Microwave Remote Sensing Tools in Environmental Science [1st ed.] 9783030457662, 9783030457679

Citation preview

Costas A. Varotsos Vladimir F. Krapivin

Microwave Remote Sensing Tools in Environmental Science

Microwave Remote Sensing Tools in Environmental Science

Costas A. Varotsos • Vladimir F. Krapivin

Microwave Remote Sensing Tools in Environmental Science

Costas A. Varotsos National and Kapodistrian University of Athens (NKUA) Athens, Greece

Vladimir F. Krapivin Institute of Radio-Engineering and Electronics Fryazino, Russia

ISBN 978-3-030-45766-2 ISBN 978-3-030-45767-9 https://doi.org/10.1007/978-3-030-45767-9

(eBook)

© Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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

Preface

The problem of global environmental change is the subject of global ecoinformatics in the context of which information technologies have been developed to ensure the combined use of various data on the past and present state of the climate-naturesociety system (CNSS). The intensification of anthropogenic effects on the environment creates a large number of CNSS optimization management problems. However, the existing tools for the solution of arising problems do not allow effective technologies to reliably diagnose numerous environmental processes and allow for the prognosis assessment of possible consequences for the population. Nevertheless, the recent research results obtained by the authors of this book are encouraging, since it is expected that the combined use of microwave remote sensing tools and ecoinformatics methods will contribute to the development of new efficient and reliable technologies for operational diagnostics and forecasting environmental processes of both regional and global scales. The global climate change discussed in recent years in relation to the global carbon cycle and greenhouse effect is of particular concern. The scientific problems arising here are related to the overcoming information uncertainties in the context of the state of the environment assessment and forecasting. Specific theoretical and applied tasks arise when the spatial image is reconstructed on the basis of monitoring data that are provided fragmentally over time and space. This book describes the theoretical and applied aspects of the combined development of radiovision and ecoinformatics methods. Particular attention is paid to the formulation and solution of applicable decision-making tasks based on microwave remote sensing monitoring data for nature-anthropogenic systems. The most sophisticated stages of the synthesis of the information-modeling systems are highlighted. Their purpose is to overcome information uncertainties and to allow the adoption of environmental monitoring systems in the real object or process. It is suggested to use a range of methodologies, algorithms, and information technologies to solve specific tasks for the diagnosis of natural environmental systems to support monitoring of data set that takes into account the specific conditions of its implementation formation and enables optimization of the observational data collection and analysis using microwave sensors. v

vi

Preface

The role of microwave radiometry in the study of environment change is taken into account given the existing scientific results associated with applications of microwave radiometry for the investigation of vegetation, soil, and water systems, including characteristics such as soil moisture, snow water equivalent, atmosphere, and water environment pollution. The main aspects of physical basis of microwave radiometry are also discussed. The use of microwave sensors located on satellites requires the elaboration of remote sensing systems to be developed with acceptable spatial resolution that is possible due to the effective data processing algorithms. Many scientists are trying to find answers to this task. The first attempts in understanding these key problems of microwave remote sensing have recently been made in a number of publications by Krapivin (2009) and Krapivin et al. (1998b, 2005, 2007, 2015a, 2018a, b; Varotsos et al. 2019). Certain results of discussions and publications allow the production of a constructive synthesis of a geoecological information-modeling system (GIMS) of natural processes by examining a set of spatial scales. The theoretical part of the book has chapters outlining various algorithms and models. The applied part of the book addresses the specific problems of the environment dynamics. The purpose of the theoretical and applied parts is the development of universal information technology for the estimation of the state of the environment subsystems operating in various climatic and anthropogenic conditions. The basic idea of the approach proposed in this book is to combine Geographical Information System (GIS) techniques with modeling technology to estimate the functionality of the CNSS. This idea is implemented using the new methods for spatial-temporal reconstruction of incomplete data. Algorithms, models, methods, criteria, and software are designed to synthesize GIS with modeling functions for complex estimation of nature-society subsystem’s state. The newly developed GIMS focuses on systematic observations and evaluation of the environment related to changes attributable to human aspects in environmental subsystems. One of the important operating aspects of an integrated system is the ability to forecast the capability to respond to adverse environmental changes. Various applications of GIMS-technology are described. There are chapters describing an application of GIMS-technology for the study of soil moisture, snow water equivalent, isolated forest fire areas, and water pollution. Particular attention is paid to the Aral Sea zone, where ecological catastrophe is taking place. An important part of the book deals with microwave radiometric methods that are traditional methods of remotely sensing Earth’s cover from planes and satellites. Combined use of microwave remote sensing, mathematical modeling of the environment, data processing, and decision-making procedures is proposed. This book aims to focus the reader’s attention on microwave radiometric technology as one of the most powerful technologies in radiophysics of remote sensing of lakes, seas, oceans, rivers, agricultural fields, irrigated lands, desert areas, forest areas, wetlands, snow-covered ground, and ice in the wavelength range from 0.5–2 cm to 21–30 cm. The basic objective of this book is to develop new approaches to the creation of environmental monitoring systems based on multidisciplinary technologies in

Preface

vii

physics, mathematics, ecology, and informatics. The effectiveness of this approach has been demonstrated by solving specific tasks from monitoring of natural systems in different latitudinal and climate zones. This book addresses the development of models of various processes performed at CNSS as one of the important scientific directions of global ecoinformatics. A global CNSS simulation model was created to be used for the study of global ecodynamics. The CNSS model has a changeability structure that allows different implementation of its items. It gives the opportunity to assess the significance of each item for accurately parametrically describing the dynamics of CNSS. The simulation experiments in the framework of studying the different principal structures of the CNSS model give the opportunity to compose the global environmental control system, using standardized means of telecommunications, existing monitoring systems, and big data processing algorithms. This book is intended for specialists in the fields of Earth aerospace research, environmental monitoring, climate change study, human and nature interaction study, geopolitics, and methodology of interdisciplinary research. The book will be useful for undergraduate and postgraduate students studying these areas of science. The book is divided into eight chapters. Each chapter introduces the reader to a specific subject field and proposes methods for solving tasks that arise in that field. Each chapter focuses on the areas of application of ecoinformatics methods and remote sensing tools and the problems encountered in solving specific tasks.

Athens, Greece Fryazino, Russia

Costas A. Varotsos Vladimir F. Krapivin

Contents

1

2

Basic Concepts of Microwave Radiometry . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Principal Concept of Remote Monitoring Technology . . . . . . . . 1.3 Microwave Emission from the Water and Land Surfaces . . . . . . 1.3.1 Brightness Temperature . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Dielectric Constant . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Emissivity Coefficient . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 Microwave Radiation Sensitivity to Variations of Basic Environmental Parameters . . . . . . . . . . . . . . . . . . . . . 1.4 Remote-Sensing Research Platforms and their Equiping . . . . . . 1.5 Microwave Polarization Characteristics of Snow . . . . . . . . . . . . 1.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Theoretical and Empirical Tools for Studying the Microwave Irradiation of Snow Cover . . . . . . . . . . . . . 1.5.3 Experimental Measurements . . . . . . . . . . . . . . . . . . . . 1.5.4 Analysis and Discussion of Empirical Results . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

1 1 3 7 7 10 13

. . . .

15 17 26 26

. . . .

28 33 38 40

Remote Sensing Technologies and Data Processing Algorithms . . . . . 2.1 Microwave Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Physical, Theoretical and Experimental Background of Microwave Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Remote Sensing Technologies in Infrared and Optical Bands . . . . 2.4 The Microwave Atmospheric Monitoring . . . . . . . . . . . . . . . . . . 2.5 Algorithms for the Remote Data Processing . . . . . . . . . . . . . . . . 2.5.1 Data Reconstruction Using the Harmonic Functions . . . . 2.5.2 Method for Parametric Identification of Environmental Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.3 Differential Approach Method . . . . . . . . . . . . . . . . . . . 2.5.4 Quasi-Linearization Method . . . . . . . . . . . . . . . . . . . . .

45 45 47 51 54 64 64 67 68 69 ix

x

Contents

2.6

Multi-channel Microwave Sensor to Measure Environmental Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Direct and Inverse Problems of the Microwave Monitoring . . . . 2.8 Interferometry Methods in Geo-Risk Assessment Tasks . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

84 89 93 95

3

Constructive Method of Vegetation Microwave Monitoring . . . . . . . 3.1 The Information-Modeling Technology . . . . . . . . . . . . . . . . . . . 3.2 Microwave Monitoring of Soil-Plant Formations . . . . . . . . . . . . . 3.3 Links Between Experiments, Algorithms, and Models . . . . . . . . . 3.4 Microwave Moel of Vegetation Cover . . . . . . . . . . . . . . . . . . . . 3.4.1 Two-Level Model of Vegetation Cover . . . . . . . . . . . . . 3.4.2 Analytical Model of Vegetation Cover . . . . . . . . . . . . . . 3.5 Microwave Emissivity of the Soil-Plant Systems . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

99 99 102 107 108 108 112 113 117

4

Microwave Remote Sensing of Soil Moisture . . . . . . . . . . . . . . . . . . 4.1 Uncertainty and Risk Sources in Remote Sensing . . . . . . . . . . . 4.2 Practical Microwave Radiometric Risk Assessment of Agricultural Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Geoinformation Monitoring System of Agricultural Function . . . 4.4 Microwave Monitoring of the Soil Moisture . . . . . . . . . . . . . . . 4.5 The State of Soils and Water Objects Evaluated by Means of Radiometry Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 121 . 121

Vegetation Screening Effect in Remote Sensing Monitoring . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Attenuation of Electromagnetic Waves in Vegetation Media . . . 5.3 Measuring System for Retrieving Attenuation of Microwaves in the Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Theoretical Generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 145 . 145 . 147 . . . . .

