Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications 3031288769, 9783031288760

Citation preview

Costas A. Varotsos Vladimir F. Krapivin Ferdenant A. Mkrtchyan Yong Xue

Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications

Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications

Costas A. Varotsos • Vladimir F. Krapivin Ferdenant A. Mkrtchyan • Yong Xue

Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications

Costas A. Varotsos Environmental Physics and Meteorology National and Kapodistrian University of Athens Athens, Greece

Vladimir F. Krapivin Applied Mathematics Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch Moscow, Russia

Ferdenant A. Mkrtchyan Informatics Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch Moscow, Russia

Yong Xue Emergency Management College Nanjing University of Information Science & Technology Nanjing, Jiangsu Province, China

ISBN 978-3-031-28876-0 ISBN 978-3-031-28877-7 https://doi.org/10.1007/978-3-031-28877-7

(eBook)

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to the distinguished scientist Professor Vladimir Fedorovich Krapivin, who died on May 5, 2021, while we were working together on writing this monograph. He left us a great scientific legacy in the field of environmental geoinformatics which we promised to share with the new generation and extend it as much as we can. In this direction is the completion of this book.

Preface

The basic problems of hydrogeochemistry that are of primary importance include the study of a wide variety of dissolved inorganic constituents as a result of chemical interactions with geological materials and to a lesser extent the contribution of the atmosphere. Hydrochemistry helps to evaluate the hydrogeochemical processes that are responsible for temporal and spatial changes in the chemistry of water resources in their various phases, including groundwater, and soil moisture (Wang-Erlandsson et al. 2022). Information on changes in water chemistry and hydrogeochemical processes is available in this book. The main threats to changing water quality include the following: • Rapid population growth increases the pressure on water resources, their quantity and quality. • Climate variability and change is monitored by changes in precipitation, evaporation, and temperature that can introduce global contamination of water resources. The importance of water quality assessment stems from the existing global problems of drinking water (Alqarawy et al. 2022; Mishra and Dubey 2023). One in nine people worldwide uses drinking water from unimproved and unsafe sources. The loss of approximately one-third of global biodiversity is estimated to be a consequence of the degradation of freshwater ecosystems mainly due to the pollution of water resources and aquatic ecosystems (Srivastava et al. 2022). Water quality monitoring is carried out using numerous technologies taking into account the physical, chemical, and biological nature of water. This book examines remote sensing technologies data processed using constructive models and algorithms. The problem of water quality monitoring is considered as an object of global ecoinformatics within the framework of which information technologies have been created, which ensure the combined use of various data on the past and present state of the climate-biosphere-society system (CBSS). The intensification of water pollution processes requires several major problems of optimizing CBSS management. Existing technologies for solving theoretical and practical problems arising here include interdisciplinary approaches that allow the vii

viii

Preface

combined use of information-modeling and instrumental tools for effective monitoring of water quality with reliable diagnostics of many environmental processes and prognostic assessment of potential consequences. This book represents the inspiring methods and results of its authors, giving a first-hand look of the results of the combined use of microwave remote sensing and optical tools and ecoinformatics methods applied to regional and global scale solutions (Varotsos and Krapivin 2020). Ecoinformatics methods are used to assess the dependence of global climate change on the level of World Ocean pollution occurring in the greenhouse effect. Specific areas are examined in relation to this effect, including the Arctic Ocean, the Sea of Okhotsk, and the South China Sea. Ecoinformatics proposes specific algorithms for solving hydrochemical tasks taking into account information uncertainty in monitoring data delivered episodically in time and parts of space. The method for overcoming information uncertainty is developed in the context of the global decision-making system that supports many current and promising environmental applications where the problem of processing big data is of utmost importance. The theoretical and applied aspects of instrumental tools are considered in this book as a basis for the monitoring systems of the water quality of various aquatic objects. Particular attention is paid to the formulation and solution of applied tasks related to decision-making based on microwave remote sensing monitoring data for hydro-ecosystems, the ecotoxicological status of which needs functional control. Types and examples of instrumental implementation of remote sensing platforms used for environmental research are described and their technical and operational characteristics are analyzed. Existing remote sensing platforms are represented as elements of an instrumental information-modeling system with universal monitoring functions and oriented toward overcoming information uncertainties and enabling them to adopt the monitoring systems in the real environmental object or process. A series of methods, algorithms, and information technologies are proposed to be used to solve hydrochemical monitoring tasks using remote sensing techniques and to provide optimization of the multi-channel observation data collection process. Special attention is paid to optical instruments that allow the formation of spectral images of hydrochemical objects. Optical tools for diagnosing water quality are represented as an alternative to traditional physico-chemical analyses of water sampling. The optical decision-making system is described as a functional tool for assessing water quality for natural water objects directly in situ without a sampling process. Different versions of optical systems are represented by multi-channel photometric and spectroellipsometric devices. The spectral images provided by these devices are the basis for diagnosing water quality using algorithms for spectral image recognition. Algorithms for optical spectral image recognition of water objects are represented. The effectiveness of the optical tools is shown in water quality control areas for a series of water bodies functioning in different climatic conditions (Molekoa et al. 2022). The results for the determination of different contaminants in the surface waters of these aquatic bodies are presented.

Preface

ix

The main goal of this research work is to increase the knowledge of the hydrogeochemistry of aquifers. This book develops new information technologies that provide solutions for different types of hydrochemical tasks using algorithms and models based on current computer technologies for processing big data. This book can help in the synthesis of efficient computer-based systems for solving problems arising from anthropogenic effects on hydrological processes and objects at various spatial scales. The basic idea of the approach proposed in this book is to combine Geographical Information System (GIS) techniques with modeling technology to estimate the function and status of hydrological environmental subsystems. This idea is implemented using new spatio-temporal reconstruction methods of incomplete data effectively carried out through the geoecological informationmodeling system (GIMS) that extends GIS functions due to the connections with the models. This book is divided into seven chapters. Each chapter introduces the reader to a specific subject area and suggests methods for solving tasks arising in that area. Each chapter focuses on areas of application of hydrogeochemistry methods and remote sensing tools as it places special emphasis on problems that arise when solving specific tasks. Chapter 1 represents new integrated information-modeling instrumental methods for hydrogeochemical analysis based on the remote sensing technology that provide the basis for a comprehensive understanding of the pollution processes of different water bodies located in different climatic zones. A review of recent advances in the combined use of remote sensing and information-modeling technologies for water quality monitoring is presented. A new perspective on global and regional water pollution problems is developed in Chap. 2 where water quality assessment and management is considered as a big data processing problem. Pollution of oceans, seas, rivers, and regional water objects is discussed taking into account the anthropogenic and natural sinks of contaminants and their changing trends. The role of aerial and satellite remote sensing observations in achieving functional diagnostics of water objects is described and demonstrated. Chapter 3 describes the remote sensing technologies that pay particular attention to microwave radiometric measurements and present problems that exist here, such as attenuation of microwaves in the vegetation layer and estimation of the snowwater content and surface water microwave radiation. The advantage of microwave observations for water quality monitoring is analyzed taking into account satellite and airborne platforms as well as algorithms for deriving quantitative information on water quality. Optical sensors for water quality monitoring are discussed in Chap. 4 where new optical tools for real-time water quality diagnostics without the use of traditional sampling and laboratory physico-chemical analysis are presented. In particular, the optical decision-making system is developed as an operational tool for the in situ assessment of water quality in natural water reservoirs. In particular, several versions of this system are analyzed using multi-channel spectrophotometers and spectroellipsometers. The spectral images obtained by these devices form the basis

x

Preface

for diagnosing water quality using new algorithms for recognizing these spectral images. More specifically, algorithms are developed to identify optical spectral images of water objects. A problem of the Arctic Basin pollution is considered in Chap. 5 where the coupled model is synthesized as the sub-system of the GIMS that implements a series of specific models describing ecological, hydrological, climatic, and hydrochemical processes in Arctic waters (Krapivin et al. 2021). The GIMS-Arctic Basin Ecosystem takes into account various sources of pollutants, including river runoffs, long-range atmospheric transport, and anthropogenic objects located in the coastal zone and on ships. Heavy metals, oil hydrocarbons, and radionuclides are considered to be the main contaminants for simulation experiments showing the high sensitivity of the Arctic ecosystem to the pollution strategy. It turns out that the current pollution level is almost critical. Biocomplexity and survivability indicators are considered as informative values for predicting the state of the Arctic ecosystem. The current state of pollution intensity leads to an increase in the accumulation of the pollutants considered here in marine waters at various rates. Chapter 6 presents various applications of remote sensing technologies to solve hydrogeochemical tasks, including the study of the dynamics of radionuclear pollutants, heavy metals, and oil hydrocarbons in the Angara/Yenisey river system, the Arctic Basin, and the Sea of Okhotsk. Microwave quality and optical water quality are given for other aquatic ecosystems such as the Peru Current, Nuok Ngot Lagoon of the Vietnamese coast of the South China Sea, the Saigon River, the Dong Nai River, the Sea of Azov, and Lake Sevan in Armenia. The hydrological model and the recovery scenario for the water balance of the Aral Sea are described in detail. The interactive nature of climate change and water quality is analyzed in Chap. 7. Various aspects that arise here discuss theoretical and applied problems based on existing knowledge and climate models. The potential impacts of climate change on water quality are considered inverse anthropogenic aftermaths that finally lead to detrimental effects. The search for mitigation processes takes place within a constructive approach to global modeling of the climate-biosphere-society system. This book is intended for specialists in the fields of environmental monitoring, the study of climate change, the investigation of human-nature interaction, geopolitics, and interdisciplinary research methodology. Water quality assessment problems are studied at both global and regional levels with an emphasis on sustainable development and human health. Athens, Greece Moscow, Russia Moscow, Russia Xuzhou, Jiangsu, China

Costas A. Varotsos Vladimir F. Krapivin Ferdenant A. Mkrtchyan Yong Xue

Preface

xi

References Alqarawy A, El Osta M, Masoud M, Elsayed S, Gad M (2022) Use of hyperspectral reflectance and water quality indices to assess groundwater quality for drinking in arid regions, Saudi Arabia. Water 14(15):2311 Krapivin VF, Mkrtchan FA, Varotsos CA, Xue Y (2021) Operational diagnosis of arctic waters with instrumental technology and information modeling. Water Air Soil Pollut 232(4):1–7 Mishra RK, Dubey SC (2023) Fresh water availability and its global challenge. Journal of Marine Science and Research 2(1): 1–55. https://doi.org/10.58489/2836-5933/004 Molekoa MD, Kumar P, Choudhary BK, Yunus AP, Kharrazi A, Khedher KM et al (2022) Spatiotemporal variations in the water quality of the Doorndraai Dam, South Africa: an assessment of sustainable water resource management. Curr Res Environ Sustain 4:100187 Srivastava A, Singhal A, Jha PK (2022) Geospatial technology for sustainable management of water resources. In: Ecological significance of river ecosystems. Elsevier, pp 105–132 Varotsos CA, Krapivin VF (2020) Microwave remote sensing tools in environmental science. Springer Nature Switzerland Wang-Erlandsson L, van der Ent R, Staal A, Porkka M, Tobian A, te Wierik S et al (2022) Towards a green water planetary boundary. Nature Reviews 349 Earth & Environment

Summary

The growing needs in almost all fields of remote environmental monitoring have led to numerous problems arising from the many unsolved tasks, including the assessment of important features of soil-plant formations and the hydrosphere. Developing new concepts and approaches to assess and address natural and anthropogenic system dynamics has become a priority. A key priority is the global problems associated with the creation of effective information technologies for data processing in the context of environmental studies. The problems of microwave remote sensing technology were analyzed as well as the documentation of environmental monitoring systems that receive, store, and process the information necessary to solve related problems. The main objective of the book is the use of information technology for the combined use of modeling technology and microwave remote sensing measurements in the assessment of system environmental conditions as well as the visualization of this technology with computer calculations for various environmental problems, including water quality assessment. Various tasks related to the assessment and forecasting of the dynamics of physical systems based on microwave remote sensing measurements using mobile platforms are solved. It is proposed to use new information technology to optimize remote sensing monitoring systems. This technology is based on sets of computer algorithms for comprehensive analysis of data from global and regional monitoring systems. Chapters in the theoretical part of the book contain a description of rigorous algorithms and environmental models. The applied part examines specific problems of environmental dynamics in areas of different regions where field experiments were carried out to test the new information technology. This book is the result of a detailed study of the environment through computer algorithms and simulation models. Its main purpose is to develop a universal information technology to assess the characteristics of environmental subsystems at both global and regional levels. Applied mathematicians, hydrologists, geophysicists, radiophysicists, and other environmental researchers will find a wealth of information in this book.

xiii

Abbreviations and Acronyms

AAMU AAPCHO ACIA ACSYS AIDS AMAP ANWAP AOGCM APAR APDA ARCSS ARCUS ARDB ARM ASA ASS AVHRR AVSS BBMSA BR CART CBD CBS CBSS CCRS CLIVAR CMCDMC CNES CSSR

Alabama Agricultural and Mechanical University Association of Asian Pacific Community Health Organization Arctic Climate Impact Assessment Arctic Climate SYstem Study Acquired Immuno-Deficiency Syndrome Arctic Monitoring and Assessment Programme Arctic Nuclear Waste Assessment Program Atmosphere–Ocean General Circulation Model Absorbed Photosynthetically Active Radiation Arctic Precipitation Data Archive Arctic Science System Arctic Research Consortium of the United States Arctic Run-off Data Base Atmosphere Radiation Measurement National Aeronautics and Space Administration Atmosphere-Snow-Soil Advanced Very High-Resolution Radiometer Atmosphere-Vegetation-Soil System Black Body-Metallic Sheet Algorithm Brundtland Report Cloud and Radiation Tested Convention on Biological Diversity Climate-Biosphere-Society Climate-Biosphere-Society System Chicago Center for Religion and Science CLImate VARiability and predictability Coupled Model of Carbon Dioxide and Methane Cycles Centre National d’études Spatiales Center for the Study of Science and Religion

xv

xvi

DPIR EIA EMW ENSO EOS EOSDIS EPA EPIC ES ESA EUP FAO FEMA FPAR FPI GARP GCOS GCP GDP GEWEX GHG GIMS GIMSAF GIS GISP GMCBSS GPM GPS GTOS HARC HDI HIS HMMS HOE HPI HSCaRS HUT IAHS IAPSO IASC IASPEI

Abbreviations and Acronyms

Drivers, Pressures, Impact, Response Earth Incidence Angle ElectroMagnetic Waves El Niño–Southern Oscillation Earth Observing System EOS Data and Information System Environmental Protection Agency Environmental Policies and Institutions for Central Asia Earth Polychromatic Imaging Camera Ecosystem Service European Space Agency Enterprise Unified Process Food and Agriculture Organization Federal Emergency Management Agency Fraction of Photosynthetically Active Radiation Food Production Index Global Atmospheric Research Programme Global Climate Observation System Global Carbon Project Gross Domestic Product Global Energy and Water Cycle Experiment GreenHouse Gas Geoecological Information Modeling System Geoecological Information Modeling System of Agricultural Function Geographical Information System Greenland Ice Sheet Project Global Model of the Climate-Biosphere-Society System Global Precipitation Measurement Global Positioning System Global Terrestrial Observation System Human Dimensions of the Arctic System Human Development Index Hydrologic Information Systems Hydrochemical Monitoring and Management System Health Organizations in Eurasia Happy Planet Index Hydrology, Soil Climatology and Remote Sensing Helsinki University of Technology International Association of Hydrological Sciences. International Association for the Physical Sciences of the Oceans International Arctic Science Committee International Association of Seismology and Physics of the Earth’s Interior

Abbreviations and Acronyms

IBP ICT IEEE IGBP IHO IIED IISD IJSD ILO IMF IPCC ISGGM ISTC ITEX IWMI KP LAI LAII LUT MACS MAHB MAIIS MBWB MCBS MDA MEM MEMLS MISR MIT MMSD MODIS MPDI MR MSRAMV NASDA NDVI NN NOAA NPP OAII ODMS OS OSE

xvii

International Biological Program Information and Communications Technology Institute of Electrical and Electronics Engineers International Geosphere-Biosphere Programme International Hydrographic Organization International Institute for Environment and Development International Institute for Sustainable Development International Journal of Sustainable Development International Labour Organization International Monetary Fund Intergovernmental Panel on Climate Change Integrated System for Global Geoinformation Monitoring International Science and Technology Center International Tundra Experiment International Water Management Institute Kyoto Protocol Leaf Area Index Land/Atmosphere/Ice Interactions LookUp Table Microwave Autonomous Copter System Multi-scale Atmospheric Transport and CHemistry Multi-channel Adaptive Information-Instrumental System Model of the Biosphere Water Balance Model of CBS Model-Driven Architecture Microwave Emission Model Microwave Emission Model of Layered Snowpacks Multi-angle Imaging SpectroRadiometer Massachusetts Institute of Technology Mining, Minerals and Sustainable Development Moderate Resolution Imaging Spectroradiometer Microwave Polarization Difference Index Microwave Radiometer Measuring System for Retrieving Attenuation of Microwaves in Vegetation NAtional Space Development Agency (Japan) Normalized Differential Vegetation Index Neural Network National Oceanic and Atmospheric Administration Net Primary Production Ocean/Atmosphere/Ice Interactions Optical Decision-Making System Ocean Salinity Okhotsk Sea Ecosystem

xviii

OSEM OSS PAH PAHO PAR PIRATA POLDER POP RAISE RAMA RAS RT SAR SD SDI SEA SEARCH SHEBA SiB SIMS SM SP SPARC SPF SPOT SWE TAO TIR TOGA TRITON TTP UAV UML UN UNCCD UNCED UNDP UNEP UNESCO UNFCCC UNWDP USA VI VSWR

Abbreviations and Acronyms

OSE Model Ocean Science Series Polycyclic Aromatic Hydrocarbon Pan American Health Organization Photosynthetically Active Radiation PIlot Research moored Array in the Tropical Atlantic POLarization and Directionality of the Earth’s Reflectances Persistrent Organic Pollutant Russian-American Initiative on Shelf-land Environment Research moored Array for Monsoon Analysis Russian Academy of Sciences Radiative Transfer Synthetic Aperture Radar Sustainable Development Scaled Shadow Index Strategic Environmental Assessment Study of Environmental Arctic Change Surface Heat Budget of the Arctic Ocean Simple Biosphere Synthesis, Integration, and Modeling Studies Soil Moisture Strategic Plan Stratosphere-troposphere Processes And their Role in Climate Soil-Plant Formation Systéme Probatoire d’Observation de la Terre Snow Water Equivalent Tropical Atmosphere Ocean Thermal Infrared Radiometer Tropical Ocean Global Atmosphere TRIangle Trans-Ocean buoy Network Technology Transfer & Promotion Unmanned Aerial Vehicle Unified Modeling Language United Nations United Nations Convention to Combat Desertification United Nations Conference on Environment and Development UN Development Programme United Nations Environment Programme United Nations Educational, Scientific and Cultural Organization United Nations Framework Convention on Climate Change. United Nations Water Development Programme United Nations of America Vegetation Indices Voltage Standing Wave Ratio

Abbreviations and Acronyms

WB WCED WCRP WHO WSSD WTO WWAP

World Bank World Commission on Environment and Development World Climate Research Programme World Health Organization World Summit on Sustainable Development World Trade Organization World Water Assessment Programme

xix

ThiS is a FM Blank Page

Contents

1

Global Problems of Ecodynamics and Hydrogeochemistry . . . . . . . . 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Advances in Information-Modeling Technology for Water Quality Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Adverse Effects on the Survivability of the Earth’s Population . . . 22 1.3.1 Natural Disasters and the Survivability of Ecological Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.3.2 A Biosphere Survivability Model . . . . . . . . . . . . . . . . . . 33 1.4 Sustainable Development and Quality of Water Resources . . . . . . 35 1.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 1.4.2 Forming the Concept for Sustainable Development . . . . . 38 1.4.3 Sustainable Development and Public Health . . . . . . . . . . 43 1.4.4 Global Change: Priorities . . . . . . . . . . . . . . . . . . . . . . . . 47 1.5 Effects of Natural and Anthropogenic Environmental Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 1.5.1 Global Ecodynamics Theory . . . . . . . . . . . . . . . . . . . . . . 54 1.5.2 The Current State of Global Ecosystems . . . . . . . . . . . . . 59 1.6 Information-Modeling Technology and Decision-Making Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 1.7 Hydrogeochemistry and Water Quality Assessment Tools . . . . . . 79 1.8 Demography and Water Resources . . . . . . . . . . . . . . . . . . . . . . . 82 1.8.1 Global Demography Aspects . . . . . . . . . . . . . . . . . . . . . 82 1.8.2 Matrix Model of Population Size Dynamics . . . . . . . . . . . 92 1.8.3 Differential Model of Population Dynamics . . . . . . . . . . . 95 1.8.4 Megapolitan Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 1.8.5 Global and Regional Water Resources . . . . . . . . . . . . . . . 100 1.9 A New Big Data Approach Based on the Geoecological Information-Modeling System . . . . . . . . . . . . . . . . . . . . . . . . . . 104 1.9.1 Big Data Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

xxi

xxii

Contents

1.9.2 1.9.3 1.9.4

Big Data Approach and Global Sustainable Development Problems . . . . . . . . . . . . . . . . . . . . . . . . . 106 GIMS as an Improvement of Big Data Approach . . . . . . . 107 Global Big Data Processing . . . . . . . . . . . . . . . . . . . . . . 110

2

Global Water Balance and Pollution of Water Reservoirs . . . . . . . . . 2.1 Global Water Balance and Sustainable Development . . . . . . . . . . 2.2 Hydrogeochemistry and Pollution Effects . . . . . . . . . . . . . . . . . . 2.3 Monitoring and Management of Water Quality . . . . . . . . . . . . . . 2.3.1 The Problems of Sustainable Water Resources . . . . . . . . . 2.3.2 Monitoring and Management Tools . . . . . . . . . . . . . . . . . 2.3.3 Hydrochemical Monitoring and Management System . . . . 2.4 Pollution of the Oceans and Seas . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Modeling of Global and Regional Water Cycles . . . . . . . . . . . . . 2.5.1 The Water Flows in the World Ocean . . . . . . . . . . . . . . . 2.5.2 Numerical Model of the Global Water Balance . . . . . . . . 2.5.3 The Regional Water Budget Model . . . . . . . . . . . . . . . . . 2.6 Drinking-Water Quality and Standards . . . . . . . . . . . . . . . . . . . . 2.7 Sources of Water Pollution and Risk to Human Health . . . . . . . .

119 119 122 127 127 130 133 137 140 140 144 148 154 155

3

Remote Sensing Technologies and Water Resources Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Contamination of Soil and Aquatic Environment . . . . . . . . . . . . . 3.3 Methods of Microwave Radiometry . . . . . . . . . . . . . . . . . . . . . . 3.4 Instrumental Tools for Microwave Monitoring . . . . . . . . . . . . . . 3.5 Microwave Airborne Platforms . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Microwave Monitoring of Soil-Plant Formations . . . . . . . . . . . . . 3.7 Measurement System to Retrieve the Attenuation of Microwaves in Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Microwave Model of Vegetation Cover . . . . . . . . . . . . . . . . . . . 3.9 Microwave Irradiation of the Snow Cover . . . . . . . . . . . . . . . . . 3.10 Passive Microwave Methods and Modeling Tools . . . . . . . . . . . .

197 210 217 229

4

Optical Tools for Water Quality Monitoring . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Photometry and Ellipsometry Methods . . . . . . . . . . . . . . . . . . . . 4.3 Optical Sensors for Water Quality Monitoring . . . . . . . . . . . . . . 4.4 Algorithms for Solving Inverse Optical Metrology Tasks . . . . . . . 4.5 Multi-functional Adaptive Information-Modeling System . . . . . .

233 233 234 235 240 243

5

Arctic Basin Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 High-Latitude Environmental Science . . . . . . . . . . . . . . . . . . . . 5.3 Arctic Basin Pollution Problems . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 General Analysis of the Problem . . . . . . . . . . . . . . . . . . .

247 247 252 260 260

163 163 167 169 178 183 192

Contents

xxiii

5.3.2

5.4

5.5

6

The Atmospheric Transport of Pollutants to the Arctic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Innovative Approach to Solving the Arctic Pollution Problem . . . 5.4.1 Geoecological Information-Modeling System . . . . . . . . . . 5.4.2 Modeling the Pollutant Dynamics in the Arctic Basin . . . . 5.4.3 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . Biocomplexity as an Indicator of the Arctic Water Reservoir State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Defnition of Biocomplexity Indicator . . . . . . . . . . . . . . . 5.5.2 The BSS Biocomplexity Model . . . . . . . . . . . . . . . . . . . 5.5.3 Conclusions and Discussion . . . . . . . . . . . . . . . . . . . . . .

Investigation of Regional Aquatic Systems . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Monitoring of Water Reservoirs in South Vietnam . . . . . . . . . . . 6.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 The GIMS Structure and Functions Adopted in the Nuoc Ngot Lagoon . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Water Balance Model of the Turan Lowland in Central Asia . . . . 6.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Simulation of Aral Sea Water Balance Modeling . . . . . . . 6.4.4 Simulation Results and Discussion . . . . . . . . . . . . . . . . . 6.4.5 Conclusions and Outlook . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Microwave Monitoring of Soil Water Content . . . . . . . . . . . . . . 6.5.1 Uncertainty and Risk Sources in Remote Sensing . . . . . . . 6.5.2 Practical Microwave Radiometric Risk Assessment of Agricultural Operation . . . . . . . . . . . . . . . . . . . . . . . . 6.5.3 Geoinformation Monitoring System of Agricultural Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.4 The State of Soils and Water Objects Evaluated by Means of Radiometric Methods . . . . . . . . . . . . . . . . . 6.6 Geoecological Information-Modeling System for the Monitoring of the Azov Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Monitoring of Sea Zones with Oil Pollution . . . . . . . . . . . . . . . . 6.7.1 Sea Zones of Oil and Gas Extraction . . . . . . . . . . . . . . . .

271 274 274 277 279 286 286 288 290 293 293 296 296 297 302 307 307 310 313 315 323 324 324 327 330 332 339 342 342 345 350 352 360 362 362

xxiv

Contents

6.7.2

6.8

7

Ecological Monitoring of the Sea Surface of the Oil and Gas Extraction Zones . . . . . . . . . . . . . . . . . . . . . . . . 6.7.3 Estimation of the Oil Hydrocarbon Pollution Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7.4 Expert System for Detecting Pollutant Spills on the Water Surface . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagnostics of the Angara/Yenisey River System . . . . . . . . . . . . 6.8.1 Angara/Yenisey River as Hydrochemical System . . . . . . . 6.8.2 Angara/Yenisey River Simulation Model . . . . . . . . . . . . . 6.8.3 Results of Simulation Experiments and In Situ Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.8.4 Assessments and Recommendations . . . . . . . . . . . . . . . .

Global Climate Change and Hydrogeochemistry . . . . . . . . . . . . . . . . 7.1 Interaction Between Globalization Processes and Biogeochemical Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 The Interplay Between Nature, Society, and Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Global Climate Diagnostics . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Some Aspects of Climate Data Reliability . . . . . . . . . . . . 7.2 Global Water Balance and Sustainable Development . . . . . . . . . . 7.3 Global Climate Change Problems . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Abnormal Situations and Climate . . . . . . . . . . . . . . . . . . 7.3.3 Impact of Natural Disasters on Habitats . . . . . . . . . . . . . . 7.3.4 Evolution of the Biosphere and Natural Disasters . . . . . . . 7.4 Use of Information-Modeling Tools for Tropical Cyclogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Definition of Instability Indicator . . . . . . . . . . . . . . . . . . 7.4.3 Search and Trace the Origin of the Tropical Cyclone . . . . 7.4.4 Calculation Results and Discussion . . . . . . . . . . . . . . . . . 7.5 Coupled Model of Global Carbon Dioxide and Methane Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Conceptual Scheme of Global Carbon Dioxide and Methane Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.4 Conclusions and Discussion of Results . . . . . . . . . . . . . .

366 372 375 380 380 383 388 397 399 399 399 409 419 433 437 437 438 442 444 448 448 449 452 455 459 459 460 470 473

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

Chapter 1

Global Problems of Ecodynamics and Hydrogeochemistry

1.1

Introduction

The origin of the twenty-first century practically does not relax but worsens a sustainable development problem of the human society. If human society was on the threshold of nuclear war in the middle of twentieth century, when there was the Caribbean Crisis, then the global climate-biosphere-society system (CBSS) is currently in crisis state for a number of reasons, including the most significant: • The gradual increase of the world population in comparison with the increase of the productivity of the agricultural and natural ecosystems leads to a decrease of the per capita volume of food. Food-deficit is a fact in many regions. Food per person remains stagnant over time and the growth of hungry peoples is expected (Mayhew 2016). • The response of the environment to anthropogenic intervention in natural cycles is monitored by the intensification of natural disasters, including the emergence of new incurable diseases (Shaw 2010; Fawaz et al. 2023). • Global climate change due to disturbance of cycles of greenhouse gases and water resources leads to a change in the spatial distribution of water resources including drinking water (Cracknell and Varotsos 2021, 2022; Weart 2008). • The development of new powerful weapons and platforms for their operational delivery in virtually every coordinate environment under the distortion of information contributes to additional uncertainties in the problem of survival of the human population (Cimbala 2012). • The intensification of both international and regional conflicts is followed by dramatic changes in the globalization and decentralization processes that do not stimulate the improvement of the living conditions of the population (Daun 2007). • One of the possible causes of ecological disaster may be pollinating insects that may occur as an ecological consequence of mobile media, including mobile phones (Sahib 2011). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Varotsos et al., Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications, https://doi.org/10.1007/978-3-031-28877-7_1

1

2

1

Global Problems of Ecodynamics and Hydrogeochemistry

• In the near future the main global problem will be the shortage of drinking water. The water crisis is a health crisis. Nearly one million people die each year from water, sanitation, and hygienerelated diseases, which could be reduced by access to safe water or sanitation. Contaminated water and poor sanitation are linked to the transmission of diseases such as cholera, diarrhea, dysentery, hepatitis A, typhoid, polio, and coronavirus. An estimated 829,000 people die each year from diarrhea as a result of unsafe drinkingwater, sanitation, and hand hygiene. Certainly, there are already serious difficulties in the equitable distribution of global freshwater resources between and within countries (Nazerali 2007; Varotsos et al. 2020b). These conflicts are escalating between new industrial, agricultural, and urban areas. Mainly, water use conflicts are inevitable due to restricted water resources of the world. Different information sources provide different assessments of these resources depending on the methodology and algorithms used for integrated assessments globally, regional, and locally. In general, water storage on Earth is 1.4 × 1018 m3 including 35 × 1015 m3 of freshwater, 13 × 1012 m3 of water in the atmosphere. Significant elements of the global water cycle are precipitation and evaporation: • Evaporation from the Earth’s surface into the atmosphere is assessed as 577 × 1012 m3/year, of which 86% comes from the oceans and 14% from the land • Precipitation falls to the land equal to 115 × 1012 m3/year Thus, the strained global relations have degrated the problem of the survivability of humanity, the solution of which is impossible on a regional scale. It is necessary to develop a constructive information technology that allows a complex description of the global ecological, demographic, socio-economic, and climatic processes taking place in the CBSS. It enables the search for constructive strategies for the CBSS survivability taking into account existing assessments and forecasts of environmental resources. The cornerstone of the concept of sustainable co-existence of nature and humans is the convention according to which all countries must seek weighed strategies for the evolution of biosphere-population system taking into account the biosphere reserves. The global population in its tendency to reduce poverty is to understand that thebiosphere reserves are depleted. Therefore, the complex goals of the world’s population are research and monitoring related to conservation and sustainability. Regarding this problem, there is a lot of research based on global models (Anderson 2015; Dufresne et al. 2013; Krapivin and Varotsos 2007, 2008; Krapivin et al. 2015b). These and other surveys of global environmental processes are based on different models of the present view of the CBSS structure. Many of them have a virtual character based on the philosophy-ideology of the world state. An acceptable constructive approach to global environmental modeling was proposed by Moisseev (1979) who clearly formulated a conceptual biosphere model that differs mainly from the well-known global models of the Club of Rome (Forrester 1971, 1979; Meadows et al. 1972, 2004; Pestel 1989). Research has developed a mathematical approach to global environmental modeling, which has provided simulation experiments with global environmental processes, including assessments of the effects of

1.1

Introduction

3

anthropogenic impacts on biosphere ecosystems (Krapivin 1993; Sellers et al. 1996a, b; Krapivin and Kelley 2009). A difference between the Club of Rome models and other models lies mainly in the methodological principles (Saavedra-Rivano 1979): • The authors of the Club of Rome models focused on the global economic processes by linking them to separate environmental processes and selecting the demographic block as the main element of the global model. • Moisseev’s (1979) starting position was in the biosphere research considering human as an element of biosphere, and demographic and economic processes are examined only in the framework of the systematic analysis of global ecological evolution. The current socio-economic theories of sustainable development are far removed from Moisseev’s ideas and certainly from Vernadsky’s theory of noosphere (Vernadsky 1944). Many indicators such as the Happy Planet Index (HPI), the Human Development Index (HDI), the Food Production Index (FPI), the Gross Domestic Product (GDP), and others undoubtedly help to assess growth trends in a particular CBSS section but create difficulties for the complex evaluation of the CBSS evolution. This is only possible by using a global model that allows the maximum number of direct and indirect couplings in CBSS to be taken into account. A tendency to improve global models is characterized by attempts to improve their precision and reduce information requirements. The complexity of organized reality simultaneously impedes this approach to improvement and brings with it a complex set of constraints connected with chaotic environmental processes and the multidimensional problem. It is an axiom for many researchers around the world. Virtually every global model is individual in nature and focusses on a limited set of environmental processes and elements. Krapivin et al. (2015a, b, 2016a) proposed a new approach to the global model synthesis based on the use of high-level media for the use of separate operations connected with the description of processes in CBSS. The geoecological information-modeling system (GIMS) was developed, the architecture of which is based on the combined use of GIS-technology and modeling tools. In general, taking into account the existing global models that describe different processes in CBSS, it is proposed to use GIMS as a universal tool of complex parameterization of the most important global processes in search of a sustainable state between nature and human society (Krapivin et al. 2016c; Pawłowski 2011). Starting from the traditional conception and civilized sense of processes in CBSS, the architecture of the global GIMS/CBSS model has been established to demonstrate an integral scheme of direct and indirect relationships between these processes (Krapivin et al. 2017a). GIMS/CBSS consists of the operation of many items, each with a separate task or range of operation. GIMS/CBSS items operate autonomously to represent a portion of the desired functionality. The problem of nature–society interaction in the context of global change in the environment and climate remains debatable in practically all world regions, where there are energetic restrictions including food and drinking water, in particular

4

1 Global Problems of Ecodynamics and Hydrogeochemistry

(Sharma and Bhattacharya 2016). In many cases, the search for solutions to overcome has insuperable restrictions that are mainly connected with the intergovernmental relations. Such situations exist both in both developed and developing regions. Globalization processes are so flexible and complex that their study, parameterization, and prediction require a trans-disciplinary approach. Van der Leeuw and Aschan-Leygonie (2000) have stated that both in nature and in the life sciences and especially in the social sciences it is impossible to avoid the trans-disciplinary approach to environmental problems. Here the theory of complex systems is a saving tool that makes it possible to understand and interpret the differences between “cultural” and “natural” processes, as well as, to some extent, to explain the difference between the concepts of “resilience” and “sustainability.” The resilience of socio-natural systems in many situations depends on the ability of the human communities involved to process all the information necessary to effectively address the complex dynamics of the system as a whole. Rosenberg (2001) develops a wise and lively critique of the contemporary globalization theory, which most experts connect with the notion of sustainability, and argues that fashionable preoccupations with spatiality have generated deep intellectual confusions that impend a clear understanding of the modern world. It shows how these confusions ultimately condemn globalization theorists to a peculiar and quixotic attitude. Advocates of globalization generally believe that all global and regional problems can be solved automatically through free trade. An unusual examination of Chomsky’s libertarian views on global economic hegemony has been given by Fox (2001). The notion of “free trade” as a universal means of solving the economic problems of Third World countries is a direct deception and leads to their further enslavement by large-scale companies. The main goal of all investigations of global environmental problems is to promote the health and well-being of human beings and the environment through: 1. Deeper community understanding of life processes, the place of humans in nature, and the health and environmental issues we face today 2. Encouraging informed debate and discussion about the practical meaning of this understanding—for individuals, families, organizations, and for society as a whole 3. Communicating the outcome of the Forum’s activities as widely as possible through publications and the Internet These and similar general postulates direct, to some extent, public opinion toward the regulation of the man-environment relations with reasonable and well-balanced result. Unfortunately, it is impossible with this approach to divide the planetary population into groups of influence. The assumed division into countries and groups of countries with the same level of economic development cannot be considered optimal. A mechanism for calculating the survivability level of one group suggested by Kondratyev et al. (2004a) enables one to develop a global model of influence and find a solution with it. One of the implementations of this mechanism is based on the criterion of biological complexity of the environment.

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

5

Understanding global environmental processes and assessing anthropogenic impacts is possible using mathematical models of global and regional water cycles. Water is one of the most widespread substances in nature. It is present in various forms almost all over the globe and plays an important role in energy and massexchange between the continents, oceans, and atmosphere. The problem of assessing the role of water in the global carbon cycle is only a small part of the general global problem of nature–society interaction. Oceans, polar ice caps, glaciers, lakes, rivers, soils, and the atmosphere contain 1.4–1.5
109 km3 of water. This mass is in constant dynamic interactions with other biospheric components and thus determines the factors of environmental variability. The developed methods of numerical experiments should be used to assess the role of these factors in the present conditions and to show the significance of the water balance in the stabilization of numerous climatic and bio-geo-coenocytic processes. Here an attempt was made, through a systematization of the information about the water balance of the planet, to create a version of the model of the biosphere water balance (MBWB) capable, within the general approach to modeling the carbon balance, to take into account the role of water fluxes.

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

Existing information and instrumental tools for water quality diagnostics basically aim to examine the chemical status of a water body based on available water quality standards (Schulz et al. 2000, 2001; Ferm et al. 2006). Water samples are usually collected for chemical analysis in the laboratory. The presence of pollutants in the aquatic environment can be detected in a variety of ways, including analysis of sediments and biota (Ahuja 2013; Munnè et al. 2016; Tidblad et al. 2012; Hunting et al. 2017). Knowledge of the ecosystem’s functional characteristics via in situ and satellite observations contributes to a better understanding and assessing trends in biodiversity, survivability, and sensitivity to environmental changes (Cracknell and Varotsos 2007, 2011; Krapivin et al. 2015b, 2018a; Krapivin and Varotsos 2017; Varotsos and Krapivin 2018). However, knowledge of the pollutants concentration in the aquatic environment enhances the possibilities of using various models for the parameterization of non-linear climate and human effects on solid or water objects (Varotsos and Cartalis 1991; Tzanis et al. 2008, 2009, 2011; Efstathiou and Varotsos 2010; Efstathiou et al. 2003, 2011; Varotsos et al. 2009, 2012a, b, 2013, 2014b; Lovejoy and Varotsos 2016; Mkrtchyan and Varotsos 2018). The process of water quality control is usually focused on the conservation and protection of environmental water resources, which is important for human safety. Existing water quality control equipment to carry out this process involves a limited set of water characteristics such as turbidity, dissolved O2, conductivity, and pH. The concentration of pollutants in the aquatic environment is assessed using various

6

1 Global Problems of Ecodynamics and Hydrogeochemistry

chemical agents. Despite the variety of instruments that control the water quality, it is necessary to create new technologies and approaches to solve emerging tasks (Prasad et al. 2015; Krapivin et al. 2016a, b, c; Plana et al. 2019; Liao et al. 2018). Aquatic systems, in their wide meaning, include lakes, ponds, wetlands, seas, and oceans. Goals and priorities of remote monitoring of the aquatic systems include numerous problems connected with the assessment of physical, ecological, and hydrochemical characteristics for the assessment of their health, detection of different processes such as the tropical cyclone beginning, aquatic weed and algae control, and understanding of the role in the climate change (Golub et al. 2022). Aquatic ecosystems are critical components of the global environment as essential contributors to biodiversity and ecological productivity. Their health is controlled, directly and indirectly, by human activities. Aquatic ecosystems are subjected to increasing anthropogenic pressure due to resource extraction (e.g., fisheries and water use). Real-time remote monitoring networks are synthesized to provide meteorological, hydrographic, and water quality information. These monitoring networks are strategically positioned along the estuary and river mouth (Hongpin et al. 2015). Monitoring of the sea and oceanic ecosystems is the subject of the World Climate Research Programme (WCRP) under which there is, for example, the Tropical Moored Buoy System: TAO, TRITON, PIRATA, RAMA (TOGA). The Global Tropical Moored Buoy Array is a multi-national effort to provide data in real-time for climate research and forecasting. Major components include the TAO/TRITON array in the Pacific, PIRATA in the Atlantic, and RAMA in the Indian Ocean. The TAO array consists of approximately 70 moorings in the Tropical Pacific Ocean, PIRATA uses ~20 buoys, and RAMA uses ~23 buoys. Arctic buoy arrays include various types of buoys directed for the measurements of: • Barometric pressure • Air and sea temperature • Ocean profile data including the ice thickness and wind characteristics Arctic marine network performs measurements to serve a range of problems such as numerical weather prediction, forecasting the climate change, and assessment of pollution levels for all basins and along the coastlines. Somehow, existing buoy arrays provide information about the state of the seas and oceans only in restricted areas. The detailed and total picture of their health is formed on the base of complex big data provided by various monitoring systems, including satellite instruments and using models and algorithms for processing big data (Krapivin 2009; Varotsos and Krapivin 2017). Zarco-Tejada and Ustin (2001) report on the progress made in improving our understanding of the biophysical and ecological processes governing the linked exchanges of water, energy, carbon, and trace gases between the terrestrial biosphere and the atmosphere by improving satellite data products for models. Usually, this exchange is analyzed by synthesizing both global and regional hydrological models, whose input data are provided by monitoring systems. The success of this analysis is defined by the use of relevant

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

7

information-modeling tools. Wang and Takahashi (1999) develop a land surface water deficit model for a large-scale heterogeneous arid and semiarid area with various soil, vegetation, and land use types, and it is used to simulate seasonal and spatial variability in potential and actual evapotranspiration and an index of water deficit (Fernandes et al. 2012). Comparisons with the results of other commonly used models and natural vegetation conditions suggest that this model can give an estimate of success for large-scale regional studies. Delpla et al. (2009) study the effects of climate change on water availability and hydrological risks, the consequences on water quality are just beginning. Numerous publications of this type usually consider very specific conditions for the hydrologic cycle of a given scale. The main problem consists in the development of maximum universal approaches to the consideration of hydrological cycles with a proper assessment of water quality. Such a universal approach was proposed by Varotsos and Krapivin (2017) when water quality assessment is considered part of global environmental problems, the solution of which is based on the main concept of geoinformation monitoring technology. The solution of the majority of applied problems of the present ecodynamics is difficult because those effective methods of control of soil-plant formations (SPF) and aquatic ecosystems have not been sufficiently developed. The need for the creation of new effective information technology for remote data processing and interpretation is dictated by the different areas of human activity. Many global environmental problems, such as greenhouse effect or natural disasters, have principal restrictions when they burden their prognosis. The set of international scientific programs makes efforts to focus research on the understanding of the processes that influence the recognition of the importance of the physical, biological, and chemical environment for life. Unfortunately, the objective of many of them is not realized. It is evident that no new paradigms were developed for the study of the Earth and its environments. One of the effective information technologies developed in recent times is Geographic Information System (GIS) technology. It has massive interest as a commercially orientated geographical computer technology. But GIS has numerous restrictions connected with its functions to predict environmental dynamics. The difficulties arising here are connected with the complexity of the earth’s surface and the absence of detailed data that reflect environmental dynamics. It is confirmed, for example, by the problems formulated by the Global Carbon Project (GCP) and the International Hydrographic Organization (IHO). Land surface properties and hydrological processes play an important role in shaping global ecodynamics, including climate change. The land surface is characterized by many parameters such as the type of soil-plant formation, leaf area index (LAI), roughness length, and albedo. These and other parameters determine the processes taking into place in the atmosphere-land system: evaporation, precipitation, and photosynthesis. The Global Climate Observation System (GCOS) and the Global Terrestrial Observation System (GTOS) supply the LAI with an accuracy of ±0.2 to 1.0 over large areas. For example, MODIS provides a 1-km global data product updated once every eight-day period throughout the year. The Multi-angle

8

1 Global Problems of Ecodynamics and Hydrogeochemistry

Imaging Spectroradiometer (MISR) supplies LAI with a special resolution of 1.1 km every eighth day. There are several classes of methods to estimate LAI (Fang and Liang 2003): • • • •

Use of the empirical relationship of LAI and vegetation indices (VI) Through the inversion of a radiative transfer (RT) model Lookup table (LUT) method Neural network (NN) algorithms

Each of these approaches has specific parameters as inputting information to the method. The VI approach is based on the correlation between LAI and VI. The RT model inversion method describes the physical processes of radiance transfer in the soil-vegetation system. LAI can be determined remotely relatively cheaply and easily using imagery from various satellites. The most commonly used satellites to determine LAI are SPOT, NOAA AVHRR, and Landsat TM, all with different spatial and spectral resolutions. Multi-channel image data, such as TM, and empirical relationships, such as NDVI-LAI relationship or SR-LAI relationship, are used to estimate LAI. Multi-angular remote sensing data provide more information for canopy structure. Particularly, the multi-angular data and model inversion method is used to estimate LAI. The Earth Observing System (EOS) provides enormous information about environmental sub-systems. EOS technologies provide the global perspective needed for an integrated, long-term, scientific, integration of our home planet. Combined change in the Earth system is inevitable. The numerous environmental problems connected with global climate change are the subject of debate among scientists. Unfortunately, the limits of future global changes have not been assessed. Kondratyev et al. (2004a) proposed a methodology for the solution of this problem based on EOS data. The Earth Observing System is a US National Aeronautics and Space Administration (NASA) program consisting of a science segment, a data system, and a space segment made up of an array of polar-orbiting and mid-inclination satellites for long-term monitoring of the Earth as an integrated system, including observations of the land surface, biosphere, atmosphere, cryosphere, and oceans. Initially conceived in the mid-late 1980s, it was implemented as a series of large “flagship” missions and smaller focused satellites, often in partnership with instruments and sometimes spacecraft from other nations (King and Platnick 2018). The numerous instruments and platform hardware of EOS provide the spacious information about all sub-systems of the Earth system: • • • • • • •

Clouds, radiation, water vapor, precipitation Oceans (circulation, productivity, air-sea exchange, temperature) Greenhouse gases and tropospheric chemistry Land surface (ecosystems and hydrology) Ice sheets, polar and alpine glaciers, and seasonal snow Ozone and stratospheric chemistry Volcanoes, dust storms, and climate change

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

9

The EOS Program includes scientific and technical support of the environmental investigations (Tianhong et al. 2003). The EOS missions and EOS Data and Information System (EOSDIS) provide data and the infrastructure to facilitate interdisciplinary research about the Earth system. EOSDIS, as NASA’s Earth science data system, enables the collection of Earth science data, command and control, scheduling, data processing, and data archiving and distribution services for EOS missions. EOSDIS science operations are performed within a distributed system of many interconnected centers with specific responsibilities for production, archiving, and distribution of Earth science data products. These data centers supply search and access of the science data and data products to many science data users. EOSDIS is managed by the Earth Science Data and Information System (ESDIS) that is a part of the Earth Science Projects Division under the Flight Projects Directorate at Goddard Space Flight Center and is responsible for: • Processing, archiving, and distributing Earth science satellite data (e.g., land, ocean, and atmosphere data products) • Preparation for tools to facilitate the processing, archiving, and distribution of Earth science data • Collecting metrics and user satisfaction data to learn how to continue improving services provided to users • Ensuring scientists and the public have access to data to enable the study of Earth from space to advance scientific understanding and meet societal needs However, there is no effective technology that makes it possible to adopt this infrastructure to the basic environmental problems. GIS and modeling technique combined gives such a technology. GIS provides efficient analytical tools for creating perspective maps with various combination processes in a knowledgedriven approach including Boolean logic combination, algebraic combination, index overlay combination, fuzzy logic and vector fuzzy logic combinations, and so on (Givant and Halmos 2009). There are many modeling techniques, including standard modeling languages such as Unified Modeling Language (UML), the Model-Driven Architecture (MDA), and the Enterprise Unified Process (EUP). However, these languages do not cover all existing models of environmental systems and processes. Many models are built based on other approaches (Cracknell et al. 2009a). The combination of such models with GIS is carried out through GIMStechnology (Krapivin and Shutko 2012). In recent years, the global carbon cycle problem has gained particular importance due to the greenhouse effect. Knowing the state of soil-plant formations (SPF) and aquatic ecosystems allows one to have a true picture of the spatial distribution of carbon sinks and sources on the Earth’s surface. As is well known, among the types of remote sensing techniques, microwave radiometry proves to be effective for observations of SPF environmental parameters. However, these observations are a function of different environmental conditions mainly depending on the SPF type. That is why it is necessary to develop data processing methods for microwave monitoring that allow the reconstruction of the SPF characteristics taking vegetation types into account and that provide the possibility of synthesizing their spatial distribution.

10

1

Global Problems of Ecodynamics and Hydrogeochemistry

As noted by Varotsos et al. (2019a, b, c, d), the problem of microwave remote sensing of vegetation cover requires the study of the attenuation of electromagnetic waves (EMW) within the vegetation layer. Solving the problems that arise here is made possible by the combination of experimental and theoretical studies. Vegetation cover is commonly characterized by varying geometry and additional parameters. Therefore, knowledge of the radiative characteristics of SPF as functions of time and spatial coordinates can be obtained through the combination of in situ measurements and models. General aspects of such an approach have been considered by many authors (Del Frate et al. 2003; Dong et al. 2010; Friedi et al. 2002). But these investigations were mainly limited to the studies of models describing the dependence of the vegetation medium on the environmental properties, as well as the correlation between the morphological and biometric properties of the vegetation and its radiative characteristics. One of the approach perspectives to solve the problems arising here is the GIMStechnology (GIMS=GIS[Model). This approach was proposed by Krapivin and Shutko (2012). A combination of an environmental acquisition system, a typical geo-ecosystem operation model, a computer mapping system, and an artificial intelligence tool will lead to the creation of a geo-information monitoring system for the typical natural element that is capable of solving many tasks arising in the remote monitoring of the global vegetation cover. The GIMS-based approach, in the framework of EMW attenuation by the vegetation canopies, allows the synthesis of a knowledge base that defines the relationships between experiments, algorithms, and models. The links between these areas are adaptive in nature giving an optimal strategy for experimental design and model structure. The goal of this chapter is to explain and assess the application of the GIMS method in the tasks of reconstructing the spatial and temporal distribution of SPF radiative characteristics. The main aspects of GIMS technology are described in Krapivin et al. (2015a). The accumulation of knowledge, the gigantic scientific and engineering progress, and the unprecedented growth of human influences on the environment already pose the problem of the global evaluation of the situation, and the possibility of its longterm forecasting. Scientific research in this field has led scientists around the world to the conclusion that solving the problem of environmental quality control is possible only through the creation of a unified international monitoring system based on a global Magnetosphere-Climate-Biosphere-Society (MCBS) system. Many international and national environmental programs are dedicated to the implementation of this system. In the framework of these programs, sufficiently large databases of environmental parameters are created, information dynamics about natural and anthropogenic processes of various scales are accumulated, and model sets of biogeochemical, biogeocenotic, climatic, and demographic processes are prepared. The technical basis of global geoinformation monitoring is covered by efficient means of data acquisition, recording, accumulation, and processing of measurement data obtained from board space, craft, ground, and floating laboratories. However, despite significant progress in many fields of natural monitoring, the main problem of designing the optimal combination of all technical means, creating an efficient and economical monitoring structure, and creating reliable methods for

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

11

assessing the environment dynamics forecasting under anthropogenic effects remain unsolved. The experience of recent years shows the possibility of creating a global model capable of using an adaptive mode of operation to provide recommendations on the monitoring structure and the formation of database requirements. The development of this model is delayed, therefore leading to unjustified costs for conducting a new expedition for environment investigations and building new observing systems. In recent years, many investigators (Krapivin and Shutko 1989, 2012; Kondratyev et al. 2002a, b; Krapivin et al. 2006) posed a problem of synthesizing a complex system for the collection of environmental information coupling GIS, remote, and contact measurements with models. Such systems are called GIMS and are aimed at the systematic observation and evaluation of the environment, its changes under the effects of economic activities of people. One of the important aspects of these systems operation is the ability to forecast the state of the environment and warn of undesirable changes in its characteristics. The realization of this monitoring function is possible by applying mathematical modeling, methods that ensure the simulation of the functioning of natural complexes (Krapivin and Varotsos 2008). The development of models of biogeochemical, biocenotic, demographic, socioand-economical, and other biospheric and climatic processes causes the necessity of formulating requirements in the GIMS structure and its database. According to the proposal of Krapivin et al. (2015b) the simulation of biosphere dynamics is one of the important functions of GIMS. As a result, a new approach to assessing the biosphere state is arising. After all, the basic aim of all investigations toward the GIMS-technology development comes to the following tasks: • The acceleration of cost optimization for the reconstruction of environmental survey systems • Creation of conditions for the optimal planning of the organizational structure of human society • Ensuring a purposeful direction of global processes so that they are for the good of mankind and so do not cause damage to nature As research has shown, there are balanced information selection criteria that span the hierarchy of causal-investigative constraints in the biosphere. They include the tuning of tolerances, the depth of spatial quantization when describing the atmosphere, land, and oceans, the degree of detail of biomes, etc. At an empirical level, expressed in expert’s evaluations by the results of computing experiments, these criteria give a possibility to select informational structure of the geo-informational monitoring system indicating hierarchic subordination of models at various levels. At the same time the creation of an effective global system for environment state control meets the requirement imposed by the regional socio-economic structure of society. This is expressed by the non-uniform development of industrial ecologically impure productions, their degree of concentration by region, the difference of the regional control and collection of information, their technical equipment, etc. Such differences inevitably affect the choice of GIMS structure and its informational technical basis.

12

1

Global Problems of Ecodynamics and Hydrogeochemistry

Thus, the hierarchical structure of the combination of mathematical models, entering the GIMS, is determined by natural-climatic and socio-economic factors, as well as by technical capabilities. The degree of detail of the models depends on their level entry into the common structure and mainly on spatiotemporal characteristics simulated at the given level of natural processes. Global ecoinformatics suggests the development of model banks for various processes in the biosphere taking into account their spatial non-homogeneity and the combination of existing global databases with already operational environmental observation systems. The cooperation of specialists who have developed climatic, biospheric, and social and economic models is proposed with the aim of creating a global model of Climate-Biosphere-Society (CBS) system. As a subsequent improvement of the model, we may study the interaction of the CBS system with processes in the near-earth space (first of all in the magnetosphere) and proceed to the creation of the MCBS model. As a result, a system that is capable of forecasting the evolution of natural processes and evaluating the long-term consequences of large-scale action on the environment will be created. The application of this system will encompass the problems of environmental protection on a global, continental, regional, and local scale with a function of implementing expert ecological examinations on the topsoil, hydrological regimes, and atmospheric air composition structure changes. The realization of the MCBS model permits to integrate into a complex structure of all international and national environmental monitoring tools and gives a tool of objective evaluation of the environmental quality in all states. Filling the system with modern effective data monitoring processing techniques allows solving a wide range of pollution source identification problems by eliminating conflicts due to transboundary pollution flows. The new information technologies in global modeling under the Integrated System of Global Geoinformation Monitoring (ISGGM) (Fig. 1.1) to be used will create a principally new structure of monitoring which will depend on a base of various qualitative data and many mathematical and physical models of various types. The evolution technology will solve many contradictions that arise from incompleteness and undetermined information base, fragmentary knowledge about nature laws, absence and under development of instrumental systems in the field of simulation experiment (Bukatova et al. 1991; Hushon and Clerman 2012; Krapivin et al. 1997a, b). The ISGGM functional structure is based on the idea of implementing evolutionary neurocomputer technology. This realization will have the architecture of a modern computer system software, including the network of evolutionary type tutorial servers. This will lead to an ISGGM structure that will simulate the forward-looking MCBS system giving the optimal evaluation of nature protection measures and other structural decisions in interaction of human society with nature. The practical embodiment of the idea about the MCBS model creation requires the realization of purposeful complex studies among which the most important questions are the following:

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

13

Fig. 1.1 Integrated System for Global Geoinformation Monitoring (ISGGM) and its structure

• Systematization of global changes and the formation of complex ideas about the biospheric processes and the structure of biospheric levels. The development of a conceptual model of the biosphere as a component of the global geoinformation monitoring system (Kondratyev et al. 2004a). • Inventory and analysis of the existing ecological databases and selection of the global database structure (Bréon 2004). • Classification of spatiotemporal features and cause-effect links in the biosphere with the goal of developing a scale of coordination of spatiotemporal scales of ecological processes (Rastetter et al. 2003). • Creation of typical model banks of ecological systems, biogeochemical, biogeocenotical, hydrological, and climatic processes (Cracknell et al. 2009a, b). • Study of biosphere and climate interaction processes. Search for regularities in the influence of the sun on biospheric systems (Krapivin and Varotsos 2007). • Systematization of information about oceanic ecosystems. Description of geophysical and trophic structures with their area division and coordination with spatiotemporal scales (Ong’anda et al. 2009). • Construction of biogenographic field models and the development of synthesis algorithms based on the ocean block of the global model (Krapivin and Varotsos 2008). • Synthesis of the co-evolutionary development scenarios of the biosphere and human society. Creation of demographic models. Parameterization of scientific and technical processes in the utilization of land resources (Norris and McCulloch 2003).

14

1

Global Problems of Ecodynamics and Hydrogeochemistry

• Search for new information technologies of global modeling providing reduction of requirements on databases and knowledge bases. Development of architectural and algorithmic principles of functioning of computer systems of neuron-like elements for evolutionary information processing with high speed and efficiency and integration of these systems into the ISGGM structure (Varotsos et al. 2018). • Development of ecological monitoring concept and creation of a theoretical basis for ecoinformatics. Development of methods and criteria for stability evaluation of global natural processes. Analysis of stability of biospheric and climatic structures (Krapivin et al. 2015b). • Synthesis of the MCBS model and development of computational tools to carry out computational experiments in the context of assessing the consistency of the implementation of the anthropogenic activity scenario (IPCC 2005, 2007; Singer 2008). The analysis of the research of the last in fields investigated by indicated tasks shows that for successful global modeling the development of new methods of system analysis of complicated natural processes and the development of data processing methods aimed at the synthesis of balanced information criteria selection and consideration of the hierarchy of cause-effect connections in the MCBS system are required. There are a series of global models that can be used as MCBS items. For example, the multi-disciplinary IMAGE model simulates the dynamics of the global society-biosphere-climate system and enables the linkages and feedbacks in this system to be explored. The model consists of three fully linked sub-systems: energy-industry, terrestrial environment, and ocean-atmosphere. Krapivin and Kelley (2009) proposed the multi-functional model of global change in the naturesociety system based on GIMS-technology. Degermendzhy et al. (2009) developed a fundamentally new approach to modeling biosphere dynamics by considering the biosphere-climate system as the hierarchy of interacting sub-systems each described by a small-scale model. The accumulated knowledge and experience of global monitoring makes it possible to separate the main blocks of the MCBS model: magnetosphere (Korgenevsky et al. 1989), climate (Randall et al. 2007), biogeochemical cycles (Kondratyev et al. 2003a), biogeocenotic processes (Cocknell et al. 2006; Wang et al. 2011), socio-economic structure (Garsey and McGlade 2006), and scientific and engineering progress (Krapivin and Varotsos 2007, 2008). The development of parameterization methods of these blocks reached a level where the synthesis of the MCBS model is possible based on the principle of coordination of the system of inputs and outputs of individual blocks. The implementation of this process requires solving the main tasks of coordination of spatiotemporal scales of natural processes and the selection of MCBS model connection algorithms with databases. Due to national and state borders two higher spatial levels in the MCBS are differentiated: global and continental. The national and state levels encompass three space scales: national, regional, and local. There are also intermediate levels. Building the MCBS model requires systematization of models and databases at national and state levels and linking them to global models and databases. One of the

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

15

results of this systematization should be the creation of typical models of natural processes as MCBS model key elements that include the national, regional, and local levels. The implementation of the hierarchical structure of the complex of mathematical models included in the MCBS model exceeds the requirements of the modeling algorithms used. Spatiotemporal fragmentary data, noise, and their incompleteness led to the necessity of researching new modeling methods that facilitate parameterization processes of phenomena in models under conditions of incomplete information and non-stationary character of data measurements of environmental parameters. Among these new approaches to modeling simulation-evolution technology was that developed by Bukatova et al. (1991). This technology allows isolating in the MCBS model, in addition to the traditional models of natural phenomena, new types of models that provide the description of weak parameterization processes. Because of this block processes such as scientific and engineering progress, agricultural production, extraction and expenditure of mineral resources, demography, etc. may appear in the MCBS model (Krapivin and Varotsos 2007, 2008). The implementation of GIMS technology needs the numerous algorithms and models to parameterize and to process the observational data. That is why the systematic description of key environmental processes and sub-systems is needed. Currently there are global datasets whose use within the GIMS technology relates to their classification and systematization. Indeed, the balance between environmental datasets of different origins does not exist and therefore one of the real problems is the coordination of observations based on airborne, satellite, and in situ measurements. It is obvious that satellite monitoring has gaps in sensor selection. Optical and infrared bands predominate among them. Many natural phenomena such as the hydrologic cycle, energy balance, and atmospheric CO2 concentration are related to the forest ecosystems. Of the many ways to measure forest characteristics, microwave remote sensing is one way which is independent of both weather conditions and the time of day when the measurements can be acquired. But the use of microwave sensors requires additional investigations. Large-scale tests of GIMS-technology have been carried out in many countries where international test sites have been organized (Shutko et al. 2010; Haarbrink et al. 2011; Varotsos et al. 2020a, b, c, d). There are many parameters describing the environmental conditions on Earth. For example, among them are parameters related to soil moisture and humidity, such as the depth to a shallow water table, contours of wetlands, marshy areas. Knowledge of these parameters and conditions is very important for agricultural needs, water management, land reclamation, measuring and forecasting trends of regional and global hydrological regime changes, and obtaining reliable information on water conservation estimates. In principle, the required information may be obtained using in situ and remote sensing measurements, by accessing prior knowledge-based data, formerly accumulated and stored in databases, in GIS. But the problem that arises here consists in addressing the following questions:

16

1

Global Problems of Ecodynamics and Hydrogeochemistry

• What kind of instruments will be used to carry out the so-called ground-truth and remote sensing measurements? • What is the cost to be paid for the contact and remote information? • What kind of balance between the information content of contact and remote observations and the cost of these types of observations should be considered? • What kind of mathematical models can be used both for data interpolation and their extrapolation in terms of time and space with the aim of reducing the frequency and thus the cost of observations and increasing the reliability of the forecast of the environmental behavior of the observed objects? These and other problems are solved by using the monitoring system based on the union of functions of environment data acquisition, creating control archives of these data, analyzing them, and forecasting the characteristics of the most important processes in the environment. In other words, this unification forms the new information technology called GIMS-technology. Evidently GIMS is a superset of systems showing in Fig. 1.1. There are two different views of GIMS. As far as the first view is concerned, the term “GIMS“is synonymous of “GIS.” Another view is that the definition of GIMS extends to GIS. Continuing with the second view, we will look at the main blocks of GIMS. A key component of GIMS is offered for consideration. It is considered as a natural subsystem interacting through biospheric, climatic, and socio-economic connections with the global MCBS system. A model is created describing this interaction and the operation of the various levels of the spatiotemporal hierarchy of the whole combination of processes in the subsystem. The model encompasses characteristic features for a typical element of natural and anthropogenic processes, and the beginning of model development is based on the existing information base. The model structure is oriented toward the adaptive regime of its use (Fig. 1.2). The combination of the environment information acquisition system, the model of functioning of the typical geo-ecosystem, the computational cartography system, and the artificial intelligent means will result in the creation of a geoinformation monitoring system of a typical nature element capable of solving the following tasks: • Assessment of the impacts of global change on the environment of the standard component of the MCBS • Evaluation of the role of environmental change occurring in the typical element in climatic and biospheric changes on Earth and in certain territories • Evaluation of the ecological state of the atmosphere, hydrosphere, and soil-plant formations • Formation and renewal of information structures on ecological, climatic, demographic, and economical parameters • Functional mapping of the state of the landscape • Forecasting of ecological consequences of the realization of anthropogenic scenario

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

17

Fig. 1.2 Conceptual block-diagram of the use of the MCBS model in the adaptive geoecoinformation monitoring regime

• Standardization of land covers, natural phenomena, inhabited sites, surface contaminations of landscapes, hydrological systems, forests • Evaluation of the population security The construction of the GIMS is linked to the separation of biosphere, climate, and social environment components that are characterized for the given level of spatial hierarchy. That their structure reflects these components (Table 1.1). One of the important items of GIMS is the parameterization of land covers, oceans, and atmosphere. This task is related to the search for efficient procedures to evaluate model parameters using various sensors. For example, Ferrazzoli and Guerriero (1996) discussed the problem of using passive microwave radiometers to solve this task when evaluating forest model coefficients. It is actually known that effective monitoring of the forest ecosystem is possible using microwave sensors. This series has an advantage compared to visible and infrared sensors. The capabilities of the microwave sensors are not revealed. Indeed, the use of microwave sensors in the context of many environmental problems such as desertification, climatic change, and greenhouse effect can help make predictive assessments of global processes more accurate. Forest extinction and health play an important role

18

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.1 Functional structure of GIMS The GIMS Item Acquisition and Express-Analysis Data Subsystem

Initial Processing and Data Acquisition Subsystem

Computer Mapping Subsystem

State of the Atmospheric Assessment Subsystem

Soil-Plant Cover State Evaluation Subsystem.

The item functions The task of an experiment planning with an indication of the structure of the environment data acquisition system using satellites, flying laboratories, mobile, and fixed ground observation points has been solved. The laboratories are equipped with software and hardware tools allow the determination of the degree of environmental contamination, ecological status, mapping characteristic formation, detection of underground centers of ecological injuries, all-weather standardization of land-covers, and detection of permafrost disturbances, oil spiels, forest states, and pollution of water bodies Methods and algorithms of simultaneous analysis of aerospace information and ground measurements are performed using space-time interpolation methods. The data is restored and reduced to a single point in time. The model parameters are determined. Thematic classification of data is carried out and spacetime combination images are performed in the optical, IR, and microwave range, trace measurements obtained from devices of various types Algorithms for the computational formation of maps with marking on them characteristics of evaluating the ecological situation are carried out. A multilevel scaling and fragmentation of the territory is envisaged. Overlaying the output maps with the information the user needs is provided through the user interface Models of the spread of atmospheric pollution due to evaporation and burning of product of oil, natural gas, and industrial enterprises are carried out. The problem of dust content evaluation is solved and gas and aerosol composition of the near-earth atmosphere layer and the creation of predictive maps of their distribution on the Earth surface are offered The software of this subsystem solves the following tasks: Standardization of the floral background taking into account the micro-relief, the soil type and its salinity, humidification, and the degree of soil brine mineralization Revealing micro- and macro-relief peculiarities and subsurface anomalies Determination of the structural topology of the land cover Indication of forests, swamps, agricultural crops, and pastures (continued)

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

19

Table 1.1 (continued) The GIMS Item Water Medium State Evaluation Subsystem

Ecological Safety and Population Health Risk Evaluation Subsystem

Subsystem for Identification of Causes of Ecological and Health Disorders

Intelligent Support Subsystem

The item functions A complex simulation model of the territory is carried out that takes into account the seasonal change of the surface and river runoff, the influence of snow cover and permafrost, the regime of precipitation and evapotranspiration. A model of water quality dynamics for a hydrologic network of territory is constructed Algorithms for assessing damage to nature, economic stability, and population health are carried out depending on the changes in the environment associate with the natural trends of meteorological, biogeochemical, biogeocenotic, micro-biological, radiological, and other natural processes, as well as with the rise of stress state of anthropogenic origin The task of revealing sources of environmental contamination sources is solved. The coordinates of the source, its strength, and possible time of unscheduled contamination substances are determined. Dynamic characteristics of pollution sources are given. Unknown sources of pollution are revealed in advance and directions of possible transboundary transport of pollution are determined Algorithms of software-mathematical are implemented to provide intelligent user support in complex analysis of objective information formed in the context of simulation experiment. Object dialog with the MCBS model is provided allowing the necessary information required in a user-friendly format. Input of data processing corrections is provided. A knowledge base is created on anthropogenic, demographic, and socio-economic processes in the territory

in regulating global biogeochemical cycles of greenhouse gases. It is known that optical systems have high efficiency in detecting leaf parameters but are unable to detect woody biomass. Ferrazzoli and Guerriero (1996) noted that in the P (0.45 GHz) and L(1.2 GHz) bands the backscatter coefficient of dense forest is much higher than that of agricultural fields, especially in HV polarization. On the other hand, a “saturation” effect is observed, such that the increase in the backscatter coefficient is small or absent when the dry biomass is larger than ~50–100 tones/ha. Of course, spaceborne microwave radiometers have the poor spatial resolution. That is why a direct use of data received by means of space or airborne microwave radiometers to evaluate forest parameters has no special perspective. But GIMStechnology guarantees the extraction of useful information from this data with high efficiency. In this connect, it is necessary to use a model describing the studied phenomenon. In the considered case, the model of the forest medium can be

20

1 Global Problems of Ecodynamics and Hydrogeochemistry

represented by a three-level crown-trunks-soil structure. The crown is filled with scatters representing leaves, needles, twigs, and branches. There are various schemes for approaching this structure. The most widespread approach consists in assuming that the scatters have the forms of cylinders, discs, and ellipses uniformly located within the crown. Usually, leaves are represented by discs of different radius, branches, and trunks—by cylinders of different categories. These widespread models have mainly theoretical meaning. Their defect is displayed in the presence of many parameters, the evaluation of which is a difficult task. Nevertheless, the backscatter coefficient calculated by means of such a model can give the representation of the electromagnetic behavior of the observed medium and thus add useful information. The GIMS-structure includes blocks which serve the following main functions: • Data collection (collects current information about the system soil/canopy: soil moisture, depth to water table, soil salinity, biomass of vegetation, rainfall rate, others) • Preprocessing, sorting, and storing data in the data bank • Modeling (simulation) of different kinds of ecological, hydrological, agricultural, climatological processes in different geophysical and environmental systems (these are blocks containing a variety of models of agricultural crops productivity, irrigation systems functioning, geo-ecology, and epidemiology models of certain vector-borne diseases, others) • Estimation of the current state of a specific geophysical system • Forecasting the state of this system in the future • Feedback support The data collection block consists of two main sub-blocks, namely: • A sub-block for collecting prior knowledge-based information • A sub-block to collect current data of in situ measurements and remote observations from mobile, aircraft, and satellite platforms The first of the two mentioned sub-blocks play a very important role in GIMS, providing a model simulation of the observed geo-system. It includes the relationships describing the different geo-systems functioning as well as the relationships between the geo-system and the surrounding media. The second sub-block consists of three boxes providing the following operations: • In situ measurements including sampling (probes) and utilizing current information accessible from meteorological stations and other accessible sources. • Remote observations from aircraft (manned and unmanned). There are different types of sensors used for remote observations. Among these are sensors operating in optical, infrared, and microwave bands of electromagnetic wavelengths. The data measured by all these can potentially be used in GIMS. In the majority of cases we have solved, the data collected by passive microwave radiometers and partly by infrared radiometers were used in this context. These sensors provide measurements of the following parameters:

1.2

Advances in Information-Modeling Technology for Water Quality Monitoring

21

(a) Soil moisture (b) Depth to shallow water table (c) Biomass of vegetation above water surface (rice crop, wetland vegetation) or above wet ground (d) The temperature assessments of a water surface, land, and dense vegetation canopy • Satellite observations of the Earth/Atmosphere/Ocean/Biosphere. Optical sensors provide crop type and land use classification with a ground resolution of about 1–50 m. Infrared sensors provide measurements of land surface temperature, water, and upper boundary of vegetative canopy with a ground resolution of 10–100 m. Microwave radiometers provide estimates of soil moisture, general condition of vegetation, temperature variations of ocean, and dry land surface with a spatial resolution of around 20–50 km. GIMS algorithmic and programming maintenance performed the following functions: • Parameterization of current and prognostic weather and climatic conditions based on in situ and remote observations and by running the required models. • Provide the cluster analysis. • Modeling of a range of agricultural crops productivity. • Modeling the operation of typical irrigation systems. • Collection of in situ, aircraft and spacecraft observations with sorting and storing capabilities. • Estimates of trends in the development of different geo-systems and evaluation of the difference (discrepancy) between measured data and model predictions. • Current service in data flow control and parameter assessments. • Data processing (interpolation, extrapolation) using different methods. • Estimates of the soil moisture profile and total water content in 1 m soil layer using the data from mobile and aircraft observations at microwaves and prior knowledge-based information about the soil. • Modeling soil concentration variations. • Modeling variations in humus characteristics. • Modeling of soil/canopy system growth for 30 plant types. • Mapping of modeling, measurement, and inter/extrapolation results. • Visualization of data in the form of figures, graphs, tables, etc. There are a few more programs and models used for global change studies, for example, the model of carbon cycle, the model of oxygen cycle, and others (Krapivin and Varotsos 2007, 2008).

22

1.3 1.3.1

1

Global Problems of Ecodynamics and Hydrogeochemistry

Adverse Effects on the Survivability of the Earth’s Population Natural Disasters and the Survivability of Ecological Systems

The survivability of the Earth’s population is discussed by many experts who by analyzing different conditions of environmental and social type try to predict trends in global demography (Krapivin and Fleishman 1968; Krapivin 2008; Krapivin et al. 2017a; Krapivin and Mkrtchyan 2019; Varotsos and Krapivin 2017, 2018; Cracknell et al. 2009a). Survival of the population is defined by parameters such as the level of food production, climate, clean water, medicine, and other living conditions, including natural and anthropogenic catastrophes. The concept of the danger of natural disasters is closely connected with the notion of vulnerability. According to Vogel and O’Brien (2004), the term “vulnerability” is, in a sense, vague, and its use in assessing the consequences of stressful situations in the environment often leads to great uncertainties. To characterize the response of the population and environment of a given area to external forcings, along with vulnerability, they use many notions, such as stability, adaptability, survivability, etc. Vulnerability is the ability of a person or a group of people to anticipate, cope with, resist, and overcome damage from a natural disaster. The concept of vulnerability is closely connected with the social characteristics of a given area and can be defined as a function of ecodynamic constituents, such as levels of urbanization and economic development, the state of environmental protection and medical service. In recent decades, there has been a trend toward increasing economic losses due to urbanization and climate change. This means that assessing the vulnerability of the natural-anthropogenic systems of a given area requires the development of a versatile approach to natural disaster risk calculations, reflecting the interaction of biophysical and socio-economic elements. The events in the beginning of the twenty-first century confirm the need to search for more appropriate technologies to assess the CBSS vulnerability, either as a whole or its elements than as a simple estimation of economic losses. The information of Table 1.2 and Fig. 1.3 characterizes the role of natural disasters in changing risks arisen due to variations of environmental parameters. The general characteristics of disasters and their outcomes can be summarized as follows (https://ourworldindata.org/natural-disasters): • Natural disasters kill on average of 60,000 people a year, worldwide. • Globally, disasters accounted for 0.1% of deaths in the last decade. This was highly variable, ranging from 0.01% to 0.4%. • Deaths from natural disasters have declined greatly over the past century—from, in some years, millions of deaths per year to an average of 60,000 over the past decade.

1.3

Adverse Effects on the Survivability of the Earth’s Population

Table 1.2 Global deaths from natural disasters

Disaster category All natural disasters Drought Earthquake Extreme temperature Extreme weather Flood Landslide Mass movement (dry) Volcanic activity Wildfire

1978 35,036 63 25,162 150 3676 5897 86 50 268 2

23 2018 10,809 0 4321 536 1666 2869 275 17 878 247

2019 9000 75 288 122 2964 1750 302 34 21 33

2020

Fig. 1.3 Absolute number of global deaths per year because of natural disasters

• Historically, droughts and floods have been the deadliest disaster events. Deaths from these events are now very low—the deadliest events today tend to be earthquakes. • Disasters affect those in poverty the most: high death tolls tend to be centered in low-to-middle income countries without the infrastructure to protect and respond to events. The Indian Ocean tsunami raises fundamental questions about the mysteries of nature, life, and death. Indeed, humanity again has the event caused by geophysical processes but not by God or other mystical forces. The Indian tectonic plate moved beneath the Burmese plate with extraordinary force causing one of the strongest earthquakes ever recorded. The movement of tectonic plates is a completely natural process that is very difficult for current science to control despite several important

24

1 Global Problems of Ecodynamics and Hydrogeochemistry

efforts (Perez-Oregon et al. 2021). The paradox of this situation consists in the seeming impotence of present science when such a catastrophe occurs. Here it is remarkable to give a comment from Guardian Weekly by Martin Kettle (Schröter 2005): “From at least the time of Aristotle, intelligent people have struggled to make some sense of earthquakes. Earthquakes do not just kill and destroy. They challenge human beings to explain the world order in which such apparently indiscriminate acts can occur. Europe in the 18th century had the intellectual curiosity and independence to ask and answer such questions. But can we say the same of 21st-century Europe? Or are we too cowed now to even ask if the God can exist that can do such things?” Estimates of vulnerability of the community (social group) or landscape (ecosystem) are possible by introducing some scale of indicators. Several approaches are possible here, one of which is to calculate the sensitivity of the CBSS elements to global changes. Such estimates can be obtained using the corresponding model. For example, Krapivin and Varotsos (2008) estimated the consequences of the greenhouse effect; Vogel and O’Brien (2004) described the human insecurity index as well as the agricultural vulnerability index in Indian territory in relation to climate change (Vogel and O’Brien et al. 2004). It should also be noted that the notion of vulnerability includes the means of survival available to the population of a given area suffering from a natural disaster. This aspect is important to assess the vulnerability, as it reflects human behavior under conditions of overcoming the consequences of the disaster and represents the internal mechanisms of correlation between social and physical factors of the formation of the vulnerability index. Another measure of vulnerability is household state and the food availability. Here vulnerability is classified into four levels: weak, moderate, high, and extreme. The household state indicator is related to socio-economic groups with a selection of different information categories: information on demography, level of agriculture development, precipitation, and market state. This enabled Bohle (2001) to construct a 2-level structure of vulnerability that reflected the interaction between political, economic, and ecological factors, assessing the internal and external aspects of the crisis and conflict of the territory under conditions of natural cataclysms. Of course, the territories of developing countries are more vulnerable, since losses due to natural disasters in poor countries, in addition to direct material damage and human victims, break their economic structure and limit their rate of progress toward sustainable development. The high vulnerability of developing countries is also explained by the high risk of major losses from natural disasters, as the dwelling houses of poor people is not protected from natural disasters. After all, a developing country needs more time to neutralize the consequences of a natural disaster. The notion of vulnerability can be used to assess seven categories of threats that shape the humans’ security: • Economic safety (secure economic development)—vulnerability to global economic changes • Food safety (physical, economic, and social availability of food)—vulnerability to extreme events, agricultural changes, etc.