150 152 157 159 159

Microwave Tools for Diagnosing Forest Fires . . . . . . . . . . . . . . . . . 6.1 Wildfire Causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Wildfires and Global Ecodynamics . . . . . . . . . . . . . . . . . . . . . 6.2.1 Fires and Forest Ecosystems . . . . . . . . . . . . . . . . . . . . 6.2.2 Wildfires, Dynamics of the Biosphere, and Climate . . . 6.2.3 Biomass Burning and Atmospheric Chemistry . . . . . . . 6.2.4 Wildfires and Carbon Cycle . . . . . . . . . . . . . . . . . . . . 6.3 Microwave Radiometric Observations of Temperature Anomalies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

163 163 165 165 167 170 172

5

6

. 124 . 129 . 131 . 135 . 142

. 173

Contents

xi

6.4

178 178 179 185 187 192

Microwave Monitoring Features of the Wildfires . . . . . . . . . . . . . 6.4.1 Microwave Model of the Wildfires . . . . . . . . . . . . . . . . 6.4.2 Forest Fire Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 SHF-Radiation of Forest Fires . . . . . . . . . . . . . . . . . . . . 6.4.4 Natural SHF-Radiation of Peat Formations . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Space Methods and Monitoring Tools for the Investigation of Aquatic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Microwave Radiometry in the Remote Monitoring of the Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Application of Big Data Approach to the Study of Arctic Basin Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Geoecological Information-Modeling System . . . . . . . . 7.3.3 Modeling the Pollutant Dynamics in the Arctic Basin . . 7.3.4 Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . . 7.3.5 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . 7.4 Implication of Geoecological Information-Modeling System for the Okhotsk Sea Ecosystem Study . . . . . . . . . . . . . . . . . . . 7.4.1 Quick Analysis of the Okhotsk Sea Ecosystem . . . . . . . 7.4.2 Model of the Okhotsk Sea Ecosystem . . . . . . . . . . . . . 7.4.3 Biocomplexity and Survivability Indicators . . . . . . . . . 7.4.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.5 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . 7.5 The Peruvian Current Ecosystem and Ecoinformatics Tools . . . . 7.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 The Peruvian Current Ecosystem Model . . . . . . . . . . . 7.5.3 Results of Simulation Experiments . . . . . . . . . . . . . . . 7.5.4 The Effects of Environmental Parameter Variations . . . 7.6 New Scenario for Recovering Water Balance of the Aral Sea . . . 7.6.1 Water Problems in Central Asia . . . . . . . . . . . . . . . . . 7.6.2 Aral-Caspian Regional Water Cycle Model . . . . . . . . . 7.6.3 Remote Sensing Database of the Aral Sea Zone Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7 Mathematical Modelling and Numerical Experiment in the Geophysical Study of the Aral Sea Region . . . . . . . . . . . . . . . . 7.7.1 Model for Structured-Functional Analysis of Hydrophysical Fields of the Aral Sea . . . . . . . . . . . . . . 7.7.2 Model of the Regional Water Balance of the Aral Sea Aqua-geosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.3 Model of the Kara-Bogaz-Gol Gulf Aqua-geosystem . . 7.7.4 Parameterization of the Water Balance in the Aral Sea Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 195 . 195 . 196 . . . . . .

201 201 203 206 208 217

. . . . . . . . . . . . . .

218 218 220 224 225 230 233 233 234 241 242 252 252 253

. 261 . 264 . 264 . 268 . 274 . 277

xii

Contents

7.8

Simulation Experiments and Forecast of the Water Balance Components of the Aral Sea Hollow . . . . . . . . . . . . . . . . . . . . . 7.8.1 Scenario for Potential Directions of Changes in Water Balance Components of the Aral Sea . . . . . . . . . . . . . . . 7.8.2 Model Estimation of Aral Sea Water Balance Dynamics in the Case of Preserved Natural-Anthropogenic Situation in the Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8.3 Recommendations on the Monitoring Regime of the Aral Sea Aqua-geosystem . . . . . . . . . . . . . . . . . . . . . . . 7.8.4 Reliability of Scenario Implementation . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Microwave Remote Sensing Monitoring and Global Climate Change Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Interactive Character of the Global Climate Problems . . . . . . . . 8.2.1 Anomalous Situations and Climate . . . . . . . . . . . . . . . 8.2.2 The Global Carbon Cycle and Its Climatic Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.3 Sources and Sinks of Carbon Dioxide in the Biosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Anthropogenic Sources of Carbon . . . . . . . . . . . . . . . . . . . . . . 8.4 Resources of Biosphere and the Greenhouse Effect . . . . . . . . . . 8.5 The Greenhouse Effect and Global Carbon Cycle . . . . . . . . . . . 8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.2 Conceptual Scheme of Global Carbon Dioxide and Methane Global Cycles . . . . . . . . . . . . . . . . . . . . . . . 8.6.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6.4 Conclusions and Discussion of the Results . . . . . . . . . . 8.7 Microwave Remote Sensing Monitoring and Environmental Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Aerosol Radiative Forcing and Climate . . . . . . . . . . . . . . . . . . 8.8.1 The Problem with Survey . . . . . . . . . . . . . . . . . . . . . . 8.8.2 Empirical Diagnostics of the Global Climate . . . . . . . . 8.8.3 The Radiative Forcing . . . . . . . . . . . . . . . . . . . . . . . . 8.9 Aerosol Long-Range Transport and Climate . . . . . . . . . . . . . . . 8.9.1 Aerosol and Climate . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9.2 Aerosol Long-Range Transport . . . . . . . . . . . . . . . . . . 8.9.3 Numerical Modeling of the Aerosol Long-Range Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

279 279

282 284 286 288 295 295 297 297

. 302 . . . .

310 313 321 322

. 327 . 327 . 328 . 337 . 342 . . . . . . . .

343 346 346 353 354 359 359 363

. 367 . 386

Contents

9

Global Climate Monitoring with Microwave Measurements . . . . . . 9.1 Microwave Monitoring of Disasters . . . . . . . . . . . . . . . . . . . . . 9.2 The Interactions of Nature and Human Society . . . . . . . . . . . . . 9.3 Natural Disasters Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Spatial and Temporal Characteristics of Natural Disasters . . . . . 9.5 Environmental Impacts of Natural Disasters . . . . . . . . . . . . . . . 9.6 Role of Natural Disasters in the Climate/Biosphere System Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.7 Reality and Expected Changes of the Environment . . . . . . . . . . 9.8 The Current Needs on Ecological-Climatic Modelling . . . . . . . . 9.9 Satellite Observations of Climate-Nature-Society System Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.10 The Earth’s Population Survivability . . . . . . . . . . . . . . . . . . . . 9.10.1 Short Description of the Problem . . . . . . . . . . . . . . . . 9.10.2 General Description of the Survivability Model . . . . . . 9.10.3 A Scenario-Based Prognosis . . . . . . . . . . . . . . . . . . . . 9.10.4 Looking to the Future . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii

. . . . . .

395 395 397 400 412 415

. 417 . 420 . 424 . . . . . . .

427 437 437 439 445 451 454

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459

Summary

This book discusses the following topics: • Remote sensing and mathematical modeling for effective forecast of dynamics of large-scale land territories and water areas behavior. • Practical applications of microwave radiometric technologies along with other remote sensing technologies in different environmental situations in hydrology, geophysics, thermal hazards phenomena, and fire hazard determination in forested and peat bog environments. • Decision making in complicated conditions. • Target-oriented models which may be effective in socioeconomic areas. • The regional and global ecological monitoring functioning based on the fundamentals of the noosphere paradigm. • Development algorithms and informational tools for the study of land covers, World Ocean, and seas using the microwave radiometric measurements. • Synthesis of the geoecological information-modeling system (GIMS) as universal instrument for the study of the evolution processes in the climate-nature-society system overcoming uncertainties in environmental big data. This book may be recommended to scientists and students to deepen science and extend knowledge to the areas described in therein. The book focuses on scientists developing new information technologies for big data processing that are provided in fragments in time and space by monitoring systems.

xv

Abbreviations and Acronyms

AAHIS AAMU AATSR ABE ABL ACMR ACS ADEOS AER AHRS AI AIMR AIS AMAP AMMR AMPR AMSR-E AMSU ANSS AOCI AOT APAR APHW ARF ARIMA ARISTI ASPPR ASTAR

Advanced Airborne Hyperspectral Imaging Spectrometer Alabama Agricultural and Mechanical University Advance Along Track Scanning Radiometer Arctic Basin Ecosystem Atmospheric Boundary Layer Airborne C-band Microwave Radiometer Aral-Caspian System Advanced Earth Observing Satellite Atmospheric and Environmental Research Altitude and Heading Reference System Aerosol Index Airborne Imaging Microwave Radiometer Airborne Imaging Spectrometer Arctic Monitoring and Assessment Programme Airborne Multichannel Microwave Radiometer Advanced Microwave Precipitation Radiometer Advanced Microwave Scanning Radiometer - Earth Observing System Advanced Microwave Sensing Unit Advanced National Seismic System Airborne Ocean Color Imager Spectrometer Aerosol Optical Thickness Absorbed Photosynthetically Active Radiation Asia Pacific Association of Hydrology and Water Resources Aerosol Radiative Forcing AutoRegressive Integrated Moving Average All Russian Institute for Scientific and Technical Information Arctic Shipping Pollution Prevention Regulations Agency for Science, Technology And Research

xvii

xviii

ASTR ASWR ATL ATSR AVHRR AVI AVSS AWSR BAMS BC BSI BVOC CALIPSO CALRS CASI CASRS CBSS CCM CCN CCSP CDNC CF CFC CFFDRS CHRIS CMCDMC CMIS CNSS CNSSGSM CRC CSIRO DA DEAD DOE DOM ECMWF EFFIS EIA EMAC EMIRAD EMISAR

Abbreviations and Acronyms

Along Scanning Trace Radiometer Absorbed Short Wave Radiation Atmospheric Top Layer Along Track Scanning Radiometers Advanced Very High Resolution Radiometer Advanced Vegetation Index Atmosphere-Vegetation-Soil System Airborne Water Substance Radiometer Bulletin of the American Meteorological Society Black Carbon Bare Soil Index Biogenic Volatile Organic Compounds Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations Cluster Analysis focused on the account of Local Reading of Sensors Compact Airborne Spectrographic Imager Cluster Analysis focused on sings Space of Remote Sensors. Climate-Biosphere-Society System Community Climate Model Cloud Condensation Nuclei Climate Change Science Program Cloud Drop Number Concentration Cloud Feedback ChloroFluoroCarbon Canadian Forest Fire Danger Rating System (). Compact High Resolution Imaging Spectrometer Coupled Model of Carbon Dioxide and Methane Cycles Conical Microwave Imager Sounder Climate-Nature-Society System CNSS Global Simulation Model Chemical Rubber Company Commonwealth Scientific and Industrial Research Organization Dust Aerosol Dust Entrainment And Deposition Department Of Energy Dissolved Organic Matter European Centre for Medium-range Weather Forecasts The European Forest Fire Information System Earth Incidence Angle European Multisensor Airborne Campaign Electromagnetic Institute Radiometer Electromagnetic Institute SAR