1.3

Adverse Effects on the Survivability of the Earth’s Population

25

• Health safety (relative freedom from diseases and infections)—vulnerability in relation to health • Environmental safety (access to maintaining sanitary norms of water, clean air, and non-degraded surface systems)—vulnerability to pollution and degradation of land • Personal safety (guarantee against physical violence and threat)—vulnerability with respect to conflicts, natural cataclysms, impending AIDS-type “accidents” • Community safety (guaranteeing cultural integration)—vulnerability with respect to cultural globalization • Political safety (preserving human rights and freedoms)—vulnerability to conflicts and war problems, both for scientists and politicians In relation to this classification, three main questions arise: 1. What technologies can be proposed by present science to better understand and evaluate the current complicated reality in which the Earth’s population exists? 2. Can the current science of making political decision-making suggest technologies for more realistic assessments of modern life and reduction of vulnerability? 3. Are there ways to conceptualize vulnerability in terms of benefits and losses under conditions of impending global change? Clearly, answers to these questions can be sought in different situations, from an individual to the whole CBSS. Vulnerability will be different in developed and developing countries, in cities and in countryside, in a desert and in the mountains. Ecoinformatics deals with the development of respective methods of vulnerability assessment. In connection with the necessity to assess the risk to the population of a given area of damage to health, structures or property due to changes in environmental parameters, the term “ecological safety” is used. These changes can be caused by both natural and anthropogenic factors. In the first case, the risk raised from fluctuations in natural processes connected with a change in the synoptic situation, epidemics, or natural disaster. In the latter case, danger occurs as a response of nature to human activity (Kondratyev et al. 2006a). It is obvious that the problem of survivability arises when environmental fluctuations are irregular. In this case living organisms do not have the ability to adapt to the fluctuating environment. Kussell and Leibler (2005) developed the model survivability of population under fluctuating environment. This model parameterizes the population behavior to survive. It is an example of how necessary it is to develop a demographical block of global biospheric models of different scales. In general, the threat of ecological danger in a given area results from the deviation of environmental parameters beyond the limits where after a long stay a living organism starts to change in the direction that does not correspond to the natural process of evolution. As a matter of fact, the notions of “ecological danger” or “ecological safety” are linked to the concepts of stability, vitality, and integrity of the biosphere and its elements. Moreover, the CBSS, being a self-organized and selfstructured system and developing with the laws of evolution, creates within itself

26

1 Global Problems of Ecodynamics and Hydrogeochemistry

ecological niches whose degree of acceptability for the population of a given area is determined, as a rule, by natural criteria (a set of pollution criteria, religious dogmas, national traditions, etc.). Nevertheless, when considering the prospects for life on Earth, one should proceed from human criteria for assessing levels of the environmental degradation because in due course, local and regional changes in the environment evolve into global ones. The amplitudes of these changes are determined by operational mechanisms of CBSS which provide optimal changes to its elements. Humanity increasingly deviates from this optimization in the strategy of interaction with the surrounding inert, abiotic, and biotic components of the environment. But at the same time, humankind as a CBSS element tries to understand the character of largescale relationships with nature, directing the efforts of many sciences toward this aim and studying the cause-and-effect relations in this system. Since the structure of human society is divided into states, under the socio-economic component of CBSS implies a country. The national safety of any country under the present conditions should be assessed based on numerous criteria of a military, economic, ecological, and social character. The development of an efficient technique for an objective analysis of the problem of national safety requires the use of the latest methods of data collection and processing on various aspects of the global system’s functioning. Such methods are provided by GIMS-technology. One of the aspects of national safety is protection against a rapidly growing number of natural disasters, which, under present-day conditions, can be sources of large-scale social unrest and, ultimately, a destabilizing factor for sustainable development. For instance, in Russia alone, the increase in the number of natural disasters over the last decade was 27.3%. Consider the economic-ecological aspect of national safety. From the viewpoint of system analysis, any country can be considered as an object of system analysis functioning in the space of other complicated systems. The interaction of these systems relates to the controlled and non-controlled exchange of elements of economic and ecological categories. The problem arises in the search for optimal strategies for each of the interacting systems. It is necessary to consider a heterogeneous scientific-technical level of these systems and, hence, the difference of approaches to the choice of criteria of assessing the national safety. GIMStechnology proposes the following solution for the problems that occur. The global CBSS model is being developed. This model describes the main processes in CBSS with their digitization in space and time. The model is based on available data and information space. It is part of a single national system of the ecological monitoring of the territory of the country and is combined with similar global and national systems, an interaction with which is possible. As a result of the combination of the CBSS model, the system of collection of data on the environmental and economic parameters of the regions of the country, the system of computer cartography and informatics means, a single national system of observation and control of the economic-ecological safety has been synthesized. This system has a hierarchical structure of information channels with the respective hierarchy of the problems to be resolved. It can provide the operational information to the regions

1.3

Adverse Effects on the Survivability of the Earth’s Population

27

about the state of ecology and economy at any spot on the globe. The system ensures the information about: • Current global changes in the environment • Expected climate changes and the role of existing or planned changes of the environment of the country in changes of climate and the biosphere, in various regions • The state of the atmosphere, hydrosphere, and soil-plant formations on the territory of a country • Availability of the needed data for ecological, climatic, economic, and demographic parameters of any region • The level of ecological safety on a given area • Occurrence of dangerous events for humans and the environment • Trends in changes of the state of forests, marshes, pastures, agricultural crops, river and lake systems, and other natural complexes • Risk of any measures to change the environment At the national level, the system can solve the following problems: • The long-term and timely planning and control of economic activity taking due account of its ecological feasibility and development of strategic rational use of nature • Operational notification and warning about processes taking place outside and within the territory of a country, which can worsen the ecological situation and cause long-term changes in the environment with an increasing risk to the health of population of individual regions • Assessment of the consequences of the implementation of anthropogenic projects for a country and other world regions • Processing of direct measurements to eliminate the causes of occurrence of ecological disasters and natural disasters The development and implementation of an efficient technology to assess the ecological safety on a global scale will be made possible within the framework of the ISGGM that enables the implementation of man-nature co-evolutionary mechanisms. The main mechanism of this combination will be new technologies of data processing based on advances in evolutionary informatics and global modeling. As a matter of fact, it is a question of implementation of an approach developed by several authors (Bukatova et al. 1991; Nitu et al. 2004) to model processes under conditions of incomplete a priori information about their parameters and the presence of mainly unavoidable information gaps. According to the scheme in Fig. 1.1, ISGGM is characterized by a set of the following functions: • Collection of information from national monitoring systems and international centers of environmental studies • Sorting-out, primary processing, and accumulation of data on natural processes • Formation of the base of knowledge of processes in the environment

28

1

Global Problems of Ecodynamics and Hydrogeochemistry

• Simulation, numerical and physical modelling of climatic, biospheric, cosmic, social, and economic processes • Forecast of the environmental state and formation of the constantly renewed bank of scenarios of anthropogenic activity • Monitoring of investigations by national and international environmental protection agencies • Issue of recommendations to national and international environmental monitoring centers The indicators characterizing GIMS as the main sub-system of ISGGM are grouped by thematic principles of organization of its structure. They are specified in the process of GIMS exploitation and cover the key characteristics of global topography, synoptic situation in energy-active zones, the content of dangerous atmospheric pollutants in characteristic latitudinal belts, and information about catastrophes. An input to GIMS is a multitude of irregular in space and fragmentary in time data of measurements of geophysical, geochemical, ecological, biogeocenotic, and synoptic characteristics. In situ and remote sensing measurements are made using devices with different degrees of accuracy. An agreement of the obtained measurements with other GIMS units is accomplished by algorithmic procedures of primary data processing. The volume of these data will be reduced in the process of GIMS functioning. The GIMS input also foresees a possibility to receive signals from scenarios of anthropogenic development of situations under study. The GIMS model is shown as a conceptual scheme in Fig. 1.4. The correlation of input and output parameters is accomplished through a composition of information fluxes mentioned here. The GIMS functions in the adaptive regime, and the final result of the system affects the input characteristics of the measuring unit. The mathematical software of the adaptive GIMS unit is shown in Fig. 1.5. Here all biogeochemical and biogeocenotic processes are described by the systems of balance equations. However, a considerable part of poorly parameterized processes is described using the method of evolutionary modeling oriented toward the unformulated parameterization of strongly non-stationary processes. The global socio-economic structure can be divided into m levels. According to Kondratyev et al. (1994a, b, 1997), m ≥ 9, and this structure has three main levels of regional development: developed, developing, and underdeveloped. The implementation of the ISGGM project will accelerate the process of leveling of this structure, since an optimization of planning an organized structure of human society will be accelerated, and the purposeful direction of global processes will be provided for the public benefit and without damage to nature, and above all, it will favor the creation of international mechanisms of the coordinated behavior of the global population in relation to the use of nature. Humankind will profit from ISGGM in the sense that finances will not be spent in vain to the accomplishment of ecologically unacceptable projects and balance with nature will be preserved. With advances in science and engineering, this profit will grow, as the transition to new kinds of resources raises no doubts.

1.3

Adverse Effects on the Survivability of the Earth’s Population

29

Fig. 1.4 Concept of adaptive adjustment of the CBSS in geoinformation monitoring: GIMStechnology

Let us formulate the problem of numerical assessment of the ecological safety of the country using the complex systems theory and vitality theory. The national system of the country, A, interacts with other similar systems but with different spatial location. For the sake of simplicity, all other systems will be denoted as B. In other words, all other countries will be identified in a first approximation with a single system, B. Further, this situation can be complicated by considering many systems with which the system A interacts. Systems А and В have aims, structures, and behaviors (strategies) The aim А (В) of system А(В) is to reach certain preferred states. These aims can be of diverse hierarchical character. The parametric presentation of the aim is one of the important

30

1

Global Problems of Ecodynamics and Hydrogeochemistry

Fig. 1.5 The main structure of information fluxes in the system of geoinformation monitoring data processing using evolutional modelling technology (Bukatova et al. 1991)

problems. As a possible suggestion, consider the following components of the system А: Аq—integral indicator Q of the quality of the environment throughout the country’s territory should not be below the threshold q; АL—the maximum permissible concentrations L(i,j) ( j = 1, mi) of substances should not be violated at the j-th part of the territory of the country in the domain i (i = 1 – soil; i = 2 – water; i = 3 – atmosphere); Аe—the economic potential of the country over the time Δt should increase by s percent. The aim В of the system В can refer to А as antagonistic, neutral, or cooperative. This relationship is determined by the type of criterial functions for both sides. The feasibility of the structure |A| (|B|) and the purposefulness of behavior A B of the system А (В) is estimated by efficiency with which the system achieves its aim. The behavior of the systems can either favor or be neutral or prevent the systems from reaching their aims and the aims of another system. In the first case, a pair of systems can be considered as a single system with the aim in common interacting with other systems. In other cases, it is a matter of systems’ relationships. In general, the spectrum of interaction of the natural-anthropogenic systems defined as either quasi-homogeneous regions or individual countries, or their groups, includes several factors among which Lomborg (2004) points to climate change, infectious diseases, conflicts, education, financial instability, corruption, migration, inferior food, starvation, trading barriers, and inclination to war. Unfortunately, a formalized consideration of these factors is still difficult. Therefore, they are further understood in the formulation of more general indicators and models. Since systems here denote national ecological systems, it is natural to introduce some statements with respect to the ways of their interaction. Such systems are open, and their interaction can be presented in the form of exchange with some resources (finances, technical means, natural resources, etc.). It can be formalized by

1.3

Adverse Effects on the Survivability of the Earth’s Population

31

introducing some resource, V, spent by the system and the resource, W, consumed by the system. As a result, an (V, W)-exchange takes place between the interacting systems. It is clear that each system wants to make this exchange profitable for itself. Hence, there is a possibility of further formalization of the systems’ functioning. In other words, let us believe that the aim of each system is the most profitable (V, W)exchange, that is, each system for a minimum of V tries to get a maximum of W, which is the function of the structure and behavior of the interacting systems: W = W V, jAj, jBj, A, B = W ðA, BÞ

ð1:1Þ

As a result, the interaction of the systems А and В is numerically reduced to the following relationships (models): W a = W a ðV a , A0 , B0 Þ = max min W ðV a , A, BÞ fA, jAjgfB, jBjg

ð1:2Þ

W b = W b ðV b , A0 , B0 Þ = max min W ðV b , A, BÞ fB, jBjgfA, jAjg where Ао and Во are optimal systems. From these relationships it appears that to determine its aim, each system should decide, which is important for itself: to obtain the most profitable (V, W)-exchange or to prevent another system from doing this. In this case, the systems can vary the values of (V,W)-exchanges within some limits W1 ≤ Wa ≤ W 1 , W2 ≤ Wb ≤ W 2 , where W1 and W2 correspond to maximum aggressive states of the systems, and W 1 and W 2 correspond to the most careful ones. With the aims of the systems known, we have a clear situation. But if each system or one of them conceals its intentions, we have a game situation with respect to the choice of the aim. Denote as А i and В j (i = 1,..., n; j = 1,..., m) the sets of aims of the systems А and В, respectively. The aims А1 and В1 consist in causing maximum damage to another system (maximum aggressiveness), and the aims Аn and Вm correspond to extreme cautiousness of both systems (maximum favor). All the other aims are scaled in {i} and {j} in the order of transition from А1(В1) to Аn (Вm), including the aims Aq, AL, and Ae. Assuming that in the situation {Аi, Вj} the systems get profits Wa = aij and Wb = bi, we obtain a bi-matrix game to determine an optimal aim with payoff matrices ||aij|| and ||bij||. In a special case, at Wa = - Wb the game becomes competitive. Note that in a general case, the situation should be studied in a probabilistic space, that is, some probability P(V, W ) of reaching its aim by each system. Moreover, it is necessary to consider various manifestations in the systems’ behavior: reliability, information content, controllability, learning capability. The systems’ elements should have different functions and purposes: protective, vital. In addition to equations of the (V, W)-exchange, dynamic relationships should be considered which describe the temporal dependence of the systems’ parameters. In this case, mathematically the problem of assessing the level of ecological safety of the territory of the country is reduced to a differential game.

32

1

Global Problems of Ecodynamics and Hydrogeochemistry

At the national level, as has been mentioned above, the criteria are numerous. The state should observe certain sanitary-hygienic and ecological norms in given climatic situations. These situations should be predicted and serve as initial conditions for the system of estimation of ecological safety. The environmental quality is a complex function of temperature, T, wind speed, U, total content of heavy metals in water, E, air, D, and soil, G; the content of gas of the k-th type (k = 1,. . ., N ) in the atmosphere Sk; biomass of vegetation cover, M, and other parameters: Q = Q(T, M, U, E, D, G, S1,. . ., SN). A similar situation exists with prescribed functional presentation of dependences L(i,j) and other environmental characteristics on natural and anthropogenic parameters. Moreover, many of these parameters can be presented as functions of the investment policy of the country. For example, investments are made into struggle against pollutions, agriculture development, road building, development of new technologies, etc. These parameters are vital for indicators of the environmental quality, and the problem will be reduced to a search for an optimal investment policy. A set of models of the dynamics of the environmental parameters and optimization relationships mentioned earlier determine the problem of synthesis of national policy in the field of nature-protection activity with due regard to respective policies of bordering countries and the whole global community. Of course, the development of regional strategy to prevent losses from natural disasters should take into account their statistics with the distribution of respective losses. Table 1.1 exemplifies a possible listing of multi-year natural disaster monitoring results. Taking this into account, the first-priority problem consists in concretization of goal functions and their dependences on parameters taking into account both internal and external national strategy in the field of ecological monitoring. Finally, the numerical problem is reduced to a boundary-value problem for the system of differential equations of the parabolic type. The system of equations describes the dynamics of pollutions over the territory of the country and the boundary-value conditions will be determined with due regard to behavioral strategies of adjacent territories. Solutions of the boundary-value problem will be introduced into equations of the (V, W)-exchange which will ultimately determine the ecological safety. It should be noted that the above-mentioned approach to the development of technology of assessing the danger of natural or anthropogenic disaster contains many free elements requiring specification or further development. In particular, as shown by Kondratyev et al. (2004b) and Posner (2004), the problem of danger assessment is interdisciplinary, and its solution requires joint efforts of the experts in sciences with lawyers, economists, psychologists, and sociologists. In principle, at present there is a necessity of a broader understanding of the term “disaster.” Many scientists have recently paid attention to ultimate cases when the danger can threaten most or even the whole humankind. It is a question of large-scale catastrophes, such as collision of the Earth with a large asteroid, irreversible global climate change, propagation of mortal viruses, unpredictable consequences of an accident with a particle accelerator, etc. For instance, Posner (2004), discussing the potential danger for humankind, speaks about possible transformation of the Earth into a super-dense dwarf with the diameter 100 m, or its populating by super-

1.3

Adverse Effects on the Survivability of the Earth’s Population

33

intellectual nano-robots, after which the humankind loses control of life development. Unfortunately, as a rule, such issues are considered alarming, fantastic, or as an object of scientific fiction. However, numerous potential events with dying-out of humankind surely contain elements of possible and conceivable scenarios and forecasts. It is important that both psychological and cultural perceptions of the elements of this multitude by the public and politicians correspond to the level of certain rationality and understanding. The problem of sustainable development and human survivability is solved by many countries without the consideration of comparative data that characterize regional correlation between negative anthropogenic impacts on the environment and the possibility of nature to be recovered. Tarko (2005) gives the obvious example of unequal difference between industrial emissions of CO2 and its sinks for different countries. As an example if Russia, Canada, and Brazil have a small deviation of these CO2 fluxes, but the US, China, and Japan redouble the situation with the greenhouse effect. This implies that the survivability problem must be solved in the world by optimizing national (V, W )-exchanges.

1.3.2

A Biosphere Survivability Model

The CBSS can be presented as a totality of nature, N, and human society, H (Homo sapiens), which constitute a single planetary system. Therefore, when developing global or regional models, their division should be considered conditional. Systems N and H have hierarchical structures |N | and |Н, goals N and Н, behaviors N and H, respectively. From a mathematical point of view, the interaction of the systems N and Н can be described by a set of relationships (parameterizations) reflecting in a general case an accidental process η(t) with an unknown law of distribution and consisting of a composition of partial processes of the interaction of these systems. Therefore, the goals and behaviors of the systems are functions of the indicator, η. There are intervals in which the η varies, and in which the behaviors of the systems can be antagonistic, indifferent, or cooperative. Humankind is adapted to some formalized division of the space into countries grouped on the principle of economic development, first. A country Нi has mi possible means to reach the goal H i, in other words, it uses a 1 m j set of strategies H i , . . . , H i i . The weight of each strategy H i is prescribed by the mi

parameter pij

j=1

pij = 1 . The resulting value of the parameter η is a function of

the characteristics mentioned above, and overall, at each moment it is described by a game theory model. An objective assessment of the environmental dynamics N = (N1, N2) is possible with certain assumptions using the models of the biosphere N1 and climate N2. Such models have been developed by many scientists, and an experience gained covers

34

1 Global Problems of Ecodynamics and Hydrogeochemistry

the point, regional, box, coupled, and spatial models. This experience enables one to synthesize a global model of a new type covering the key connections between the hierarchical levels of natural and anthropogenic processes. In general, the state of the systems H and N can be described with vectors xH(t) = x1H , . . . , xnH and xN(t) = x1N , . . . , xm N , respectively. The combined trajectory of these systems in the n + m – dimensional space is described by the function η(t) = F (xH, xN), the form of which is determined by solutions of the global model equations. The form of F is determined within the knowledge of the laws of co-evolution, and therefore here we have a wide field to investigate in different spheres of knowledge. The available estimates of F indicate an interaction of the concepts “survivability” and “stability.” It is evident that the dynamic system is “alive” in the time interval (ta, tb), if the phase coordinates determining it are within “permissible limits” xiH, min ≤ xiH ≤ xiH, max ; xjN, min ≤ xjN ≤ xjN, max . And since the systems H and N have the biological bases and limited resources, one of these boundary conditions is unnecessary, that is, for the components of vector x = {xH, xN} = {x1,...,xn + m} the condition xmin ≤ η (t)

nþm i=1

xi should be met. This simple scheme includes the

requirements to preserve the total energy in the system and a variety of its components. Of course, the concept of biospheric survivability is more capacious and informative. As many authors believe, in the system ecology this notion means stability and integrity of the system, which is the ability of the biosphere to withstand an external forcing. In other words, the survivability of the biosphere is measured by its tendency to suppress strong oscillations of its structure and elements, restoring their equilibrium. Thus, the biospheric survivability is its ability to actively resist the external forcings, to keep its characteristics for a long time, with due regard to probable states of its subsystems in which it is still functioning, and to continue its functioning in case of deviations in these external forcings. The development of the so-called strategies of natural and anthropogenic components of the biosphere relates to a search of life-securing technologies (self-protection technologies) able to help people in crisis situations to adapt themselves to extreme environmental changes. These technologies are genetics, robotics, artificial intelligence, and nanotechnology. One of the strategies of humankind survival under conditions of large-scale disasters is to create adapted-to-danger transport systems, engineering constructions, water resources protective systems, robotic mechanisms for rescue and reconstruction operations, etc. For instance, the existing transport highways in the zones of possible origins of tsunami cannot ensure an operative evacuation of population to secure regions. It is clear that for these purposes, in the coastal regions, air, underground, or sub-water means are needed to rescue people. Similarly, in the zones of extreme natural phenomena like flood, tsunami, or earthquake, it is necessary to construct systems to protect drinking water and food resources. An alternative solution of these problems is to build protective dams which can resist tsunami, lava, and water fluxes and protect people. With this aim in view, for instance, in 1998 in the USA the Federal Emergency Management Agency (FEMA) was organized

1.4

Sustainable Development and Quality of Water Resources

35

with the aim to develop protective means and constructions which would make it possible to avert extreme situations in the regions where these situations are highly probable. Clearly, it is high time for humanity to think of these problems. The civilization progress achieved during the last 5000 years can be annihilated by an accidental meteoritic storm, the onset of glaciation, the global-scale land flooding, or the southward turn of the Gulfstream. All possible scenarios, even almost improbable, should be studied during this century. Humankind cannot stop further progress; people must use the achievements of cosmonautics to start a new stage of using solar system’s resources instead of the resources existing on our planet.

1.4 1.4.1

Sustainable Development and Quality of Water Resources Introduction

Although the problem of sustainable development has been widely discussed in scientific literature (Klarin 2018; Krapivin and Varotsos 2007; Agueman et al. 2003; Altinbilek et al. 2005; Chamine et al. 2018; Springett 2005), the respective information flux prompts to return to analysis of the respective conceptual ideas. However, first it is necessary to remind the ambiguity of the applied terminology. It has been mentioned already that the translation of the words “sustainable development” (in Russian) is inaccurate. “Maintained stable development” would be more correct, but for the sake of convenience and taking into account that the term “sustainable development” has become of common use, we shall use this term. The concept of development sustainability in its present understanding first appeared in 1974 in the documents of the International Church Council (Grimstead 2003) in response to the appearance in developing countries of objections concerning the exaggerated fears about the environmental condition, when millions of people suffer from poverty and hunger. In 1980, the concept of sustainable development was put forward by the International Unit for Nature and National Resources Protection and became widely known in 1987 after publication of the report “Our Common Future” (Bruntland 1987) prepared by the UN Commission on the Environment and Development headed by Gro Harlem Brundtland, who is now the head of the World Health Organization. According to the “Brundtland Report” (BR), the sustainable development is defined as development which meets the needs of the present generation, without damaging future generations. In this context, equality both within and between generations is of particular importance (Bermejo et al. 2010). A much more complete and concrete definition of the notions of sustainability (in the context of CBSS dynamics) has been suggested by Kobayashi (2005) (several reviews of these problems can be found in Cropp et al. (2005) and WB (2003)). According to this definition, sustainable development (SD) means “actions

36

1 Global Problems of Ecodynamics and Hydrogeochemistry

accomplished by humans that show respect to a variety of living beings, which ensures the preservation of life, nature and culture for future generations within the ability of the environment, as well as establishing relationships in order to build a better society in the future and seeking a higher level of happy life for maximum population independent of time and space.” Chapin III et al. (2004) consider four categories of sustainability that refer to nature (A), economy (B), society (C), and well-being (D). Based on analysis of data for Japan, the choice of 20 indicators of SD has been substantiated for these four categories of sustainability as applied to four spheres (Kobayashi 2005): A. Nature: (1) biodiversity, (2) global warming, (3) cycle of resources, (4) water, soil, and air, (5) ecological formation. B. Economy: (1) energy, (2) resources productivity, (3) food, (4) financial status, (5) international cooperation. C. Society: (1) security, (2) mobility, (3) sex characteristics, (4) traditions and culture, (5) financial fluxes. D. Wellbeing: (1) life satisfaction, (2) science and education, (3) participation in social life, (4) health, (5) differences in living standards. Analysis of the estimates of the SD indicators listed above showed that for Japan, a conditional integral SD indicator in 2005 was 33.5 (versus 100 for the supposed stable society in 2050), while in 1990 this indicator was 41.3, that is, sustainability decreased by 19%. The slogan “sustainable development” has been picked up by governments and international organizations and is widely used. At the UN Conference on the Environment and Development held in Rio de Janeiro in 1992 and in UN Climate Change Summit (UNFCCC, 2019), this slogan was supported by the leaders and governments of many countries, although, on the other hand, such a vigorous official support raised doubts of specialists in the environmental problems with respect to SD concept as vaguely formulated and differently interpreted (Garaguso and Marchisio 1993; Espinosa 2019). Executive Secretary of the UNFCCC Patricia Espinosa characterizes results of Paris Climate Forum 2019: “The Paris Agreement was an unprecedented turning point in the global struggle against climate change. It was a commitment by nations of the world that, for the first time, they would work together to combat climate change, adapt to its effects and assist developing countries in doing the same. It was a multilateral success, charting a new path, and offering both hope and action to people around the world.” Dresner (2002) analyzed the historical development of the SD concept, the content of present debates about these problems as well as obstacles to achieving SD and prospects for bridging these obstacles. Jacobus (2006) gives an overview of the origin of the concept of sustainable development by going far back in history to trace its roots and shows how the idea of sustainability has evolved over the centuries in opposition to notions of progress. Mensah (2019) mentions that sustainable development has become a popular buzzword in contemporary development discourse. However, despite its spread and the massive popularity over the years, the

1.4

Sustainable Development and Quality of Water Resources

37

concept still seems unclear as many people continue to ask questions about its meaning and history, as well as what it entails and implies for development theory and practice. As early as 1627, F. Beckon (Lucas 2018) expressed an idea that science should be able to dominate nature reaching a better understanding how nature develops: “I now understand how to put Nature with all its children at your disposal in order it served you and was your slave.” However, soon after this, Decarte (1994) expressed the idea that reasons of Nature cannot be understood and separated a rationally thinking human from the rest of Nature which cannot be considered as a machine. Newton’s laws of motion and the subsequent rapid development of science in the seventeenth to eighteenth centuries agreed with these ideas. Political theory (Belov 1999; Scoones 2016) based on the deductive analysis of rights to life, freedom, and property has been important. This theory promoted T. Jefferson (Thompson 2010) to develop the American Declaration of Revolution inspired by philosophers such as Voltaire and Rousseau (Majksner and Oswald 2016; Messner 2015). In Great Britain the idea of rationality has been widely recognized in the sphere of economics. The works of the Scottish economist A. Smith (Weinstein 2001) have been an intellectual support to capitalism and market. The creation of the steam-engine at the end of eighteenth century marked the beginning of a new period which Fridrich Engels (1880) called the “industrial revolution” (Elster 1986; Mohajan 2019). In the course of only two generations, people’s life has changed radically. Technical progress in social organization has enabled humans to achieve a certain degree of control over Nature on unprecedented scales. As early as 1798, when the industrial revolution had just begun, Malthus published his “Essey on principle of population” (Maltus 1992) in which he expressed now widely known ideas on the growth of population size in geometric progression, considerably exceeding the rate of food production. Disagreeing with his opponents (Karl Marx and Fridrich Engels were the most radical opponents), Malthus (1798) wrote: “I’ve read some ideas about the perfection of humans and society with great pleasure. The respective appealing picture warms and delights me. I heartily wish to reach this perfection, but I see great and, to my mind, insoluble difficulties on this way.” In this regard, it should be noted that the problem of natural resources turned out to be not as urgent as it seemed earlier (Lomborg 2001, 2004), rapid increase of population (concentrated in poor developing countries) is actually a serious problem for the present world. Even the socialist Charles Fourier (1876), many years ago, expressed his concern about the physical limits of natural resources, and the writer Mary Shelley (Brocklesby 2003; Shelley 1994) in her novel “The Last Man” (Shelley 1826) gave a first warning of the impending global ecological disaster. The same subject was touched upon by John Stuart Mill (Mill 1909) in his famous book “Principles of Political Ecology.” At present, this concern is best formulated in terms of the broken closure of global biogeochemical cycles and the broken mechanisms of biotic regulation of the environment. In this connection, one can recall the words of Friedrich Engels (Engels 1880): “Thus at every step we are reminded that we do not control Nature like conquerors, being outside Nature. In fact, we, with our body, blood and mind,

38

1 Global Problems of Ecodynamics and Hydrogeochemistry

belong to and exist in Nature. Our regulation of Nature consists only in our having an advantage over all other living beings, in our ability to know and correctly apply the laws of Nature.” The concern discussed was first formulated internationally at the UN Conference on the Human Environment held in Stockholm in 1972 (Boudes 2011) in the form of the slogan “Only one Earth” (or “Space Vehicle Earth”). This metaphor was intended to highlight two main circumstances: 1. Limits to human activity 2. Needed human control of environmental dynamics It is precisely the time for the re-appearance of Malthusianism (Malthus 1798) and the comprehension of global ecological limits (Fragnière 2016). A number of publications have appeared dedicated to these problems stimulated by the Roman Club (first of all, this is mentioned in the book by Meadows et al. (1972)). An important stage was the preparation of the report initiated by US President Carter (1977–1981) with the aim of analyzing the global ecological situation and forecasting its development until the year 2000. In particular, it was mentioned in the report: “If current trends continue, the world in the year 2000 will be more densely populated, more polluted, and more prone to disasters. Serious stresses are coming connected with the problems of population resources and the environment. Despite the increase in material production, the people of the world will become in many ways poorer than they are now.”

1.4.2

Forming the Concept for Sustainable Development

The fundamental role in understanding the fact that the environmental problems are a critically important aspect of socio-economic development was played by the UN Conference on Environmental Problems held in 1972 in which a Declaration on environmental problems was adopted, and then UNEP created the UN organization on environmental problems. In 1974, the concept of sustainable society was first presented at the Ecumenical Conference on the problems of science and technologies related to socio-economic development convened by the World Church Council (WCC 2005). According to this concept (WCSDG 2004): “First, social stability cannot be reached without a uniform distribution of deficit or without possibility of combined participation in resolving the social problems. Second, the global society cannot be stable if food needs at any time are not much below the global level of their provision, and the level of pollutants emissions is below the ability of their accumulation by ecosystems. Third, a new social organization will be stable only under conditions that the scale of the use of non-retrievable natural resources does not exceed the growth of resources due to technological investment. Finally, a stable functioning of society needs a level of societal activity which can never be affected negatively by the unceasing strong and frequent natural global climate changes.”

1.4

Sustainable Development and Quality of Water Resources

39

An important feature of the concept of sustainable society is that it is based, first of all, on the principle of uniform distribution of resources, which then became the corner stone of the “Brundtland Report” (BR) as well as on the idea of the “democratic participation” whose importance was then emphasized at the Second UN Conference on the Environment and Development in Rio de Janeiro. The term “sustainable development” has first appeared in the document “the World Conservation Strategy” published in 1980 by the International Union for the Conservation of Nature and Natural Resources (Workman 2005), and the notion itself has been defined as an “integration of preservation (nature protection) and development to provide a planetary change which can ensure safe survival and welfare of all people.” The “development” is defined as “a modification of the biosphere with the use of human, financial, living, and non-living resources to satisfy the humans’ needs and improve life standard.” The development should be combined with preservation (nature and resources protection) defined as the humans’ control of the biosphere which can ensure a maximum usefulness for the present generation with maintained biospheric potential that would meet the needs and aspirations of future generations. In 1982, by a decision of the UN General Assembly, the World Commission on Environment and Development (WCED) was organized. It was headed by Gro Harlem Brundtland (later on this Commission was called the Brundtland Commission (BC)). The key thesis of the BC report “Our common future” published in 1987 was as follows (Ebel et al. 2004): “Many contemporary trends of development lead to the growing number of the poor and are subjected to various negative effects, and at the same time, the environment is degrading. How can this development serve the world during the next century in the same environment but with a doubled population size? Understanding this situation has broadened our views of the future. We should perceive a necessity of such a new way of development which would provide a stable progress in humans’ life not in several places during several years but over the whole planet till the far-away future.” This “new way” was called “sustainable development” (SD) capable to meet the present needs without any damage to future generations. It should be emphasized that this simple and vague definition has been further interpreted in a varied and contradictory way, although the BC report contains a rather detailed comment on the SD concept. In particular, the following vies attract attention: “The SD conception states the presence of limits—not absolute limits but limitations imposed by the present level of technologies and social organization development on the environmental resources and the ability of the biosphere to ‘assimilate’ the consequences of humans’ activity. However, new technologies and social organization can be controlled and improved to establish the ways of development of a new era of economic growth. The commission believes that the widely spread poverty is no more inevitable. Poverty is not only evil in itself. It is important that SD requires satisfaction of the basic needs of all people and giving everybody a possibility to satisfy their aspirations for a better life. The poor world will be always subjected to ecological and other disasters.” And then:

40

1

Global Problems of Ecodynamics and Hydrogeochemistry

“In the past, we were concerned about the impacts of the economic growth on the environment. Now we should show our concern with respect to the effects of ecological stresses—degradation of soils, water resources, atmosphere and forests— on the prospects of economic development. During the recent past, we had to face an intensive economic interdependency of the countries. At present, we are to get accustomed to their accelerating ecological interdependency. Ecology and economy have become more and more interrelated—on local, regional, national and global scales—under the influence of numerous cause-and-effect feedbacks.” The BC report was an important stage of preparation of the UNCED held in Rio de Janeiro in 1992. The most important documents adopted by the Conference included: the Framework Convention on the problem of climate change; convention on biodiversity; statement on forest problems; Rio declaration; agenda for the twenty-first century. As Dresner (2002) has justly noted, the major value of UNCED consists in perception of global environmental problems as one of the key aspects of the international politics. One of the important indicators of the changed situation was the participation in UNCED of more than 100 leaders of various countries, whereas at the Stockholm Conference (5–16 June 1972) only prime ministers of Sweden and India took part. Since the respective problems have been widely discussed in scientific literature (Krapivin and Varotsos 2007, 2008; Cracknell et al. 2009a), we will not dwell on them here as well as on further events connected with the 19th Special Session of the United Nations General Assembly (23–27 June 1997, New York) and with the World Summit on Sustainable Development held in Johannesburg in 2002. An analysis of situation development concerning sustainable development has recently become more urgent. The debates in the UN Special Assembly have demonstrated the presence of a gigantic gap between declarations and reality. The creation of the World Trade Organization (WTO) has caused a serious concern of ecologists in connection with fears that WTO can show a trend of decreasing the ecological standards in the shortterm interests of increasing the goods’ competitive ability. Also important is the fact that favoring the free trade expansion, the WTO involves economically weak countries into the system of international competition, which they cannot endure and which strongly secures for them the status of economically underdeveloped countries without resources, in particular, to resolve the problems of ecological security. Other negative aspects of WTO activity are also important. One of the convincing illustrations of inadequacy of numerous declarations adopted at the World Forums is the history of working out of the Kyoto Protocol (KP) and its coming into effect on February 16, 2005. Since these problems have been discussed earlier (Kondratyev 2004), we shall only note a paradoxical absurdity of the present situation: it has been widely acknowledged that even a complete realization of KP recommendations on reduction of GHG emissions to the atmosphere will not affect the global climate, but on the other hand, despite a great expenditure connected with emissions reduction, the real situation is that in the world, as a whole, they go on growing. Note, by the way, that global change has recently been characterized mainly by negative trends: deforestation and the loss of

1.4

Sustainable Development and Quality of Water Resources

41

biodiversity have continued, the problems of freshwater resources and the impact on the coastal and marine environment have become more intense. One of the possible reasons of the gap between declarations and reality is connected with the vague definition of the notion of sustainable development, which, on the one hand, concentrates attention on the stability problems, but, on the other hand, emphasizes the importance of development (in this connection, it is apparent that the notions of “stability” and “sustainable development” cannot be considered identical and therefore their interchangeable use in the “Agenda for the 21st century” (Messner et al. 2019) is, of course, incorrect). Provision of the balance of two aspects of the discussed notion is a difficult problem, the more so that manifestations of “extremism” exist on both sides. The situation becomes complicated even more by an ambiguous notion of “development” (of course, the matter concerns the socio-economic development and hence, the human dimension should occupy the key place). Since the concentration of effort on provision of economic growth has led to an underestimation of other aspects of the social progress, the concept of meeting the “base needs,” widely used in the mid-1970s, should foresee both material (food, home, health, education, etc.) and non-material needs (the basic human rights, participation in public activity, etc.). In the early 1980s, the problem of debts, however, seriously intensified, which forced many governments of developing countries to reduce the level of expenditure on reaching the goals concerning the “human dimensions.” This change has also been favored by the requirement to cut the social expenditure and structural change as a term for further loans, brought forward by the WB and IMF. As for the structural change aimed at accelerated movement toward a free market, it was linked to measures such as a weaker role of the government in economy, liquidation of subsidizing some spheres of economy, liberalization of prices, expansion of privatization, an unlimited openness of national economies to international trade and finances. In this connection, of importance was an announcement in the Brandtland Report that meeting the base needs is of first priority. However, the report emphasized an importance to ensure the economic growth which should be favored by structural changes. In this context, an alternative conception of human development is put forward by the Indian economist Amartya Sen, the 1998 Nobel Prize winner (Amartya and Gates 2006). According to this conception, the life standard should foresee not only the ensuring of an average level of income, but also satisfying the basic vital values of society. The Human Development Index (HDI) worked out within the framework of the UN Development Programme (UNDP) can serve as a criterion of the level of human development and involves such indicators as lifetime, education, and material well-being. With HDI taken into account, one could rank the countries, which gave rather interesting results. For example, it has been shown that poor countries such as Costa Rico, Cuba, and Sri Lanka can compete with developed western countries due to high levels of lifetime and literacy. Despite a low level of income in the Kerala state (India) determined by per capita GDP (it is the poorest state in India, the average life expectancy is 73 years compared to 61 years in India, as a whole, and at 76 years in Great

42

1 Global Problems of Ecodynamics and Hydrogeochemistry

Britain and the USA). Literacy of grown-ups in Kerala is 91% (compared to 65% in India on the whole and 99% in the western countries). The level of birth rate averages 1.7 children per family (as in Great Britain) but for India the respective index averages 3.1. This could be reached in Kerala because with limited financial resources a considerable part of them was spent on health service and education. An opposite example is Brazil, where an economic growth has been reached at the expense of a low level of human development. Some of the west-Asian economic “tigers” have demonstrated the societal development from the priority of human development and slow economic growth to mutually intensifying progress of both aspects during the next decades. Births per 1000 population change from 43.7 (Angola) to 6.5 (Monaco) that depend on the wide parameters both natural and human induced. The analysis of statistical data shows that there really does not exist a dependence of fertility rates on such important parameters as freshwater and food resources. Although, according to WB estimates, the progress of east-Asian countries is determined by successive application of the principles of market economy, in fact, these countries were characterized by an intensive intrusion of governments into economic development, the use of subsidies (especially in agriculture), and taking protective measures in trade. An investment in the development of education and health service was important. The economic development of the east-Asian countries has been achieved, however, at the expense of serious ecological losses (deforestation, environmental pollution, biodiversity decrease, etc.). In this context, there is no doubt that the development of east-Asian countries (as well as the West, as a whole) has been ecologically unstable. A certain role in the evolution of the idea of SD was played by the notion of “natural capital” defined (in contrast to the notions of financial or “human” capital) as an expression of value for humans of the Earth itself. This approach is similar to the conception of biotic regulation of the environment proposed later (and more constructive). Having analyzed various ideas of SD, Dresner (2002) concluded that in this context three principles of SD (proposed by Daly 2002) deserve special attention, which determine the necessity: 1. To limit the scale of the increase in the size of the population, taking into account the “carrying capacity” of the Earth. 2. To ensure technological progress that guarantees an increase in efficiency but not the production level. 3. To balance the relationship between the use of renewable natural resources and their supplies, the level of emissions not exceeding the assimilating ability of the environment. The contradictory evolution of developments connected with the SD notion has stimulated further discussions. In particular, a critical analysis of this evolution has been undertaken in a series of papers published in the journal “Sustainable Development” (Bagheri and Hjorth 2005; Mont and Dalhammar 2005; Koroneos and Xydis 2005). So, for instance, Redclifte (2005) has accomplished an overview of the

1.4

Sustainable Development and Quality of Water Resources

43

conceptual history of the notion of sustainable development from the time of the BC report till now, emphasizing the necessity of broadening this notion with “human dimensions” (social aspects, ecological fairness) considered. Such aspects are sometimes ignored in the market (liberal) conception of development. Views expressed by Redclifte (2005) largely correspond with the ideas of the “Fair Globalization” report (ILO 2004). From Luke’s (2005) point of view, the notion of sustainable development has become mainly the subject of rhetorical discussions as an ideological “construction” used in the context of the problems of functioning of the present global society. The term is used more and more as a “label” denoting the regimes of existence which are neither stable nor reflecting the socio-economic development. Discussing the sustainable development of economy in the context of ecodynamics of natural systems, Hudson (2005) emphasized the priority of two aspects of these problems: (1) natural resources consumption; (2) emissions of pollutants and waste into the environment. Orsato and Cregg (2005) analyzed possibilities of radical reforms (with the data of ecological sociology and organization theory taken into account), which could be able to ensure the sustainable industrial development. Two basic related problems are particularly important in the context of sustainable development: globalization and prospects of the present consumption society development (Saltık et al. 2013).

1.4.3

Sustainable Development and Public Health

Different workshops and other international initiatives realized during recent years tried to answer on the main question: Do we know enough about CBSS dynamics or is more research needed and how to achieve sustainability? The topics on sustainability that were discussed during recent years practically did not consider the problem of human health in the context of globalization and sustainable development. Environmental changes—both at global and local levels—have an increasing effect on health, particularly that of poor and vulnerable populations. A healthy environment is one in which people have access to safe food and water, have adequate sanitation, and are protected from risks associated with disasters and environment pollution and degradation. Environmental health includes various aspects that can improve human health to achieve sustainable development of the CBSS. Jain et al. (2015) found that globalization processes are linked to the global biofield. It is known that one of the conditions where the individual exists safely is the possibility to adapt to the changing environment. In the twenty-first century, a person is to adapt to the numerous changing factors having social, political, economic, informational, and religious sense. In the globalization conditions, when a person has contact with human society, he can form his behavior depending on the aspects mentioned in the preceding text. Kogan (2004, 2006) proposed a model of global biofields. This model parametrizes the person biofield as an element of human society. Biofield is defined as a system of fields having different nature and arising

44

1 Global Problems of Ecodynamics and Hydrogeochemistry

during the living process. The term “Biofield” is debatable. Kogan (2004) views it as some form of a manifestation of vital activity for man and human society. A holism of this term gives possibility to combine the physical and spiritual aspects of globalization and sustainable development. Anderson et al. (2006) explain that global public health problems can be solved through the emergency medicine approach as a global discipline. The core concepts and strategies of emergency medicine care require focused medical decision-making and action with the goal of preventing needless death or disability from time-sensitive disease processes. It assumes the conditions that must be treated within a certain time period to prevent or minimize mortality or morbidity. It says that an interaction of medicine with the environment is therefore essential for an assessment of the situation in order to take preventive and therapeutic measures. One of the features that show the interactive harmony between sustainable environment and public health is the Environmental Public Health Indicators (EPHIs). EPHIs were first developed by Council of State and Territorial Epidemiologists (CSTE) in 2000 (Goldman and Coussens 2004; CDCP 2006). These indicators were identified in part to provide a means of placing non-infectious diseases and conditions under surveillance toward building a comprehensive National Public Health Surveillance System. The purpose and goals of EPHIs include: • Describe elements of environmental sources, hazards, exposures, health effects, and intervention and prevention activities that may stand alone or be combined to describe their interaction • Assess positive and negative environmental determinants of health, including measures of the built environment • Serve as communication tools for making environmental health information available to stakeholders • Identify areas for intervention and prevention and evaluate the outcomes of specific policies or programs aimed at improving environmental public health Measures and sources for EPHIs include numerous CBSS characteristics and different databases that can be used as input information for the Global Model of CBSS (GMCBSS) (Kondratyev et al. 2002a, b). Hazardous or toxic substances in different environments can be considered in the simulation experiments through GMCBSS to assess the global public health distribution pattern. Knowledge of the environmental dependence of health effects is key to establishing the stability of the CBSS. The development of culture depends on the philosophy of life, spiritual traditions, religion convictions, and other aspects of the population culture. Spiritual dimension of sustainable development was discussed in the World Summit on Sustainable Development (April 30–May 2, 2001, New York) where a focus on the spiritual dimension of human reality was made. It was pointed out that development policies and programs can truly reflect the experiences, conditions, and aspirations of the planet’s inhabitants and elicit their heartfelt support and active participation. Spiritual principles are to be taken into account when the governments form their

1.4

Sustainable Development and Quality of Water Resources

45

strategies in framework of global CBSS dynamics. In seeking to give importance to spiritual principles into its deliberations, it is necessary to focus on religions and spiritual values, particularly as they relate to and impact the development processes. The importance of spiritual values in development was noted in the Resolution of the 55th Session of UN General Assembly (February 5, 2001, New York). Environmental ethics and philosophy developed by many authors during the last years help to understand the role of knowledge in a larger relationship with nature (Heynemann 2005). Without discussion on these subjects, it is impossible to formulate cardinal principles for the constructive consideration of spiritual values in the GMNSS. It is very important for all countries throughout world, but for Russia in the first place where new political philosophy is only formed, and environmental ethics does not observe. In this context, both Russia and other countries are to develop long-term environmental strategy that could become the basis of practical policy making. It will help governments to identify their new place and role in world dynamics. Religion has an important role in sustainable development (Gas-Aixendri and Albareda-Tiana 2019). Many scientific centers study this role. For instance, the Earth Institute’s Center for the Study of Science and Religion (CSSR) was founded in 1999 as a forum for the examination of issues that lie at the boundary of two complementary ways of comprehending the world and our place in it. By examining the boundaries and intersections of science and religion, the CSSR is to stimulate dialogue and promote understanding. Another center, the Chicago Center for Religion and Science (CCRS), is dedicated to relating religious traditions and scientific knowledge in order to gain insight into the origins, nature, and future of humans and their environment, and to realize the common goal of a world in which love, justice, and ecologically responsible styles of living prevail. The purpose is to provide a place for research and discussion between scientists, theologians, and other scholars on the most basic issues concerning: • How we understand the world we live in and our place in that world • How traditional concerns and beliefs of religion can be related to scientific understandings • How the joint reflection of scientists, theologians, and other scholars can contribute to the well-being of the human community Conflicts and occasional agreement between science and religion are key mechanisms for the development of constructive approach to global environmental science. The relationship between science and religion is mainly determined by disagreements in two main areas: • There have been hundreds of disputes since the end of the sixteenth century in which scientists and theologians have taught opposing beliefs. At any given time, over the past few centuries, there has been at least one active, major battle. Dozens are active right now. • Science evaluating religion involves using the scientific method to evaluate the validity of a religious belief.

46

1

Global Problems of Ecodynamics and Hydrogeochemistry

A place for religion in science is defined by Pollack (2006): “Science and Religion” both deal with explanations and understanding mechanisms, but if one can say, “so what?” to either of the first two pairs, why not say it about this third? Are the shared attributes of science and religion rich enough to outweigh their manifold intellectual, emotional, and intentional differences? I say yes. And further: “Globally, all that makes us human in a biological sense is that despite these differences the six billion different human genomes are all in principle capable of coming together with each other through sperm and egg to make another generation of people. The biology of us makes us truly all equal.” Chernavsky et al. (2005) expand the biological sense of present person and give a new view on the human intellect and his constructive activity. These characteristics will be to play a principal role in the nearest future when the spirit of contradiction between Nature and Society comes to a critical level and when religion cannot help to overcome this level. In this sense, the prospects of the global model development return again to the Club of Rome model (Forrester 1971; Meadows et al. 1972). Having reviewed approaches to assessing CBSS dynamics, from Forrester and Meadows to recent publications (Krapivin and Varotsos 2007, 2008; Krapivin et al. 2015a; Nitu et al. 2020), one can draw a conclusion that an appreciable progress in a search of ways to reach the global sustainable development can be made with a system approach to the many-functional monitoring of the CBSS. Thanks to the Clube of Rome authors, more than 30 years ago the contradiction was first emphasized between the growth of population size and limited natural resources, and for the first time after the works by Vernadsky (1944) an attempt has been made to use the numerical modeling to study the CBSS evolution. Of course, the CR model oversimplifies real internal connections in CBSS, describing interactions of its elements by averaged indirect relationships, without direct account of economic, ecological, social, and political laws. The possibility of such an account appeared later in connection with the studies in the field of simulation and evolutionary modeling and theory of optimization of interaction for complex systems resulting in the creation of the methods and algorithms of prognostic estimation of dynamic processes in conditions of a priori uncertainty. However, the problem of creation of the global model adequate to the real world still cannot be solved even in the present conditions. From the one hand, a complete consideration of all the CBSS parameters leads to insurmountable multi-variance and information uncertainty with irremovable problems. Besides, in the spheres such as physics of the ocean, geophysics, ecology, medicine, sociology, etc., an adequate parameterization of real processes will be always problematic because it is impossible to have a complete database. Nevertheless, a search of new efficient ways of synthesis of the global system of the CBSS control based on adaptive principles of the use of the global model and renewed databases seems to be perspective and raising hopes to get reliable forecasts of the CBSS dynamics. Preliminary calculations with the use of the GMCBSS have shown that the role of biotic regulation in CBSS has been underestimated, and the forecasts, for instance, of the levels of the greenhouse effect have been overestimated. Therefore, in this book an attempt has been made to synthesize the GMCBSS considering past experience and accumulated databases as well as knowledge about the environment and human society.

1.4

Sustainable Development and Quality of Water Resources

47

Further improvement of the GMCBSS will be linked to the balanced development of studies both in the sphere of parameterizations in CBSS and in modernization of the Earth observation systems, covering the whole thematic space of the CBSS (Kramer 1995; Lakshmi 2011; Zhao et al. 2022): • Sun–land interactions (physical mechanisms of the transport of mass, momentum, and energy in the geosphere) • Atmospheric dynamics (atmospheric chemistry, atmospheric physics, meteorology, hydrology, etc.) • Dynamics of the World Ocean and coastal zones (winds, circulation, sea surface roughness, color, photosynthesis, trophic pyramids, pollutions, fishery) • Lithosphere (geodynamics, fossil fuel and other natural resources, topography, soil moisture, glaciers) • Biosphere (biomass, soil-plant formations, snow cover, agriculture, interactions at interfaces, river run-off, sediments, erosion, biodiversity, biocomplexity) • Climatic system (climate parameters, climate-forming processes, radiation balance, global energy balance, greenhouse effect, long-range climate forcings, delay of climate effects) • Socio-political system (demography, geopolicy, culture, education, population migration, military doctrines, religion, etc.)

1.4.4

Global Change: Priorities

Global ecodynamics is a unifying element for the globalization and SD problems. Problems of global ecodynamics (changes in the CBSS on a global scale) attract the growing attention (Krapivin and Varotsos 2007, 2008; Krapivin et al. 2015a; Cracknell et al. 2009a, b). The respective developments are aimed at assessing changes in the CBSS taking place in the past, observed now, and possible in the future. Even estimates of the present ecological situation are more than contradictory, varying from substantiation of well-being (as has been done, for instance, in the much-talked-of monograph by Lomborg (2001)) to conclusions about a threatening global disaster (such statements are most popular, especially in mass media). Illustrating apocalyptic prognoses, Lomborg (2001, 2004) selected a series of ecological “litanies” such as, for instance, “for more than 40 years the Earth has been sending out distress signals” (Lomborg 2001). Perhaps, the most vivid case of the politically motivated “ecological extremism” is the statement of Sir David King, the Science Advisor to the Prime Minister of Great Britain, according to which climate change is a more threatening global danger than terrorism. Specifically, he has said in one of the lectures on global warming (King 2002; Kreisler 2006): “The earth’s energy budget is a key to understanding this process and I’m very keen to explain to people it wasn’t discovered yesterday, this goes back to the great French mathematician Fourier who first understood the greenhouse effect and simply formulated it by saying the sun’s energy comes in through the atmosphere, warms up

48

1 Global Problems of Ecodynamics and Hydrogeochemistry

the earth and the earth being warm then generates heat back into space and the atmosphere around the earth contains some of that heat which means that daytime night time temperatures are not that different as they would be, for example, on the moon where there is no atmosphere. That’s the greenhouse effect and rather like going to bed with a duvet, if you’re not warm enough you put another blanket on and you’ll warm up. If you add to the greenhouse gases then something has to warm up and that something is, of course, the earth. So what we have here is a fairly complex picture. We’ve got solar energy coming in and you’ve got the energy penetrating the atmosphere is essentially the high energy part of the solar spectrum, the visible and ultraviolet light that gets through very effectively. Warms up the earth and the lower atmosphere and then we get what we call black body radiation, you get infrared radiation back out. So this is at the other end of the spectrum and what this means is that this can be absorbed, this energy coming back from the earth, by the atmosphere.” A detailed realistic analysis of the prospects of civilization development is contained in monographs (Kondratyev et al. 2002a, b; 2003a, b; 2004a, b). The main priorities of studies of the CBSS dynamics include: • Conceptual and analytical approaches to studies of interactions in the CBSS • Discovering connections of the environment and socio-economic development with ecological policy • Establishing connections between environmental changes taking place now and those likely in the future • Substantiation of the means that can be used to study these connections • Guaranteeing the complex meeting of commitments when following the laws of international ecological right in national programs dedicated to all directions of developments • Revealing of gaps in scientific developments and estimates The enumerated basic principles should be implemented taking into account the following four general requirements: 1. 2. 3. 4.

Coordination of scientific developments, ecological policy, and their achievement Examining the interaction of ecodynamics with socio-economic development Examining special features of processes at different spatial scales Reflection of particular features of the processes at different spatial scales

The following fact is the necessity to take into account an interaction of the key problems of ecodynamics determined by the presence of numerous feedbacks in the CBSS and non-linearity due to which the “threshold effects” (Frankham 1995) can occur as well as synergism of technologies and ecological policy. Numerous illustrations of the urgency to consider different interactions are contained in the problems of global climate change. An adequate analysis of the role of feedbacks and CBSS non-linearity is seriously complicated by available fragmentary information. In this context, it is disappointing that the concept of biotic regulation of the environment developed in Russia, which could have served a conceptual basis for resolving the problems of global

1.4

Sustainable Development and Quality of Water Resources

49

ecodynamics, has not been recognized yet (Gorshkov 1995; Gorshkov et al. 2000). Unfortunately, the concept of biotic regulation conception remains “unnoticed” in the West, which has been illustrated by recent polemics with respect to the Gaya concept (Ebel et al. 2004; Kerr 2005). The concept of biotic regulation is based on the following conclusions: 1. Earth is a unique planet of the Solar system, since on this planet, life exists in the form of biota—a totality of all living organisms, humans including. Important properties of life include biological stability of species and their communities as well as a very rigid distribution of energy fluxes absorbed by biota among organisms of different sizes. Biota is responsible for the formation of environmental properties and their stability according to biotic needs. Only for this reason the long existence of biota on the Earth based on the principle of biotic regulation has become possible. Maintaining environmental stability is one of the major goals of all living organisms. 2. Like many other species, homo sapiens is one of the biotic species and therefore its important problem consists also in maintaining the global biospheric stability. Otherwise, sustainable development would be impossible. Very long ago, humans left their natural ecological niche and began to consume much more biospheric resources than it is permitted by requirements of ecological balance. After the beginning of industrial revolution, this process of breaking the natural balance was accelerated under conditions of rapid population growth. 3. Approximate estimates have shown that to provide the stable state of the biosphere, one can use not more than 1% of its resources. At present, this share is close to 10%. A similar situation exists with respect to the evolution of global biogeochemical cycles of matter. So, for instance, the completeness of global carbon cycle before the beginning of industrial revolution had been close to 0.01% (biodiversity had played an important role in establishing the biosphere stability). By now, the completeness has decreased to 0.1% and a threat of global ecological disaster becomes more and more pronounced. So, the biosphere should be considered not a resource but a fundamental condition for life on Earth. At present, a major goal should be a restoration of the biosphere, already subjected to substantial disturbing forcings, and its maintenance in a state that would ensure the sustainable development. However, a difficult problem is the presence of many insufficiently studied aspects of biospheric dynamics. In this context, the creation of adequate observational systems and further improvement of numerical modelling methods play a decisive role. The first of these problems is especially urgent, since until now an adequate system for global change monitoring not only does not exist but has not even been conceptually substantiated. 4. An extremely important feature of the current development of civilization consists in the inequality between developed and developing countries, which is manifested, first of all, through non-equivalent use of the existing resources. The per-capita consumption of the “golden billion” (this term is popular in the Russian-speaking world, referring to the relatively wealthy people in industrially developed nations, or the West) is incomparably higher than for the rest of the

50

1

Global Problems of Ecodynamics and Hydrogeochemistry

world. Therefore, it is necessary to achieve an agreement on a new social order based on the agreement on cooperation and partnership. Achieving adequate life support systems is only possible based on corresponding international agreements. 5. It is becoming increasingly clear that the current market economy system does not provide the transition from the unstable to stable trajectory of development. The respective effort undertaken in the spheres such as education, legislation, and management cannot be considered sufficient. Non-governmental and religious organizations can play a more constructive role. 6. To stimulate the transition to sustainable development, a complex accomplishment of a series of measures is needed based on understanding the present situation complexity. It is extremely important to realize the fact that further development of the consumption society will lead to a global ecological disaster and civilization collapse. The only resolution of the problem consists in rejection of the traditional paradigm of the consumption society and in radical change of the way of life based on admission of priorities of spiritual values. To overcome the North-South social contrasts, immediate measures are needed to aid developing countries based on the implementation of accepted UN recommendations. It is the concept of biotic regulation that should serve as the basis for the first priority of the UNEP Programme (UNEP 2002, 2004), which is defined as an interaction between man and the environment, the conceptual approach to the analysis of interactions. This problem can be resolved by using the UNEP DPIR (Drivers, Pressures, Impact, Response model), which describes the cause-and-effect links between ecological and socio-economic NSS components. The concrete directions of the ecological development include: • Natural and anthropogenic environmental changes. In this context, of key importance is a system classification and priorities of interaction between natural and anthropogenic changes of the environment (it should be emphasized that this unresolved problem is one of the major reasons of extremely contradictory character of the existing conclusions with respect to the nature of the present global climate change (Kondratyev et al. 2004a, b). The Programme UNEP document has justly emphasized the priority of the problems of biogeochemical interactions and cycles (even in the case of carbon cycle we are still far from an adequate understanding of the most substantial mechanisms of its formation). • Ecological factors and humans’ well-being. Both positive and negative ecological factors require attention. Positive factors include ecosystems that perform functions of life support (water, food, recreations, and many others). Various factors of ecological stress (diseases, agricultural pests, natural disasters, etc.) are negative. • Anthropogenic impacts on the environment. In this context the analysis of demographic dynamics and socio-economic development conditions is of particular interest.

1.4

Sustainable Development and Quality of Water Resources

51

• Ecological policy and its connections with ecodynamics. In particular, taking into account the use of technologies, institutional measures, and risk control. Quite separate are problems of interaction of science and ecological policy. As for concrete factors of ecodynamics, the Programme UNEP (2004) has justly emphasized the first priority of analysis and assessment of the role of interaction between problems such as biodiversity, climate change, degradation of soil fertility, freshwater, coastal and marine environment, local and regional air quality, ozone layer depletion, persistent organic pollutants (POPs), and heavy metals (Operacz et al. 2022). The questions to be answered are the following: • What are the key interactions between the various environmental changes and what factors determine them? • How do the interactions between the various anthropogenic impacts take place and how can they be “separated” if necessary? • How are the interactions between products provided by ecosystems and the functioning of ecosystems manifested? • What are the current interactions between impacts and responses to them and how can they be either re-grouped or removed if the interactions in the NSS need to be changed? In terms of socio-economic development goals mainly in the period up to 2019 (compared to 2000 conditions), the main problems include (UN 2020a, b): 1. Liquidation of extreme poverty and starvation (halving the number of people living on less than 1 dollar per day). 2. Implementation of universal elementary education. 3. Providing sexual equality and highlighting the role of women. 4. Reduction of infant mortality (children under five) by 2/3 by the year 2015. 5. Improvement of mothers’ health (reduction of mortality in childbirth by 3/4). 6. Struggle against HIV/AIDS, malaria, and other diseases. 7. Providing ecodynamic stability (liquidation of decreasing trends in environmental quality, halving the number of people without access to good-quality drinking water, improvement of living conditions for, at least, 100 million people living in slums). 8. Development of cooperation in the interests of socio-economic development (with an emphasis on the debt problem of developing countries). Serious efforts to substantiate the priorities of the environmental studies in the twenty-first century have been undertaken by the NOAA scientists who developed the Strategic Plans (SP) for the period 2003–2007 and later (NOAA 2003; Uccellini 2018; Lautenbacher 2005). The SP sets out the priority of four directions of developments for NOAA including the main long-term goal of climate adaptation and mitigation, as well as responding to climate and its impacts (Christodoulakis et al. 2022).

52

1

Global Problems of Ecodynamics and Hydrogeochemistry

1. Protection, restoration, and control of the use of coastal and ocean resources based on understanding the laws of ecosystems’ dynamics. The urgency of these problems is explained by that about half the USA population is located in the coastal zone, whose extent along the coastline constitutes about 1/5 of the total length of the coastline. The coastal regions are developed faster than others. Here, in particular, the population size grows by 3600 people per day, there are more than 28 million places for work, and the gross production of goods and services exceeds 54 billion dollars. 2. Understanding the laws of climate change to increase the ability of the society to respond to them (note that the document uses two synonymous notions: climate variability and change without clearly defining them). In this context, it should be noted that supply of about 1/3 GDP in the USA depends directly or indirectly on weather conditions and climate. The annual and inter-annual climate change like that determined by El Niño brought forth in 1997–1998 an economic damage of about 25 billion dollars with losses of property estimated at 2.5 billion dollars and yield 2 billion dollars. Under US conditions, providing reliable weather forecasts in agriculture alone makes it possible to avoid about 700 million dollars in losses. 3. Providing information on climate and water resources. On average, the annual economic damage caused in the USA by hurricanes, tornado, tsunami, and other disasters constitutes about 11 billion dollars. About 1/3 of national economy (~3 trillion dollars) is “weather-sensitive.” 4. Maintaining national commercial interests by provision of ecologically reliable information to ensure safe and efficient transportation. Of course, safe and efficient systems of transportation are one of the key components of the economy. This applies to all kinds of transportation: land, sea, and air. Six directions of developments are considered the most substantial: 1. Creation of the system of integral (complex) observations of the global environment, processing, and analysis of observational data. 2. Effective solution to the problem of spreading knowledge about the environment. 3. Carrying out the necessary scientific studies. 4. Providing effective scientific cooperation. 5. Solving national security problems. 6. Adequate resolution of organizational problems. The NOAA strategic plan provides for the achievement of the following strategic goals for the first of the four directions of developments mentioned in the preceding text: • Increasing the number of coastal and marine ecosystems is not subjected to unfavorable changes and being in a stable condition. • Raising the socio-economic importance of the marine environment and respective resources (sea food, recreations, tourism, etc.). • Improving the living conditions for coastal and marine flora and fauna. • Increasing the number of species protected from unfavorable impacts.

1.4

Sustainable Development and Quality of Water Resources

53

• Increase the number of cultivated species to the optimal level. • Improving the environmental quality in coastal and marine regions. The achievement of the listed goals should be based on the use of the ecosystem approach, and in this connection, on the expansion of the scale of studies on the various ecosystem’s dynamics in the framework of three strategic directions: (a) Protection, reconstruction, and control of the use of resources of coastal and marine regions as well as the Great Lakes. (b) Reconstruction of species and their habitats for protection. (c) Expansion and maintenance of stable fisheries. To achieve the goals of the NOAA Strategic Plan, of key importance is to create a complex system of the environmental monitoring which would provide the obtaining, processing, and analysis of the data on the ocean-atmosphere-land interactive system from local to global scales. Assessing the NOAA SP as a whole, one would have to say that it is unfortunately skewed too strongly toward the satisfaction of national interests. Even in this context it is impossible to adequately resolve the problems of ecodynamics without taking into account the interactivity of processes of local, regional, and global scales. The part of the plan dealing with climate problems is seriously confusing. With an extremely urgent nature of these problems (mainly due to their highly politicized nature), a concrete understanding of the limitations of numerical modeling tools and climate observing systems and planning of efforts to overcome this limitation is of utmost importance. Without dwelling upon concrete characteristics of the present global ecodynamics, we only emphasize: the absence of long-term perspectives of development of the modern consumption society illustrated by estimates of global ecodynamics raises no doubts. Therefore, at the World Summit on Sustainable Development held in Johannesburg in 2002, the necessity was emphasized to accomplish 10-year programs on reaching a stable production and consumption including the following recommendations (Southerton et al. 2004): • Developed countries taking the lead in providing stable production and consumption. • Achieving these goals based on the general but differentiated responsibility. • Imparting the key role to the problem of stable production and consumption. • Laying emphasis on participation of young generation in resolution of the problems of sustainable development. • Use of the “polluter pays” principle. • Control of the cycle of products evolution from their production to consumption and waste to increase the productive efficiency. • Backing the policy that favors both the output of ecologically acceptable products and ecologically adequate services. • Development of more ecological and efficient methods of energy supply. An exception of energy subsidies.

54

1

Global Problems of Ecodynamics and Hydrogeochemistry

• Support of free-will initiatives of industry in order to raise its social and ecological responsibility. • Study and application of ecologically pure production especially in developing countries as well as in small and medium business. Although the listed recommendations are rather declarative, still they demonstrate a necessity to change the paradigm of the socio-economic development (first of all, this refers to developed countries) from the consumption society to priorities of social and spiritual values. A concrete analysis of the ways of this development requires a participation of many specialists in the field of social sciences. As mentioned in the preceding text, the main aspect of global ecodynamics (in the context of biotic regulation of the environment) is the state of ecosystems on the planet. Using the data of the recent fundamental UNEP Report (Reid 2005), let us consider the basic related problems. This is surely a unique opportunity to leverage the collaborative power to drive forward innovation that can help solve the many crises our planet already faces, as well as those to come in the future. Numerous world summits such as Paris 2020 Venture Capital World Summit (26 October 2020), UN Climate Action Summit 2019 (December 2–13, 2019), World Conference on Climate Change (October 12–13, 2020) etc., usually discuss the themes restricted by limited problems that do not allow to make the conclusions and to give recommendations of global scale. This effect is explained by the fact that each country sees the problem of sustainable development through the prism of its own context, and not from global opinion (Wilson and Doz 2012).

1.5 1.5.1

Effects of Natural and Anthropogenic Environmental Changes Global Ecodynamics Theory

The UN General Assembly report presented by UN Secretary-General K. Annan (“We the Peoples: The Role of the United Nations in the 21st century”) contained, in particular, an appeal to prepare for discussions an analytical report on the problems of global ecosystems dynamics “The Millennium Ecosystem Assessment” (MEA). In response to this appeal, the respective document was prepared in 2001 aimed mainly at assessing the consequences of global change in ecosystems for humans’ well-being, scientific substantiation of measures needed to ensure and maintain the sustainable development of ecosystems, and analysis of the importance of ecosystems dynamics for sustainable development of society (Reid 2005). Changes in ecosystem services influence all components of human well-being, including the basic material needs for a good life, health, good social relations, security, and freedom of choice and action. Humans are fully dependent on Earth’s ecosystems and the services that they provide, such as food, clean water, disease regulation, climate regulation, spiritual fulfillment, and aesthetic enjoyment. The

1.5

Effects of Natural and Anthropogenic Environmental Changes

55

relationship between ecosystem services and human well-being is mediated by access to manufactured, human, and social capital. Human well-being depends on ecosystem services but also on the supply and quality of social capital, technology, and institutions. The main content of the MEA (2005b) report prepared by the four working groups (“Conditions and Trends,” “Scenarios,” “Responses to Forcings,” “Subglobal Assessments”) with participation of more than 2000 experts and reviewers was to answer the following questions: 1. What changes have taken place in ecosystems? 2. How have “ecosystem services” (functioning of ecosystems as life-support systems) and their applications changed? 3. How have changes of ecosystems affected the humans’ well-being and reduction of poverty scale? 4. What are most substantial factors that cause changes in ecosystems? 5. What are possible scenarios of future changes of ecosystems and associated systems of life support (“ecosystem services”)? 6. What can be learned about the consequences of ecosystems changes for humans’ well-being by analyzing the sub-global processes? 7. What is now known about time scales, risk, and non-linear dynamics of ecosystems? 8. What are possibilities of the choice to ensure the ecosystems’ stability? 9. What are the most substantial uncertainties that hinder the making of adequate decisions to ensure the stable functioning of ecosystems? Of great importance for the answers to these questions were the results of developments accomplished within the programs of four International Conventions: The Convention on Biological Diversity (UNEP 2006); the United Nations Convention to Combat Desertification (Lean 1995); the Ramsar Convention on Wetlands (Ramsar COP8 2002; Ramsar COP9 2005); and the Convention on Migratory Species (UNEP 2005). So, the Convention on Wetlands, signed in Ramsar, Iran, in 1971, is an intergovernmental treaty which provides the framework for national action and international cooperation for the conservation and wise use of wetlands and their resources. There are presently 153 Contracting Parties to the Convention, with 1629 wetland sites, totaling 145.6 million hectares, designated for inclusion in the Ramsar List of Wetlands of International Importance. “The Convention’s mission is the conservation and wise use of all wetlands through local, regional and national actions and international cooperation, as a contribution towards achieving sustainable development throughout the world” (Ramsar COP8 2002; Ramsar COP9 2005). Naturally, the MEA report is based on the use of results published earlier. The emphasis in the report (Reid 2005) has been on analyzing the interconnections between ecosystems and humans’ well-being, in particular, in the context of “ecosystem services.” In this case the ecosystem is defined as a single interactive dynamic complex of communities of plants, animals, and microorganisms together with the inert environment. The MEA report contains a consideration of practically all diversity of ecosystems ranging from relatively weakly changed (as, for instance,

56

1 Global Problems of Ecodynamics and Hydrogeochemistry

natural forests) to landscapes with anthropogenically changed structures and agricultural lands as well as urban agglomerations, where ecosystems have been radically altered by man. As for the “ecosystem services” (ES), they are defined as performing functions of life support by using the respective potential of ecosystems. Such “services” can be classified as provisioning with food, water, wood, fiber; regulating, connected with impacts on climate, water regime, diseases, waste, and water quality; cultural, on which depend conditions of recreation, esthetic, and spiritual aspects; and supporting, which include soil formation, photosynthesis, and biogeochemical cycles. In this connection, it is important that humans as one of the species, while being protected from ecological impacts due to achievements in technologies and culture, nevertheless depend critically on the functioning of the “ecosystem services.” The fundamental goal of the MEA report is to analyze the ES impact on humans’ well-being which is determined by a totality of such aspects as basic material for a good life, including secure and adequate means of living, sufficient amount of food (at any time), dwelling, clothes, and various goods; health, including good state of health and favorable environment (pure air and water); good social relations which ensure the social prosperity, mutual respect, ability, and readiness to render help (especially to children); security that foresees an access to natural and other resources, personal security, as well as security against natural and anthropogenic disasters; freedom of choice and action that guarantees an achievement of goals with individual qualities taken into account. Freedom of choice and action is also determined by other components of prosperity (as well as some other factors, especially education), and it is a condition for achieving its other components, especially equality and justice. An important conceptual side of the MEA report consists in consideration of humans as an integral part of ecosystems and in taking into account their interaction with these ecosystems which (as humans’ living conditions change) leads (directly or indirectly) to changes in ecosystems, causing thereby changes in humans’ wellbeing. On the other hand, the impact of social, economic, and cultural factors not related directly to ecosystems causes changes in humans’ living conditions, and many natural factors affect the ecosystems’ dynamics. Though the MEA report pays special attention to connections of ecosystems’ dynamics with humans’ well-being, it emphasizes that humans’ impacts on ecosystems are not only consequences of the care about humans, but also the result of recognition of the value of individual species and the whole ecosystems. Such inherent values are properties independent of their use by anyone else. The irrational use and over-exploitation of natural resources head the list of unresolved problems of ecosystems’ stability. Though some ecosystems ensure the growing volume of production in the form of fish, cattle, and various agricultural commodities, their integrity has turned out to be broken, and in many cases the efficiency of ecosystems’ functioning decreased. As Stokstad (2005) noted, of 24 kinds of ecosystem services considered in the MEA report about 60% have degraded. Therefore, the Co-chair of the Board of Directors of the Millennium

1.5

Effects of Natural and Anthropogenic Environmental Changes

57

Ecosystem Assessment Robert T. Watson (2002) emphasized that population undermines the ecological capital in the world. One of the main dangers of this trend relates to that it enhances the risk of sudden and severe changes in ecosystems (e.g., collapse of fishery or the growth of diseases such as HIV/AIDS or COVID-19). An important aspect of the problem is that negative consequences of the impact on ecosystems tell, first of all, on the poorest (this refers, in particular, to arid regions of sub-Saharan Africa, Central Asia, and Latin America, where about one-third of the global population is concentrated). Provision of the ecosystems’ stable functioning is of key importance for overcoming the poverty and achieving the UN-formulated goals of the socio-economic development. The co-Chairman of the WG on business and industry J. Lubchenko (Lubchenko et al. 1991) has justly noted that only by protecting and restoring the ecological functions of ecosystems can we adequately resolve the problems of starvation and poverty. In the context of the problem of ecosystems’ stable functioning, special attention is paid to anthropogenic impacts on global biogeochemical cycles of nutrient salts. So, for instance, the wide use of nitrogen fertilizers has resulted in a dangerous intensification of the processes of eutrophication, algae blossoming, large-scale episodes of the death of mammals, and ground water contamination. The MEA report contains an informative analysis of the aspects of ecosystems’ global dynamics, but, unfortunately, one key aspect of the problem has been ignored. The matter concerns the systems’ ability to self-regulate, and in this connection, the conception of the biotic regulation of the environment. Among numerous participants of the MEA preparation, there were no specialists familiar with numerous publications on these problems (Gorshkov 1995; Gorshkov et al. 2000; Kondratyev et al. 2004a; Kondratyev and Krapivin 2005). Meanwhile, the biotic regulation conception is of fundamental importance both for understanding the laws of the present ecodynamics and for prognostic estimates. To stimulate further discussions on the problems of ecosystem dynamics, bearing in mind mainly their key ethical aspects concerning the forecasts of civilization development, Ehrlich and Kennedy (2005) proposed to carry out “Millennium Assessment of Human Behaviour” (MAHB) on the basis of MEA and IPCC experience in order to understand what actions are needed to resolve the ecological problems from the viewpoint of changes needed to turn to the ecologically stable society peaceful and providing equal rights for everybody. It is important to ask the people of rich countries whether they are ready to agree with the estimates of the US President Bush according to which their “way of life” cannot be the subject of discussions. It is important to analyze, which (from the ethical point of view) is most adequate, considering the needs and possibilities on a global scale. Limited mainly to the discussion of social aspects, the MAHB initiative should be linked to the organization of open and transparent forums dedicated to discussions of the ethical “measurements” of adequate interactions between people and governments as well as approaches to the exploitation of natural systems of life support. To this end, working groups should be set up to discuss, in particular, the following problems:

58

1

Global Problems of Ecodynamics and Hydrogeochemistry

1. What is known now about the evolution of cultures and in this connection, which democratic changes are possible. 2. How deficient and non-uniformly distributed non-renewable natural resources are used and what are ethical relationships between their distribution, economic capabilities, and accessibility. 3. Ethical aspects concerning the global commercial system. 4. Conflicts between the needs for resources and ecological consequences of their usage. 5. Inequality of economic conditions, races, and sex as factors of the environmental degradation. Other possible directions of discussions can be problems of war and peace, international management, health maintenance, and others. The main goal of the MAHB should be an integration of efforts of different WGs on continuing basis and substantiation of respective recommendations. The MAHB can favor, for instance, the coordination of efforts of public organizations to limit (under certain conditions) the monopoly of some corporative actions. One of the examples of the fruitfulness of such efforts can be the preparation and further signing of the Montreal Protocol on reduction of anthropogenic impacts on the global ozone layer, which has demonstrated a successful interaction of governmental and public organizations (Varotsos 2005a, b). It is supposed to concentrate efforts of MAHB on the analysis of mechanisms of decision making concerning the use of natural resources and respective risks. The problem of relationships between the interests of people and society demands a serious attention, as well as a search of institutional structures to resolve this problem, which is especially important from the viewpoint of coordination of the interests of the groups of people with different traditions and little contact. Much more serious efforts should be applied to stimulate interdisciplinary developments. In the context of the problems of globalization and sustainable development, especially taking into account that present losses of biodiversity and damage to natural ecosystems are sometimes considered as having a positive value for humans’ well-being from the viewpoint of short-range perspectives, the preservation of biodiversity and stable functioning of “ecological services” are most important. In this connection, Balmford et al. (2005) have analyzed the prospects for achieving by 2010 the goals formulated in the Convention of Biodiversity at the World Conference on Sustainable Development in 2002 as “ . . . an achievement by 2010 of substantial reduction of the present rate of biodiversity losses on global, regional, and national scales.” However, the problem is that the actual data on biodiversity are rather incomplete, and the present understanding of the role of interactive biological, geographical, and geochemical processes as factors of humans’ well-being is only rudimentary. Therefore, the concrete substantiation of the ways of achieving the goals of the Convention on Biological Diversity (CBD) by 2010 is a complex problem. In this context, of particular importance is the problem of an adequate choice of quantitative criteria (indicators) of biodiversity and ecosystems functioning (“ecological services”).

1.5

Effects of Natural and Anthropogenic Environmental Changes

59

In early 2004, the CBD participants formulated preliminary framework conditions which determine an approach to the choice of the discussed indicators that need further serious discussions and optimization. An Advanced Workshop was organized by Global Biodiversity Information Facility in this connection in July 2004 in the United Kingdom of Great Britain (Heywood 2004) that concluded about an expedience of selecting 18 indicators which ensure a complex quantitative estimate of biodiversity losses, though there are many uncertainties with respect to the causes of these losses (MMSD 2001). Therefore, it is possible that later on there may appear a need of additional indicators. An analysis of ecosystems dynamics in the USA has led, for instance, to the conclusion that 102 indicators should be used. On the other hand, there is no doubt that the solution of the problem cannot be attributed only to assessments of indicators. Of course, it is necessary to develop respective simulation models of CBSS (interactive biological, chemical, and physical processes with human dimensions taken into account), which is also important for the substantiation of the need for information about ecosystems dynamics obtained, in particular, using the remote sensing method. Given that until recently, mainly biologists dealt with the problem of biodiversity, the participation of corresponding developments of experts in the field of socio-economic sciences, geography, as well as experts in geophysical and geochemical processes is of particular importance.

1.5.2

The Current State of Global Ecosystems

In the last 50 years, the global ecosystems have been subjected to anthropogenic changes, and they had not suffered during the evolution in the Holocene period. Such changes have related to the growing population size and an increase of the need of food, freshwater, wood, fiber, and fuel (Fotaki et al. 2022). Naturally, these changes have favored the progress of socio-economic development and people’s well-being, but so selectively (in different global regions and for different groups of population) that in some cases the consequences of the use of biosphere resources for people have turned out to be negative. It is also important that actual consequences of the impact on the global biosphere have been understood only during the last years. For part of the people these consequences have been negative due to three problems considered in the following text. And solving these problems will make long-term sustainable use of biosphere resources possible. First, as estimates of the state of ecosystems at the turn of millennia (Reid 2005) have shown, about 60% of ecosystems (15 of 24 ecosystems studied in MEA) have suffered either degradation or unstable use, including such resources as freshwater, fish catches, local and regional climate, as well as the impact has been observed on natural disasters and pests. Though material losses are difficult to assess, there is no doubt that they are negligible and characterized by increasing trends. Many ecosystems have been damaged due to impacts on other “ecosystem services,” for instance, those connected with food production.