Abbreviations and Acronyms

EMW ENSO EOF EOS EOSDIS ERB EPIC EPIC ERS ESA ESDIS ESS ESTAR EUP EVI EWT EXPRESSO FAA FAO FCCC FDR FFDI FMC FOV EPAR FSU FWI GCOS GCP GCS GeoDAS GHG GIMS GIMSAF GIS GMNSS GOES GOME GOOS GPS GPS/MET GSM

xix

Electro Magnetic Waves El Niño–Southern Oscillation Empirical Orthogonal Functions Earth Observing System EOS Data and Information System Earth Radiation Budget Environmental Policies and Institutions for Central Asia Earth Polychromatic Imaging Camera European Remote Sensing European Space Agency Earth Science Data and Information System Effective Scattering Surface Electronically Scanned Thin Array Radiometer Enterprise Unified Process Enhanced Vegetation Index Equivalent Water Thickness EXPeriment for REgional Sources and Sinks of Oxidants Federal Aviation Administration Food and Agriculture Organization Framework Convention on Climate Change Fire Danger Rating Forest Fire Danger Index Fuel Moisture Content Fields Of View Fraction of Photosynthetically Active Radiation Former Soviet Union. Fire Weather Index Global Climate Observation System Global Carbon Project Ground Control Station Geo Data Acquisition System Green House Gas Geoecological Information Modeling System Geoecological Information Modeling System of Agricultural Function Geographical Information System Global Model of the Nature-Society System Geostationary Operational Environmental Satellite Global Ozone Monitoring Experiment Global Ocean Observing System Global Positioning System Global Positioning System/METeorology Global Simulation Model

xx

GST GTOS HSCaRS IASTED ICGGM ICLIPS ICSU IEEE IGAC IGBP IHDP IIASA IMARC INDOEX INPP IPCC ITBL JCSDA KBGG KP LACE LAI LERMA LMD LOSAC LPJ-DGVM LSAT LUT LWC LWRF MABL MACS MATCH MBGC MCRS MDA MEM MERIS MGCDC MISR MODIS MR

Abbreviations and Acronyms

Global Surface Temperature Global Terrestrial Observation System Hydrology, Soil Climatology and Remote Sensing International Association of Science and TEchnology for Development International Centre for Global Geo-eco-information Monitoring Integrated assessments of CLImate Protection Strategies International Council for Science Institute of Electrical and Electronics Engineers International Global Atmospheric Chemistry International Geosphere-Biosphere Programme International Human Dimensions Programme International Institute for Applied Systems Analysis Intelligent Multi-frequency Airborne polarimetric Radar Complex INDian Oceanic EXperiment Integrated Net Primary Production Intergovernmental Panel on Climate Change Internal Thermal Boundary Layer Joint Center for Satellite Data Assimilation Kara-Bogaz-Gol Gulf Kyoto Protocol Lindenberg Aerosol Characterization Experiment Leaf Area Index Laboratoire d’Études du Rayonnement et de la Matière en Astrophysique et Atmosphères Laboratory of Meteorological Dynamics L-band Ocean Salinity Airborne Campaign Lund-Potsdam-Jena Dynamic Global Vegetation Model Land Surface Air Temperature LookUp Table Liquid Water Content Long-Wave Radiative Forcing Marine Atmospheric Boundary Layer Microwave Autonomous Copter System Multi-scale Atmospheric Transport and CHemistry Modelling of Biogeochemical Global Cycles Multi-channel radiometric system Model-Driven Architecture Microwave Emission Model Medium Resolution Imaging Spectrometer Instrument Model of Global Carbon Dioxide Cycle Multi-angle Imaging SpectroRadiometer Moderate Resolution Imaging Spectroradiometer Microwave Radiometer

Abbreviations and Acronyms

MRSD MSD MSRAMV MW NASA NDVI NDWI NEHRP NFDRS NH NMAT NMHC MN NOAA NPOESS NPP NRI NSS NVR OCCAM OCTS OIP OMI OSAVI OSCORA OSE OSEM PACE PAGES PALS PAR PBL PCE PDF PIRATA PMR PNW POAM POLDER PSR/C RAMA RAS

xxi

Microwave Remote Sensing Division Mean-Square-Difference Measuring System for Retrieving Attenuation of Microwaves in Vegetation Micro Wave National Aeronautics and Space Administration Normalized Differential Vegetation Index Normalized Vegetation Water Index National Earthquake Hazards Reduction Programme National Fire Danger Rating System Northern Hemisphere Nocturnal Marine Air Temperature Non-Methane Hydro Carbons Neural Network National Oceanic and Atmospheric Administration National Polar Orbiter Environmental Satellite System Net Primary Production Neural-Robotics, Inc. Nature-Society System Nadir Viewing Radar Ocean Circulation and Climate Advanced Model Ocean Color and Temperature Sensor Optical Instruments and Products Ozone Monitoring Instrument Soil-Adjusted Vegetation Index optimized for Agricultural Monitoring Okhotsk Sea & Cold Ocean Research Association Okhotsk Sea Ecosystem OSE Model Plankton, Aerosol, Cloud, ocean Ecosystem PAst Global changES Passive and Active L and S band system Photosynthetically Active Radiation Planetary Boundary Layer Peruvian Current Ecosystem Probability Density Function PIlot Research moored Array in the Tropical Atlantic Passive Microwave Radar Pacific North-West Polar Ozone and Aerosol Measurement POLarization and Directionality of the Earth's Reflectance Polarimetric Scanning Radiometer/C-band Research moored Array for Monsoon Analysis Russian Academy of Sciences

xxii

RF RIV RMD RMSE ESAR SAR SAT SARVI SAVI SCE SCIAMACHY SCOPE SDI SeaWiFS SES SGPE SHF SL SLAR SMASHF SMMR SMOS SPF SPOT SR SRB SSAPP SSM/I SST SVI SWRF TAR TEM THC TIM TIR TOA TOGA TOMS TPP TRITION TRMM TTP

Abbreviations and Acronyms

Radiative Forcing Residual Income Valuation Radiation-Moistening Dependence Root Mean Square Error Radar SAR Synthetic Aperture Radar Surface Atmospheric Temperature Soil Adjusted Total Vegetation Index Soil Adjusted Vegetation Index Snow Cover Extent SCanning Imaging Absorption 5 spectroMeter for Atmospheric CHartographY Statewide Communities Of Practice for Excellence Scalled Shadow Index Sea-viewing Wide Field-of-view Sensor Seismic Electric Signals SGPE Southern Great Plains Experiment Super High Frequency Surface Level Side-Looking Airborne Radar Simulation Model of the Aral Sea Hydrophysical Fields Scanning Multichannel Microwave Radiometer Soil Moisture and Ocean Salinity Soil-Plant Formation Systéme Probatoire d’Observation de la Terre Simple Ratio Surface Radiation Budget Simulation System for the Atmosphere Pollution Physics Spatial Sensor Microwave/Imager Sea Surface Temperature Spectral Vegetation Index Short Wave Radiative Forcings Third Assessment Report Transverse Electric and Magnetic ThermoHaline Circulation Theory-Information Model Thermal Infrared Radiometer Top-Of-Atmosphere Tropical Ocean Global Atmosphere Total Ozone Mapping Spectrometer Total pure Primary Production TRIangle Trans-Ocean buoy Network Tropical Rainfall Measuring Mission Technology Transfer & Promotion

Abbreviations and Acronyms

UML UNEP UNESCO USGS USSR VDC VI VIIRS VOC VSWR VWC WAAS WCRP WDI WEP WISE WMO WR WSSD

xxiii

Unified Modeling Language United Nations Environment Programme United Nations Educational, Scientific and Cultural Organization USA Geological Survey Union of Soviet Socialist Republics Volt Direct Current Vegetation Indices Visible Infrared Imaging Radiometer Suite Volatile Organic Compounds Voltage Standing Wave Ratio Vegetation Water Content Wide Area Augmentation System World Climate Research Programme Water Deficit Index Water Evaporation/Precipitation World Information Service on Energy World Meteorological Organization Wood Remains World Summit on Sustainable Development

Chapter 1

Basic Concepts of Microwave Radiometry

1.1

Introduction

Super High Frequency (SHF) radiometry began to develop in the 1950s mainly in the former Soviet Union and the United States, with the aim of studying the emission of natural and anthropogenic objects in all weather conditions, the composition of radio-landscapes maps, the development of the recognition methodology for the land and water surfaces and on their basis the application of the radio-landscape navigation. Remote sensing of land surfaces is based on recordings of natural or reflected and scattered electromagnetic emissions. A possibility to obtain information about the characteristics of land covers depends on the mechanisms of electromagnetic emission transformation that are functions of physical and geometrical characteristics of the surface. Practically, the investigation of land and water objects is accomplished using the wave range from 0.8 cm to 21 cm. Parameters that govern the intensity and polarization of microwave emission are dielectric permittivity, temperature, mineralization, roughness of earth and water surfaces, vegetation covers, foam formations, pollutant films on the water surface, etc. Waves of electromagnetic radiation used for remote sensing of land cover occupy a light range, infrared range, and radiowaves. Alternative spectrum for remote sensing is defined by the character of the phenomenon studied and the absorption and scattering of electromagnetic waves in the atmosphere. Transparency of the atmosphere for a given band of electromagnetic waves is the principal factor of this band for remote sensing. Therefore, remote sensing measurements give acceptable results when multi-channel system is used. One example of these systems is the Scanning Multichannel Microwave Radiometer (SMMR) which measures dualpolarized microwave radiances from Earth’s atmosphere and surface providing all-weather measurements of sea surface temperature, wind speed, and atmospheric liquid water and water vapor.