60

1

Global Problems of Ecodynamics and Hydrogeochemistry

The following events refer to specific manifestations of ecosystem impacts: • During the 30 years after 1950, a much larger area of land was transformed to agricultural land than in the previous 150 years (1700–1850). Now cultivated land (regions, where C then hypothesis H1 is chosen The sequential procedure of the decision-making process allows the minimization of the detection time when the existing phenomenon occurs (Fig. 1.9). The sequential procedure has the following basic characteristic (Wald 1947, 2004):

Fig. 1.9 Principal scheme of the decision-making system based on the sequential analysis procedure. A description of the sub-units is given in Table 1.7

74

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.7 Description of the decision-making system (DMS) and sub-units of the decision-making sequent procedure (DMSP) of Fig. 1.9 Sub-unit VMD CSC SETDF VP RUA VVAD DMSP-I AMLR ET CLRT VSPS FTDPUQ EV CPCSP CTT MOI

Description Visualization of the measurement data in the form of direct soft copy, discrete distribution, and statistical parameters Calculation of the statistical characteristics (mean, central second- and third-order moments, asymmetry and excess coefficients, expression of the entropy, etc.) Synthesis of the empirical and theoretical distribution functions Valuation of the parameters used in the Neyman–Pearson and the sequential procedures of the hypothesis’s decision Realization of the user access to all the functions of the decision-making system Visualization of the accepted decision DMSP input: α, β, a,{xi} Accumulation of measurements as the likelihood ratio Evaluation of the thresholds A and B Comparison of the likelihood ratio with the thresholds A and B Visualization of the sequential procedure state The FTDP unit query with respect to the form of fa(x) and the activation of the appropriate knowledge base level Evaluation of Eν, Dν, c Computation of the probability of completion of the sequential procedure Control of the task type for the choice between H0 and H1 taking the errors of the first and second kind into consideration Management of the operative intervention to the functioning of the DMSP unit

LðaÞ ≈ AhðaÞ - 1 = AhðaÞ - BhðaÞ , where A and B are the boundaries for the Ln(x) and h(a) is the solution of the equation: 1

½f a1 ðxÞ=f a0 ðxÞhðaÞ f a ðxÞ dx = 1 -1

The values of A and B have the estimates: B ≈ β=ð1- αÞ, A ≈ ð1- βÞ=α Therefore, L(a0) = 1 - α and L(a1) = β, and so it follows that the average number of observations in a sequential procedure equals to: E a ν = ½ð1 - αÞ ln½β=ð1 - αÞ þ α ln½ð1 - βÞ=α=Ea0 ξ,

when a = a0 ,

E a ν = ½β ln½β=ð1 - αÞ þ ð1 - βÞ ln½ð1 - βÞ=α=E a1 ξ,

when a = a1

For a = a* and when Ea* ξ = 0 and Ea* ξ2 > 0 we have:

ð1:4Þ

1.6

Information-Modeling Technology and Decision-Making Systems

E a ν ≈ ½- ln½β=ð1- αÞ ln½ð1- βÞ=α=Ea ξ2

75

ð1:5Þ

According to Eqs. (1.4) and (1.5), the number of observations of a sequential procedure is a random variable ν the average value of which (Eaν) can be smaller or larger than n. It is necessary to have the distribution P(ν = n) = Pa(n) in order to judge the possible values of ν: Ea νPa ðnÞ = wc ðyÞ = c1=2 y - 3=2 ð2π Þ - 1=2 exp - 0:5c y þ y - 1 - 2 ,

ð1:6Þ

where 0 ≤ y ≤ njE a ξj < 1, c = K jE a ξj=Da ξ = ðE a νÞ2 =Da ν > 0, Da ν = KDa ξ=ðEa ξÞ3 , E a ν = K=Ea ξ, K = ln A for E a ξ > 0 and K = ln B for E a ξ < 0: According to (1.6), the Wald’s distribution function Wc( y) has the form x

W c ð xÞ =

wc ðzÞ dz,

ð1:7Þ

0

where wc(z) = (c/2π)1/2z-3/2exp[-0.5c(z + z-1 - 2)] and c = [E(ν)]2[D(ν)]-1. The universality of the distribution (1.7) follows from its duality in the Gaussian distribution. Already in 1960 Wald showed that if |Eaξ| and Daξ are sufficiently small in comparison to lnA and lnB, the distribution of ν/Ea defined by the expression (1.6) will be a close approximation to the real one even for ξ not distributed by the Gaussian law. The theoretical aspects of the universality of the distribution Wc(x) are important for the integrated estimation of the sequential procedure efficiency. However, these are not the principal aspects for experimental applications. For this reason, as a rule, the synthesis of the decision-making system is perceived without these considerations. In reality, the volume of the measurements, as a rule, is small and the central limit theorem does not work. The statistical difficulties arising from this can be overcome by evolutionary modeling (Bukatova et al. 1991), intelligent technology (Nitu et al. 2000), and the use of other algorithms. Unlike the Neyman–Pearson criterion, the sequential procedure does not separate the stages of measurement and data processing but alternates them. The algorithmic load of the sequential procedure changes dynamically, while at the same time the classical procedure realizes the data processing stage only on the finishing step of the experiment. Hence, the synthesis of the efficient decision-making system (DMS) poses the following problems: (i) Selection of the criterion for the parameter estimation (ii) Revealing of the probabilistic characteristics of the studied process

76

1

Global Problems of Ecodynamics and Hydrogeochemistry

(iii) A priori estimation of the possible losses concerning the precision of the decisions taken (iv) Prognosis of dynamic stability for experiment results The DMS should have a wide range of functions: (i) Visualization of the measurement data in the form of direct soft copy, discrete distribution, and statistical parameters (ii) Calculation of the statistical characteristics (mean, central second and third order moments, asymmetry and excess coefficients, expression of the entropy, etc.) (iii) Synthesis of the empirical and theoretical distribution functions (iv) Valuation of the parameters used in the Neyman–Pearson and sequential procedures of the hypothesis decision (v) Making the user access to all functions of the decision-making system According to the scheme presented in Fig. 1.9 the decision-making system should have an expert control level. The unit DMSP-I controls the decision-making procedure through its inputs and outputs. According to the functions of the sub-units described in Table 1.7, the user can promptly interfere in any arbitrary stage of the computer experiment, correcting the parameters of the decision-making procedure or even terminating it. The sub-unit CTT manages the calculation process taking into account the character of the task. It forms the variants that correspond to the concrete combination of the errors of the first and second kind, α and β. Based on this combination, the sub-unit CTT produces a set of parameters to manage the other sub-units. Depending on α and β the simplified procedures are possible. For example, two variants for the asymmetric thresholds A and B are: 1. B = β∕(1 - α) → 0, A = (1 - β)/α → const 2. B = β∕(1 - α) = const, A = (1 - β)/α → 1 In other words, the errors α and β are unequal in value. Particularly: 1. β → 0, α = const 2. β = const, α → 0 In these cases, the sequential procedure will end with probability equal to 1 if the following conditions are met: 1. Ea ζ > 0, ζ = ln[fa1(ξ)/fa(ξ)] 2. Ea ζ0 < 0 The probability of completing the procedure is slow when one of the following conditions is not met: 1. β ≫ α, Ea ζ > 0 2. α ≫ β, Ea ζ < 0

1.6

Information-Modeling Technology and Decision-Making Systems

77

Fig. 1.10 Block scheme of data processing in the sequential analysis procedure

The user can visualize the state of the procedure as shown in Fig. 1.10. The unit VMD coordinates system input with the separate channels of measured data. As each random process {xi} is executed, analyses are carried out to eliminate errors and to represent input signals in a form that is acceptable for the other units. The unit CSC calculates signal characteristics: M1 = M3 =

1 n

n

xi , M 2 = i=1

n ð n - 1Þ ð n - 2Þ

1 n

n i=1 n

ð xi - M 1 Þ 3 , r 2 = i=1

nðn þ 1Þ M4 = ð n - 1Þ ð n - 2Þ ð n - 3Þ Ri = xi, max - xi, min , M M 6 = 42 - 3: M2

- 3=2

ð xi - M 1 Þ 2 , r 1 = M 3 M 2

p , V=

3ð n - 1Þ 2 M4 , M 42 ðn - 2Þðn - 3Þ 1

n 4

ðxi - M 1 Þ , H = i=1

M2
100%, M1

f a ðxÞ ln½f a ðxÞdx, -1

78

1

Global Problems of Ecodynamics and Hydrogeochemistry

These characteristics and other standard parameters are used to reconstruct fa(x). For instance, if M3 ≈ 0 and r1 ≈ 0, fa should be searched for in symmetric distributions. Unit RUA performs this search by selecting continuous distributions from the knowledge base and estimating their distance from the empirical distribution using the criterion: L

χ2 = i=1

½mi - f a ðxi ÞΔxi 2 f a ðxi ÞΔxi

The continuous distribution f * is chosen to satisfy the following condition: min χ 2 ðf a Þ = χ 2 ðf  Þ: fa

The decision-making system is composed according to the main scheme of Fig. 1.9. Its functioning scheme is shown in Fig. 1.10. The choice of the architecture of expert system is made taking into account the availability of suitable algorithms, models, and software. In recent years, with the advancement of technology, applying different methods has become considerably simpler for decision makers and users who are involved in complicated mathematical problems and multiple alternatives (Nowak et al. 2016; Zelkowicz et al. 2015). Standard approaches to decision-making problems are classified into different classes. Lu et al. (2007) list the most significant classes as follows: • Structured, where the process to reach the optimal solution is known as the standard method, which can be described using statistics to compare products in terms of cost or quality. • Unstructured, where problems are generally vague in nature in which human intuitions are the basis of most of the decision making. • Semi-structured, where problems are a combination of both the unstructured and structured ones and the ideal solutions for these problems are based on a mixture of both formal approaches and human judgement. Ada and Ghaffarzadeh (2015) examine the role of information systems in the decision support process when management information system provides knowledge about the relative position of the organization and basic forces at work. Basically, there exist many types of information systems like management information system, decision support system, transaction processing system, and expert system, an important element of which is database. The following list of information systems includes: • • • • • •

Transaction Processing System (TPS) Expert System (Specialist) (ES) Office Automation System (OAS) Personal and Work Group Information Systems (WGSS) Management Information System (MIS) Decision Support System (DSS)

1.7

Hydrogeochemistry and Water Quality Assessment Tools

79

The use of these information systems has practical solutions to economic problems. Solving environmental problems needs additional tools for the big data processing when these data are characterized by temporal and spatial distributions. GIMS technology helps to organize the big data processing procedure using elements of the mentioned information systems.

1.7

Hydrogeochemistry and Water Quality Assessment Tools

Natural water systems are affected by the input of various contaminants, such as organic matter, hazardous chemicals, and nutrients, produced by domestic, industrial, and agricultural activities. Park et al. (2020) review the recent advances in information and communications technology (ICT) with particular attention to water quality monitoring, focusing mainly on water resources, such as rivers and lakes. Water quality data includes various physical, chemical, and microbial parameters provided during laboratory analyses or by specific sensors in field measurements. Data are collected on a weekly, monthly, or seasonal basis depending on the decision-making process for effective water resource management and operational detection of accidental events such as a toxic pollutant input. The most important real-time monitoring of water quality occurs when the drinking-water-supply systems are monitored. Natural and anthropogenic pressures on aquatic environments and resources have increased significantly in the last decades. Ecologically critical situations, such as floods, eutrophication, and offshore oil spills, require the development of water quality monitoring systems capable of controlling water quality fluctuations over multiple timescales. Water quality assessment comprises physical and biological indicators, such as pH, electrical conductivity, dissolved oxygen, turbidity, temperature, total organic content, total suspended solids, and nutrient concentrations. There are sensors that can assess these indicators both directly in situ and through laboratory analysis of samples collected in the water system. Typically sensors include physical, chemical, optical, and microwave types (Tables 1.8 and 1.9). Zhang et al. (2018) proposed the integrated method of hydrochemical analysis, multivariate statistics, and geochemical modeling for surface water, groundwater, and thermal water samples. Water quality is determined by the hydrochemistry affected by different hydrochemical processes determined by natural physicalchemical activities including ion change, mineral dissolution and precipitation, water–rock interaction, and redox transformation. Li and Liu (2019) present a review of smart sensors applied in water quality monitoring management including a consideration of newly introduced technologies like bulk data handling techniques. Nesaratnam (2014) discusses the basics of the hydrological cycle and describes the natural aquatic environment including the normal composition of surface waters. Von Sperling (2007) shows that biological wastewater treatment process is very

80

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.8 Examples of water quality parameters monitored by sensing technology (Park et al. 2020) Content Basic-item monitoring

Parameter pH value, DO concentration, EC, temperature, oxidation-reduction potential, and turbidity

Organic-compound monitoring Nutrient monitoring

COD Nitrate

Nitrate, ammonium, phosphate

Harmful algal blooms (HABs) monitoring

Chl-a

Phycocyanin Cyanobacteria biomass

HABs monitoring using hyperspectral image (HSI)

Chl-a

Phycocyanin

Cyanobacteria biomass

Physical status for water quantity monitoring

Water level

Velocity

Sensor type In situ electrodes, colorimetry, conductivity cell, membrane electrode, optical sensor, potentiometric, thermistor, nephelometric, etc. In situ electrochemical sensor Using an optical sensor where nitrate concentration is determined from the relationship between UV light absorbance and nitrate concentration in a water sample Wet chemistry sensor where the nutrient concentration is measured based on a colorimetric reaction Using satellite images (Chl-a concentration is determined from the empirical relationship between satellite image and Chl-a concentration) In situ optical sensor with wireless data transport network In situ fluorometric sensor Using satellite images (cyanobacteria biomass concentration is determined from the empirical relationship between satellite image and cyanobacteria biomass) Chl-a concentration is determined from the empirical relationship between HSI and Chl-a concentration Phycocyanin concentration is determined from the empirical relationship between HSI and phycocyanin concentration Cyanobacteria biomass concentration is determined from the empirical relationship between HSI and cyanobacteria biomass In situ acoustic sensor where the distance from the surface of the water to bottom is measured from the echoes of the acoustic waves Velocity sensor (e.g., ADV)

1.7

Hydrogeochemistry and Water Quality Assessment Tools

81

Table 1.9 Information-modeling instrumental decision-making systems of optical and microwave types (Varotsos et al. 2019a, b, c, d, 2020a, b, c, d) System name Microwave radiometers Multi-channel statistical analyzer (MCSA)

Adaptive information-modeling instrumental system (AIMIS)

Recognition optical spectroellipsometric system (ROSS)

An adaptive universal identifier of the water quality (AUIWQ)

Expert system for an adaptive identification of the environmental parameters (ESAIEP)

Spectroellipsometric decision-making system (SEDMS)

System functions An assessment of surface water quality including spotness of contaminants and water quality. It is based on a series of microwave radiometers organized into multi-channel measuring system, the general structure of which assumes that the flux of data from all radiometers can be analyzed both separately and together with the data from other channels. Final decision-making about the environmental sub-system identification is realized by means of parallel-sequential procedure It is based on 35-channel spectrophotometer. The refraction coefficient –s assessed with precision 0.0003. Water quality is assessed by means of the spectral image recognition algorithm It is based on 128-channel spectropolarimeter with binary polarization modulation. Recognition of spectral images of the water samples is realized by means of cluster analysis It is based on 8-channel spectrophotometer principal structure which has sky-light adapter for the location at the water medium. It can use sun light or an artificial light source. Registration of spectral image of water sample is realized during 1 second Schematic diagram of the ESAIEP consists 128-channel spectroellipsometer, digital transformer, multi-channel minispectrograph, and interface sub-system. ESAIEP can diagnose the water sampling using spectral images and algorithms for their identification The SEDMS construction has two vertically mounted sub-systems (array of LEDs and digital light sensor) that are sinked at water environment by 10–15 cm. Light flow from LEDs after its passing through water environment is registered by a sensor and after that the spectral image is formed

much influenced by climate and temperature plays a significant role in the treatment process. These and other publications confirm that water quality assessment process has numerous aspects complicated a synthesis of decision-making system with functions of the water quality diagnostics especially when water quality monitoring is realized at local, national, or international level. Continental water bodies are of various types including flowing water, lakes, reservoirs, and ground waters, the inter connection between which is provided by the hydrological cycle with many intermediate water bodies, both natural and artificial (Bartram and Balance 1996; Li et al. 2014).

82

1

Global Problems of Ecodynamics and Hydrogeochemistry

One of the main hydrogeochemical problems is the assessment of harmful and toxic effects on plants, animals, and humans. Metals are very important for human life where it is found in the human body with trace amount and these metals do different roles in it. Heavy metals refer to any metal and metalloid element having density ranging from 3.5 to 7 g/cm3 as well as toxic and poisonous at law concentration such as mercury (Hg), cadmium (Cd), arsenic (As), chromium (Cr), thallium (Tl), Zinc (Zn), nickel (Ni), copper (Cu), and lead (Pb) (Khamis and Mohammed 2018). Heavy metals differ widely in their chemical properties and their sources have both natural and anthropogenic character. Table 1.10 shows the standards for metal concentration in drinking water and the health effects. Bartram and Balance (1996) have shown that residence time is an important concept for water pollution studies because it is associated with the time taken for recovery from a pollution incident. Figure 1.11 shows common ranges of water residence time for various types of water bodies.

1.8 1.8.1

Demography and Water Resources Global Demography Aspects

The notion of globalization and sustainable development in the first place pertains to human society. Exactly this problem was invented, by humanity realizing the inadequacy available in the Earth’s resources. As a result, the greatest number of uncertainties appear over time in the development of human society. One of the possible approaches, which helps to overcome these uncertainties, is a new scientific direction (called global ecoinformatics) that has been intensively developed in recent years, in the context of which information technologies have been created which ensure the combined use of various data about the past and present state of CBSS. The creation of a CBSS operational model based on knowledge and available data and combined with an adaptive-evolutionary concept of geoinformation monitoring, which enables one to realize an intercorrection of the CBSS model and the regime of the global data collection, can be considered an important step in global ecoinformatics. As a result, the CBSS structure can be optimized to achieve sustainable interaction between nature and human society and to create an international strategy for the coordinated use of natural ecosystems. But, for this it is necessary to have efficient mechanisms for the parameterization of demographic processes, the description of which in a global model will increase validity of global ecodynamics forecasting. The demographic situation determines the dynamics of anthropogenic processes. Efforts to develop models of demographic processes are therefore justified. Existing predictions of changes in population size and variations in its spatial distribution allow one to synthesize the scenarios to be used in a global model as well as to try to solve the problem of its verification. An adequate model of the global demographic process requires an extensive database covering the characteristics of changes in the

1.8

Demography and Water Resources

83

Table 1.10 Typical concentration of metals in drinking water and health effects (Gautam et al. 2014) Metal Lead

Nickel

Effects Toxic to humans, aquatic fauna, and livestock High doses cause metabolic poison Tiredness, irritability, anemia, and behavioral changes of children Hypertension and brain damage Phytotoxic High conc. can cause DNA damage Eczema of hands High phytotoxicity Damaging fauna

Chromium

Necrosis nephritis and death in man (10 mg/kg of body weight as hexavalent chromium) Irritation of gastrointestinal mucosa

Copper

Causes damage in a variety of aquatic fauna Phytotoxic Mucosal irritation and corrosion Central nervous system irritation followed by depression Phytotoxic Anemia Lack of muscular coordination Abdominal pain etc.

Zinc

Cadmium

Causes serious damage to kidneys and bones in humans Bronchitis, emphysema, anemia Acute effects in children

Mercury

Poisonous Causes mutagenic effects Disturbs the cholesterol

Drinking water standard By the Environmental Protection Agency maximum concentration: 0.1 mg/L By European Community: 0.5 mg/L Regulation of water quality (India) 0.1 mg/L By the Environmental Protection Agency maximum concentration: 0.1 mg/L By European Community: 0.1 mg/ Regulation of water quality (India): 0.1 mg/L By the Environmental Protection Agency maximum concentration: (hexavalent and trivalent) total 0.1 mg/L By European Community: 0.5 mg/L Regulation of water quality (India): 0.1 mg/L By the Environmental Protection Agency maximum concentration: 1.0 mg/L By European Community: 3 mg/L Regulation of water quality (India): 0.01 mg/L By the Environmental Protection Agency maximum concentration: 5 mg/L By European Community: 5 mg/L Regulation of water quality (India): 0.1 mg/L By the Environmental Protection Agency maximum concentration: 0.005 mg/L By European Community: 0.2 mg/L Regulation of water quality (India): 0.001 mg/L By the Environmental Protection Agency maximum concentration: 0.002 mg/L By European Community: 0.001 mg/ L Regulation of water quality (India): 0.004 mg/L (continued)

84

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.10 (continued) Metal Arsenic

Effects Causes toxicological and carcinogenic effects Causes melanosis, keratosis, and hyperpigmentation in humans Genotoxicity through generation of reactive oxygen species and lipid peroxidation Immunotoxic Modulation of co-receptor expression

Drinking water standard World Health Organization guideline of 10 mg/L By European Community: 0.01 mg/L Regulation of water quality (India): 0.05 mg/L

Fig. 1.11 Typical water residence times in inland water bodies (Bartram and Balance 1996; Van der Ent and Tuiinerbug (2017)

standards of demographic behavior, detailed information on intensities of demographic processes in various regions of the planet, assessments and criteria of demographic policy, and, especially important, the structural indicators of human society (Krapivin and Varotsos 2007). Many authors have considered the problem of parameterization of demographic processes. Demographic processes are considered part of planetary biosphere processes in which Homo sapiens play the role of user of products provided by the biosphere sub-systems and a regulator of energy and matter fluxes between them. With the development of global infrastructures in trade, industry, agriculture, science, politics, and other spheres of humans’ activity at the beginning of the

1.8

Demography and Water Resources

85

twenty-first century, the problem appeared of not only CBSS sustainable development but also, and to a greater extent, the search for balanced relationships between the environmental state, the resources accumulated by individual countries, and human health. It is evident that biosphere reserves are limited. The last 50 years have turned out to be an important and rapid transition of humanity from isolated social, political, and economic relationships to large-scale political and growing connections between even distant regions. All this has already led to appreciable changes in biodiversity, structure of land, and water ecosystems as well as changes in global biogeochemical cycles of important biosphere elements, such as water, nitrogen, carbon, sulfur, and ozone. The course of the biogeochemical cycles of these elements directly affects living conditions almost everywhere over the globe and, eventually, human health. Water plays a special role. The per-capita daily need for water exceeds 1.4 liters. At present, 0.65% of available water on the planet is actually available (rivers, lakes, ground waters, soil moisture). Tables 1.11 and 1.12 show some data on the distribution of water resources by types and compartments considering the existing uncertainties and deviations. Its biogeochemical cycle is a Table 1.11 Water distribution in the biosphere

Component World amount of water Water amount in oceans and seas The land water Freshwater Salt water Atmospheric water Water vapor in the atmosphere Water droplets in the atmosphere Ice crystals in the atmosphere Water in the ice of Arctic countries and glaciers Ground ices of perennial frozen earth Liquid water of upper part of terrestrial cortex Reservoirs Lakes Ground waters Renewable ground waters Soil moisture Water of marshes Rivers Biological water

Quantitative characteristics Occupied square Volume Level (106 km2) (106 km3) (m) 509 1454.193 2856.96 361.3 1370.323 3792.76 149 47.9871 322.06 148.8 28.2403 189.79 509 23.7468 46.65 509 0.0129 0.025 509 0.011481 0.023 509 0.001129 0.0025 509 0.000129 0.0005 16.2275 24.0641 1482.92

Part from total volume of water (%) 100 93.96 3.3 1.94 1.63 0.001 0.0008 0.00009 0.000009 1.65

21

0.3

14/29

0.021

149

23.62593

158/56

1.62

0.4 2.1 134.8 134.8 82 2.682 148.8 148.8

0.005 0.1764 23.4 0.01332 0.0165 0.01147 0.00212 0.00112

12.5 84 174 0.1 0.2 4.28 0.01 0.008

0.0003 0.12 1.61 0.0009 1.13 0.0008 0.00001 0.00008

86

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.12 Earth’s major water compartments (Matta 2010) Compartment Oceans Ice and snow Saline groundwater Freshwater Fresh lakes Saline lakes Soil moisture Atmosphere Marshes, wetlands Rivers, streams Living organism Total

Volume (1000 km3) 1,338,000 24,364 12,870 10,530 91 85 16.5 12.9 11.5 2.12 1.12 1,386,000

Percent of total water 96.5 1.76 0.93 0.76 0.007 0.006 0.001 0.001 0.001 0.0002 0.0001 100

natural mechanism which guarantees the water supplies recovery, making it a renewable source. The heterogeneity of the spatial distribution of water supplies determines the size of the population in a given territory. Unfortunately, there is practically no scientifically based concept of the CBSS development that predicts a balanced development of all its vital important indicators. For the first time, a note appeared in the IPCC Second Assessment Report (Fankhauser and Tol 1997) that “the sustained health of human populations requires the continued integrity of Earth’s natural systems.” This statement should be understood as an invitation to study the relationships between the health of the population in general and an individual, the dependence of which is not obvious. As Kalb et al. (2004) and Goodhand (1999) noted that globalization has created many purely intellectual problems, the solution of which will require consideration of many concepts such as livelihood, identity, governance, transnationalism, and knowledge. The present is characterized by many ongoing conflicts whose impact on global ecodynamics has yet to be evaluated. For the first time, the problem of sustainable development was formulated on a constructive level in the works of Krapivin and Varotsos (2007, 2008). It is the theory of biotic regulation that is the basis for this constructive formulation, as it creates a link between CBSS key elements. From this theory a conclusion emerges that the structural units of the living space are the cells of the mechanism that provides regulation and sustainability of the environment and thus determines the ecological stability of humankind. As the process of globalization expands, this stability depends more and more on the decisions made by humans. A truly existing division of human society into those living in developing and developed countries uniquely determines everyone’s standard of living. Although the notion of globalization is rather vague, it nevertheless implies the enhancement of the economic and political influence of global corporations in the life of the population in general. This influence results in the intensification and broadening of global economic integration, when the flows of finance, goods, technologies, population, and service are

1.8

Demography and Water Resources

87

independent of borders. This is where the economic elite inevitably comes into play, and political conservatism stems from their desire to create a new world order when the health and life of the population of many countries is not a priority for this elite. In relation to this, Galeano (1997) noted that only the wealth of the exploited colonies and their natural and human resources allow the developed countries to become richer, increasing the poverty level in the poor countries. The rhetoric about globalization and its benefits remains a shield for these processes. Today’s globalization has the following special features: • The increasing scale and pace of transnational movement of goods, services, and finances limit the possibilities of many countries to intervene in international commercial activity. • There is a certain asymmetry between the levels of legal protection in the field of trade relations, where many international agreements and regulations apply, for instance, within the WMO, and social and environmental relations, where the responsibility of governments vis-à-vis international organizations is limited. • The GDP of some multinational corporations exceeds the GDP of many countries, which enables them to dictate a price policy and govern the global labormarket practically independently of the governments of those countries. • The enhancement of the impact of the market liberalization processes with the participation of World Bank (WB) and the International Monetary Fund (IMF) forces many countries to join the global economic integration without any prospect of improving the lives of populations. • Social, economic, environmental, and health issues are becoming “inherently global,” rather than purely national or domestic: Thee environmental impacts of human activities are planetary in scale and scope; disease pandemics and economic stagnation partly underpin state collapse and regional conflict. Despite the apparent negative aspects of globalization, it accelerates the propagation of new knowledge and technologies, including medicine. Of course, it promotes an expansion of the National Health Service, education, and, in particular, the protection of the health of women and children. In poor countries, globalization can reduce poverty and lead thereby to a desirable level of human health. On the other hand, critics of globalization ascertain that it promotes the propagation of infectious diseases and creates conditions for imposing western living values in opposition to traditional local culture. And it is not apparent that integration into the global economy will always and under any conditions lead to economic growth and poverty reduction. China, Korea, Thailand, Malaysia, Indonesia, and Vietnam can be an example of a moderate attitude toward globalization while maintaining national values. Countries with weaker internal protection are those in Africa and South America, strongly influenced by the WB and the IMF, and where domestic economic activity is suppressed, with worsening balance of payments and, consequently, increasing poverty and deterioration of health. Human health does not only depend on the available biospheric resources and on the most dangerous diseases such as cholera and COVID-19 (He et al. 2020; Tiwari 2020). There seems to be a distinct correlation between the spread of cholera and the

88

1 Global Problems of Ecodynamics and Hydrogeochemistry

provision of clean water and food to the population. In general, a long-range transport of aerosols whose intensity depends on the state of land ecosystems strongly promotes the spread of dangerous diseases. Agents of dangerous diseases that can reach the lungs directly can be carried by dust particles over long distances. From estimates of Griffin et al. (2003), in this way 10 species of bacteria, 5 species of fungi, and 5 viruses can be carried over long distances, and as a result, anthrax, tuberculosis, diphtheria, bacterial meningitis, smallpox, etc., can propagate. The dust sources themselves are usually characterized by increased susceptibility to asthma. Along with direct impact on human health, long-range transport of aerosols can affect health through food prepared from yield collected in ecosystems affected by delivered pesticides and other pathogens. For instance, fungal spores from the territory of Cameroon in Africa across the Atlantic can reach Dominican Republic in the Caribbean in 9 days. Sugarcane rust (Puccinia melanocepthala) is a classic example of ecosystems’ contamination in South America due to the transport of diseases from Africa. From the estimates of Noji (2001), in the present world along with indirect human’s impacts in the process of propagation of dangerous diseases there is the risk of direct interference into this process. The notion of bioterrorism has emerged as one of the negative manifestations of the today’s globalization. A human being can cause an epidemic of a dangerous disease and a panic in the population of a vast region, destroying its social infrastructure and leading to a lot of victims. Thus, the range of dangerous risks for the population of individual regions is quite wide and is not limited only by direct economic mechanisms that regulate the population’s standard of living. Mathematical epidemiology tools, population biology models, and statistical methods allow the study of different processes of various viruses that spread both locally and globally. Mathematical methods are used in microbiology and virology to detect important factors that control the virus spreading and explain how its parameters are obtained. The traditional mathematical model can predict the virus evolution between different territories which is the focus of the World Health Organization (WHO) mainly for some Western African countries. A forecasting of the evolution of the epidemic with a lethal human disease was carried out for the Ebola virus disease discovered in 1976 in Central Africa. In this case, the Ebola virus spread was restricted by two regions, including Zaire and Sudan, which did not require global efforts to limit its spread to other regions. Understanding the pathways of any virus transition is achieved usually using different mathematical models adapted to the epidemiologic virus parameters. The situation with the coronavirus is significantly differed from many events of the past few decades. Global panic began in early December 2019 when the first COVID-19 victim was diagnosed in Wuhan, China. After that, the number of infected populations in many countries increased rapidly. Governments began to introduce restrictions on human movement within their countries aimed to restrict the spread of the disease. Mainly these restrictions include the following elements aimed at removing the spread of coronavirus:

1.8

Demography and Water Resources

89

• • • • •

Restrictions on international flights and destination of domestic routes Veto mass population groups Limitation of different processes in marketing Transfer of many organizations and peoples to a remote work regime Establishing a regime of self-isolation for many groups of the human population (social distancing) • Using a quarantine regime to isolate individuals or their groups • Introduction of financial support for various sectors of the national economy

The situation with COVID-19 is significantly differed from past pandemic outbreaks both because of the very high speed of global spread and because of the dramatic increase in infected and sick peoples as well as the absence of a vaccine (Bai et al. 2022). Therefore, early developed epidemic models help to understand the role of different quarantine parameters on the transition rate of infected individuals. There are mathematical models that can be adapted to short-term forecasts of the stochastic processes identified with the COVID-19 pandemic (Karako et al. 2020; Chen et al. 2020). Most of these models use systems of differential equations, some parameters of which reflect characteristics of the COVID-19 pandemic process (Varotsos and Krapivin 2020a, b; Varotsos et al. 2021a, b, c). He et al. (2020) have developed discrete-time stochastic epidemic model parameters that were estimated using statistical data from January 11 to February 13, 2020, in China. Plank et al. (2020) based on a continuous-time branching process model considered different scenarios for the isolation of population cases and assessed the consequences of restrictive control implemented in New Zealand. Tiwari (2020) has assessed the efficiency of Susceptible-Infected-Removed (SIR) and SusceptibleInfectious-Quarantined-Recovered (SIQR) models to limit the COVID-19 spread using personal isolation and determined parameters and indicators that quantify the spread of infections in India. Karako et al. (2020) using stochastic transmission model assessed the effectiveness of strategies to control the COVID-19 spread in Japan. Chen et al. (2020) analyzing outbreaks in China proposed a time-independent susceptible-infected-recovered model, using which the number of infected and dead individuals can be predicted. This model is based on two time-invariant variables reflecting average contacts of an individual with others per unit time and the recovery rate that shows a number of dead individuals. Varotsos and Krapivin (2020a, b) developed the prediction method of COVID-19 spread depending on the serial restrictions of population movement and the interactions between people considered in the framework of the appropriate scenarios (Krapivin 1970; Krapivin and Mkrtchyan 2019). The COVID-19 decision making system (CDMS) is synthesized to be the evaluator of epidemic parameters and the predictor of epidemic consequences. A particular danger for countries under the influence of the WB and the IMF is the external funding of the import of cheap goods. For instance, in Zambia, the opening of the domestic market for textile imports led to the collapse of the domestic textile sector with closure of 132,140 textile factories and respective unemployment, closure of schools, reduced financing of health service and agriculture. In contrast,

90

1 Global Problems of Ecodynamics and Hydrogeochemistry

countries such as Cuba, Costa Rica, China, and others did not allow their borders to be completely opened to imports and therefore, despite low indicators of per capita GDP, they are characterized by a high level of human health. This testifies to the fact that the impact of globalization on the commonwealth and health of a concrete nation can manifest itself both through both their improvement and aggravation, depending on a combination of a host of external and internal factors. It is clear that the uncontrolled liberalization of many branches of industry and agriculture is one of the triggers for the aggravation of the national standard of living. Protectionist policies, including subsidies, may preserve rural life and livelihood—arguments frequently advanced by the EU and Japan (Labonte 2001; Labonte and Sanger 2006a, b). This benefits the health and quality of life of rural people. But such policies can also support ecologically unsustainable forms of production and increase oligopolistic corporate control over global food production. Thus, the question arises “Who wins and who loses?” This question can be answered by solving another problem consisting in evaluating the impact of the present globalization on health in the context of the political, social, and economic traditions of the country as well as depending on the level of the economic development, the natural resources supply, and the general state of the society. Lee et al. (2002) analyzed the state of the debate on the merits and shortcomings of globalization in the context of the health of population in general, noting that depending on ideological motivations and opinions of experts are diametrically opposed. Their conclusions are classified from the complete usefulness of globalization to its complete denunciation. In this connection, two questions arise: 1. What mechanisms and consequences of globalization lead to an improvement in the humans’ health on the planet as a whole and in each region separately? 2. How can humankind control the consequences of globalization, directing them to improve living standards? Answers to these questions can be found in the publications of Kondratyev et al. (2003b, 2004a), where a constructive procedure has been developed to evaluate the consequences of implementing growth scenarios based on simulation experiments using GMCBSS. In any case, a limit has exhausted in the global field experiments with CBSS conducted by the WB and the IMF. The GMCBSS quantifies the fundamental links between population health, the environment, and economic growth. Without a clean and prosperous Earth, it is impossible to create conditions acceptable for life. The realization of this dream in the present world faces many contradictions between the desire to raise the standard of living and preserve the environment. GMCBSS provides a method to coordinately resolve these contradictions due to simulation experiments which take into account global and regional geoinformation monitoring data. Among many programs providing information on such experiments, one can point out SPARC in which data on stratospheric processes are obtained. This data is important for the ERB specification. The problem of health constantly emerges as an inevitable consequence of globalization, when the developing processes of urbanization, local violence, and regional conflicts (Anderson et al. 2006) enhance the risks of morbidity and

1.8

Demography and Water Resources

91

mortality. This trend has led to the development of emergency medicine as a global discipline. First of all, the environmental state is one of the determinants of the public health of countries at all levels of socio-economic development. Therefore, the problem of looking for information indicators for a comprehensive evaluation of the state of public health becomes important. Many national and international organizations are trying to resolve the problems shown here: • United States Environmental Protection Agency. Protecting human health is an integral part of EPA’s mission. The EPA conducts numerous research programs throughout the world that study the effects of pollution on the human body. Research efforts include studies on how pollution affects children and people with asthma and other illnesses and water contaminants may affect swimmers and beachgoers. Monitoring environmental quality also plays an important role in protecting human health. EPA works with state and local agencies, as well as volunteer and other citizens groups, to monitor air and water quality and to reduce human exposure to contaminants in the air, land, and water. • Environmental Protection Agency Ireland. The EPA’s viewpoint papers examine a variety of environmental topics, highlighting key environmental issues, the role of the EPA, government departments, local authorities, and other state agencies, as well as listing recommendations for future action including agriculture and the environment, air quality, anaerobic digestion, bathing water quality, brownfield site redevelopment, climate change, drinking water quality, forestry and the environment, renewable energy, waste management, and waste prevention and minimization. • Environment Protection Authority, Victoria, Australia. EPA Victoria’s purpose is to protect, care for, and improve the environment. The basic question is how can people help protect the environment? Different approaches are considered as means to solve this question, including: 1. Use environmentally friendly transportation. 2. Recycling is good news for the environment. It helps conserve natural resources and reduces pollution and litter. 3. Compost and reduce waste. 4. Dispose of litter carefully. 5. Save energy. Saving energy reduces air pollution and greenhouse gases. • World Health Organization. WHO projects, initiatives, activities, and information products include numerous topics. Every year the World Health Report takes a new and expert look at global health, focusing on a specific theme, while assessing the current global situation. Using the latest data gathered and validated by WHO, each report paints a picture of the changing world of health and shows how, if recent lessons are understood and heeded, unprecedented health gains can be achieved. • The Pan American Health Organization. The PAHO topics include: child health, epidemiology, food and nutrition, gender and health, health promotion, injuries and violence, lifestyles, maternal health, non-communicable diseases, etc.

92

1

Global Problems of Ecodynamics and Hydrogeochemistry

• Health Organizations in Eurasia. HOE controls many medical associations, institutes, and centers from countries of Former Soviet Union and Asian countries organizing conferences and publications. • Association of Asian Pacific Community Health Organization. AAPCHO announcements: 1. New UDS Fact Sheet—Patient demographic trends at AAPCHO member centers. 2. AAPCHO issues a statement in response to CDC report on hepatitis. 3. AAPCHO collaborates with UCSF in development of updated AAPI HIV/AIDS Fact Sheet. Combination of the words “health and wealth” is used by numerous socially oriented organizations both for the introduction of basic ideas of the social justice in the world and for the development of fundamental criteria of CBSS development. To a large extent, these two notions are used to look for a strategy of an individual’s behavior (Andrews 2004) and to a lesser extent, at a societal level. Clearly, the state of public health is directly dependent on the state of the economy. In developed countries, 5–10% GDP is spent on medical service and other measures to maintain public health, which is impossible for many developing countries. Therefore, one of the globalization priorities should be the equalization of economic potentials to be used in sectors directly related to the public health maintenance. And this equalization must take place at the UN level. Man causing the environmental pollution processes leads to damage to the ecosystems themselves. The environmental crisis resulting from the biosphere deterioration has many sources where soil-plant formations (SPF) play a significant role. The impact of man on SPFs through his economic activities transforms and modifies natural conditions and processes leading to changes in the biotic and abiotic components of the biosphere. As a result of human activities, many natural hydrological objects are polluted through the basic factors of environmental crisis. One of main sources of water pollution is agriculture where the increase in production is achieved due to the use of chemical fertilizers, expansion in irrigational facilities, and development of high-yielding seed varieties (Appannagari 2017).

1.8.2

Matrix Model of Population Size Dynamics

According to the possibility and needs of the global model, the unit of population dynamics includes the impact of the following factors (Logofet 1993, 2002): • Per-capita food provision F (calculated as the sum of several shares of vegetation and animal population of the region as well as fish catches) • The share A of animal protein in the humans’ diet (determined by the contribution to the F value from animals and fish)

1.8

Demography and Water Resources

93

• The level of public medical service M (in this model version it is proportional to per-capita financing) • Genetic load for human population G (increases slowly with population growth and depends on the level of environmental pollution) The gender structure of the population and the processes of population migration between regions are ignored. The age structure contains three groups (0–14 years, 15–64 years, 65 years and older). There is also a fourth group of population (people with disabilities from these three age groups). For terminology convenience the fourth group is considered not affecting the age structure. Let St be the age structure at a time moment t, then the population size dynamics can be described by the following matrix equation: Stþ1 = D × St where D is the demographic matrix 4 × 4 including the effect of the factors F, A, M, and G:

D=

d11

d 12

0

0

d21 0

d 22 d 32

0 d 33

0 0

d41

d 42

d 43

d 44

Matrix D differs from Leslie’s traditional matrix for the age-structured population model, first, by inequality to zero of its diagonal elements, which is explained by the overlapping of subsequent generations, and, secondly, by the last line that reflects the non-age character of the fourth group. The diagonal elements of matrix D are determined by apparent balance relationships: 4

d ii = 1 - μi -

d ji

ði= 1 ÷ 4Þ

j>i

which include the coefficients of mortality μi of the i-th group, descending functions of the per-capita food provision, and the medical service level: μi = μi (F, M) with lim

F, M → 1

μi ðF, M Þ = μi, min > 0

where μi, min characterizes a minimum of physiological mortality with optimal food provision and medical service. It is assumed that the reproductive potential of the human population is concentrated entirely in the second age group. The d12 coefficient value is considered to be a regional constant. Note that the birth-rate coefficient is a complex and poorly studied function of many variables, the ethnic traditions being one of the important

94

1 Global Problems of Ecodynamics and Hydrogeochemistry

variables. The birth-rate is affected by religion, for instance, the Moslems consider children to be their wealth. And in South Asia the religious norms order each family to have at least one son. Catholicism also affects markedly the birth-rate index. Therefore, if the function μi is well parameterized by the statistical data, the coefficient d12 is still an unidentified parameter. Nevertheless, the d12 value for different global regions can be estimated from the birth-rate data. The coefficients of transition in the following age groups d21 and d32 are determined by the duration of the respective age group and the hypothesis of a uniform distribution of ages within the group, namely: d21 = 1/15; d32 = 1/50. Genetically stipulated diseases and a deficit of protein in the food of children are the main causes of transitions from a younger age group to the disability group. Thus d41 = d41 (G, A) is the age function of its arguments. Coefficients d42 and d43 are small compared to d41 and determined by the sum of the vital parameters of the environment, the level of medical service, and other indicators of anthropogenic means (investments). In a first approximation, suppose d4k = Δk μk (k = 23), where Δk ≪ 1. The demographic matrix D has a certain set of properties which make it possible to reveal the typical trends of the demographic process. A maximum eigenvalue is: λmax ðDÞ = maxfλ1 ; d33 ; d44 g > 0

ð1:8Þ

where λ1 = 0:5ðd 11 þ d22 Þ þ 0:25 ðd11 þ d22 Þ2 þ d 12 d22 –d 11 d22

1=2

:

From (1.8) it follows that if λmax (D) ≥ 1, then λmax (D) = λ1, that is, the growth rate of Homo sapiens population in the neighborhood of the stationary age distribution is determined by λ1 value. The stationary age structure is calculated as an eigenvector corresponding to λmax (D). The coefficients and modifying dependences of matrix D calculated, as a rule, from the data of Stempell (1985), give the dominating eigenvector PD = (0.369; 0.576; 0.05; 0.005). The first three components of vector PD coincide, up to the second sign, with global data on the age complement of the world population. The matrix version of the global model demographic unit has a time step of 1 year. An inclusion of this unit in the chain of other global model units with an arbitrary time step Δt < 1 year is needed as adjusting procedure. This is possible, for instance, if the demographic unit is only included at time moments that are multiples of 1 year. In this version spasmodic changes of population size will cause some imbalance in the continuous trajectories of the world system. Another procedure without this drawback is to use a prognostic equation: StþΔt = St þ Δt ðD–I ÞSt

ð1:9Þ

1.8

Demography and Water Resources

95

where I is an identity matrix. One can demonstrate that for the age structure vectors coinciding in direction with the eigenvector of matrix D, the main term of a relative error accumulated during one year with a time step Δt = 1/n constitutes (1–1/n) (η – 1)2/2. Let the right-hand part of (1.8) be denoted by f (S, Δt). Then if e is the eigenvector of matrix D with an eigenvalue η, then with the time step Δt = 1/n f ðe, 1=nÞ = e þ ð1=nÞðD–I Þe = ð1 þ ½η–1=nÞe: It is obvious that St + nΔt = f (n) (St, 1/n). And with St = e f(n) (e, 1/n) = (1 + [η – 1]/n)n e. But if Δt = 1, then St + 1 = f (e, 1) = η e, so that a relative error resulting from a subdivision of the 1-year time interval is ð1 þ ½η–1=nÞn –η = nðn–1Þðη–1Þ2 = 2n2 þ 0 ðη–1Þ3 : In other words, the accuracy of an approximation of (1.9) increase as Δt approaches unity.

1.8.3

Differential Model of Population Dynamics

The matrix model considered above allows one to use the demographic statistics by age groups, but requires knowledge of many parameters, which leads to uncertainties in the global model. Therefore, a second version of the demographic unit is suggested which simulates the dynamics of only the total population size. This version assumes that the impact of numerous environmental factors and social factors on the population size dynamics Gi in the i-th region is manifested through birth-rate RGi and mortality MGi: dGi =dt = ðRGi- M Gi ÞGi ,

ðI= 1, . . . , mÞ

ð1:10Þ

Birth rate and mortality rate depend on food supply and quality, as well as environmental pollution, living standards, energy supply, population density, religion, and other factors. In the global model all these factors will be taken into account according to the principle: RG = ð1–hG Þk G GH GV H GO H GG H GMB H GC H GZ M G = μG H μMB H μG H μFR H μZ H μC H μO G þ τGO τGC ωðGÞ where for simplicity purposes we shall omit the i index which assigns the relationship to the i-th region; hG is the coefficient of food quality (hG = 0 when the quality of food is perfect); the coefficients kG and μG point to levels of birth and mortality, respectively; the indices τGO and τGC characterize the dependence of the regional population mortality on O and C—indicators of the environmental state (in the global model it is the content of O2 and CO2 in the atmosphere) manifested through

96

1 Global Problems of Ecodynamics and Hydrogeochemistry

the humans’ physiological functions; the coefficient ω(G) characterizes an extent of the influence of population density on mortality (in the present conditions ω(G) ≈ 0.6); the functions HGV(HμFR), HGO(HμO), HGC(HμC), HGMB(HμMB), HGG(HμG), and HGZ(HμZ) describe, respectively, an impact on birth-rate (mortality) of the environmental factors, such as food supply, atmospheric O2 and CO2 concentrations, living standards, population density, and environmental pollution. The functions HμO and HμC approximate the medico-biological dependences of mortality on the atmospheric gas composition. Consider all these functions in more detail. For this purpose, we shall formulate a series of hypotheses concerning the forms of dependences of mortality and birth-rate on various factors. The results of numerous studies with national specific features taken into account allow one to assume the following dependence as an approximation of the function HGV: HGV = 1 – exp(-VG), where VG is an efficient amount of food determined as a weighted sum of the components of Homo sapiens’ food spectrum: V Gi = k GΦi Φ þ kGFi ðF i þ

aFji F i Þ þ kGri I i ð1 - θFri - θuri Þþ j≠i

þk GLi Li þ k GXi ½ð1 - θFXi ÞX i þ ð1 - vFXi Þ

aXji X j j≠i

Here Φ is the volume of vegetable food obtained from the ocean; F—protein food; L—food given by forest; X—vegetable food produced by agriculture; I— fishery products; coefficients kGΦ, kGF, kG, kGL, and kGX are determined using the technique described in Chap. 6; aFji and aXji are, respectively, shares of protein and vegetable food in the i-th region available for use by the population of the i-th region; θFXi and vFXi are shares of vegetable food produced and imported by the i-th region, respectively, to produce the protein food; θFri and θuri are shares of fishery spent in the i-th region on the protein food production and fertilizers, respectively. With an increasing food supply the population mortality falls to some level determined by the constant ρ1,μG at a rate ρ2,μG, so that HμFR = ρ1,μG + ρ2,μG /FRG, where the normalized food supply FRG is described by the relationship FRG = FRG(t) = VG/G(FRG(t0) = FRGO=VG(tO)/G(t0)). Similarly, we assume that the birth-rate depending on the living standard MBG of population is described by the function with saturation so that a maximum of birth-rate is observed at low values of MBG, and at MBG → 1 the birth-rate drops to some level determined by the value of a*GMB. The rate of transition from the maximum to the minimum birth-rate with the change of MBG is defined by the constants a1,GMB and a2,GMB: H GMB = aGMB þ a1,GMB expð- a2,GMB M BG Þ where M BG = ðV=GÞf½1–B–U MG –U ZG =½1–Bðt o Þ - U MG ðt o Þ - U ZG ðt o Þg × × ½E RG ðt Þ=E RG ðt o Þ, E RG ðt Þ = 1– exp½ - k EG M ðt Þ=M ðt o Þ:

1.8

Demography and Water Resources

97

The dependence of mortality on the living standard is described by the decreasing function: H μMB = b1,μG þ b2,μG exp - bμG M BG : This function shows that population mortality with an increasing per-capita share of capital falls by the rate coefficient b* μG at the level b1,μG. The birth-rate and mortality at certain limits are, respectively, decreasing and increasing functions of population density: H GG = G1,G þ GG expð- G2,G Z GG Þ,

H μG = θ1,μG þ θ2,μG Z ωμG GG ,

where ZGG = G(t) / G(to). Finally, an important aspect of the Homo sapiens ecology is the environmental state. In this context, the problems of anthropobiocenology have been widely discussed in the scientific literature and many authors are trying to find out the necessary regularities. Without going into details of these studies, most of which cannot be used in the global model, we shall limit ourselves to the following dependences: H G Z = l1,G expð- lG Z R G Þ, H μZ = n1,μ G þ n2,μ G Z R G , τGC =

τ1,GC þ τ2,GC ðC a - C 1,G Þ for τ1,GC

for

C a > C1,G , 0 ≤ C a ≤ C 1,G

H μO = f 1,μ G þ f 2,μ G =Oðt Þ, H μ C = exp k μ G C a , H G O = 1 - expð- kG O OÞ, H G C = expð- k G C C a Þ, Z R G = Z ðt Þ=Z ðt 0 Þ, τGO =

τ1,GO τ2,GO - ðτ2,GO - τ1,GO ÞO=O1,G

for for

O > O1,G , 0 ≤ O ≤ O1,G

where Ca is the atmospheric CO2 concentration, C1,G and O1,G are human safe levels of atmospheric CO2 and O2 content.

1.8.4

Megapolitan Zones

A megapolitan zone is an Earth’s surface area with a high urbanization, developed industry, and other human activity attributes concentrated in a limited territory (urban, suburban, and peri-urban areas). A number of areas like this is constantly growing in the world and their total area increases with the growing size of the global population. In this context, geographer Gottman (1987) writes: “. . . the Megapolitan concept seems to have popularized the idea that the modern cities are better reviewed

98

1 Global Problems of Ecodynamics and Hydrogeochemistry

not in isolation, as centers of a restricted area only but rather as parts of ‘citysystems,’ as participations in urban networks revolving in widening orbits.” According to European Spatial Planners (Faludi 2002), the primary urban unit for integration into the global economy is a trans-metropolitan area, or what Gottman (1964, 1987) refers to as a “Megalopolis.” Andoh and Iwugo (2002) uses the term “Megapolis.” Typical examples of large megapolises are Moscow, Tokyo, New York, HoChiMinh, etc. For example, the total megapolitan population in USA amounts to 68% of the US population (Lang and Ghavale 2005). The Moscow megapolitan zone is characterized by high concentrations of sources of anthropogenic pollution in the limited territory (energy enterprises, chemical industry, and automobile transport). Their share in total emissions is 90.4%. The polluted air plume from Moscow can be observed at about 100 km from the city. The state of the water bodies within the megapolis is determined by the input of sewage, surface run-offs, as well as run-offs from industrial enterprises to the basic rivers Moskwa and Yausa, as well as into 70 rivers and springs on the territory of the megapolis. The concentration of chemicals in the river Moskwa both in the city and along the downstream varies widely depending on the season. For instance, the copper ion content varies during the year from 0.004 to 0.013 mg
l‐1 (4–13 permissible concentration level—PCL) with a maximum in the spring. The content of oil products varies within 0.25 to 0.6 mg
l‐1 (5–12 PCL). A similar situation exists in the small river Yausa where the concentration of copper varies from 0.007 to 0.12 mg
l‐1 and that of oil products from 0.38 to 0.7 mg
l‐1. The ecological service of the megapolis monitors the local concentrations of pollutants forming the respective time series of data on the state of the environment. There is a global network of megapolises, the consideration of which in the global model results in an increase of its accuracy. Many megapolises are in the stage of formation. Khoshimin is an example of a young megapolis as well as adjacent provinces of South Vietnam— Dong Nai, Bin Zyong, and Baria-Vung Tau. This megapolis occupies an important place in the economy of Vietnam covering 75% of industrial production of South Vietnam and 50% for the whole country (Si and Hai 1997). The development of the megapolis infrastructure includes services of ecological and sanitary control of the environment at an early stage, which makes possible the prospective planning of the environment. The analysis of the data on the structure of the environment of megalopolises of the world suggests the conclusion that for its complex assessment it is possible to develop a sample system of the models simulating the transport and propagation of pollutants in the atmosphere and in water bodies. The input information for this model can be both from the monitoring systems and the global model. The characteristic linear dimensions of a megapolis constitute tens of kilometers. For example, Osaka-Kyoto-Kobe prefecture and New York City areas equal to 11,170 km2 and 54,520 km2 with population density 1669 and 402 person per square kilometer, respectively. Such areas mean that to simulate the processes of the atmospheric transport of pollutants a model of the Gaussian type can be used. To assess the quality of the atmosphere in the megalopolis it is sufficient to form a composition of the Gaussian streams and at the points of their intersections to

1.8 Demography and Water Resources

99

summarize the concentrations of the respective types of pollutants. Population density is a key indicator for the assessment of atmosphere pollution as well as of the water quality. Water resources in many megapolises for domestic, industries, and commercial are becoming scarce as a result of pollution of water bodies by wastewater, which contains heavy metals, bacteria, etc. Flooding is a serious problem in megapolises where drainage systems are poor and there is a relatively high water table and flat topography. All these aspects need to be taken into account in the GMCBSS to make the ecodynamics forecast more precise (Table 1.13).

Table 1.13 The current status of global CBSS core components Global CBSS component Grain production Total, mln t/yr Per capita, kg/yr Meat production Total, mln t/yr Per capita, kg/yr Area of irrigated soils Total, mln ha Area per 1000 people, ha Fossil fuel expenditure, mln t of oil equivalent Coal Oil Gas Energy production by Nuclear power stations, GW/yr Wind-driven systems, MW/yr Mean global temperature, °C Carbon emission due to fossil fuel burning, mln t C/yr Partial pressure of CO2 in the atmosphere, ppm Production of metals, mln t/yr Production of round timber, mln m3/yr Oil spills due to anthropogenic activity, thousand t/yr Gross product Total, bln US$ /yr Per capita, dollars/yr Foreign debt of developing countries and countries of the former Eastern Bloc, bln US$ Global population Total, billions Annual increment, millions

Estimate of the component 1836 302 232 38.2 274 45.7 2186 3504 2164 348 18,100 14.4 6480 370.9 902 3336 48.6 44.9 7392 2.53

7.8 83

100

1.8.5

1

Global Problems of Ecodynamics and Hydrogeochemistry

Global and Regional Water Resources

Water is a very important environmental element for human life compared to other resources extracted from the Earth’s depths. Water resources are large, but their use is restricted by ecological factors. Water resources include surface and underground reservoirs, the use of which in liquid, frozen, and gaseous state depends on the regional and local situations. Human global population growth leads to a concomitant growth in water demand by agriculture, consumers, and industry which leads to the water resources crisis around the world. In fact, freshwater consists of about 3% of the world’s storage and only 0.3% is available for use. Nearly 70% of worldwide use is for irrigation. Average water use varies widely. Approximately 22% of freshwater use is industrial, only 8% is used for household purposes. Average water consumption per person per day is 575 liters in the USA and only 4 liters in Mozambique. 100% of freshwater is divided by 68.9% of glaciers and permafrost; 29.9% of fresh ground water; 0.9% of soil, swamp water, and permafrost moisture; and 0.3% of freshwater lakes and river storage. Additional to this, the total water resources are divided by salt water (97.5%) and freshwater (2.5%). These data are specified in Tables 1.14, 1.15, 1.16, 1.17, and 1.18. Table 1.14 Average renewable freshwater resources per person per year (https://ourworldindata. org/water-use-stress) Region South America Oceania Eastern Europe North America Central America and Caribbean Western and Central Europe Sub-Saharan Africa Central Asia East Asia Middle East South Asia Northern Africa

Renewable freshwater resources per person per year, m3 30,428 29,225 21,383 12,537 8397 4006 3879 2420 2115 1444 1131 256

Table 1.15 Distribution of freshwater resources

Region World Asia Southern America

Volume of river outflow % km3/year 47,000 100 13,190 28.1 10,380 22.1

Personal water availability, 103 m3/person/year 8.8 4.5 34.0 (continued)

1.8

Demography and Water Resources

101

Table 1.15 (continued)

Region Northern America Africa Europe Australia and Oceania Russia

Volume of river outflow % km3/year 5960 12.7 4225 9.0 3110 6.6 1965 4.2 4270 9.1

Personal water availability, 103 m3/person/year 15.0 6.5 6.0 83.0 28.5

Table 1.16 Ten first countries by the freshwater resources Country Brazil Russia Canada China Indonesia USA Bangladesh India Venezuela Myanmar

Water resources, km3 6950 4500 2900 2800 2530 2480 2360 2085 1320 1080

Water resources per capita, 103m3/person 43.0 30.5 98.5 2.3 12.2 9.4 19.6 2.2 60.3 23.3

Table 1.17 Global hydrological cycle components (Oki and Kanae 2006) Hydrological cycle component Fluxes, 103 km3/year Precipitation over oceans Evaporation over oceans Net water vapor flux transport in the atmosphere Total terrestrial precipitation: Rainfall Snowfall Surface runoff Subsurface runoff River Precipitation on: Forests Grassland Cropland Lakes Wetlands Others

Value 391.0 436.5 45.5 98.5 12.5 15.3 30.2 45.5 54.0 31.0 11.6 2.4 0.3 11.7 (continued)

102

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.17 (continued) Hydrological cycle component Total terrestrial precipitation Evaporation from: Forests Grassland Cropland Lakes Wetlands Others Total terrestrial evapotranspiration Storage, 103km3 Water of seas Rivers Soil moisture Lakes Groundwater Wetlands Permafrost Biological water Water vapor over land Glaciers and snow Water vapor over sea

Value 111.0 29 21 7.6 1.3 0.2 6.4 65.5 1,338,000 2 17 175 23,400 17 300 1 3 24.064 10

Table 1.18 The GIMS functions and their short description The GIMS function Planning and analysis of big data clouds

Modern analysis of big data fluxes using spaciotemporal interpolation and extrapolation methods

Evaluation of the state of the atmosphere

The function description Analysis of the structure of the environmental data acquisition system using satellite data, flying laboratories, and mobile and fixed ground observation means as well as providing socio-economic information (Krapivin and Shutko 2012) Retrieval of the data and their reduction to the common time scale is performed. Global model parameters are determined. A thematic classification of big data is carried out and spatiotemporal combination of measurements obtained from various type sources is made (Kondratyev et al. 2002a, b) The gas and aerosol composition of the nearearth atmospheric layer is provided and forecasting maps of their distribution are created (Kondratyev et al. 2006a, b; Krapivin et al. 2012) (continued)

1.8

Demography and Water Resources

103

Table 1.18 (continued) The GIMS function Evaluation of the state of soil-plant covers

Evaluation of the state of the water medium

Modeling global biogeochemical cycles

Modeling photosynthesis

Modeling demographic processes

Climate change modeling

Identifying causes of ecological and sanitary disorders in the environment

Intelligent support

The function description Determine the structural topology of land cover revealing soil-plant formations according to spatial analysis (Krapivin and Shutko 2012; Kondratyev et al. 2002a, b) The simulation model is used for hydrological processes taking into account seasonal changes of surface and river runoff, the influence of snow cover and permafrost, and the precipitation and evaporation regime (Krapivin and Varotsos 2007; Krapivin et al. 2015b). Mathematical models of global cycles of greenhouse gases are used taking into account the roles of soil-plant formations, the World Ocean and the geosphere, as well as anthropogenic processes (Kondratyev et al. 2003b; Krapivin and Varotsos 2016; Varotsos et al. 2014a, b) Photosynthetic processes in the oceans and vegetation layers are described by proper mathematical models (Sellers et al. 1986; Krapivin and Kelley 2009) Population dynamics is described by two models with consideration of the role of environmental and social factors. The models differ in their mathematical approaches (Kondratyev et al. 2002a, b; Krapivin and Varotsos 2007) Climate change processes are described by simple functional models reflecting the roles of greenhouses gases and air pollution (Mintzer 1987; Krapivin and Varotsos 2008) Hazardous environmental processes are searched for and identified, including the detection and forecasting of tropical cyclones, floods, and excessive air pollution (Krapivin and Shutko 2012; Nitu et al. 2013) Software-mathematical algorithms are implemented to provide the user with intelligent support when performing complex analysis of simulation experiment results (Nitu et al. 2004)

104

1.9 1.9.1

1

Global Problems of Ecodynamics and Hydrogeochemistry

A New Big Data Approach Based on the Geoecological Information-Modeling System Big Data Problems

Big data problems arise when it is necessary to solve complex tasks based on both structured and unstructured series of data collected from various sources with different precision. Hydrogeoecochemical investigations both generate and need a large amount of information that demands a set of algorithms for storage and processing distributed data sets. Schematically this is represented in Fig. 1.12. Varotsos and Krapivin (2017) have developed a new approach to big data processing based on GIMS-technology that captures the fundamental processes controlling the evolution of the climate-biosphere-society (CBSS) system. Today’s development of civilization promotes the problems of assessment and forecasting the expected climate changes and the related variations of human and animal habitat. In the first place, the beginning and expansion of dangerous natural processes leading to human losses and economic damages is one of the main problems of environmental monitoring data processing. The complexity of this problem is related to the heterogeneity and multiformity of available information

Fig. 1.12 Principal scheme of information-modeling tracking tropical cyclones, including the search and detection stages of the time when the phase state of the atmosphere-ocean system begins to steadily change with its amplitude growing

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 105

from various sources, such as Earth monitoring systems and existing databases (Sudmanns et al. 2017). Indeed, human civilization must solve a more important problem of sustainable development between nature and society. What is needed is the investigation of reliable and efficient informational technology providing spatial scope of global and regional relationships between complex structures of relations within nature and society, considering possible constraints and multi-fold structures. According to Krapivin and Varotsos (2007, 2008), the fundamental problem lies in the conception of globalization and its understanding. The interaction of humanity and nature is a function of a vast set of factors acting both in human society and in the natural environment. The main problem of this interaction is the globalization of the anthropogenic impacts on natural systems of population growth and the expansion of polluted areas. Existing big data approaches for the sustainable problem solution mainly focused on traditional economic challenges (Ammn and Infanuddin 2013; Belaud et al. 2014; Dhamodaran et al. 2015; Guo et al. 2014, 2016, 2017) as well as in limited environmental problems (Baumann et al. 2016; Craglia et al. 2012; Edwards 2010; Evangelinos and Hill 2008; Goodchild et al. 2012; Olivier et al. 2016; Zhong et al. 2014). Nowadays, big data from satellites play a major role in climate change dynamics (Cracknell and Varotsos 1994, 1995, 2007, 2011). Over the last decades, atmospheric chemical composition such as carbon dioxide, ozone, and other greenhouse gases have been systematically monitored by satellite and ground-based instruments (Efstathiou et al. 1998, 2003; Gernandt et al. 1995; Kondratyev et al. 1994b; Reid et al. 1994, 1998; Varotsos and Cracknell 1993, 1994; Varotsos et al. 1994, 1995, 2000). Such measurements are now necessary to monitor extreme weather events by observing surface temperature and wind field, through advanced optical satellites and remotely sensed ground-based instrumentation (Efstathiou et al. 2011; Efstathiou and Varotsos 2010; Tzanis et al. 2008; Varotsos 2002, 2005a, b; Varotsos and Cartalis 1991; Varotsos and Cracknell 2004; Varotsos et al. 2009; Varotsos and Tzanis 2012). Varotsos and Krapivin (2017) develop the approach and methodology to be used to solve the problem of sustainable development of the climate-biosphere-society system (CBSS) taking into account both natural and demographic processes. Among the existing tools for environmental data visualization, the geophysical information system (GIS) is the most demanded approach to environmental monitoring data processing and representation. Basic GIS imperfection consists in that it does not focus on multi-pronged prognosis of monitoring objects. Important improvement of GIS technology was made by Kondratyev et al. (2004a) when geoecological information-modeling system (GIMS) was presented as a combination of GIS and modeling technology. Key aspects of GIMS have been discussed in many publications (Cracknell et al. 2009a, b; Krapivin and Shutko 2012; Krapivin et al. 2006, 2015b).

106

1.9.2

1

Global Problems of Ecodynamics and Hydrogeochemistry

Big Data Approach and Global Sustainable Development Problems

With the development of society, the CBSS sustainable development problem becomes increasingly critical practically covering the world. Even the use of satellite environmental monitoring does not provide data that can help to assess the CBSS characteristics with high reliability and, in particular, to predict the CBSS evolution. Big data tools help to solve limited economic problems, but encounter difficulties when environmental considerations are taken into account and when considering the nature protection problems. Therefore, widening the big data tools by providing functions to process and analyze extremely large volumes of environmental information delivered by different sources irregularly at the time and fragmented in space is a real problem. The solution of this problem is mainly realized by the use of global models oriented to the study of the CBSS limited aspects, including climate and biospheric models that practically consume a limited amount of pre-historical structured data (Krapivin 2009; Krapivin and Kelley 2009). The sustainability development problem of global CBSS for its solution requires the collection of data on an unprecedented scale. Decisions based on existing global models of different environments, including the biosphere, geo-sphere, hydrosphere, and atmosphere, cannot answer the main question: what is the optimal structure of global monitoring data that reflects different aspects of individual CBSS items and help to overcome non-removable uncertainties in many fields (Held and McGrew 2007a, b). As demonstrated by Krapivin and Varotsos (2007, 2008), the development of biogeochemical, biocenotic, hydrophysical, hydrochemical, climatic, and socioeconomic processes taking place in the CBSS inevitably requires a balanced criterion for information selection taking account the hierarchy of causal effects in the CBSS with the coordination of spatial digitization. Existing environmental monitoring systems such as the Earth Observing System (EOS) and the Global Ocean Observing System (GOOS) provide long-term global observations of the land surface, biosphere, atmosphere, solid Earth, and oceans and enable improved understanding of the Earth as an integrated system. These systems have allowed for the synthesis of Earth Observing System Data and Information System (EOSDIS) that provides data management capabilities from various sources of different types, including satellites, aircraft, field measurements, and various other sources. EOSDIS contains a growing database consuming about 8.5 terabytes daily. The size of the EOSDIS file has been increasing since recent years from about 0.2 PB in 2000 to 14 PB in 2015. These data flows can be supplemented with global and regional socioeconomic information from the Socioeconomic Data and Applications Data Center (SEDAC), the list of which summarizes the available data in 52 countries. Combined EOSDIS and SEDAC data processing can play a key role in solving the CBSS sustainable development problem using the GIMS approach (Krapivin and Shutko 2012).

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 107

Fig. 1.13 The GIMS as a tool for integrating big data fluxes provided by different monitoring systems and other data sources

1.9.3

GIMS as an Improvement of Big Data Approach

The generalized concept of GIMS is shown in Figs. 1.13 and 1.14. The GIMS key item is a global CBSS model (Krapivin and Varotsos 2007, 2008). The basic GIMS principles are: • Integration, unification, and coordination of big data fluxes provided by existing monitoring resources based on the unique organizational and scientificmethodological basis • Coordination and compatibility of big data fluxes using the unique timecoordinate system, common system of data classification, coding, format, and structure • Providing the independence of big data fluxes from ecosystem and state boundaries The construction of the GIMS is linked to the consideration of the components of the biosphere, climate, and social environment characterized by the given level of spatial hierarchy. The implementation of the GIMS function is provided by its subsystems listed in Table 1.18. Basic block of GIMS is GMCBSS, the structure of which is shown in Fig. 1.15. The GMCBSS assimilates big data fluxes considering a complex of biospheric, climatic, and socioeconomic processes taking into account their spatiotemporal hierarchy. According to the procedure shown in

108

1

Global Problems of Ecodynamics and Hydrogeochemistry

Fig. 1.14 Conceptual block-diagram of GIMS functional operations

Fig. 1.15 The structure of GMCBSS

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 109

Fig. 1.14, the GMCBSS structure is oriented to the adaptive functioning mode by inputting the global model into a geoinformation monitoring system (Krapivin and Kelley 2009; Krapivin and Shutko 2012). The approach to the GMCBSS synthesis is based on the two mathematical methods: • Balance equations are used when knowledge of subject fluxes and information between CBSS components is exhaustive. • The evolutionary algorithm is applied when the build-up of an adequate balance model is, in principle, impossible because of the lack of information completeness and the knowledge of environmental and socioeconomic laws is insufficient. The GIMS-based method makes it possible to create a global monitoring system in which the GMCBSS provides the entire system to be categorized as a class of subsystems with variable structures and makes the system adaptable to changes in natural and socioeconomic processes. Figure 1.16 demonstrates the general structure of GIMS as a set of approaches, instruments, and methods for processing structured and unstructured data characterized by big volumes and significant variety. GIMS allows the combined use of different approaches to processing big data fluxes that primarily solve a new decision-making process to optimize these fluxes at the expense of effective monitoring alternatives and data processing tools (Krapivin et al. 2017a, b).

Fig. 1.16 The GIMS/GMCBSS block-diagram. The abbreviation expansion is given in Table 1.17

110

1.9.4

1

Global Problems of Ecodynamics and Hydrogeochemistry

Global Big Data Processing

The interaction level between society and nature has reached global planetary scales when anthropogenic impacts on the natural subsystems and processes become dangerous habitat changes for both animals and people. There is only a single approach to the search for CBSS sustainable development, which consists in assessing the consequences of the anthropogenic scenario implementation through simulation experiments. The GIMS approach can help in these experiments by allowing the estimation of environmental impacts from the implementation of anthropogenic scenarios, including global and regional scales of impacts. The variety of these impacts covers almost all environments, including pollution of the atmosphere and hydrosphere at the expense of the release of toxic compounds into the environment, habitat destruction through agriculture and urban sprawl, agricultural expansion into forested areas, etc. In other words, human society aiming at living comfort depletes resources, destroys the plant and animal kingdom, and pollutes the environment. To demonstrate the GIMS functions, several forest restoration scenarios are considered. In particular, GIMS could implement different scenarios that are aligned with our understanding of possible environmental impacts. The land cover is characterized by the heterogeneity of biomes and other environmental objects. Currently, the main areas of the land are woodland (41.2%) and cropland (24.7%). The land use is a basic problem, the solution of which involves the management and modification of the natural environment mainly related to the reduction of forest areas (Kargel et al. 2014; Lambin and Geist 2006; Ramachandran and Garrity 2012). Unfortunately, forest areas are subject to the realistic application of anthropogenic scenarios related to the withdrawal of their biomass. It is known that boreal and tropical forests suffer the most anthropogenic impacts. The phenomenon of wildfire by lightning strike or by human actions is the primary determinant of forests. GIMS allows the realization of different impact scenarios on these forests with a spatial resolution 1° × 1° (Hengeveld et al. 2015). Simulation experiments show that total burning of all coniferous forests up to 42°N leads to an increase in atmospheric carbon by 21.7% with a 4 °C increase in global temperature. Over the next year, the oceans absorb 10% of the emitted carbon, and over the next century only 1.3% of the increased atmospheric carbon has remained in the atmosphere. The atmosphere-ocean system over 100 years leads to a 9% increase in the carbon balance and carbon content of the deep oceans (Krapivin et al. 2017a). The real impact on coniferous forests is reflected in the humus layer dynamics, which over 30 years has lost 4% of its reserves. Burnt coniferous forests are restored by 68% in 100 years. Other forests absorb 1/30 of the emitted carbon. Tables 1.19, 1.20, and 1.21 show estimates of changes in carbon stocks in basic biospheric reservoirs when forests of different climatic zones are partially burned. It appears that large-scale impacts on land biota are damped over 60–100 years. Under these circumstances the biosphere is more stable in impacts in tropical forests than in boreal forests. The simulation results show that Northern hemisphere forests (42°N and higher) play a significant stabilizing role in

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 111

Table 1.19 The GIMS/GMCBSS functional blocks Block DM SCM CMCM GSCM GCOO GNCM GPCM RHCM BMSPF PMTM PMAA ABPM MATP FPM AIFI UEM MWEL AHIS DMEP BDP GSAE BDFM SSIP SEMC

Block functions Demographic models (Kondratyev et al. 2004a) Simple climate model (Mintzer 1987; Krapivin et al. 2015b) Coupled model of the carbon dioxide and methane cycles (Krapivin et al. 2017a) Global sulfur cycle model (Krapivin and Varotsos 2008) Coupled model of global cycles of oxygen and ozone (Krapivin and Varotsos 2008) Global nitrogen cycle model (Varotsos et al. 2014a) Global phosphorus cycle model (Kondratyev et al. 2006b) Regional hydrological cycle model (Krapivin and Shutko 2012) Biocenotic model of the soil-plant formations (Krapivin 1993; Nitu et al. 2004) Photosynthesis model for the tropical and moderate oceanic zones (Krapivin 1996) Photosynthesis model for the Arctic and Antarctic zones of the World Ocean (Kondratyev et al. 2003a; Krapivin et al. 2017a; Krapivin and Soldatov 2014) Arctic Basin pollution model (Varotsos and Krapivin 2018) Model of long-range atmospheric transport of pollutants (Kondratyev et al. 2002a, b) Food production model (Tuyet et al. 2019b; Nitu et al. 2015) Evolutionary algorithm for the indicator calculation of the food industry (Nitu et al. 2004) An upwelling ecosystem model (Krapivin and Varotsos 2016) Model of the typical water ecosystem on the land (Krapivin and Shutko 2012) An algorithm for the human indicator survivability calculation (Krapivin et al. 2017b) Dynamic model of the environmental pollutants (Kondratyev et al. 2006a, b) The big data processing using sequential and cluster analyses (Soldatov 2010, 2015; Krapivin et al. 2012; Varotsos and Krapivin 2017) The GIMS structure adaptation to the simulation experiment conditions (Krapivin et al. 2015b) Big data cloud formation and management Synthesis of the scenarios for the interaction between the population and the environment Simulation experiment management and control

the global carbon cycle. Within these scenarios it is assumed that the forest areas during the post-fire restoration are covered by the same plants. Certainly, post-fire restoration leads to a new forest structure and diversity of other species which is the result of many environmental processes, including global climate change and natural forest succession. Table 1.21 shows the changing role of vegetation in atmospheric carbon absorption under the reconstruction of soil-plant formations. Anthropogenic change of vegetation covers can significantly alter the balance components of the global carbon cycle. It is clear that such hypothetical vegetation cover transformations must take into account climatic zones and biological compatibility. GIMS partially helps in carrying out similar simulation experiments. GIMS provides an opportunity to evaluate the mosaic picture of carbon dioxide sinks in vegetation biomes in its dynamics. Knowledge of this mosaic makes it possible to assess the role of concrete

112

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.20 Model estimates of carbon stock fluctuations when all forests of the Northern latitudes up to 42°N are partially burned Deviation in the carbon Deviation in the carbon reserve reserve (Gt) (Gt) Years after Years after ΔCA ΔCS ΔCU ΔCX influence ΔCU ΔCX ΔCA ΔCS influence Scenario: Coniferous forests are partially burned in 25% of the area 2.15 3.24 6.31 60 0.02 3.95 37.71 0 1.63 3.32 7.53 4.23 70 0.78 7.71 1.44 28.54 10 1.32 3.45 2.82 80 1.85 5.43 20.82 20 1.16 3.47 6.41 1.41 90 2.12 4.82 8.53 15.87 30 0.72 3.41 0.52 100 2.61 3.45 11.63 40 3.38 5.39 200 2.93 10.91 3.01 9.21 50 0.25 2.22 4.44 10.86 3.67 10.17 0.83 8.42 Scenario: Coniferous forests are partially burned in 50% of the area 4.33 6.81 6.86 0.05 60 12.85 0 75.12 16.32 1.54 70 8.28 14.89 3.07 6.29 10 56.15 2.56 2.44 6.79 10.53 3.61 80 5.24 20 39.52 4.42 90 2.83 12.78 1.93 7.04 30 29.62 16.71 9.7 1.31 6.64 7.19 5.08 100 1.09 40 24.13 6.62 5.6 200 8.89 50 16.54 22.54 2.99 0.45 4.35 8.56 18.87 7.29 19.53 1.65 14.53 Scenario: Coniferous forests are completely burned 7.75 13.15 24.79 60 15.79 0.11 150.05 0 16.67 30.47 5.89 13.53 70 30.84 3.13 114.63 5.72 10 5.17 14.08 11.62 80 22.56 7.42 83.91 20 25.71 4.33 14.33 5.89 90 34.14 18.84 8.55 64.44 30 2.81 13.98 2.23 13.96 10.42 100 47.82 40 13.56 21.53 44.27 12.14 11.74 200 35.12 50 0.94 8.45 17.91 43.91 14.87 40.72 3.49 33.88 Nomination for CO2 concentration: CA—atmosphere, CU—upper photic ocean layer, CS—dead organic matter on the land, CI—intermediate photic ocean layer under the thermocline, CD—deep ocean, and CB—near bottom ocean layer, CX = CI + CD + CB

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 113

Table 1.21 Model estimates of carbon stock fluctuations when all forests of northern latitudes up to 42°N are partially burned. The nominations are given in Table 1.19 Deviation in the carbon reserve (Gt) Years after Years after ΔCA ΔCS ΔCU ΔCX impact impact Scenario: 25% of the northern (up to 42°N) forests are burned 60 0.03 -1.9 6.22 59.52 0 70 11.97 1.23 43.02 10 80 2.49 9.8 7.64 33.75 20 90 3.45 7.95 27.08 30 100 4.05 16.42 5.78 19.68 40 200 4.83 4.47 15.04 50 22.03 15.75 13.99 Scenario: 50% of the northern (up to 42°N) forests are burned 13.47 0.54 60 0 119.05 22.56 2.74 70 10 86.92 3.95 18.14 4.98 80 20 72.36 90 30 51.68 15.35 16.48 6.61 11.06 8.49 100 40 41.98 9.99 200 50 30.32 33.59 8.81 41.17 32.92 29.13 Scenario: 100% of the northern (up to 42°N) forests are burned 60 25.18 0.08 239.02 0 70 48.07 4.93 174.28 7.94 10 39.27 11.01 80 138.91 20 107.39 31.62 32.24 13.82 90 30 24.11 16.89 100 83.08 40 67.64 17.89 19.21 200 61.55 50 90.33 64.32 56.92

Deviation in the carbon reserve (Gt) ΔCA ΔCS ΔCU ΔCX 11.05 8.14 5.23 3.11 1.81 3.17

12.13 10.31 8.71 7.49 6.17 1.48

3.54 2.52 1.91 1.43 0.91 9.43

5.34 5.41 5.88 5.89 6.02 5.42

23.95 16.48 10.82 6.17 3.94 -6.6

25.67 20.59 17.85 17.45 12.32 3.06

6.53 5.35 3.68 2.97 2.07 0.79

10.82 11.17 12.31 12.48 12.87 10.81

43.78 33.14 20.72 12.91 7.33 12.74

48.91 42.18 35.03 30.12 24.74 5.94

15.67 10.43 7.54 5.52 3.71 1.74

20.71 21.93 22.82 23.33 23.64 21.73

biomes in the regional balance of carbon and, on this basis, to estimate the possible consequences of anthropogenic interference with these biomes. GIMS provides an opportunity to estimate atmospheric CO2 sequestration by vegetation sites as they evolve in different regions. It is assumed that CO2 emissions in 2015 are estimated at 36.1 GtCO2 worldwide and 1.7 GtCO2 from the Russian territory with a sequential

114

1 Global Problems of Ecodynamics and Hydrogeochemistry

decrease of 10% in 2150. It is also accepted that deforestation processes in Russian territory are not taking place. In the context of this scenario, CO2 assimilation rates by plants on the territory of Russia will increase from 206.1 MtC/year in 2015 to 292.3 MtC/year in 2150 which is a result of climate change. With this role of Arctic deserts the tundra is characterized by an increase of 256% in the period from 2000 to 2150. Global warming effects on these biomes are shown in the extension of the vegetation period and transformation plant types at the expense of the successional processes of the tundra–taiga boundary. Such effects are reduced for mid-taiga forests and dry steppes by up to 205% and 114%, respectively. Natural land cover transformations are real human processes to improve living habitat and food production growth. These actions lead to the change of many evolutionary processes, including changes in biogeochemical cycles, which directly leads to climate change. Table 1.22 demonstrates some modeling results when various biomes are transformed. Such hypothetical experiments lead to an understanding of the limits of natural stability and to the possible ranges of anthropogenic interventions in natural environments. Simulation experiments show a significant dependence of global climate on the overall health of global forests. For example, a 19% reduction in global forest area by 2050 leads to a 53% increase in CO2 concentration by the end of the twenty-first century, and conversely, a 10% increase in forest area over the same time period results in a decrease of CO2 concentration by 12%. Figure 1.17 represents a climate change under different forest impact scenarios. We see that forests and climate are intrinsically linked through atmospheric carbon sequestration as well as through direct and indirect effects on the global hydrological cycle (Roberts 2009). The GIMS reflects the interactions of natural and anthropogenic factors that play a significant role in the greenhouse effect formation depending on energy use and economic development. Figure 1.18 gives a comparison of modeling results on future global temperature change under IPCC scenarios. It is evident that GIMS forecasts more low deviations in average global temperature compared to the results from the atmosphere-ocean general circulation model (AOGCM) of Hadley Centre. For example, the implementation of the A1FI pessimistic scenario is oriented toward a very rapid economic growth and intensive use of fossil resources, giving the global temperature rise to 2100 by 4 and 2.6 °C according to AOGCM and GIMS, respectively. But these changes in 2200 are 5.5 and 4 °C, respectively. The conclusion that can be drawn from the results of Fig. 1.18 is that GIMS gives more precise results due to the wider components considered. The co-evolution of climate, biosphere, geosphere, hydrosphere, and human society depends on how the Earth’s system generates and maintains thermodynamic imbalance. Understanding and evaluating processes in the climate-nature-society system requires big data processing algorithms under their exponential growth and when the use of traditional data processing tools eventually becomes obsolete. Most of the existing climate models and global biospheric models do not provide a comprehensive analysis of the processes present in the Earth system. GIMS as shown in Table 1.22 and Fig. 1.16 can play the role of big data informationmodeling system that can simultaneously analyze heterogeneous data delivered by

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 115

Table 1.22 Model assessments of fluctuations in carbon reserves under different tropical forest burning rate scenarios. The nominations are given in Table 1.20 Deviation in the carbon reserve (Gt) Years after ΔCA ΔCS ΔCU ΔCX influence Scenario: 25% of tropical forests are burned 42.2 0.2 406.2 0 264.4 20.0 74.1 8.0 10 48.0 14.9 162.2 20 93.7 27.9 19.1 90.6 30 15.0 21.3 45.4 40 22.4 84.8 7.5 18.3 50 38.6 36.5 21.6 Scenario: 50% of tropical forests are burned 42.2 0.2 0 406.2 10 264.4 20.0 74.1 8.0 48.0 14.9 20 162.2 30 90.6 93.7 27.9 19.1 15.0 21.3 40 45.4 22.4 50 18.3 84.8 7.5 38.6 36.5 21.6 Scenario: 100% of tropical forests are burned 42.2 0.2 406.2 0 264.4 20.0 74.1 8.0 10 48.0 14.9 162.2 20 93.7 27.9 19.1 90.6 30 15.0 21.3 45.4 40 22.4 84.8 7.5 18.3 50 38.6 36.5 21.6

Years after influence

Deviation in the carbon reserve (Gt) ΔCA ΔCS ΔCU ΔCX

60 70 80 90 100 200

2.9 -5.8 11.6 13.2 14.5 13.2

12.4 7.5 4.2 2.6 1.9 2.3

3.0 0.5 0.9 1.7 2.1 1.9

22.8 22.8 22.6 22.5 21.8 17.7

60 70 80 90 100 200

2.9 -5.8 11.6 13.2 14.5 13.2

12.4 7.5 4.2 2.6 1.9 2.3

3.0 0.5 0.9 1.7 2.1 1.9

22.8 22.8 22.6 22.5 21.8 17.7

60 70 80 90 100 200

2.9 -5.8 11.6 13.2 14.5 13.2

12.4 7.5 4.2 2.6 1.9 2.3

3.0 0.5 0.9 1.7 2.1 1.9

22.8 22.8 22.6 22.5 21.8 17.7

different monitoring systems with mismatched scales and non-removable uncertainties. Tables 1.18, 1.19, 1.20, and 1.21 demonstrate such functions of GIMS as a new big data approach. As a result of the simulation results, there are significant impacts of the saturation of greenhouse effect due to CO2 growth, which is in agreement with the physical laws and justified by many modeling results (Miskolczi

116

1

Global Problems of Ecodynamics and Hydrogeochemistry

Fig. 1.17 The dynamics of CO2 concentration for different scenarios of changing land cover areas: (1) the rates of changes of the forest areas over the next 100 years remain as those of the period 1970–2015 (0.45%); (2) forests will be cleared by 2050; (3) by 2050 forest area will be reduced by 50%; (4) as in 3 but for a 10% reduction; (5) by 2050 the area of forests increases by 50%; (6) as in 5 but for a 10% increase; (7) by 2050 coniferous forests will be reduced by 50%; (8) by 2050 agriculture fields will increase by 50%

Fig. 1.18 Projections of changes in average global temperature evaluated via AOGCM and GIMS under three IPCC emission scenarios

1.9

A New Big Data Approach Based on the Geoecological Information-Modeling System 117

2007). Simulation experiments analogous to Table 1.20 can help to search for the optimal forest management strategy when climate change forecast will be acceptable for a long time. It is necessary to carry out a series of simulation experiments considering reasonable scenarios (e.g., Table 1.23) (Krapivin et al. 2017b). In addition, GIMS has the function of merging data when data is delivered from disparate sources irregularly in time and fragmented in space. This function allows answering the following questions that inevitably arose during the management of environmental monitoring (Nitu et al. 2013; Krapivin and Shutko 2012): • What tools, remote sensing platforms, and instruments should be used to build the GIMS database? • What is the cost of GIMS simulation experiment information? • What balance should be struck between different data sources? As shown by many applications of GIMS technology, it was able to show how to address these and other questions, mainly related to the CBSS sustainable development using simple biosphere models and complex simulation models that require the big data processing (Bartsev et al. 2008; Degermendzhy et al. 2009; Sellers et al. 1986). In any case, the search for alternative pathways to global CBSS sustainable development is carried out through simulation experiments in the context of scenarios proposed by experts. GIMS expands the realm of scripting and optimizes big data flows. Varotsos and Krapivin (2017) describe the main structure of GIMS and give examples of its use to demonstrate the functional efficiency of the big data analysis and processing. The scenarios studied here show the presence of alternatives in environmental anthropogenic strategies.

Table 1.23 Dynamics of the ratio of total CO2 assimilation rates by vegetation covers from the atmosphere under the scenario when the natural biome is globally replaced by another biome Scenario where the actual existing biome is replaced by another biome globally at a scale of 1-degree. The scenario is implemented in 2015

Present vegetation Arctic deserts and tundras Tundras Mountain tundra North-taiga forests Sub-tropical deserts Broad-leaved coniferous forests

Future vegetation Forest-tundra Forest-tundra Forest-tundra Mid-taiga forests Mid-taiga forests Mid-taiga forests

F6 (Future vegetation) /F6 (Present vegetation) Years 2020 2030 2050 2100 2.55 0.94 1.42 1.63 1.98 3.92

2.12 0.92 1.15 1.44 1.66 4.07

1.89 0.96 1.01 1.11 1.45 2.84

2.16 1.12 1.04 1.12 1.33 1.96 (continued)

118

1

Global Problems of Ecodynamics and Hydrogeochemistry

Table 1.23 (continued) Scenario where the actual existing biome is replaced by another biome globally at a scale of 1-degree. The scenario is implemented in 2015

Present vegetation Sub-tropical broad-leaved and coniferous forests Xerophytic open woodlands and shrubs Moderately arid and arid steppes Pampas and grass savannas Forest-tundra Alpine and sub-alpine meadows Variably humid deciduous tropical forests Tropical xerophytic open woodlands Tropical savannas Tropical deserts Sub-tropical and tropical grass-tree thickets of the toga type Mangrove forests

F6 (Future vegetation) /F6 (Present vegetation) Years 2020 2030 2050 2100

Future vegetation

Humid evergreen tropical forests

3.07

2.55

2.44

1.77

Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests

21.52

19.47

17.94

18.37

22.15

18.33

16.32

14.77

99.23

77.12

68.54

70.09

187.65

153.2

138.3

140.67

791.01

766.4

751.2

766.93

Humid evergreen tropical forests

1.42

1.33

1.23

1.25

Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests

68.05

60.19

56.59

57.34

5.98

4.98

4.68

5.08

25.99

24.75

23.57

22.65

17.12

15.78

14.92

14.09

0.95

1.21

0.97

7.11.09

Chapter 2

Global Water Balance and Pollution of Water Reservoirs

2.1

Global Water Balance and Sustainable Development

The main problem of sustainable development is directly related to water resources whose spatial distribution is defined by the oceans, seas, and coastal hydrological systems where water quality depends on the multiple human activities carried out in the terrestrial and the aquatic environment (Hafeez et al. 2018; Arias and Botte 2020). To overcome the problems arising here, remote sensing technology provides spatially synoptic and near real-time measurements that can be effectively used to detect, map, and track many pollutants such as oil and chemical spills, algal blooms, and high concentrations of suspended solid. Of course, the problems of the water cycle are critically important not only in the context of climate change studies but also (and indeed to a greater extent) as a factor supporting life on Earth. Due to the respective feedbacks, the water cycle is functioning as a unifier of various processes taking place in the nature-society system. The corresponding questions include, in particular, the following: • What are the mechanisms and processes responsible for the formation and variability of water cycles and to what extent are they anthropogenically affected? • How are interactions between the global water cycle and other cycles (carbon, energy, etc.) controlled through feedback processes, and how do these processes change over time? • What are the uncertainties in predicting annual change and interannual variability as well as in the long-term projections of various parameters (components) of the water cycle and what are the possibilities to reduce the levels of these uncertainties? • What are the potential consequences of water cycle variability of various spatiotemporal scales for human activity and ecosystems, how might this variability influence the Earth’s system by affecting transport of deposits and biogenes and affecting biogeochemical cycles?

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Varotsos et al., Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications, https://doi.org/10.1007/978-3-031-28877-7_2

119

120

2

Global Water Balance and Pollution of Water Reservoirs

• What are the possibilities of using information about the global GHGs cycle in decision making in the field of ecological policy with regard to water resources? Improvements in methods for calculating shortwave (SW) and longwave (LW) radiation fluxes have favored much more reliable calculation of radiative fluxes and RF. However, the problem of the influence of cloud cover dynamics is far from being resolved. Importantly, the development carried out in 1990 led to the conclusion about a systematic underestimation of the earlier calculated values of solar radiation absorbed by clouds by about 40%, which, of course, has shown the reliability of numerical climate modeling results in the context of the cloud feedbacks functioning. Credibility can be increased by using the results of numerical modeling of the climatic implications of clouds with processes of different scales considered, including the direct reproduction of cloud cover dynamics, especially a powerful convective cloudiness in the tropics. The use of modern models has shown promising results and provides an adequate reproduction of the diurnal trend of precipitation. A considerable progress has been achieved that has provided a reduction of the uncertainty level from 25% and more to less than 3%, especially using the remote sensing means such as microwave radiometers (Chukhlantsev 2006; Varotsos and Krapivin 2020a, b). The most important developments in the field of the water resources study are: • Accumulation of a comprehensive hydrological database. • Substantiation of an integral strategy of the global observing system with emphasis on the problem of water cycle. • Preparation and application of new space-borne remote sensing equipment to measure water content in the troposphere and lower stratosphere. • The retrospective analysis of all available observational data (especially from satellites) for parameters of the water and energy cycles (prioritizing long-term global precipitation data). • Re-analysis of regional data on climate dynamics for the historical period. • Global precipitation monitoring using satellites with a complex of instruments replacing those carried by TRMM and SSM/I. • Cloud climatic feedback (with emphasis on convective clouds in the tropics). • Impact of changes in temperature and hydrological characteristics on concentrations of pollutants and pathogens in the atmosphere near the Earth’s surface and in groundwater (the main motivation is determined in this case by the urgency of drinking water quality problems). • Monitoring of soil moisture in drought study problems. • Prediction of climate in the interest of solving the problems of water resources control. The global precipitation measurement (GPM) satellites will answer the following questions as part of NASA’s Earth Science Enterprise major category of “how does precipitation affect our changing Earth?”: • How are global precipitation, evaporation, water, and cycling changing? • What are the effects of clouds and surface hydrological processes on Earth’s climate?

2.1

Global Water Balance and Sustainable Development

121

• How are variations in local weather, precipitation, and water resources related to global climate variation? • How can the duration and reliability of weather forecasts be improved with new space-based observations, data assimilation, and modeling? • How well can transient climate variations be understood and predicted? • How well can long-term climatic trends be assessed or predicted? Undesirable pollutant fluxes into the hydrosphere are formed due to many human activities, including industrial production, fossil fuels combustion, agriculture, and product use. These sources produce pollutants that can find their way into the World Ocean and other hydrological bodies. Ocean and sea life is affected via rivers or through the atmosphere. The variety of pollution sources is increasing as measures are taken to improve coal power plants, reduce emissions into the atmosphere, and use microorganisms to break down pollutants in sewage, wetlands, and buffer zones created along rivers and streams to absorb excess fertilizers, dispersant oils, and spills. Oil and gas exploration, oil production, and transportation-related spills are significant sources of oil in the oceans. On a global scale, all sources of oil products in the hydrological systems include individual boats, marine vessels, airplanes, and land runoff. For oil spill cleanup, biological agents and materials are used to absorb the oil and gelling agents that facilitate the removal of the oil from the water surface. Along with oil hydrocarbon, nutrient pollution of the hydrosphere has sensible effects on aquatic ecosystems. Excess nutrients are bad for marine life, including particularly excess nitrogen and phosphorus that promote explosive growth of algae. Understanding ecological, geological, and hydrological modes of contaminants is necessary to search for precursors and critical factors the awareness of which can help to assess the CBSS sustainable level. Certainly, definitive and consistent evidence for hydrological and hydrochemical precursors has some ambiguity with the application of GIMS technology, for now. CBSS sustainability is significantly defined by both integrated and country-distributed water resources that can be represented as sustainably managed or unmanaged. In common case, water resources in the context of sustainable development of a society must be considered from all aspects, including economic and social (Saritas et al. 2015; OECD 2010; EPA 2012; Mishra et al. 2016; Mishra and Hussain 2018). The maintenance of population life and the level of economic activity depend on the availability of adequate water resources. However, the amount of water available and the amount of water needed are often out of balance. The lack of balance is observed in many regions such as Central Asia or Africa (Varotsos et al. 2020a). The data in Table 2.1 shows the distribution of water resources by regions of the world and some countries.

122

2

Global Water Balance and Pollution of Water Reservoirs

Table 2.1 The distribution of water resource by regions and countries Total population, million 793.288 72.4

Region/ Country Africa Central America and Caribbean Central 78.563 Asia Eastern 217.051 Europe Near East 257.114 Northern 409.9 America Oceania 25.388 and Pacific 3332 Southern and Eastern Asia Southern 346.7 America Western 0.511 and Central Europe World 7800 Selected countries Australia 25.661 Brazil 212.834 Canada 37.742 China 1440.298 Colombia 50.883 India 1382.419 Indonesia 273.523 Kazakhstan 18.64 Peru 33.054 Russia 146.7 USA 331.352

2.2

Average precipitation, mm/year 681 2010

Internal water resources, km3/year 3950 781

External water resources, km3/year 0 6

Percent of the world water resources 9.0 1.8

4.655

273

261

28

0.6

18.095

467

4449

244

10.2

6.348 21.9

217 611

488 6662

3 47

1.1 15.2

8.058

592

911

0

2.1

21.191

1133

11,712

8

26.7

17.853

1603

12,380

0

28.3

4.898

836

2170

11

5.0

133.795

8423

43,764

0

100

7.692 8.515 9.984 9.597 1.142 3.287 1.911 2.717 1.285 17.098 9.834

534 1761 537 647 3240 1083 2702 254 1738 460 715

256 5418 2850 2812 2112 1261 2838 109 1616 4313 2000

0 2815 52 17 20 647 0 16 297 195 71

0.5 18.8 6.6 6.5 4.9 4.4 6.5 0.3 4.4 10.3 4.7

Total area, 106km2 39.044 0.749

Hydrogeochemistry and Pollution Effects

One of the environmental problems caused by the increase in pollutant loads discharged into natural water systems requires the water quality study using more efficient methods such as new environmental monitoring tools and mathematical models. In this context, chemical and biological problems emerge as key themes for

2.2

Hydrogeochemistry and Pollution Effects

123

Table 2.2 Some of the important sources of water pollution and environmental effects (Singh and Gupta 2016) Pollution source Urbanization

Sewage and other oxygen demanding wastes Industrial wastes

Agrochemical wastes

Nutrient enrichment

Thermal pollution

Oil spillage The disruption of sediments

Acid rain pollution

Radioactive waste

Introduction of invasive species

Environmental effects Runoff from urbanized surfaces adds into hydrosphere phosphorus which creates the impairment conditions for the land covers Important nutrients such as nitrogen and phosphorus are carried into the hydrosphere which can cause a massive increase in the growth of algae or plankton Industrial activity is followed by the discharge and disposal of various chemicals, including spent acids, alkalis, dyes, heavy metals, hydrocarbons, benzene, organic components, and others. All these discharges eventually reach water bodies in the form of sewage affecting human health and the organism living there Agrochemical wastes include fertilizers, pesticides which may be herbicides, and insecticides that are widely used in crop fields to enhance productivity. They can reach humans through the food chain Nutrients can reach hydrospheric systems both in natural and anthropogenic ways causing widespread eutrophication and ultimately slow entanglement Changes in water temperature adversely affect water quality and aquatic biota leading to changes in the physical, chemical, and biological characteristics of water bodies. High temperatures disrupt reproductive cycles and lead to negative physiological changes causing difficulties for aquatic life Long-term oil spills on the water surface decrease the ecosystem productivity causing suffocation of living conditions The traditional transport of pollutants by water flows is changed by the effect of dams for hydroelectric power or water reservoirs, which can reduce the flow of nutrients from rivers to the seas Atmospheric sulfur dioxide and nitrogen dioxide emitted from natural and human-made sources form sulfuric and nitric acids in the air, the fall of which in water or on the vegetation surface leads to negative effects on the natural ecosystem Sources of radioactive waste include nuclear power plants, mining of radioactive minerals, and use of radioisotopes in medical and research purposes. Small doses of radioactive elements in the aquatic environment stimulate the metabolism. Large doses of radioactive elements cause mutagenic effects Sources of invasive species include sewage washed up by ships in 90% of cases. Invasive species rapidly reproduced and depress natural elements of the aquatic environment

many investigations (Singh and Gupta 2016; Burguet et al. 2018). The principal significance is freshwater ecosystems covering only about 0.5% of the Earth’s surface and have a volume of 2.84 × 105 km3. Polluted water affects both human and aquatic life. Table 2.2 summarizes these effects. Table 2.3 characterizes the sources of freshwater and their distribution. The total quantity of freshwater

Country Algeria Argentina Australia Brazil Canada Chile China Egypt France Germany India Italy Mexico Morocco Nigeria Peru Russia South Africa Sudan USA Zimbabwe

Population (September 2020) 43,851,044 45,194,774 25,560 212,867,328 37,742,154 19,116,201 1,439,323,776 102,334,404 65,273,711 83,783,942 1,380,004,385 60,461,826 128,932,753 36,910,560 206,139,589 32,971,854 145,934,462 59,308,690 43,849,260 331,404,570 14,862,924

Total freshwater withdrawal, km3/year 6.07 29.19 24.06 59.30 44.72 12.55 549.76 68.30 33.16 38.01 645.84 41.98 78.22 12.60 8.01 20.13 76.68 12.50 37.32 477.00 4.21

Per capita withdrawal, m3/ person/year 185 753 1193 318 1386 770 415 923 548 460 585 723 731 400 61 720 535 264 1030 1600 324

Table 2.3 Distribution of renewable freshwater resources and their using in selected countries Domestic use, % 22 17 15 20 19 11 7 8 16 12 8 18 18 10 21 8 19 31 3 13 14

Industrial use, % 13 9 10 18 69 25 26 6 74 68 5 37 5 3 10 10 63 6 1 46 7

Agricultural use, % 65 74 75 62 12 64 67 86 10 20 87 45 77 87 69 82 18 63 96 41 79

124 2 Global Water Balance and Pollution of Water Reservoirs

2.2

Hydrogeochemistry and Pollution Effects

125

available at any given time is characterized by certain variability due to climate change and many human-induced management efforts such as building reservoirs and draining wetlands. Examples of this variability are the following: • Extreme water scarcity is observed in Texas in North America, whose renewable water supply totals just 26 km3 in an area of 695,622 km2. • South Africa has only 44 km3 of renewable water resources on 1,221,037 km2 of area. • Many regions have the highest concentration of renewable water sources, including the Amazon and Orinoco Basins with a total of 6500 km3 or 15 percent of global runoff, Yangtze Basin—1000 km3, and Siberian Rivers—1960 km3. • Many countries permanently sustain water crisis such as the Central African Republic and Congo Republic where water resources per person do not exceed 10 m3/year. As can be seen from Tables 2.1 and 2.3, the world’s water resources are theoretically infinite, but their spatial distribution is irregular in space which leads to the water crisis in many regions and countries. Unfortunately, about 96.5% of the world’s water resources are saline waters. Freshwater reserves are distributed by rivers (0.0002%), fresh lakes (0.007%), fresh underground water (0.76%), glaciers (1.74%), soil water (0.001%), Greenland (0.17%), Antarctic (1.56%), atmosphere (0.001%), permafrost (0.022%), bogs (0.0008%), and biological objects (0.0001%). This distribution of freshwater shows that water pollution and human health effects are multifaceted which complicates the management of water resources and the search for acceptable scenarios for a future regional strategy for water resource control (Varotsos et al. 2020b). The water pollution problem and the ecological consequences are in the initial stage of their solution. Table 2.4 presents the types of water pollution and explains the general environmental consequences. Verma and Dwivedi (2013) represent the consequences from heavy metals pollution of the hydrosphere. Some of the pollutants like lead, arsenic, mercury, chromium, nickel, barium, cadmium, cobalt, selenium, and vanadium are very harmful, toxic, and poisonous. Some contaminants such as zinc, copper, and iron are useful for human and animal health in small doses. Zinc, copper, manganese, sulfur, iron, and baron are useful for agricultural vegetation. The biotic effect of heavy metals occurs when the bio-constituent limits are exceeded. The ecological effects from heavy metals have a specific character for each metal and these effects appear depending on the biological objects. Verma and Dwivedi (2013) have analyzed the effects of metal contaminants introduced into food tissues and have shown a specific role of some of them: • Cadmium is toxic at extremely low levels. Cadmium is also associated with bone defects, including osteomalacia, osteoporosis and spontaneous fractures, increased blood pressure, and myocardial disfunctions. • Lead is the most important heavy metal toxin that causes inhibition of hemoglobin synthesis, kidney, joint dysfunctions from food and water, and inhalation.

126

2

Global Water Balance and Pollution of Water Reservoirs

Table 2.4 The most common types of water pollution and its effects Type of water pollution Surface water pollution Ground water pollution Chemical pollution

Nutrients pollution Oxygen depletion pollution Microbiological pollution

Ecological effects Surface water includes lagoons, rivers, oceans, and lakes contamination of which features result from the dissolution or mixing of the water with pollutants. The main effects of this are diseases The hazardous chemicals and particles (pesticides and fertilizers) applied to the surface by humans leaching into the ground from rainwater can kill aquatic life. Chemicals used in industry and agriculture can be delivered to both surface and underground water bodies that can destruct aquatic ecosystems Excessive concentration of nutrients is essentially dangerous for vegetation and algae of the aquatic environments The depletion of oxygen leads to the death of aerobic microorganisms but promotes the well-being of anaerobic organisms Water containing microorganisms (protozoa, viruses, and bacteria) can cause diseases such as cholera and bilharzia. The effects of microbiological pollution are common in areas where people drink untreated water

• Zinc causes the signs of illness, vomiting, diarrhea, bloody urine, icterus, liver failure, kidney failure, and anemia. • Mercury in organic forms can cause spontaneous abortion, congenital malformation, and corrosive esophagitis and hematochezia. The lists of water pollution sources in Tables 2.2 and 2.4 give a general representation of the existing pollution processes in hydrology. A more detailed description of the hydrochemical factors usually occurs when regional or local water quality problem is solved. Knowledge of potential pollutants can be obtained as part of environmental monitoring. Examples of sources of potential pollutants are given in the following text: • Wastewater discharges from sewage treatment projects can produce organic pollutants, pathogens, solids, and wastes resistant to nitrogen and phosphorus. • Industrial effluent discharges treatment can generate nitrogen, oxygen-depleting substances, and a wide range of chemicals. • Leaking pipelines can deliver sewage, oil. • Mining provides heavy metals, acid mine drainage. • Contaminated land delivers hydrocarbons, organic chemicals, heavy metals, oxygen-depleting substances. • Farm waste and silage can deliver pathogens, oxygen-depleting substances, nitrogen, and phosphorus. • Oil storage facilities contribute to the supply of hydrocarbons. • Urban storm-water discharges resulting from storm water runoff can deliver nitrogen and phosphorus, oxygen-depleting substances, heavy metals, hydrocarbons, pathogens, persistent organic pollutants, suspended solids, settleable solids, and litter.

2.3

Monitoring and Management of Water Quality

127

• Landfill sites are a source of nitrogen, ammonia, oxygen-depleting substances, and a wide range of chemicals. • Fish farming can provide nitrogen and phosphorus, oxygen-depleting substances, and pathogens. • Organic waste recycling to land as diffuse source can deliver nitrogen, phosphorus, and pathogens. • Agricultural fertilizers are a source of nitrogen and phosphorus. • Soil cultivation promotes the diffuse streams of soil, nitrogen, and phosphorus. • Power generation facilities can provide nitrogen and sulfur.

2.3 2.3.1

Monitoring and Management of Water Quality The Problems of Sustainable Water Resources

The availability of adequate water resources is critical to sustain life and to support economic progress in any area. However, the real distribution of world water resources is uneven between regions and countries, which needs the initiatives and solutions aimed at the development of a management plan for sustainable use of water resources in each region or country. This problem is a real issue in arid and semiarid watersheds where limited water resources lead to planning for shortmedium and long-term water availability. The sustainable use of water resources must assess the surface water and groundwater potential, develop a water balance model, and prepare a water management plan based on hydrological and climatic zones. The problem of the water quality monitoring and management is dictated by real restrictions of clean freshwater resources that do not exceed 2% of global water resources and their regional availability is characterized by uniformity. Li and Liu (2019) present recent innovations in operations management for water quality monitoring and cover newly introduced technologies like bulk data handling techniques as well as include aspects on advanced optimization, system and platform, wireless sensor network, selection of river water quality, groundwater quality detection, and more (Singh et al. 2022). Borden and Roy (2015) described key components of monitoring systems and hydrologic information systems (HIS). HIS is a system for measuring, processing, storing, and disseminating interlinked aspects of watershed data including the quantity and quality of climate (hydro-meteorological, e.g., precipitation, evapotranspiration), surface water (hydrological), and groundwater (hydrogeological). The HIS physical infrastructure includes observation networks, laboratories, data communication systems and data storage, and processing centers equipped with databases and tools for data entry, validation, analysis, retrieval, and dissemination. Real examples of HISs were represented in different situations when the water quality of hydrological systems is controlled (Nitu et al. 2019; Varotsos et al. 2019b, c).

128

2

Global Water Balance and Pollution of Water Reservoirs

UN water facts and initiatives are directed on the understanding and consideration of the fact that water is a precondition for human existence and for the sustainability of the planet. Water is at the core of sustainable development and is critical for socioeconomic development, healthy ecosystems, and for human survival itself. It is vital for reducing the global burden of disease and improving the health, welfare, and productivity of populations. Really, freshwater reserves are the main factor of the human life-support together with energetic resources. Freshwater problems attract attention understanding that water is a key component of the ecosystem and pessimistic prognoses concerning the availability of water for many human groups in the nearest future. In this context, UN coordinates the efforts and activities of many other international, regional, and local agencies to assess trends in the water quality changes understanding the current state of global water resources: • The total volume of the Earth’s water resources is about 1.4 × 109 km3 including 35 × 106 km3 (2.5%) of freshwater. • 68.9% of freshwater resources exist as ice, stable snow cover in mountains, glaciers in the Antarctic and Arctic. • About 8 × 106 km3 (30.8%) of freshwater is accumulated in soil moisture, ground water, bogs, and permafrost. • Lakes and rivers contain about 1.05 × 106 km3 (0.3%) of freshwater. • The total volume of freshwater available for use by ecosystems and population is about 2 × 105 km3 or less than 1% of the total reserves of freshwater on Earth. Overall, a fact needs to be stated that providing the population with freshwater in adequate volumes is of fundamental significance for the achievement of socioeconomic development goals and the environmental protection. Growing impacts on the environment are causing serious stress, including the reduction of selfcleaning functions of freshwater ecosystems. For example, the area of freshwater wetlands that play an important role in natural water treatment and the formation of the water cycle has been halved in recent decades. The geographical distribution of freshwater resources is extremely irregular— about half of freshwater resources belong to Brazil, Russia, Canada, Indonesia, China, and Columbia. For example, a significant area in China is arid and 7% of the global freshwater resources under the existing population cannot provide water to the entire population of China, which leads to extensive use of groundwater with an observed lowering of their level. Such stories of situations practically exist in many countries. Therefore, water quality monitoring and management tools are the main issues of the present hydrogeochemistry, the careful study of which can seek sustainable water management when freshwater resources of the country will provide minimal risk to the health of the population and are acceptable for other human activities. A fairly acceptable tool for water resources management is the HIS, which is usually synthesized taking into account comprehensive data and knowledge about the regional elements of the water cycle. Indeed, HIS aims to measure, process, store, and disseminate interrelated aspects of watershed data, including the quantity and quality of hydrometeorological, hydrogeological, and hydrological elements of the water cycle (Borden and Roy 2015).

2.3

Monitoring and Management of Water Quality

129

Following several publications (UNWDP 2015; WWAP 2015) the following data and knowledge can be represented: • Over 1.7 billion people live in river basins where water use exceeds replenishment, resulting in river drying, groundwater depletion, and ecosystem degradation. • Two-thirds of the world’s population will live in water-stressed countries by 2025 if current consumption patterns continue. • Demand for water will increase by 55% by 2050. • Economic losses from inadequate water supply and sanitation amount to 1.5% of the gross domestic product of the countries included in a WHO study on that achievement of the MDGs. • Some estimates suggest that over 80% of wastewater is discharged untreated. • About 40% of the world’s population lives in basins that overlap two or more countries, which account for about 60% of the global freshwater flow. About 2 billion people worldwide depend on groundwater for their basic water needs. • Water scarcity has been identified by industry, government, academia, and civil society as one of the top three global risks of greatest concern. • Water-related disasters are the most economically and socially devastating of all natural disasters. Since the original Rio Earth Summit in 1992 floods, droughts, and storms have affected 4.2 billion people (95% of all people affected by disasters) and caused US$ 1.3 trillion in damage (63% of all damage). The Food and Agriculture Organization of the United Nations (FAO) issues some assessments and conclusions: • Agriculture accounts for 70% of water withdrawals worldwide, although this figure varies considerably across countries. Agriculture is by far the largest consumer of water globally. • Agriculture uses 11% of the world’s land surface and 30% of total global energy consumption, when considering food production and the supply chain. • Rain-fed agriculture is the dominant system of agricultural production around the world. This method is slightly more than half as effective as optimal agricultural management. • By 2050, world agriculture should produce 60% more food globally, and 100% more in developing countries. To achieve sustainable agriculture, FAO proposes five interconnected principles: 1. 2. 3. 4.

Improving efficiency in the use of resources. Direct action to conserve, protect, and enhance natural resources. Protect and improve rural livelihoods and social well-being. Enhance the resilience of people, communities, and ecosystems, especially to withstand climate change and market volatility. 5. Good governance is essential for the sustainability of both natural and human systems.

130

2

Global Water Balance and Pollution of Water Reservoirs

Unfortunately, twenty years of the twenty-first century show that global environmental degradation continues at critical levels for major ecosystems. Integrating sustainability into ecosystem management is key: • Integration, collaboration, coordination among sectors and natural resource managers. • Implementation as well as policy setting (regulation, enforcement, and compliance). • Economic rationale incorporated into solutions (e.g., natural infrastructure). • Valuing ecosystem services (including oceans), making them more relevant to people on the street. • Investment and implementation of watershed management plans.

2.3.2

Monitoring and Management Tools

Monitoring and assessing the state of the aquatic environment requires knowledge of many characteristics, which can be classified into: 1. Forming factors of water chemistry. 2. Water quality. 3. Dynamic characteristics. The first group includes characteristics of the processes affecting the water quality and determining the dynamics of the parametric space that describes the complex state of the aqua-geosystem under study. Depending on the conditions of the experiment, these factors are connected to the spatial scale and can be pointwise or local. To describe a set of factors and determine their spatial significance, we denote points on the aqua-geosystem’s surface by latitude φ and longitude λ. Choose a rectangular system of coordinates with the axis z pointing up from the surface, so that the value z to denote the depth at the point with coordinates (φ, λ). Then any measurement will be described by the parameter ξ(φ,λ,z, t), which in the general case is a non-stationary random value. In the concrete experiment, the formation of the set Ξ = {ξ} is connected to the factors of the hydrological cycle. Depending on the spatial scales of the hydrophysical object under study, a variety of possible schemes of formation of water quality and other characteristics of the aquageosystem covers a wide range of physico-chemical interactions of the elements of the water basin ecosystem. A variety of all these schemes is evidence of the complexity of the water quality assessment and aqua-geosystem state. Therefore, an efficient monitoring of the aqua-geosystem depends on an adequate description of these interactions and an adequate list of parameters to be measured. The parameters that directly determine water quality are acidity, turbidity, and chemical content. There is a set of criteria of water quality depending on its application. Table 2.5 illustrates the sets of factors to assess water quality with different criteria.

2.3

Monitoring and Management of Water Quality

131

Table 2.5 Some criteria and factors—the structure of substances and parameters that determine water quality Factors Type of water pollution Biological Chemical Physical Elements Group of characteristics Basic ions Dissolved gases Biogenic elements Microelements Organic substances

Factors Microorganisms favoring the fermentation of organic substances Toxic substances or changing the composition of water Heating, radioactivity Elements Chloride C -, sulfate SO24 - , hydrocarbon HCO3- , carbon CO23 - , natrium Na +, potassium K +, magnesium Mg 2+, calcium Ca 2+ Oxygen O2, carbon dioxide CO2, nitrogen N2, hydrogen sulfide H2S Nitrogen, phosphorus, and silicon compounds Zn, Cu, Pb, Ni, Co, Li, Rb, Cs. Be, Br, I, F, etc. Content of carbon, oxidizability, biochemical consumption of oxygen

These circumstances testify to the fact that it is very difficult to draw a distinction between hydrophysical and hydrochemical experiments. However, it is clear that water quality is, to a greater extent, a hydrochemical function. The aim here is neither to draw this distinction nor to substantiate a set of problems to be solved in the hydrophysical experiment. Apparently, when talking about data series of the hydrophysical experiment, one should not ignore the hydrochemical data. Ultimately, the assessment of the state of the aqua-geosystem requires a certain combination of such data and especially when studying the dynamic characteristics. A change of ξ(φ,λ,z,t) is connected with examining the correlations between all factors affecting water quality. When forming the spatial image of the water body under study, it is necessary to take into account an interaction of climatic, biogeochemical, ecological, and hydrological processes. This is possible by using a numerical model and, hence, forming new data series in the form of the models’ coefficients. The stages of the hydrophysical monitoring relate to different levels of databases and therefore require the respective algorithms of analysis and interpretation of measurement results. Moreover, matching a set of algorithms of data processing to the problems of the experiment cannot guarantee an adequate complex of methods to solve these problems. A combination of the hydrophysical monitoring problems with the system of automated data processing is possible only in an adaptive regime. Monitoring can be spot and areal. In the first case, data processing relates to the use of a number of algorithms providing an assessment of the needed set of parameters without consideration of spatial scales of the process or object under study. In the second case, the system of data processing requires special methods aimed at reducing their variability and forming a spatial image based on measurements that are episodic in time and fragmented in space. In this case it is essential to choose the scales of the cartographic grid and relate it with the problems to be

132

2 Global Water Balance and Pollution of Water Reservoirs

solved. Only then can one guarantee a certain level of reliability in the assessment of the state of the aqua-geosystem. Algorithms for the formation and processing of spatial databases are approved in GIS-technology. However, there is a gap between traditional methods of GIS-technology and models as an instrument of prediction. To fill the gap, select the matrix-identifier structures with the hierarchical topology in both space and in elemental filling of the aqua-geosystem model. Each element of the identifier describes in the symbol space the image of an element (fragment) of the system under study. In other words, the territory Ω is presented in the form of the hierarchical spatial structure: Ω = [ Ωij , Ωij = [ Ωis jl etc: s, l ði, jÞ According to this scheme, the territory Ω in the fifth level of the database is represented by a set of matrices Ak = akij , (k = 1,. . ., N ) where algorithms and data from other levels are used to determine the elements akij . An identifier as a simplest binary approximation of the territory Ω can be exemplified as follows: aij =

0,

ði, jÞ= 2Ω;

α,

ði, jÞ 2 Ω;

where the symbol α can identify the sets of elements of the aqua-geosystem that are supposed to be taken into account in the data processing system. This semantic reduces the dimensionality of the fifth level of the database but increases the probability of information distortion in dialogue mode. In other words, here is the problem of optimizing the fifth level of the database. Overall, a set of models of hydrophysical processes in a limited area or on a global scale allow to carry out a numerical experiment and estimate the parameters of a hydrophysical object. The use of numerical experiment in hydrophysical studies has much in common with other fields of its application. A closed set of algorithms for analyzing the measured data is constructed and the fourth and fifth levels of the database are formed on its basis. An adaptive procedure of using the aqua-geosystem function model makes it possible to start a dialogue function to estimate many parameters that cannot be reliably estimated from measurement data or that raise doubts. The most essential link in the hydrophysical experiment automation system is the processing of data on the hydrophysical factors, whose knowledge, other conditions being equal, plays a certain role in assessing the complex state of the aquageosystem. These are factors that introduce some elements of instability into the model and are the source of the aqua-geosystem’s dynamics. Therefore, in all algorithms for the hydrophysical experiment, the hydrodynamical equations serve as a benchmark for rigor and accuracy. An adequate system of hydrodynamic equations describes changes in velocity, pressure, and current density components

2.3

Monitoring and Management of Water Quality

133

Fig. 2.1 Structure of HMMS. Notation is given in Table 2.6

and contains five equations: a three-component equation of motion, an equation of continuity, and an equation of state. Since we are interested in the problem of pollutant transport in the hydrological network, these equations should be supplemented with equations of water balance in the territory.

2.3.3

Hydrochemical Monitoring and Management System

The problem of the composition of the hydrochemical monitoring and management system (HMMS) in a given territory requires a classification of the elements of aquageosystem, the consideration of which results from the experience gained in solving such problems applied to other territories. Analysis of the scheme in Fig. 2.1 shows that many parts of HMMS can be realized with the help of GIS-technology. These are formations of the spatial database, topographic structure modeling, computer cartography, and visualization. Other HMMS fragments are realized in accordance with GIMS-technology. The GIMS-technology recommends in the case of organizing studies in areas with heterogeneous structures to form series of maps of different thinness. For any level of spatial resolution Δφ × Δλ, the database contains an information layer of the sets of identifiers controlled by the control unit and whose data are received by the respective functions—units of the HMMS. This is where the cartographic modeling scheme widely used in GIS is implemented.

134

2

Global Water Balance and Pollution of Water Reservoirs

Table 2.6 Decoded notations in the scheme of Fig. 2.1 Identifier of the HMMS block GSM SEM SMMT SMPE SMWQ PPWR CIF MERS MESS HM MT MWTF MBAB PM MATP MI MSPF MFRO MWMC MGE MPG MFOL CDM DI FSOD

Description of the block Global simulation model System of ecological monitoring Simulation model of the moisture motion over the territory Simulation model of the process of moisture evaporation Simulation model of water quality Realization of the procedure of predicting the state of the water regime and quality in a given territory Control of information fluxes Modelling the evaporation from the river surface Modelling the evaporation from the soil surface Horton model Model of transpiration Model of the water temperature formation Modelling the biotic and abiotic balance of the water body Penman model Model of atmospheric transport of pollutants Model of infiltration Model of the structure of soil-plant formations Model of the formation of the run-off with the topography of watershed and soil characteristics taken into account Model of water motion in the canal Model of gas exchange on the border “water-atmosphere” Model of pollution generation Model of the formation of the oxygen level in water Coordination of database with models Dialogue interface Formation of the structures of output data

As indicated in the preceding text, the organization of any kind of monitoring requires a solution of the problems of spatiotemporal interpolation. This problem is solved by many methods applied in GIMS-technology. In HMMS these methods are used to integrate information from various sources. This procedure includes the following operations: • Integration into a single system of spatial data from various sources, such as geographic maps, satellite photographs, remote measurements, ground observations, etc. • Creation of the geometrical description of the Earth surface within compatible topological structures. • Modeling and software for transformations between vector and expanded data, that is, between maps and images. • Provision of agreement between the range of hydrophysical and hydrochemical characteristics at each level of the spatial scale of the system.

2.3

Monitoring and Management of Water Quality

135

• Provision of co-ordination of information fluxes of spatial, linear, and pointwise data within a unit or for the entire model. • Synthesis of the scattered spatial information and formation of a representative image of either the aqua-geosystem or its fragments. There are elements in the HMMS database which cover the entire set of information levels. As a result of the simulation experiment, at each moment the computer memorizes a set of schematic maps. Depending on the user’s query, the FSOD unit forms an output file with the needed information structure. Computer visualization tools convert the symbol matrix into a colored schematic map, and the user obtains information that is easy to analyze. Big data clouds are formed based on of historical information and the current water quality monitoring system, the operation of which provides the information needed to support management objectives. The SEM and CIF blocks provide a decrease of uncertainty introduced into hydrologic problems through the following (Bras 1990): • Inherent unexplainable variability of nature. • Lack of understanding of all causes and effects in physical systems. • Lack of sufficient data. To parameterize the hydrospheric part of the investigated site of the Earth’s surface and create a universal schematic envelope of the system, cover the territory with the grid with a step Δφ in latitude and Δλ in longitude. The area of each cell will be σΔ = ΔφΔλkφkλ where kφ and kλ are kilometers in 1о latitude and longitude, respectively. A discrete numbering of the cells gives a set of the sites Ξ = {Ωij} where each site Ωij has an area σij. The coordinates of the center of the site Ωij are equal to: i

φi = φ1 þ 0:5 φi þ

ðs- 1ÞΔφs , i = 1, . . . , N; s=1 j

λj = λ1 þ 0:5 λj þ

ðs- 1ÞΔλs , j = 1, . . . , M; s=1

The imported area digitization grid allows one to create a global reference procedure to a given object Ξ under study using the following symbolic 2D arrays:

TERRITORYðI, J Þ =

0 1

for for

ΩIJ2 = Ξ, ΩIJ 2 ΞS ,

2

for

ΩIJ 2 ΞW ,

where ΞS[ΞW = Ξ, ΞS is part of the land territory; ΞW is part of the territory under a hydrospheric object. The topological structure of the hydrospheric part of the territory is described by the identifier of the HYDROL(I,J) type in which possible configurations of different types of hydrospheric elements are described. The

136

2 Global Water Balance and Pollution of Water Reservoirs

HYDROL array allows the selection of a moving part of the water environment in the territory Ω and a more accurate examination of water chemistry variability due to heterogeneous catchment areas. To organize the numerical procedure of calculating the dynamic water flows between the cells ΩI J, enter an array of the heights above sea level: TOPOGRðI, J Þ =

0

when

ΩIJ 2 ΞW ,

ωIJ

when

ΩIJ 2 ΞS ,

where ωIJ is the mean cell height ΩI J above sea level in meters. Each cell ΩIJ is characterized by a certain relationship between water basins, wild and agricultural vegetation, urban constructions, and roads. Determine this relationship by a set of the following 2D arrays: PARTW(I,J, TIME) = S1IJ 2[0,1] is part ΩIJ under water basins; PARTA(I,J,TIME) = S2IJ 2[0,1] is part ΩIJ under cultural vegetation; PERTS(I,J,TIME) = S3IJ 2[0,1] is part ΩIJ under wild vegetation. Anthropogenic objects cover the rest part of the territory: S4IJ = 1 - S1IJ - S2IJ - S3IJ . Each cell ΩI J is occupied by one of n types of wild vegetation and one of m types of cultural vegetation. The patterns of this occupation are presented with 2D arrays: VEGETA (I, J, TIME) for agricultural vegetation, VEGETS (I, J, TIME) for wild vegetation and ANTROP (I, J, TIME) for anthropogenic landscapes. Water can be polluted through the atmosphere, washing-out from soils and anthropogenic emissions directly into water reservoirs (common and industrial sewage, leakages, and discharges from ships). Information about these processes is compiled in the form of an identification scheme TYPEC (I, J, TIME). Based on the information contained in the TYPEC array, the statistical mean state of pollution in the absence of information is controlled. The system is filled with data by entering the results of measurements provided by either the monitoring or compliance control services of anthropogenic systems. The system contains the TYPEP (I, J, TIME) array designed to enter more accurate information about pollutant productions. The TYPEP array includes production characteristics of the area due to which the system provides the information network with average emission estimates in the absence of other information. Information on the characteristic compositions of sewage in the producers’ territory is used as a database. Depending on HMMS performance requirements, corrective information can be obtained from satellites. For instance, information about the arrays TERRITORY, HYDROL, TOPOGR, PARTW, PERTS, VEGETA, VEGETS, and ANTROP can be obtained from satellites. The arrays TYPEC and TYPEP can be replenished and corrected by the regular use of the flying laboratory. The structure of the HMMS information base is determined both by the possibilities of its formation and by the need for practical solutions to the problems of water quality control in each area. The experience gained in the creation and use of such systems suggests a hierarchical structure of the database constructed on the principle of successive digitization of the territory. Following the scheme of hierarchical

2.4

Pollution of the Oceans and Seas

137

digitization of the space, one can, without damage to the system, constantly increase the minuteness of digitization, with the subject orientation of the territory ΩIJ preserved. Thus, the structure and functions of the HMMS are adjusted to natural composition of data on various aspects of functioning of natural and anthropogenic landscapes. The HMMS synthesizes only the available data and suggests which other data should be obtained. Models in the HMMS structure provide an integration of data in space and by subject areas, due to which a comprehensive image of the territory with processes taking place in it is formed. HMMS has a set of formal structures to control the monitoring regime. Use of spatiotemporal and subject identifiers cue the HMMS to the topology and morphology of the natural or anthropogenic object. The sets of identifiers listed in the preceding text parameterize the spatial and subject structure of the territory Ω, so that at each level of spatial digitization a complete description of an image of the system under study is provided. The spatial topology of this parameterization can be heterogeneous and non-uniform as well as excessive. The user, following the hierarchy of the HMMS menu, can perform the following operations: • To request data for any identifier (array) and correct any of its fragments. • To request estimates of all or part of the parameters of the simulation units and correct them. • To select parameter sets and identifiers for faster access to them. • To compose a symbolic schematic map of the distribution of estimates of environmental characteristics. • To predict the state of the environment at a given depth or until the fulfillment of the a priori formulated criterion for assessing the state of the water environment. The user, through the interface, sends a permission at each step of the command dialogue, which is assessed in the query analysis unit, and from its response, the control unit perceives a chain of needed system actions. Through query return channels, the resulting prediction is ordered into the required format which can change at each cycle of service. The final result is presented in the form of the protocol with enumerated characteristics of the water environment by objects and areas as well as in the form of schematic maps or digital information combined with the map.

2.4

Pollution of the Oceans and Seas

The ocean covering 71% of the Earth’s surface provides 1% of the total amount of food consumed by humankind, the remaining 99% comes from cultivated lands. Rough estimates put the total biomass of nekton at 5.3 billion tons. Industrial fishing from the World Ocean is estimated at 70 mln t/year, which is 20% of the protein consumed by humankind. Industrial fishing is close to a threshold (≈90 ÷ 100

138

2 Global Water Balance and Pollution of Water Reservoirs

mt/year). However, this is not a threshold for the industrial capabilities of ocean ecosystems in general, as stocks of krill and other biological objects are underutilized. This inconsistency between the role of land and ocean ecosystems in food production is explained, first of all, by the fact that on land the cultural economy is intensively developing, which is poorly developed in seas and oceans. Therefore, possible ways to raise the ocean bioproductivity have not yet been used. First, humankind mainly uses the final trophic levels of natural communities of the World Ocean—fish and whales. Each next trophic level receives only ~0.1 share of energy accumulated from the previous level. On land, two levels of organisms (plants, herbivorous animals) are used, and in the oceans and seas, up to five levels are used. The direct use of industrial objects without fish will make it possible to strongly increase the amount of protein products obtained from the ocean. Secondly, the problem of the transition from free fishing to a cultural method of economic activity in the World Ocean arises, that is, the problem of artificially increasing the productivity of the biological communities of the ocean. For this purpose, first of all, it is necessary to study methods of controlling the production of the final product in the biological systems of the World Ocean. To determine the rational ways of impact on ocean communities, it is necessary to study their structure and functioning, to have a clear idea about production processes, transformation of substance, and energy fluxes at different trophic levels of the ocean ecosystems. It is necessary to develop a theory of control in the biological systems of coastal waters and the open ocean, which differ both in natural hydrophysical and biogeochemical parameters and in degree of anthropogenic forcing. Marine communities are complex biological systems consisting of populations of individual species whose interaction results in the dynamic development of the community. Their spatial structure is strongly determined by the composition of numerous biotic and abiotic factors which depend on a totality of the oceanic fields. The latter are determined by the laws of the oceanic general circulation which is the sum of rising tides and falling tides, the zones of convergence and divergence, wind and thermohaline fluxes, etc. In the later twentieth and early twenty-first centuries, an urgent problem appeared to forecast the ocean systems’ dynamics under conditions of the growing anthropogenic forcing (chemical poisoning, mechanic destruction of living organisms, environmental change) and to evaluate their role in the dynamics of the whole biosphere. Recent studies of the climatic impact of GHGs have shown that the role of the World Ocean in this process is underestimated. In particular, Kondratyev and Johannessen (1993) gave data on the role of the Arctic basins of the World Ocean in the formation of global CO2 cycle, from which it follows that this role has been wrongly assessed. This is connected to the fact that a consideration of biological and gravitational processes, together playing the role of a pump which is pumping over CO2 from the atmosphere to deep layers of the ocean, did not correspond to reality in the earlier models of the global biogeochemical carbon cycle. Therefore, a specification of the models of the regime of this pump functioning with due regard to climatic zones can strongly affect the prognostic estimates of the greenhouse effect (Kondratyev et al. 2004a, b; OSS 2007).

2.4

Pollution of the Oceans and Seas

139

Ocean ecosystems influence the intensity of biogeochemical cycles through the atmosphere-water boundary, and it is usually parameterized based on observational data. However, in this impact the vertical structure of processes taking place in the ocean plays a substantial role. The character of these processes depends strongly on external phenomena, including tsunami, typhoons, thunderstorms, earthquakes, and tornados. According to Legendre and Legendre (1998), in the Arctic zones of the World Ocean the patchy structure of the springtime blossoming of phytoplankton is determined by winter conditions of ice formation and subsequent process of thawing. In other zones such external circumstances are factors of pollution of the atmosphere and ocean surface, changing conditions of phytoplankton existence and the functioning of the carbonate system. Phytoplankton is at one of the primary stages of the trophic hierarchy of the ocean ecosystem. As observations have shown, the World Ocean has a patchy structure formed by a combination of heterogeneous spatial distributions of illumination, temperature, salinity, nutrient element concentrations, hydrodynamic characteristics, etc. The vertical structure of phytoplankton distribution is less diverse and has more universal properties. These properties manifest themselves through the presence of 1–4 maxima of phytoplankton biomass in depth, of a certain shape and reference at concretion level. The variability of topology and vertical structure is connected with the seasonal cycles and has been experimentally well studied in many climatic zones of the World Ocean. Model qualitative and quantitative indicators of this variability have been found. Combined distributions of abiotic, hydrological, and biotic components of the ocean ecosystems have been studied. The complexity and interrelationship of all processes through the oceanic thickness considerably complicates a search of the laws of formation of plankton spots and the establishment of correlations between various factors of regulation of the intensity of trophic relationships in the ecosystems of the oceans. For example, a close relationship between primary production and the amount of phytoplankton has been established in many studies. At the same time, this relationship gets broken depending on the combination of synoptic situation and illumination conditions. It turns out that the degree of breaking depends considerably on the combination of groups of phytoplankton (Legendre and Legendre 1998). The analysis of the accumulated observational data to assess the production of seas and oceans, and an attempt of many experts to reveal the laws of production formation inherent to various water basins have led to numerous specific laws of local relationships between productivity and environmental parameters. An efficient method for studying the vertical structure of the ocean ecosystems is their numerical modeling. The creation of the model needs knowledge of the structure of trophic relationships in the ecosystem, features of hydrological conditions and information about other characteristics of the environment. The modeling experience has shown a possibility of efficient forecasting of the dynamics of the World Ocean communities. These models can be exemplified by a 3-D model of the ecosystem of the Peru upwelling (Krapivin 1996), the Okhotsk Sea (Krapivin and Soldatov 2014; Krapivin and Varotsos 2019), etc. In all these models, the unit of parameterization of the ecosystem’s vertical structure is the central one.

140

2

Global Water Balance and Pollution of Water Reservoirs

The release of undesirable substances into the oceans and seas because of human activities in industry and agriculture, among others, leads to the degradation of the appropriate ecosystems. Pollutants often originate far inland and are transported to the ocean via rivers or through the air. Pollutants of particular concern include oil, excess nutrients from fertilizers, debris, and industrial contaminants. Unfortunately, oceanic and marine ecosystems do not neutralize pollutants enough to eliminate their impacts. It is now known, however, that some pollutants can significantly alter marine ecosystems and cause harm—sometimes deadly—to species from the top to the bottom of the food web. Therefore, study of the structure and functioning of the ocean ecosystems becomes one of the most important and rapidly developing directions of marine biology. Its various aspects are being developed in many countries within the International Biological Program (IBP). One of the problems of this study is to obtain a possibility to forecast the system’s behavior because of changing some of its parameters. However, due to uniqueness and great spatial extent of the World Ocean’s ecosystems, it is difficult to quantitatively evaluate all elements of the system at different moments of its development and in different regions of the ocean, and the more so, to assess the impact of their change on the functioning of the system, overall. Therefore, using a model approach is one way to solve these problems. Table 2.7 summarizes some pollutants in their role for marine ecosystems. The main danger to ocean ecosystems is plastic waste that is delivered to ocean waters by almost all countries without control. There are an estimated 5.25 trillion pieces of plastic in our oceans, and this plastic waste poses a violent threat to all marine life (Fig. 2.2). The following text has some statistics on ocean pollution: • 100 million marine animals die every year from plastic waste alone. • Plastics take 500–1000 years to degrade; currently 79% is sent to landfills or the ocean, while only 9% is recycled, and 12% gets incinerated. • 500 marine locations are now recorded as dead zones globally, currently the size of the United Kingdom’s surface (245,000 km2). • 80% of global marine pollution comes from agriculture runoff, untreated sewage, discharge of nutrients, and pesticides. • 90% of the worldwide ocean debris comes from just 10 rivers. • About 270 million pieces of waste drift to the oceanic surface.

2.5 2.5.1

Modeling of Global and Regional Water Cycles The Water Flows in the World Ocean

The first place among all water reservoirs on Earth is occupied by the World Ocean whose present volume exceeds 50 times the volume of water in glaciers, second to the World Ocean. This comparison is important for understanding the correlation between the steps of hierarchy of water basins and determining their structure in the

2.5

Modeling of Global and Regional Water Cycles

141

Table 2.7 Some ocean and sea pollutants (Ballerini et al. 2018; Ivanina and Sokolova 2015) Pollutant Oil products

Nutrient pollution

Industrial contaminants

Plastic pollution

Heavy metals

Comments Energy demands continue to rise as the population increases and the developing world becomes more industrialized. Worldwide oil consumption is projected to rise sharply in the coming decades. Oil runoff into the oceans and seas occurs from ships, transportation platforms, individual cars and boats, lawn mowers, jet skis, marine vessels, airplanes, land runoff from oil slicks on urban roads, and deposition of hydrocarbons from the atmosphere. As a result, the oceanic surface is more effectively covered by oil spills. The living marine ecosystem elements are very sensitive to oil pollution due to the disruption of the oxygen cycle Nutrient pollution can significantly change production processes and cause a lowering in the marine ecosystem survivability. Land-use data provide information about agriculture, industrial activities, and residential developments that affect trends in nitrogen and phosphorus inputs from runoff. The majority of nutrient pollution flowing into the sea can be attributed to agriculture, primarily runoff of dissolved nitrogen and phosphorus from fertilizers applied to agricultural fields, golf courses, and lawns A lot of pollution is caused in the oceans and seas by sources located in or upstream of urban ports, through sewage discharges, automobile emissions, and other waste-producing activities. Storm-water runoff also carries contaminants from distant sources In recent decades there has been much attention from the public and governments concerning the continued accumulation in the World Ocean of plastic, physical and chemical properties of which the degradation time of the plastic can vary from several weeks to centuries. Plastic helps to transport microorganisms over long distances that they practically do not control Ocean acidification is an emerging concern in estuarine and coastal ecosystems, which are especially vulnerable to anthropogenic stress due to their location at the interface between the marine and terrestrial environments and the impacts of the coastal development and agricultural and urban runoff. Some metals (such as Cu and Zn) are essential cofactors in a number of biochemical processes while others (such as Cd, Hg, and Pb) have no known biological functions in animals; however, all metals are toxic in high concentrations

model. Within a priori scenarios of anthropogenic activity and possible changes in the biosphere, the correlation between these steps is important. For instance, 1.6% of the global supplies of water are accumulated in the Antarctic. A comparison of these supplies with the volume of the Arctic Ocean where the water content is 20% less than in the Antarctic glacier cover, the conclusion suggests the inadequacy of the global model of the hydrological cycle without the role of Antarctica. Total freshwater resources are distributed at 2.4 percent of the world’s water resources. Liquid freshwater averages about 13 percent. Soil moisture has only 2 percent of groundwater (Keeling and Visbeck 2001; Matta 2010). The hydrology and marine currents of the Southern Ocean under the influence of glacier cover have been described in numerous monographs, and circulation models of different complexity and degree of detail have been derived to simulate them.

142

2

Global Water Balance and Pollution of Water Reservoirs

Fig. 2.2 Annual metric tons of mismanaged plastic waste entering the sea from some countries. file:///L:/100+%20Ocean%20Pollution%20Statistics%20&%20Facts%20(2020).htm

Such models for the World Ocean, as a whole, are based on configurations of the non-penetrating boundaries and straits topology. Numerous numerical experiments with such models made it possible to reveal the principal structure of the global ocean circulation consisting of the hierarchy of closed ring circulations with the centers of upwelling and downwelling waters and water cores and including the geometry of water basins with the straits between them. To describe the water circulation in the Southern basin, it is necessary to consider the Drake Passage. A scheme of the hydrological field circulation in the World Ocean, acceptable for simulation, has been proposed by Chahine (1992). The model is a system of equations and boundary conditions considering coastal contours, the bottom relief, and ice formation and melting. However, on a global scale, to simulate the ocean circulation, a simplified scheme is necessary mainly reflecting the role of straits. Such a scheme is shown in Fig. 2.3. The final unit responsible for the modelling of the World Ocean circulation has the following form: σOF

dW OF = H OF þ RF þ I LF þ SLF - SFL - H FO - AF þ M IF þ DPF þ ðwAOF - E FA ÞσOF þ AF , dt dW OI = AFI þ CPI þ N PI þ K I þ RI þ ðwAOI - E IA ÞσOI - AIP - M IF , σOI dt dW OP σOP = AIP þ RP þ ðwAOP - EPA ÞσOP þ I P - BPL - DPF - C PI - N PI , dt dW OL σOL = RL þ BPL þ ðwAOL - E LA ÞσOL þ SFL - I LF - SLF , dt dW A = ðE PA - wAOP ÞσOP þ ðE FA - wAOF ÞσOF þ ðE IA - wAOI ÞσOI þ ðE LA - wAOL ÞσO : σ dt

In the framework of this large-scale approach to the formation of the biosphere water balance model, the dependences of the fluxes of water in its different phases on environmental parameters remain uncertain. Apparently, the mass exchange between the reservoirs s and l can be described by the simplest linear scheme:

2.5

Modeling of Global and Regional Water Cycles

143

Fig. 2.3 Elements of the global water balance considering the role of the ocean. The notation is given in Table 2.8

wsl = |WOSσOS - WOLσOL |/Tsl, where Tsl is the time for equalizing the levels WOS and WOL, σOS and σOL are the areas of water basins s and l. For the scheme in Fig. 2.3 we have AFI = maxfðV OF - V OI Þ=T FI , 0g, M FI = maxfðV OI - V OF Þ=T IF , 0g, AIP = maxfðV OI - V OP Þ=T IP , 0g, N PI = maxfðV OP - V OI Þ=T PI , 0g, C PI = maxfðV OP - V OI Þ=T PI  , 0g, DPF = maxfðV OP - V OF Þ=T PF , 0g, SLF = maxf0, ðV OL - V OF Þ=T LF g, SFL = maxf0, ðV OF - V OL Þ=T FL g, βPL = maxf0, ðV OP - V OL Þ=T PL g,

where VOS = WOS σOS, (S = F,I,P,L). To estimate the flux KI, consider information on the moisture balance in the region of the Red Sea. According to available estimates, the input of water to the Red Sea via the Suez Canal and by precipitation can be neglected. Not a single river flows into the Red Sea. The main component of the flux KI through Bab el Mandeb is rather persistent. Hence, we can assume KI = max{0, wAK σ KMP - EKMA σ KM}, where wAK and σKMP are the level and the area of the mainland run-off to the Red Sea, respectively, EKMA is the evaporation from the area σKM of the Red Sea. Water expenditure through the Strait of Gibraltar HFO (HOF) is determined by the relationship between the levels of WOF and the Mediterranean Sea. To avoid complicating

144

2

Global Water Balance and Pollution of Water Reservoirs

Table 2.8 Quantitative estimates of water fluxes in the scheme in Fig. 2.3 (103km3/yr) Flux wAOL—precipitation SLF—straits RL—rivers HOF—Strait of Gibraltar wAOF—precipitation AFI—the Antarctic Current DPF—Drake Passage NPI—Indonesian seas AIP—the Antarctic Current wAOI—precipitation KI—Red Sea EPA—evaporation IP—Antarctic ice

Evaluation 3.6 436 5.14 23.97 72.5 6780.24 5771.09 66.86 6338.74 84 0.005 200.4 0.975

Flux βPL—Bering Strait ILF—Arctic ice SFL—straits RF—rivers EFA—evaporation MIF—the Cape Igolny Current CPI—the East-Australian Current EIA—evaporation RI—rivers RP—rivers wAOP—precipitation ELA—evaporation

Evaluation 80.5 0.57 400 19.33 96.6 952 437 115.4 5.386 13.12 206.7 1.7

Table 2.9 Water in the biosphere Reservoir World Ocean Glaciers Groundwaters Fresh lakes Saline lakes Soil moisture Rivers Atmosphere

Volume, 103 km3 137,0000 29,000 4000 125 104 67 1.2 14

Part of the total volume, (%) 97.61 2.08 0.29 0.009 0.008 0.005 0.00009 0.0009

Regeneration time 3100 years 16,000 years 300 years 1–100 years 10–1000 years 280 days 12–20 days 9 days

the model structure, the level of water in the Mediterranean Sea is determined by its watershed and the difference between precipitation and evaporation. Since the intraannual distribution of the water inflow into the Atlantic Ocean varies within a score of percent, we can reliably assume: WFO = -WOF = const.

2.5.2

Numerical Model of the Global Water Balance

Water is one of the most abundant substances in nature (Table 2.9). It is present in various forms in practically all sectors of the planet and plays an important role in energy and mass exchange between continents, oceans, atmosphere, and other, smaller land territories and water basins. This role in recent years is increasingly manifested in a complicated system of human-society-environment interactions that requires the creation of scientific principles of rational control of water resources. Human-induced changes in the global water cycle are now globally significant and are being modified without adequate understanding of how this cycle works. Therefore, the problem of evaluating the role of water in the global carbon cycle is only a small part of the general global problem of nature–society interaction.

2.5

Modeling of Global and Regional Water Cycles

145

Oceans, polar ice caps, glaciers, lakes, rivers, soils, and the atmosphere contain 1.4–1.5 × 109 km3 of water. This mass is in continuous dynamic interactions with other biospheric components and thus determines the factors of environmental variability. The developed methods of numerical experiments should be used to assess the role of these factors in present conditions and to show the significance of water balance in stabilization of numerous climatic and biogeocenotic processes. Here an attempt has been made, through a systematization of the information about the water balance of the planet, to create a version of the model of the biospheric water balance (MBWB), within the general approach to modeling the carbon balance, to take into account the role of water fluxes. An important moment of the MBWB functioning is its conjunction with the methods of determination of various parameters of the water cycle. Such methods are based on the use of surface, satellite, and air-borne measurements. The unit MBWB as a global model makes it easier to understand the role of the oceans and land in the hydrological cycle, to choose the main factors that control it, as well as to trace the dynamics of its interaction with plants, soil, and topographic characteristics of the Earth surface. It is based on the interaction between elements of the water cycle, as well as natural and anthropogenic factors considered through information interconnections with other units of the global model (Varotsos and Krapivin 2018). Consider the block-scheme of the global water exchange and write the corresponding equations. The basic regularity of the global water exchange is the invariability of water supplies on the Earth in the time period of hundreds of years, that is, we can reliably write the balance equation WE = WS + WO, where WE, WS, and WO are water supplies on the Earth, on land, and in the oceans, respectively. A compartment of the atmosphere is related to the respective watershed area. The relationship is valid: dW E dW S dW O = þ =0 dt dt dt or dWS/dt = -dWO/dt. Hence, the trend in changes of water supplies on land is opposite to the similar trend in the oceans. With the water supply in the atmosphere WA = WAO + WAS, we obtain WE = WA + WS1 + WO1, where WAO and WAS are water supplies in the atmosphere over the oceans and land, respectively; WS1 = WS - WAS, WO1 = WO - WAO. The balance equation will be: dW E dW A dW S1 dW O1 = þ þ =0 dt dt dt dt As can be seen, the structure of trends in water supply ratios is complicated, and to analyze it additional considerations are needed. This complication becomes considerable with further subdivision of the biosphere. In MBWB, small corrections for the water exchange between Earth and space are not taken into account. The model of the global water cycle can be based on the

146

2

Global Water Balance and Pollution of Water Reservoirs

Fig. 2.4 Water fluxes across the border of a limited land area Fig. 2.5 Water fluxes across the border of a limited area with a water object

method of describing the hydrology of comparatively large areas. In this case the basic unit of such an area is a compartment Ωij of the Earth’s surface of size Δφi in latitude and Δλj in longitude. The state of the water component of the compartment Ωij with the coordinates (φi,λj) can be characterized by the magnitude of an equivalent column of liquid water per unit area. Possible water fluxes along the boundary of Ωij are shown in Figs. 2.4 and 2.5. The intensities of these fluxes depend on the phase state of water, temperature, wind speed, and other geophysical and ecological factors. It is difficult to account for all fine details of these fluxes in the global model because their interactions are poorly studied. Therefore, the degree of detail chosen here has been orientated toward considering the most important components of their states. Water is considered in liquid, solid, and gas phase. Within the compartment Ωij

2.5

Modeling of Global and Regional Water Cycles

147

there is only one state although in the future with the necessary information, a vector parameter can be introduced, which determines the share of precipitation over Ωij in the form of snow, pellets of snow, granulated snow, pellets of ice, ice rain, hail, rain, drizzle, wet snow, and more. The global water balance consists of the mosaic structure of local balances at the level of Ωij. The proposed description of water fluxes enables one to trace their balance at any level of spatial digitization—region, water basin, continent, ocean, hemisphere, or biosphere. Clearly, the general balance of evaporation and precipitation at the level of the biosphere is maintained. In other cases, on the average, with decreasing spatial sizes of the selected unit of the biosphere, one should expect an increasing difference between the precipitation amount and evaporation. In this case the water transport through the atmosphere, with river run-off and sea currents, will serve as an equalizer. Although the quantitative estimates of all these parameters are well known, the water cycle dynamics can be described only using the model. As a first approximation, to assess the role of precipitation in the global CO2 cycle, one can use only the components WAU and WAS. However, considering the spatially heterogeneous distribution of CO2, the biosphere should be digitized. With the notations considered in Figs. 2.4 and 2.5, the equilibrium equations of water cycle in the level of Ωij are written as follows: dW S ðt, i, jÞ = wAS þ wGS þ wSS þ wHS - wSO - wSG - wST - wSA - wSH , dt dW H ðt, i, jÞ = wSH þ wOH - wHO - wHG - wHS , dt dW O ðt, i, jÞ ½wIO ðt, k, nÞ þ wHO ðt , k, nÞ þ wAO þ wO - wOA - wOG - wOR - wT = dt ðk , nÞ2I kn dW A ðt, i, jÞ = wOAA - wAOO þ wSA þ dt

wV

for water surface

wST

for land

Detailing of the right-hand sides of these equations with the functional presentations of fluxes with varying parameters of the environment will determine the level of the qualitative and quantitative reliability of the model. In particular, the model can be simplified by approximating the average value of WO: WO =

p 2500 þ 350 t

for

6400 - 3200 expð - t=62:8Þ for

0 ≤ t ≤ 70, t > 70,

where the average depth of the World Ocean is measured in meters and the age of the ocean t is calculated in millions of years. Variations of the ocean volume are also approximated by the formula: ΔV = ΔWOAO + 59.5(ΔWO)2, where AO = 361.06106 km2.

148

2.5.3

2

Global Water Balance and Pollution of Water Reservoirs

The Regional Water Budget Model

Consider the scheme of Fig. 2.6 as the basis for modeling the hydrological regime of a limited territory ΩL, occupied by the aqua-ecosystem under study. Each territory has the river network, water bodies, and land. According to the landscapehydrological principle, to derive a simulation model in the zone of the hydrological system functioning, it is necessary to select the views, which are associated with the typical representation of the floral background, whose concrete appearance is determined by the micro-relief, type and properties of the soil, surface moistening, depth of ground waters, and other factors. In general, the territory ΩL is characterized by the presence of m facies, and the water network has n of heterogeneous sites. Bearing this in mind, according to the scheme in Fig. 2.6, the closed system of balance equations takes the form:

Fig. 2.6 The block-scheme of the sample water balance model in the limited area

2.5

Modeling of Global and Regional Water Cycles

149

n

σij dW A i j =dt = Eij Ri j þ

m

ð V k - B k Sk Þ þ D i j þ k=1

ðLl þ T l - W l σl Þ l=1

m

Sk dGk =dt = Y k - V k þ Bk Sk - H k þ J k þ

ðK l k - F k l –V k l - M k l Þ - Γk l=1

þSk ðCk - 1 V k - 1 =Dk - 1 - C k V k =Dk Þ, n

σl dΦl =dt =

m

ðF k l þ V k l þ M k l Þ þ k=1

ψ kl θl - Ll - T l - Pl - θl þ N l þ W l σl ,

k=1 n

σi j dGi j =dt = I i j - Z i j - Di j þ

m

ðH k - J k Þ þ k=1

ðPl - N l Þ, l=1

In these formulas the following notations are assumed: σi j, σl, and Sk are the areas of the territory Ωij, of the l-th facies, and the k-th compartment of the river network in km2, respectively; Δk is the linear size of the k-th compartment of the river network, km; WAij, Gk, and Φl are, respectively, the levels of water in the atmosphere, in the kth compartment of the river network, and the l-th facie on the territory Ωij; θij is the level of ground waters, m; ψ kl is the share of the run-off of the k-th facie, getting to the territory of the l-th facies; the rest of the notations are given in the scheme in Fig. 2.6. An application of the model to other regions is carried out via the variables E, R, Yi, Γi, I, Z. Besides, in the analysis of the concrete situation, the configuration of the waterway and the level of the water body are considered. The necessary equations are written as described in the preceding text, based on the water volume balance situation. Operationally, all the fluxes in the scheme in Fig. 2.6 can be described based on the laws of hydrodynamics and taking into account the available observational information. The inflow Eij and outflow Rij of moisture can be determined from the remote sensing data. Between the measurements, the wind speed information, Vi j, is used, and the functions Eij and Rij are calculated with the formulas: Eij = EH,ij; Rij = WA, * * * ijl /(l + k1V ), where l = 2√σ/H, EH is the atmospheric moisture on the windward border of the region, k1 is the constant coefficient reflecting the contribution of wind into the circulation of precipitation. Information on precipitation and run-off is entered into the information catalogues of the hydrometeorological service (Table 2.10). Based on this data, the respective model units can be derived. Assuming that the distribution of precipitation is proportional to the relevant areas, we obtain: Bk = W A,ij σk =σ ij ; W l = W A,ij σl =σij The model of the river run-off formation should take into account the watershed topography and the spatial distribution of its soil characteristics, as well as the special features of the vegetation covers. Let θl = (gl + Klexp[-al Xl - Cl Al])σl, where Xl and Al are, respectively, the vegetation density (m/km2) and the soil layer

150

2

Table 2.10 Estimates of temperature at the beginning and end of the period with solid precipitation for some global regions

Global Water Balance and Pollution of Water Reservoirs

Region Taimyr Peninsula South-east of the West-Siberian plain Western Siberia South-eastern coastline of Russia Kolyma North of 70oN The Caucasus: 500–1000 m above sea level 1500–2000 m above sea level Finland Japan

TAF (°C) -2.4 -7.6 -6.0 -4.4 -4.0 -4.0 -6.2 -4.8 -3.0 -7.0

thickness (m) over the area σl; gl is the coefficient of the relief run-off in the l-th facies; kl is the coefficient of water penetration through vegetation and soil covers over the area σl; al and Cl are the coefficients of precipitation retained by plants or soil in the l-th facies, respectively. The parameters of this dependence can be determined from field measurements that establish, for a given type of soil and plants, a connection between precipitation intensity, the rate of taking up water by the soil, and water resistance of its structure. So, for reservoirs, for instance, run-off is equal to precipitation. This rather rough approach can be specified, since the radiometric methods make it possible to classify the soil moisture, at least, into three types: firmly bound, loosely bound, and free water. The bound water is a film moisture adsorbed by the surface of ground particles with the film thickness 6–8 molecular layers. The content of bound water is estimated at 2–3% in sands, and 30–40% in clay and loess. The bound water cannot be assimilated by plants, it does not dissolve salts. In the considered models these specific features are taken into account when determining the respective coefficients of evaporation and transpiration. The run-off θl is distributed among facies and in the form of return water Klk flows into the river. In a general form this is reflected through the coefficients of the run-off distribution ψ ls

mþ2 s=1

ψ ls = 1 , where ψ lmþ1 is the share of the run-off from the l-th

facies beyond the region, ψ lmþ2 is the share of the run-off from the l-th facies into the river. The coefficients ωl k

n

k=1

ωlk = 1 characterize the run-off distribution from

the l-th facies by the river compartments and are determined by the landscape relief and the spatial location of the facies and the waterway compartments. Thus Klk = ωlkψ lmþ2 θl. Evaporation from the soil surface can be described by the formulas of Hitchcock, Horton, Weissman, and others (Bras 1990). For instance, the formula by Priestley and Taylor (1972) for latent heat of evaporation qE is: qE = αS(q* - qi)/(S + γ), where qi is the soil heat flux, W/m2; q* is the net radiation flux, W/m2; γ = 0.066103 Pа/К is the psychometric constant; S is the slope of the saturated moisture pressure versus temperature curve (Pa/K),

2.5

Modeling of Global and Regional Water Cycles

151

1:06 for wet soil, α=

1:04 for dry soil, > 1:26 at a warm air advection over wet surface:

The Horton formula gives: V = 0:36½ð2- expf- 0:44θgÞlV - la ðmm=dayÞ; where θ is the wind speed (m/s); lV is the vapor pressure near the water surface, la is the water vapor elasticity. The Rower formula is written as: V = 0:771ð1:465 - :007ρÞð0:44 þ 0:26θÞðlV- la Þ, ðmm=dayÞ where ρ is atmospheric pressure (mm Hg). The variety of parameterization forms of the dependence of the rate of evaporation from the soil surface on environmental parameters allows one to easily adapt the model of water balance to the information base. The flux T in Fig. 2.6 reflects the impact of vegetation cover on the hydrological regime of the territory. One of the simple models of transpiration is the following dependence: T = yð24α þ β Þ, ðcm=dayÞ; where y is the specific water return of the soil; α* is the rate of the ground water lifting (cm/hr)); β* is the daily change of the level of ground waters (cm). Identify the components of the block diagram in Fig. 2.6 characterizing the leakage and infiltration processes of water from the river. Both leakage and infiltration depend on the quality of the riverbed and water level. Let Hi =

μ i C i Si μi C i, min

for for

0 ≤ C i ≤ Ci, min , C i > C i, min ,

where μi is the coefficient of penetration of water through the riverbed. Filtration Fi increases with increasing Ci between two critical values of Ci,min, when there is no infiltration, and Ci,max, when it is at a maximum:

F i, max =

0

for

0 ≤ C i ≤ Ci, min ,

μi ðCi - Ci, min ÞSi μi ðC i, max - Ci, min ÞSi

for for

C i, min < C i < C i, max , C i ≥ C i, max

The distribution of water filtering from the river between facies depends on the distance ri j between the i-th compartment and the j-th facies, as well as on the

152

2 Global Water Balance and Pollution of Water Reservoirs

structure of soil and landscape relief. In particular, this dependence can be described by the function Fij = Fi,maxχ(rij), where χ(rij) is the decreasing function that satisfies the condition m

χ r i j = 1: j=1

Evaporation from the river surface depends on the environmental temperature and can be described by the formula Vi = V i Tω or by the relationship Vi = μ(θ)(ρV - ρ2), where μ(θ)is the function reflecting the impact of the wind; ρV is the water vapor pressure at the temperature of the evaporating surface, mb; ρ2 is the absolute air humidity at a height of 2 m, hPa . The overflow volume is determined by the binary regime of the waterway functioning within a maximum possible water level Ci,max, so that: V i =

0 Ci - Ci, max

for for

0 ≤ Ci ≤ C i, max , C i > C i, max

The distribution of U i between facies depends on the landscape relief, characterized by the matrix of the relief run-off Ψ = ||Ψi j ||, and is written as m

Ψij = 1, Ψij ≥ 0: ijj = 1

As a result, Uij = ΨijU i . Water withdrawal for irrigation from the i-th compartment of waterway is an anthropogenic factor, and should be considered as a free parameter M i =

m j=1

Mi j .