© Springer Nature Switzerland AG 2020 C. A. Varotsos, V. F. Krapivin, Microwave Remote Sensing Tools in Environmental Science, https://doi.org/10.1007/978-3-030-45767-9_1

1

2

1 Basic Concepts of Microwave Radiometry

Table 1.1 Different wavelength bands in the microwave region

Band P L S C X K

Frequency, GHz 0.3–1 1–2 2–4 4–8 8–12.5 12.5–40

Wavelength, cm 30–100 15–30 7.5–15 3.8–7.5 2.4–3.8 0.5–2.4

The theory and practice of microwave remote observations widely use such basic notions as brightness and noise temperature a precision of calculation of which depends on the sensitivity limit of a microwave radiometer. Therefore, a quality of remote measurement results is defined by the types of passive microwave devices including their specified physical features and transformation type of a signal (Janssen 1993; Krapivin and Shutko 1989, 2002, 2012; Pampaloni 2004). Microwave region extends from 0.3 to 300 GHz frequency in different microwave regions represented by P-, L-, S-, C-, X-, K- bands as indicated in Table 1.1. Real production of remote sensing monitoring can be received after an inverse task solution that is usually incorrect due to different errors of radiometric measurements, noises of the used sensors, and inaccuracy of the used functions. Therefore, remote sensing tools include a stage of radiometer calibration using in-situ test measurements (Andreas and Wilcox 2016). The quality of microwave radiometer calibrations determines the accuracy of geophysical retrievals from delivered brightness temperatures after proper inverse task solution. The quality and reliability of data collected with a microwave radiometer depends on the instrument’s thermal stability, noise level, and the calibration accuracy (Solheim et al. 1998). The calibration is needed to convert measured voltages/counts into brightness temperatures (Tb). Remote sensing methods include two main classes: active and passive. This book mainly considers passive radiometric tools that analyse natural (heat) radiation emitted by natural objects. In particular, microwave sensors of different range are used effectively to solve environmental problems in different applications including the study of: • • • • •

crops, forest cover, snow and ice fields, soil moisture and soil types; hydrological processes such as floods; surface temperature with detection of anomalies; water surface pollution from different sources; and productivity of natural and agricultural ecosystems.

Areas of application for microwave remote sensing technologies include the following themes: • On land: estimation of soil moisture, identification of crops, mapping of floods, evaluation of snow characteristics, assessment of parameters in geology, forestry, urban land use and control of hydrocarbons dynamics.

1.2 Principal Concept of Remote Monitoring Technology

3

• Oceanographic applications include such significant themes as ocean circulation, shallow-water system evolution depending on the climate change, and ocean pollution by oil products. • Meteorological predictions depending on the ocean-state, including ocean circulation and its pollution. • Assessment of mesoscale atmospheric processes such as cyclones (hurricanes, typhoons) and profiling of moisture and temperature. The use of microwave bands for remote sensing monitoring is based on their all-weathering when the atmosphere is transparent to microwave waves. However, a reservation that radio-transparency of the atmosphere is relative it must to be made. For example, the water vapor has an absorption line at 1.35 cm wave-length and oxygen absorption at 0.5 cm wave-length. At the same time non-compact and short vegetation weakly absorbs microwave radiation that allows for the realization of radio-observation through vegetation cover. A microwave attenuation problem in the vegetation covers is discussed separately. One of the problems arising under the application of microwave remote sensing consists of low spatial resolution that is mainly defined by the altitude of the onboard platform radiometer (Migliaccio and Gambardella 2005; Küchler et al. 2016). It is well known that spatial resolution of optical sensors is better than microwave radiometers. Scanning radiometer data processing can allow enhancing the limited intrinsic spatial resolution. Camps and Tarongí (2010) showed that the radiometric resolution of noise-injection radiometers can be optimized by dynamically adjusting the integration times devoted to the three measurements: antenna, antenna plus noise, and reference load. According to Park et al. (2004) discussed the possibility to improve spatial resolution of microwave radiometers using Fourier Transform Method and spatially adaptive Capon beam formulation method (Monjardin et al. 2009). Spatial resolution is an important variable in the processes controlling the energy and water fluxes at the interface between the Earth’s surface and the atmosphere. For continental to global scale modeling of these land surface processes there is a need for long term remote sensing–based Tb for validation and data assimilation procedures. The spatial resolution of thermal infra-red (TIR) measurements ranges from 1 to 5 km for polar orbiting satellites to 50 km for geostationary platforms. Low spatial resolution makes it difficult to take into account the land surface roughness and the heterogeneity of vegetation cover.

1.2

Principal Concept of Remote Monitoring Technology

The elements of SHF-radiometry relate to the 1950s when in the USA and the Former Soviet Union all-weather control of the Earth begun with both satellite launches and remote sensing sensors designed for: • realization of latent all-weather radio observations; • synthesis of maps for the radio landscapes;

4

1 Basic Concepts of Microwave Radiometry

• development of methods for landscape recognition; and • radio landscape navigation. SHF-radiometry is based on the registration of natural emission from natural objects such as land, water, atmosphere, clouds, reservoirs, vegetation, etc. Various different SHF-sensors and platforms for their placement have been described in many publications (Krapivin and Shutko 2012; Krapivin et al. 2019; Woodhouse 2005; Mickelsen 1971). The solution of the majority of applied problems of present ecodynamics is difficult because the effective methods of controlling soil-plant formations (SPF) and ocean ecosystems are insufficiently developed. The need for the creation of new effective information technology to remote data processing and interpretation is dictated by different areas of human activity. Many global environmental problems such as greenhouse effect or natural disasters have principal limitations when their prognosis is considered. The set of international scientific programs make efforts to focus investigations on understanding the processes that affect the recognition of the importance of the physical, biological and chemical environment for life. Unfortunately, the objective of many of them is no realized. It is evident that new paradigms for studying the Earth and its environs did not develop. One of the most effective information technologies that have been developed in recent times is Geographic Information System (GIS) technology. It has great interest as a geographically oriented computer technology with commercial orientation. But GIS has numerous limitations associated with its functions to predict environmental dynamics. The difficulties arising here are related to the complexity of the earth surface and the lack of detailed data that reflect environmental dynamics. It is corroborated, for example, by the problems that were formulated within the Global Carbon Project (GCP). Land surface properties and processes play an important role in the formation of global ecodynamics including climate change. Land surface is characterized by many parameters such as soil-plant formation type, leaf area index (LAI), roughness length, and albedo. Other parameters determine the processes taking into place in the atmosphere-land system: evaporation, precipitation, and photosynthesis. The Global Climate Observation System (GCOS) and Global Terrestrial Observation System (GTOS) supply the LAI with accuracy of
0.2 to 1.0 over large areas. For example, MODIS gives a 1-km global data product updated once every eight-day period throughout the year. The Multi-angle Imaging Spectroradiometer (MISR) supplies LAI with a special resolution of 1.1 km every 8 days. There are several categories of methods to estimate LAI (Fang and Liang 2003): • • • •

using 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 input information of 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

1.2 Principal Concept of Remote Monitoring Technology

5

soil-vegetation system. LAI can be determined relatively cheaply and easily using imagery from various satellites. The most commonly used satellites for determining LAI are SPOT, NOAA AVHRR and Landsat TM, all with differing spatial and spectral resolutions. Multi-channel image data, such as TM, and empirical relationship, such as NDVI-LAI relation or SR-LAI relation, are used to estimate LAI. While multi-angular remote sensing data provide more information for canopy structure. Particularly, multi-angular data and model inversion method is used to estimate LAI. Enormous information about the environmental sub-systems is provided by the Earth Observing System (EOS). 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. Numerous environmental problems associated with global climate change are the subject of debate among scientists. Unfortunately, the limits of future global changes remain to be assessed. Kondratyev et al. (2004) proposed a methodology for solving this problem based on EOS data. The numerous instruments and platforms hardware of EOS supply the spacious information really 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.

The EOS Program includes scientific and technical support of the environmental investigations (Asrar and Dozier 1994; Tianhong et al. 2003). The EOS missions and EOS Data and Information System (EOSDIS) provide data and infrastructure to facilitate interdisciplinary research about the Earth system. EOSDIS, as NASA’s Earth science data system, enables the Earth science data collection, command and control, scheduling, data processing, and data archiving and distribution services for EOS missions. The 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 provide search and access of science data and data products to many science data users. The EOSDIS is managed by the Earth Science Data and Information System (ESDIS) which is 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 of tools to facilitate the processing, archiving, and distribution of Earth science data.

6

1 Basic Concepts of Microwave Radiometry

• 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. But there is no effective technology that enables this infrastructure to adapt to the basic environmental problems. The GIS and modeling technique being coupled give such technology. The GIS provides efficient analytical tools for generating prospective maps by several combination processes in a knowledge driven approach including Boolean logic combination, algebraic combination, index overlay combination, fuzzy logic and vector fuzzy logic combinations, and so on (Givant and Halmos 2009; Anaokar and Khambete 2016). There are several synthetic modeling techniques including standard modeling languages such as Unified Modeling Language (UML), Model-Driven Architecture (MDA) and Enterprise Unified Process (EUP). However, these languages do not cover all existing models of environmental systems and processes. Many models have been created based on other approaches (Cracknell et al. 2009). Combination of such models with GIS is realized by means of GIMS-technology (Krapivin et al. 2015; Krapivin and Shutko 1989; Krapivin et al. 2006). Over the last few years, the global carbon cycle problem has gained particular prominence because of the greenhouse effect. Knowledge of the state of the SPF and ocean ecosystems allows one to have a real picture of the spatial distribution of carbon sinks and sources on Earth’s surface. As is well known, among the types of remote sensing techniques, microwave radiometry proves 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 reconstruction of the SPF characteristics with consideration of vegetation types and that provide the possibility of synthesizing their spatial distribution. As noted by Chukhlantsev (2006), the problem of microwave remote sensing of vegetation cover requires the study of the attenuation of electromagnetic waves (EMW) within the vegetation layer. The solution to the problems arising here is made possible by the combination of experimental and theoretical studies. The vegetation cover is usually characterized by varied geometry and additional parameters. Therefore, knowledge of the radiative characteristics of the SPF as functions of time and spatial coordinates can be acquired by means of a combination of in-situ measurements and models. General aspects of such an approach have been considered by many authors (Del Frate et al. 2003; DeWitt and Nutter 1988; Dong et al. 2003; Friedi et al. 2002; Lopez-Iturri et al. 2015). But these investigations were mainly restricted to investigating models describing the dependence of vegetation medium on environmental properties, as well as the correlation between morphological and biometrical properties of vegetation and its radiative characteristics. One of prospective approach to solving the problems arising here is GIMStechnology (GIMS ¼ GIS[Model). This approach was proposed by Krapivin and Shutko (1989, 2002, 2012) and developed by Varotsos and Krapivin (2017). A

1.3 Microwave Emission from the Water and Land Surfaces

7

combination of an environmental acquisition system, a model of the functioning of the typical geo-ecosystem, a computer cartography system, and an artificial intelligence tool will result in the creation of a geo-information monitoring system for the typical natural element that is capable of solving many arising tasks in remote monitoring of global vegetation cover. The GIMS-based approach, within the framework of EMW attenuation by the vegetation canopies, allows the synthesis of a knowledge basis that establishes the relationships between the experiments, algorithms and models. The links between these areas have an adaptive character giving an optimal strategy for experimental design and model structure. The purpose of this chapter is to explain and assess the application of the GIMS method to reconstruct the spatial and temporal distribution of the SPF radiative characteristics.

1.3 1.3.1

Microwave Emission from the Water and Land Surfaces Brightness Temperature

Practically microwave radiometry uses millimeter, centimeter and decimeter waves. Really the following waves were used for remote sensing monitoring tasks (Haarbrink et al. 2011; Krapivin and Shutko 2012): 0.8, 1.35, 1.55, 1.6, 1.9, 2.25, 3, 3.2, 3.4, 4.3, 5.5, 6, 8.5, 18, 21, 43, and 73 cm. The SHF-radiometer captures the radiant energy emitted by the monitoring object. Usually the SHF-radiometer consists of the following elements: • antenna section providing spatial and polarization selectivity of measured radiant energy and its transformation into brightness temperature; • radiometric receptor enabling it to measure brightness temperature with certain precision and sensitivity; and • recording device allowing unique comparison of the emitting element location in space with its radiation intensity. The intensity of natural radio-thermal radiation of the subjacent media in microwave range according to Releigh-Jeans approximation and Kirchhoff’s law is characterized by brightness temperature Tb: T b ¼ κT eff :

ð1:1Þ

where κ ¼ 1-R is the emission coefficient (or absorbing surface ability, or blackness level), Teff (K) is the effective surface temperature, R is the reflectivity of the ground at the land-air interface:

8

1 Basic Concepts of Microwave Radiometry

8


η cos θ η cos θ
2 > <
ηa cos θaa þηg cos θgg
for horizontal polarization, a g R¼

2 > η cos θ η
a g a cos θ g
:
ηg cos θa þηa cos θg
for vertical polarization: where ηa(ηg) is the intrinsic impedance of the air (ground), θa is the incidence angle, θg is the transmission angle. Emissivity coefficient of uniform by depth media restricted by slot surface at the wavelength λ of the electromagnetic wave is described by means of reflective Fresnel formulas that include environmental characteristics such as complex dielectric transmissivity ελ ¼ ε0λ  iε00λ , incidence angle θ and are defined by vertical (V) and horizontal polarizations (H). Microwave radiometers measure emitted microwave radiation, expressed in terms of brightness temperature, for vertical or horizontal polarization. The emission of microwave energy is commonly referred to as microwave brightness temperature (Tb). Brightness temperature, in common case, can be calculated starting from the following expression:    T b ¼ τ ðH, θÞ Rτsky þ ð1  RÞT surf þ T atm ðH, θÞ

ð1:2Þ

where τ is the atmospheric transmittance, τsky is the atmospheric downward thermal emission, Tatm(H,θ) is the upwelling thermal emission of the atmosphere received by the radiometer, Tsurf is the soil surface thermometric temperature, R is the smooth surface reflectivity, H is the altitude of the radiometer, and θ is the incidence angle. Fairly often the brightness temperature is calculated using the following formula:   hc 1 2hc2 Tb ¼ 1þ ln kB λ I λ λ5

ð1:3Þ

where λ is wavelength (cm), h is Planck’s constant (6.62607004  1034 m2kg/s, c ¼ 3  108 m/s is the speed of light (km/s), kB ¼ 1.38  1023 J/K is Boltzmann constant, Iλ is the spectral radiance of the black body: Iλ ¼

2hc2 1 , 5 exp ðhc=k TλÞ  1 λ B

ð1:4Þ

where T is the temperature of black body (K). Figure 1.1 gives an illustrative representation of the physical parameters averaged by spectrums and wave bands for typical environments such as metallic surface, water surface, wet and dry soil. All physical bodies with a temperature that does not equate to absolute zero emit energy in the form of electromagnetic waves. This energy is reflected, absorbed and reradiated by others bodies. The idealized body that absorbs all incident electromagnetic radiation, regardless of frequency or angle of incidence, is named as absolute black body. Exact dependence between the emission

1.3 Microwave Emission from the Water and Land Surfaces

9

Fig. 1.1 Averaged SHF-radiation characteristics of typical environments

Fig. 1.2 Example of spatio-temporal Tb variations depending on the rainfall regime. Wavelength λ ¼ 21 cm

intensity of the black body If, its thermodynamic temperature T (by the Kelvin scale) and frequency f (Hz) of the emission is given by the Planck law: If ¼

2hf 3 1  c2 exp hf  1 kB T

ð1:5Þ

Figure 1.2 demonstrates a dependence of radiobrightness temperature on the rainfall regime. Measurements made just after heavy rainfall show the heterogeneity of the landscape for monitoring. Existing lowlands are filled with water that results in a Tb decrease. Figure 1.3 shows microwave emissivity for different surface types

10

1 Basic Concepts of Microwave Radiometry

Fig. 1.3 The frequency dependence of the emissivity of different surfaces. Notation: 1 – sea water; 2 – wet land; 3 – re-frozen snow; 4 – multi-year ice; 5 – dry snow; 6 – second year ice; 7 – dry land, new ice; 8 – wet snow

which allows the understanding of the common dependence of radiobrightness temperature dynamics on frequency.

1.3.2

Dielectric Constant

Relative permittivity is the factor by which the electric field between charges is reduced relative to vacuum. Relative permittivity is also commonly known as the dielectric constant that is typically denoted as εr and is defined as εr ¼ ε/ε0 where ε is the complex frequency – dependent absolute permittivity of the material, and ε0 is the vacuum permittivity. Microwave emission from a soil depends on its moisture content because of the large contrast between the real part of dielectric constant of water and that of dry soil. Dielectric characteristics of the soil can be determined by the following expression: pffiffiffiffiffiffiffiffi εsoil ¼ 1 þ 0:5ρsoil

ð1:6Þ

where ρsoil is the soil density (g/cm3). For dry sand and loam εsoil ¼ 3–4. Such materials as dry air, water and vacuum have dielectric constants equaled to 1.000 59, 80 and 1.000 00, respectively. Figure 1.4 shows the dependence of dielectric constant on the frequency for ice and water in the microwave part of the spectrum. Considering the vegetation and soil as double-component mixtures of dry matter and water, the dielectric constants can be determined by the following expressions:

1.3 Microwave Emission from the Water and Land Surfaces

11

Fig. 1.4 Real and imaginary parts of dielectric constant as functions of frequency

q
ffiffiffiffiffiffiffiffiffiffi
ffi ffi pffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffi pffiffiffiffiffiffi
εveg
¼ ρ pffiffiffiffi jεsoil j ¼ ρw εB þ ð1  ρw Þ εW ; P εB þ ð1  ρP Þ εW ;

ð1:7Þ

where εveg is the dielectric constant for the vegetation cover, ρP and ρw are the relative volumetric concentrations of water in the plants and soil, respectively; and εB and εW are the dielectric constants of dry soil and water, respectively; εsoil ¼ ε0soil þ iε00soil , εveg ¼ ε0veg þ iε00vegl . Mineral soil part consists mainly of sand (particles larger than 0.05 mm) and clay (particles less than 0.05 mm). Basic soil component is quartz with a touch of the feldspar, mica and kaolin. The densities of these substances vary slightly and are within the limits of 2.65
0.15 g/cm3. The dielectric constant for mineral particles equals to 4.7 + iε where ε is a function of frequency and varies in the following limits ε2[0.02, 0.12]. Significant contribution to the dielectric constant is added by water. Therefore, radiometry tasks take into account the following three types of water in the soils: gravitational water, capillary water, and hygroscopic water. These types play different roles in the soil environment: • Gravitational water is contained in large cellular or soil fissures. This water can freely drip under the influence of gravity and flow into (to) the soil medium. Its electrodynamic characteristics correspond to the usual water. • Capillary water is the water held in the soil micropores, and is the water that composes the soil solution. Capillary water is held in the soil because the surface tension properties (cohesion and adhesion) of the soil micropores are stronger than the force of gravity. However, as the soil dries out, the pore size increases and gravity starts to turn capillary water into gravitational water and it moves

12

1 Basic Concepts of Microwave Radiometry

Fig. 1.5 The dependence of the dielectric constant of water on temperature

down. Capillary water is the main water available to plants as it is trapped in the soil solution right next to the plant roots. • Hygroscopic water forms as a very thin film surrounding soil particles and is generally not available to the plant. This type of soil water is so tightly bound to the soil by adhesion properties that very little of it can be taken up by plant roots. Since hygroscopic water is found on the soil particles and not in the pores, certain types of soils with few pores (clays for example) will contain a higher percentage of it. The complex dielectric constant ε ¼ ε1 + iε2 of coupled gravitational and capillary waters can be described by the following semi-empirical formulas: ε1 ¼ ε0 þ

2πftr ðεs  ε0 Þ εs  ε0 , ε2 ¼ 2 1 þ ð2πft r Þ 1 þ ð2πftr Þ2

ð1:8Þ

where ε0 (4.9) and εs are dielectric constant constituents of high and low frequencies, respectively; tr is the relaxation time for water molecules. The dielectric constant also depends on temperature. Figure 1.5 gives an example for the dielectric constant of water as a function of temperature. Knowledge of dielectric constant helps to determine the penetration depth at a given frequency and thus to explore the potential of ground-penetrating microwave sensors (Mätzler and Murk 2010). Figure 1.6 shows the dependence of the real and imaginary dielectric constant of a sandy soil on the soil water content.