To take into account a possible heterogeneity of the distribution of M i between facies, take the matrix of the coefficients of the distribution of watering v = ||vi j || (vi j

≥ 0,

m

j=1

vi j = 1, i = 1,. . .,n; j = 1,. . .,m), so that Mij = vijM i .

The relationship between surface water and ground water fluxes depends largely on the flux of water infiltrating through the soil layer downward. This flux, called infiltration, considering only the vertical heterogeneity of the soil can be described in a general form by the equation: ∂P ∂ ∂P = pðPÞ þ K z ðPÞ ∂t ∂z ∂z

ð2:1Þ

2.5

Modeling of Global and Regional Water Cycles

153

Bras (1990) gave various versions of solutions for this equation. For practical use the following solution can be suggested: f = f c þ ðf 0- f c Þ exp - Pl2 t , where f = (Pi - P0)P/(πt)-1, fc is the asymptotic value of the rate of filtration, f0 is the initial value of the rate of infiltration. Groundwater infiltration and evaporation processes are highly dependent on the vertical profile of the soil layer. The following soil layers can be selected: saturated and unsaturated. The saturated layer usually covers the depths >1 m. The upper unsaturated layer includes the soil moisture in the plant’s roots zone, the intermediate level, and the level of capillary water. The water motion through these layers can be described by the Darsy law, and the gravitation term Kz(P) in the Eq. (2.1) is calculated from the equation: K z ðPÞ = 256:32 δs- 7:28 - 1:27 δ1:14 ðcm=dayÞ, s where δs is the volume mass of soil (g cm-3). Thus, the system of equations of the regional water budget with the indicated functional descriptions of water fluxes in the region under study at initial values of W(t0), G(t0), Ci(t0), Φj(t0) prescribed for a time moment t0 enables one to calculate the characteristics of the water regime of the whole region for t ≥ t0. The initial values are provided by the monitoring system. The regularity of surveys depends on the required accuracy of prognosis and can be realized by planning the monitoring regime. Based on the synthesis of the model and the remote sensing system, monitoring can be organized practically in any irrigated agro-ecosystem. In this case the problems of identifying the air-borne measurements with the values of geophysical, ecological, and hydrological parameters appear. An example of the successful solution of such problems is a determination of the dependence between the coefficient of spectral brightness τJ = τz + (τ0 - τz)exp(-αW c) + dW n, where τ0 is the coefficient of the dry soil brightness, τz is the coefficient of the brightness of soil with the moisture content close to a minimum of the field moisture capacity (when there is no free water in the soil); α, с, d, and n determine the type of soil (α, d, n < 1; c > 1; for achromatic loamy soils we have: τz = 0.09; τ0 = 0.28; α = 0.01; c = 2.3; n = 0.9; d = 0.0001). Obtaining these estimates is an important problem in the remote sensing of the environment. Finally, note that the deterministic approach to modeling the water cycle in the zone ΩL described here cannot be considered as the only possible one. Such an approach gives only average trends in changes of the water cycle components. Their distribution and probabilistic prognosis can only be obtained based on dynamicstochastic water balance models. When modeling the global carbon cycle, this approach enables one to account for the absorption of atmospheric CO2 in the region due to washing out.

154

2

Global Water Balance and Pollution of Water Reservoirs

2.6

Drinking-Water Quality and Standards

Certainly, the monitoring of water quality is a complex and multifaceted problem, a solution of which provides for the study of tasks such as selection sensors for in-situ measurements, definition of elements of the water cycle with interaction of surface and groundwater, search data for short and long-term - temporal changes in surface and groundwater recharge and storage. Water quality management involves the systematic collection of physical, chemical, and biological information, and the analysis, interpretation, and reporting of those measurements, according to a pre-planned design and structure. The solution of the tasks arising here depends on the water quality standards accepted in different regions and countries. Water quality standards are acceptable for regions or countries to provide health-based targets which include numeric guideline values which define drinking water that does not represent any significant risk to human health. According to WHO (2018) adapted regulations and standards can help: • Protection of human health after the consumption of drinking water. • Complementing new ideas in water resources management. • Achieving a balance between health-related drinking-water issues and other health-related priorities. • Ensure the drinking-water management strategies are based on current scientific knowledge and proven good practice. • Allow appropriate targeting of resources. Drinking-water standards are indicators for ecological, social, and economic status of a given region. Usually, two levels of templates (primary and secondary) are given. US Environmental Protection Agency (EPA) regulators develop Primary Standards for drinking water contaminants based on three criteria (Hassinger and Watson 2016): • Pollution causes adverse health effects. • Risk assessment and cost-benefit assessments. • Known to occur in drinking water. The US EPA has established limits on the concentration of certain drinking water contaminants that are allowed in public water supplies. These limits, or standards, are set to protect human health and ensure that drinking water is a good quality. Each country chooses drinking water standards based on national traditions and following international standards. A specific situation occurs in the People’s Republic of China where the drinking water quality is determined according to the type of source and its functions. Water bodies are divided into five classes according to the utilization purposes and protection objectives (http://english.mee.gov.cn/SOE/soechina1997/ water/standard.htm):

2.7

Sources of Water Pollution and Risk to Human Health

155

Table 2.11 Chinese surface water standards (μg/L) Water standard Grade I Drinking

As 10 10

Mn – 100

Cr 10 10

Ni – 20

Cu 10 1000

Cd 1 5

Pb 10 10

Zn 50 1000

• Class I: applies mainly to water from sources, and national nature reserves. • Class II: applies mainly to the first class of protected areas for central drinking water sources, the protected areas for rare fish, and fish and shrimp spawning areas. • Class III: applies mainly to the second class of protected areas for central sources of drinking water, protected areas for common fish and swimming areas. • Class IV: applies mainly to water areas for industrial use and entertainment which are not directly touched by human bodies. • Class V: applies mainly to water bodies for agricultural use and landscape requirements. Table 2.11 characterizes the Chinese national quality standards for drinking water and surface water quality (Shen 2012). The intensive social and economic development of China society during the last 30 years have promoted the increasing discharge of pollutants into national water bodies, which inevitably lead to very negative effects on the overall quality of the waters of the country. Principally, standards or some of the criteria play a very important role in water quality management following the economic and technological feasibility, enforceability, and suitability to different circumstances in various regions of the country. It is evident that drinking water quality and introduction of its standards directly depends on the freshwater quality. According to Waststrate et al. (2019), the water quality assessment has various aspects listed in Table 2.12. Table 2.13 characterizes water availability in various countries.

2.7

Sources of Water Pollution and Risk to Human Health

Ecosystem dynamics and change of living conditions define levels of risk for human health on given territory. The following key questions need the answers: (i) What are the most important feedbacks (and their quantitative relations) between the ecosystems’ dynamics and global changes (first of all, climate)? (ii) What are possible consequences of the impact of global changes on ecosystems? (iii) What are possibilities of the choice to provide a sustainable development of ecosystems and respective needs of society in the production of ecosystems with supposed global changes taken into account?

156

2

Global Water Balance and Pollution of Water Reservoirs

Table 2.12 Various aspects of life activity linked (directly or indirectly) with the freshwater problems Millennium Declaration Goal 1 End extreme poverty and hunger Goal 2 Provide universal elementary education Goal 3 Promote equality between men and women and expand the capacity of women Goal 4 To reduce infant mortality Goal 5 Improving mothers’ health Goal 6 Combating HIV/AIDS, malaria, and other diseases Goal 7 Providing ecological stability

Goal 8 To arrange a global-scale partnership in the interest of development

Millennium Summit Implementation Plan

Problems to be resolved Between 1990 and 2015, halve the share of the population living on less than 1 dollar a day and halve the share of the starving people By the year 2015, provide the opportunity for boys and girls in the world to have elementary education Eliminate the inequality of boys and girls in their access to elementary and secondary education preferably by 2005 and at all levels of education by 2015 at the latest To reduce by two-thirds the mortality of children under the age of 5 years over the period 1990–2015 To reduce the mothers’ mortality by three-fourths during the period 1990–2015 By the year 2015, stop the spread of HIV/AIDS, malaria, and other major diseases and start reducing their scale To take into account the principles of sustainable development in national strategies and programs and to reduce the tendency to lose ecological resources. By the year 2015, halve the share of the population without access to clean drinking water, substantially improve the lives of at least 100 million slum dwellers To continue shaping transparent, predictable, and impartial commercial and financial systems based on legal norms. To provide control, development, and poverty reduction at national and international level. To satisfy the special needs of the least developed countries having no access to the sea as well as small island developing countries (SIDC). In cooperation with the private sector, measures should be taken to enable everyone to take advantage of the benefits of new technologies By 2015, halve the share of the population without access to safe drinking water, partly due to the lack of money (as foreseen in the Millennium Declaration) and the share of the population without access basically hygiene. By the year 2005, elaborate plans for complex control of water resources and water use efficiency

2.7

Sources of Water Pollution and Risk to Human Health

157

Table 2.13 Annual per capita renewable freshwater resources Country Greenland

Renewable freshwater resource per capita (m3) 10, 522, 275

Renewable freshwater resource per capita (m3) 9939

French Guiana Iceland Guyana Suriname Congo Papua New Guinea Gabon Bhutan Canada Peru Liberia Chile Lao People’s Dem. Rep. Brazil Panama

608, 817 538, 878 315,678 236, 836 230,142 121,791

Country Bosnia and Herzegovina Estonia Guatemala United States Belarus Lithuania Greece

113,247 106,292 83,931 66,339 61,159 54,868 53,752

Mexico Mauritania Luxembourg Cuba Japan Ukraine France

4353 3546 3421 3402 3328 3034 3003

43,891 43,539

2936 2683

Russian Federation Sierra Leone Finland Argentina Sweden Australia Serbia Bulgaria Mongolia Turkmenistan

31,877

Italy United Kingdom Uzbekistan

28,778 20,737 20,410 20,226 18,372 17,824 14,123 13,176 12,067

2389 2285 1882 1582 1065 779 417 237 213

Austria

10,075

Afghanistan Germany Belgium India Egypt Kenya Algeria Israel China, Hong Kong SAR Kuwait

9205 8130 7951 7866 7377 6465

2656

7

To answer these questions, of primary importance are results of the following developments directed on the development of a new vision of environmental health for the twenty-first century. The health of hydrological systems is an important indicator of many environmental processes. Chemicals in water that affect human health are the presence of heavy metals such as fluoride, arsenic, lead, cadmium, mercury, petrochemicals, chlorinated solvents, pesticides, and nitrates. Concentration of fluoride below 0.5 mg/l causes dental carries and mottling of teeth but exposure to higher levels above 0.5 mg/l for 5–6 years may lead to adverse effect

158

2 Global Water Balance and Pollution of Water Reservoirs

on human health leading to a condition called fluorosis. Arsenic is a very toxic chemical that reaches the water naturally or from wastewater of tanneries, ceramic industry, chemical factories, and from insecticides such as lead arsenate, effluents from fertilizer factories, and from fumes coming out from burning of coal and oil. Arsenic causes a respiratory cancer and leads to bladder and lung cancer. Lead in the drinking water source affects the blood, central nervous system, and the kidneys. Mercury pollution impacts the quality of fish production, the consumption of which can lead to death. Cadmium reaches human body through food crop from soil irrigated by affected effluents (Schwarzenbach et al. 2010; Lufingo 2019). Huq et al. (2013) have characterized main types of water pollutants based on the experience of the Buriganga River (Bangladesh) study: • • • • • • • •

Liquid organic wastes. Liquid inorganic wastes. Nutrient substances. Synthetic compounds. Inorganic chemicals. Silt and sediment. Hot water. Industrial, municipal, and urban wastes.

Degree of hazard to public health depends on the pollutant concentration and its toxicity. Serious damage is given by microorganisms/germs spreading in the water bodies the sources of which are feces of animals, wastes of sewage, latrines, various kinds of bacteria, virus, and other organisms. Toxic substances directly affect living beings through drinking water. Some toxic substances accumulate in the body and then express its contamination. Harmful effect on living animals and organisms that came in contact with contaminated water is defined by toxic chemicals, materials, contaminants, and harmful materials delivered to the rivers and lakes from international and un-international releases (Schwarzenbach et al. 2010; Singh et al. 2020). With respect to human health, the most dangerous situations are arisen when sources of drinking water are polluted by chemical toxicants accumulated to food chains. Certainly, human health depends on the various kinds of environmental changes, including climate change, stratospheric ozone depletion, land degradation, freshwater decline, biodiversity losses, and ecosystem functions. Unfortunately, human health is a function of human activity that affects weather, climate, and the environment. Therefore, human health assessment can be realized in the framework of global model of climate-biosphere-society system that can help to classify living conditions on a global scale with the separation of negative areas (Krapivin and Varotsos 2007, 2008). Realization of this model needs many data about the world, regional, and local environmental processes. In reality, such data concerning, for example, the African continent at present time have fragmentary character. The major environmental problem in Africa is linked to water resources, the volume and quality of which need improvement. In the context of human health, the

2.7

Sources of Water Pollution and Risk to Human Health

159

improvement of drinking water quality can decrease sanitation risks and can help to realize the effective control of water resources taking socio-economic aspects into consideration (Pare and Bonzi-Coulibaly 2013): • • • •

Low economic development of West and Central Africa. Rapidly growing population particularly in big cities. Low educational level of the population. Low social conditions in terms of accommodation, energy, sanitation, health, drinking water. • Growing need for food security. • Increasing demand for water for drinking and agricultural purposes. The majority of African countries face similar water pollution patterns, mainly due to: • • • •

Intensive use of pesticides and fertilizers in agriculture. Improper and unjustified disposal of solid wastes. Discharge of untreated wastewaters from households and industries. Penetration of some natural pollutants of geological origin into freshwater environment.

Some selected water quality data from the Sub-Saharan regions show the large variations in freshwater quality indicators between countries. Water scarcity and problems of drinking water quality are seen in Asian countries such as China, India, and Pakistan where much of these countries face serious water deficit. According to the World Health Organization, more than 1.2 billion people lack clean water, and more than 5 million people die each year from contaminated water or water-related diseases. Unsafe water causes 4 billion cases of diarrhea each year. Unsafe or inadequate water, sanitation, and hygiene cause approximately 3.1 percent of all deaths worldwide (Matta 2010; RSC 2010; Rimin 2018). Lufingo (2019) represented data on water standards in East African regions (Table 2.14).

Table 2.14 East African portable water standards Parameter Bicarbonate as CaCO3 Carbonate Chloride Sodium Potassium Sulfate Magnesium Calcium Nitrate

Formula HCO3CO23 ClNa+ K+ SO24 Mg2+ Ca2+ NO3-

Natural portable water, mg/L Not specific Not specific 250 200 50 400 100 150 45

160

2

Global Water Balance and Pollution of Water Reservoirs

Table 2.15 List of WHO guidelines for chemicals held in GEMStat (Rickwood and Carr 2007) Chemical Aluminum, mg/L Ammonia, mg/L Antimony, mg/L Arsenic, mg/L Atrazine, mg/L Barium, mg/L Benzene, mg/L Boron, mg/L Cadmium, mg/L Chloride, mg/L Chromium, mg/L Copper, mg/L Cyanide, mg/L DDT, Metabolites, mg/L Endrin, mg/L

Guideline 0.1 1.5 0.02 0.01 0.002 0.7 0.01 0.5 0.003 250 0.05 2 0.07 0.001

Source Agriculture Human Treatment Natural Agriculture Natural Industrial Natural Industrial Human Natural Treatment Industrial Pesticides

Chemical Hydrogen sulfide Iron, mg/L Lead, mg/L Lindane, mg/L Manganese, mg/L Mercury, mg/L Nickel, mg/L Nitrate, mg/L Nitrite. mg/L pH, minimum pH, maximum Selenium, mg/L Sodium, mg/L Sulfate, mg/L

Guideline 0.05 0.3 0.01 0.002 0.4 0.001 0.02 50 3 6.5 8 0.01 200 250

Source Human Treatment Treatment Agriculture Natural Industrial Treatment Human Human Human Human Natural Human Human

0.0006

Agriculture

600

Human

Fluoride, mg/L Hardness, mg/L

1,5 200

Natural Human

Total dissolved solids, mg/L Turbidity, NTU Zink, mg/L

5 3

Human Human

Table 2.16 Comparison of WHO drinking water guidelines for selected parameters with guidelines from the European Union (EU), United States (USEPA), and Australia (Rickwood and Carr 2007) Parameter Ammonia, mg/L pH Chloride, mg/L Iron, mg/L Lead, mg/L Arsenic, mg/L Copper, mg/L Fecal coliform bacteria, counts/100 ml

WHO 1.5 6.5–8.0 250 0.3 0.01 0.01 2.0 0

EU 0.50 No 250 0.2 0.01 0.01 2.0 0

USEPA No 6.5–8.5 250 0.3 0.015 0.01 1.3 0

Australia 0.50 6.5–8.5 250 0.3 0.01 0.007 2.0 0

Rickwood and Carr (2007) demonstrate a list of WHO guidelines for chemicals adhered to in the Global Environment Monitoring System (GEMS) (Table 2.15). Table 2.16 compares different indicators of drinking water quality. Practically all countries control microbial pollution of drinking water to provide a human heath perspective and overcome existing uncertainties connected with undetected limits. Detection limits are provided by suitable instrumental tools and technologies. Examples of detection limits are given in Table 2.17.

2.7

Sources of Water Pollution and Risk to Human Health

Table 2.17 The detection limit parameters greater than that acceptable for human heath WHO guideline

Parameter Arsenic, mg/L Cadmium, mg/L Chromium, mg/L Copper, mg/L Lead, mg/L Manganese, mg/L Mercury, μg/L

161 Detection limits 0.013–0.8 0.0038–5.0 0.07–2.0 5 0.011–1.0 1.0–1.1 2–200

Guideline 0.01 0.003 0.05 2 0.01 0.4 1

Chapter 3

Remote Sensing Technologies and Water Resources Monitoring

3.1

Introduction

The intensification of anthropogenic processes in the environment poses a number of principal problems of the nature management optimization, including the control and management of hydrological and hydrochemical systems. However, the existing tools for the solution of the problems arising here do not provide effective technologies to have reliable diagnosis of numerous disasters and allow the prognostic assessment of the possible consequences for the population. Nevertheless, the encouraging results obtained last time by many authors make it possible to expect that the combined use of microwave remote sensing tools and ecoinformatics methods will help to develop new efficient and reliable technologies for operative diagnosis and forecasting of environmental processes both in regional as well as on a global scale (Krapivin and Shutko 2012; Krapivin et al. 2019; Varotsos and Krapivin 2020a, b; Thenkabail 2015; Kummerow 2020). Of particular concern is global climate change that has been discussed in recent years in relation to the global carbon cycle and the greenhouse effect and the important role of the water cycle at both global and regional scales. The scientific problems that arise here relate to the overcoming of information uncertainties and the big data processing in the context of the assessment and forecasting of the state of the environment. The growing number of published works dedicated to global ecological studies leads to the realization that the protection of nature has become an urgent problem. The question of working out of principles of co-evolution of man and nature is raised with ever-increasing persistence. Scientists in many countries are trying to find ways to formulate laws governing the processes that occur in the environment. Numerous national and international programs of biosphere and climate studies contribute to the search for means to resolve the conflict between human society and nature. However, attempts to find efficient methods of regulating human activity on a global scale encounter difficulty of a primary nature. The majority of the difficulties are the absence of an adequate knowledge base on climatic and biospheric processes as well © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Varotsos et al., Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications, https://doi.org/10.1007/978-3-031-28877-7_3

163

164

3 Remote Sensing Technologies and Water Resources Monitoring

as the large incompletion of databases on global processes occurring in the atmosphere, ocean, and land. Another difficulty is due to the inability of modern science to formulate requirements that must be met by global databases that are necessary for a reliable evaluation of the environment state and forecasting of its evolution for sufficiently long periods of time. Over the past 20 years, remote sensing techniques have increasingly demonstrated their capability to monitor components of the water balance of large and small water basins on time scales ranging from months to decades. Satellite altimetry routinely monitors water level changes in large rivers, lakes, and flood plains. The theoretical and applied aspects of the combined development of remote sensing and ecoinformatics methods are addressed in many environmental issues (Nayak and Zlatanova 2008; Cazenave et al. 2016). Particular attention is paid to the formulation and solution of applied tasks related to decision making on microwave remote sensing monitoring data for nature-anthropogenic systems. The most complex stages of the synthesis of information-modeling systems are presented, which are oriented toward overcoming information uncertainties and enable the adoption of monitoring systems in the real object or process from the environment. Cazenave et al. (2016) have collected review articles showing how space-based observations, combined with hydrological modeling, have considerably improved our knowledge of the continental water cycle and its sensitivity to climate change. Two main issues are highlighted: 1. The combined use of space-based observations to monitor water storage changes in river basins worldwide 2. The use of spatial data in hydrological modeling either through data assimilation or as external constraints A series of methodological, algorithmic, and information technologies are proposed to be used for the solution of specific tasks related to the diagnosis of natural hydrological systems to support the entire formation of monitoring data considering the specific conditions of its implementation and to enable the optimization of observational data collection and analysis processes using microwave sensors located on flying platforms (Krapivin and Shutko 2012; Varotsos and Krapivin 2020a, b). The role of microwave radiometry in the study of environment change is considered taking into account existing scientific results connected with applications of microwave radiometry to investigate vegetation, soil, and water systems including characteristics such as soil moisture, snow water equivalent, atmospheric and water pollution. The principal aspects of physical basis of microwave radiometry are discussed. The use of microwave sensors located on satellites requires the elaboration of remote sensing systems with acceptable spatial resolution as possible due to the effective data processing algorithms. Many scientists try to find answers to this task. The first steps toward understanding these key problems of microwave remote sensing were recently made in a series of publications by Krapivin (2009), Krapivin et al. (2006, 2015a, 2018a, 2019), and Varotsos and Krapivin (2020a, b). Some

3.1

Introduction

165

results of discussions and publications permit to produce a constructive synthesis of a geoecological information-modeling system (GIMS) of natural processes considering a set of spatial scales. GIMS technology allows the study of dramatic aspects of anthropogenic impacts on the biosphere’s water cycle. GIMS technology has been developed to solve the wide range of problems arising in agriculture (Krapivin and Shutko 2012; Nitu et al. 2019). The properties of the interaction of electromagnetic energy with soil-plant formations are of great interest to the scientists and engineers who designed the GeoInformation Monitoring System for Agricultural Function (GIMSAF). The microwave radiometric technique for measuring soil moisture characteristics has been developed and is widely used in many regions (Haarbrink et al. 2011). This experience was used at GIMSAF to carry out the procedure of determining water content and moisture profiles by remote sensing in a meter-thick soil layer with and without vegetation cover. The procedure is based on the dependence of microwave emissivity of soil on its moisture. The procedure takes into account variations in temperature, density, and roughness of natural soils. GIMSAF performs: • Collection of information on the current parameters of the soil-vegetation system (moisture, shallow water table, soil salinity, biomass of the vegetation) in various regions • Processing of the preceding information in the context of a simulation model of biogeocenotic, physical, and chemical processes in the soil-vegetation system in order to assess its current state and to predict its development and potential productivity • Representation of current and predicted information as a schematic map with a controlled structure of spatiotemporal details • Making technological decisions that are optimal in terms of providing the maximum biological productivity of agriculture vegetation The GIMSAF structure includes units from: • Collection of information • Initial data processing and data accumulation to simulate agricultural vegetation productivity and water conditions • Predicting the state of the geosystem • Evaluating the difference between measured and predicted trends in geosystem processes • Decision-making to measure geosystem control • Support service in operation with input and output data The data acquisition module is based on remotely sensed and ground-based data, prior knowledge of an object’s state, and meteorological parameters. The remote receivers trigger a microwave radiometric complex equipped with computer programs that allow the acquisition of operational area information to the desired degree of detail on soil moisture content at a depth of 0 to 100 cm, subsoil water level at a depth of 0 to 3 m, salt concentration of the soil in a range of 3 to 90 g
l-1, vegetation

166

3 Remote Sensing Technologies and Water Resources Monitoring

biomass, degree of mineralization, and contamination in reservoirs. The operating range of the moisture measurement is 0.0–0.6 g
cm-3 with a maximum absolute error equal to the vegetation biomass less than and greater than 200 t/ha to 0.05 and 0.05–0.08 g/cm3, respectively. The operation of the information processing units is based on a computer program package providing the spatiotemporal interpolation of remote and point surface trace measurements as well as stabilization of the information flow in the event of failure in the information collection system. The program package also includes the description of the moisture dynamics and biological, physical, chemical, and energy processes in the interaction between soil, vegetation, atmosphere, and energy sources, including factors of anthropogenic effects. The adaptation of information processing units to the real conditions in the studied region is accomplished by introducing sets of coefficients and characteristic dependencies. The operation of the trend correction and decision making units is based on the evaluation of the object state, the prediction of its development, and the sensitivity of the system to potentially possible productional and technological measures formulated by the experts. Peculiar features of GIMSAF and its distinction from similarly designed systems are the following: • Use of remote radiophysical sensors providing area, operational, and quantitative information on the state of objects • Ability to operate at various levels of information support (remote, surface, and a prior data set or part of them or without such data) • The system is fully open for further development in terms of technological and software support and is compatible with any sensor and information, consulting, and expert systems • Due to the use of adaptation algorithms, the system is self-regulating and highly noise immune • The system can be made compatible with means of implementing the productive and technological decisions made Expected application results include: • Obtaining reliable operational, area, and quantitative information on the parameters of the object and its general state with prediction • Evaluation of feasibility and efficiency of anthropogenic effect on agriculture and melioration objects • Optimal production and technological decisions made with the help of a simulational experiment and providing an increase in object control performance.

3.2

3.2

Contamination of Soil and Aquatic Environment

167

Contamination of Soil and Aquatic Environment

Soil pollution is often caused by the uncontrolled disposal of sewage and other liquid wastes resulting from domestic uses of water, industrial wastes containing a variety of pollutants, agricultural effluents from animal husbandry, and drainage of irrigation water and urban runoff. Typically, water pollution through runoff and drainage water is diffuse in nature when pollutants actually accumulate on the ground and are transported through the air-soil-water system due to changes in wind fields and precipitation. Therefore, diffuse pollution processes in their impact on the environment and human health are generally unknown (Rodrigues-Eugenio et al. 2018). One of main water pollution sources is agriculture which is directly connected with population growth and increasing food demand. Human demands for safe living are both the driving force behind agricultural expansion and intensifying impacts on water quality due to pollutants leaching from the soil surface. Certainly, water pollution from agriculture has direct negative effects on human health (WWAP 2017): • High levels of nitrates in water can cause methemoglobinemia—a potentially fatal illness—in infants. • Pesticide accumulation in water and the food chain leads to acute and likely chronic health effects. • Accumulation of nutrients in lakes and coastal waters causes eutrophication which has an impact on biodiversity and fisheries. The role of agriculture in global water-quality crisis is increasing in both developed and developing countries due to economic growth and poor wastewater management. Globally, 80 percent of urban wastewater is discharged into hydrological systems untreated (Mateo-Sagasta et al. 2017). Three to seven million tons of pesticides are produced annually the using of which is varied between 9.2 and 2 kg of active substance per hectare of arable land in developing versus developed countries, respectively. Water contamination can arise in drainage and sewer systems in non-agricultural/urban areas through runoff of rainwater over sealed surfaces, such as roofs and roads. Schwarzenbach et al. (2010) indicated five cognitive elements of the type and concentration of pollutants in the aquatic environment that allow a proper assessment of water quality: 1. Knowledge of the type and origin of pollutants 2. The availability of analytical methods for quantification of the temporal and spatial variability in the concentrations of the chemical(s) present 3. A profound understanding of the processes determining the transport and fate of the chemical(s) in the system considered 4. Mathematical transport and fate models of appropriate complexity to design optimal sampling strategies and to predict future developments of a given pollution case 5. Methods for quantification of the adverse effects of the chemicals on aquatic life and human health

168

3

Remote Sensing Technologies and Water Resources Monitoring

Angelovičovά and Fazekašovά (2014) analyzed numerous anthropogenic sources of pollutants that can contaminate the soil and water environment, considering inputs from waste waters following from mines and waste storage, runoff of pesticides from agricultural land or atmospheric deposition. Heavy metals as non-biodegradable pollutants can persist in soils for a long time reaching toxic levels in soil, water, and biota. Heavy metals can migrate over long distances in soil achieving water environment with washed off the ground. Soil contamination by heavy metals leads to changes of soil characteristics limiting soil productivity and environmental functions which is no longer appropriate for agricultural production. The sources of soil contaminants include the following (Rodrigues-Eugenio et al. 2018): • Several soil parent materials are natural sources of certain heavy metals and other elements, such as radionuclides. • Natural sources of arsenic (As) include volcanic releases and weathering of As-containing minerals and ores, but also naturally occurring mineralized zones of arsenopyrite (gossans), formed by the weathering of sulfide-bearing rock. • Many toxic elements are released into the environment such as heavy metals (Hg, Cr, Cu, Ni, Zn), polycyclic aromatic hydrocarbons (PAHs), and dioxin-like compounds which are realized from volcanic eruptions. • Naturally occurring asbestos are fibrous minerals that occur naturally in soils formed from ultramafic rock, especially serpentine and amphibole. It can be easily dispersed by wind erosion, and their mobilization will depend on the characteristics of the asbestos-containing materials, soil properties, humidity, and local weather conditions. The main anthropogenic sources of soil pollution include chemicals produced or used in industrial and agricultural activities, domestic and municipal wastes. A schematic representation of contaminant ways for soil pollution is given in Fig. 3.1. Figure 3.2 explains processes of soil pollutant spreading and downstream

Fig. 3.1 Potential interrelated pathways for soil-subsurface chemical contamination (RodriguesEugenio et al. 2018; Yaron et al. 2012)

3.3

Methods of Microwave Radiometry

169

Fig. 3.2 Transport pathway of contaminants in the soil-water environment

transport owing to surface runoff, flood events, atmospheric transport and deposition, and soil erosion.

3.3

Methods of Microwave Radiometry

Remote sensing of the land covers, atmosphere, and water aquatories is based on the recording of background or reflected and scattered electromagnetic radiation. A possibility to receive the data about the properties of environmental elements is connected with those knowledges which explain the dependence of thermal emission on its physical and geothermal parameters. Also the scattering mechanisms and active radiation reflection are functions of these parameters (Shutko 1982, 1986, 1987, 1992, 1997; Shutko et al. 1994, 2010; Krapivin and Shutko 2012; Macelloni et al. 2001, 2003; Paloscia et al. 2018; Yan et al. 2022). The waves (or frequencies) of electromagnetic emission used for remote sensing in the environment monitoring systems occupy a wide spectrum from 0.3 μm to 1.3 m with the sub-ranges: near ultra-violet (0.3–0.4 μm), visible (0.4–0.76 μm), near infrared (IR: 0.76–1.5 μm), middle and far IR (1.5 μm–1 mm), and super high frequency (SHF: 1 mm–1.3 m). The SHF-range is divided by three basic sub-range: millimeters (1–10 mm), centimeters (1–10 cm), and decimeters (10–130 cm). Two sub-ranges called usually as L-band (15.8–63 cm) and P-band (63–100 cm) are used by many authors. The real range selected for the study of environmental objects is defined by many conditions, such as the absorption and scattering of electromagnetic waves by the

170

3 Remote Sensing Technologies and Water Resources Monitoring

Earth’s atmosphere, as well as its interactions with earth covers and water objects. An atmosphere is an extremely limiting factor for selecting the range to be used as a working range in remote monitoring. For instance, P-band can be used for in-depth detection by sky-laboratories, but its application to satellite monitoring systems has a set of problems due to ionosphere being its screen. An analogous situation is the case of ultra-violet radiation that is absorbed intensively by the atmosphere gases. In order words, the question that arises is the atmosphere clearness for the range of specific waves. Thus, for example, some waves in IR and sub-millimeter bands are strongly absorbed by the water vapor. The clouds powerfully relax the light radiation and create obstacles to observe the land surface from space in many areas. Knowing the operating atmosphere under the given synoptical and geographical conditions is the key task for the composition of the remote monitoring system. As a rule, this task is resolved when the measurements are made. For this, one or more channels, using the waves, which are relatively strongly absorbed or dispersed by different atmospheric components, are added to the basic channels. The content of the relative component is determined by the effect of the relaxation of these waves on different latitudes (or the integrated content, depending on the task). The correction of the measurement results is introduced by the main channels using this additional data. The defined physical special features of the environmental remote sensing are inherent in the ultra-high frequency and ultra-shortwave bands that form the basis of radiometric geoinformation monitoring systems. The use of these systems allowed the solution of many tasks for the functional identification of the natural phenomenon and the development of new methodologies and technologies for the remote diagnosis of natural and nature-technogenic systems. Microwave methods are conditionally divided into two classes: active and passive. Methods that study the character of reflection, scattering, and absorption of waves emitted by the source with known spectral density are defined as active. Within the optical range, these sources are the sun, lasers, and other light emitters. In the radio-range spectrum, active methods are widely used in the design of radiolocation systems. In this context, the power of reflected and dispersed radiation, its spectral composition and polarization characteristics, phase and propagation time are interesting subjects. Passive methods are based on heat emission analysis of natural formations. Here we take into account the fact that the character of thermal emission is determined by the substance temperature and its physical parameters. This is why passive methods are used for the temperature measurement and for the determination of different environmental parameters. These measurements are effective primarily under the multi-channel sensing coordinated with data processing algorithms (Varotsos and Krapivin 2020a, b). Let us now look at the specific features of radiophysical methods, and in particular those of the methods in which radio waves are used. In this context, the main focus is on the SHF-range, since this range is applied to remote sensing systems installed on fly-laboratories and satellites (Krapivin and Phillips 2001a, b; Shutko et al. 2010). Microwave radiometer systems are strongly used in the many

3.3

Methods of Microwave Radiometry

171

satellites. These systems provide operational control over twenty basic geophysical parameters that shape weather and climatic processes. The main specific features of the radiometric methods are associated with the high radio-clearness of the atmosphere. It is one of its advantages compared to optical and infrared methods. The ability of the latter is mainly limited by the absorption and dispersion of atmospheric properties. Larger problems arise here through the clouds that are opaque to these wavelengths, and often prevent receiving the necessary data on the environment state. It is difficult to obtain functional data when it is necessary to solve tasks such as the diagnosis of extraordinary and destructive natural or technological anomalies. Certainly, the use of optical and infrared devices is only possible in areas with low cloudiness. Therefore, the main advantage of radiophysical methods for remote monitoring is its visibility. Certainly, there are also some problems in the solution that many scientists are working on. For example, the absorption line of water vapor exists for λ = 1.35 cm, and the oxygen absorption band results for λ = 0.5 cm. Radio waves can penetrate below the vegetation cover canopy and deep into the soil layer. That is why the use of radiophysical methods allows the estimation of vegetation and soil state and the determination of its properties. Macelloni et al. (2003) demonstrate how using a multifrequency microwave radiometer at L, C, and X bands (1.4, 6.8 and 10 GHz) can be used to measure the soil moisture and vegetation biomass in agricultural areas. Certainly, the precision of such results depends on the many factors including soil properties (hydraulic and dielectric characteristics, permeability) and meteorological situation. Microwave radiation from the soil is usually expressed through the Rayleigh Jeans law using the brightness temperature factor Tb which can be related to the physical temperature Tsoil T b = ð1- r ÞT soil = eT soil

where r is the soil emission coefficient, e is the soil emissivity. It is considered if the sensitivity of the microwave sensor is higher than 0.5 K, and its ability to measure soil moisture is very accurate. Remote sensing topics cover many theoretical and experimental investigations of the most advanced disciplines in geoscience, ecology, radiophysics, biochemistry, oceanology, applied mathematics, cybernetics, climatology, and other sciences (Chukhlantsev 2006; Grankov and Milshin 2004, 2010, 2016; Haarbrink et al. 2011). The microwave radiometry or passive microwave remote sensing are one of the radio-physical methods used for remote environmental observations. It is based on measurements of the natural electromagnetic radiation of the objects in millimeters to the decimeter range of wavelengths. The theoretical aspects of radiophysical monitoring are determined by the physical foundation of the measurement procedure organization in the active or passive regimes of the electromagnetic radiation propagation. An active sensing technology is based on the radiolocation methods, and a passive one is based on the background radiation capture. In both cases, theoretical tasks of microwave monitoring are related to the study of the

172

3 Remote Sensing Technologies and Water Resources Monitoring

electromagnetic waves propagation in the environment and, of course, in the atmosphere and near the ionosphere (Yakovlev 2001; Chukhlantsev 2006). In the active regime, the debate concerns the propagation of electromagnetic waves of various ranges from the emitter to the receiver. Here are two possible cases. In the first case, the transmitter and receiver have a different position in the environment and the decision to assess the environmental parameters is accepted on the basis of the registered distorted transmitted signal. In the second case, the transmitter and the receiver are combined to evaluate the environmental parameters based on the analysis of the emitted signal, the absorption and distortion of the received one, and its reflection. Certainly, the time of signal propagation in the environment is an information parameter. An active form of microwave monitoring is typical for the radiotranslusense method in the atmosphere. Passive methods of microwave monitoring are based on SHF-radiometry. The existence of resonance absorption fields in the SHF-range allows remote reconstruction of meteorological atmospheric parameters such as vertical temperature and humidity profiles. The ability to capture data not only about the properties of the water and land surfaces but also about the deep characteristics depends on the choice of electromagnetic spectrum. In the infrared range, the total emission is formed on a very thin surface layer. Electromagnetic waves of microwave range are strongly absorbed by the land and water surfaces. The depth of its spread into the water environment changes from one hundredth to one millimeter. At the same time, on dry soils, continental ices, and dry snow this value can reach several tens of wavelengths. It allows remote investigations of soil, ice, and snow cover up to considerable depths. The penetrating capability of radio waves gives the advantage especially under the detection of land coverings. Vegetation without a dense canopy (grass, cereals, etc.) slightly absorbs radio waves, so it is possible to carry out radio-observation of soil covers through this vegetation. Radio waves can spread to the soil up to the depth of 1 m. The main defect of microwave radiometric observations is the comparatively low spatial resolution compared to the optical range. In the radio-spectrum, high spatial resolution is achieved by the multichannel application and specific data processing methods. It requires large financial investments. Let us look at some aspects of active position. It is assumed that irradiation takes place at the nadir of smooth plot. In this case the signal power reflected from the land surface is W = pGA|κ0|2(16πH2)-1, where p is the emitted power, G is the coefficient of directional action of antenna, A is the efficient antenna area, H is the antenna height above the ground surface, κ0 is the coefficient of surface reflection. If all instrumental parameters and antenna height are known, the ratio of power accepted to the received one determines the reflection coefficient value. In the case when the soil is uniform in depth, the reflection coefficient is κ0 = (ε1/2–1)/(ε1/2 + 1), where ε is the dielectric ground permittivity. For the estimation of ε there is the following approximate formula:

3.3

Methods of Microwave Radiometry

173 2

1=2 ε = ε1=2 w pw þ εs ½1 - pw  ,

ð3:1Þ

where εw is the dielectric water permittivity, pw is the relative volume concentration of the free moisture in the soil, εs ≈ (1 + 0.5ρs)2 is the dielectric permittivity of the dry ground, ρs is the dry ground density (1–2 g
cm-3). From the formula (3.1) it is obvious that the reflected signal power allows the determination of the reflection coefficient and, consequently, of the dielectric ground permeability. The value of ε defines the ground density when it is dry, or the water content, if it is wet. The dielectric properties of the land covers are essential for the use of radiometric methods to diagnose them under different conditions. Specifically, the knowledge of the spectral coefficient for emission in the millimeter range allows the properties of the snow or ice layer to be estimated. The reflection coefficient is also determined by passive methods using the measurements of radiothermal emission intensity. The intensity of the background radiation at microwave range according to the Kirchhoff’s law is characterized by the brightness temperature: T j = κT e

ð3:2Þ

where κ is the emission coefficient (or absorbing surface ability, or the blackness level), Te is the effective surface temperature. The expression (3.2) characterizes the thermal emission of the surface and does not take into account the emission falling on the surface and reflecting by it. The coefficient κ is described by Fresnel reflective formula. The thermodynamic and brightness temperatures are measured in Kelvins: T(K) = Te(°C) + 273. Emissivity is a function of dielectric permittivity of the object/surface of observation. For a land surface, the dielectric permittivity is primarily a function of soil moisture. The higher the soil moisture content, the higher the permittivity of the soil, the lower the emissivity/intensity of radiation/brightness temperature of that piece of land. For a water surface, the dielectric permittivity is first of all a function of electric conductivity of a water solution that is dependent on the concentration of salts, acids, in the presence of oil films and many other chemical substances. For example, the higher the salinity of the water, the higher the dielectric permittivity of water solution, the lower the emissivity/intensity of radiation/brightness temperature of this water body. Within the 2–30 cm band, for Te = 10–30 °C, the radiation characteristics of several surface types are shown in Table 3.1. Table 3.2 shows Table 3.1 Basic microwave radiation characteristics of some typical surface covers

Surface Metal Water surface Very wet soil Very dry soil

Tj(K) 0 90–110 160–180 250–270

κ 0 0.3–0.4 0.55–0.65 0.85–0.93

174

3

Remote Sensing Technologies and Water Resources Monitoring

Table 3.2 Sensitivity of a bare soil microwave radiation to variations in soil moisture (W ), soil density (D), salinity (S), and surface temperature (T ) Wavelength (cm) 2–3 18–30

Spectral band X L

ΔTj/ΔW (K/g/ cm3) -200 -(200 to 300)

ΔTj/ΔD (K/g/ cm3) -15 -10

ΔTj/ΔS (K/ppt) 0.05 -0.5

ΔTj/ΔT (K/oC) 0.5 0.1

the sensitivity of X-band (2–3 cm) and L-band (18–30 cm) to changes in bare soilfree water content, soil density, salinity, and temperature of the soil surface. These data show that the main parameter affecting the intensity of a bare soil radiation, practically independent of the spectral band, is the soil moisture. Based on this sensitivity, it is feasible to estimate the value of soil moisture without a priori data on the soil parameters. The power recorded by a fully tuned receiver is W = κTjΔf where Δf is the received emission branch. In the case when the model of soil with the flat surface is considered, the emission and reflection coefficients are correlated by the formula κ = |κ0|2. Consequently, the knowledge of the emission coefficient allows the estimation of the reflection coefficient and electrophysical properties of ground. Under this it is necessary to know the soil temperature T or to calibrate the radiometer using the land covers with emission coefficients which are known. The theoretical bases of remote methods, as a rule, are approximate. Among the relevant simplifications are often used the uniformity of the distribution of environmental parameters by the depth or height, the absolute transparency of the area, and the land cover smoothness. The models compiled under these simplifications reflect the limited spectrum of properties of the studied phenomenon. It is therefore necessary to assess the adequacy of the model. The problem of wavelengths choice and the combination of its ranges with the classes of solved tasks is now in the field of vision of many researchers. The theoretical foundation of this choice lies in the field of the theory of thermal radiation transmission. In particular, for the monochromatic case the transmission equation is: dI ðzÞ=dz = J ðzÞ - αI ðzÞ,

ð3:3Þ

where α is the absorption coefficient, J(z) is the emission source distributed by z, I(z) is the emission intensity at point z. Under this for the non-scattering atmosphere the following equation holds: J(z) = ε1(z)B(z), where ε is the atmosphere emissivity, and BðzÞ = 2hf 3 c - 2 exp hf ðkT Þ - 1

-1

;

ð3:4Þ

h, c, and k are fundamental constants, f = νc is the frequency. For the microwave branch where the observed frequency is in the region of a few hundred gigahertz or less, the condition hf < 0:5: 4:2857ðNDVIÞ2 - 1:5429

where H is the canopy vertical size (m). The curves of Fig. 3.21 show a dependence of the attenuation coefficient on both the gravimetric vegetation moisture content and the type of vegetation fragments. In particular, the attenuation coefficient increases from 0.2 dB/kg/m2 to 2.0 dB/kg/m2 monotonically when frequency changes in the 0.8–4.0 GHz band and its value practically does not change in 4.0–8.5 GHz band (curve 1). In the case of thick branches without leaves (curve 3), the attenuation coefficient decreases with increasing frequency from 0.9 dB/kg/m2 to 0.25 dB/kg/m2. Increasing the thickness of branches from 5 to 50 mm without leaves leads to an increase in the attenuation coefficient from 0.06 to 0.9 dB/kg/m2 for low-frequency band, and a decrease from 2.0 to 0.25 dB/kg/m2 at high-frequency band. This effect is explained by the fact that the thin branches for long-waves represent media with an effective dielectric penetration close to 1, but the thick branches are compared to the wavelength that promotes resonance effects.

3.7

Measurement System to Retrieve the Attenuation of Microwaves in Vegetation

207

The results of Fig. 3.22 summarize the attenuation coefficient dependence on frequency for different trees when their gravimetric moisture content is varied from 42 to 52 percent. As a result, tree crones can attenuate electromagnetic waves with the following law: μ = 2.6f 0.44we. Figure 3.23 presents the dependence of the attenuation coefficient on the gravimetric moisture of the trees. The following conclusion is drawn from these results that the attenuation coefficient is directly proportional to the water content of tree branches. Under this, the curve’s slope increases with increasing frequency. Finally, the specific attenuation coefficient can be approximated by the formula (3.7), where the coefficients c and β are defined by the vegetation type and the canopy density. The main contribution to the coefficients c and β is defined by the value of the vegetation optical depth and other characteristics of the atmospherevegetation-soil system (Pampaloni and Paloscia 1986; Krapivin et al. 2006; Pretzsch 2014; Disney et al. 2006). Figure 3.24 illustrates the dependence of β on the gravimetric vegetation water content. The final purpose of this discussion is to introduce an approach to estimate the attenuation of microwaves under their propagation in the vegetation layer. The natural vegetation is characterized by the variety of canopy types and forms. The canopy of a tree consists of the mass of foliage and branches determined by spatial distribution and orientation, length, density, and categories as well as water content and dielectric permittivity.

Fig. 3.23 Dependence of the attenuation coefficient on the canopy water content

208

3

Remote Sensing Technologies and Water Resources Monitoring

Fig. 3.24 Dependence of the coefficient β on the gravimetric vegetation water content averaged over branch diameters

The vegetation canopy structure has a significant impact on the microwave signal as it propagates through the vegetation layer. There are different approaches to study this impact including 3D modeling tools (Disney et al. 2006) in coordination with laboratory and in situ observations (Chukhlantsev 2006; Krapivin et al. 2006). During MSRAMV measurements, typical canopies are formed in the camera allowing the evaluation of c and β coefficients presented in Table 3.17 (Scaggs 2007; Kimmins 2004). The coefficient c in Eq. (3.7) is a function of canopy parameters such as canopy volumetric density and dielectric constant ε = ε1 - iε2 that are specific for a given tree type and ε is a function of temperature, salinity, frequency, vegetation bulk density, and gravimetric vegetation water content. Real dielectric values can vary depending on the wood structure as well. For example, according to Zhu and Guo (2017) ε1 and ε2 of pine as a function of moisture content change in the ranges of 1.9–5.2 and 0.2–1.7, respectively. The dielectric constant varies with various depths in the tree body (Ranson et al. 1992). The coefficient β determines a speed of the attenuation coefficient change depending on the frequency of the electromagnetic wave (Van de Griend and Wigneron 2004; Kruopis et al. 1999). The variety of such dependencies is defined by the canopy diversity that is usually characterized by statistical distributions of foliage and branches (Liang 2004; Liang et al. 2005; Ishimaru 2017). A model of effective dielectric permittivity as a function of the canopy density is given by Chukhlantsev (2006). For example, the effective dielectric permittivity for coniferous canopy is estimated to be 2.008-i0.0006 for

3.7

Measurement System to Retrieve the Attenuation of Microwaves in Vegetation

209

Table 3.17 Examples of attenuation models summarizing the results of measurements performed with MSRAMV Forest canopy type Young aspen forest with thin branches (0.5–1.0 cm) Duration of the vegetation period Wintertime Young maple forest with thin branches (0.5–1.0 cm) Duration of the vegetation period Wintertime Regular aspen forest during vegetation period: leaf radius is 1.9 ± 0.5 cm, branches—radius is 4.5 ± 0.8 cm and length is 20.7 ± 6.5 cm. Regular maple forest during vegetation period: leaf radius is 5.1 ± 0.7 cm, branches—radius is 3.9 ± 0.6 cm and length is 31.9 ± 8.4 cm. Pine forest: needle length is 7.2 ± 0.4 cm, branches—radius is 2.8 ± 0.7 cm and length is 40.1 ± 6.2 cm. North-taiga forest: needle length is 2.9 ± 0.3 cm, branches— radius is 3.2 ± 0.6 cm and length is 29.7 ± 4.4 cm. Traditional mixed forest of boreal zone.

The model (1) coefficients. c β 0.99 ± 0.11 1.47 ± 0.21

0.77 ± 0.19 0.14 ± 0.09

1.36 ± 0.19 4.41 ± 1.23 1.13 ± 0.22

0.62 ± 0.17 0.12 ± 0.05 0.48 ± 0.18

1.78 ± 0.45

0.43 ± 0.16

0.61 ± 0.18

0.35 ± 0.12

1.32 ± 0.19

0.44 ± 0.14

1.17 ± 0.23

0.39 ± 0.13

f = 2.4 GHz. According to Chukhlantsev (2006), the main canopy parameter is optical depth τ that can by estimated using the following formula: τ = 4pk0ε2h/ (3cosθ), where k0 is the free space wave number, h is the linear dimension of the canopy in the direction θ. In this case, the total attenuation of microwaves during their propagation in the vegetation canopy will be equal to b = μh. Model (3.7) allows the mapping attenuation effect for microwave propagation in a given area covered by the vegetation of a given type under the conditions when the necessary parameters are estimated by the existing monitoring system. Table 3.18 gives an example of the attenuation of microwaves at the frequency of 1.2 GHz under θ = 0° incidence in the forest canopy of some types. The seasonal parameters of these forests are estimated based on various publications (Shugart et al. 1992; Disney et al. 2006; Gamon et al. 1995; Camacho et al. 2013; Shabanov et al. 2005; Yang et al. 2017). Varotsos and Krapivin (2018) provide a new experimental tool for studying microwave propagation through vegetation and a model of 0.8–10 GHz narrowband radio signal attenuation in the vegetation layer. The applicability of this attenuation model is based on knowledge and understanding of the propagation modes arising in vegetation layer and on a possibility to study experimentally these modes with the MSRAMV. The model was tested by comparing the modeling results and the results of measurements made with the MSRAMV and with radiative method in real situations. Table 3.17 characterizes the precisions obtained. Undoubtedly, it is impossible during this work to cover all really probable situations of the microwave propagations through vegetation layers. Really, the

210

3

Remote Sensing Technologies and Water Resources Monitoring

Table 3.18 Seasonal distribution of averaged canopy attenuation estimates (dB) at frequency f = 1.2 GHz Vegetation cover North-taiga forest Mid-taiga forest South-taiga forest Broad-leaved coniferous forest Broad-leaved forest Subtropical broad-leaved and coniferous forest Humid evergreen tropical forest Variable-humid deciduous tropical forest

Summer 5.6 10.3 10.8 11.2 13.5 12.9 14.3 13.9

Fall 5.2 9.6 10.2 10.6 9.1 12.7 14.2 13.7

Winter 4.9 9.1 9.5 10.1 7.7 12.5 14.2 13.6

Spring 5.3 9.7 10.3 10.8 12.3 12.7 14.3 13.8

measuring system for retrieving attenuation of microwaves in vegetation (MSRAMV) was synthesized to be an experimental tool for the study of attenuation effects when radio waves of 0.8–10.0 GHz are propagated in the vegetation layer. The MSRAMV needs the knowledge of biometric vegetation characteristics such as canopy density and vegetation water content. The variation of these features can be realized in the MSRAMV camera, which allows the two-parameter analytical attenuation model (3.7) to be estimated. The implementation of this model does not need to form the image of the canopy through the representation of leaves and branches with disks and cylinders, respectively, which, of course, involves some knowledge of the user. Of course, this user knowledge is needed when the canopy image is formed within the MSRAMV camera, but model (3.7) has only two unknown parameters unlike more complex models. Model (3.7) can be improved at the expense of search of functional dependencies of its parameters on the vegetation biometric and physical characteristics such as biomass, geometry, density, structure, and optical depth that are estimated using existing monitoring systems (Aires 2013; Achard and Hansen 2012). The results of this study show that a simple model (3.7) together with MSRAMV measurements gives a possibility to assess the attenuation of microwaves under their propagation in the vegetation layer of various types in the arbitrary direction. Such on-site measurements were realized for the isolated trees (Milshin and Grankov 2000; Chukhlantsev et al. 2003; Grankov and Milshin 2010).

3.8

Microwave Model of Vegetation Cover

Haarbrink et al. (2011) and Verba et al. (2014) proposed approaches for the assessments of radiation intensity sensitivity of a series of parameters of the water and land surfaces. With reference to the water objects, the relation between the variations of brightness temperature and physical temperature, water salinity S, wind speed V, and soil moisture can be calculated through the following expressions:

3.8

Microwave Model of Vegetation Cover

211

ΔT b K ffi a0 , a 2 ½0:3, 0:6 for λ = 2 cm; ΔT surf °C 0 ΔT b K ffi b0 , b 2 ½0:4, 0:45for λ = 8 cm, ΔT surf °C 0 ΔT b K ffi 0:1 for λ = 21 cm ΔT surf °C ΔT b K ffi - a2 , a 2 ½0:1, 0:8for λ = 21 cm, ΔS ppt 2 K ΔT b , b 2 ½0:7, 0:9 for λ = ð0:8–2Þ cm: ffi b2 ΔV m=c 2

ð3:8Þ

ΔT b Δρw

K ffi - b1 g=cm 3 , b12[200–250] for range λ = 2–21 cm, Tsurf = 0–30 °C, S = (0–60) ΔT b ΔT b K ppt and ρw 2 [0.1, 0.45] g/cm3; Δρ ffi - a1 g=cm a12[10,20]. ΔT  3, surf soil ΔT b ð0:3 - 0:6Þ K= ° C at the wavelength λ = 2 cm; ΔT surf  0:1 K= ° C at the wavelength λ = 21 cm. Formulas (3.8) give acceptable accuracy for estimating emissivity and soil moisture, and show that when there is vegetation cover and the reflectance coefficient of the vegetation cover is small, the natural temperatures of the soil and vegetation are close. Under this we have:

T bsv ffi T bsoil β þ ð1- βÞT v , β ffi e - 2τ , τ = γ v hv ≈ ηv Qv , β ffi

ΔT bsv=Δρ

w

ΔT bsoil=Δρ

w

=

ΔT bsv ð3:9Þ ΔT bsoil

where Tv is the vegetation temperature, T bsv is the brightness temperature of soil/ vegetation system. Specifically, as follows from (3.9) coefficient β 2 [0,1] has the meaning of the slope lowering coefficient of radiation-moisture dependence. For example, β 2 [065,0.9] at the λ = 21 cm practically for all agricultural crops. In common case, the following algorithm is used to retrieve soil moisture under vegetation (Wen et al. 2005): T Bp = T au þ Latm L2veg T ad þ Latm Lsky 1 - εp þ Latm Lveg εp T soil þ þLatm T veg 1 - ωp 1 - Lveg 1 þ 1 - εp Lveg

ð3:10Þ

where TBp is the satellite-measured brightness temperature at p polarization, which refers to horizontal (h) or vertical (v) polarization; Tau and Tad are the upwelling and downwelling atmospheric temperatures, respectively; Tveg and Tsoil are the vegetation and soil thermal temperatures, respectively; L is the vegetation or atmospheric transmittance expressed as L = exp.(-τsecθ), where θ is the incident angle of the observation; τ is the vegetation or atmospheric optical depth, which depends upon the vegetation or atmosphere extinction coefficient; ωp is the vegetation single scattering albedo; εp is the soil emissivity at p polarization, which is related to soil water content and a soil surface roughness parameter; and Tsky is the cosmic brightness temperature.

212

3

Remote Sensing Technologies and Water Resources Monitoring

The capability of passive microwave sensors to measure soil moisture content as an important parameter for the agriculture of crop covered fields is the subject of extensive research (Ferrazzoli et al. 1992). There are different empirical and theoretical propagations and pass-loss models allowing to assess the parameters of agricultural fields used for crop mapping and forecasting (Cookmartin et al. 2000). A typical case of sub-surface heterogeneities is the existence of over-moisture media or subsoil waters under the dry soil layer. For this case there are two moisture profiles: • Fixed (and usually small) moisture level at the boundary from the surface to the level of subsoil waters (z) • Smooth change of moisture (usually decreasing) between the level of subsoil waters and the soil surface (the so-called “Capillary Fringe”) (Choudhury et al. 1995) A profile of the first type is specific to arid dry zones. Under such a moisture profile the radiation contrast caused by the heterogeneity is defined by the following approximation: Δκ ≈ ð1 - r1 Þ2 r 2 e - 2τ ,

ð3:11Þ

where τ = γz, r1 and r2 are reflection coefficients for the boundary’s atmosphere-dry soil and dry-soil-wet soil, respectively. A second type of profile is specific for humid regions. In this case, soil moisture depends on the depth of subsurface waters, the evaluation of which is carried out by the indirect method, that is, based on the measured values of water density ρw. Each soil level Δz contributes to the brightness temperature: ΔTb = T(z)γ(z)Δz, where γ is the absorption coefficient of the radio waves at depth z. Finally, the radio brightness temperature at the soil surface will be: 0

T b ð 0Þ =

z

T ðzÞγ ðzÞ exp zmax

γ ðhÞdh dz

ð3:12Þ

0

To understand the dependence on the environmental parameters of the radio brightness temperature recorded by a radiometer at the height H above the soil, which is covered by plants, it is useful to consider simple parametric descriptions of this dependence. A simplified microwave model can be written as the following integral: H

T B ðH Þ = es T s exp -

γ ðxÞdx þ 0

y

H

T ðyÞγ ðyÞ exp 0

γ ðxÞdx dy þ ζ ðH Þ 0

ð3:13Þ

3.8

Microwave Model of Vegetation Cover

213

where γ is the absorption coefficient, es is the soil emissivity coefficient, Ts is the soil temperature, T is the atmospheric temperature, and ζ is the precision of the model. There are various simplifications of Eq. (3.13). For example, various approximations for T( y) and γ( y) are often used: T 2 , 0 ≤ y ≤ h; T ðyÞ = or T ðyÞ = T 0 þ T 1 y þ ⋯ þ T n yn and γ( y) = γ 0 + γ 1y T 1 , h < y ≤ H: or γ( y) = const. For the satellite monitoring case it can be shown that T B = es T s psat þ

n-1

Dl ð0Þ

ð3:14Þ

l=1

where psat is the atmospheric transmittance between the soil surface and the satellite: D1 = T ðhÞ, D2 =

1 dT ðhÞ 1 dDl - 1 ðhÞ ,:...
, . . . , Dl =
dh γ ðhÞ dh γ ð hÞ

Considering different analytical representations for the vertical profile T(h), formula (3.14) will calculate the brightness temperature TB with an error equal to Dn(0). The most simplified microwave model (3.13) for the canopy-surface brightness temperature is given as: T B ðλ, θÞ = e2 T 2 þ R21 ðθÞT B,sky ðθ1 , θ2 Þ

ð3:15Þ

where θ is the angle of incidence and TB,sky is the sky brightness temperature incident on the canopy level from a direction (θ1,θ2), with zenith and azimuth angles θ1 and θ2, respectively. Model (3.15) simplifies the analysis of the influence of canopy temperature and wind speed, for example, on the variations of the brightness temperature. This analysis is possible with knowledge of canopy roughness as a function of wind speed. The atmosphere-vegetation-soil system (AVSS) is commonly considered by many authors (Fig. 3.25). The microwave emission of the AVSS is formed by a combination of absorption and diffraction processes within the media and by repeated reflection upon their boundaries. A most thorough study of microwave emission model (MEM) for possible AVSS configurations has been undertaken by many authors (Ferrazzoli and Guerriero 1996; Varotsos et al. 2019b). Usually, the AVSS emissivity ability is described as e = 1 - jRj2 where

ð3:16Þ

214

3

Remote Sensing Technologies and Water Resources Monitoring

Fig. 3.25 Model-based calculation of the shielding influence of vegetation cover in microwave monitoring

R=

R21 exp½ - 2iχh þ R32 2π ; χ = χ 1 - iχ 2 = ε - sin 2 θ λ 2 R21 R32 þ exp½ - 2iχh

1=2

;

θ is the angle between the direction of electromagnetic wave propagation in the atmosphere and the atmosphere/vegetation interface; R21 and R32 are the complex Fresnel coefficients for the atmosphere/vegetation and vegetation/soil interfaces, respectively; and h is the vegetation cover height. The Fresnel coefficients for the interface between the ith and kth medium are defined by the expression: Rik = jRik j expð- iqik Þ

ð3:17Þ

where qik is the phase shift. From (3.16) and (3.17) one gets the following representation for the emissivity: 2

2

r - R32 expð - 4χ 2 hÞ - R21 - 2κ1 sin q21 sin ξ e= r þ κ1 cosðq21 þ ξÞ where ξ = q32 þ 2χ 1 h; κ 1 = 2R21 R32 expð - 2χ 2 hÞ; 2

2

Rik = jRik j; r = 1 þ R21 R32 expð - 4χ 2 hÞ; ðγ i - γ k Þ2 þ ðδi - δk Þ2 Rik = ðγ i þ γ k Þ2 þ ðδ i þ δ k Þ2

1=2

;

ð3:18Þ

3.8

Microwave Model of Vegetation Cover

2ð δ i δ k - γ i γ k Þ ; γ 2i - γ 2k þ δ2i - δ2k

tgqik =

γk =

Ak ak Ak ε0k þ βk ε00k ε0k

δk =

2

þ ε00k

ðak þ bk Þ=2 ;

2

þ ε00k

βk =

ð3:19Þ

for horizontal polarization;

βk ak Ak ε00k - βk ε0k ε0k

Ak =

2

215

for vertical polarization; for horizontal polarization;

2

for vertical polarization;

ðak - bk Þ=2 ;

2 ε0k - sin θ;ε2 = ε02 - iε002 is the dielectric ε3 = ε03 - iε003 is the dielectric permittivity of the permittivity of the air with ε01 = 1, ε001 = 0 .

ak =

b2k þ ε00k

2

;

bk =

permittivity of the vegetation cover; soil; and ε1 = ε01 - iε001 is the dielectric

Following Chukhlantsev (2006) Fresnel coefficients are determined as functions of vegetation cover parameters. The dielectric constants of vegetation and soil are input parameters to formulas (3.18) and (3.19). Considering vegetation and soil as two-component mixtures of dry matter and water, the dielectric constants can be determined by the following expressions: p p jε2 j = ρP εB þ ð1- ρP Þ εW ;

p p jε3 j = ρB εB þ ð1- ρB Þ εW ;

ð3:20Þ

where ρP and ρB are the relative volumetric concentrations of water in plants and soil, respectively; and εB and εW are the dielectric constants of dry soil and water, respectively. As shown empirically by Engman and Chauhan (1995) ε03 > ε003 and both the real and imaginary parts of soil dielectric constant are increasing functions of volumetric moisture content. These functions can be accessed by the following formulae: ε03 ≈ c1 þ c2 ρB þ c3 ρ2B expðc4 - c5 =λÞ, ε003 ≈ d1 ρB þ d2 ρ2B expðd 3 - d 4 =λÞ,

ð3:21Þ

where λ is the wavelength (cm), and ci and di are constants depending on the type of the soil. For example, a soil that consists of 30.6% sand, 55.9% silt, and 13.5% clay is characterized by the dependencies (3.21) with c1 = 2.35; c2 = 52.4; c3 = 31.1; c4 = 0.057; c5 = 1.22; d1 = 7.1; d2 = 46.9; d3 = 0.0097; and d4 = 1.84. Thus, taking into account the dependencies of (3.21), it becomes possible to optimize the microwave range for passive remote sensing. Of course, a set of unsolved problems exists relating to the model corrections necessary in order to

216

3 Remote Sensing Technologies and Water Resources Monitoring

take into consideration the surface roughness and other obstacles distorting the brightness temperature TB, as well as a set of special features which arise in the solution of the inverse tasks. A basis for future model refinements is the correlation between TB, the atmospheric transmissivity for a radiometer at height H above the soil, the smooth surface reflectivity R, and the thermometric temperatures of the SPF Ts - v and atmosphere Ta. This correlation can be expressed by the Schmugge– Shutko formula (Schmugge 1990; Shutko 1987): T B = t ðH Þ RT sky þ ð1- RÞT s - v þ T a

ð3:22Þ

where Tsky is the contribution from the reflected sky brightness. Typical remote sensing applications use microwave wavelengths λ ≥ 1 cm and in this case the atmospheric transmission approaches 99% and Ta + Tsky ≤ 5°K (Engman and Chauhan 1995). The more precise correlation in (3.22) can be achieved by considering the influence of surface roughness on soil reflectivity: R′ = R exp (-g
cos2ω), where ω is the angle of incidence, and g is the roughness parameter (g = 4σ 2k2, where σ is the root mean square height variation of the soil surface and k = 2π/λ). Formulas (3.20) and (3.21) are simplified using the correlation between soil water content ρB and vegetation water content ρP (DeWitt and Nutter 1988): ρB = 78:9 - 78:4½1- R0 expð0:22ρP Þ For uniform media, the expression (3.22) is often represented in the following way: 2

TB =

1 - R21

2

T 3 1 - R32

2

e - τ2 þ T 2 ð1 - e - τ2 Þ 1 þ R32 e - τ2 2

2

1 - R21 R32 expð - 2τ2 Þ

where the opacity coefficient τ2 can be evaluated by means of formula τ2 =

4πQ p : νρ1 λI m ε2

where ν is the vegetation volume density and ρ1 is the specific density of the wet biomass. The coefficient R21 is estimated by the following expression: R21 ≈ α2 ½1- expð- τ2 Þ: There are many more simplified or more complicated models of AVSS. For example, the model describing the forest medium, as sketched in Fig. 3.16, was developed by Ferrazzoli and Guerriero (1996) and is based on the Rayleigh–Gans approximation of the electromagnetic properties of coniferous leaves (needles).

3.9

Microwave Irradiation of the Snow Cover

217

Vegetation cover is represented as a two-layered structure. The first layer is the canopy consisting of leaves and branches, represented by discs and cylinders, respectively. The second layer of the vegetation cover consists of trunks described by near-vertical cylinders. Such a model structure allows one to simulate the microwave forest emission. Models for layered vegetation have been studied by many authors (Ferrazzoli and Guerriero 1996; Karam et al. 1992; Krapivin et al. 2006). The main mathematical task follows from the solution of the radiative transfer equation. Different models consider a wide frequency range for both deciduous and coniferous forests and account for the distributions of branch size and leaf orientation. The basic conclusion of these studies indicates that detailed knowledge of forest structure is important for the adequacy of the model. For example, taiga forests and forest tundra have such an important component as lichens that are characterized by lowered photosynthesis and can influence the attenuation index. The work arising here is the subject of future studies.

3.9

Microwave Irradiation of the Snow Cover

Remote sensing investigations of the snow cover are of significant interest in the following theoretical and applied areas: • Monitoring and modeling of regional and global hydrological processes • Assessment of temporally stored snow volumes for avalanche and flood warning as well as for agriculture • Modeling and prediction of regional and global climate change based on the database of snow covered areas, snow depth (SD), snow water equivalent (SWE), surface albedo, snow-soil irradiance, and other snow properties Knowledge of potential changes in seasonal snow covers ice fields and permafrost as components of Earth’s cryosphere allows the future discussions to better understand the global climate response to the changing cryosphere parameters. Snow covers play a special role in the ecological processes of the northern latitudes, including the Arctic Basin, through influencing surface energy and water balances thermal regimes and trace gas fluxes. Snow covers of many latitudes are characterized by very rapid change with time. Therefore, forecasting snowmelt runoff is important for many flood-prone areas, depending mainly on the snow cover depth. The correlation between the natural microwave irradiance of the snow cover and its parameters is studied by many authors (Schwank et al. 2014; Takala et al. 2011; Pulliainen 2006; Varotsos et al. 2019a). However, the development of methods for determining snow water equivalent based on microwave monitoring data puts a number of difficult tasks on scientists. In particular, centimeter and millimeter wavelengths are characterized by powerful volumetric scattering in dry snow. On the one hand, it leads to the growing of the snow layer reflection coefficient with an increase in its thickness, which principally allows the assessment of snow layer

218

3 Remote Sensing Technologies and Water Resources Monitoring

depth based on radiometric observations. On the other hand, it complicates the modeling task of microwave irradiation of snow layer (Proksch et al. 2015). In this regard, the determination of snow layer depth using microwave observations is usually performed through semi-empirical models based on correlations of transmission and reflection coefficients with its geophysical characteristics such as layer depth, crystal sizes, density, etc. (Dai et al. 2017; Varotsos et al. 2019a). However, the complex layered structure of the snow layer complicates the use of these models to solve inverse tasks for the assessment of the snow layer characteristics based on the radiometric data. A series of studies have shown that the microwave polarization difference index (MPDI) of snow layer can be an effective tool to evaluate snow layer parameters more precisely (Xie et al. 2015). Knowledge of the polarization indices of snow layers leads to significant lowering of uncertainties in numerical assessments of snow water equivalent. Passive polarimetric microwave observations allow more precise evaluation of brightness temperature depending on snow structure. Varotsos et al. (2019a) propose theoretical and empirical results of polarized measurements of the integral reflection coefficient of the snow layer. Measurements were made for the frequencies 6.9 and 18.7 GHz when considering different snow types: • • • •

Fresh snow (the granular structure is absent) Small-grained snow (the typical size of ice crystals is less than 1 mm) Mid-grained snow (the typical size of ice crystals is 1–2 mm) Coarse snow (the typical size of ice crystals is 2–5 mm)

The general approach to the evaluation of polarized characteristics of microwave irradiation from the layer-rough surface scattering system is based on brightness temperature measurements. The polarization characteristics of the scattering and emission of electromagnetic waves over the rough surface can be evaluated from the solution of the following equation (Chukhlantsev 2006): cos θ

d I ðθ, φ, zÞ = - κ о ðθ, φÞ
I ðθ, φ, zÞ þ dz π

×

0

0

0

0

0

2π

dφ0 ×

0

dθ sin θ Pðθ, φ; θ , φ Þ
I ðθ , φ0 , z0 Þ, 0 ≤ θ ≤ π, 0 ≤ φ ≤ 2π,

ð3:23Þ

0

where I ðθ, ϕ, zÞ is 4×1 vector-column composed of modified Stock’s parameters in direction (θ, ϕ), Pðθ, ϕ; θ0 , ϕ0 Þ is the 4×4 matrix of the phase function of unitary volume from direction (θ′, ϕ′) to the direction (θ, ϕ) and κо ðθ, ϕÞ is the relaxation matrix components of which are defined by the scattering amplitude in the forward direction. When heat emission is considered, the right-hand side of Eq. (3.23) is completed by κ п 2k0λT2 ðzÞ, where κ п is the absorption matrix Ξ, T is the surface physical temperature, λ is the wavelength, and k0 is the Boltzman constant (1.3807 × 10-23, JK-1).

3.9

Microwave Irradiation of the Snow Cover

219

Equation (3.23) can be considered as an approximation for the characteristics of microwave irradiation of parallel-plane snow cowers when the attenuation and absorption matrices are diagonal. In this case, the radiation transmission can be described by the following equation (Pulliainen et al. 1999: Macelloni et al. 2001): cos ϑ uðz, ϑ, φÞ =

ωγ 4π

dJ ðz, ϑ, φÞ = - γJ ðz, ϑ, φÞ þ uðz, ϑ, φÞ, dz J ðz, ϑ0 , ϕ0 Þ ζ ðϑ, φ, ϑ0 , φ0 Þ sin ϑ0 dϑ0 dφ0 þ u0 ,

ð3:24Þ ð3:25Þ

where J(z, ϑ, φ) is the spectral radial intensity of radiation flux for the given polarization, γ is extinction coefficient, ω is the single scattering albedo, z is the distance along the normal to the snow layer, ϑ is angle between the direction of ray and normal, u0 is the intensity of inherent sources of radiation per unit volume, ζ is the dispersion index of the unit volume. The simplified models (3.23) to (3.25) can be synthesized using a two-level representation of the snow layer as shown in Fig. 3.26. The microwave emission of the atmosphere-snow-soil (ASS) system is formed depending on absorption and diffraction processes. The ASS emissivity capability can be described as κ = 1 - jρj2 where

Fig. 3.26 Profile layers along the ASS

ð3:26Þ

220

3

ρ=

Remote Sensing Technologies and Water Resources Monitoring

ρSA exp½ - 2iχz þ ρSS 2π , χ = χ 1 - iχ 2 = ε - sin 2 θ λ 2 ρSA ρSS þ exp½ - 2iχz

1=2

ð3:27Þ

θ is the incidence angle, ρSA and ρSS are the complex Fresnel coefficients for the snow layer–atmosphere and snow layer–soil interfaces, respectively; z = SD is the snow layer thickness. From Eqs. (3.26) and (3.27) we have κ=

r - ρSS expð - 4χ2 zÞ - ρSA - 2κ1 sin qSA sin ξ r þ κ1 cosðqSA þ ξÞ

ð3:28Þ

where ξ = qSS þ 2χ 1 z; κ 1 = 2jρSA j × jρSS j expð - 2χ 2 zÞ; r = 1 þ jρSA j × jρSS j expð - 4χ 2 zÞ; ðγ snow - γ atm Þ2 þ ðδsnow - δatm Þ2 ρSA = ðγ snow þ γ atm Þ2 þ ðδsnow þ δatm Þ2 ðγ soil - γ snow Þ2 þ ðδsoil - δsnow Þ2 ρSS = ðγ soil þ γ snow Þ2 þ ðδsoil þ δsnow Þ2

1=2

; 1=2

ð3:29Þ tgqSA =

γm =

Am am Am ε0m þ βm ε00m ε0m

δm =

2

2

þ ε00m

2

βm am Am ε00m - βm ε0m ε0m

Am =

2ðδsnow δatm - γsnow γatm Þ ; γ2snow - γ2atm þ δ2snow - δ2atm

2

þ ε00m

2

ðam þ bm Þ=2; βm =

ð3:30Þ

for horizontal polarization; for vertical polarization; for horizontal polarization; for vertical polarization; ðam - bm Þ=2; am =

2

b2m þ ε00m ;

bm = ε0m - sin θ;εsnow = ε0snow - iε00snow is the dielectric permittivity of the snow cover; εsoil = ε0soil - iε00soil is the dielectric permittivity of the soil; and εatm = ε0atm - iε00atm is the dielectric permittivity of the air with ε0atm = 1 and ε00atm = 0: Sub-index m symbolically represents one of three media: atmosphere, snow and soil.

3.9

Microwave Irradiation of the Snow Cover

221

Following Krapivin et al. (2006) Fresnel coefficients are determined as functions of snow cover parameters. The dielectric constants of snow and soil are input parameters in formulas (3.29) and (3.30). Considering snow and soil as doublecomponent mixtures of dry matter and water, the dielectric constants can be determined from the following expressions: p p jεsnow j = ρP εd þ ð1- ρP Þ εW ;

p p jεsoil j = ρB εB þ ð1- ρB Þ εW ;

ð3:31Þ

where ρP and ρB are the relative volumetric concentrations of water in snow and soil, respectively; and εd and εW are the dielectric constants of dry snow and water, respectively. The real and imaginary parts of the dielectric constants of snow and soil can be approximated by the following formulas (Chukhlantsev 2006): ε0d = 1 þ 1:7ρ þ 0:7ρ2 , ε00d = 0:52ρ þ 0:62ρ2 ε00ice ε0soil ≈ c1 þ c2 ρB þ c3 ρ2B expðc4 - c5 =λÞ, ε00soil

≈ d 1 ρB þ

d 2 ρ2B

ð3:32Þ

expðd 3 - d 4 =λÞ,

where ε00iceis the imaginary part of the dielectric constant of pure ice (~8× 10-4), ci and di are constants depending on the soil type. Thus, following Krapivin et al. (2006), the brightness temperature of the ASS for uniform media can be represented as follows: T bASS =

1 - ρ2SA

T 3 1 - ρ2SS e - τ2 þ T 2 ð1 - e - τ2 Þ 1 þ ρ2SS e - τ2 1 - ρ2SA ρ2SS expð - 2τ2 Þ

ð3:33Þ

An assessment of snowpack water storage of restricted area can be performed based on knowledge about SD and SWE. Different models exist to calculate these snow characteristics (Tsutsui and Maeda 2017; Liu et al. 2017). The following approaches SD = a(MPDI) + and SWE = q1MPDI = q2 are used here, where the coefficients a, b, q1 and q2 are defined during in-situ experimental measurements. Snow water content (SWC) is assessed depending as a function of brightness temperature and can ultimately be calculated using the empirical approaches represented by Kelly and Chang (2003) and Clifford (2010). In the isothermal case, the emission characteristics of the scattering layer are represented by the value of emission coefficient κ which according to Kirchhoff’s law is defined by the following formula: κ = 1 - r - q,

ð3:34Þ

where r is the integral reflection coefficient, q is the transmission coefficient of snow layer. The coefficients r and q are defined through the solution of the radiative transfer equation. Analytical solutions of Eqs. (3.34) and (3.25) are known for

222

3 Remote Sensing Technologies and Water Resources Monitoring

one-dimensional and anisotropic scattering index (Carcolé and Ugalde 2008). In other cases, the numerical solution of Eqs. (3.24) and (3.25) is needed. In particular, this solution is defined using the Monte-Carlo method (Macelloni et al. 2001; Chen et al. 2003). However, the snow layer has scatterers within its volume coherent interactions between which the use of a radiative transfer theory in the dense medium is needed (Wen et al. 1990). The solution of Eq. (3.24) when single scattering is considered gives (Chukhlantsev 2006) κ = ð1- ωÞ 1- e - τ sec ϑ , r = ω 1- e - τ sec ϑ , q = e - τ sec ϑ

ð3:35Þ

where τ = γz is integral attenuation of radiation in the layer under observation at nadir. In the case that the microwave emission of the snow layer is recorded by C- and K- band radiometers, its brightness temperature can be calculated by means of the equation (Macelloni et al. 2001): T bASS = T snow ð1–r–qÞ þ κsoil T soil q þ T snow ð1–r–qÞð1–κsoil Þq,

ð3:36Þ

where the first term of the right-hand side of the equation characterizes the emission of snow layer, the second term describes the soil emission attenuated in the snow layer, and the third term represents the snow layer emission reflected from the soil and attenuated by the snow layer. In the isothermal case, when Tsnow = Tsoil = T, the emission coefficient of the soil-snow layer can be represented by the following expression: T bASSS = κsoil 1- 1- q2 ½1- ðr 0 þ Rsnow Þq T þ ½1- ðr 0 þ Rsnow Þ - ðr 0 þ Rsnow Þq 1- q2

κ=

ð3:37Þ

where r0 is integral reflection coefficient of the snow layer, Rsnow is the Fresnel reflection coefficient for the optically thick snow layer. For (r0 + Rsnow)q Ξ2 or Ξ1  Ξ2. In other words, there is always such value of the scale ρ that defines a biocomplexity level Ξ → ρ = f(Ξ), where f is certain transformation of the biocomplexity concept into the number. To try to look for a satisfactory model that reflects the picture of verbal biocomplexity in the field of conceptions and signs subject to formal description and transformation. For this purpose, subsystems of the BSS are selected. The correlations between these subsystems are defined by the binary matrix function: X = ||xi j||, where xi j = 0, if subsystems Bi and Bj do not interact; xi j = 1, if subsystems Bi and Bj interact. Then any point ξ 2 Ξ is defined as a sum ξ =

m

m

i=1 j>i

xi j . Certainly here arises the

uncertainty to overcome which it is necessary to describe the scale Ξ. It can be performed, for example, by introducing weight factors for all BSS subsystems. The origin of these coefficients depends on the subsystem type. That is why three main types of subsystems are chosen: living and nonliving subsystems and plants. Living subsystems are characterized by estimating their density with element numbers or biomass value per unit area or volume. Vegetation is characterized by the type and the part of the area it occupies. Nonliving subsystems are measured by the concentration of matter related to the square or volume of the environment. In common case, specific characteristics {ki}, corresponding to the subsystems {Bi} significance, are recorded to every subsystem Bi (i = 1, ..., m). As a result, we obtain the narrower definition of the rated formula to move from the biocomplexity concept to the scale Ξ of its indicator: m

m

ξ=

k j xi j : i=1 j>i

It is clear that ξ = ξ(φ,λ,t), where φ and λ are geographical latitude and longitude, respectively; t is the current time. For the area Ω, the biocomplexity indicator is defined as a mean value: ξΩ ðt Þ = ð1=σ Þ

ξðφ, λ, t Þdφdλ, ðφ, λÞ2Ω

where σ is the area of Ω. Thus the indicator ξΩ(t) is the embedded BSS complexity characterization reflecting the individuality of its structure and behavior at each time t in the space Ω. According to natural evolution laws, the decrease (increase) in ξΩ will correspond to the increase (decrease) in biocomplexity and the ability of natural-anthropogenic systems to survive. As the decrease in biocomplexity disrupts the exclusivity of

288

5 Arctic Basin Pollution

biogeochemicl cycles and leads to a decrease in stress on nonrenewable resources, the binary structure of the matrix X changes in the direction of intensification of resource enhancement technologies. The vector of energy exchange between BSS subsystems moves into position when the survivability level of the BSS decreases. The global simulation model is oriented to the spatial discretization of the earth’s surface with Δφ in latitude and Δλ in longitude. In other words, the BSS space Ω is divided by a set of cells Ωij(Ω = [Ωij; Ωij = {((φ,λ); φi ≤ φ < φi + 1; λj ≤ λ < λj + 1; i = 1,..., N; j = 1,..., M; N = [180/Δφ]; M = [360/Δλ]}). Each cell Ωi j has its biocomplexity indicator value: ξΩ ði, j, t Þ = 1=σ ij

ξðφ, λ, t Þdφdλ

ð5:1Þ

ðφ, λÞ2Ωij

The value ξΩ(i,j,t) calculated by the formula (5.1) reflects the topological structure of matrix X(i,j,t). Consequently, there are n = NM matrixes and biocomplexity indicators to characterize BSS biocomplexity. A set of numerical features of BSS biocomplexity has been derived in the context of a computer experiment. These features are distributed in space and time. Integrated BSS biocomplexity indices can be calculated for an arbitrary region ω 2 Ω: ξω ðt Þ = ð1=σ ω Þ

ξΩ ði, j, t Þ

ð5:2Þ

ði, jÞ2ω

It can be average BSS biocomplexity by longitude or latitude zone, by ocean or sea aquatory, by country or state territory, etc.