1.3 Microwave Emission from the Water and Land Surfaces

13

Fig. 1.6 The dependence of soil permittivity on the water content

1.3.3

Emissivity Coefficient

The emissivity of the surface of a material is its effectiveness in emitting energy as thermal radiation. Quantitatively, emissivity is the ratio of the thermal radiation from a surface to the radiation from an ideal black surface at the same temperature as given by Stefan–Boltzmann Law (Surhone et al. 2010). The ratio varies from 0 to 1. Emissivity, or radiation efficiency, of most materials (surfaces) is a function of the surface condition, temperature, and wavelength of the measurement. Emissivity coefficient of black body equals 1. All real objects have emissivities less than 1.0, and emit radiation at correspondingly lower rates (Trefil 2003). Emissivity is a measure of the ability of a surface to emit energy by radiation, and can be highly dependent on the wavelength of the radiation. For microwave bands the emissivity coefficient is usually calculated using empirical formulas such as (Krapivin and Shutko 2012): κ¼

κ¼

dρ2

pffiffiffiffiffiffiffiffiffiffiffiffi =εsoil = cos δ=2 pffiffiffiffiffiffiffiffiffiffiffiffi , =εsoil = þ 2 =εsoil = cos δ=2 þ 1 4

aρw þ bρsoil þ c , 2 w þ eρw þ f ρw ρsoil þ gρsoil þ hρsoil þ q

ð1:9Þ

ð1:10Þ

14

1 Basic Concepts of Microwave Radiometry

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi where =εsoil = ¼ ðε0 Þ2 þ ðε00 Þ2 is a modulus of soil dielectric constant, δ ¼ arctg (ε00 /ε0 ), tgδ ¼ 102  103; and a, b, c, d, e, f, g. H, q are empirical coefficients that are functions of wavelength λ, temperature Teff and soil salinity S. Dielectric constant of soil-water mixture changes from 3 to 4 when ρw has specific value for soil moisture absorbed by soil particles. Real and imaginary components of the dielectric constant of the moisture soil vary from 3–4 to 15–25 for ε0 and from 0 to 3–5 for ε00 when soil contains moisture. A depth of the emitting layer (skin layer) at wavelength λ can be calculated by means of the following formula: l¼

1 λ ¼ pffiffiffiffiffipffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi γ 2 2π =ε=  ε0

ð1:11Þ

where γ is the absorption coefficient. Knowledge of microwave emissivity for different natural object helps solve various tasks arising in remote sensing monitoring. For this purpose microwave emissivity atlases were synthesized: • AER Global Gridded AMSR-E Emissivity Atlas. Global gridded monthly mean Aqua/AMSR-E emissivities (EIA ¼ 55 ). Frequencies: 10.65 to 89 GHz. FOV size: 51 km x 29 km. Earth grid: 28 km sinusoidal grid. • LERMA/LMD Global Gridded MW Emissivity Atlas. • Global land surface emissivity atlases computed using observations from various sensors including: – AMSU-A: frequencies 23, 31, 50 and 89 GHz, atlases for high and low zenith angles – AMSU-B: frequencies 89 and 150 GHz, atlases for high and low zenith angles – SSMI/I: frequencies 19V, 19H, 22V, 37V, 37H, 85V, 85H. LERMA/LMD Atlas provides microwave data at frequencies (GHz) – 19V&H, 22V, 37V&H, and 85V&H with 1 degree regular grid and monthly time resolution. These data are added from ultraspectral and/or hyperspectral spaceborne IR soundings (Zhou et al. 2011). Microwave land surface emissivity database includes global gridded monthly emissivity products at the AMSR-E 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz V-pol and H-pol channels with a spatial resolution 27.79 km. These data can help characterize the land surface properties. Analysis of the microwave satellite observations allows the assessment of the penetration depth of the wavelength in the soil which is an important parameter for different models describing soil moisture, ground water depth and topography. Prigent et al. (2006) noted that in sand dunes, penetration depth can be as large as five wavelengths.

1.3 Microwave Emission from the Water and Land Surfaces

15

It is clear that microwave responses to land surface include contributions from soil and vegetation including their water content or snow cover depth. The response of the bare soil depends on the soil dielectric properties and roughness. Vegetation absorbs, emits, and scatters microwave radiation. Increasing vegetation density increases the emissivity to horizontal polarization and reduces the emissivity polarization difference. This effect is observed in tropical rainforest areas such as Africa and South America.

1.3.4

Microwave Radiation Sensitivity to Variations of Basic Environmental Parameters

Haarbrink et al. (2011) proposed approximations for estimates of radiation intensity sensitivity of a range of water and land surface parameters. Concerning water objects, the relationship between the variations of brightness temperature and physical temperature, water salinity S, wind speed V and soil moisture can be calculated by the following expressions (Shutko 1982; Haarbrink et al. 2011): ΔT b K ffi a0 o , a0 2 ½0:3, 0:6 for λ ¼ 2 cm; ΔT surf C ΔT b K ffi b0 0 , b0 2 ½0:4, 0:45 for λ ¼ 8 cm, ΔT surf C ΔT b K ffi 0:1 o for λ ¼ 21 cm ΔT surf C ΔT b K , a 2 ½0:1, 0:8 for λ ¼ 21 cm, ffi a2 ppt 2 ΔS

ð1:12Þ

ΔT b K , b 2 ½0:7, 0:9 for λ ¼ ð0:8  2Þcm: ffi b2 ΔV m=c 2 ΔT b Δρw 

K ffi b1 g=cm 3 , b12[200–250] for the range λ ¼ 2 – 21 cm, Tsurf ¼ 0 – ΔT b K ffi a1 g=cm 30 C, S ¼ (0  60)ppt and ρw2[0.1, 0.45] g/cm3; Δρ 3 , a12[10, 20]. soil ΔT b ΔT b  

ð 0:3  0:6 ÞK= C at the wavelength λ ¼ 2 cm;

0:1K= C at the ΔT surf ΔT surf wavelength λ ¼ 21 cm. Formulae (1.1)–(1.11) have universal character and are used in many microwave models. Formulae (1.12) give acceptable precision for the assessment of soil emissivity and moisture when vegetation cover is present and reflection coefficient of the vegetation cover is small, the physical temperatures of soil and vegetation are close. Under this we have:

16

1 Basic Concepts of Microwave Radiometry

T bsv ffi T bsoil β þ ð1  βÞT v , β ffi e2τ , τ ¼ γ v hv  ηv Qv , β ffi

ΔT bsv=Δρ

w

ΔT bsoil=Δρ

w

ΔT bsv ¼ ΔT bsoil

ð1:13Þ

where Tv is the vegetation temperature, T bsv is the brightness temperature of soil/ vegetation system. In particular, as it follows from (1.13) coefficient β2[0, 1] has the meaning of slope lowering coefficient of radiation-moisture dependence. For example, β2[065, 0.9] at the λ ¼ 21 cm practically for all agricultural crops. In general, the following algorithm for soil moisture retrieval under vegetation is used (Jackson et al. 2002; Wen et al. 2005):    T Bp ¼ T au þ Latm L2veg T ad þ Latm Lsky 1  εp þ Latm Lveg εp T soil þ       þ Latm T veg 1  ωp 1  Lveg 1 þ 1  εp Lveg

ð1:14Þ

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 ¼ exp.(τsecθ), where θ is the incident angle of the observation; τ is the vegetation or atmospheric optical depth, which depends on 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. Equation (1.14) shows that 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 various empirical and theoretical propagation and pass-loss models that allow estimating agricultural field parameters used for mapping and forecasting crops (Cookmartin et al. 2000). A typical case of sub-surface heterogeneities is the existence of over-moistured media or subsoil waters under the dry soil layer. There are two moisture profiles for this case: • fixed (and usually small) moisture level in the boundaries from surface to the level of subsoil waters (z), and • smooth change of the moisture (as a rule decreasing) between the level of subsoil waters and soil surface (so-called “Capillary Fringe”; Choudhury et al. 1995; Shi et al. 2009). A profile of the first type is specific to arid dry zones. Under such moisture profile radiation contrast caused by the heterogeneity is defined by the following approximation:

1.4 Remote-Sensing Research Platforms and their Equiping

17

Δκ  ð1  r 1 Þ2 r 2 e2τ ,

ð1:15Þ

whereτ ¼ γz, r1 and r2 are reflection coefficients for the boundaries atmosphere/dry soil and dry soil/wet soil, respectively. A second type of profile is specific for moisture humid regions. In this case when soil moisture depends on the depth of the subsurface waters an evaluation is performed by the indirect method namely based on the measured water density values ρw. Each soil level Δz gives its contribution to the brightness temperature:ΔTb ¼ T (z)γ(z)Δz, where γ is the absorption coefficient of the radiowaves at the depth z. Finally, radiobrightness temperature on the soil surface will be: Z0 T b ð 0Þ ¼ zmax

2 T ðzÞγ ðzÞ exp 4

Zz

3 γ ðhÞdh5dz

ð1:16Þ

0

The heterogeneities in the media boundaries according to (1.15) must be taken into account with high precision which allows the generated microwave model to be sufficient for the controlled environmental object. Equation (1.16) is used to solve the inverse task of microwave radiometry.

1.4

Remote-Sensing Research Platforms and their Equiping

Solution of specific environmental tasks requires instrumental and algorithmic optimization. This can be realized by means of the GIMS-technology methods. The ratio between experimental and theoretical methods in this case is determined by the complexity of environmental system to be studied. The cumulative experience of the GIMS application is based on the solution of national problems in many countries where different remote sensing platforms were created. Historically this experience began in 1980 with the equipping of flying laboratories based on Russian bi-plane AN-2, twin-engine plane IL-14, 4-engine plane IL-18, helicopters MI-2, MI-8 and Ka-26 (Krapivin and Shutko 2012; Nitu et al. 2013). During the ensuing years, most of these carriers were widened. One of the first satellite platforms (Cosmos-243) was launched on September 23, 1968. It has four nadir looking radiotelescopes onboard. They measured thermal radio emission of the Earth’s surface and atmosphere at wavelengths of 0.8, 1.35, 3.4 and 8.5 cm. An infrared radiometer installed onboard the satellite measured the radiation in the 10–11 μm band within the same viewing angle as the radio telescopes. The unique radio and infrared emission data of different Earth’s covers were acquired. The new era - of satellite radiophysical remote sensing investigations of the Earth’s environment was opened. The following problems can be solved based on the data provided by this satellite:

18

1 Basic Concepts of Microwave Radiometry

• The determination of water vapour content in the atmosphere; • The total water content in the clouds and the determination of precipitations zones; • The temperature and condition (roughness rate) of the ocean surface; • Sea ice condition characterization; • Soil moisture determination. • The choice of the optimal operating wavelengths for the Cosmos-243 satellite microwave radiometric complex for the most effective characterization of the Earth’s environmental parameters. These wavelengths are: 0.8; 1.35; 3.4 and 8.5 cm. One of effective multi-frequency polarimetric synthetic aperture radar system was created in the 90s by the Russian Corporation “Vega” for surface and subsurface sensing. It is an airborne IMARC SAR system and its principal description is given in Krapivin et al. (2019). This Corporation created radiometer system “Radius” operating at wavelengths of 0.8 cm, 2 cm, 5.5 cm, 21 cm and 43 cm (Table 1.2). Main features of the IMARC SAR complex are given in Table 1.3. The IMARC is a four-wavelength polarimetric airborne SAR system designed at Radioengineering Corporation “Vega” (Kutuza et al. 1998, 2000). The basic technical characteristics of this radar are given in Table 1.2. The radar system operates at wavelengths: X (3.9 cm), L (23 cm), P (68 cm), and VHF (2.54 m); polarizations in all bands: VV, HH, VH, and HV; spatial resolution is around 12
8 m; maximum swath is 24 km. The carrier of this system was a twin-turbine jet airplane TU-134A and other planes. The main IMARC SAR mission goals were to map the characteristics of the Earth’s covers (including soil hydrological regimes), to map the ground terrain in a presence of vegetation by eliminating the influence of vegetation and, to Table 1.2 Scanning radiometer «Radius» characteristics. The power consumption is 200-300 W, the power supply is 27 VDC; weight is 100–120 kg; H is the height above ground Frequency 37.5 GHz 15.2 GHz 5.5 GHz 1.4 GHz 0.7 GHz

Wavelength 0.8 cm 2 cm 5.5 cm 21 cm 43 cm

Table 1.3 The IMARC SAR complex parameters

Band Ka X C L S

Pixels/scan 32 16 6 2 1

Parameter Frequency Wavelength, cm Polarization Resolution, m Antennas: gain, dB Width in azimuth, deg Width in elevation, deg

Resolution 0.02 H 0.06 H 0.13 H 0.5 H H

Mode Scanning Scanning Scanning Scanning Non scanning

Value X L P 3.9 23 68 VV, HH, VH, HV 4–6 8–10 10–15 30 14–17 14–17 18 24 24 24 24 24

VHF 254 15–20 9–11 40 60

1.4 Remote-Sensing Research Platforms and their Equiping

19

produce elevation models, to detect areas with on-ground and underground irregularities, etc.(Shutko et al. 2007, 2010). The airborne SAR system IMARC can be used to solve the following tasks: • sensing surface of the ocean, ice and vegetation; • subsurface sensing of scattering objects and deep layers. Multi-frequency polarimetric SAR provides new remote sensing possibilities for sea, soil, vegetation, ice cover and other Earth’s surfaces. Deep layer-by-layer remote sensing algorithms that are used allow solving a narrow set of environmental problems. Fieuzal et al. (2013) investigated the sensitivity of multi-temporal SAR data acquisition at frequencies X-, C- and L – bands, polarizations HH, VV, VH and HV and incidence angles from 23 to 53 . It has been shown that the angular sensitivity of random backscatter decreases with increasing vegetation index of agricultural fields. This result has shown the existence of uncertainty information under SAR monitoring and suggests the need to take into account angular sensitivity under environmental monitoring. It is evident that an effective approach to using SAR measurements for solution of environmental tasks is based on the empirical relationship of NDVI and LAI values with backscattering coefficient. As a result it can help build an information basis with estimated characteristics of environmental ecosystems that is needed to study climate change-facing ecosystems. It is well-known that the larger the wavelength, the higher is the influence of the deeper soil layers. This fact allows the development of deep layer sensing methods using multi-frequency radar systems. For subsurface sensing the use of long waves of P and VHF bands is required. Information on soil properties (soil moisture) profiles can be obtained from the analysis of scattering measurements at different wavelengths. The influence of soil moisture profile on backscattering cross section makes it necessary to develop reflection models from layers located at different depths. The solution of the inverse problem can be obtained by measuring the backscatter at several wavelengths and at different polarization modes. To have complete information about soil moisture profile it is necessary to solve the problem by interpreting images in broad band of wavelengths including meter band where attenuation in soil and vegetation is comparably low. The results of multi-band radar survey obtained with the help of 4-bands airborne SAR IMARC (Radioengineering Corporation “Vega”) illustrate the possibility of measuring hydrological soil regimes and water lens allocation in the Kara-Kum desert. Underground water lenses were detected at a depth of 50–70 m. Results were validated by well-boring control. We can see radar images at: 1. 2. 3. 4.

dry river-Uzboy bed; sand dunes of 6–15 m height; underground water lenses; and transmission facilities.

The other multi-functional remote sensing airborne system was the IL-18 (hull No 75423) shown in Fig. 1.7 where allocation of the antennas is presented. This

20

1 Basic Concepts of Microwave Radiometry

Fig. 1.7 Scheme of antenna positioning systems and photo-hatches on-board of IL-18 flying laboratory. Notation: Antennas: 1,3 – radiolocators with synthetic aperture, wavelengths – 2.0 and 10.0 cm; 2,6- trace polarimeters, wavelength – 0.8 and 2.25 cm; 4 – six-channel scanning polarimeter, wavelength – 0.8, 1.35, 2.25, 10, 20 and 27 cm; 7,9 – precision altimeter and interferometer of side looking, wavelength – 2.2 cm; 13 – sub-surface sounding station of decimetric range; Photo-hatches: 5,10,12 – large-format and frame TV, aerocamera; 11, 14 – radiometers of mm range; 16 – trace radiometers, wavelength – 0.8, 1.35, and 2.25 cm; 15 – gravimetric and inertial devices; 17 – astro-hatch

system for the first time realized geoecological information-modeling system (GIMS) was proposed and developed in series of publications by the authors of this book. The IL-18 flying laboratory has been able to solve numerous environmental tasks practically in real time. The on-board system represented in Fig. 1.8 registered environmental characteristics and at the same time processed existing and delivered by sensors information. Figure 1.8 illustrates an on-board information-modeling system structure that interrupts all sensors with a time interval equal to 0.01 seconds and taking into account the navigation information all results are referred to the geographical coordinates. Long-term exploration of flying laboratory IL-18 in Former USSR has demonstrated the availability of using the multi-purpose remote sensing systems under the solution of environmental problems on large territories. Unfortunately, this laboratory was destroyed in 1990 when the USSR was broken. However, this experience was developed in Russia (Krapivin and Shutko 2012). Application of the GIMS-technology depends on the complexity of the environmental tasks that need to be completed during a given time period with a certain precision. One of the most effective instruments for using GIMS-technology was synthesized by a private Dutch\Holland Company “Miramap” based on the

1.4 Remote-Sensing Research Platforms and their Equiping

21

Fig. 1.8 Typical structure of the on-board information-modeling system carried out in the flying laboratory IL-18

European Space Incubator initiative from the ESA Technology Transfer & Promotion (TTP) office. The TTP office is contributing to the capitalization of space-based technology and know-how to benefit Europe’s economy and science. The innovation of microwave radiometer (MR) mapping company (Miramap) was nominated for the Dutch Innovation Price in 2005 and was quoted in several newspapers and magazines such as the Dutch Financial Times (Haarbrink et al. 2011). The Miramap instrument consists of three microwave sensors in X-band, C-band and L-band that are all GMNSS integrated. The X-band and C-band sensor performs a conical scan at constant incidence angle over a wide swath, while the L-band sensor makes a twin-beam oscillating scan (Fig. 1.9 and Tables 1.4, 1.5). The small instrument sizes and weights enable the use of a low-cost light aircraft as the observing platform, providing decision makers with a new affordable tool. The platform on which these instruments are flown is a reliable and safe twin Aero Commander. The aircraft is specially modified to carry simultaneously a range of other than microwave instruments, such as (digital) photogrammetric cameras, lidar scanners, and thermal infrared and multi-spectral sensors. The ability to measure such a wide range of remotely sensed parameters from a single low-cost airborne platform is unique worldwide. With the full set of sensor shown in Tables 1.4, 1.5 and 1.6, the Miramap company provides customers exactly with the parameters and environmental conditions given in Table 1.7 including: • surface soil moisture, • underground moistening, • depth to a shallow water table (down to 2 meters in humid areas and down to 3–5 meters in arid/dry areas),

22

1 Basic Concepts of Microwave Radiometry

Fig. 1.9 Principal scheme of remote sensing monitoring using a flying laboratory equipped with the microwave scanning radiometric system Table 1.4 Miramap microwave sensor specifications

Parameter/Band Frequency (GHz) Wavelength (cm) Pixels/Scan Incidence Angle ( ) Beam Width ( ) Polarization Sensitivity (K/s) Absolute Accuracy (K)

X-band 15.2 2.0 16 30 3.5 H 0.15
5

C-band 5.5 5.5 6 30 5 H 0.2
5

L-band 1.4 21 2 15 25 H 1
5

Table 1.5 Microwave Radiometer Mapping Company (Miramap, Noordwijk, the Netherlands): Sensor specification Sensor Digital photo camera

Type Rollei AIC 50 mm lens

Wavelength Visible 0,4– 0,7 micron

Lidar scanner

Optech altimeter Radius (IREEVega design) Flir systems

SW infrared 1064 nm Microwave 2, 5, 21 cm LW infrared 7,5–13 micron

Passive microwave scanner Thermal camera

Project specs 10 cm GSD subpixel precision 2 cm GSD 0,1 m precision 5 m GSD 0,15 K 3 m GSD 0.1  C

Use Detailed visible interpretation Elevation model (Sub) surface detection of wet and dry areas Surface temperature

1.4 Remote-Sensing Research Platforms and their Equiping

23

Table 1.6 Miramap company platforms and remote sensing instrumentation Platforms/ Instrumentation Microwave radiometers Infrared radiometers Optical color digital cameras Lidar (3-D) land surface relief measurer Georadar

Aircraft laboratory Exists Exists Exists

Unmanned plane/helicopter Exis Exists Exists

Specific car/Rover Exists Exists Exists

In-situ data collecting instruments Exists Exists Exists

Exists

No exists

No exists

No exists

No exists

No exists

Exists

Exists

Table 1.7 Miramap product specifications (Haarbrink et al. 2011) Parameters Soil moisture (g/cm3) Depth to water table (m) Plant biomass (kg/m2) Pollutant concentrations (ppt)