5.5.2

The BSS Biocomplexity Model

The BSS consists of the subsystems Bi (i = 1, ..., m), the interactions between which are formed over time as functions of many factors. The BSS biocomplexity is composed by the structural and dynamic complexity of its components. In other words, the BSS biocomplexiry is formed under the interaction of its subsystems {Bi}. In due time, the subsystem Bi can change its state and, consequently, will change the topology of the relationships between them. The evolutionary mechanism of the subsystem Bi adaptation to the environment allows us to hypothesize that each subsystem Bi regardless of its type has a structure Bi,S, a behavior Bi,B and a goal Bi,G. So that Bi = {Bi,S, Bi,B, Bi,G}. Subsystem’s Bi efforts to achieve certain preferred conditions for it are its goal Вi,G. The feasibility of the structure Bi,S and the feasibility of the behavior Bi,B for the subsystem Bi are estimated by the effectiveness of achieving the goal Bi,G. As the example, the fish migration process is considered. The investigations of any authors revealed that this process is accompanied by an external appearance of

5.5

Biocomplexity as an Indicator of the Arctic Water Reservoir State

289

purposeful behavior. From these investigations, it follows that fish migrations are subordinated to the principle of complex maximization of effective nutritive ration, given preservation of favorable environmental conditions (temperature, salinity, dissolved oxygen, pollution, depth). In other words, traveling of migration species takes place at velocities characteristic for them in the direction of the maximum gradient of effective food, given adherence to ecological restrictions. That is why we can formulate that goal Bi,G of fish subsystem is in the increase of their ration, the behavior Bi,B consists in the definition of moving trajectory securing the attainability of goal Bi,G. Since the subsystems Bi (i = 1, ..., m) interactions are connected by chemical and energy cycles, then it is natural to suppose that each subsystem Bi carries out geochemical and geophysical transformation of matter and energy to remain in a steady state. The formalism of this process approach consists in the assumption that the interactions between BSS subsystems are represented as a process where the systems exchange a certain amount V of spent resources in exchange for a certain quantity W of consumed resources. Give the name of (V, W) – exchange to this process. The goal of the subsystem is the most advantages (V, W) – exchange, i.e., it tries to get maximum W in exchange for the minimum V. W is a complex function of the structure and behavior of the interacting subsystems. W = W(V, Bi, {Bk, k 2 K}), where K is space of subsystems’ numbers interacting with the subsystem Bi. Define BK = {Bk, k 2 K}. Then the following (V,W) – exchange results of interaction between subsystem Bi and its environment BK: W j:O = max min W j V j , Bj , BK = min max V j , Bj , BK = W j V j , Bj,opt , BK,opt Bj

BK

BK

Bj

W K:O = max min W K V K , Bj , BK = min max V K , Bj , BK = W j V K , Bj,opt , BK,opt BK

Bj

Bj

BK

Therefore, it follows that there is some target range of subsystem Bi when setting the levels of Vi and VK. Since the limiting factors apply in nature, then in this case it is natural to assume that there is some level Vi,min when the subsystem Bi stops spending its active resource to acquire an external resource, i.e., if Vi ≤ Vi,min, the subsystem Bi transfers to internal resource regeneration. In other words, when Vi ≤ Vi,min, the decrease of biocomplexity indicator ξΩ(t) takes place at the expense of interrupting the interactions of the subsystem Bi with other subsystems. In the common case, Vi,min is a structural function of scalar type, i.e., the transition xij from the state xij = 1 to the state xij = 0 takes place not for all j simultaneously. Indeed, in any living food experiment subsystem, producer/consumer relationships cease when the consumption biomass concentration falls below some critical level. In other cases, interactions of the subsystem {Bi} can stop at the expense of various combinations of its parameters. The parametric description of possible states of subsystem interactions {Bi} can be performed in the context of the BSS simulation model. The biocomplexity calculation can be performed for local scale of Ω (see Table 5.7). In any case, a biocomplexity change reflects a level of ecosystem survivability that is principal indicator of trends in Arctic ecosystem evolution.

290

5 Arctic Basin Pollution

Knowing the history of the biocomplexity spread of Arctic aquatories as a function of external factors allows knowledge of the subsequent effects that global society assigns to Arctic latitudes. A transitivity relationship of trophic groups can be considered as the predictor of the ecosystem state and as an indicator of its survivability: n

J ðt Þ =

i = 1 ðφ, λ, zÞ2Ω n i = 1 ðφ, λ, zÞ2Ω

U i ðφ, λ, z, t Þdφdλdz

U i ðφ, λ, z, t 0 Þdφdλdz

where Ui is the biomass of i-th ecosystem element, t0 is the time the ecosystem is considered known.

5.5.3

Conclusions and Discussion

According to Krapivin et al. (2017a), global population growth can inevitably produce results, which forces the search for ways to reduce anthropogenic impacts on the Earth’s biosphere. Of course, there are many solutions that can lead to direct international efforts in this direction. One way to overcome the difficulties in assessing the ecosystem dynamics of the Arctic basin is to use GIMS-ABE which describes the most important processes of pollutant transformation when it comes to Arctic environment. GIMS-ABE follows the trend of Big Data technology to analyze environmental monitoring data using useful information and corresponding models that link them into a unique system that includes the consideration of different hypothetical scenarios of human activity (Varotsos and Krapivin 2017). One of the main features of GIMS-ABE is the structural independence of its blocks, and the exchange of information between them takes place only through inputs and outputs. This structure allows adding new blocks or updating existing blocks without changing other blocks. Big data clouds provide information on all blocks according to their thematic functions. The testing and validation of GIMS-ABE are carried out by comparing the simulation results with results of other authors and a check of the observed data taking into account the qualitative agreement between the simulation results and the observed data. In this study, water temperature variability estimated in the AWTM block is followed by corresponding trends in the Arctic ecosystem that are consistent with the observed effects (Nagato and Tanaka 2012). Simulation results show different contaminant concentration stabilization time intervals for Arctic aquaria and various pollutants. Factors that limit primary production (light, temperature, and nutrients) appear to change biodiversity when phytoplankton biomass reaches minimum levels. Trends in Arctic ecosystem change, including the vertical distribution

5.5

Biocomplexity as an Indicator of the Arctic Water Reservoir State

291

of ecosystem biomass, correspond to trends described in several publications (Cousteau 1963; Fernández-Méndez et al. 2015; Krapivin 1996; Wang et al. 2006). The simulation results of this study alert the global human society in advance to the need to reduce anthropogenic impacts on the Arctic regions, and it is necessary to do so in the coming years. The simulation results show that Arctic seas have distinctly different ecosystem regimes and speeds to reach a steady state. For example, fluctuations in the productivity of coastal zones and open waters can vary by 5–10 and 2–4 times, respectively, which is explained by mainly reducing the role of pollutants with offshore movement. In contrast, ecosystem productivity of coastal waters is higher than that of remote aquifers as a result of river flows with nutrients. The main conclusion of this study is that the effective solution of the Arctic basin ecosystem pollution could be by developing prediction models without their strong validation and verification in a traditional way, continuing the development of the Arctic database (Zweng et al. 2017) using drift and buoy stations as well as satellite observations. Unfortunately, the level of safe survival of the Arctic ecosystem is unknown. As can be seen from Fig. 5.7, the survivability of the Arctic ecosystem decreases sharply when the pollution level increases. This study examined only a limited range of pollutants. It is known that there are pollutants with the most negative consequences for the living elements of the Arctic ecosystem (Cousteau 1963; Duarte et al. 2012; Fisher 2011; Libes 2009; Ma et al. 2011).

Chapter 6

Investigation of Regional Aquatic Systems

6.1

Introduction

Aquatic systems, in their broadest meaning, include lakes, ponds, wetlands, seas, and oceans. Goals and priorities of remote monitoring of aquatic systems include numerous problems related to the assessment physical, ecological, and hydrochemical characteristics for the assessment of their health, detection of different processes such as tropical cyclone start, aquatic weed, and algae control, understanding of the role in the climate change. Aquatic ecosystems are critical components of the global environment as essential contributors to biodiversity and ecological productivity. Their health is controlled, directly and indirectly, by human activities. Aquatic ecosystems have been subjected to increasing anthropogenic pressure due to resource extraction (e.g., fisheries and water use). Real-time remote monitoring networks are synthesized to provide meteorological, hydrographic, and water quality information. These monitoring networks are strategically positioned along the estuary and river mouth (Hongpin et al. 2015). Monitoring of the sea and oceanic ecosystems is the subject of the World Climate Research Program (WCRP) in which there exist such as the Tropical Moored Buoy System: TAO, TRITON, PIRATA, RAMA (TOGA). The Global Tropical Moored Buoy Array is a multinational effort to provide data in real-time for climate research and forecasting. Major components include the TAO/TRITON array in the Pacific, PIRATA in the Atlantic, and RAMA in the Indian Ocean. The TAO array consists of about 70 moorings in the Tropical Pacific Ocean, PIRATA uses ~20 buoys, and RAMA uses ~23 buoys. The Arctic buoy arrays include several types of buoys directed at measurements of: • Barometric pressure • Air and sea temperature • Ocean profile data including the ice thickness and wind characteristics

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Varotsos et al., Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications, https://doi.org/10.1007/978-3-031-28877-7_6

293

294

6

Investigation of Regional Aquatic Systems

The Arctic Marine Network makes measurements to serve a range of problems such as numerical weather prediction, climate change prediction, and assessment of pollution levels for all watersheds and along coasts. Somehow, existing buoy arrays provide information on the state of the seas and oceans only in limited areas. The detailed and comprehensive picture of their health is formed on the basis of complex big data delivered by various monitoring systems, including satellite instruments and using models and algorithms for processing big data (Krapivin 2009; Varotsos and Krapivin 2017). A problem of the diagnosis of the aquatic environment is considered in many national and international programs. Knowledge of water quality resources is important to many living spheres of the world’s population. It is well known that our planet’s freshwater resources are limited, and their quality depends on numerous natural and anthropogenic factors. Major problems of disturbance in aquatic ecosystems are associated with sewage, soil erosion, agriculture, waste treatment facilities, etc. Overcoming the problems arising here is linked to the synthesis of effective tools for diagnosing water quality. The traditional approach to hydrophysical and hydrochemical system monitoring is based on water and soil sampling based on which the series of empirical water characteristics is synthesized and then a mathematical model is developed to describe the structural and dynamic changes of the system under study. One of the problems that arise in this way is connected to the practically nonremovable information uncertainty as a result of the fragmentation of the irregularity of the monitoring data. Addressing this problem is provided by the development of big data processing tools. The assessment of fluid quality and its testing methods in Earth conditions dominates the standards based on the use of special manufacturers, sensors, and chemical reagents. A variety of existing methods for testing fluids in Earth conditions are not associated with significant difficulties. However, this problem is complicated when considering the Mars mission (Varotsos et al. 2022b; Krapivin et al. 2014, 2016c). This section develops new informational tools for the diagnosis of water bodies, including the detection of negative processes in their operation. These tools are based on the big data approach and use tools such as multichannel optical sensors, the use of which provides the efficiency and reliability of the diagnostic results. The information modeling instrument technology described in this section has an adaptation function to spatial water bodies and provides their water quality diagnostics based on episodic field measurements. Information-modeling instrumental system (IMIS) structure has blocks implementing a set of models and algorithms that provide big data processing (Varotsos and Krapivin 2017). The IMIS base is a compact multichannel optical device (MCOD, spectrophotometer or spectroellipsometer). IMIS performs adaptation to the spatial structure of the water object by providing the formation of pixels Ξij = {φi ≤ φ < φi + 1; λj ≤ λ < λj + 1}, where φ and λ are geographical coordinates. The choice of Δϕ = ϕi + 1 - ϕi and Δλj = λj + 1 - λj depends on the solved task significance and the available database. The vertical structure of the water body is

6.1

Introduction

295

Fig. 6.1 Main scheme for the multichannel decisionmaking system (MCDMS). Notations are given in Table 6.1

Big Data x1

Clouds xi

MCDMS

xn

Synchro n of the d ization ata flux es

CSC

EDFP

CSPP

TPDF

ESSI

DSSI

CAI

AIE

Resolver

Table 6.1 Elements of MCDMS Block CSC EDFP TPDF CSPP DSSI ESSI CAI AIE

Block function Calculation of statistical characteristics. Creation of the empirical distributions of frequency and probability. Creation of theoretical probability distribution function. Calculation of sequential procedure parameters. Definition of the system state indicators. Evaluation of the system state indicators. Choice of alternative indicators. Analysis of the indicator evolution.

considered and described by the appropriate hydrological model implemented in IMIS as a separate block. Two outer boundaries of the water body are considered sources of transboundary pollutant flows. The detection of negative or dangerous effects with the indicator of the studied system or process based on the monitoring data mainly depends on the big data processing algorithms and decision-making process. The classical method orients the observation system to accumulate a constant amount of data and process it using statistical methods. This method is characterized by the time delay that can lead to the omission of the expected phenomenon. The sequential analysis algorithm implements the decision-making process practically in a real-time regime by alternating between observation and decision-making stages. In other words, tracking data processing occurs after each data stream. Schematically, the sequential analysis decision-making algorithm is shown in Fig. 6.1. Tracking data provided by different channels are characterized by different arrival rates, accuracy, and reliability. Operational decision making is possible when decisions are made based on the combination of information channels. MCDMS provides control of the studied system state indicator calculated by a selected algorithm and oriented toward the integral representation of the system evolution. The structure and functionality of the indicator are defined by the MCDMS user. There are different natural or man-made systems, the state of which is assessed based on integrated indicators.

296

6.2 6.2.1

6

Investigation of Regional Aquatic Systems

Monitoring of Water Reservoirs in South Vietnam Introduction

Vietnam as an intensive developing economic needs operational and efficient tools to monitor water quality (Tuyet et al. 2019a, b). One of economically significant water object is Nuoc Ngot Lagoon. Nuoc Ngot Lagoon is located in South Vietnam (Binh Dinh province, 14°9′0′′N, 109°10′59′′E) in the zone where there are intense anthropogenic processes. These processes are mainly connected with agricultural activity (McElwee 2010; Piazza et al. 2010). The importance of the lagoon ecosystem is defined by its productivity assessed by fishing and shrimp production. The support of the high efficiency of the lagoon ecosystem is carried out with a traditional monitoring system that includes on-site water quality measurements. Krapivin et al. (2015a, b) proposed a geoecological information modeling system (GIMS) that develops GIS technology functions through the use of modeling processes. This section adopts GIMS in the Nuoc Ngot Lagoon area. The optical sensors used during field measurements and laboratory water sampling analysis are shown in Figs. 4.2, 4.3, 4.4, 4.5, and 4.6 (see Chap. 4). The main GIMS function is to integrate and coordinate the monitoring data delivered by different tools, episodic at the time and piecemeal from the space provided as part of this optimization of the monitoring process. The structure of GIMS depends on the complexity of the physical object. The Nuoc Ngot Lagoon area is characterized by the diversity of soil-plant formations, including their ecological characteristics as functions of climatic parameters. The water regime of the lagoon is controlled by exchanges with the South China Sea and river systems. Adaptation of GIMS to the lagoon area needs the development of new or specifications of standard GIMS items, such as a regional hydrology model and instrumentation. There are numerous studies of coastal lagoons on their heat budgets, energy, and hydrological regime. In each case, specific models are composed to form a basis for processing monitoring data and optimizing the monitoring regime. RodriguezRodriguez and Moreno-Ostos (2006) studied a natural heat budget model for the Nueva Lagoon in Southeast Spain. This model is only linked to the tracking process specific to the Nueva Lagoon region. Ferrarin et al. (2013) created and investigated the model of the Lesina lagoon (Italy) based on artificial neural networks to study the water balance of the lagoon and assess the spatial variability of lagoon biogeochemistry. Jakimavičius and Kovalenkovienė (2010) using traditional modeling technique analyzed the problem of the hydrological regime of the Curonian lagoon depending on the modernization projects of the Klaipėda seaport, taking into account climate change and the influence of the Baltic Sea. These and similar models are characterized by the individuality of the model’s structure and functions. A direct use of this kind of model for the study of other lagoons needs additional investigations and really requires new model synthesis. GIMS simplifies the solution of this task.

6.2

Monitoring of Water Reservoirs in South Vietnam

297

The structure and functions of GIMS are oriented to the variety of typical parameters of the environmental system that allows the identification of elements of the nature-society system that are typical for given spatial and temporal scales concerning the environmental system under study. In this case, a model development stage comes in the GIMS fitting process when the actual model is automatically synthesized through the formation of subject identifiers in the GIMS database. The GIMS structure has five levels of functional elements. The elements of the first level form a peripheral image of the studied environmental system that interferes with the elements of the system. The list of second-level structure elements are algorithms for tracking data processing. The third-level elements form a spatial image of the studied environmental system by referencing the system elements to the spatial digitization pixels. Fourth-level elements produce the connections between structures and GIS models. The fifth-level elements provide the prediction process and an evaluation of the simulation results.

6.2.2

The GIMS Structure and Functions Adopted in the Nuoc Ngot Lagoon

According to the general technology of GIMS synthesis, it is necessary to create a simulation model of the studied environmental object taking into account a priori information about them and subsequent assessment of discrepancies between modeling results and observations that allows correcting the model and the monitoring regime. As a result, stable coordination of model calculations and monitoring is established, and reliable forecasting is achieved. As shown by Krapivin et al. (2015a), the standard GIMS structure has a set of models presented in Fig. 6.2 and Tables 6.2 and 6.3. The GIMS adaptation process is implemented by the GSI component that parameterizes the hydrospheric portion of the investigated lagoon site and creates a general schematic envelope of the lagoon zone covering the gridded area with a step Δφ in latitude and Δλ in longitude that are given by the user. A discrete enumeration of pixels Ξij = {φi ≤ φ < φi + 1, λj ≤ λ < λj + 1} gives a set of sites, where each site Ξij has an area σ ij. The introduced grid of digitizing the lagoon zone is identified with series of subject identifiers that correspond to the lagoon elements. According to this procedure, the lagoon zone Ξ in the GIMS/NNL database is represented by a set of matrices Ak = akij , where the akij is the identifier of the subject or process in pixel Ξij. Figure 6.3 shows the main stages of lagoon zone identification and water quality assessment. The hydrological regime of the lagoon is the main component of the environmental processes that determine water quality. The WBM and MCBO components describe typical hydrological processes in the lagoon zone considering the interaction of the lagoon with the South China Sea. This interaction is linked to the

298

6

Investigation of Regional Aquatic Systems

Fig. 6.2 Structure of the geoecological information-modeling system of the Nuoc Ngot Lagoon (GIMS/NNL). The system components are described in Tables 6.2 and 6.3 Table 6.2 The GIMS/NNL management items Item UII FWSM

CPP DFS CIF FMI MSDM CLPT

Item functions Universal information interface. Formation of the water balance simulation model. Management of the models and algorithms for the parameterized description of the hydrophysical, hydrochemical, and hydrological processes Control of the parameterization process for the energy and matter flows in the lagoon. Realization of the transformation mechanisms for chemicals in the water environment. Database formation and synthesis of anthropogenic scenarios which are possible in the lagoon region. Control of information flows between the GIMS/NNL items. Formation and use of the water quality criteria. Management of the statistical decision makings. Control of the lagoon phase transitions.

exchange of water at the mouth of the lagoon Γ (strait). Figure 6.4 represents a set of water flows possible in each pixel of the lagoon zone. The water cycle is the continuous circulation of water within the lagoon zone that is described by the system of differential equations taking into account the external water flow and an entrained water flow (Krapivin and Shutko 2012; Krapivin et al. 2015a; Jakimavičius and Kovalenkovienė 2010).

6.2

Monitoring of Water Reservoirs in South Vietnam

299

Table 6.3 The GIMS/NNL functional items Item GSI PEI RRD SUA FMI CSC CGF

DWQ WBM MCSO PWL SHP EWQI MTM SEP SEC UDA AODR RHI CIO CWQ DOI AWQ CEW NPS SAP CFG IUOP FSC CIUB FCS

Item functions Generation of the subject identifiers to adopt the GIMS to the lagoon region configuration taking into account of geophysical, ecological, and socioeconomic structure. Perception of experimental information, its scaling, and the entry to the database. Realization of the requests to the database. Supporting the user actions when a decision about the interface corrections is needed. Forming the maps with information about water quality in the lagoon. Change of the scales for the cartographic information with the selection of the lagoon bordering territory. Control of the GIMS/NNL functions to provide the coordination of interial information fluxes, to detect the defective requests and messages, to notify about the incorrect or unlawful user commands, to support the user actions Detection of the water quality disturbances and the user informing about it. Water balance model of the lagoon influence zone (Krapivin et al. 2015a). Model of complex multifactor surface outflow taking into account of the catchment area topography and soil-plant formations. Parameterization of the wastewaters to the lagoon (Tran 2011; Son 2016). Simulation of hydrophysical processes (Bui 2002; Krapivin et al. 2015b). Evaluation of the water quality indicators (Zhen-Guang et al., 2013; Cribb 2017; Romano et al. 2017). Modeling the transformation mechanisms of the chemicals in the water environment (Bui 2002). Simulation of the exchange processes on the boundary lagoon-sea including the tide (Rodriguez-Rodriguez and Moreno-Ostos 2006; Romano et al. 2013). Simulation of the exchange processes by chemicals between the lagoon and atmosphere (Bui 2002; Wells 2011). Updated data archive of pollutant characteristics that can be delivered to the lagoon zone from the agriculture, municipal, and industrial sources. An assessment of official data reliability concerning the sources of pollutants. Reduction of the heterogeneous information to the unique standard. Coordination of the inputs and outputs of the GIMS/NNL items and their connections with the database. Control of the water quality criteria (Zhen-Guang et al., 2013; Cribb 2017). Documentation of operative information concerning the lagoon water quality. Accounting the water quality analyses realized in the chemical laboratory. Complex evaluation of the lagoon water quality. Neyman-Pearson statistical decision-making procedure (Nitu et al. 2013). Sequential analysis procedure to make the statistical decision (Wald 1947, 2004). Control of the functioning the GIMS/NNL items. Information uncertainty overcoming procedure (Krapivin et al. 2015a, b). Formation of the series for meteorological and geophysical characteristics that are specific for the lagoon zone (Giuliani et al. 2011; Ferrarin et al. 2013). Calculation of the indicators that characterize an unstability of the environment and the lagoon ecosystem biocomplexity. Forming the cluster space of the lagoon water quality characteristics (Krapivin and Shutko 2012).

300

6

Investigation of Regional Aquatic Systems

Fig. 6.3 The GIMS/NNL application for Nuoc Ngot Lagoon monitoring and water quality diagnosis. Note: (1) NO3- concentration exceeds 0.25 ppm; (2) pH ≥ 9.0

The process of the contaminants diffusion in lagoon water depends on their state and is managed by the water circulation process. Dissolved fractions of the contaminants (ξ) take part in biogeochemical processes more intensively that suspended particles (μ). But as suspended particles, contaminants fall out more rapidly to the sediment. Therefore, the MTM item describes the processes of contaminant transformations, including the absorption of the dissolved fraction ξ by plankton (HZξ), sedimentation of the solid fraction (K1μ), the deposition with the detritus (HDξ), the absorption by detritophages from bottom sediments (K μξ L ), and release from bottom ). sediments owing to diffusion (K μξ a Thus, the equations describing the pollutant cycle in the lagoon environment become: ∂μw ∂μ ∂μw ∂μw þ vwϕ w þ vwλ þ vwz = ∂t ∂ϕ ∂λ ∂z

3 i=1

βi Ωiξμ - K 1μ þ α1 K μξ a

∂ξw ∂ξ ∂ξ ∂ξw w ∂ξw - H Zξ - H Dξ - H aξ þ vwϕ w þ vwλ w þ vwz = ð1- α1 ÞK μξ a þ k2 ∂t ∂ϕ ∂λ ∂z ∂z2 ∂μ μξ = K 1μ - α1 K μξ L þ Ka ∂t

6.2

Monitoring of Water Reservoirs in South Vietnam

301

Fig. 6.4 The block diagram of the sample water balance model at each pixel of the lagoon zone

∂ξ μξ = H Dξ - ð1- α1 Þ K μξ L þ Ka ∂t where μ*(μw) and ξ*(ξw) are the concentrations of contaminants in the bottom sediments (water) as solid and dissolved phases, respectively; Haξ is the output of contaminants from the sea to the atmosphere by evaporation and spray; Ωiξμ is the input of contaminants to the lagoon with river waters (i = 1), atmospheric deposition (i = 2), and ship’s wastes (i = 3); βi is the part of the suspended particles in the i-th flow of contaminants; V = vwϕ , vwλ , vwz is the flow velocity; and α1 is the part of the solid fraction of contaminants in the bottom sediments. The water salinity is important characteristic of the Nuoc Ngot Lagoon. The EWQI item controls the water salinity indicator following the balance equation: σ ij zij

dC ij = dt

r ij Rij C ij þ E ij C ij- Cji ði, jÞ2Ξ

- 1- qij C ij þ F ij ;

302

6

Investigation of Regional Aquatic Systems

where Cij is the concentration of salts in the Ξij pixel; zij is the average depth; Eij = DijAij/Lij is the volumetric diffusion coefficient; Lij is the average length of the pixel adjoining boundaries; Aij is the pixel contact area with other pixels; Dij is the turbulent diffusion coefficient; Fij is the source function describing the external inflow of the salts to the Ξij pixel (with precipitation, river runoff, from South-China Sea, and bottom sediments); Rij is the total water outflow from Ξij pixel; rij is the part of the Rij directed to the border pixels; r ij þ qij = 1; rij = 0 ði, jÞ2Ξ for the no bordering pixels. Hydrochemical characteristics of the Nuoc Ngot Lagoon are formed mainly by the water exchange with the South-China Sea across the barrier lagoon strait Γ that is characterized by small tidal basin area. The water masses are moving across the lagoon border Γ periodically to the lagoon or from them to the sea. The speed of this process is evaluated with the following equation (SHP item): V Γ ðφ, λ, t Þ = V Γmax j cos½π ðt- t max Þ=τ; ðφ, λÞ 2 Γ, t max - τ=2 ≤ t ≤ t max þ τ=2 where τ (≈6.5 hours) is the tide length, tmax (11, 17, 24 by local time) is the time when velocity of water flow across Γ reaches maximum value VΓmax (≈425 m/s). The water level variations in the lagoon depend mainly on the tide regime as well as on the river flow. The lagoon depth is parameterized by the following formula: zp ðφ, λ, t Þ = z0 ðφ, λ, t Þ þ σ Γ 0:5V Γmax t=σ where σ Γ (≈45 × 103 m2) is the pixel area of the strait Γ, z0 is the lagoon depth at the time of the tide finishing, σ is the lagoon area. External inflow of the salts to the lagoon aquatory is formed to a greater or lesser extent by the river outflow. The salt sources such as precipitation and direct coastal outflow are the negligible components considering topography (PWL and MCSO items). Thus, the flux F(φ, λ, ζ, ω, θ, t) of salts of type ζ at the state ω with parameter θ is described by the following: F ðφ, λ, ζ, ω, θ, t Þ = Ψðφ, λ, ζ, ω, θ, t ÞQðφ, λ, t Þεðζ, ω, θ, t Þ where Ψ is the salt concentration in the soil, ε is the washout coefficient, Q is the runoff.

6.2.3

Simulation Experiments

The Nuoc Ngot Lagoon is connected to the South China Sea by the inlet De Gi and it is characterized by parameters listed in Table 6.4. The main problem is to optimize the water quality of the lagoon to support maximum productivity of the ecosystem, considering that the lagoon is located in the zone where anthropogenic processes are

6.2

Monitoring of Water Reservoirs in South Vietnam

303

Table 6.4 Parameters of the Nuoc Ngot Lagoon Parameter Area, km2 Wind speed, m/s Average Maximal Air temperature, °С Average Minimal Maximal Relative air moisture, mb Average Minimal Maximal Solar radiation, Kcal/cm2

Parameter value 14.7 1.9 39.6 27.0 15.8 39.9 27.9 20.0 32.7 144

Parameter value -44–+59

Parameter Lagoon level variations, cm Temperature of upper layer, оС Dry season Wet season Precipitation, mm/year Average Minimal Maximal Distribution of the depths Average depth, m Maximal depth, m

1.6 9.8

Radiation balance, Kcal/cm2

92.5

26 29 1692.9 778.0 2587.0

Fig. 6.5 Layout plan for the field locations where the water samples are taken

intensively developed. The solution to this problem is achieved through monitoring the lagoon based on field measurements at sites No. 1–10 depicted in Fig. 6.5 and by managing the water flow along the Γ strait. Field measurements are carried out with the methodical prediction for the separation of three water levels: upper, middle, and

304

6

Investigation of Regional Aquatic Systems

Table 6.5 Evaluation of GIMS/NNL effectiveness in reconstructing the spatial distribution of Nuoc Ngot Lagoon water quality Measurement site 1 2 3 4 5 6 7 8 9 10 Average error, %

Water salinity, o/oo M E 33.5 30.15 26.0 23.14 29.1 26.48 32.2 27.37 31.7 28.53 26.3 28.40 25.7 27.50 25.4 26.16 30.5 26.54 26.5 20.95 10.7

Water turbidity, mg/l E M 10 12.40 21 24.78 27 29.70 24 26.88 47 45.59 27 29.97 21 23.94 38 34.58 51 48.45 63 60.48 11.0

рН M 7.04 7.71 7.66 8.01 7.29 7.22 7.45 7.21 7.09 7.63 6.0

E 7.82 7.79 7.58 7.78 7.84 7.52 7.30 7.75 7.71 7.00

РО4- 3 , mg/l M E 0.033 0.03 0.034 0.03 0.068 0.45 0.023 0.02 0.042 0.04 0.082 0.10 0.067 0.35 0.023 0.02 0.023 0.02 0.039 0.05 15.4

The initial data for the simulation experiment are used only from site No.1. Note: M is the modeling result, E is the field measurement Table 6.6 Evaluation of GIMS/NNL accuracy when predicting water salinity of Nuoc Ngot Lagoon

Site 1 (strait Γ) 2 3 4 5 6 7 8 9 10

In-situ measurement of the water salinity (о/оо) at the time t0 30.2

Prognosis and error (%) t0+ t0+ t0+ 7 days 14 days 21 days 32.0(5) 32.8(9) 26.4(10)

t0+ 1 month 29.3(12)

t0+ 1.5 month 34.4(14)

33.6 33.6 33.7 33.8 33.8 30.7 32.4 32.5 30.4

32.8(2) 32.4(5) 33.4(4) 33.4(4) 31.0(8) 31.4(3) 34.7(7) 33.0(6) 31.9(9)

26.4(19) 25.9(19) 26.6(20) 25.2(20) 27.5(20) 30.2(9) 26.6(19) 28.3(20) 33.2(10)

25.7(23) 26.2(22) 25.6(24) 26.5(22) 25.3(25) 34.5(19) 25.2(22) 25.1(23) 27.8(24)

34.4(10) 33.7(12) 28.3(12) 28.8(11) 30.9(12) 34.9(13) 30.9(9) 29.4(10) 33.2(12)

27.6(15) 37.5(17) 37.8(17) 27.6(17) 27.8(17) 27.9(17) 34.9(16) 28.6(18) 34.5(17)

The initial data at time t0 are used by site No.1

lower. The chemical characteristics of the water and the analysis of the bottom samples are evaluated in the hydrochemical laboratory of the Southern Branch of Vietnam Petroleum Institute (Ho Chi Minh City). Weather information is provided by Nha Trang weather station. GIMS/NNL enables spatial reconstruction and prediction of water quality based on field samples. Tables 6.5 and 6.6 characterize the accuracy of this reconstruction. Simulation experiments show that the correlation between water turbidity and its salinity has a maximum located at 25°/oo. Below this, variations in water salinity are 7–8% in the Γ Strait, 81–85% in the estuary, and 53–55% in other pixels. The commercial importance of the lagoon ecosystem requires management of water

6.2

Monitoring of Water Reservoirs in South Vietnam

305

Fig. 6.6 Dependence of forecast deviation on depth at time for water quality characteristics

quality including its salinity. It can be carried out through the combined use of GIMS/NNL and control of tidal processes with a managed dam. As can be seen from Table 6.5, the optimal water quality level can be achieved when GIMS/NNL uses data from measurements at site No. 1 (Γ strait) only. As can be seen from Tables 6.5 and 6.6, the spatial distribution of water salinity is reconstructed during two weeks with a precision no greater than 84%. The largest error occurred at the sites 3 and 7 for РО34 - : The cause of this is the observed anomalous emission of chemical substances from sediments at sites 3 and 7 which first needs the correction of the model coefficients in the MTM, SHP, and SEC items. However, as can be seen from Fig. 6.6, an accuracy of 85–90 percent can be achieved by field measurements at Site 1 with a frequency of one month and using GIMS/NNL to calculate the water quality characteristics during that month. Undoubtedly, this result depends on the reliability of the meteorological forecast. Figure 6.7 shows the results of simulation experiments when different meteorological conditions are considered. It appears that consistent forecast accuracies occur during the dry season and the wet season is characterized by the least reliability of modeling results. The average forecast error during the dry season was less than 9.7% with the exception of 2010 when it exceeded 10.6%. Wet seasons are characterized by higher forecast errors due to overall errors in meteorological data and assessments of coastal and river discharges. However, the prediction of water salinity and pH is characterized by errors in the region of up to 10.4% not depending on the season. Optimum ecological conditions in the Nuoc Ngot lagoon zone are achieved through the management of the tidal process when other factors such as riverine pollutant flows, rainfall, and coastal runoff are controlled. Table 6.7 characterizes a dependence of water salinity according to tidal processes. The average ecologically acceptable water salinity ranges between 22.5°/oo and 24.5°/oo. The reduction in seawater inflow into the lagoon leads to a reduction in water salinity due to river

306

6

Investigation of Regional Aquatic Systems

Fig. 6.7 A dynamics of the maximum errors of the Nuoc Ngot Lagoon water quality forecast by GIMS/NNL performed monthly during the year when the initial data are measured at site 1 and the errors are evaluated at sites 2–10 Table 6.7 Dependence of lagoon water salinity on the volume of seawater inflow into the lagoon Velocity of the sea water inflow, VΓ (m3/s) 0 50 100 200 300 400 500 600

Dry season 1 week 2 weeks 20.01 18.22 21.41 22.03 22.11 23.05 22.74 22.95 23.53 23.64 23.55 23.65 24.92 24.97 27.58 28.55

3 weeks 16.34 22.54 23.49 23.51 23.65 23.67 25.24 30.94

Wet season 1 week 2 weeks 19.36 16.86 20.13 21.16 21.29 21.54 21.71 22.23 22.97 23.48 23.31 23.42 23.78 23.89 27.12 28.34

3 weeks 14.77 22.22 22.62 23.47 23.52 23.53 24.56 30.49

outflow. As can be seen from Table 6.7, the optimal water inflow management regime is to support water inflow to the lagoon in the range of 300–500 m3/s. The duration of the experiment is one, two, and three weeks. When the strait Γ is closed, the water salinity in the lagoon decreases during 1 week from 23.5°/oo to 20.01°/oo during the dry season and continues to decrease to 16.34°/oo after 3 weeks. When VΓ > 500 m3/s, water salinity increases rapidly during both wet and dry seasons. It says that river inflows do not balance the salinity of the lagoon water not supporting it at an average value of 23.5°/oo. The implementation of water quality control in a regional or local hydrochemical object needs a solution of the optimization project for the economic and organic provision of this function. GIMS technology enables the solution of this task using a combination of mathematics and instrumentation. The Nuoc Ngot Lagoon is a typical hydrochemical coastal feature with medium-scale hydrological processes

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

307

managed by river discharge and water exchange with the South China Sea. Simulation experiments have shown that in this case it is possible to optimize water quality control through regular field measurements and simulations. The most important element of this process is updated information on anthropogenic changes in the lagoon area. Therefore, GIMS/NNL allows optimization of lagoon monitoring by reducing field measurements in both time and number of locations. Reliable monitoring results are obtained when field measurements are made at location 1 (Γstrait) with a frequency of 3 weeks in the dry season and 2 weeks in the wet season. The monitoring process can be automated by using a fixed Γstrait adaptive spectroellipsometric sensor that allows the assessment of water quality characteristics in a real-time regime and the management of the tidal regime. Solution of this work is the next stage of future research realized by the authors. Finally, optical instruments were used to monitor the water quality of other water bodies in the territory of Vietnam, including: • The Saigon River in the Ho Chi Minh City zone has been diagnosed for water quality assessment and prognosis in the aquatory port • The Dong Nai River in the water supply zone for both Dong Nai Province and Ho Chi Minh City • Fishery reservoirs in Ba Ria – Vung Tau Province to identify oil and salt spills on the water surface • Nuoc Ngot Lagoon in Tinh Binh Dinh Province in search of a cost-effective monitoring regime • South China Sea to detect oil spills on the water surface Figures 6.8 and 6.9 show results characterizing spectral images of different water bodies and water quality in Nuoc Ngot Lagoon.

6.3 6.3.1

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools Introduction

Lake Sevan is one of the freshwater lakes in the world located in the mountains at an altitude of 1915.89 m above sea level. It is the largest lake in the Caucasus. Lake Sevan is a large reservoir of fresh water not only for Armenia, but also for the countries of the region. Rivers feeding Lake Sevan that flow through densely populated settlements produce different pollutants in the lake waters (Margaryan et al. 2020; Varotsos et al. 2020b). Knowledge of the water quality of Lake Sevan is important for the economy and population of Armenia in relation to fishing and tourism. For the first time, the quality of Sevan water was measured in 1891 when three ions (Ca, Cl, PO4) were detected (Gluskov and Davydov 1932). Over time, the

308

6

Investigation of Regional Aquatic Systems

Fig. 6.8 Spectral images of different water objects in Southern Vietnam

Fig. 6.9 Distribution of salt spots in the Nuoc Ngot Lagoon during the dry season under weak winds

development of industry and agriculture in Armenia, the water quality of Sevan is getting worse due to polluted river waters and atmospheric transport of pollutants. The hydrometric indicators of Lake Sevan have changed due to anthropogenic impacts, including the reduction of the lake surface from 1416 km2 in 1930 to 1242 km2 in the present state. Anthropogenic impacts on Lake Sevan began when

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

309

the Sevan-Hrazdan irrigation complex was built. Consequently, an average Sevan depth changed from 41.3 m to 25.9 m during this period and its volume became 40% smaller (Hovsepyan et al. 2019a, b). Since 1981, the water level of Lake Sevan has risen by 2.44 meters to date due to the Arpa-Sevan tunnel built in 1981 under the Vardenis ridge. As of 2017, approximately 170 million m3 of Kechut Reservoir water is annually diverted to the lake through this tunnel. The drainage area and volume of Lake Sevan before 1930 were 3475 and 58.5 billion m3, respectively. These indicators are added today with the following values: altitude above sea level is 1896.46 m. The catchment area is 3649 km2. The average and maximum depth are 25.9 m and 79.4 m, respectively. The amount of water is 32.8 km3. It is obvious that the consequence of the reduction of the water level of Lake Sevan is the reduction of the biocomposite and bioproductivity of the ecosystem. Wang et al. (2004) and Sargsyan (2007) have proposed a stochastic modeling approach to study the ecological balance of Lake Sevan and assess the content of planktonic organisms in the lake waters taking into account the existing requirements of the country’s energy needs. Whole changes of the geoecosystem of Sevan occurred in the last 80 years and their overcoming posed the problem of effective technological development for complex monitoring of the lake environment taking into account the existing real external effects. This problem is discussed in the Armenian National Assembly where the current situation of Sevan is assessed as negative for the Armenian economy. The Lake Sevan research was supported by the UNEP GRID – Arendal Program under which the study of the assessment of the lake’s evolution and the search for an effective strategy to save the lake was initiated by Danielian (2011). Effective monitoring of the Sevan requires the combined use of satellite data and episodic field measurements of water quality in the lake and flowing rivers. An experience of such complex monitoring was demonstrated in the case of the Nuoc Ngot Lagoon and the Sea of Okhotsk (Krapivin et al. 2015b, 2016b). This work uses and develops this experience in relation to Lake Sevan. The existing results of the investigations of the Lake Sevan ecosystem make possible a general representation of its state: • The concentration of dissolved salts in the lake’s water can change from 543.2 mg/L to 562.8 mg/L during year reaching higher values in spring • The vertical distribution of the water temperature and density is characterized by a negligible increase from surface to the bottom during winter • Lake Sevan is characterized by the higher concentration of dissolved oxygen under its small annual variations • The salinity and pH of the lake water decreased and increased from the shoreline at a distance of about 200 m, respectively The information modeling and instrumentation tools proposed here allow knowledge of many characteristics of lake water to assess its quality without significant financial cost. There is a problem associated with processing data that is inevitably delivered episodic in time and fragmented in space that needs big data processing algorithms. Varotsos and Krapivin (2017) developed such an algorithm based on the

310

6 Investigation of Regional Aquatic Systems

Geoecological Information Modeling System (GIMS) technology using a modeling and tools convention. GIMS technology provides efficient combination of models with large data streams and realizes the prediction of the observed evolution of the environmental system providing the detection of stressful situations (Krapivin et al. 2017a, 2019). This study develops the GIMS technology to monitor the water quality of the Sevan, following the cost-effective monitoring regime and achieving a reliable assessment of the water quality of the lake. The combination of model forecasting and sampling data processing with the use of optical adaptive tools enables the organic information modeling system for the reliable functional diagnosis of the water quality of Lake Sevan.

6.3.2

Method

The proposed method is based on the combination of optical instruments, geoecosystem simulation model of Lake Sevan, and algorithms for solving optical inverse tasks. According to this, the hydrological, biological, and anthropogenic aspects of Sevan are evaluated based on literature sources. Optical instruments include 8- and 35-channel spectrophotometers and a 128-channel spectroellipsometer (Figs. 4.4 and 4.5, see Chap. 4), which are used for in situ measurements of lake water quality. The implementation of this method allows the comprehensive control of lake resources and the long-term forecast of ecological and socioeconomic interactions in the lake area. The main scheme of this control provides for covering the lake basin with a geographic grid of magnitudes Δφ and Δλ in latitude and longitude, respectively. This geographic grid helps to account for spatial heterogeneity in the distribution of pollutant sources, topography, and population density, as well as to describe the river basin and soil-vegetation formations that can influence the hydrological regime of the lake. The observation of the degradation of the Lake Sevan ecosystem and its reduction or countermeasures set the task of creating an effective complex technology of information modeling instruments that allows solving the existing problem taking into account the maximum number of pollution sources located in the Lake Sevan basin (Fig. 6.10). A central focus for the assessment of the water quality of Lake Sevan in its evolution is to develop both a conceptual and a constructive approach acceptable for decision-making and the implementation of instruments for multiscale management of internal resources. Thus, the proposed study method includes both qualitative and quantitative sampling and control procedures: • Statistical analysis of historical data for the reconstruction of trends in the dynamics of pollutant flows in Lake Sevan and its disclosure • Search for sites for sampling the water of Lake Sevan as optimal as possible to reduce the uncertainty of the information • Using the test procedure based on the comparison of the modeled results and the measured results at separate locations of the water samples

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

311

Fig. 6.10 Location of five sampling areas in Lake Sevan and main sources of contaminants

The water-geosystem of Lake Sevan is characterized by the variety of parameters characterizing both the water quality and the geosystems of the adjacent areas, including the types of land covers, the spatial distribution of anthropogenic objects, the quality of the soil, the Sevan-Hrazdan hydroelectric cascade, and of irrigation. The joint assessment of the state of Lake Sevan must take into account all these environmental and anthropogenic elements. The rivers running into Lake Sevan bring the most polluted water and promote the transformation of the lake into a large swamp in the coming years. The existing level of pollution of the inflowing rivers, caused by human activities, is steadily directed toward the lake. The water quality of Lake Sevan depends on socioeconomic demands in Armenia, including agricultural development, industrialization, and population demands for drinking water, which creates environmental risks and security difficulties. The investigation and assessment of the existing challenges is possible with an appropriate model that can integrate the disparate hydrological and hydrochemical processes taking into account the environmental situations around Lake Sevan (Danielian 2003, 2011). The current degradation trends of Lake Sevan must be parameterized both in terms of ecosystem functioning and indirect effects from neighboring areas. The assessment and understanding of the processes in the Sevan basin is possible only through its complex model, the structure and functions of which take into account the existing and handed down information. The Lake Sevan water quality model (MLSWQ) is synthesized using a weighted combination

312

6 Investigation of Regional Aquatic Systems

of separate models for different processes present in the lake basin following existing modeling technology (Krapivin et al. 2015b). The process of pollutant mixing in lake water depends on their state and is managed by the water circulation process (Gevorgyan et al. 2016). The dissolved fraction of the contaminant (ξ) is more intensive compared to the suspended particles (μ) that take part in the biogeochemical processes. Heavy metals entered in water can bioaccumulate in organisms and increase in food chains. But as suspended particles, the contaminants fall out more rapidly to the sediment. River’s water currents significantly stimulate the lake water mixing. A contaminant concentration dynamics in the lake is described by the following equation: ∂Cνγ ðφ, λ, z, t Þ ∂Cνγ ðφ, λ, z, t Þ ∂Cνγ ðφ, λ, z, t Þ þ Vϕ þ Vλ = ∂t ∂ϕ ∂λ ∂ Cνγ ðφ, λ, z, t Þ ∂C νγ ðφ, λ, z, t Þ - θRΦ þ K0 þ ð β d Þ ∂z2 ∂z 2

Qνγ

ð6:1Þ

where C νγ ðϕ, λ, z, t Þ is the concentration of contaminant of ν type and in the phase γ (ξ, μ) at geographical coordinates (φ, λ), depth z, and time t; Vφ and Vλ are projections of wind velocity on the latitude and longitude, respectively; K0 (1.5–2.1 m2/ day) is the diffusion coefficient, β (3.8–4.3 m/day) is the upwelling velocity; d (3.7–4.9 m/day) is the sedimentation velocity; θ (0.009–0.012) is the indicator of contaminant C νγ assimilation by the living elements; RΦ is the average net primary production of Lake Sevan ecosystem; Qνγ is the inflow of contaminant C νγ with rivers: Qνγ =

18 j=1

σ j M νγ ðjÞ σ LS þ σ j

ð6:2Þ

where σj is the j-th river inflow to Lake Sevan (m3/day); σ LS is water volume of Lake Sevan before the pressure point; M νγ ðjÞ is the concentration of contaminant of ν type and in the phase γ (ξ, μ) in j-th river flowing into Lake Sevan. Wind force is an important factor influencing water mixing. Spectral polarization-optics tools are used to evaluate the characteristics of different physical and hydrochemical environments in real time. The use of optical instruments for the diagnosis of natural water bodies is based on the analysis of their optical spectra (Krapivin et al. 2017b). Spectrophotometers yield one spectrum and the spectroellipsometer two spectra that characterize the relative changes in amplitudes and phases of the electromagnetic wave vector at and across its plane of emission. The optical devices used in this study are characterized by the following functions: 1. 8-channel optical universal decision-making system (OUDMS-8) presented in Fig. 4.2 (see Chap. 4) is based on the eight light diodes with peak wavelengths from 350 nm to 710 nm. OUDMS-8 can use sunlight or artificial lightning

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

313

depending on the on-site measurement strategy and weather conditions. When sunlight is used, the OUDMS-8 performs a user-set time-frequency calibration procedure. The luminous flux registration accuracy is 99.995%. OUDMS-8mounted on an unmanned helicopter can provide spectral images of oil slicks on the water surface. 2. A 35-channel spectrophotometric decision-making system (SDMS-35) shown in Fig. 4.3 (see Chap. 4) measures the optical characteristics of water samples in the wavelength range from 300 nm to 800 nm. Measurements can be carried out both on site and in laboratory conditions. The luminous flux registration accuracy is 99.997%. 3. 128-channel spectroellipsometric decision-making system (SEDMS-128) illustrated in Fig. 4.5 (see Chap. 4) simultaneously measures the tangent of the spectroellipsometric angle Ψ and the cosines of spectroellipsometric angle Δ in the wave range from 350 nm to 910 nm. Spectroellipsometric angles Δ and Ψ are measured with a precision 0.001 and 0.005, respectively. Each of the decision-making measuring systems has the following functions: • Recording of optical characteristics of the water object under study, formation of its spectral image, and composition of a database of standard spectral images • Solving visual inverse tasks to assess water quality parameters • Recognition of spectral images Universal OUDMS-8 can use artificial light source or sunlight. In the latter case, OUDMS-8 is automatically calibrated with a time-delay of 0.1 second. SEDMS-128 measures spectroellipsometric angles and provides a photodetector light intensity {CosΔ(ηi)} and tangent of the relative phase shift of two orthogonal polarization components {TanΨi((ηi)}, where ηi is the i-th wavelength.

6.3.3

Algorithms

The knowledge of the optical spectra enables a decision to be made about the fluid quality and the concentration of different contaminants using a set of algorithms for spectral image recognition and the solution of optical inverse tasks. The simple answer that emerges from this knowledge is whether or not the studied liquid meets a given quality standard. The assessment of water or other liquid quality needs the assessment of the contaminant concentration, which is usually carried out using algorithms for the solution of optical inverse work. There are different situations depending on the type of liquid. In the case of a liquid solution of a component, a contaminant concentration can be assessed through spectral image recognition with an interpolation process within the spectral space. In this case, an optical system training procedure is performed. The multicomponent liquid solution needs the optical reverse working solution for its diagnostics.

314

6

Investigation of Regional Aquatic Systems

Let us consider possible algorithms that provide reliable results of water quality diagnostics. The first optical spectral image recognition algorithm is performed by comparing the image index S(η) with a series of standard images prepared and located in the spectral database during the training of the optical system. The spectral standard for OUDMS and SDMS has one component. The spectral template for SEDMS has two components reflecting the ellipsometric angles Δ (0°–360°) and Ψ (0°–90°), where Δ is the phase difference before and after reflection (phase shift). The angle Ψ corresponds to the amplitude ratio during reflection. The functional representation of CosΔ and TanΨ is defined by the ratio of complex Fresnel coefficients. SEDMS provides two spectra SΨ(η) = TanΨ and SΔ(η) = CosΔ, an analysis of which allows the assessment of physical and chemical properties of the studied water sample. The precision of these assessments depends on the software used. According to Krapivin et al. (2015b), the transformation of spectral space to vector space can increase the precision of the water quality diagnostics. The formation of the vector image can be realized by many methods. One of them is approximation S(η) = {Ξ1, . . ., Ξm}, where Ξi is i-th characteristic of optical spectrum. In the case of SEDMS using for the spectral measurements, distance δ between specters of unknown and standard water sample is assessed by the following formula: δ

=

ρ SsΔ - SxΔ þ SsΨ - SxΨ min s

=

1 min 4m i m

þ

m

þ X j - ΞΔ,i j

j=1

Y j - ΞΨ,i j

= m j=1

2

X j - ΞΔ,i j

2

m

þ

Y j - ΞΨ,i j

j=1

ð6:3Þ



j=1

where SxΔ and SxΨ are spectrums of the water sampling with unknown characteristics; SsΔ and SsΨ are spectrums of the water samplings with known characteristics; ΞΔ,i j and ΞΨ,i are vector components ( j = 1,. . .,m) from spectral standard database, X j and Yj j ( j = 1,. . .,m) are vector components of spectral approximation for the water sampling with unknown characteristics. In this study, the case m = 9 is used with the following classification of spectral parameters: • Ξ 1 is the area below the spectral curve. • Ξ 2 and Ξ 3 are maximum and minimum ordinates of the spectral curve, respectively. and • Ξ 4 is the distance |ηmax - ηmin|, when Sðηmax Þ = max SðηÞ Sðηmin Þ = min SðηÞ. η

η

• Ξ 5 and Ξ 6 are maximum values of dS(η)/dη and d2S(η)/dη2, respectively. • Ξ 7 is a number of events, when dS(η)/dη = 0.

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

315

• Ξ 8 and Ξ 9 are the values of spectral curve ordinates for specific wavelengths η* and η**. During a learning of the decision-making system, all vectors of S(η), SΨ(η), and SΔ(η) are accumulated in the decision-making system database after forming different structures such as clusters as a basis for the conclusion on the water quality, including assessment of chemicals’ concentration. Diagnosis of multicomponent water sampling is performed by means of solution of the following system equations: N

aij xj = T i þ ξi ði= 1, . . . , M Þ

ð6:4Þ

j=1

where xj ( j = 1,. . ., N ) is the concentration of j-th pollutant, Ti is value of specter for i-th wavelength (N = 8 and 35 for OUDMS and SDMS, respectively, and N = 128 for SEDMS). The coefficients {aij} are evaluated during the learning procedure of the optical decision-making system.

6.3.4

Results and Discussion

The water quality of Lake Sevan is determined by many natural and anthropogenic conditions, including a drop in the water level due to the reckless use of water resources, an increase in the average concentrations of hydrochemical parameters in the rivers of the Lake Sevan basin (the area is 4750 km2) and climate change (Sargsyan 2000). Table 6.8 characterizes the water quality of the rivers flowing into Lake Sevan. Certainly, many negative changes in the ecosystem of Lake Sevan were associated with the imprudent management of its water resources, which reduced the original surface and volume of the lake. The maximum decrease in the depth of the lake reached 20 meters in 1970. Since 2002, the water level (the elevation of the lake was 1896.34 m) began to rise by 7 cm in 2008 and 47 cm in 2010, it reached 1903.5 m in 2018. The water balance of Lake Sevan is characterized by annual precipitation and evaporation from 340 to 720 mm and from 560 to 890 mm, respectively. The river inflow varies from 1424 to 1688 km3/year and the discharge of the Hrazdan River is 0,564 km3/year. Anthropogenic removal of water resources from Lake Sevan is currently estimated at 0.17 km3/year. The weather in the Lake Sevan basin is usually windy with an average wind speed of 3 m/s in spring and autumn, 4 m/s in summer, and 6 m/s in winter. The depth distribution of the water pool is shown in Fig. 6.11. Water quality monitoring of Lake Sevan was carried out using three visual tools illustrated in Figs. 4.2, 4.4, and 4.6 (see Chap. 4). Periodic direct measurements and water sampling were carried out at the five sites illustrated in Fig. 6.10 during different periods within a year. The locations of the sampling sites are: 1 – Hrazdan River, 2 – Gavar, 3 – Martuni, 4 – Vardenis, and 5 –

River Argichi Arpa-Sevan Artanish Astghadzor Bakhtak Daranak Dzhil Drastik Dzknaget Hrazdan Gavaraget Karchaghbyur Litchk Martuni Masrik Pambak Tsakkar Tsapatakh Vardenic

Average runoff, km3/yr 0.177 0.118 0.00294 0.0105 0.0202 0.00821 0.00306 0.00757 0.0341 0.0265 0.12 0.0385 0.0593 0.0549 0.125 0.00662 0.0222 0.00315 0.059 Cd 1.22 0.93 0.16 1.43 0.91 0.84 0.12 0.47 0.73 1.25 1.87 1.02 0.96 0.78 1.86 0.06 0.55 0.41 1.56

Fe 56.31 44.41 4.89 34.51 37.64 23.27 168.21 15.23 10.65 214.54 234.01 360.23 65.54 66.71 54.82 31.19 63.78 137.45 95.32

Ni 3.41 2.69 1.91 2.09 2.28 1.41 3.14 2.24 2.41 3.31 4.32 6.18 1.07 1.55 3.32 1.73 1.54 2.06 3.41

Pb 0.084 0.234 0.067 0.073 0.066 0.046 0.009 0.073 0.069 0.059 0.082 0.067 0.19 0.47 0.031 0.052 0.035 0.002 0.164

Selected trace and toxic heavy metals, μg/L

As 6.48 5.29 4.73 6.32 3.44 2.97 5.89 7.31 6.33 7.43 8.21 7.79 7.85 3.66 8.76 6.63 7.57 8.84 8.12

PO4- 3 , mg/L 0.203 0.102 0.012 0.034 0.026 0.021 0.023 0.009 0.158 0.212 0.493 0.178 0.053 0.095 0.223 0.014 0.021 0.024 0.162

NO3+ NO2mg/L 3.67 0.19 0.21 0.17 2.09 1.18 0.74 0.67 1.33 1.17 6.35 4.07 0.05 2.99 6.24 0.44 0.32 0.79 2.17 NHþ 4 mg/L 0.23 0.52 0.15 0.32 0.24 0.11 0.05 0.09 0.26 0.29 0.32 0.14 0.13 0.46 0.36 0.03 0.23 0.24 0.35

CO3 mg/L 35.7 22.8 6.6 13.2 10.6 4.8 1.8 3.9 9.2 7.5 10.3 5.1 2.7 9.1 7.3 1.2 8.9 3.8 5.3

SO24 mg/L 4.75 3.44 2.41 1.89 2.11 3.12 3.44 0.98 2.32 4.13 2.56 6.18 4.78 5.31 6.19 1.86 4.87 2.13 2.67

Table 6.8 Average hydrologic and hydrochemical parameters of the rivers of the Lake Sevan basin (Pepoyan and Manvelyan 2008; Petrosyan et al. 2019)

316 6 Investigation of Regional Aquatic Systems

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

317

Fig. 6.11 Depth distribution of Lake Sevan (Vardanian 2009). Distribution of the Lake Sevan depth (Vardanian 2009)

Babadzhan. The training process for the visual decision-making systems is based on spectral measurements at sites 2–5 and long-term data of the Environmental Impact Monitoring Center of the Ministry of Nature Protection of Armenia (Yu et al. 2015). The hydrometeorological and bioproductive characteristics of Lake Sevan were modeled mainly based on data from the Water Resources Management Service of Armenia and literature sources (Hovsepyan et al. 2019a). Figure 6.12 represents seasonal spectral images of Lake Sevan measured at position number 1 (40°33′43″N, 62°39′1″) by OUDMS in 2019. The natural sunlight intensity E0 is used in the sky adapter input. The attenuated light flux E is fixed in a digital converter and delivered to the computer. A variety of spectral images characterize the seasonal evolution of lake water quality. Seasonal changes in absorption coefficient are reversed for wavelengths below and above 559.5 nm. The simultaneous intersection of the spectral curves at a wavelength of 559.5 nm indicates the existence of a substance whose concentration is seasonally constant. It is the yellow substance that is washed away from decaying debris and organic matter. It shows that the geoecosystem of Lake Sevan not depending on the polluted river runoff remained geochemically stable and there was no catastrophic situation in the lake in 2019. Additional specifications of the spectral image of the lake are provided by the measurements made with SEDMS-128 and are depicted in Fig. 6.13. The spectroellipsometric parameters CosΔ and tanΨ decrease and increase during the season, respectively. It justifies the seasonal stability of the main geochemical trends in the Lake Sevan geoecosystem. Finally, these spectral measurements provide seasonal estimates of lake water quality by assessing specific pollutant concentrations. Tables 6.9 and 6.10 show such results for toxic heavy metals (Cd, Hg, Pb, Cr, As), phosphates, and nitrates.

318

6

Investigation of Regional Aquatic Systems

Fig. 6.12 Seasonal spectral images of Lake Sevan fixed in 2019 by means of OUDMS at Hrazdan River source

Fig. 6.13 Seasonal spectroellipsometric spectral images of Lake Sevan registered with SEDMS based on the lake water samples delivered during 2019 from sampling site number 1. (Hrazdan River source)

Water sampling site 1 2 3 4 5

Toxic heavy metals, μg/L 3.87 3.77 3.12 4.12 2.31

Spring

PO4- 3 , mg/L 0.041 0.041 0.059 0.034 0.038 Turbidity, mg/L 45.59 34.58 29.70 12.40 26.88

Toxic heavy metals, μg/L 3.52 3.89 2.58 3.87 1.36

Summer PO4- 3 , mg/L 0.034 0.042 0.067 0.039 0.023 Turbidity, mg/L 30.54 24.55 21.09 11.04 24.73

Toxic heavy metals, μg/L 2.95 3.08 1.96 2.76 1.16

Autumn PO4- 3 , mg/L 0.039 0.051 0.068 0.041 0.037

Turbidity, mg/L 30.09 22.48 19.62 8.56 17.74

Table 6.9 Results of in situ measurements in the sites, shown in Fig. 6.10, using SDMS-8 and SEDMS-35 in springer, summer, and autumn 2019

6.3 Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools 319

Year 2015 2016 2017 2018 2019

PO4- 3 , mg/L 0.048 0.051 0.046 0.046 0.039

NO3- , mg/L 0.36 0.38 0.33 0.26 0.28

Summer Toxic heavy metals, μg/L 3.38 3.21 2.67 2.58 2.53

PO4- 3 , mg/L 0.044 0.046 0.041 0.042 0.037

NO3- , mg/L 0.28 0.28 0.26 0.21 0.24

Autumn Toxic heavy metals, μg/L 2.56 2.16 1.97 2.04 2.03

PO4- 3 , mg/L 0.043 0.042 0.038 0.036 0.032

NO3mg/L 0.16 0.19 0.18 0.16 0.15

6

Measurements were made via OUDMS-8 and SDMS-35

Springer

Toxic heavy metals, μg/L 3.92 3.79 3.18 3.21 3.31

Season

Table 6.10 Results of in situ measurements averaged over five sites of the water samplings (Fig. 6.8)

320 Investigation of Regional Aquatic Systems

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

321

Undoubtedly, the water quality of Lake Sevan is formed by hydrochemical materials delivered to the lake by river runoff and rainfall. These sources of lake water pollution are characterized by some volatility and uncertainty that require knowledge of statistics and forecasting of socioeconomic trends. River water quality is not constant and depends on the presence of pollution sources along the rivers. For example, fluctuations in the amount of hydrochemical materials annually transported to Lake Sevan by rivers can reach more than 30 times for NO3- and 11 times for PO4- 3 (Asatryan and Dallakyan 2015; Danielian 2011). Agricultural activity in the lake basin contributes to this instability due to the high concentration of organic compounds in rivers, including the logging zone (~100 ha) near the villages of Lichk and Artsvaqar, which provides nitrogen to the lake and increases water eutrophication. In particular, a significant change in the turbidity of the lake water depends on the commercial peat mining that took place near the village of Torfavan. As a result, the concentration of peat in lake water can reach 0.94 g/m3. As can be seen from Table 6.9, the current water quality level of Lake Sevan does not have high spatial instability, which can be explained by mixing processes under the effects of the wind field as well as bioaccumulating processes when many pollutants entering the water, they can escape into food chains and by other selfcleaning processes. The actual assessment of the water quality of Lake Sevan can be performed through episodic measurements using visual instruments and regular modeling. Table 6.11 shows the comparative accuracy levels of the optical devices and the modeling assessments of the concentration of different pollutants. Initial data for MLSWQ were distributed on April 10, 2019 at sampling site number 1. Final modeling and measurement results correspond to September 10, 2019. Numerous field measurements using optical instruments and model calculations during 2019 showed that lake water quality in sampling area number 1 can be an indicator of water quality for the entire lake body. The deviation of lake water quality assessed at the five sampling sites by visual instruments and using MLSWQ from laboratory analyses does not exceed during the 5 months 17.4%. As derived from this result, the monitoring regime for Lake Sevan can be as follows. Lake water Table 6.11 Assessment of the precision of optical and modeling tools on comparative analysis of optical measurements, water sampling control in laboratory, and modeling results, performed on 10 September 2019 Data source OUSDMS-8 SDMS-35 SEDMS-128 Model Lab analysis Minimal error, % Maximal error, %

Fe, μg/ L 175.34 168.15 146.12 162.67 163.46 0.5

Ni, μg/L 19.22 20.14 22.31 20.45 21.73 2.6

As, μg/L 5.81 5.34 5.52 5.49 5.68 0.5

Pb, μg/L 0.22 0.18 0.21 0.18 0.19 0.06

Cu, μg/L 1.53 1.63 1.59 1.61 1.68 0.06

PO4- 3 , mg/L 0.041 0.046 0.037 0.042 0.038 2.7

NO3- , mg/L 0.167 0.148 0.152 0.159 0.161 3.7

11.9

13.1

6.0

13.6

9.8

17.4

8.1

Turbidity, mg/L 31.14 29.56 30.93 29.36 30.17 2.1 3.1

322

6

Investigation of Regional Aquatic Systems

Fig. 6.14 Comparison of modeling results and in situ measurements taken during springautumn 2019

quality is assessed in situ at sampling point number 1 using OUDMS and SEDMS optical devices at a frequency of every 5 months and water sample is collected for SDMS and laboratory control. ODMS functions allow the reconstruction of spatial and temporal distributions for the hydrochemical parameters of Lake Sevan. Figure 6.14 shows model calculations when the initial data correspond to water quality at sampling site number 1 and the accuracy check is performed at sampling site number 4 (Fig. 6.10). Controlled measurements of water quality were carried out at water sampling site number 4 on 10 May, 10 July, and 10 September 2019. Numerous model calculations were performed when the numbers of control sites were changed to the original data and control measurements. These calculations show that the accuracy of the modeling results and the timely monitoring regime have consistent characteristics that do not depend on which site is used as the initial data for the MLSWQ. Control measurements should be carried out with a frequency of every fifth month. The concentrations of selected hydrochemical parameters have practically similar temporal distributions without significant fluctuations. Numerous analogous modeling calculations with a change of locations for initial data and for control measurements have shown that the ODMS provides the water quality assessment with errors not exceeding 18% depending on the accuracy of official data on the concentration of pollutants in the river waters delivered to Lake Sevan. The practically acceptable early monitoring regime can be selected based on the comparative analysis of MLSWQ results with field measurements through ODMS. The results presented in Fig. 6.14 show that MLSWQ can be used to predict water quality for a month with an error of no more than 10% and for a year with an error of about 20%.

6.3

Lake Sevan Water Quality Diagnostics Using Optical Instrumental Tools

6.3.5

323

Concluding Remarks

The geosystem of Lake Sevan belongs to the complex natural objects of significant socioeconomic importance, the monitoring of which is required not only to assess their current state, but also to predict overall processes in its zone of influence. It is important under environmental conditions when there are uncertainties about pollutant fluxes. Rivers flowing into Lake Sevan with industrial, agricultural, and domestic waste form its water quality, the control of which is important for many areas of human activity in Armenia. The main indicator of lake water quality is the concentration of chemical components in the Hrazdan River when it flows from Lake Sevan. The study carried out in this work confirms this. The monitoring regime proposed in this study allows the control of characteristics for the formation of the water quality of the Sevan and the periodic assessment of pollutant concentrations taking into account existing tributaries flowing through densely populated areas located in the lake’s watershed. The accuracies provided by this monitoring regime range from 0.1% to 18% which depends on the knowledge of external factors such as the concentration of pollutants in rivers and streams of Lake Sevan and rainfall as well as the type of optical decision-making system. More accurate analysis of water sampling can be completed when N ≤ M corresponds to N ≤ 8 for OUDMS, N ≤ 35 for SDMS and N ≤ 128 for SEDMS. These constraints determine the decision-making system to be used for the water diagnosis of Lake Sevan. Numerous calculations with Eq. (6.4) have shown that the decrease in diagnostic accuracy of water quality occurs below N >> M. Therefore, the choice of optical decision-making system must be matched with the number of contaminants whose concentrations are required for assessment. Certainly, the proposed monitoring regime needs improvement considering many negative impacts on Lake Sevan related to the ecological balance of the lake, its hydrological and social aspects (Vardanian 2009, 2012; Vardanyan et al. 2014). Visual decision-making software can add new algorithms for big data processing and hydrologic models geared toward forecasting lake water quality under regional climate change and scenarios of future trends in anthropogenic impacts, including lake water use for agriculture, industry, and satisfaction of drinking water resources (Sargsyan 2007). These improvements can provide the synthesis of effective organic information modeling technology for a sustainable management of Lake Sevan water resources. Finally, the proposed water quality monitoring regime of Lake Sevan has the following stages: • The current level of water quality of Lake Sevan makes it practically necessary to carry out regular water sampling or field measurements every 5 months by recording the visual characteristics of the lake water in the Hrazdan River catchment area (site number 1). • Accumulation of the water quality spectral images into the spectral database as standards for the detection of trends.

324

6

Investigation of Regional Aquatic Systems

• Modeling reconstruction of spatial distribution of the water quality characteristics with the use of existing anthropogenic data. • Decision making about the possible change of monitoring regime (change of water sampling frequency and sampling site numbers as initial data for the MLSWQ). Scientific and technological objectives of the Lake Sevan geoecosystem operational monitoring need further improvement taking into consideration different factors both hydrological, hydrochemical, and social-economic character. Developed in this section, monitoring tools are to be added series software elements realizing forecasting functions in framework of scenarios reflecting the trends in the socioeconomic development of Armenia.

6.4 6.4.1

Water Balance Model of the Turan Lowland in Central Asia Introduction

Climate-induced changes are observed and recorded in many studies. According to the future climate scenarios, the global-scale hydrological cycle is going to be strengthened due to expected global warming. This enhancement will lead to increased energy availability in the atmosphere due to latent-heat release during precipitation. The latter may be combined with the fact that hydrological sensitivity is greater for solar radiation forcing compared to the effects of greenhouse gases. In addition to anthropogenic intervention in the issue of climate change and global warming, there are also significant regional human interventions to the environment with severe consequences. The Turan plain in Central Asia is the most illustrative example of when the regional water balance has changed significantly and dangerously. This regional change is linked to both global climate change and anthropogenic processes such as the implementation of Kara-Kum Channel project, damming of the water outflow from the Caspian Sea over the Kara-BogasGol Gulf, and the turn of Uzbekistan rivers to the south for the cotton industry development. It is historically known that the Caspian Sea level has significant fluctuations that are not accompanied by similar hydrological changes in Central Asia. Nevertheless, the Aral Sea as the largest inland body of saline reservoirs in the world, located in the center of Central Asian deserts (Kara-Kum, Kyzyl-Kum, and Betpakdala) is almost dried. However, there are many other water bodies experiencing significant desiccation, or are otherwise endangered because of either unsustainable anthropogenic pressures or global climate change. The negative consequences are manifold, ranging from deterioration of environmental conditions (desertification processes, increase of continental climate) to economic and social impacts (decay of fisheries, agriculture

6.4

Water Balance Model of the Turan Lowland in Central Asia

325

and horticulture, tourism, and other related businesses). Zavialov (2005) argues that because the Aral Sea represents an extreme case of lake degradation, insight obtained from the Aral Sea may have a broader applicability to other water bodies. These cases include several other enclosed seas which are suffering severe alterations in circumstances similar to those of the Aral Sea, of which we just note the Dead Sea that is a deep terminal lake at the border between Israel and Jordan. The present Dead Sea surface is located at about 416 m below the World Ocean level, which makes it the lowest land spot on Earth. The Dead Sea whose maximum salinity is above 340 g/l and density is about 1237 kg/m3 (1.237 g/ml) is considered to be one of the saltiest lakes in the world. The Dead Sea desiccation continues at rates of 0.5–1 m/year. The shallowing is believed to have been anthropogenic and resulted from major water management interventions in the drainage basin, manifested mainly through water diversions from the Jordan River feeding the lake. The river waters have been diverted for agricultural and industrial uses by Jordan, Syria, and Israel; the political issues and conflicts are discussed by Pearce (2018). The discharge into the Dead Sea has reduced from 1.5 km3/year in the 1950s to only 0.15 km3/year at present. Other notable examples are the Lake Eyre in Australia, the Lake Sevan in Armenia, and the Lake Chad in central Africa. Desiccation characteristic for the Aral Sea, Dead Sea, and Lake Chad as well as River discharge drop is an approximate difference between the characteristic predesiccation and present-day inflow as given in Zavialov (2005). Focusing on the Aral Sea, the shrinking water volume continues as an example of dramatic events that took place in the current century. The water Aral Sea level has dropped approximately 26 meters since the onset of its primary sources of water being diverted (Micklin 1988; Sun and Ma 2019; Burr et al. 2019). Historical data show that the Aral Sea had stable fluctuating level between 50 m and 53 m during the last 200 years prior to 1960. During this period, the Aral Sea surface was (51–61) × 103 km2 and its water balance supported by Kara-Bogaz-Gol and Amy Darya and Syr Darya rivers due to precipitation and river outflow. Really, 50–60 km3/yr are evaporated from the sea surface, 9–10 km3/yr water arrived with precipitations, and 33–64 km3/yr delivered with river inflow. Aral Sea volume and its area began to decline significantly when water flow from the Caspian Sea to KaraBogaz Gol was cut off in 1980. This decision making was based on the observed decrease of the Caspian Sea level during 1950–1970. Dam between Caspian Sea and Kara-Bogaz Gol was erupted in 1992 (Gippius et al. 2016). Undoubtedly, human-induced negative processes in Central Asia not only brought economic, ecological, and social insecurity to the resident population, but also created negative habitats with unfavorable human health conditions. The dried up seabed produced dust storms laden with chemicals that led to increased air and water pollution levels, and crop damage as much as 1000 km away. Existing forecasts of environmental dynamics in Central Asia are largely uncertain due to the existing and ongoing widespread environmental degradation. Reducing water use scales is impossible and optimal water distribution in Central Asia requires a cooperative agreement between countries, which requires consolidating efforts. Water for these countries is the most valuable and conflicting natural resource

326

6 Investigation of Regional Aquatic Systems

despite the fact that large quantities of water are stored in the mountain glaciers of Pamir and Tien Shan. The main water user in Central Asia is the irrigation of agricultural land. The water withdrawn from Amy Darya and Syr Darya Rivers partially returns to the rivers (≈0.34  0.07%) and other different reservoirs (≈0.26  0.05%). Many authors discuss the Aral Sea problem as a key element of Central Asia hydrology that makes it impossible to change human-induced irrigation strategies and to renew the runoff of Amu Darya and Syr Darya in the Turan plain. Krapivin et al. (2015b) promoted the idea that the hydrological regime of Aral Sea could be reconstructed using simple scenarios when Caspian Sea waters are directed to Turan plain by evaporation and precipitation that could provide sustainable water management in Central Asia. During the Former Soviet Union, several scenarios were discussed to solve the Central Asian water problem and some of them began to be understood. The Former Soviet Union also discussed the transfer of Siberian River water to Central Asia and some relevant decisions were made (Micklin 1987, 1988, 2014). In particular, the irrigation-recording channel linked to Irtysh River with Kazakhstan was built in 1971. However, many famous Soviet scientists and the Academy of Sciences call for this project to be canceled. The main argument was that the implementation of this project would lead to a decrease in temperature in the Arctic waters (primarily in the Kara Sea) and to unpredictable changes in the global climate. This problem remains important for the independent countries of Central Asia. Its solution is complicated by the different economic strategies of Kazakhstan and Uzbekistan that are oriented toward the use of existing water resources. Kazakhstan proposes the project of the Aral Sea recovery through control of Syr Darya’s water regulations. This project is economically unacceptable for Uzbekistan. But at present, many existing national and international projects are in conflict with the nationality of the countries of Central Asia. There were other proposals to solve the Aral Sea problem. At the 48th session of the UN General Assembly on 28 September 1993 and at the 50th session of 24 October 1995, the recommendation was made to support the countries of Central Asia by appealing to international financial institutions and developed countries. Micklin (2016) raised the question “What could be the future for the Aral Sea and its surrounding environments?” and replies that “the return of the sea to its 1969s situation is possible but very unlikely in the foreseeable future.” Considering the various scenarios that were materialized, Micklin (2016) concluded that the recovery of the average river inflow in the Aral Sea at 56 km3/yr requires more than 100 years. If the control of water resources in Central Asia was effective in the Former Soviet Union, the use of water for irrigation and electric power generation today is more complicated due to different national interests. The scenario proposed may be acceptable for Kazakhstan, Tajikistan, Kyrgyzstan, and Uzbekistan and is useful for the wider region, including Azerbaijan and Turkmenistan. This stabilizes the regional water cycles and can be easily realized. Indeed, it can lead to an agreement between Central Asia countries and closer cooperation on the implementation of this scenario.

6.4

Water Balance Model of the Turan Lowland in Central Asia

6.4.2

327

Material and Methods

The plain water balance of Turan is mainly formed in the Aral Sea zone, the evolution of which is subject to natural-climatological and man-made processes of both regional and global scales. Dukhovny and Stulina (2001) described the scheme of the transboundary return flow to the Aral Sea basin, which can form the basis for the scenarios. The main flows of water into the Turan plain include the natural runoffs of Syr Darya and Amu Darya, cross-border waters, and precipitation. Undoubtedly, Turan’s plain water balance is dependent on the hydrological regime of the Caspian Sea (Panin et al. 2014). A key indicator of this balance is the level of the Aral Sea. Unfortunately, the external water resources for the Aral Sea have declined over the last few decades and there is really a resource, that is atmospheric precipitation. According to existing data, the average precipitation level is estimated here from 90 to 120 mm/yr. However, the water balance of the Aral Sea can be represented by the following equation, where many components are small or zero (Micklin 2007, 2014; Fernandez et al. 2012): dAðt Þ = ½H 33 ðt Þ þ H 8A ðt Þ þ H 8S ðt Þ - H 7 ðt Þ=σ A ðt Þ, dt

ð6:5Þ

where σ A is the Aral Sea area (km2); H8A and H8B are runoffs of Amy Darya and Syr Darya rivers to the Aral Sea, respectively. The components of Eq. (6.5) are shown in Fig. 6.15 and Table 6.12. As can be seen from Fig. 6.15, almost all water flows in Central Asia do not intersect with the Aral Sea zone. In addition, the rivers Amu Darya and Syr Darya do not deliver their waters to the Aral Sea. The water flows of these two rivers are regulated by older built dams and reservoirs that cannot be changed without intergovernmental solutions that continue in recent decades without a positive result. The idea is to propose the simple solution to this problem. An analysis of this situation shows only one possible and acceptable method for all Central Asian countries to raise the Aral Sea level. This method involves moving Caspian Sea water into the Turan valley through their extra evaporation, which can be achieved if the Caspian waters move to the natural reservoirs-evaporators on the east coast and have levels below the sea. This watering / evaporation / precipitation (WEP) scenario can be carried out as part of the project that Kazakhstan plans to implement in the twenty-first century using the Ashicor lowland and may be Karagie for watering. Generally, the bottoms of many lowlands on the eastern Caspian coastline have a level below the sea. The Former Soviet Union planned to direct the Caspian waters into the Karagie (Batyr) lowland and build a 35,000-kilowatt hydroelectric power station. In this case, the Karagie lowland could play the role of the Kara-Bogaz-Gol Gulf (-32 m). The lowland Karagie (-132 m) is located on the Mangyshlak Peninsula, about 50 km from Aktau City. Among other lowlands that could be used in the WEP scenario as natural evaporators may show the following lowlands. The Kaundy lowland has a depth of 57 m and is about 40 km away from Zhanaozen City. It has dimensions 50–17 km.

328

6

Investigation of Regional Aquatic Systems

Fig. 6.15 Block scheme of the water-flow diagram for the Turan Lowland. The notations are explained in Table 6.12

The Chagala Sor and Zhasgurly lowlands are close to the Kara-Bogaz-Gol coast. Lowland Lifeless Kultuk (-27 m) is located on the north-east coast of the Caspian Sea and is about 100 km long. A hollow Kaundy (-57 m) is located on the Mangyshlak Peninsula near the Karynzharyk lowland. Finally, the following hollows can be additionally used as natural evaporators to perform the WEP scenario: Kaidak (-31 m), Chagala Sor (-30 m), and Karin Arik (-70 m). Their absolute levels are below the recent level of the Caspian Sea (-25.7 m). The total area of these evaporators is approximately 90–103 km2. Additional evaporation from these reservoirs can increase precipitation in the Turan plain and other parts of Central Asia. Moreover, additional water vapor can reach the mountain zone by increasing the river outflows. The WEP scenario is accomplished by the following steps: • Classification of typical wind directions in the Aral-Caspian area • The waters of the Caspian Sea are directed to the natural reservoirs-evaporators (saline soils and hollows) located on the East Caspian Sea coast • The simulation model of the Aral-Caspian hydrological area is used to control water sinks in the Turan plain due to precipitation (forced, rainmaking technology likely)

6.4

Water Balance Model of the Turan Lowland in Central Asia

329

Table 6.12 The water flows in the Turan lowland The water flow, mm/yr Runoff from Caspian Sea to the Gulf of Kara-Bogaz-Gol Buried waters Simulated evaporators Evaporation from the surface: Gulf of Kara-Bogaz-Gol Caspian Sea Aral Sea Reservoirs of the drainage waters Irrigation systems Amu Darya River Syr Darya River Lakes and reservoirs Soil Artificial evaporators River runoff into Caspian Sea Aral Sea Inflow due to the drainage waters: Amu Darya Syr Darya Thawing of glaciers and snowfields The water use for the irrigation Inflow of the waters into the accumulators of drinage waters Leakage from irrigation systems Accumulation of the waters in the lenses Elimination of the waters from lenses for domestic using Drawoff for the irrigation use Surface runoff into rivers Surface runoff from irrigated areas Precipitation on: Lakes and reservoirs Glaciers and snowfields Soil Gulf of Kara-Bogaz-Gol Caspian Sea Accumulators of drainage waters Amu Darya River Syr Darya River Aral Sea Irrigation systems Artificial evaporators Inflow of the reservoirs at the expense of the ground waters Inflow of the rivers at the expense of ground waters

Identifier H1 H2 H3 H4 H5 H7 H18 H19 H20A H20S H21 H23 H37 H6 H8 H9A H9S H10 H24 H12 H13 H14 H15 H11 H22 H25 H26 H27 H28 H29 H30 H31 H32A H32S H33 H34 H38 H36 H35

330

6

Investigation of Regional Aquatic Systems

The technical implementation of the WEP scenario does not require significant financial costs and can be achieved through joint efforts by the five-independent republics of Central Asia.

6.4.3

Simulation of Aral Sea Water Balance Modeling

The water balance of the Aral Sea has been studied by many authors (Kuzmits 2006; Micklin 2016; Krapivin et al. 2015b). The main results are the establishment of negative trends both in the Aral Sea region and throughout Central Asia with its water resources. The scheme of Fig. 6.15 allows the model synthesis of the water balance of Central Asia (WBCA) region taking into account hypothetical water flows, including additional flows in the atmosphere over Central Asia. The whole area Ξ is divided by pixels Ξij = {(φ, λ): φi ≤ φ < φi + Δφ, λj ≤ λ < λj + Δλ. As a result, the basic system of balance equations for water flows in Central Asia has the following view: dF ðt Þ = ðH 3 þ H 38 - H 37 Þ=σ E ; dt

ð6:6Þ

dC ðt Þ = ðH 1 þ H 29 - H 37 - H 38 Þ=σ KBG ; dt

ð6:7Þ

dK ðt Þ = ðH 30 þ H 6 - H 1 - H 3 - H 5 Þ=σ C ; dt

ð6:8Þ

dW ðtÞ = H 4 þ H 5 þ H 7 þ H 17 þ H 18 þ H 20 þ H 21 þ H 23 þ H 37 dt

34

H s =σ T s = 26

ð6:9Þ where σE, σKBG,σC, and σT are the areas of the evaporators, Kara-Bogaz-Gol, Caspian Sea, and Central Asia, respectively. All symbols are explained in Table 6.12 and Fig. 6.15. Equations (6.5, 6.6, 6.7, 6.8, and 6.9) are analogous to the balance equations for the other elements presented in Fig. 6.15. The solution of these equations is made by the difference algorithm taking into account the pixel structure {Ξij} (Krapivin 1969). The water flows Hi (i = 1,. . .,38) are functions of temperature, wind speed and direction, atmospheric pressure, and topography (Sitterson et al. 2017). The numerical procedure is used to calculate the water balance components between the pixels Ξij with the σ ij areas. It is assumed that p all pixels have an equal area σ. Discrete time interval for calculations is no less σ =V where V is the wind speed. Physical

6.4

Water Balance Model of the Turan Lowland in Central Asia

331

processes that determine the wind effect on water flows into the atmosphere are major and can be described as the following equation: W Ξij , t þ Δt = ð1- aÞW Ξij , t þ aW ðΞls , t Þ

ð6:10Þ

p where a = σ=ðVΔt Þ, Ξls is the pixel located on the windward side to the pixel Ξij. The water balance equations for all reservoirs are converted to linear difference equations as shown for the Caspian Sea: K ðt þ Δt Þ = K ðt Þ þ ðH 6 þ H 30 - H 1 - H 3 - H 5 Þ=σ C

ð6:11Þ

The variety of existing parametric description of hydrological processes allows the selection of efficient equations that require a minimum number of coefficients (Bras 1990). This option minimizes the uncertainty in modeling results at the expense of errors in many parameters of the environmental processes taking place in the Central Asia area. The water flows in Eqs. (6.5, 6.6, 6.7, 6.8, 6.9, 6.10, and 6.11) and others in Fig. 6.15 are described using hydrological equations (Varotsos and Krapivin 2017). The evaporation from the water surface is described by the equation reflecting the dependence of the flow Hk (k = 4,5,7,18,19,20,21,37) at the air temperature Ta (°C), wind speed V (km/h) at a height of 2 m, and a dewpoint temperature Td (°C): H k = ηð0:42 þ 0:0029V Þ

Ta - Td Ta þ Td

ð6:12Þ

where the coefficien η depends on the type of water reservoir: η = 25.03 for the sea shallow waters; η = 24.91 for the deep sea waters; η = 25.57 for other fresh reservoirs. Flow H23 is described by Penman’s empirical equation: H 23 = 2:6ð0:5 þ 0:15V Þew ð1- r Þ

ð6:13Þ

where ew (kPa) is the saturated water vapor pressure at the prevaling temperature, r is the relative humidity. Water vapor as a secondary component of the atmosphere plays the most important role in the hydrological cycle of Central Asia. Generally, Caspian waters that evaporate from Kara-Bogaz-Gol and other evaporators are an important source of water that can increase the level of Aral Sea as a result of precipitation (Leroy et al. 2006). Therefore, realising the WEP scenario could intensify the precipitation and thus begin to raise sea level. Taking into account the existing experience of parameterization of the precipitation process, the following equation is used to describe the flows Hm (m = 26,. . .,36, 38):

332

6

Investigation of Regional Aquatic Systems

Table 6.13 Average monthly air temperatures (°C) in the Central Asia region based on weather reports collected during the period 2000–2018 Month January February March April May June July August September October November December

Uzbekistan 4.56 5.84 14.53 23.33 29.83 34.47 36.06 34.83 29.17 20.84 9.17 3.61

H m = qW

Kazakhstan -3.84 -2.37 3.82 11.65 16.71 21.64 24.03 23.25 17.86 10.51 3.44 -2.03

Turkmenistan 2.48 4.52 10.03 16.06 21.03 25.57 28.19 28.17 21.12 15.23 9.54 4.49

Kyrgyzstan -10.05 -11.02 -5.12 25.09 30.13 26.32 2.34 -12.42 -9.78 6.43 7.26 7/98

μ T V -V V max - V þ 1 a exp - α max μ2 þ T a V max þ V V max þ V

Tajikistan -15.43 -10.46 -3.62 8.61 9.67 12.58 15.91 15.45 10.57 4.07 -2.13 -7.78

ð6:14Þ

where Vmax is the maximum regional wind speed (km/h); q (0.312 mPa/day), α (0.85), μ1 (17.27), and μ2 (237.3) are empirical coefficients.

6.4.4

Simulation Results and Discussion

After describing the above WEP scenario, we are now proceeding to evaluate its results under the assumption that a synoptic situation is formed in Central Asia with the 2000–2018 state with a normal distribution of temperature and dispersion of 15% relative data from Table 6.13 and depending on possible variations in the climate trend. It is estimated that the average annual temperature increases by 0.04 °C every decade (Gong et al. 2017). The coefficients of equations (6.5, 6.6, 6.7, 6.8, 6.9, 6.10, 6.11, 6.12, 6.13, and 6.14) are defined based on the approach that existed during the hydrophysical processes over the period 2000–2018. The average long-term wind rose was studied by many authors (Bortnik and Chistyaeva 1990; Micklin et al. 2014; Micklin 2014). According to this, the isobars are mainly oriented from north-east to south-west in summer and from north-west to south-east in winter. As a result, wind direction recurrence over the Aral Sea region is given in Fig. 6.16. Of course, wind regimes of the Aral Sea region are formed according to the main types of synoptic processes, such as wave activities, peripheries of anticyclones and confining cyclones. Numerous historical data show that around 130–160 days a year, the winds in the Aral Sea region have northwest, west, and southwest directions, mainly from May to September when atmospheric temperatures change (Lioubimtseva 2014).

6.4

Water Balance Model of the Turan Lowland in Central Asia

333

Fig. 6.16 Prevailing wind directions in the Aral Sea zone and their recurrence (Bortnik and Chistyaeva 1990; Varotsos et al. 2018)

Data on surface covers and spatial distribution of reservoirs were obtained in a remote sensing monitoring framework using the IL-18 flying multifunctional laboratory during 1970–1990 (Borodin et al. 1982). Also, data from Small and Sloan (1999) on the distribution of geophysical and hydrological objects in Central Asia pixel structure {Ξij} are considered. Part of the water is diverted to Kara-Bogaz-Gol and the evaporators (fluxes H1 and H3) evaporate at the rates H4 and H37, respectively. After the evaporators are filled, a Caspian water outflow of about 20–25 km3/yr and 110–115 km3/yr is required in Kara-Bogaz-Gol and other evaporators, respectively. Vapor volume H4 + H37 is delivered to different pixels in Central Asia depending on wind speed and direction and partly falls. Water vapor can reach the glaciers in Pamir Mountains and Tien Shan that can increase water flowing on the Amu Darya and Syr Darya Rivers. It indicates the possible optimum regime of Caspian water flowing (H37) into the evaporators. Figure 6.17 shows the correlation between the water volume evaporated from Karas-Bogaz Gol and the precipitation in the Aral Sea zone based on historical data and forecasted by the WBCA model without taking into account additional evaporators. As can be seen from Fig. 6.17, the water evaporated from Kara-Bogaz Gol to a maximum volume fall in the Aral Sea zone in a small amount. Additionally, with the use of evaporators, precipitations can be added 3–4 times. However, a significant portion of all evaporating water from the Caspian Sea, KaraBogaz Gol, and natural evaporators during the summer season passes through the Aral Sea zone due to the high atmospheric temperature. The solution to this problem is possible with the use of forced (artificial or rainmaking) rainfalls (Liu et al. 2009). In this case, precipitation in the Aral Sea zone can reach 50–60 cm/yr. Figure 6.18 shows the dependence of the relative humidity of the atmosphere on air temperature. One can see that about 10 km3 of Caspian water from Kara-Bogaz Gol evaporated during I-III and IV-IX, corresponding to 4.8 km3 of precipitation

334

6

Investigation of Regional Aquatic Systems

Fig. 6.17 An evaporation-precipitation dependence in the WEP scenario

Fig. 6.18 The atmospheric humidity-temperature dependence in the WEP scenario

6.4

Water Balance Model of the Turan Lowland in Central Asia

335

Fig. 6.19 WEP scenario modeling results. Note: (1) WEP scenario 1 is used; (2) WEP scenario 2 is used; (3) WEP scenario 3 is used for μ = 0% and ξ = 5%; (4) WEP scenario 3 is used for μ = 90% and ξ = 10%

over the Aral Sea, while about 20 km3 of water evaporated during IV-X corresponding to 4.4 km3 of precipitation. Of course, this imbalance will be right for other natural evaporators. Exceeding this imbalance is possible using artificial rainfalls over the Aral Sea. There are many techniques for the implementation of rainmaking (Pelley 2016). The WBCA contributes to a better understanding of the hydrological processes and how the water components in Fig. 6.15 change depending on the possible conditions within the WEP scenario. Figure 6.19 provides an understanding of the Aral Sea recovery processes when proposing realistic solutions to Central Asia’s water balance. The future of the Aral Sea is considered optimistic, and its level fluctuations can be relatively stable after many years of WEP scenario implementation. As can be seen from Figs. 6.19 and 6.20, there is a prospect of returning the sea to its 1960s state. The retention time of the Aral Sea depends on the version of the WEP scenario, notably: WEP scenario version 1: Use only Kara-Bogaz Gol as a Caspian water evaporator. WEP scenario version 2: In addition to Kara-Bogas Gol, other natural Caspian water evaporators are used (the area is equal to σ E)

336

6

Investigation of Regional Aquatic Systems

Fig. 6.20 Aral Sea recovery dynamics according to natural evaporators area and WEP scenario versions: (1) WEP scenario 2 is used; (2) WEP scenario 3 is used for μ = 60% and ξ = 0%; (3) WEP scenario 3 is used for μ = 90% and ξ = 0%; (4) WEP scenario 3 is used for μ = 60% and ξ = 5%; (5) WEP scenario 3 is used for μ = 90% and ξ = 5%; (6) WEP scenario 3 is used for μ = 60% and ξ = 10%; (7) WEP scenario 3 is used for μ = 90% and ξ = 10%

WEP scenario version 3: The process of evaporation of Caspian water by KaraBogas Gol and additional natural evaporators takes place along with the reduction of river water for the irrigation by ξ percent and using rainmaking with efficiency of μ percent. The simulation experiments show that the implementation of WEP scenario version 1 will restore the Aral Sea level of the 1960s over the next 500–600 years. The WEP scenario version 2 will offer a significant chance to solve the Aral Sea problem over the next 100–130 years using forced rainfalls. Using the WEP scenario version 3, it will solve the Aral Sea problem with high reliability when the irrigation strategy in Central Asia is revised and optimized. Curves 3 and 4 in Fig. 6.19 indicate possible periods for increasing Aral Sea volume under potential reduction in water withdrawal from Amur Darya and Syr Darya by 5% (3.5  0.4 km3/yr) and 10% (7.1  0.8 km3/yr) leading to the 1960s state in 120–140 and 70–95 years, respectively. This strategy can be easily achieved by using drip irrigation. More detailed assessments of the Aral Sea recovery process are presented in Fig. 6.20. Water from Amu Darya and Syr Darya Rivers and rainmaking are significant factors in

6.4 Water Balance Model of the Turan Lowland in Central Asia

337

Table 6.14 Some hydrological characteristics of the Aral Sea region when the WEP-version 3 scenario (see Fig. 6.19) is performed for μ = 90% and ξ = 10% Time after the WEP scenario beginning, years 0 10 20 30 40 50 70 90 120 140 160

Aral Sea volume, km3 68 157 297 431 662 883 1039 1059 1079 1081 1083

H33/σ A, mm/yr 250 961 1095 1023 1062 1104 1099 1087 1078 1106 1105

H7, km3/yr 25.9 28.9 39.4 49.2 51.3 61.8 66.1 62.7 63.9 64.7 63.5

(H4 + H37), km3/yr 128 131 129 133 128 136 132 129 135 134 136

H8, km3/yr 0 0.42 1.13 1.41 1.91 2.22 2.97 3.42 4.76 5.23 5.47

H9, km3/yr 0.17 0.16 0.17 0.21 0.19 0.20 0.22 0.21 0.23 0.23 0.24

solving the Aral Sea problem. The results of Figs. 6.19 and 6.20 and Table 6.14 show the existence of numerous strategic technical implementation solutions that are possible in the context of adequate management of regional water resources. Thus, the WEP scenario could stabilize the hydrological status of the Aral Sea under the constructive cooperation of the Central Asian governments. Irrigation systems lose about 50% of the river’s water due to irrational use and delay of technical systems. All of these confirm the marketability of the WEP scenario version 3. The maximum rainfalls (H3) in the Aral Sea zone occur when the evaporation of Caspian waters from natural evaporators reaches 100–140 mm/month. The subsequent increase in evaporated waters (H4 + H37) as a result of the increase in air temperature does not give any positive results. This effect is agreed by reducing the air humidity by increasing air temperature, too. Therefore, forced rainfalls can damp this negative effect and significantly increase the H33 flow. Air humidity is a significant indicator of the prerain situation. Table 6.15 shows a distribution of the average monthly air relative humidity in Central Asia zones in the Aral Sea Basin. The implementation of WEB scenario 3 is followed by an increase in air humidity practically in all parts of Central Asia. In dry zones, the air humidity increases by 5–8%. The Ustyurt plateau and Aral Sea hollow receive an increase in air humidity by 10–18%. This increase in air humidity is stabilized in the first year of the implementation scenario and is maintained when the simulation experiment continues. The most important result is the increase in air humidity during the summer season. It is noted that air humidity increases by 2–5% in mountain areas. It is envisaged that the technical implementation of the WEP scenario would not require significant financial costs and could be achieved through joint efforts by the five independent republics of Central Asia.

Ustyurt Plateau Δt = 0 Δt = 10 71.3 80.4 59.6 67.9 54.7 61.3 43.2 49.7 32.5 37.7 27.4 37.1 26.8 37.6 25.7 37.5 33.9 42.8 49.7 51.1 59.9 66.8 68.8 79.3 Δt = 50 83.2 67.4 61.6 49.5 38.1 37.5 37.9 37.4 42.7 50.8 66.7 78.9

Kara Kum desert Δt = 0 Δt = 10 62.5 65.6 53.5 55.1 45.9 46.8 39.2 40.8 28.5 29.9 22.7 24.1 22.4 23.9 21.7 23.2 25.8 26.7 37.4 38.5 49.5 50.5 62.9 64.1 Δt = 50 64.7 55.4 46.7 41.2 29.9 23.8 24.1 23.6 26.9 38.3 50.4 63.8

Aral Sea hollow Δt = 0 Δt = 10 81.2 87.6 74.8 85.4 72.3 84.3 53.9 81.4 44.8 79.9 29.2 72.7 30.1 72.5 42.6 73.1 51.7 75.2 59.8 80.2 75.1 82.4 80.9 86.7

Δt = 50 87.5 85.7 84.2 81.3 79.8 71.9 73.2 73.1 74.9 80.1 82.6 88.1

6

Note: Δt is the time in years after the beginning of the simulation experiment

Month January February March April May June July August September October November December

Table 6.15 Fragments of spatial distribution of average monthly relative air humidity (%) delivered by the WBCA model when the WEP scenario 3 was performed

338 Investigation of Regional Aquatic Systems

6.4

Water Balance Model of the Turan Lowland in Central Asia

6.4.5

339

Conclusions and Outlook

According to recent climate and precipitation forecasts by 2021 and 2050, climate change will affect the dynamics of hydrogeological systems and their chemical water quality. Consequently, the water balances for the discrete aquifers should be recalculated. As mentioned above, the Aral Sea region is an interesting example. This is geographically the border between Kazakhstan and Uzbekistan being in the past the fourth largest inland sea in the world. Since the 1960s, the volume of water has decreased by 14. The water reserves to the Aral Sea come from the rivers Amu Darya (originating in Tajikistan) and Syr Darya (originating in Kyrgyzstan). In the early twentieth century, the demand for river water for local agriculture, especially the cotton industry, led to the construction of irrigation systems. However, the failure to maintain infrastructure, along with heavy pollutant emissions, had serious consequences for residents of the areas around the Aral Sea. A significant role in the water balance of the Central Asia plays Kara Kum and the Kyzyl Kum Deserts, as well as the semi-dry steppes. The sources of Amu Darya and Syr Darya are located in the mountain zone of Tien Shan and Pamir. The discrete pixel structure of the WBCA model covers these zones with a geographical grid of Δφ = Δλ = 0.1°. Information on the characteristics of these morphological zones was synthesized based on multiyear remote sensing observations using IL-18 flying laboratory of the Kotelnikov Institute of Radioengineering and Electronics of the Russian Academy of Sciences and relevant publications (Bortnik and Chistyaeva 1990; Borodin et al. 1982; Chen et al. 2013) and dating the Aral Sea Basin events starting from 1950 until today. The waters supplied by the natural evaporators into the atmosphere come in part from the mountain glaciers which provide additional precipitation ≈850 mm/yr and the river outflow can increase. Using WBCA model simulations shows that there is the main solution to the Aral Sea problem. The results of the WEP scenario version 3 show that the volume of the 1960s Aral Sea can be achieved during a considerable time of the 5.4% evaporation of the river inflow into the Caspian Sea using Kara-Bogaz Gol and other evaporators on the east coastline. In this case, the evaporated waters are distributed to the Aral Sea Basin under the influence of wind fields and synoptic situations considered in the literature sources. In general, the WEP scenario mainly brings positive changes to all components of the water cycle in Central Asia. For example, the surface runoff in rivers increased by 2.3 km3/yr (H22 = 19.1  4.3 km3/yr) and the inflow of rivers at groundwater increased by 1.8 km3/yr (H36 = 16.3  2.2 km3/yr). WBCA model verification was performed by comparing historical data on Aral Sea volume dynamics and modeling results during 1950–2010, showing the model error of about 11–14% (see Table 6.16) depending on the variation of historical data. Specifically, this precision can be increased when the WBCA model is used along with the regional climatic model and detailed descriptions of the topographical and

340

6

Investigation of Regional Aquatic Systems

Table 6.16 Comparison of observed and calculated Aral Sea volumes (Cretaux et al. 2013, 2019; Satybaldiyev et al., 2023) Aral Sea volume, km3

Year 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2018

Observed 1089.0 1030.3 928.2 762.4 670.4 468.3 304.1 239.0 195.4 112.3 98.1 48.3 42.2

Calculated 1067.9 999.2 1010.4 846.3 603.4 505.8 347.7 262.9 218.8 105.6 105.9 52.6 46.0

WBCA error, % 1.94 3.02 8.86 11.00 9.99 8.01 14.34 10.00 11.98 5.97 7.95 8.90 9.00

Historical data used in WBCA Caspian water flow River runoff to Aral Sea, to Kara-Bogaz-Gol, H1 (km3/yr) H8 (km3/yr) 24.3 41.9 20.3 0.3 15.2 0.2 12.2 0.2 1.2 1.2 4.9 0.3 13.0 3.1 22.5 4.7 17.7 9.7 25.2 7.4 20.8 4.5 19.3 2.3 22.1 2.4

ecological parameters of the Aral Sea Basin will be evaluated. Nevertheless, the WBCA model presents positive changes in the Central Asian water cycle when the WEP scenario version 3 is being made. Many elements of the water cycle, as shown in Table 6.13, are back in the 1960s. For example, flow H10 increases by 0.4%, flows H27 and H28 reach 750–920 mm/yr and 93–178 mm/yr, respectively. Micklin (2016) points out that the solution to the Aral Sea problem is only possible in the case where the governments of the five Central Asian countries can reach to an agreement sharing water resources in the Aral Sea Basin. This cooperation is provided by the Multipartner Human Security Trust Fund whose program “Building the resilience of communities affected by the Aral Sea disaster through the Multi-partner Human Security Fund for the Aral Sea” (https://news.un.org/en/ story/2018/11/1026701). This Program can support the stabilization of the water use process in Central Asia by mobilizing communities, social infrastructure projects, and optimizing water cycles through the introduction of new irrigation technology systems. The WEP scenario can be a key tool for decision making and developing a strategy for sharing the Aral Sea area. Modeling results of this study show the prospect of solving the Aral Sea problem through cooperative efforts by the Central Asian countries. As shown above, the Aral Sea water balance disturbances have natural and anthropogenic causes that require a weighed and separate analysis that can help resolve this contradictory problem. Natural causes include climate change as a major regulator of water flows to the Aral Sea basin. In this case, it is necessary to consider a Caspian Sea water balance and thawing the Pamir glaciers. The Aral Sea does

6.4

Water Balance Model of the Turan Lowland in Central Asia

341

indeed have very limited external water sources that according to Pearce (2018) relates to the complex scientific, economic, and historical dimensions of the world water crisis that are possible within the framework of international agreements. We have argued that the drying of the Aral Sea is almost entirely anthropogenic, and we have noted that there have been quite serious climatic consequences. The summers have become hotter, the winters have become colder, and the length of growing season for crops such as cotton in irrigated areas has become shorter. When it comes to the Dead Sea, the totality of anthropogenic causes appears to be less clear. According to Zavialov (2005), the changes in Lake Eyre are essentially natural. In this case, there is no human interference in the water entering or leaving the lake, except possibly any changes in rainfall in the area caused by some sort of human activities. It is probably the closest example we can find to variations of a large lake that are almost entirely due to natural causes. The WBCA model shows that the volume of the Aral Sea 1960 can be restored over the 90–240 years depending on the WEP scenario versions. In particular, the use of the maximum possible area (about 90,000 km2) of natural evaporators of the Caspian water and the reduction of river water withdrawal by 10%, and the introduction of rainmaking technologies, allow Aral Sea recovery during the closest century. In other situations, the WBCA model presents longer time periods. The use of weather radars and other monitoring tools (Krapivin and Shutko 2012; Krapivin et al. 2018b) allows the operational assessment of water storage in the atmosphere and the regime selection of rainmaking procedure. Such an experience of rainmaking procedures has been found in many countries. Undoubtedly, many WBCA blocks can be improved, and new blocks can be synthesized taking into account the long history of the Aral Sea at timescales ranging from years to millennia (Burr et al. 2019). Present-day remote sensing technologies deliver additional data for more detailed consideration of the Central Asian water components represented in Fig. 6.15 (Sun and Ma 2019). The general message of the above-mentioned discussion is that there are some climate changes that are (almost) entirely anthropogenic having nothing whatsoever to do with CO2 emissions from the combustion of fossil fuels. Another message is that water shortage or conflicts of interest between states, or even physical conflicts, over the diversion of water resources constitute one of the any threats to our way of life (e.g., Pearce 2018). It is an area where the human influences (6.5) different environmental changes that are caused by different combinations of human activities and natural causes and (6.6) there are important environmental changes—and therefore possible threats to our way of life—that have nothing whatsoever to do with CO2 emissions from the burning of fossil fuels (Krapivin and Varotsos 2016; Krapivin et al. 2017a, b; Varotsos et al. 2007, 2020d).

342

6.5 6.5.1

6

Investigation of Regional Aquatic Systems

Microwave Monitoring of Soil Water Content Uncertainty and Risk Sources in Remote Sensing

Uncertainty and risk are key features of agricultural production. The main sources of uncertainty and risk are related to the causes affecting agricultural production: • The amount and quality of output that will result from a given set of inputs are typically not known with certainty, i.e., the production function is stochastic. • Presence of uncontrollable elements, such as weather and absence reliable prognosis of many agricultural processes. • Price uncertainty is a standard attribute of agricultural activities. • Technological uncertainty is always the cause of overcoming risk. • Policy uncertainty poses a considerable risk to agricultural investments. Unfortunately, the basic postulates of rationality in agricultural risk are tied to economic models. Technological and nature-oriented risks are the least studied of all. Decisions about environmental risks usually focus on the assessment of consequences, and assessments of consequences are complicated by uncertainty. Some environmental hazard problems are discussed by Krapivin and Varotsos (2007, 2008). It is clear that agricultural risks are a part of policy decisions for sustainable management based on the reasonably concrete knowledge base, the effective use of which is possible within the framework of the Global Nature-Society System Model (GMNSS). One of the main problems in the context of modern science is the assessment of the ability of the biosphere to survive in the conditions of increasing anthropogenic influence. Such an assessment can be carried out using the biosphere model. Known attempts toward the synthesis of a global model give unsatisfactory results. The created global models are educational in nature and do not enable real estimations. In recent years, many researchers have raised the problem of creating reliable and effective systems for monitoring the environmental situation on a global scale, including in the agricultural sphere. In general, this problem involves the development of technical means for the collection, storage, and transfer of the data of the state of the nature medium and (on the other hand) the development of methods of processing this data. The existing means of collecting information on natural objects and processes make it possible to form the data set, covering large areas up to the entire biosphere. Remote means of environmental monitoring are becoming particularly effective. Varotsos and Krapivin (2020a, b) have formulated the basic model of biosphere survival and proposed a new perspective on global modeling. The behavior of any system is determined by the value which can take on different terms characterizing the state of the system. Upon interaction with an external medium and in particular with other systems, the values of these terms can vary in one way or another. For any technological or biological system, it is

6.5

Microwave Monitoring of Soil Water Content

343

always possible to show the field of change of the characteristic parameters, wherein the system can be considered to be functioning. Outside this field, the system does not exist. Thus, one can substitute the complex behavior of a system by a description of the behavior depicting this system by a point in phase space of the characteristic parameters. If the change of any coordinate leads to the disappearance of the depicting point from the allowable field, the system is demolished (the organism as a whole is destroyed). A system is defined by its structure and behavior. The behavior of such a system is aimed at providing uninterrupted functioning by means of a correspondingly organized structure and behavior. This characteristic of the complex system to actively withstand the hostile action of an external medium, we shall refer to as survivability (Krapivin and Shutko 2012). The key questions to be answered within the numerous investigations of global ecodynamics are: • What is the level, interactions, and significance of “human dimension” (socioeconomic factors) in the development of society and its role in global environmental changes? • What are present and possible future impacts of the global environment variability on economic development, what factors determine the capability of society to react to the occurring changes, what are possibilities to provide a sustainable development and to reduce the man’s sensitivity to forcings? • What are the possible decision-making methods for the benefit of sustainable development under conditions of NHS complexity and high-level uncertainties about global environmental variability? • What are possible impacts of the global environmental changes on human health; what information about ecodynamics and socioeconomic factors is needed to evaluate the respective cumulative risks for human health? The most significant directions of developments related to the answers to these questions include, in particular, the following: 1. Regional problems of ecodynamics. Of primary importance here are studies of the problems of power supply, reconstruction of ecosystems, recovery of human health, and special phenomena such as droughts and forest fires at local and regional levels. In this connection, the integration of physics, chemistry, and biology with respective socioeconomic aspects taken into account should play a decisive role, which will help to make adequate decisions in ecological policy. Studies of the dependence of forest fires on climatic conditions can serve as an illustration of possible approaches to the solution of such problems, and the main goal is the prediction of anthropogenic wildfires. 2. Analysis of economic efficiency. The complexity of quantitative assessments of the economic efficiency of one or another ecological policy is determined by an ambiguous choice of this policy with priorities taken into account. In this context, of great importance is the use of simulation models of ecodynamics, especially

344

6

Investigation of Regional Aquatic Systems

the models of changes in land supplies of carbon to assess the response of carbon supplies in vegetation and soil to specific features of land use and changes in land surface characteristics, to variations of the atmospheric CO2 content, and to climate. Solution of such problems will provide a more reliable prediction of global trajectories of CO2 emissions to the atmosphere. 3. Possible consequences of global climate changes for forestry and agriculture over the country territory. Analysis of such changes with various scenarios of possible changes in global climate suggested the conclusion that in the case of several scenarios, the impact on forestry and agriculture in the country will be economically favorable. Partly, it is connected with the growth of forests production (with a CO2 concentration increase) and determined by capability of forests to adapt for climate change. As for agriculture, according to available prognostic estimates for a long period, positive impact of the global warming on agriculture in the country will be less economically favorable than it follows from the earlier estimates. 4. Effect of UV solar radiation on human health. Intensive developments in this direction are being carried out to study the impact of the lowering level of biologically active UV solar radiation due to decreasing total ozone content in the atmosphere on the agricultural production. By risk assessments, we mean at least fivefolds (five levels) interpretation or definition: 1. Theory of different kinds of risks 2. Experimental studies of risks in condition of natural and man-made disasters 3. Remote sensing application to identify risks (microwave radiometry allows obtaining hazard assessments in a practically instantaneous manner) 4. Risks assessments in GIMS 5. Creation of state-of-the art technological complexes for strategic identification of risks of hunger around the World by developing aerospace complexes Uncertainty and risk sources in remote sensing arise due to limited spatial resolution and under specific applications when the use of existing technologies, models, algorithms, and tools generate errors in the big data processing. Certainly, the word uncertainty has resisted a narrow definition. Particularly, a reasonable definition of the uncertainty is that accurately measured, estimated, or predicted values will have little uncertainty; inaccurate measurements, estimates or predictions should be associated with high uncertainty. Typical uncertainty sources connect with the precision of initial parameters for model used for the remote sensing data processing. The uncertainty that is always present in the model form is usually called structural uncertainty. When a set of models are considered, each of which gives different results, uncertainty is associated with the fact that the most accurate model is unknown. The uncertainty level is diminished when input variables are measured in situ regime and modeling results are compared with ground observations.

6.5

Microwave Monitoring of Soil Water Content

6.5.2

345

Practical Microwave Radiometric Risk Assessment of Agricultural Operation

Advanced technology for the assessment of soil hydrological regimes for agricultural purposes using multichannel passive (radiometric) and active (SAR) microwave measurements has been developed by many authors (Sharkov 2003; Krapivin and Shutko 2012). The physical background of the technology, the equipment used, examples of experimental data, and the perspectives of technology application were described by Shutko et al. (2007, 2010). The problem of Earth environmental monitoring and land use control has become increasingly important in the past 20–30 years. Successfully solving this problem required the research, development, and application of different remote sensing technologies. Among these technologies are radiophysical remote sensing methods and instruments that play an important role in Earth surface research. These methods and instruments are based either on the measurements of the parameters of natural radiothermal electromagnetic radiation from the Earth’s surface or on the measurements of the parameters of artificially radiated electromagnetic signals scattered on the Earth’s soil. In the first case, the remote sensing tools are called either Microwave Radiometers (MR) or Passive Microwave Radar (PMR); in the second case, the tools are called either Side-Looking Airborne Radar (SLAR) or Synthetic Aperture Radar (SAR), or Nadir Viewing Radar (NVR) with ultrashort (nanoseconds) length of pulses, etc. installed on aircraft and satellites and orbital stations and tested in many scientific campaigns. The research team of the Kotelnikov’s Institute of Radioengineering & Electronics of Russian Academy of Sciences and Radioengineering Corporation “Vega” of Russia has developed an advanced technology for the assessment of soil hydrological regimes using multispectral passive (radiometric) and active (SAR) microwave measurements (Haarbrink et al. 2011; Shutko et al. 2006). These technologies are aimed to measure: • Soil surface moisture • Underground moisture at different depths (profiling) • Depth to shallow water table (up to 2 meters in wet areas and up to 3–5 meters in arid/dry areas, (up to 10 m for an active system) • Presence of groundwater resources • Contours and amount of water seepage through hydrotechnical constructions (levees) • Biomass of vegetation above water surface or wet soil The passive system includes an airborne multichannel scanning radiometer “Radius” (2.25, 5.5, 21, 43 cm) and a set of nonscanning radiometers (2.25, 6, 18, 21, 27 cm). The active system includes an airborne multichannel SAR system (3.9, 23, 68, 254 cm). Successful tests of this equipment have been conducted in Russia, Ukraine, Uzbekistan, Turkmenia, Moldova, Bulgaria, Cuba, USA (Krapivin and Sutko 2012).

346

6

Investigation of Regional Aquatic Systems

Microwave radiometry or passive microwave remote sensing is based on measurements of the natural electromagnetic radiation of objects and Earth covers in the millimeter to decimeter wavelength range. Within this zone, land surface radiation is primarily a function of soil-free water content and it is also influenced by other parameters of the soil-canopy system, such as aboveground vegetation biomass, soil density, water salinity, and the temperature of the system. Free water content in soil depends on rainfall rate, artificial watering, shallow groundwater, and processes of soil drying at the interface soil-atmosphere. Hence, microwave radiometry is an important tool for the assessment of soil hydrological regimes. The measure of radiation intensity in the microwave band is referred to as the brightness temperature Tb, which is the product of the emissivity κ, and the thermodynamic temperature Te, within the effectively emitting layer of the object: Tb = κTe. Emissivity is a function of the dielectric permittivity/permeability of the object/ surface of observation. For a land surface, the dielectric permittivity is primarily a function of soil moisture. The higher the soil moisture content, the higher the permittivity of the soil, the lower the emissivity/intensity of radiation/brightness temperature of this piece of land. For a water surface, the dielectric permittivity is primarily a function of the electric conductivity of a water solution which depends on the concentration of salts, acids, the presence of oil films, and many other chemicals. For example, the higher the salinity of the water, the higher the dielectric permittivity of the water solution, the lower the emissivity/intensity of radiation/brightness temperature of this water body. Within the 2–30 cm band, for air temperature t = 10–30 °C, the radiation characteristics of several surface types are shown in Table 6.17. Table 6.18 shows the sensitivity of radiation in the X-band (2–3 cm) and L-band (18–30 cm) to changes in bare soil-free water content, soil density, salinity, and temperature of the soil surface (Krapivin and Shutko 2012). These data show that the main parameter affecting the intensity of a bare soil radiation, practically independent of spectral band, is soil moisture. Based on this sensitivity, it is feasible to estimate the

Table 6.17 Microwave radiation characteristics of some typical surface types

Surface Metal Water surface Very wet soil Very dry soil

Emissivity, κ 0 0.3–0.4 0.55–0.65 0.85–0.93

Tb (K) 0 90–110 160–180 250–270

Table 6.18 Sensitivity of a bare soil microwave radiation to variations in soil moisture (W ), soil density (D), salinity (S), and surface temperature (T ) Wavelength (cm) 2–3 18–30

Spectral Band X L

ΔTb/ΔW (K/g/cm3) -200 -(200 to 300)

ΔTb/ΔD (K/g/cm3) -15 -10

ΔTb/ΔS (K/ppt) 0.05 -0.5

ΔTb/ΔT (K/oC) 0.5 0.1

6.5

Microwave Monitoring of Soil Water Content

347

value of soil moisture without a priori data on the soil parameters. It has also been shown that even for rather high values of biomass, up to 2–3 kg/m2, the plant canopy is still transparent in the decimeter wavelength range. There are different sources of natural microwave radiation in microwaves, which can influence the accuracy of measurements: Milky Way, ionosphere, atmosphere (clouds), land. The minimum influenced by different sources of radio-noise is 2–21cm wavelength band, which is window of transparency at microwaves. The centimeter and decimeter wavelength microwave radiometers are not influenced by the conditions of illumination, by presence of fog, smoke, clouds, and are very useful for measurements in different meteorological conditions. Examples of the operating range and errors for the three main parameters that can be set with the system are listed below: Soil moisture content • The operating range is 0.02–0.5 g/cm3 • Maximum absolute error If the vegetation biomass is less than 2 kg/m2–0.05 kg/m2 If vegetation biomass is greater than 2 kg/m2–0.07 kg/m2 Depth to a shallow water table • Operating range: Humid, swampy areas are 0.2–2 m Dry arid areas, deserts are 0.2–5 m • The maximum absolute error is 0.3–0.6 m Plant biomass (above wet soil or water surface) • The operating range is 0–3 kg/m2 • The maximum absolute error is 0.2 kg/m2 Radar (SAR) survey or active microwave remote sensing is based on measurements of the parameters of artificially radiated electromagnetic signals scattered by the Earth’s covers and objects. Due to high spatial resolution, radar systems are very important instruments of remote sensing from space. Studies of the scattered signal from soil and vegetation have previously been carried out using ground and airborne scatterometers and radars. The results of these studies are the dependences of the scattered signal on soil moisture, soil surface roughness, vegetation parameters, etc. (Chukhlantsev 2006). Theoretical models for electromagnetic waves scattered by bare soil are based on the Kirchhoff approximation. Different expressions for the backscattering coefficient σ 0s were obtained, but most of them can be written as σ 0s = he - h F ðklSinϑÞRs Cos2 ϑ þ σ h2 , h = 4k 2 σ 2 Cos2 ϑ,

ð6:15Þ

348

6

Investigation of Regional Aquatic Systems

where σ is the standard deviation of roughness height, l is the roughness correlation length, k is the wave number in free space (2π/λ), ϑ is the nadir viewing angle, R is the soil reflectivity (Chukhlantsev 2006; Varotsos et al. 2019a). Analysis of formulas (6.15) showed that • The sensitivity of electromagnetic wave scattering by bare soils to reflectivity variations in bare soil surface does not depend on roughness parameters, wavelengths, and observation angle; the dynamic range of scattering by bare soils is no more 9 dB, when the reflectivity varies from 0.05 (very dry soil) to 0.4 (very high soil moisture); 6–8 reflectivity gradients may be obtained with an accuracy of soil backscatter mesurements of 1.5 dB. • The maximum dynamic range of backscatter by bare soils is equal to about 18 dB, when the standard deviation of the roughness height σ ranges from 0.5 to 4 cm. • There are two independent parameters: the standard deviation of the roughness and the reflectivity. It is obvious that to separate and estimate the reflectivity (soil moisture) and roughness parameters, it is necessary to use measurements of at least two frequencies. The best separation of these parameters is observed for observations at wavelengths satisfying the condition: h (λ1) ≈ 1, h (λ1) 0 with depth. In these coordinates, any measurement is represented as a function ξ(φ, λ, z, t) which is a time-dependent random variable. The processing of sets of such values requires the application of special methods (Bukatova et al. 1991). Typically, these methods involve nonstationarity reduction procedures that use spatiotemporal discretization. The choice of spatiotemporal scales is determined by the dynamic characteristics of the water space and by the specific tasks to be achieved. The datasets containing the experimental estimates of circulation characteristics for the marine environment always include periodic, nonperiodic, steady, and nonstationary fluctuations. Therefore the measurement sets should be corrected, taking into account the scalability of the processes under study. So next is the programming of the special features of the measurements. The study of oil pollution of the World Ocean is one of the important problems of environmental monitoring. The global scale of this process requires the implementation of monitoring systems that enable the control of the aquatic environment on a massive scale. Satellite-based systems are one such approach. The analysis of measurement data shows that remote sensing of the water surface through devices using various wavelengths makes it possible to detect oil slicks on the water surface, determine the type of oil, and estimate the oil slick parameters (area, thickness, SOUND intensity). Remote sensing techniques based on microwave radiometry allow the determination of oil pollution of the water surface under arbitrary weather conditions. The widespread application of remote sensing techniques depends on knowledge about the processes of interaction of oil with seawater, its optical and electrical characteristics, the effect of the atmosphere, and other factors that affect the

364

6

Investigation of Regional Aquatic Systems

Fig. 6.31 The emittance of oil spills of different thicknesses. Wavelengths in centimeters are given on the curves. The water temperature is 10 °C and the oil dielectric permeability ε is 2.2

propagation of electromagnetic waves. Combining microwave and infrared (IR) arrays with mathematical modeling techniques is an effective method for discovering oil spills on the water surface. Distinguishing between the emittance and temperature of contaminated and surface freshwater regions is the physical basis for remote sensing of oil spills by microwave and IR radiometers. The emittance, κ of the three-layer atmosphere-oil-water system having smooth section boundaries can be calculated with the formulas proposed by Kondratyev et al. (1997). Figure 6.31 gives an example of the dependence of the emittance variations, Δκ for the atmosphere-oil-water system as a function of oil spill thickness, κ. The oil spill dielectric properties occupy an intermediate position between free space (atmosphere) and water. As a result, the appearance of a film leads to the effect of the resonant medium (to the decrease of the reflection coefficient) and to the increase of the surface radio-brightness temperature. As the oil film thickness increases, the value TY = κT0 (T0 is the surface temperature) initially increases, after which alternating maxima and minima are observed. From Fig. 6.31, it can be seen that in order to remove the uncertainty in the determination of the film thickness, it is necessary to simultaneously measure the radio-brightness temperature by means of radiometers with different wavelengths. Thin films, which form under small volumes of spilled oil or in the vicinity of oil spills, do not change the emissivity of a smooth water surface in the microwave region. However, disturbed surfaces covered by thin oil films are characterized by lower TY, values, which is caused by the suppression of high-frequency components in the sea turbulence spectrum. The value and sign of the radiation contrast of spills on a clean water background depend on the thickness and optical properties of oil films, hydrometeorological conditions, time of day, etc.

6.7

Monitoring of Sea Zones with Oil Pollution

365

Fundamental experimental investigations of oil water pollution by means of microwave and IR radiometers are described by many authors (Nitu et al. 2020). Field experiments showed that satisfactory results were achieved when radiometers with wavelengths of 8–12 μm and 0.34, 0.8, 1.5, and 8.5 cm were used. The sensitivity of the radiometer with respect to the inputs of its antennas is equal to 0.1–0.3 K under the time constant of 1 sec. Experiments were carried out with flying laboratory heights of 100 to 200 m. Calibration of the radiometers was done using blackbody radio luminosity temperatures or through calculations for a calm water surface under a cloudy sky. Thin films are identified with high precision by means of infrared radiometers. Most thick films are recorded with high reliability by microwave radiometers. The oil film thickness can be estimated from the dependence of the radiobrightness temperature variation, ΔTY, on the emissivity ability, Δκ: ΔT Y = Δκ ½T 0- ð1- T Y,atm =T 0 Þ, where T0 is the surface temperature, TY,atm is the atmosphere radiobrightness temperature calculated by radiosensing data, and the value (1 - TY,atm/T0) characterizes the influence of the atmosphere. The geometric parameters of the oil films are defined by means of photogrammetric methods, the basis of which is the spectroscopic photoimage at the various wavelengths. The ranges 0.4–0.5 μm and 0.7–0.8 μm are the most informative for solving this task. Oil products registered with wavelengths 0.4–0.5 μm are a light spot on the dark background of the water image. The image captured by wavelengths of 0.7–0.8 μm helps decipher the water surface. The recording of the oil spills can be carried out by means of active sensing methods. So, for example, an oil spill exposed by the near ultraviolet radiation begins to fluoresce in the visible range (0.6–0.7 μm). This fluorescence can be registered by the adaptive identifier in the real-time mode. The above methods allow us to consider two versions of the monitoring system for gas extraction zone (GEZ) control. The first version corresponds to the oil extraction system located completely below the surface of the water where the stationary position of the remote sensing systems is impossible. In this case, the structure of the monitoring system has submerged measurement subsystems fixed by anchors and emerging subsystems mounted on flying or floating laboratories. The estimation of the concentration of pollutants emitted into the atmosphere is carried out by modeling calculations. For this purpose, the gradient of the gas components and the speed of movement are measured in the surface layer. It is also possible to use the emerged measuring subsystems.

366

6.7.2

6

Investigation of Regional Aquatic Systems

Ecological Monitoring of the Sea Surface of the Oil and Gas Extraction Zones

GEZ according to GIMS technology must have a structure with a selection of different subsystems that reflect various hydrological and hydrochemical conditions. The design of the GEZ is linked to the interpretation and detailing of the structural and functional properties of these subsystems. The experience of such investigations shows that the first stage requires solving the following tasks for the design of the environmental monitoring (SEM) system: • An elaboration of a scientific and economically covered criterion for the assessment of the state of the atmosphere and water environment in the area of influence of the GEZ • Analysis of hydrophysical structures and aquatic formation to be taken into account by the GEZ monitoring system • The preparation of proposals for the structure of software and hardware for the GEZ monitoring system taking into account the actual conditions of the initial information field, the presence of developed technologies for monitoring data processing and environmental quality standards • An elaboration of a version of the project describing in detail the hierarchy of the subsystems and their elements with instructions for the algorithmical and technical infrastructure of the SEM • Preparation of the SEM technical project with the indication of the final variant choosen, and with the substantiation of the set of devices, the software, the informational network components, the control structure, and recommendations for the operation of the system • Preparation of technical documentation for the full set of SEM components with detailed calculation of reliability, operational efficiency, and stability, and with recommendations for possible modernization stages reflecting consideration of the system operational experience The design of the SEM depends on its operating conditions. These conditions are basically related to the hydrophysical properties of the environment. The compact state affects the speed of degradation processes and the transport of the oil product through food chains. All water bodies can be classified by hydrophysical parameters into two categories: frozen and unfrozen. It is obvious that from a modeling point of view, the second category is a special case of the first category when the snow and ice cover thickness is zero. Therefore, the first class is considered in detail. For example, the Shtockman’s GEZ situated in the Barents Sea with high variability of the synoptic situation is accompanied during the year by periods of sharp change in the hydrophysical state of the sea environment. In this zone, the air masses brought from the Atlantic Ocean and the Central Arctic aquatories collide and mix up. The monsoon character of the Barents Sea climate is shown by the presence of winds in the low atmospheric layers which blow from the ocean to the land in summer and in the opposite direction during the whole winter. The winter

6.7

Monitoring of Sea Zones with Oil Pollution

367

season (October–March) is characterized by strong cyclonic activity (the largest number of days with storms and maximal repetition of strong winds). The ice cover achieves its maximum size in April. During the period June to August, cyclonic activity is minimum. The above properties allow us to come to the obvious conclusion that the technical implementation of Shtockman’s GEZ oil pollution measurement system requires the construction of devices protected from large natural loads and significant temperature fluctuations. At the same time, the strong turbulence of the lower atmosphere near the water surface in winter reduces the amount of measurement points in space required to provide the necessary information. If the wind speed is greater than 5 ms-1 and the atmospheric turbulence is high, the atmosphere can also be considered mixed with sufficient accuracy in the 50 × 50 km region. In this case, the measurements can be carried out in two or three places of the aquarium. On the contrary, in summer (June–August) when the intensity of atmospheric turbulence decreases, the measurements should be carried out in the area of influence of each oil well. The estimation of the dispersion of the pollutant and the calculation of its spatial distribution are carried out through corresponding atmospheric dynamics models. The most important stage of the analysis and design of the measuring system to determine the oil pollution level consists in the description of the sea environment dynamics. It is known that an oil spill spreading on the sea surface is subordinated to the superposition of two processes. The first is the spill drift due to sea current, wind, and surface waves. The second is the spill spreading over a calm surface. For the Barents Sea, the second process cannot be taken into account under the synthesis of the GEZ measurement network. This process should only be considered in the hydrophysical model to reflect the full range of the hydrophysical processes. An analysis of many models describing oil spreading over the sea surface gives the following results. The speed of movement of the oil spill equals 60 percent of the current speed and 2–4 percent of the wind speed. When there is ice cover, the wind component is absent. The character of the GCD measuring subsystem that ensures the assessment of the oil concentration into the sea water thickness essentially depends on the possibility of putting algorithms into the information processing unit and reconstructing the forms of the oil spatial distribution based on data fragmentary in space and episodic in time. The synthesis of an adequate model to describe the oil product kinetics in the marine environment under the Arctic conditions here is an important process. Calculations show that oil pollution in the Arctic climate can persist for several years. Therefore, the risk of its accumulation is great. The transformation processes of oil pollution into other forms under the Arctic climate are slowed down substantially by comparison with the analogous processes in warmer waters. It is known that the contributions to the self-cleaning process of sea water are 0–70% for evaporation, 15–30% the photooxidation, and 2–7% for biological use. In summer conditions, the transformation of 0–60% of the raw oil mass takes place during 40 days. In winter, these estimates are reduced by about three times. In this period, the process of oil products’ accumulation from the ice comes into action.

368

6 Investigation of Regional Aquatic Systems

The intensity of this process depends on the ice porosity. A noticeable contribution to water purification is introduced by drift ice which ensures the removal of oil in quantities estimated at 25% of its weight. However, the involvement of ice in the process of cleaning the surface of the sea from oil pollution has had negative results. The oil trapped by the ice is transported to other bodies of water and practically the same volume returns to the aquatic environment when the ice melts. This exchange between aquariums must be considered in SEM design, as oil contaminants can reach a controlled aquarium in this way. That is why in winter it is necessary to control the level of ice pollution in adjacent water bodies. Shtockman’s GCD zone is characterized by such generalized synoptic and hydrophysical conditions that provide the conditions when this zone is ice-covered for 5 months (from February to June). During the period from February to March, the ice drifts to the North or North-West. Below this, the drift speed is estimated at 100 km/month. As a result, the contaminated ice reaches the central Arctic basin, where it thaws and the oil enters the water. Shtockman’s GCD zone is under the influence of the West Novaya Zemlya branch current which forms the eastern boundary of the cyclonic cycle in the central trough region. The configuration of the formation of moving water masses suggests that in order to discover the pollutants transported to Shtockman’s GCD by other aquatic species, it is necessary to check its southern and eastern boundaries. The oil products are characterized by a multicomponent process that expands the set of conditions of its behavior in seawater. Among the most important transformation processes of oil products in seawater, it is necessary to mention the following: dissolution, evaporation, spreading in the deep layers in the form of droplets, oxidation, absorption by suspended organic matter, biosedimentation, and bacterial decomposition. The objective laws of formation of all these processes were studied and therefore it is necessary to take them into account in SEM design. This will allow to reduce the standards in the composition of the measuring equipment and the regime of making the measurements. Biodegradation of oil hydrocarbons is an important oil removal process in the marine environment. This process is linked to the functioning of bacterioplankton, phytoplankton, and other marine animals. Accounting for this process in SEM is possible at the expense of including Shtockman’s GCD aquatic ecosystem model in the SEM software. The basis of the conceptual model is represented in Fig. 6.32. An elaboration of Shtockman’s GCD ecosystem model requires consideration of the physical structure of the environment. This structure is seasonal in nature. The parameterization of its vertical structure is possible considering the results obtained earlier by many authors (Legendre and Krapivin 1992; Legendre and Legendre 1998; Kondratiev et al. 2002a, b; Nitu et al. 2020). The scheme of Fig. 6.32 represents the typical vertical division structure of the marine environment. This allows the realization of the modeling scheme of Fig. 6.33.

6.7

Monitoring of Sea Zones with Oil Pollution

369

Fig. 6.32 Vertical structure of the ice-covered aquatory

Fig. 6.33 Main structure of the ecological monitoring system in relation to the gas condensation deposits zone

370

6

Investigation of Regional Aquatic Systems

An ecological monitoring system of practically any anthropogenic loading object requires consideration of all the elements predicted by GIMS technology. However, the physical operating conditions of SEM require the revision of the formal structure of GIMS. It is obvious that in the case under consideration, the following standards must be provided: • Measuring devices must operate reliably at low temperatures. • The measurement network infrastructure does not require additional construction. • The information network must ensure the relevance of the data in the context of international standards. • The data processing subsystem must reduce the conditions created by the demands on the measurement subsystem. • The data presentation subsystem must allow the GCD administration the ability to evaluate multiple forms of the state of the environment. • The SEM is to be combined with other information systems (regional, national, and international). Measuring devices are to function reliably under low temperatures. The experience of the composition of GIS and expert systems that have the function of nature protection speaks of the necessity of observing the conception and subsequent stages when implementing natural environment monitoring systems. This experience allows us to propose the SEM infrastructure in the form depicted in Fig. 6.33. The SEM software package must be oriented toward the implementation of an algorithmic set that allows the implementation of the following minimum set of functions: • Comprehensive assessment of the ecological status of the water table • Assessment of the ecological status of the reservoir for each of its elements • Assessment of the integrated ecological situation of the reservoir at the point, in the square and in the total area • Identification of the source of pollution • Predictive assessment of the ecological state of the reservoir, integrated assessment of the ecological state of the aquatory Answers to questions that arise when implementing these functions and making decisions require the conversion of measurement data into an acceptable format. This can be done through the software package listed below: • Calibration of measurement data • Filtering measurement data • Technical tools for the scanning and mixing functions of the measuring devices with the information unit • Making decisions about the existence of an external situation • Spatiotemporal coordination of multiple data types

6.7

Monitoring of Sea Zones with Oil Pollution

371

• Spatial interpolation of measurement data and formation of spatial images • Reconstruction of the spatial distribution of environmental characteristics based on measurements that are fragmentary in the water table and at depth • Calculation of the kinetic characteristics of pollutants in seawater under climatic conditions of the sea area • Calculation of the content of gases and solid particles in the atmosphere above the GCD aquifer • Assessment of the ecological status of the control zone according to the given criterion • Synchronization of information flows and ensuring their availability to the data processing center in a volley • Realization of physicochemical processes in the atmosphere-sea-GCD system • Formulating short-term and long-term forecasts of common ecological conditions in the GCD zone • Identification of the sources that cause the disturbance of the ecological standards in all the controlled parameters, taking into account the accepted criteria that have been set • Implementation of computer mapping algorithms • Selection of the program system for database control, data processing in the SEM information network, and their accumulation in the database • User interface software • Database maintenance software. • Reconstruction of functions that are omitted due to incomplete and inaccurate information • Accumulating knowledge about different specific and typical situations • Formation of operational data in emergency situations The algorithmic support of the SEM database has a double burden. On the one hand, the accumulation of data on the functioning of the ecological system of the GCD zone allows us to increase the reliability of obtaining assessments of ecological states and reduce the demands on the measurement subsystem. On the other hand, the SEM database can be used as part of the regional or national database. The operation of the SEM is ensured by the correlation between the measurement subsystem, the reconstructed database, and the model. Continuous monitoring can be performed with sufficient stability only under adaptive algorithmic support. The dynamic correlation of operating regimes for all SEM subsystems is shown schematically in Fig. 6.34. According to this scheme, the measurement subsystem can operate in discrete regimes together with the model. Emergency detectors work only in continuous mode.

372

6

Investigation of Regional Aquatic Systems

Fig. 6.34 A mutual adaptive scheme using the ecosystem model and measurements for realizing the GIMS technology in the SEM mode operation

6.7.3

Estimation of the Oil Hydrocarbon Pollution Parameters

The problem of identification and assessment of oil pollution parameters in the water environment has been studied sufficiently widely. Existing methods of oil discovery in the water may be divided into two groups, surface film and suspension, corresponding to the state of the oil hydrocarbons. The oil and oil products, by their physical-chemical properties, can form in the sea water films, clots, emulsions, and solutions. The oil films have thicknesses from fractions of a millimeter to several centimeters. All these determine the methods of monitoring oil pollution. The choice of monitoring method is determined by the oil pollution level. It is known that oil pollution effects affect the physical-chemical processes taking place in sea water. Specifically, the surface strength for oil and oil products is two to four times less than for nonpolluted water. The thermal conductivities and thermal capacities of water and oil are distinct and equal respectively to 0.599 W/m/K and 4.187 KJ/kg/K for water and to 0.15 W/m/K and 1.7–2.1 KJ/kg/K for oil. These distinctions influence the many processes in the atmosphere-water system. Oil pollution films decrease the thermal conductivity and thermal capacity of the upper sea layer. They alter the evapotation process reducing it by 1.5 times or more, they disrupt gas exchange between the atmosphere and sea water, and change water temperature. All these results are used for the design of measuring devices to estimate the oil pollution level, including microwave and optical tools described by Krapivin and Shutko (2012). Here we consider optical tools.

6.7

Monitoring of Sea Zones with Oil Pollution

373

Fig. 6.35 Spectral reflection coefficients for oil and water

Along with the aforementioned effects of sea water oil pollution, there is an optical effect which is determined by the change in sea surface albedo and by the variation of the internal optical properties of sea water. Theoretical approaches to the problem of light spreading in the sea water environment or its reflection from the sea surface are connected with the consideration of various tasks. For example, the calculation of the reflection coefficient in different sea environment states is given by Kabanov et al. (2000). Theoretical and experimental studies show the existence of contrasts in the reflecting properties of oil films and a nonpolluted water surface. Figure 6.35 shows these contrasts. Certainly these contrasts are functions of many parameters: wavelength, oil film thickness, vision angle, salinity, water roughness, light intensity, and content of other contaminants. As shown by Krapivin et al. (2016b) using the spectral measurements at a range of 380 nm to 700 nm allows one to have a reliable technique for the detection of oil pollution on the water surface. The adaptive identifier as optical decision-making system (ODMS) (Mkrtchyan et al. 2018) designed for use in different conditions of illumination is described in Chap. 4 (Fig. 4.2). The ODMS consists of an 8-channel device that records light scattered in water or reflected from the surface in the visible range, of an informational interface and software (Fig. 6.36). A principle of the functioning of the system consists in entering the numerical code n2[7128] of spectra {Tn(λ): λ2[λi, λi + 1], i = 0,1,...,n - 1} on the basis of which the solution about the existence of oil pollution is obtained and the assessment of its parameters is carried out. Specifically for n = 7, the basic spectral characteristics of the channels are given in Fig. 6.37. We see that the spectral characteristics of the ODMS interference filters have pronounced maxima and insignificant crossings. The maximum conductance of the channels is observed under λ1 = 398 nm, λ2 = 439 nm, λ3 = 480 nm, λ4 = 510 nm, λ5 = 583 nm, λ6 = 631 nm, and λ7 = 680 nm.

374

6

Investigation of Regional Aquatic Systems

Fig. 6.36 Scheme of the functioning of the adaptive identifier. The results of the measurements are represented by the scale of the analog-to-digital converter (a range from 0 to 212). The ODMS first transforms the light signal to an electric one in the range 5 V and then to digital code in the interval [0,4096] Fig. 6.37 Basic spectral characteristics of ODMS8 light filters

%

40

30

20

10

0 380

440

500 560 Wavelengths, nm

620

680

ODMS is designed to learn from the measurement of spectral characteristics and the simultaneous independent measurement of the content of chemical elements in the aquatic environment. As a result, a standard bank is formed in the knowledge base, comparison with which provides the solution of the identification problem. The software of the adaptive identifier provides different algorithms for the solution of this problem, and cluster analysis is among them.

6.7

Monitoring of Sea Zones with Oil Pollution

375

ODMS can be used in different fields where the quality of water should be estimated or the presence of a particular set of chemical elements should be revealed. The adaptive identifier solves these problems by real-time monitoring of the aquatic environment. In the stationary version, it allows the tracking of the dynamics of the water quality in a stream, and when placed on a ship, it allows the measurement of water pollution parameters along the route. The functionality of the adaptive identifier can be extended by increasing the volume of standards in the knowledge base. The use of a natural light source allows the examination of soils, the identification of oil products on a water surface, the determination of the degree of pollution of atmospheric air, and the estimation of the conditions of other objects of the environment, whose spectral images may change. That is why the adaptive identifier is a universal measuring device, the use of which in the SEM allows one to have operative information about not only on arising of oil pollution but about other pollutants which can appear in the GCD zone.

6.7.4

Expert System for Detecting Pollutant Spills on the Water Surface

Adaptive environmental recognition technology allows us to synthesize an expert system for adaptive identification of environmental parameters (ESAIEP). The ESAIEP has the following main components: • • • •

A compact multichannel spectropolarimeter (MSP) An informational interface with the computer (IIC) Software (STW) An extending database (EDB)

The STW implements a set of algorithms for processing the data flows being received from the MSP and secures the service functions such as visualization and control of the measurements regime. The EDB consists of the set of standards for spectral images of pollutant spots represented by points in the multidimensional vector space of indications calculated beforehand on the strength of teaching data retrieval sets. The working principle of ESAIEP is based on stabilizing the light flux fluctuations at the MSP output and converting it into a digital code. The subsequent processing of this data depends on the STW structure that includes different 2D image recognition algorithms. The adaptability of the identification process is determined by the level of accumulation of knowledge about the variation characteristics of the intensity and polarization properties of the light reflected from the water surface. STW includes the algorithms that allow, in case the identification of the pollution point is indeterminate, to make a specific decision based on a visual analysis of the spectral image. This process is carried out in dialogue with ESIEP. In this case, the operator can specify the decision made in the knowledge base as a template.

376

6

Investigation of Regional Aquatic Systems

The principal scheme of the STW unit that ensures the recognition process is some transformation F. The light intensity ξji recorded at time ti by the channel λj comes to the algorithm F where the process of distinguishing between hypotheses H0 and H1 is performed. The ESAIEP operator determines the initial data γ, α, and β, and also decides which parameters ui = (u1,...,uτ) will be calculated based on the measurements ξji . The service unit IIC enables the formation of the vector ui using the statistical characteristics of the sets ξji or using direct measurements. A priori information characterizes the type of distribution fα(ui). The function n

Li =

n

Ψj = j=1

j=1

f α1 uji =f α0 uji

is compared to its limit values Li, min and Li,max. In the first stage, these values are selected as rather arbitrary, but then they are changed till the maximum discriminating accuracy between the hypotheses H0 and H1 is achieved. We also have Li, min → Li, min and Li, max → Li, max . The values Li, min and Li, max are stored in the EDB. After training the operation of the expert system is limited only by the measurements volume determined by the operator, which is based on the reason of the possibility of achieving statistical reliability and regime adherence in real time. The operator has two possibilities to regulate this regime by specifying the volume of set ξji or by specifying the time interval for its accumulation. Usually the time interval is fixed. For example, ODMS ensures reliable dicrimination between the competing hypotheses under a fixed time interval of observation equal to 1 s. Operator contacts with the different ESAIEP units occur via the man-machine interface IIC which secures a selectivity in the control for the functions of all units. Under the existence of an oil spill on the water surface, the ESAIEP analyzes its thickness, age, source, and geometrical configuration. In this case, the most informative channels have wavelengths λ = 398, 439, and 480 nm. In the case when there are dissolved and suspended oil components, the system estimates their concentration. If in the EDB there exists data about the hydrodynamic parameters, it calculates the spatial distribution of the concentration of the oil components. The ESAIEP was tested more than once during several Russian/American and Vietnam/Russian hydrophysical expeditions. The results of these expeditions spread over many contaminants of anthropogenic origin. Here we consider only one example of oil pollution. Namely it is a matter of investigations by means of ODMS of some water bodies situated on the territory of South Vietnam. The most important aquatory of South Vietnam is the area Ω of South-China Sea bordering on Vung Tau city where there exists the exploration and extraction of oil from the sea bottom. In 1975, the Vietnam Oil & Gas General Directorate was set up. The majority of the oil companies (Vietso Petro, PSCT, Gas Company, Petroleum Assembling Enterprise Cooperation) realize national and international projects concerning the exploration and exploitation of the GEZ.

6.7

Monitoring of Sea Zones with Oil Pollution

377

The quality of sea water in this area is mainly dependent on the GCD zone and the flows of the Mekong and Saigon rivers. The aquatory Ω is restricted on the north by the coast line running in a north-easterly direction, on the west and east by the meridians 106° and 109°, respectively, and on the south by the parallel 8.5 degrees north latitude. The water current scheme of this aquatory is considered as given. According to the existing data, it can be approximated by a seasonal scheme with the attachment to winter (τw), spring (τs), summer (τu), and fall (τa). On average during the fall and winter, the currents are directed along the coast line in a north-easterly direction (α = 45°). During the remaining time, the currents have the opposite direction (α = 225°). The speed value is varied insignificantly both in the space and in the time. Near the coastline this speed equals to 2–6 km/h. The further one gets from coastline, the speed variations are 0.7–4.8 km/h. It is supposed that depths the hij are constant on the whole aquatory Ω. The vertical gradient of the water temperature is considered as negligibly small. The synoptic regime during the year is approximated by a binary situation: the rainy season exists during May–October, from November to April the dry season takes its place. Table 6.23 consists of some results of oil pollution measurements by means of AI-1. The simulation results are given in Figs. 6.38 and 6.39. As it follows from Fig. 6.39 under the above suppositions, the average annual distributions of oil hydrocarbons on the aquatory Ω are essentially nonuniform. Taking an average by the seasons eliminated the influence of pollution drift by the currents and revealed the places where oil hydrocarbons always come to the upper sea layer. The degree of oil hydrocarbon accumulation in the surface-water layer varies between the limits of 25–130 mg/m2. This is several times higher than the oil hydrocarbon concentration in the open ocean and significantly lower than in heavily polluted waters.

Table 6.23 Data processing results of measurement during the field study of oil products’ content in water (mg/L) in the South Vietnam territory

Depth (m) 0.1 0.5 1.0 1.5 2.0 2.5 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

Saigon River Day hours 9 15 0.052 0.101 0.061 0.094 0.063 0.085 0.066 0.103 0.054 0.077 0.051 0.049 0.062 0.068 0.043 0.094 0.048 0.083 0.059 0.074 0.062 0.052 0.041 0.043 0.042 0.049 0.037 0.055

18 0.047 0.06 0.053 0.061 0.088 0.064 0.048 0.034 0.026 0.055 0.084 0.054 0.077 0.038

Dongnai River Day hours 9 15 0.019 0.019 0.012 0.013 0.012 0.012 0.010 0.010 0.009 0.010 0.008 0.007 0.006 0.008 0.004 0.009 0.001 0.002 0 0 0 0 0 0 0 0 0 0

18 0.011 0.015 0.013 0.011 0.007 0.006 0.005 0.004 0.006 0 0 0 0 0

378

6

Investigation of Regional Aquatic Systems

Fig. 6.38 The ratio, d, of the oil hydrocarbon content in the upper sea layer of 1 m depth to the content in the thick water layer as a function of geographical coordinates. The modeling results are averaged over all seasons. It is assumed that the oil hydrocarbon concentration in ΩR[ΩP and in GCD zone is constant and is equal to 0.06 mg/L

Fig. 6.39 The map-scheme of yearly averaged distribution of oil hydrocarbons in the GCD zone of Vung Tau city in 2001. These estimates are calculated through the model based on the initial data of 1994. The scale step is 0.01 mg/L

Figure 6.38 gives the forecast of oil hydrocarbon distribution over the entire aquatory Ω. Here it is assumed that the oil hydrocarbon concentration at the boundary ΩB is constant equal to 0.001 mg/L and is brought into Ω according to the scheme of the currents stored in the ESAIEP database. As can be seen from Fig. 6.38, the stabilized distribution Oðϕ, λ, z, tÞdzÞ is quite Ω

well tuned to the direction of the currents. The formation of a field with an increased oil hydrocarbon concentration located near the Vung Tau shelf zone is explained by the assumption that at the GCD site the following conditions take place: O(φ, λ, z, t) = 0.09 mg/L in the upper layer, and O(φ, λ, z, t) = 0.05 mg/L in the remaining water depth. Under other assumptions, the structure of the distribution O(φ, λ, z, t) is unchanged. Only pollution scales are changed.

6.7

Monitoring of Sea Zones with Oil Pollution

379

Table 6.24 Results of model-based calculations of pollution levels of the Saigon river based on the ODMS measurements. The river speed is equal to 3 km/h x 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 M 15A Δ

NO3 mgL-1 0.80 0.81 0.83 0.92 0.96 1.11 1.26 1.35 2.09 3.41 4.18 6.01 6.12 5.99 6.03 6.07 6.8 17.08

P2O5 mgL-1 0.10 0.12 0.15 0.21 0.24 0.35 0.42 0.44 0.91 1.23 2.42 3.09 3.17 3.18 3.21 3.19 7.32 20.45

TDS mgL-1 50.1 50.1 49.9 50.1 52.3 54.2 60.9 65.8 70.3 77.7 80.1 90.4 91.3 91.2 91.1 91.1 4.01 30.19

TSS mgL-1 15.5 15.5 15.4 15.5 15.9 16.7 17.2 19.8 25.1 30.2 37.3 41.8 42.7 41.9 41.8 41.9 130.5 28.13

BOD5 mgO2L-1 2.2 2.4 2.5 2.8 2.9 3.3 4.0 5.1 6.2 6.8 7.7 8.3 8.4 8.3 8.5 8.6 58.3 30.08

Oil mkgL-1 0.40 0.40 0.41 0.42 0.44 0.45 0.46 0.47 0.53 0.56 0.58 0.60 0.61 0.63 0.65 0.65 0.92 29.34

Measurements were made during 15 November 1994 from 10:00 to 15:00 hours Designations: x is the distance from HoChiNinh river port along the Saigon river downstream (km), Δ is the model error (%), TDS Total dissolved solids, TSS Total suspended solids, BOD Biological oxygen demands, symbols M and A correspond to the model and experiment, respectively

The establishment of the SEM for the GCD located in the South China Sea provides for the discrimination of oil pollution sources. This is why the ODMS was used to estimate the oil pollution of the main rivers of South Vietnam (Saigon and Dongnai). In addition, the task of distinguishing between oil and salt spills on the water surface of reservoirs where industrial biological production is carried out was solved. Also, controlling the water quality in the Dongnai River is important for the ecological service of Ho Chi Minh City. Table 6.24 and Fig. 6.40 represent some experimental data. It appears that the Saigon River is characterized by unstable formations of oil clots that move randomly by depth. The predictability of the Saigon River’s pollution level is 60–70 percent over the course of a day. It is clear that inclusion in SEM operations to control the pollution of the Saigon River requires additional information on the dynamics of rising and falling tides. This information must be parameterized and stored in the SEM knowledge base or come from the measurement subsystem.

380

6

Fig. 6.40 The results of field experiments during the Vietnamese/Russian hydrophysical expedition of 1994 when the oil pollution of the Saigon and Dongnai rivers was studied by ODMS. The results of the measurements are marked by the symbols «•» and «o» for the Saigon river and by the symbols «+» and «□» for Dongnai river at 14 and 15 November 1994, respectively. The scale step is 0.05 mg/L

Investigation of Regional Aquatic Systems

Oil

2

1

0 9

12 15 The day-hours (HoChiMinh City)

18

Currently, the Dongnai River has a low level of pollution. The measurement data obtained with the ODMS show a high stability of the distribution of oil pollution in this river. Therefore, the implementation of the SEM function to control the oil pollution of the Dongnai River can be considered as a future stage. Finally, Fig. 6.40 represents the hourly dynamics of the oil pollution level for both rivers. ODMS measurements are made for the Saigon River in its middle in downtown Ho Chi Minh City and for the Dongnai River at the drinking water station 30 meters from the waterfront. The comparison of the curves from Fig. 6.40 shows that the control of oil pollution near this station should be carried out by ODMS in the continuous regime and has the ability to control the water standard. It is more difficult to control the water standard in the Saigon River where the hourly fluctuations of the pollution level range between 100 and 300 percent. Such oscillations are caused by complex hydrodynamic conditions of pollutant spreading. A combination of high current velocity, ebb and flow of tides, and high turbulence causes the formation of plumes of pollutants that move as closed volumes of water.

6.8 6.8.1

Diagnostics of the Angara/Yenisey River System Angara/Yenisey River as Hydrochemical System

The Angara River is a major tributary of the Yenisei River. It flows rapidly north of Lake Baikal for about two-thirds of its 1779 km, before turning west at its confluence with the Yenisei River, which flows north to the Kara Sea, draining an area of about 2.58 million km2 along a 4102 km journey. The Yenisei’s flow rate in the Kara Sea

6.8

Diagnostics of the Angara/Yenisey River System

381

fluctuates widely, averaging 19,800 m3/s, and up to 130,000 m3/s, during the spring run-off (Polyak 2004). The share of the Angara-Yenisei River system (AYRS) in the total inflow of rivers into the Kara Sea varies by 22.1–26.4%, which requires an assessment of the role of the Angara-Yenisei River system in Arctic water pollution (Holmes et al. 2001; Herrault et al. 2016; Obolkin et al. 2016; Makkaveev et al. 2019; Varotsos and Krapivin 2020a, b; Kondratyev et al. 2002a,b). The growing interest in Siberia’s environmental problems is mainly due to the potential global consequences of Siberian Rivers pollution, given a variety of possible sources of pollution and their pathways of spread, including atmospheric and river transport. The Angara River as the main tributary of the Yenisei River flowing into the industrial area has a negative cumulative effect on water quality and contributes to the negative changes of the Angara/Yenisei hydrological and hydrochemical system. The Angara River is the only run-off of Lake Baikal (Obolkin et al. 2016; Dementiev et al. 2015; Karnaukhova 2008; Khodzher et al. 2018; Bychkov et al. 2018). Very negative environmental effects on the Angara/ Yenisei River system come from biogenic substances, including nitrogen and phosphate compounds (Karnaukhova 2008). Existing AYRS experimental water quality measurements reflect pollutant content mainly at local sites. Savichev and Matveenko (2013) characterized the mineralization of the surface waters of the Angara River in the Boguchan reservoir zone as 20–40 mg/L. Sorokovikova (1993) showed that many contaminants have an irregular distribution along the Yenisey. Concentrations of nitrogen, phosphorus, and organic compounds were found to increase and their dynamics in space and time changed. Seasonal concentrations of mineral nitrogen, phosphorus, and sulfates were estimated in Kuzmin et al. (2014) and Tarasova and Mamontova (2014). This section examines the pollution of the Angara/Yenisei River as a whole using spectral optical monitoring tools and the AYRS Simulation Model (AYRSSM). This provides an adequate description of the pollution of the Angara/Yenisei River flow taking into account for the first time the role of bottom relief, improving the accuracy of the AYRS pollutant concentrations in the Kara Sea (Krapivin and Phillips 2001a, b; Krapivin et al. 1998, 2015a, b). Many authors note that the coastal waters of the Kara Sea are characterized as moderately polluted, especially near the settlements of Amderma and Dikson (Andreeva 1998). The AYRSSM gives consistent modeling results for these concentrations compared to their episodic measurements (Polyak 2004; Flint 2015). The difference between data from different authors can be greater than tens of percent. AYRSSM provides stability of modeling results, which is one percent. The AYRS study was conducted by many authors. Savenko et al. (2016) presented much evidence from previous hydrochemical water studies at the Yenisei estuary and in neighboring aquatories of the Kara Sea, paying attention to the transformation processes of trace elements, phosphates, and organic matter. As mentioned above, the Angara River is one of the largest tributaries of the Yenisei River (≈24 percent of its runoff) and plays an important role in the pollution of the Yenisei River below the village of Strelka (Kondratyev et al. 2002b; Vyruchalkina 2004). The Angara-Yenisei region is characterized by significant industrial activity,

382

6 Investigation of Regional Aquatic Systems

including aluminum production in Krasnoyarsk and Angarsk. Many studies on the water quality of the Angara/Yenisei River system have focused on monitoring and improving the evaluation process of highly reliable drinking water resources (Yu and Grebenshchikova 2020). The main regulation of pollutant flows—discharged into the Kara Sea from the discharge of the Angara/Yenisei River system—is the five hydroelectric dams at Krasnoyarsk and the Sayano-Shushenskoe at Yenisei and Irkutsk, the Bratsk and Ust-Ilimsk at Angara. These dams are responsible for fluctuations in the flow regime and water balance of the Angara River (Vyruchalkina 2004). The Angara River has an area of about 1.1 × 106 km2 and its outflow from Lake Baikal is estimated at 1855–1910 m3/s, while toward the Yenisei River it is 4, 350–4530 m3/s at the expense of its tributaries. A special feature of the Angara River is the uniformity of its runoff during the year in contrast to the Yenisei River, where the ratio of maximum to minimum runoff is equal to 80 in the Krasnoyarsk zone (Tarasova and Mamontova 2014). The Irkutsk reservoir receives water from Lake Baikal with annual fluctuations of 15.5%. The consequent long-term dynamics of contaminant concentrations in water and sediments is determined by the location of the dams and the bottom relief (Dementiev et al. 2017; Karnaukhova 2007a, b; Pastukhov et al. 2019; Karnaukhova 2007b). The impact of industrial development in the area under consideration can be evaluated taking into account all the cities with their dams, reservoirs, and industrial structures. The main component of all pollutants discharged into the Angara River is sewage water (85%), which is mainly distributed between the Bratsk Reservoir (45.2%) and the Ust-Ilimsk Reservoir (39.8%). Every year, the Angara River receives 0.66 km3/year of sewage water, about 2% of which is treated at normal water quality levels. Oil products, toxic heavy metals, phenols, and organic substances from the chemical, industrial, timber industry, and municipal services are considered top pollutants. In the timber industry, this plays a significant role in the pollution of reservoirs due to submerged wood in the dams’ areas where the wood pulp decomposes slowly due to the low temperature of the upper water. As a result, areas flooded with decomposed wood waste are sources of nitrogen, phosphorus, and phenols in the river water. In relation to this, the overwhelming volume (93%) of wastewater produced in the Irkutsk region reaches the Angara River basin. The variety of estimates of AYRS pollution levels are made in different studies, as explained by the measurement in different years and seasons, as well as by the use of different instrumental tools. Most in situ surveys are carried out locally in existing water reservoirs and especially in the Angara source, Irkutsk and Bratsk reservoirs (Yushkov et al. 2015). Yushkov et al. (2015) examines the impact of Bratsk-based economic entities on the environmental quality, taking into account the stable sources of nonferrous metallurgy, the heat power industry, and the pulp and paper industry. Significant research was carried out in Ruhakana (2004) to assess changes in the hydrological regime of the Angara River before and after the construction of the fourth dam. As a result of this regulation, the seasonal water cycles of Lake Baikal and the Angara River change the long-term aftereffects of which are not

6.8

Diagnostics of the Angara/Yenisey River System

383

assessed. Nemirovskaya (2015) found that the wider variability of hydrocarbon concentrations in surface waters was characteristic of the frontal zones of the Yenisei River mouth (4.8–69 μg/L). Herrault et al. (2016) have demonstrated the effectiveness of optical remote sensing satellite observations in the operational assessment of AYRS water quality including dissolved organic carbon. The main idea of this study is to combine the use of optical sensors with data processing algorithms and modeling tools, both to develop a functional AYRS water quality data source and to evaluate the final pollutant flow to the Kara Sea at the Yenisei’s estuary. Specifically, this study presents key empirical results from field measurements and water sampling analysis that includes a specific list of chemicals. Concentrations of these pollutants are used to evaluate AYRS through the optical inverse task solution based on spectral optical observations. Because the bottom sediments are not analyzed in this study, the results of previous relevant studies are used (Karnauchova 2008, 2011; Jagus et al. 2013; Mazaeva et al. 2014). It is therefore obvious that the present work stems from the need to extend previous studies in this area.

6.8.2

Angara/Yenisey River Simulation Model

The Yenisei River divides Western and Eastern Siberia. Figure 6.41 explains the geographical location of the Angara/Yenisei River system. As can be seen from this map, the availability of many sites along the AYRS for in situ measurements and sampling is low. Field measurements in AYRS outlets are constrained by limited access to sampling sites. Therefore, real knowledge of the AYRS status under the Angara mouth is often only possible with AYRSSM (Krapivin et al. 2017c). According to the current literature, the main sources of pollution of the Angara / Yenisei River are the main industrial centers of the cities of Krasnoyarsk, Irkutsk, Bratsk, Angarsk, Usolye-Sibirscoe, and Svirsk. The study of these sources and the subsequent conclusions about water quality have already been conducted by many authors (Vakulovsky et al. 2008; Phillips et al. 1997). Specifically, for the pollution of the Angara-Yenisei River, in the past, detailed information has been received from various missions, such as: 1. In the summer of 1995, the US-Russian environmental and hydrophysical expedition took place on the Angara and Yenisei rivers in Siberia. The following organizations participated in this mission: US Naval Research Laboratory (Washington), US Naval Academy (Annapolis), Global Technologies Inc. (Idaho Falls), Institute of Ecoinformatics of Russian Academy of Natural Sciences (Moscow), Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences (Moscow), Irkutsk State University (Irkutsk). The 44 main results of this mission have been published in Krapivin et al. (1998). 2. In the summer of 2019, the hydrochemical expedition was organized by the Institute of Ecoinformatic Problems, Russian Academy of Natural Sciences

384

6

Investigation of Regional Aquatic Systems

Fig. 6.41 Location of the Angara/Yenisei River system in Central Siberia, Russia

(Moscow). This study was based on spectral optical field measurements and water sampling. During this mission, three optical multispectral devices were used to measure water quality directly in situ and by water sampling. Water samples were delivered to the laboratory where optical spectral and chemical analyses were performed. Maps of in situ measurements and sample locations are shown in Fig. 6.42. Three spectral optical devices and a cluster of computer components with suitable software, were used in this study, including data processing algorithms and AYRSSM. The features of the optical devices are shown in Table 6.25. As an example, Fig. 6.43 shows the main structure of the universal 8-channel spectrophotometer (US-8). Field measurements were performed using an 8-channel universal pretraining spectrophotometer (US-8) to identify spectral images from 380 to 700 nm. The software of US-8 has algorithms for the identification and recognition of spectral images of the aquatic environment almost in real time. The main structure of the US-8 is shown in Fig. 6.43. The US-8 can use two light sources including sunlight or artificial light (a halogen bulb, a tiny xenon pulse lamp, etc.). Using sunlight requires a calibration process that runs automatically for a period of 1 second. The in situ measurement procedure using the US-8 consists of immersing the sky-light adapter (1 m long) in the water environment. Measurements were made by vessels in areas of interest and are shown in Fig. 6.42. The incoming light reaches the adapter, and the

6.8

Diagnostics of the Angara/Yenisey River System

385

Fig. 6.42 Fragments of the Angara/Yenisei River system and locations of spectral optical measurements and water sampling at a distance from Lake Baikal to Lesosibirsk Table 6.25 Characteristics of the spectral optical systems used in the study of the Angara/Yenisei River system System Universal 8-channel spectrophotometer (US-8)

35-channel spectrophotometer (SP-35)

128-channel specroellipsometric system (SS-128)

Characteristics The wavelength range is 380–700 nm. The weight of the measuring device is 6.2 kg. The spectrum recording time is 0.8 sec. The US-8 can carry out in situ measurements (with and without sampling). The wavelength range is 300–800 nm. The weight of the device is 3.9 kg. The spectral image recording time is 0.5 sec. Spectral measurements are performed when water samples are delivered. The spectral range is 380–780 nm. The weight of the measuring device is 5.4 kg. The time to record two spectral images is 0.6 sec. The water sampling is required.

386

6

Investigation of Regional Aquatic Systems

Fig. 6.43 View of an 8-channel adaptive optical universal spectrophotometric decision-making system (US-8) used to study the characteristics of in situ spectral water and in the laboratory during water sampling

analog-digital converter provides the relaxation coefficient as an indicator of water quality. The 35-channel spectrophotometer was used to analyze a water sample under laboratory conditions. In this case, the US-8 software is used. For more accurate spectral monitoring results, the 128-channel spectroellipsometer is used for spectral analysis of water sampling. In all cases, the average measurement time is 0.5–1.0 second. The US-8 universality consists in adapting it to the following three measurement modes: • Direct measurement of the water relaxation coefficient by immersing part of the sky-adapter into the water environment • Formation of a spectral image of the water sampling located in the special reservoir • Formation of a spectral image of the water surface when the sky adapter is directed toward it Water quality is assessed through spectrophotometry / spectroellipsometry inverse task solution or/and spectral images’ recognition. US-8 and SP-35 provide an S(λ) spectrum that reflects the relaxation coefficient depending on the wavelength λ. The recognition of such spectrum is performed using cluster analysis and calculation of the distance between the spectra based on the spectral etalons database. The most effective algorithm for spectral images recognition is the transformation of spectral space into vector space when the optical spectrum is changed by the vector that reflects the shape of the spectrum. An example of a database item is given in Fig. 6.44. Spectrum processing and optical inverse task solution are applied for the universal case of spectroellipsometric measurements. According to the basic equation:

6.8

Diagnostics of the Angara/Yenisey River System

387

Fig. 6.44 Spectral images of ZnSO4 dissolved in water at concentrations shown on the curves

ρ = r p =r s = TanΨ expðiΔÞ, where rp and rs are complex amplitude reflection coefficients, SS-128 provides spectra for polarizations p and s for the water sample η: • SΨ(λ, η)—spectral distribution of the tangent of the spectroellipsometric angle Ψ • SΔ(λ, η)—spectral distribution of the cosine of the spectroellipsometric angle Δ The transformation of the optical spectral space into the vector space is achieved by direct evaluation of the specific characteristics of the spectrum. In this case, the spectra SΨ(λ, η) and SΔ(λ, η) are converted into two vectors: ΞΨ(η) = (C1Ψ,. . ., CnΨ) and ΞΔ(η) = (C1Δ,. . ., CnΔ), where the values CjΨ (CjΔ) reflect the physical parameters of the spectra: • C1Ψ (C1Δ) is an area below the spectral curve. • C2Ψ (C2Δ) and C3Ψ (C3Δ) are the maximum and minimum coordinates of the spectral curves, respectively. • C4Ψ (C4Δ) is the maximum distance between maximum and minimum coordinate. • C5Ψ (C5Δ) and C6Ψ (C6Δ) are the maximum values of the first and second derivatives of the spectral curve, respectively. • C7Ψ (C7Δ) is the number of maximum spectral curves. • C8Ψ (C8Δ) and C9Ψ (C9Δ) are the values of the spectrum coordinates at selected wavelengths λ* and λ**.

388

6

Investigation of Regional Aquatic Systems

• C10Ψ (C10Δ) is the relationship between the wavelength range evaluated for the maximum and minimum coordinates of the spectral curve. Spectral recognition of the unknown spectra SΨ(λ, x) and SΔ(λ, x) is carried out by achieving a minimum value δ = min ρ ΞsΔ - ΞΔ ðxÞ þ ΞsΨ - ΞΨ ðxÞ s

1 min 4n i 1 min 4n i

n

=

n

j=1

X jΔ - C ijΔ þ

n

j=1

X jΔ - C ijΔ

2

þ

ð6:16Þ

n

X jΨ - C jΨ þ j=1

X jΨ - C jΨ j=1

Another approach assumes that spectrum formation is linearly dependent on fluctuations in the concentration of contaminants. In this case, the solution of optical inverse task is solved by the following system of algebraic equations: a11 x1 þ . . . þ a1m xm = Sðλ1 , X Þ :: . . . . . . . . . . . . . . . . . . . . . . . . . . . ak1 x1 þ . . . þ akm xm = Sðλk , X Þ

ð6:17Þ

where xj ( j = 1,. . ., m) is the concentration of j-th contaminant in the water environment. The coefficients aij are evaluated during the training procedure considering the facts of the over-defined (m > k) or unspecified (m < k) system (Varotsos et al. 2019a, b, c, d). The AYRSSM block diagram is shown in Fig. 6.45 where its blocks are selected from the model functions. A description of the AYRSSM blocks is given in Table 6.26. The operational capabilities of the AYRSSM are more general than is required if the AYRS water quality assessment is being considered.

6.8.3

Results of Simulation Experiments and In Situ Measurements

Figure 6.42 illustrates the sites for optical spectral measurements and water sampling. Table 6.25 shows optical decision-making systems used for in situ spectral measurements and water samples analyses in laboratory conditions. The combined process of water quality monitoring, algorithms, and AYRSSM allows the results to characterize the spatial distribution of contaminants along the Angara River and the Yenisei River after its intersection with Angara starting from the Strelka Village. Radionuclides content in river bottom sediments and their

6.8

Diagnostics of the Angara/Yenisey River System

389

Fig. 6.45 Structure of the AYRSSM. The description of the main blocks is given in Table 6.26 Table 6.26 List of main blocks of AYRSSM Block MRRO SPMM SMPE CWQA MWR MSR MI MSSC MVU MF EMP SMT MSM SMSA FAFP MPWT MKPW FDSE CFS

Description of the block Model of the river runoff. Simulation procedures for modeling the water masses motion. Set of models to parameterize the evaporation process. Criteria for water quality assessment. Model of water regime in a water body. Model of spreading the river runoff across the riverbed. Model of infiltration. Model of the sink taking into account the effect of vegetation and soil cover. Model of vertical uplifting of ground water during evaporation, feeding, and exfiltration. Model of filtration. An empirical model of precipitation. A specified model of transpiration. Model of snow melting and evaporation from the snow surface. A simulation model of sedimentation and biological assimilation of pollutants. The formation of anthropogenic fluxes of pollutants. Model of the process of the water temperature formation. Model of kinetics of chemical pollution of water. Formation of database of spectral etalons. Choice and formation of scenario for the simulation experiment.

390

6

Investigation of Regional Aquatic Systems

Table 6.27 The concentration of heavy metals (μg/L) in the water samples of the Angara River evaluated on the basis of the US-8 data In situ water sampling 20 km of Angara from Baikal 30 km upstream from Irkutsk Angarsk (Angara) Bratsk (Angara) Kazachinskoe (Yenisey) Strelka (Angara flows into Yenisei)

As 3.15 3.42 8.3 11.2 10.4 7.6

Cd 0.21 0.22 0.36 1.15 1.17 1.32

Cr 0.09 0.11 0.23 0.32 0.19 0.24

Cu 1.21 1.24 1.43 2.48 1.54 1.67

Ni 10.2 10.4 13.3 15.4 15.3 16.9

Pb 0.36 0.41 1.07 1.09 1.13 1.19

Zn 10.5 10.6 11.2 13.6 13.7 12.2

transport to the Kara Sea were studied earlier (Krapivin et al. 1998; Varotsos et al. 2019a, b, c, d; Bolsunovskii et al. 2011). The analysis of the existing data on the radioactive pollution of Angara and Yenisei waters shows that continuous selfremoval of radionuclides is observed. For example, the self-removal of 137C is estimated at 0.19 1/year, which corresponds to a half-purification time of 3.6 years for a 600 km section of the Angara/Yenisei riverbed (Vakulovsky et al. 2008). Therefore, radioactive pollutants are not considered. The initial source of pollution in Angara is Lake Baikal, whose water is affected by industrial objects located on the shores of the lake, according to Russian Federal Law defining the protection zone of the lake (Cotte et al., 2023; Boulion 2018; Krapivin and Varotsos 2008; Suslova and Grebenshchikova 2020). Industrial systems and cities along the Angara River add pollutants to the water. After a crossroads, the water quality of Yenisei and Angara is determined by the average water properties. Further quantitative assessment of the pollutant budget in the Angara/ Yenisei River system is assessed using a mass balance model (Bychkov et al. 2016; Holmes et al. 2001). The results of optical monitoring and model calculations are given in Tables 6.27, 6.28, 6.29, and 6.30 and Figs. 6.46 and 6.47. The Angara River, the main subject of this study, has its source in the northern Lake Baikal and flows at the junction with the Yenisei River below the Strelka Village. The current of the Angara River is characterized as fast with the existence of many jumps and rifts. These peculiarities play a significant role in the distribution of contaminants in the Angara waters (Polyak 2004; Obolkin et al. 2016; Kuzmin et al. 2014; Savenko et al. 2016; Savenko 2018). The following result was detected. Almost all heavy metals have an irregular distribution in the Angara and Yenisei Rivers. It can be explained that the rivers have fast currents with high turbulence and bottoms mainly with rocks. Particularly, the bottom of the Angara River has sediment depressions where heavy metals accumulate and are washed out episodically. This is confirmed by them that some bottom areas located mainly above the dams can accumulate faster chemicals and the latter become less average concentrations (Varotsos and Krapivin 2018; Kondratyev et al. 2002a; Phillips et al. 1997). Indeed, the Bratsk, Irkutsk, and Ust- Ikimsk reservoirs manage fluctuations in the water level of the Angara River and thus provide the links between the content of the chemicals. According to Vyruchalkina (2003), water discharges from the Angara River area near reservoirs dams increased from a

Site 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Distance from Lake Baikal, km 27 68 109 123 142 157 178 547 561 643 670 1665 1779 1888

Cd 0.22 0.22 0.33 0.44 0.69 0.78 1.01 1.14 1.17 0.95 0.97 1.21 1.16 1.18

Fe 76.3 84.4 84.8 84.5 87.6 93.2 98.2 95.3 100.5 114.4 124.1 120.3 115.4 116.7

Ni 10.4 10.6 10.9 11.1 11.8 11.4 13.1 12.4 12.4 13.3 14.2 16.1 16.1 16.3

Pb 0.34 0.34 0.67 0.73 0.66 0.46 0.29 0.73 0.69 0.59 0.82 0.67 0.79 0.92

Selected trace and toxic heavy metals, μg/L

As 3.52 5.91 4.34 6.28 3.46 2.75 5.92 7.12 6.33 7.34 8.13 7.92 7.58 7.66

PO4- 3 , mg/L 0.009 0.011 0.012 0.034 0.026 0.021 0.023 0.059 0.128 0.112 0.193 0.178 0.153 0.195

Table 6.28 Average hydrochemical parameters of the Angara River evaluated during the 2019 expedition NO3+ NO2mg/L 0.017 0.019 0.021 0.017 0.039 0.081 0.074 0.067 0.052 0.067 0.055 0.076 0.082 0.089 NH4mg/L 0.002 0.013 0.011 0.018 0.019 0.016 0.021 0.033 0.029 0.035 0.032 0.019 0.008 0.003

HCO3mg/L 58.9 64.2 62.7 65.8 65.9 64.1 68.2 63.6 65.3 68.4 67.3 71.7 72.8 73.4

SO24 mg/L 4.75 5.44 4.51 5.89 5.11 472 5.44 5.98 6.32 6.13 5.56 6.18 4.78 5.31

6.8 Diagnostics of the Angara/Yenisey River System 391

392

6

Investigation of Regional Aquatic Systems

Table 6.29 Comparison of concentrations of heavy metals and oil hydrocarbons in the Angara/ Yenisei River system carried out in 1995 and 2019 1995

Site (1) Angara source (2) Irkutsk, reservoir origin (3) Irkutsk, below dam (4) Above the Angarsk (5) Angarsk City zone (6) Below the Angarsk (7) Usolye Sibirskoe (8) Bratsk Sea (9) Bratsk City zone (10) Osinovka (11) Below Energetic (12) Angara, Strelka (13) Yenisei, Strelka (14) Below Strelka junction

Heavy metals, μg/L 7.5 8.9

2019 Oil hydrocarbons, mg/L 0.016 0.019

Heavy metals, μg/L 7.3 7.7

Oil hydrocarbons, mg/L 0.023 0.034

8.7

0.021

7.6

0.045

10.3

0.041

9.6

0.062

11.5

0.054

9.9

0.067

11.8

0.057

10.3

0.072

13.7 12.6 12.8 11.5 10.9

0.077 0.084 0.089 0.114 0.091

11.4 11.6 11.8 10.9 10.7

0.083 0.088 0.094 0.092 0.099

12.4 13.6 13.7

0.182 0.099 0.095

11.6 12.7 12.5

0.176 0.112 0.064

minimum in April to a maximum in September. As shown in Tables 6.2 and 6.3, heavy metals such as As, Ni, Fe, and Zn have high fluctuations in their concentrations depending on the distance from the Angara mouth. This result can be explained by their irregular concentrations in local waste waters, as defined by the types of industrial and socioeconomic production. An analysis of the monitoring results presented in Table 6.28 shows the reduction of Angara pollution level almost all the way from Lake Baikal to the junction with the Yenisei River as part of the comparative expedition of Strelka Village in 1995. This result is explained by the reduction of industrial activity and the use of new technologies in the commercial sector. In practice, measurements of concentrations of different chemical elements at sampling sites 1–14 suggest that the Angara River has a low level of mineralization and can be transferred to the hydrocarbon’s category. A special role in shaping the water quality is played by the reservoirs located along the Angara River with their narrow spots and the widening of the lake-type, which significantly affect the process of turbulence and sedimentation. Mazaeva et al. (2014) studied the dynamics of coastal processes with a digital relief model and showed that friction processes

Distance from the Angara junction (Strelka) to Kara Sea, km Strelka, 0 Lesosibirsk, 45 Novoselovo, 543 Igarka, 1161 Dudinka, 1405 Karaul, 1837 Dickson, 2338

Heavy metals, μg/L 12.7 12.1 9.2 7.7 6.9 5.7 4.4

PO4- 3 , mg/L 0.153 0.188 0.176 0.088 0.063 0.013 0.0015

+NO2mg/L 0.082 0.089 0.067 0.034 0.012 0.0052 0.0019

NO3Oil hydrocarbons, mg/L 0.112 0.083 0.098 0.066 0.045 0.043 0.039

HCO3- , mg/L 72.8 72.9 68.7 64.5 57.3 45.1 35.4

Table 6.30 Model evaluation of the hydrochemical characteristics of water delivered from the Yenisei River to the Kara Sea

Mineralization, mg/L 87.5 89.3 75.4 62.1 54.8 41.6 39.7

SO24 mg/L 4.78 5.22 6.21 6.33 5.25 4.92 4.44

6.8 Diagnostics of the Angara/Yenisey River System 393

394

6

Investigation of Regional Aquatic Systems

Fig. 6.46 Distribution of water quality of the Angara River depending on the distance from Lake Baikal

Fig. 6.47 Optical spectral images of selected zones in the Angara/Yenisei River system

play a dominant role in the sedimentation of contaminants and the movement of bottom sediments. These explain the occurrence of instability in the data listed in Tables 6.3, 6.4, 6.5, and 6.6. Overall, according to Table 6.28, the concentrations of heavy metals in the water have decreased in recent years, but the concentrations of oil hydrocarbons have increased slightly.

6.8

Diagnostics of the Angara/Yenisey River System

395

In addition, it is important to estimate the concentrations of chemicals in the water of the Yenisei estuary. These evaluations were performed using the AYRSSM described in detail by Krapivin et al. (1998) and Krapivin and Varotsos (2008). The AYRSSM was improved by further examination of the AYRS structure, including four dams located along the Angara River and bottom profile. The AYRS watershed has an area Ω separated from the uniform geographic grids Ωk (k = 1,. . ., N ) located along the AYRS and other adjacent areas Ωij as sources of pollutant fluxes to AYRS. Cells Ωk are located along the AYRS starting with Ω1 at the Angara River source to ΩN at the Yenisei River mouth. Balance equations are used to parameterize the dynamics of water quality and pollutant concentration. The AYRSSM calibration in its versions was performed with the following studies: • Monitoring of water quality in the Nuok Ngot Lagoon (South Vietnam) (Krapivin et al. 2016b, 2017c) • Study of heavy metal radionuclear pollutants in AYRS and the Arctic Basin (Varotsos and Krapivin 2018; Krapivin and Phillips 2001a, b; Krapivin et al. 1998; Phillips et al. 1987) • Monitoring of water quality in the Lake Sevan (Armenia) (Varotsos et al. 2020b, d) The AYRSSM calibration procedure in this study is mainly based on the assessment of model sensitivity and stability of modeling results by variations in the number and location of sampling sites shown in Fig. 6.42. The displacement and subtraction of 25% of the sampling sites give the deviation of the modeling results not more than 3%. The AYRSSM verification process involves the comparison of modeling results with in situ measurements. The range of modeling results at sampling sites (Fig. 6.42) is 2.2% to 3.4%. The model was used for the Yenisei River at a distance from the Angara junction to the Kara Sea. The modeling results are shown in Table 6.30. The sedimentation and self-decomposition processes cause the chemical concentration to decrease slowly as Yenisei water moves toward its estuary. It should be noted that the accuracy of the spectral optical systems used in this study was evaluated by Krapivin et al. (2015b, Fig. 9.22) where it was shown that the accuracy of the evaluation of the contaminant concentration did not exceed 2.7% for concentrations below 10%. The accuracy of optical decision-making systems changed by up to 5.6% when the aqueous solution of chemical elements was increased by up to 12.7%. This section demonstrates the possibility of spectral optical monitoring in the evaluation of water quality for the Angara/Yenisei River system taking into account hydrological, hydrochemical, and anthropogenic processes. Overall, this work covers the results of the 2019 hydrochemical expedition study on water quality in the Angara/Yenisei River system, considering the location of existing man-made objects located along the Angara Cascade. Knowledge of the movement of pollution and its concentration at different distances from Lake Baikal allows the assessment

396

6 Investigation of Regional Aquatic Systems

of the concentration of contaminants in Lake Baikal and the Kara Sea. Monitoring of water quality from the Angara source provides important data for assessing the current anthropogenic impacts on Lake Baikal. According to the data in Tables 6.3 and 6.4, the water quality at site 1 can be considered to correspond to chemical requirements for drinking water quality, which is in line with the conclusion of Suslova and Grebenshchikova (2020) based on the in situ measurements in September 2019. The use of spectral optical devices to analyze the quality of Angara water allows the functional distinction of the hydrochemical characteristics and the formation of their long-term indicators to control the instability characteristics in their distribution along the Angara Cascade. During the 2019 mission, limited series of measurements were performed at different times of the day, and it was found that the concentration of heavy metals in the water during the day could change by 2.8% in reservoirs, regardless of the distance from Lake Baikal. This result is mainly explained by the irregular outflow of domestic and industrial wastewater in Angara and proposes the synthesis of such a monitoring system, which will use tools and means of information modeling, providing a complex examination of the possible natural and man-made impacts on the water quality of the Angara River. The problem of functional water quality control in the Angara/Yenisei River system can be simplified using the AYRSSM taking into account its 3-D bed model (Bychkov et al. 2016). Digitization of bed relief is possible on the basis of pilot maps. The combination of such models and in situ measurements in individual areas can optimize a process for assessing water quality. According to Krapivin et al. (2015a, b), the hybrid geoinformation system can be set up on the basis of information modeling instrumental technology with the decision-making function to minimize economic factors for progress in assessing the anthropogenic impact on water quality in Lake Baikal, the Angara/Yenisei River system, and the Kara Sea. Moreover, the US-8 spectrophotometer can be mounted on a fixed platform and could provide hydrochemical characteristics at a frequency of several seconds. In situ measurements at 14 selected sites have shown the existence of spatial and temporal variability of the concentrations of chemical elements. Water samples collected in the Irkutsk Reservoir in July and August at the same site show that HCO3- and SO24 - concentrations are 66.51  1.3 mg/L and 4.53  0.47 mg/L, respectively. Computer experiments show that the entry of heavy metals into the Kara Sea from AYRS has a constant value with a depression of 26%. The impact of the AYRS ecosystem on the heavy metal conversion process does not exceed 2.8%. The concentrations of heavy metals in samples taken up the Angara River upstream of the Angara-Yenisei junction are estimated to vary 1.4 mg/L. Table 6.28 shows that the comparative concentrations of heavy metals and oil hydrocarbons in 1995 and 2019 decrease and increase on average 9.7% and 5.6%, respectively.

6.8

Diagnostics of the Angara/Yenisey River System

6.8.4

397

Assessments and Recommendations

The results presented in this section illustrate the features of the combined use of optical spectral instruments for in situ measurements and water sampling analysis to assess the water quality of such a large-scale water system as the Angara-Yenisei River. An advantage of the method developed in this study is the ability to form a database of spectral images of AYRS elements recorded during the year, which allows the assessment of trends in changing AYRS water quality. Comparing spectral images that meet the same times of different years can help decide on possible in situ measurements. Finally, according to this study and many literature data, the main difficulty in assessing water quality in the Angara/Yenisei River system is the significant variations in hydrochemical characteristics both during the year and during the day explained by the irregular water flow and the structure of the bottom relief. The special feature of the AYRS function is determined by the Siberian climate when the lowest temperatures until October to March are below zero and the AYRS drainage area (3.479 × 106 km2) is covered by snow that accumulates air pollutants. It should be noted that this must be taken into account when developing AYRSSM. Therefore, the composition of the information modeling system, based on the suitable hydrochemical model and the fixed position of the US-8 at selected sites can optimize the solution of the operational control problem and detect dangerous water quality disturbances in the AYRS. In general, the water quality of the Angara River is at the expense of the feeders from the drainage basin with an area of 1.039 × 106 km2. In particular, the AYRS model takes into account this situation of which is possible with the use of remote sensing tools to assess the structure of land cover and soil moisture content. Remote sensing tools have been developed to diagnose water surface and vegetation and are widely used to solve many tasks (Varotsos and Krapivin 2020a, b). Effective diagnosis of vegetation and soil cover is made using microwave instrumental tools based on wavelengths of centimeters. Microwave remote sensing tools help to collect data on the spottiness of oil products on the water surface. The detection of pollutant spills can be performed simultaneously with optical and microwave instruments, which improves the precision of AYRSSM input data. Future modernization of the AYRSSM can be achieved with additional blocks, some of which are intended to parameterize the functional dependencies of hydrochemical processes on nonlinear climatic parameters and socioeconomic decisions. The results of this study show the adaptability of the developed information modeling instrumental technology to the water quality monitoring of every river system that can improve and raise the productivity of monitoring systems responsible for the water quality. Real application of the AYRSSM to control the water quality of the other river system needs a revision of big data clouds, spatial pixel structure, and vegetation covering. Stationary location of optical spectral sensor US-8 in sites for the formation of spectral images of local water sampling provides automatic monitoring regime. Realization of this monitoring regime can be a subject of the relevant scientific project.

Chapter 7

Global Climate Change and Hydrogeochemistry

7.1 7.1.1

Interaction Between Globalization Processes and Biogeochemical Cycles The Interplay Between Nature, Society, and Climate

The problem of nature-society interaction in the context of a global change in the environment and climate has been discussed in detail at the All-World Conference on Climate Change in Moscow, the APEC Summit-2007 (September 2007, Sydney, Australia). The “Sydney Declaration on Climate Change“was signed on 8th September 2007 by 21 APEC leaders. It indicates the wish of signatories to work toward nonbinding “inspirational” goals on energy efficiency per unit of GDP. In this connection, Australian Prime Minister John Howard said that 21 leaders agreed on three very important and quite specific things: “Firstly, the need for a long-term inspirational, global emissions reduction goal... Secondly, the need for all nations, no matter what their stage of development, to contribute, according to their own capacities and their own circumstances, to reducing greenhouse gases. Thirdly, we have agreed on specific APEC goals on energy intensity and forestry and we’ve also agreed on the important role of clean coal technologies.” In particular, Bolin (2004), while emphasizing the anthropogenic character of the observed climate change, still recognizes the uncertainties in assessments of sensitivity of the climate system to humans’ impacts. This uncertainty leads to the fact that neither models nor expert estimates can determine in detail the likely characteristics of climate changes, or how quickly and where they will occur and to what extent they will affect the well-being of the population. Here a limited knowledge of biogeochemical cycles and the role in them of the human factor contributes most to this uncertainty. The impact of the growing concentration of CO2 and aerosols in the atmosphere on the greenhouse warming is directly proportional, and this takes place both naturally and due to anthropogenic factors.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Varotsos et al., Constructive Processing of Microwave and Optical Data for Hydrogeochemical Applications, https://doi.org/10.1007/978-3-031-28877-7_7

399

400

7

Global Climate Change and Hydrogeochemistry

The greenhouse effect estimated by the equivalent concentration of CO2 and aerosol in the atmosphere constitutes 2.7 W/m2 and -1.3 W/m2, respectively. But here the functional difference between these effects should be pointed out which is that while the lifetime of aerosols in the atmosphere is from weeks to months, GHGs can reside in the atmosphere for decades to centuries. In this difference lies the inertial uncertainty of climate change. Climate changes manifest themselves both on global and regional scales. Natural catastrophes are one of manifestation of these changes. Their intensity and number is increased year-by-year. The serious increase in great natural catastrophes was between 1960 and 2005. The frequency of these events more than doubled during this period. Subsequent years were characterized by various anomalous phenomena which confirm the instability and poor predictability of the occurrence of natural anomalies unfavorable to the population. To confirm this, it is enough to mention some events taking place in 2007 as very typical: • The formation of Subtropical Storm Andrea on May 9, 2007 marked an earlier beginning of the Atlantic hurricane season. This was the second occasion in 5 years that a storm formed before the official season start date • Hurricane Dean (13–23 August 2007) at a rate of 270 km hr-1 devastated much land in the Caribbean basin and Mexico • A heavy heat covered vast areas of Europe. For instance, the air temperature in Greece, Romania, Hungary, and Bulgaria reached 46 °C in shadow, which has led to numerous forest fires. The forest fires in Greece reached a scale of a national disaster • Unprecedented flooding in the last 60 years covered central and southern areas of England, destroying a million homes and leaving tens of thousands of people without electric power Environmental anomalies take place annually at various spatial and temporal scales (IIT 2020; Leaning and Guha-Sapir 2014). A natural disaster is a sudden event that causes widespread destruction, major collateral damage or loss of life, brought about by forces other than the acts of human beings. For instant, only in 2020, major natural disasters such as bush fires, floods, hurricanes, volcanic eruptions, earthquakes, locust swarms, and windstorms take place. As has been repeatedly mentioned, the interacting components of the today’s climate system include a wide spectrum of natural and natural-anthropogenic sub-systems and processes, without a complex study of which it is impossible to reliably single out prevailing trends in a climate change. In this connection, here are the most important ones. • • • •

Global water cycle. Impact of cloud feedbacks. Global carbon cycle. Water-carbon cycles interaction. Land use and land surface changes. Present-day trends in the GHGs content in the atmosphere and their control mechanisms.

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

401

• Interactions between climate and productivity of land ecosystems. Land ecosystems dynamics. • Impact of the climate regime shifts on marine ecosystems. • Control of natural resources to neutralize the negative consequences of human activities. • Socioeconomic aspects of ecodynamics and climate, and their analysis to optimize the land use strategy. • Interactions between processes in the geosphere and the biosphere, and their dependence on space. Infrared (IR) active gases, like water vapor (H2O), carbon dioxide (CO2), ozone (O3), methane (CH4), nitrous oxide (N2O), chlorofluorocarbons CFC-11 (CCl3F), and CFC-12 (CCl2F2) naturally and anthropogenically present in the Earth’s atmosphere, absorb thermal IR radiation emitted by the Earth’s surface and atmosphere. This phenomenon is known as the “atmospheric greenhouse effect,” and the IR active gases responsible for the effect are referred to as “greenhouse gases.” The rapid increase in concentrations of GHGs since the industrial period began has given rise to concern over potential resultant climate changes. Total combination of climatic effects is explained by the series of natural and anthropogenic processes connected mainly with the biogeochemical cycle of CO2 as main of them. However, as has been mentioned in publications (Kondratyev and Varotsos 1995; Kondratyev 1999b; Kelley 1987), many scientists and even politicians draw conclusions on the problem of the “greenhouse” role of CO2 based on the one-sided estimates without consideration of many important feedbacks and especially without considering the role of other GHGs. As shown by numerous studies, this role is rather substantial: • Although there is about 220 times more CO2 than methane in the Earth’s atmosphere (Keppler et al. 2006), each kg of CH4 averaged over 100 years, warms the Earth 23 times stronger than the same mass of CO2. • Water vapor is the most important absorber (its share in the greenhouse effect constitutes 36–66%), and together with clouds it makes up 66–85%. CO2 alone contributes 9–26%, while O3 and other minor GHG absorbers contribute to the greenhouse effect 7% and 8%, respectively. As Monin and Shishkov (1990) noted, the difficulty is assessing the change in the greenhouse effect with a change in the content of any gas in the atmosphere consists in that the atmosphere-ocean-land system involves numerous positive and negative feedbacks. Leaving out of account any of them can lead to rather distorted and erroneous conclusions and estimates. So, for instance, with an increasing CO2 content and, hence, temperature, evaporation should intensify and, respectively, the water vapor content should increase, which, in its turn, absorbs additional energy and leads to a new temperature increase. Besides, when the temperature rises, the CO2 solubility in the ocean becomes worse. But at the same time, the albedo changes, and the regime of the aerosols removal from the atmosphere changes, too. A 70% decrease (increase) of the planetary albedo depending on clouds leads to an increase (decrease) of the assimilated amount of solar energy, which leads to a

402

7 Global Climate Change and Hydrogeochemistry

warming (cooling) of climate. Estimates of the present-day greenhouse effect vary round the value ΔT = 33.2 K, which is mainly formed due to water vapor (20.6 K, 62%), CO2 (2.4 K, 7.2%), nitrous oxide (1.4 K, 4.2%), and CH4 (0.8 K, 2.4%). Aerosol particles in the atmosphere play significant role in the climate change. They influence climate in two main ways, referred to as direct forcing and indirect forcing. Many scientific groups study the aerosol effects on climate forming processes developing various techniques to compute the flow of solar radiation through an atmosphere containing aerosols, clouds, and gases. Various conceptual aspects of the climate problem are also discussed in numerous documents of the international organizations. In particular, this refers to the main conclusion of the summary of the IPCC-2001 report (IPCC 2001) which claims that an increasing body of observations gives a collective picture of a warming world and most of the observed warming over the last 50 years is likely to be due to human activities. It is unfortunate that the former Chairman of IPCC Working Group-1 (WG-1) Professor J. Houghton in a recent article (Houghton 2003) in the British newspaper The Guardian, compared the threat of anthropogenic climate changes to weapons of mass destruction and admonished the USA for their refusal to support the concept of dangerous, anthropogenic global warming, and thus the Kyoto Protocol. No matter how paradoxical it may seem, such claims are in fact being made against the background of an increasing understanding of the imperfections of the current global climate models and their still inadequate verification. This makes predictions on the basis of numerical modeling no more than conditional scenarios. As for the USA, one should welcome huge efforts of this country to support climate studies, manifested through both special attention to as improvement of observational systems and to developments in the field of climate problems in general (Mahoney 2003). The U.S. spends about 2.0–2.5 billion dollars a year on climate research. In 2004, the USA spent 4.5 billion dollars on these problems. From 1993 to 2016, U.S. spent on climate science 47.56 billion dollars. The statement of the Intergovernmental Group G-8 published on 2 July 2003 (WSSD 2003) has justly emphasized that in the years to come efforts will be concentrated on three directions: • Strengthen international cooperation on global observation • Accelerate the research, development, and diffusion of energy technologies • Agriculture and biodiversity The Earth’s climate system has indeed changed markedly since the industrial revolution, with some changes being of anthropogenic origin. The consequences of climate change do present a serious challenge to the policy-makers responsible for the environmental policy and this alone makes the acquisition of objective information on climate change, of its impact, and possible responses, most urgent. With this aim in mind, the World Meteorological Organization (WMO) and the UN Environmental Program in 1988 organized the Intergovernmental Panel on Climate Change (IPCC) divided into three working groups (WG) with spheres of responsibility for the:

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

403

1. Scientific aspects of climate and its change (WG-I) 2. Effects on and adaptation to climate (WG-II) 3. Analysis of possibilities to limit (mitigate) climate changes (WG-III) The IPCC has so far prepared four detailed reports (Houghton et al. 1990; IPCC 2001, 2005, 2007) as well as several special reports and technical papers. Griggs and Noguer (2002) have briefly reviewed the first volume of the Third IPCC Report (TIR) prepared by WG-I for the period June 1998 – January 2001 with the participation of 122 leading authors and 515 experts. Four hundred and twenty experts reviewed the first volume and 23 experts edited it. Several hundred reviewers and representatives of many governments made additional remarks. With the participation of delegates from 99 countries and 50 scientists recommended by the leading authors, the final discussion of TIR was held in Shanghai on 17–20 January 2001. A “Summary for decision-makers” was approved after a detailed discussion by 59 specialists. Analysis of the observational data as contained in TIR led to the conclusion that global climate change is taking place. The Reports IPCC (2001, 2007) give a detailed review of the observational data of the spatial-temporal variability of the concentrations of various GHGs and aerosol in the atmosphere. The adequacy of numerical models was discussed from the viewpoint of the climate-forming factors and the usefulness of models to predict climate change in the future. The main conclusion about anthropogenic impacts on climate was that “there is new and stronger evidence that most of the warming observed during the last 50 years has been determined by human activity.” According to all prognostic estimates considered in TIR, both SAT increase and sea level rise should take place during the twenty-first century. In characterizing IPCC data for empirical diagnostics of climate, Folland et al. (2002) drew attention to the uncertainty of the definitions of some basic concepts. According to IPCC terminology, climate changes are statistically substantial variations of an average state or its variability, whose stability is preserved for long time periods (for decades and longer). Climate changes can be natural in origin (connected both with internal processes and external impacts) and/or may be determined by anthropogenic factors, such as changes in the atmospheric composition or land use. This definition differs from that suggested in the Framework Climate Change Convention (FCCC) where climate changes are only of anthropogenic origin in contrast to natural climate change. In accordance with the IPCC terminology, climatic variability means variations of the average state and other statistical characteristics (MSD, repeatability of extreme events, etc.) of climate on every temporal and spatial scale, beyond individual weather phenomena. Hence climate variability can be both of natural (due to internal processes and external forcings) and anthropogenic origin: possess both internal and external variability. As Folland et al. (2002) have noted, seven key questions are most important for the diagnostics of observed changes and the climate variability: 1. How significant is climate warming? 2. Is the currently observed warming significant?

404

3. 4. 5. 6. 7.

7

Global Climate Change and Hydrogeochemistry

How rapidly had the climate changed in the distant past? Have precipitation and atmospheric water content changed? Are there changes in the general circulation of the atmosphere and ocean? Have climate variability and climate extremes changed? Are observed trends internally coordinated?

In order to answer the above questions, the reliability of observational data is fundamental. Without such observational data, adequate empirical diagnostics of climate remains impossible. Yet the information concerning numerous meteorological parameters, so very important for documentation, detection, and attribution of climate change, remains inadequate for the drawing of reliable conclusions. This is especially true for the global trends of those parameters (e.g., precipitation), which are characterized by large regional variability. Folland et al. (2002) have answered some of the above questions. A comparison of the secular change of global average annual sea surface temperature (SST), land surface air temperature (LSAT), and nocturnal air temperature (NAT) over the ocean for the period 1861–2000 on the whole revealed some similarity, though the warming in the 1980s from LSAT data turned out to be stronger, and the NAT data showed a moderate cooling at the end of the nineteenth century not demonstrated by SST data. The global temperature trend can be interpreted cautiously as equivalent linear warming over 140 years constituting 0.61 °C at a 95% confidence level with an uncertainty range of ±0.16 °C. Later on, in 1901, a warming by 0.57 °C took place with an uncertainty range of ±0.17 °C. These estimates suggest that beginning from the end of the nineteenth century, an average global warming by 0.6 °C took place with the interval of estimates corresponding to a 95% confidence level equal to 0.4–0.8 °C. The spatial structure of the temperature field in the twentieth century was characterized by a comparatively uniform warming in the tropics and by a considerable variability in the extratropical latitudes. The warming between 1910 and 1945 was initially concentrated in the Northern Atlantic and the adjacent regions. The Northern Hemisphere was characterized by cooling between 1946 and 1975, while in the Southern Hemisphere some warming was observed during this period. The temperature rise observed during the last decades (1970–2000) turns out, on the whole, to have been globally synchronous and clearly manifested across Northern Hemisphere continents in winter and spring. In some Southern Hemisphere regions and in the Atlantic, however, a small all-year-round cooling was observed. A temperature decrease in the Northern Atlantic between 1960 and 1985 was later followed by an opposite trend. On the whole, the climate warming over the period of measurements was more uniform in the Southern Hemisphere than in the Northern Hemisphere. In many continental regions between 1950 and 1993, the temperature increased more rapidly at night than during daytime (this does not refer, however, to coastal regions). The rate of temperature increase varied from 0.1 to 0.2 °C/10 years. According to the data of aerological observations, air temperature in the lower and middle troposphere increased after 1958 at a rate of 0.1 °C/10 years, but in the upper troposphere (after 1960) it remained more or less constant. A combined

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

405

analysis of the aerological and satellite information has shown that in the period 1979–2000, the temperature trend in the lower troposphere was weak, whereas near the land surface it turned out to be statistically significant and reached 0.16 ± 0.06 ° C/10 years. The statistically substantial trend of the difference between the Earth’s surface and the lower troposphere constituted 0.13 ± 0.06 °C/10 years, which differs from the data for the period 1958–1978, when the average global temperature in the lower troposphere increased more rapidly (by 0.03 °C/10 years) than near the surface. The considerable differences between the temperature trends in the lower troposphere and near the surface are most likely to be real. So far, these differences cannot be convincingly explained. The climate warming in the Northern Hemisphere observed in the twentieth century was, according to Folland et al. (2002), the most substantial over the last 1000 years. Special attention has been paid in the IPCC (2001, 2007) reports to the possibility for predicting future climatic changes. The chaotic character of the atmospheric dynamics limits the long-term weather forecasts to 1 or 2 weeks and prevents the prediction of a detailed climate change (e.g., it is impossible to predict precipitation in Great Britain for the winter of 2050). However, it is possible to consider climate projections, that is, to develop scenarios of probable climate changes due to the continuing growth of GHGs’ concentrations in the atmosphere. Such scenarios, if credible, may be useful for decision-makers in the field of ecological policy. The basic method to make such scenarios tangible involves the use of numerical climate models that simulate interactive processes in the climatic system “atmosphereocean-land surface-cryosphere-biosphere.” As Collins and Senior (2002) noted, because there are so many such models, the serious difficulty arises as to which is the best model to choose. As this selection problem is unsolved, it is possible to compare climate scenarios obtained using different models. According to IPCC recommendations, four levels of projection reliability are considered: 1. From reliable to very likely (in which case there is agreement between results for most models) 2. Very likely (agreement of new projections obtained with the latest models) 3. Probable (new projections with agreement for a small number of models) 4. Limited potential (model results are not certain, but changes are naturally possible) A principal difficulty in giving substance to projections is the impossibility of determining agreed predictions on how GHGs’ concentrations will evolve in future, which makes it necessary to take into account a totality of various scenarios. The huge thermal inertia of the World Ocean dictates a possibility of delayed climatic impacts of the GHGs’ concentrations, which have already increased. Calculations of annual average global SAT using the energy-balance climate model with various scenarios of the temporal variations of CO2 concentrations have led to SAT intervals in 2020, 2050, and 2100 to be 0.3–0.9, 0.7–2.6, and 1.4–5.8 °C respectively. Due to the ocean thermal inertia, a delayed warming should manifest itself within 0.1–0.2 °C/10 years (such a delay can take place over several decades).

406

7

Global Climate Change and Hydrogeochemistry

The following conclusions can be attributed to the category of projections with the highest reliability (Collins and Senior 2002): 1. Surface air warming should be accompanied by a tropospheric warming and stratospheric cooling (the latter is due to a decrease of the upward longwave radiation flux from the troposphere) 2. Faster warming on land compared to oceanic regions (as a result of the great thermal inertia of the ocean); a faster warming in the high-mountain regions (due to albedo feedbacks) 3. Aerosol-induced atmospheric cooling holds a SAT increase (new estimates suggest the conclusion about a weaker manifestation of the aerosol impact) 4. Presence of the warming minima in the North Atlantic and in the circumpolar regions of the oceans in the Southern Hemisphere due to mixing in the oceanic thickness 5. Decrease of the snow and sea ice cover extent in the Northern Hemisphere 6. Increase of the average global content of water vapor in the atmosphere, enhancement of precipitation and evaporation, as well as intensification of the global water cycle 7. Intensification (on average) of precipitation in the tropical and high latitudes, but its attenuation in the sub-tropical latitudes 8. Increase of precipitation intensity (more substantial than expected as a result of precipitation enhancement, on average) 9. Summertime decrease of soil moisture in the middle regions of the continents due to intensified evaporation 10. Intensification of the El Niño regime in the tropical Pacific with a stronger warming in the eastern regions than in the western ones, which is accompanied by an eastward shift of the precipitation zones 11. Intensification of the interannual variability of the summer monsoon in the Northern Hemisphere 12. More frequent appearance of high temperature extremes but infrequent occurrence of temperature minima (with an increasing amplitude of the diurnal temperature course in many regions and with a greater enhancement of nocturnal temperature minima compared to daytime maxima) 13. Higher reliability of conclusions about temperature changes compared to those about precipitation 14. Weakening of the thermohaline circulation (THC) that causes a decrease of the warming in the North Atlantic (the effect of the THC dynamics cannot however compensate for the warming in West Europe due to the growing concentration of GHGs) 15. More intense penetration of the warming into the ocean depth in high latitudes where vertical mixing is most intensive As for the estimates characterized by a lower level of reliability, the conclusion (at level 4) about the lack of an agreed view on the changing frequency of storms in middle latitudes, is of special interest here, as is a similar lack of agreement about the

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

407

changing frequency of occurrence of tropical cyclones under global warming. An important future task is to improve climate models aimed at reaching eventually a level of reliability that would enable the prediction of climatic changes. Allen (2002) has discussed the basic conclusions contained in the “Summary for policy-makers” (SPM) of the Third IPCC Report and especially of its main conclusion that “There is new and stronger evidence that most of the warming observed during the last 50 years should be attributed to human activity.” This conclusion supplements the statement according to which “as follows from the present climate models, it is very unlikely that the warming taking place during the last 100 years was determined only by the internal variability” (“very unlikely” means that there is less than one chance of ten for an opposite statement to be well-founded). McKitrick (2007) writes that “The following concluding statement is not in the Fourth Assessment Report but was agreed upon by the ISPM writers based on their review of the current evidence. The Earth’s climate is an extremely complex system, and we must not understate the difficulties involved in analyzing it. Despite the many data limitations and uncertainties, knowledge of the climate system continues to advance based on improved and expanding data sets and improved understanding of meteorological and oceanographic mechanisms. The climate in most places has undergone minor changes over the past 200 years, and the land-based surface temperature record of the past 100 years exhibits warming trends in many places. Measurement problems, including uneven sampling, missing data, and local land-use changes, make interpretation of these trends difficult. Other, more stable data sets, such as satellite, radiosonde, and ocean temperatures yield smaller warming trends. The actual climate change in many locations has been relatively small and within the range of known natural variability. There is no compelling evidence that dangerous or unprecedented changes are underway. The available data over the past century can be interpreted within the framework of a variety of hypotheses so as to cause and mechanisms for the measured changes. The hypothesis that greenhouse gas emissions have produced or are capable of producing a significant warming of the Earth’s climate since the start of the industrial era is credible, and merits continued attention. However, the hypothesis cannot be proven by formal theoretical arguments, and the available data allow the hypothesis to be credibly disputed. Arguments for the hypothesis rely on computer simulations, which can never be decisive as supporting evidence. The computer models in use are not, by necessity, direct calculations of all basic physics but rely upon empirical approximations for many of the smaller scale processes of the oceans and atmosphere. They are tuned to produce a credible simulation of current global climate statistics, but this does not guarantee reliability in future climate regimes. And there are enough degrees of freedom in tunable models that simulations cannot serve as supporting evidence for any one tuning scheme, such as that associated with a strong effect from greenhouse gases.

408

7

Global Climate Change and Hydrogeochemistry

There is no evidence provided by the IPCC in its Fourth Assessment Report that the uncertainty can be formally resolved from first principles, statistical hypothesis testing or modeling exercises. Consequently, there will remain an unavoidable element of uncertainty as to the extent that humans are contributing to future climate change, and indeed whether or not such change is a good or bad thing.” Clearly, the reality of such a statement depends on an adequate modeling of the observed climatic variability. Analysis of the results of the relevant calculations using six different models has shown that three of six models reproduce climate variability on time scales from 10 to 50 years which agrees with the observational data. Another conclusion in SPM (TAR) is that “reconstruction of data on climate for the last 1000 years shows that the present warming is unusual and unlikely to be of only natural origin” (“unlikely” means that there is less than one chance of three for an opposite conclusion). This conclusion is supplemented with the following: “Numerical modeling of the response to only natural disturbing forces does not explain the warming that took place in the second half of the twentieth century.” This view is based on the analysis of the results from the numerical modeling of changes in the average global SAT during the last 50 years. It follows from this that a consideration of natural forcings (solar activity, volcanic eruptions) has demonstrated a climatic cooling (mainly due to large-scale eruptions in 1982 and 1991) which has allowed the conclusion that the impact of only natural climatic factors is unlikely. However, there is only one chance in three that it was so: such carefulness is due to insufficient reliability based on indirect information concerning natural forcings in the past. Numerical modeling results cannot explain pre-1940 climate warming considering only anthropogenic factors but are quite adequate considering both natural and anthropogenic effects (GHG and sulfate aerosol). As stated in SPM of TAR, “these results. . . do not exclude possibilities of contributions of other forcings.” It is possible therefore that good agreement of the calculated and observed secular trends of SAT may in part be determined by a random mutual compensation of uncertainties. Another important illustration of the inadequacy of the numerical modeling results is their difference with observations concerning temperature changes near the Earth’s surface and in the free troposphere. If, as models suggest, the temperature of the troposphere is increasing faster than near the surface, then analysis of observational data between 1979 and 2000 reveals that the free troposphere warming is slower and possibly absent. In evaluating the content of the IPCC-2001 Report, Griggs and Noguer (2002) argued that this report: 1. Contains a most complete description of the current ideas about the known and unknown aspects of the climate system and the associated factors 2. Is based on the knowledge of an international group of experts 3. Is prepared based on open and professional reviewing 4. Is based on scientific publications

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

409

Unfortunately, none of these statements can be convincingly substantiated. The IPCC-2001 Report has therefore been strongly criticized in the scientific literature, the most important items of which we shall now discuss. The problems of global warming were discussed earlier (Kondratyev 2004; Krapivin and Varotsos 2007, 2008; Varotsos et al. 2019b; Cracknell et al. 2009a). In principle, the positions of the anthropogenic global warming proponents and the “reasons for climate” have not changed the IPCC (2007) publication.

7.1.2

Global Climate Diagnostics

The main cause of contradictions in studies of the present climate and its changes is the inadequacy of the available observational databases. They remain incomplete and of poor quality. In this connection, Mohr and Bridge (2003) have carried out a thorough analysis of evolution of the global observing system. As is well known, climate is characterized by many parameters, such as: • Air temperature and humidity near the Earth surface and in the free atmosphere • Precipitation (liquid or solid) • Amount of cloud cover and the height of its lower and upper boundaries, microphysical and optical properties of clouds • Radiation budget and its components; microphysical and optical parameters of atmospheric aerosols • Atmospheric chemical composition, and more However, empirical analysis of climatic data is usually limited by the results of SAT observations, with data series available for no more than 100–150 years. Even these data series are heterogeneous, especially with regard to the global database; the main source of information for proving evidence for global warming idea. Also, it should be borne in mind that the globally averaged secular trend of SAT values is based, to a large extent, on the use of incomplete observed SST data. The most important (and controversial) conclusion by Jacobson (2002a, b) concerning the anthropogenic nature of present day climate change is based on analysis of the SAT and SST combined data, that is on the secular trend of mean average annual global surface temperature (GST). In this context, two questions arise: • First on the information content of the notion of GST (this problem was formulated by Essex and McKitrick • Secondly on the reliability of GST values determined, in particular, by fragmentary data for the Southern Hemisphere, as well as the still unresolved problem of urban “heat islands” Studies on the reliability of the SAT observations are continuing from the perspective of observational techniques. For more than 100 years, SAT was measured with the glass thermometers, but now arrangements to protect the

410

7 Global Climate Change and Hydrogeochemistry

thermometers from direct solar radiation and wind have been repeatedly changed. This dictates a necessity for filtering out SAT data to provide homogeneous data series. In the period from April toll August 2000 at the station of the Nebraska State University, USA (40°83′N; 96°67′W), HUBBARD et al. (2002) carried out comparative SAT observations over smooth grass cover with the use of various protections of thermometers. At the same time, direct solar radiation and wind speed were measured. Analysis of observations has shown that differences of observed data can reach several tenths of a degree. Therefore, a technique has been proposed to increase the homogeneity of observation series which substantially increases the homogeneity of the series. However, it does not permit the exclusion of the effect of calibration errors and drift of the temperature sensor’s sensitivity. For the diagnosis of the observational data, emphasis should be placed on the analysis of climate variability in which a consideration not of averages but moments of higher orders is important. Unfortunately, there have been no attempts to use this approach. The same approach refers to estimates of the internal correlation of observation series. McKitrick (2007), having analyzed the secular trend of SAT, showed that with the filtered-out contribution to temperature variations during the last several decades at the expense of internal correlations (i.e., determined by the climatic system’s inertia), it turns out that practically the temperature has not changed. There is a paradox: an increase of the global average SAT during the last 20–30 years is the principal basis for the conclusion concerning the anthropogenic contribution to the present-day climate changes. From satellite observations (starting from 1979), the trend of global average temperature for the lower troposphere (0–8 km) was +0.07 °C per ten years (Christy and Spencer 2003). According to the data of aerological sensing, there was an increase of the global average temperature of the lower troposphere by about 0.03 °C per ten years, being much below the SAT increase (~0.15 °C per ten years) (Waple and Lawrimore 2003). This difference in warming manifests itself mainly in the oceanic regions in the tropics and sub-tropics, and it is not clear why this is so (Christy and Spencer 2003). The results of the numerical climate modeling show that the global warming should be stronger in the free troposphere than near the surface. The difference in temperature trends near the surface and in the troposphere has caused heated discussion in the scientific literature (Christy and Spencer 2003; Waple and Lawrimore 2003). Since the reliability of the satellite remote sensing data raises no doubts, and their spatial representativeness (on global scales) is more reliable than that of the data of surface measurements, this difference should be interpreted as necessitating further analysis of the SAT and SST data adequacy. Data on changes in the height of the tropopause have recently attracted rapt attention. As Santer et al. (2003) noted, starting from 1979, the height of the tropopause increased by several hundred meters, agreeing with the results of numerical climate modeling taking into account the growth of GHGs’ concentrations, whose contribution prevails, again, in “enigmatic” agreement of the observed and calculated data.

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

411

Studies of the dynamics of the tropical tropopause layer are of great interest for quantitative estimates of climate change and an understanding of mechanisms for the troposphere-stratosphere interactions. These circumstances have stimulated recent serious attention to studies of the climatic structure and variability of the tropical tropopause as well mechanisms responsible for the formation of this structure. Serious attention has also been paid to analysis of data on the content of water vapor in the stratosphere and mechanisms for the formation of thin cirrus clouds in the tropics. Randel et al. (2003) undertook studies of the structure and variability of the temperature field in the upper troposphere and lower stratosphere of the tropics (at altitudes about 10–30 km) from the data of radio occultation observations for the period from April 1995 till February 1997 using the Global Positioning System (GPS). A comparison with a large number (several hundreds) of synchronous aerological sensing has shown that a retrieval of the vertical temperature profiles from GPS/MET data provides reliable enough information. Analysis of the obtained results suggested that the spatial structure and variability of the tropopause altitude determined by a “cool point” (minimum temperature) of the vertical temperature profile are governed mainly by wave-like fluctuations like Kelvin waves. A strong correlation was observed between temperature from GPS/MET data and outgoing longwave radiation, which can serve as an indirect indicator of penetrating convection in the tropics. This correlation confirms a reality of temperature fluctuation revealed from GPS/MET data and opens possibilities of quantitative assessments of the response of large-scale temperature field in the tropics to time-varying conditions of convection revealing coherent wave-like variations at altitudes between 12 and 18 km. In the Northern Hemisphere, sea ice and snow cover reach their minimum and maximum extent in September and February, respectively. This variation determines the significance of global snow and ice cover for climate change. Recent trends of snow and ice conditions assessed by global monitoring systems show that variations in snow and ice cover reflect a number of effects of a shift in climate, including changes in both air temperature and precipitation patterns. Seasonal variations in snow cover are the main source of runoff in the dry season in many mountain regions determining both the water supplies and possible natural disasters (floods, avalanches, and landslides). Numerical modeling using global climate models has shown (from considering the growing concentration of GHGs and aerosols) that climate warming should increase in the Arctic because of feedback determined by the melting of the sea ice and snow cover causing a decrease in surface albedo. On the other hand, from the observed data, SAT has increased during the last decades over most of the Arctic. One of the regions where a warming has taken place is northern Alaska (especially in winter and in spring). In this connection, Stone et al. (2002) have analyzed the data on climatic changes in the North of Alaska to reveal their impact on the annual trend of the snow cover extent (SCE) and the impact of SCE changes on the surface radiation budget (SRB) and SAT.

412

7

Global Climate Change and Hydrogeochemistry

Numerous satellite-derived data provide useful information on the thermal structure of the upper ocean. Sea surface variations are given by the measurements with TOPEX/POSEIDON. Sea surface temperature is derived from NOAA/AVHRR. In this context, Arruda et al. (2005) presented a semi-dynamic model that combines sea surface height anomalies, infrared satellite-derived SST, and hydrographic data to generate maps of the upper ocean heat content anomaly, which are suitable for climate variability studies. McPhaden and Hayes (1991) examined the surface layer heat balance using wind, current, and temperature data from equatorial moorings along 165°E focused primarily on daily to monthly time scale variations during the 1986–1987 El Niño/Southern Oscillation event. They inferred that evaporative cooling related to wind speed variations accounts for a significant fraction of the observed SST and upper ocean heat content variability. This evaporative heat flux converges nonlinearly in the surface layer, giving rise to larger temperature variations in the upper 10 m than below. Other processes, such as wind-forced vertical advection and entrainment, lateral advection, were negligible or of secondary importance relative to evaporative cooling. A large fraction of the SST and surface layer heat content variance could not be directly related to wind fluctuations; this unexplained variance is probably related to shortwave radiative fluxes at the air-sea interface. Cai and Whetton (2002) drew attention to the fact that the ocean dynamics can considerably affect future global-scale precipitation. Developments in these difficult problems are based on the use of both observed data and results of numerical modeling and have led to quite different conclusions. The climatic warming of the last decades was characterized by the spatial structure similar to that of the El Niño/ Southern Oscillation (ENSO) event. But since there are no data on such a structure for the whole century, the observed structure of warming is assumed to be a manifestation of the multidecadal natural variability of climate, not the result of the greenhouse forcing. Moritz et al. (2002) revealed a substantial inadequacy of climate models as applied to the Arctic conditions. In most cases, the calculated AO (Arctic oscillations) trends turned out to be weaker compared to observed ones. The calculated climate warming is greater in the fall over the Arctic Ocean, while the observed warming is at a maximum in winter and over the continents in spring. Data on GST are important for climate diagnostics. As Majorovicz et al. (2002) have noted, an analysis of the GST data obtained in different regions of Canada by measuring the ground temperature in boreholes revealed considerable spatial differentiation both in GST increase observed in the twentieth century, and in the onset of the warming. For instance, from measurements in 21 boreholes covering a period of the last 1000-year, warming was detected (within 1–3 °C) during the last 200 years. The warming was preceded by a long cooling trend in the region 80°–96°W, 46°– 50°N, which continued until the beginning of the nineteenth century. According to data for ten boreholes in central Canada, the temperature had reached a minimum about the year 1820 with a subsequent warming by about 1.5 °C. In western Canada, warming has reached 2 °C over the past 100 years.

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

413

An analysis has been made by Majorovicz et al. (2002) of more adequate information on GST from data of measurements in 141 boreholes at a depth of several hundred meters. The holes were drilled in 1970–1990. The results obtained revealed an intensive warming that started in the eighteenth to nineteenth centuries, which followed a long period of cooling (especially during the Little Ice Age) continuing during the rest of the millennium. The time of the onset of the present warming differed between regions. An analysis of the spatial distribution of the GST changes over the territory of Canada revealed a substantial delay in the onset of the present warming in the east-to-west direction, with a higher level of the GST increase in the twentieth century in western Canada. This conclusion is confirmed by the data of SAT observations. It should be noted that the GST increase in eastern Canada had begun about 100 years before the industrial age. The characteristics of the atmospheric general circulation are important components of climate diagnostics. As Wallace and Thompson (2002) pointed out that the west-eastern zonal wind component averaged over the 55°N latitudinal belt can be a representative indicator of the primary mode of the surface air pressure anomalies— the Northern Annual Mode (NAM). Both NAM and a similar index SAM for the Southern Hemisphere are typical signatures of symbiotic relationships between the meridional profiles of the west-eastern transport in the respective hemisphere and wave disturbances superimposed on this transport. Their index determined (using a respective normalization) as a coefficient for the first term of NAM expansion in empirical orthogonal functions can serve as the quantitative characteristic of the modes. The presence of the positive NAM (or SAM) index indicated the existence of a relatively strong west-eastern transport. In recent years, it has been recognized that dynamic factors contribute much to observed temperature trends. For instance, in 1995 a marked similarity was observed between the spatial distributions of the SAT field and NAM fluctuations for the last 30 years, with a clear increase of the NAM index. The increasing trend of the index was accompanied by mild winters, changes of the spatial distribution of precipitation in Europe, and the ozone layer depletion in the latitudinal belt >40°N. Similar data are available for the Southern Hemisphere. The main conclusion is that along with the ENSO event, both NAM and SAM are the leading factors of the global atmospheric variability. In this connection, attention should be focused on the problem of the 30-year trend of NAM toward its increase, the more so that after 1995 the index lowered. It is still not clear whether this trend is a part of long-term oscillations. Observational data show that during the twentieth century, an increase of precipitation constituted 0.5–1% per ten years over most of land surface in the middle and high latitudes of the Northern Hemisphere, but a decrease (by about 0.3% per ten years) took place over most of land surface in sub-tropical latitudes, which has recently weakened, however. As for the World Ocean, the lack of adequate observational data has not permitted the identification of any reliable trends of precipitation. In recent decades, intensive and extreme precipitation in the middle and high latitudes of the Northern Hemisphere has probably become more frequent. Beginning from the mid-1970s, the ENSO events have been frequent, stable, and

414

7 Global Climate Change and Hydrogeochemistry

intensive. This ENSO dynamics was reflected in specific regional variations of precipitation and SAT in most of the zones of the tropics and sub-tropics. Data on the intensity and frequency of tropical and extratropical cyclones and local storms remain patchy and insufficient, and do not allow conclusions to be drawn about any trends. Changes in the biosphere are also important indicators of climate. One of them is the bleaching of corals. It is important to recognize that enhanced atmospheric forcings on coral reefs lead not to their disappearance but to their transformation into more resistant species. Changes of sea water properties are another indicator. The transport, diffusion, and chemical transformation of pollutants in the atmosphere over many regions of the world are main factors that regulate the greenhouse gases’ role in climate change. In this context, Otero et al. (2004) analyzed physical and optical properties of biomass burning aerosols in a continental dry region of South America and Argentina to understand the atmospheric radiative processes in the region. It is known that biomass burning generates small particles, water vapor, and gases like CO, CO2, nitrogen oxide, and VOCs. These emissions are not evaluated in global scale with high precision to have useful additional information for climate models. Regarding the properties of the atmospheric aerosol and its climatic impact, the respective current information has been reviewed in detail in many publications (Melnikova and Vasilyev 2004; Varotsos et al. 2001, 2005). In this connection, it is pointed out again that the supposed anthropogenic nature of the present global climate warming was explained by the warming caused by the growth in GHGs’ concentrations (primarily CO2 and CH4) as well as cooling due to anthropogenic aerosols. However, if the estimates of the “greenhouse” warming can be considered as sufficiently reliable, then the respective calculations of radiative forcing (RF) due to aerosol are very uncertain. Of no less importance is the fact that while the global distribution of the “greenhouse” RF is comparatively uniform, in the case of the “aerosol” RF it is characterized by a strong spatial-temporal variability (including changes in the sign of radiative forcing). Paleoclimatic information is an important source of data for the comparative analysis of the present and past climates. Analysis of the data of paleoclimatic observations reveals large-scale abrupt climate changes in the past when the climate system had exceeded certain threshold levels. Though some mechanisms for such changes have been identified and the existing methods of numerical climate modeling are being gradually improved, the existing models still do not permit a reliable reconstruction of past climatic changes. With emphasis on the climatic implications of the growth of GHGs’ concentrations in the atmosphere, less effort has been made to study possible sudden climate changes that may be of natural origin though possibly intensified by anthropogenic forcings. Since such changes lie beyond the problems addressed in the UN FCCC, Alley et al. (2002) undertook a conceptual evaluation of the problem of large-scale abrupt climate changes. Though the available long-term stabilizing feedbacks have determined the existence on the Earth of comparatively persistent global climate for about 4 billion years, with characteristic time scales from one year to one million years,

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

415

feedbacks prevailing in the climate system had favored an enhancement of forcings on climate. So, for instance, changes of global average SAT within 5–6 °C during the glaciation cycles apparently resulted from very weak forcings due to variations of the orbital parameters. Even more amazing is that over several decades and in the absence of external forcings, regional changes have taken place reaching 30–50% of those that had taken place in the epochs of glaciations. Data from the period of instrumental observations have revealed abrupt climatic changes, quite often accompanied by serious socioeconomic consequences. So, for instance, the warming in many northern regions in the twentieth century took place in two rapid “steps,” which enables one to suppose that in this case there was a superposition of the anthropogenic trend on interannual natural variability. Special attention was paid to the role of the ENSO event. The latter also refers to a sharp change of the climate system in the Pacific region in 1976–1977. Considerable abrupt changes of regional climate in the period of Paleocene were detected from paleoclimatic reconstructions. They had been manifested as changes of the frequency of occurrence of hurricanes, floods, and especially droughts. Regional SAT changes reaching 8–16 °C had happened in the periods of 10 years and shorter. Dansgaard-Oeschger (DO) oscillations can serve as an example of sudden large-scale changes (Dansgaard et al. 1993). The climatic system involves numerous factors that intensify climatic changes with minimum forcings. The withering or death of plants, for example, may cause a decrease of evapotranspiration and hence lead to precipitation attenuation, which may further increase drought conditions. In the cold-climate regions, the snow cover formation is accompanied by a strong increase of albedo, which favors further cooling (the so-called “albedo effect”). Substantial climatic feedbacks are associated with the dynamics of the thermohaline circulation. While the drivers of either change or stability of climate are comparatively well known, understanding is very much weaker of the factors in the spatial distribution of anomalies over large regions, including the globe. In this connection, further studies of various modes of the general circulation of the atmosphere and the ocean (ENSO, DO oscillations, etc.) are important, as it is the respective improvement of general circulation models. Most important here are the potential effects of abrupt climatic changes on ecology and economy as current estimates are generally based on the assumption of slow and gradual change. Abrupt climate changes were especially substantial in periods of the transition of one climatic state to another. Therefore, if anthropogenic forcings of climate can favor the drifting of the climate system toward a threshold level, the possibility of raising the probability of abrupt climate changes also increases. Of great importance is not only the amount but also the rate of anthropogenic forcings on the climate system. So, for instance, a faster climate warming should favor a stronger attenuation of the thermohaline circulation as this may promote an acceleration of the shift to the threshold of climatic changes (it is important that under these conditions, the thermohaline circulation dynamics becomes less predictable). To accept adequate solutions in the field of ecological policy, a deeper understanding of the whole

416

7 Global Climate Change and Hydrogeochemistry

spectrum of possible sudden climate changes is extremely important. Difficulties in the identification and quantitative estimation of all possible causes of sudden climate change and low predictability near threshold levels testify to the fact that the problem of abrupt climate changes will always be aggravated by more serious uncertainties than the problem of slow change. Under these conditions, the development of ways to provide the stability and high adaptability of economics and ecosystems is of great importance. The estimates of RF changes contained in IPCC-2007 Report, which characterize an enhancement of the atmospheric greenhouse effect and are determined by the growth of concentrations of MGCs well mixed in the atmosphere, gave the total value 3.44 Wm-2, with the following contributions of various MGCs: CO2 (1.49 to 1.83 W m-2), CH4 (0.43 to 0.53 W m-2), halocarbon compounds (0.31 to 0.37 Wm-2), N2O (0.14 to 0.18 Wm-2). The ozone depletion observed during the last two decades could lead to a negative RF constituting 0.15 W m-2, which can be reduced to zero in this century in case of successful measures to protect the ozone layer. The growth of the tropospheric ozone content beginning from 1750 (by about one-third) could produce a positive RF of about 0.33 Wm-2. Since the time of the IPCC-1996 Report, the RF estimates have substantially changed, being determined not only by purely scattering sulfate aerosol considered above, but also by other types of aerosol, especially carbon (soot) characterized by considerable absorption of solar radiation as well as organic, sea-salt, and mineral aerosol. The strong spatial-temporal variability of the aerosol content in the atmosphere and its properties seriously complicates an assessment of the climatic impact of aerosol (Kondratyev et al. 2006a). New results of numerical climate modeling have radically changed the understanding of the role of various factors of RF formation. According to Kondratyev et al. (2006a), there is an approximate mutual compensation of climate warming due to the growth of CO2 concentration and cooling caused by anthropogenic sulfate aerosol. Under these conditions, anthropogenic emissions of methane (mainly due to rice-fields) and carbon (absorbing) aerosol should play a more important role. Estimates of RF obtained with due regard to GHGs and aerosol are of importance in giving substance to conclusions concerning the contribution of anthropogenic factors to climate formation. The correctness of these conclusions is restricted, however, by three factors. One of them is that the interactivity of these factors seriously limits (if not excludes) the possibility of adequate estimates of contributions of individual factors. The second, not of less importance, factor consists in that the above calculated estimates refer to average global values and therefore are the results of smoothing the RF values characterized by strong spatial-temporal variability. Finally, the most complicated problem is an impossibility of reliable assessment of the aerosol RF considering its direct and indirect components. According to estimates by Podgorny and Ramanathan (2001), the value of direct RF at surface level can increase to 50 Wm-2, and Chou et al. (2002) obtained values exceeding 100 W m-2 in the period of forest fires in Indonesia. Vogelmann et al. (2003) estimated the RF due to radiative heat exchange from which it follows that during daytime near the surface, the RF value is usually equal to several Wm-2. From the

7.1

Interaction Between Globalization Processes and Biogeochemical Cycles

417

data of Pavolonis and Key (2003), the total RF at surface level in the Antarctic varies within 0.4–50 Wm-2. Yabe et al. (2003) obtained the average value 85.4 W m-2, and from Lindsey and Simmon (2003), the RF in the USA constitutes 7–8 W m-2. Weaver (2003) has analyzed the possible role of changes of the cloud RF (CRF) at the atmospheric top level, especially in extratropical latitudes, as a climateforming factor whose role consists in regulating the poleward meridional heat transport. The cloud dynamics in extratropical latitudes and related changes in CRF depend on the formation in the atmosphere of vortices responsible for the evolution of storm tracks. It is vortices determining the formation of storm tracks that contribute most to the meridional heat transport. It has been shown by Weaver (2003) that the average annual radiative cooling of clouds in high latitudes has the same order of magnitude as a convergence of the vortices-induced meridional heat flux but of an opposite sign. Since there is a close correlation between CRF and storm-track dynamics, one can suppose two ways of the impact of the storm-tracks dynamics on the poleward heat transport: 1. Directly—via the vortices-induced heat transport in the atmosphere. 2. Indirectly—via CRF changes. The efficiency of heat transport by vortices is reduced by radiative cloud cooling. Changes of efficiency can be a substantial climate-forming factor. Different performance levels can determine the likelihood of different climatic conditions. In the context of the problem of CRF formation due to longwave radiation, Wang et al. (2003) considered specific features of the spatial distribution of cloud cover in the period of an unusually intensive El Niño event in 1997–1998 from the data of observations from SAGE-II satellite. Data on the cloud cover frequency of occurrence in this period and CRF are unique information for verification and specification of schemes of interaction parameterization in the system “clouds – radiation – climate” used in the models of atmospheric general circulation. Based on the use of the occultation technique of remote sensing (RS), the SAGEII data provide the vertical resolution above 1 km and a quasi-global survey (70°N– 70°S). Analysis of the results under discussion revealed: 1. An occurrence of the upper-level opaque clouds exceeding the normal level in the eastern sector of the tropical Pacific and an opposite situation in the regions of the “warm basin” of the Pacific; a combined distribution of anomalies of an opaque cloudiness located at altitudes above 3 km can be explained by the impact of the spatial structure of anomalies of SST fields and precipitation observed in the tropics. 2. The same laws are characteristic of cloudiness near the tropical tropopause recorded at detection threshold. 3. The zonal mean distribution is characterized by a decrease of the amount of opaque clouds in low latitudes (except the SH tropics at altitudes below 10 km) and an enhancement of clouds in high latitudes as well as by an increase (decrease) of cloud amount (at detection threshold) in the SH tropics (in the upper troposphere of the NH sub-tropics).

418

7

Global Climate Change and Hydrogeochemistry

4. The geographical distribution of calculated CRF anomalies which agrees well with the data of satellite observations of the Earth radiation budget. New estimates of direct and indirect RF have been obtained by Giorgi and colleagues. Markowicz et al. (2003) have undertaken a study to estimate the aerosol RF due to longwave radiation (radiative heat exchange). It was studied the aerosol radiative forcing at infrared (IR) wavelengths using data from the Aerosol Characterization Experiment, ACE-Asia, cruise of the National Oceanic and Atmospheric Administration research vessel Ronald H. Brown. The mean IR aerosol optical thickness at 10 m was 0.08 and the single-scattering albedo surface forcing reached 10 W/m2 during this cruise, which is a significant contribution compared to the total direct aerosol forcing. The surface IR aerosol radiative forcing was between 10 and 25% of the short-wave aerosol forcing. Over the Sea of Japan, the average IR aerosol radiative forcing was 4.6 W/m2 at the surface, and 1.5 W/m2 at the TOA. The IR forcing efficiency at the TOA was a strong function of aerosol temperature (which is coupled to vertical structure) and changes between 10 and 18 W/m2 (per IR optical depth unit), while the surface IR forcing efficiency varied between 37 and 55 W/m2 (per IR optical depth unit). From these and other data, it follows that an accuracy of the estimate of radiation balance as a function of space coordinates depends on clouds distribution, their state, and atmospheric pollution, as well as on the chosen size of pixels for spatial averaging. In this connection, Henderson and Chylek (2005) used image data from the Multispectral Thermal Imager to evaluate the effect of spatial resolution on aerosol optical depth retrieval from satellite imagery. It has been shown that the aerosol optical depth (AOD) depends weakly on pixel size in the range 40 × 80 m2 to 2040 × 4080 m2 in the absence of clouds and changes monotonically with increasing pixel size in clouds. The multifaceted and ambiguous role of aerosols in climate changes and influence on human beings is demonstrated in the works of Otero et al. (2004), where the temporal variability of aerosol optical properties has been investigated during a period of intense biomass burning in Córdoba-CETT site (continental dry region in South America and Argentina). Due to the high frequency of occurrences of biomass burning in the dry season, it was important to characterize aerosol optical properties to understand the atmospheric radiative processes in the region. Such a study is important in general not only for evaluation and prediction of climate changes but also for total control of the environmental quality. Anyhow, particles with diameter