Operating range 0.02–0.5 0.05–5 0–3 1–30

Max abs error 0.07 0.3–0.6 0.2 1–5

• located on the surface and shallowly buried metal objects of a reasonable size under the conditions of dry ground, • contours of water seepage through hydrotechnical constructions (levees, dams, destroyed drainage systems, different kinds of leaks), • biomass of vegetation above a water surface or wet ground, • increase in temperature in land, forested and volcano areas, • changes in salinity/mineralization and temperature of a water surface, • water surface pollution, oil slicks on a water surface, • on-ground snow melting, • ice on a water surface and on roads, runways. Some indices of the effectiveness of the GIMS-technology realized in Miramap’s flying research laboratory framework are as follows: 1. Soil moisture content • operating range is 0.02–0.5 g/cm3 • maximum absolute error is: – when vegetation biomass is less than 2 kg/m2–0.05 g/cm3; – when vegetation biomass is greater than 2 kg/m2–0.07 g/cm3. 2. Depth to a shallow water table • operating range is: – for humid or swampy areas – 0.2–2 m; – for dry arid areas, deserts – 0.2–5 m;

24

1 Basic Concepts of Microwave Radiometry

• maximum absolute error is 0.3–0.6 m. 3. Plant biomass (above wet soil or water surface): • operating range is 0–3 kg/m2; • maximum absolute error is 0.2 kg/m2. 4. Salt and pollutant concentration of water areas (off-shore zones, lakes): • operating range is 1–300 ppt; • maximum absolute error is 1–5 ppt; • relative error is 0.5 ppt. Large-scale investigations into microwave research carrying platforms were conducted by the Microwave Remote Sensing Division (MRSD) in 2002 through 2005 within the NASA Center for Hydrology, Soil Climatology and Remote Sensing (HSCaRS) at Alabama Agricultural and Mechanical University (AAMU). This Division was capable of performing microwave radiometric data interpretation and conducting studies in field conditions, from mobile platform and unmanned helicopter (Krapivin and Shutko 2012). The antennas, radiometers, data collection system and an embedded Global Positioning System (GPS) receiver were mounted on the manned “Rover” type mobile platform and the unmanned helicopter platform “Microwave Autonomous Copter System” (MACS) for measuring soil-plant radiation system. All radiometers were mounted on a folding mount panel to observe horizontally polarized radiation when folding the panel between the nadir and zenith looking angle. The GPS information was used to record microwave reading to a common coordinate system of the study area. All data was stored on a 256 mb memory card. The data capture rate was set to 1 measurement per second in each of the radiometric channels and GPS readings. The manned “Rover” type mobile platform is a modified “Gator” utility vehicle. This two-seater vehicle has a 286 cc, air-cooled, 4-cycle gasoline engine. Its towing capacity is 500 lb (226 kg) with a top speed of 20 mph (32 km/h). The instrument platform (or mounting frame) for radiometers and other instruments was assembled at AAMU research station. The aluminum folding panel of 1.5 m  1.5 m connecting all system components was designed so that the incidence angle from 0 (nadir) to 180 (sky) could be easily obtainable (Table 1.8). The radiometers were mounted with the antennas viewing off to the right hand side of the platform at an incidence angle of 10 . The radiometer “Rover” shuttles back and forth in a northsouth direction at a speed of 2–5 mph, using the developed remote sensing system data obtained from a height of 2 m provided the spatial resolution of 1.4 m of land area. The MACS unmanned helicopter platform was equipped with a 6 cm radiometer (incidence angle 5 ) mounted on the nose of the AutoCopter™ on a stabilized gimbal with a pan/tilt interface which attenuates vibrations. The MACS is a modified AutoCopter™, a small unmanned helicopter platform that can fly autonomously

1.4 Remote-Sensing Research Platforms and their Equiping

25

Table 1.8 Characteristics of the microwave platform “Rover” Parameter Frequencies Polarization Antennas Incidence angle Sensitivity Beam width Weight Power required

Value 1.41, 1.67 and 5 GHz H or V 19.700  19.700 ; 16.300  16.300 ; slot array; 7.900  6.500 ; dipole array Fixed angles 0–30 off nadir 0.5 K (3-dB); 30 12 kg 30 VA

Krapivin and Shutko (2012)

(a pre-programmed flight path) or semi-autonomously (with an operator directing the maneuvers). This is a product of Neural-Robotics, Inc. (NRI) of Huntsville, AL. The unmanned helicopter advantage is its patented flight control system consisting of multiple neural network modules working together. The result is an autonomous helicopter that adapts to changing conditions and provides an extremely stable platform for hundreds of applications. The AutoCopter™ is 2.18 m in length (from tip of tail rotor to tip of main rotor) and weighs approximately 13.6 kg. It carries a payload of up to 6.8 kg. Basic avionics consist of a PC/104 computer, altitude and heading reference system (AHRS), GPS receiver (WAAS-compatible), downward pointing range finder (ultrasonic sensor), barometric pressure sensor, and heading-hold gyro. The standard transmitter is used as the “ground station”. The Ground Control Station (GCS) with a flight planning program “WayPlanner” was used with the AutoCoper™. This is a self-contained Windows-based application that unlocks the power of fully autonomous flight. The program enables mission planning in 2D using stored satellite images. Flight plans were uploaded to the AutoCopter™ via data link enabling the aircraft to takeoff climbs, fly its programmed route, and land fully autonomously. The scheduled flight consisted of an autonomous launch with 16 waypoints, a climb to 30 meters, followed by a transition to forward flight at a velocity of 2 m/s and auto-landing. During the flight the aircraft has the ability to state data (aircraft altitude, speed, and other parameters in real time on the GCS screen in 2D and 3D. While maintaining airspeed, altitude and heading, 8 flight lines were flown at a distance of ~500 m at 30 m intervals. The time it took the helicopter to fly from waypoint to waypoint (north-south direction) was ~4.5 minutes totaling ~40 minutes of flight time. Because the aircraft flew below 500 ft. it was exempt from FAA regulations. Using the developed remote sensing system, data obtained from an altitude of 30 m provided the spatial resolution of 20 m of land area. Numerous remote sensing experiments have showed that using cm range radiometers provide reliable information on soils and vegetation cover as well as on surface characteristics of water objects. Table 1.9 presents the technical characteristics of microwave radiometers.

26

1 Basic Concepts of Microwave Radiometry

Table 1.9 Technical characteristics of microwave radiometers. Notation: H is height (m) Frequency, GHz 13.3 5 1.4

Wavelength, cm 2.25 6 18/21

Range X C L

Field of vision 30 30 30

Spatial resolution 0.65  H 0.65  H 0.65  H

Model Single-beam Single-beam Single-beam

Fig. 1.10 Examples of remote sensing images provided by sensors of L-, P- and SHF ranges

Figure 1.10 presents a comparison of the different penetrating ability of L, P and SHF radar signals received in the Spass-Klepiki region, Ryazan Province, Russia. Penetrating ability of L-range signal depends on vegetation cover characteristics. The P-range signal practically does not depend on vegetation cover and reflects the underlying surface characteristics. Image in the VHF range demonstrates the absence of signal dependence on the vegetation cover and delivers information about soil cover and water surface characteristics.

1.5 1.5.1

Microwave Polarization Characteristics of Snow Introduction

Remote sensing surveys for snow cover are of great importance in several theoretical and applied areas, such as: • Monitoring and modeling of regional and global hydrologic processes; • Assessment of temporally stored snow volumes for warning about snow slides and floods as well as for agriculture; • Modeling and predicting regional and global climate change based on the database of snow covered areas, snow depth (SD), snow water equivalent (SWE), snow albedo, snow-soil irradiance and other snow properties.

1.5 Microwave Polarization Characteristics of Snow

27

Knowing the possible changes in seasonal snow cover, ice fields and permafrost as characteristic components of Earth’s cryosphere allows for future discussions to better understand the global climate response to changing sensitive cryospheric parameters. For example, snow cover plays a special role in the ecological processes of the northern latitudes, including the Arctic Basin, through its influence on surface energy and water balances, as well as thermal regimes and trace gas fluxes. Snow cover in many latitudes is characterized by a very rapid change over time. Therefore, the forecast of snow-melt runoff is important for many areas where there are floods depending mainly on the depth of snow cover. The correlation between natural microwave irradiance of snow cover and its parameters has been studied by many authors (e.g., Hofer and Mätzler 1980; Meier 1980; Koike 1993; Schwank et al. 2014; Takala et al. 2011; Pulliainen 2006). However, processing of SWE determining methods on microwave monitoring data raises a series of difficult tasks. In particular, centimeter and millimeter wavelengths are characterized by powerful volumetric scattering in dry snow. On the one hand, this leads to an increase in the reflection coefficient of snow layer by increasing its thickness, which mainly allows the snow layer depth to be assessed based on its radiometric observations. On the other hand, it complicates the task of modeling the microwave irradiation of the snow layer (Proksch et al. 2015). In this respect, the determination of the depth of the snow layer using microwave observations is usually performed by semi-empirical models based on correlations of its transmission and reflection coefficients with its geophysical characteristics, such as the depth of the layer, the crystal size, density, etc. (Butt 2004; Che et al. 2008; Dai et al. 2017). However, the complex layered structure of the snow layer complicates the use of these models to solve the inverse task that is to estimate the characteristics of the snow layer based on its radiometric data (Xie et al. 2015). Related studies have shown that microwave polarization difference index (MPDI) of the snow layer can be an efficient tool for more accurately assess the basic parameters of the snow layer. Knowledge of the polarization indices of snow layers leads to a significant reduction in the uncertainties in the numerical estimates of the snow water equivalent, in particular. In this connection, passive polarimetric microwave observations allow for a more accurate evaluation of brightness temperature according to snow structure (Battaglia and Simmer 2007; Dong et al. 2007; Lemmetyinen et al. 2018; Langlois et al. 2007). The present paragraph presents the theoretical and empirical results of polarized measurements of the integral reflection coefficient of snow layers. Measurements were made at 6.9 and 18.7 GHz frequencies testing different types of snow, such as: • • • •

Freshly fallen snow (the granular structure is missing), Small-grained snow (typical size of ice crystals is less than 1 mm), Mid-grained snow (typical size of ice crystals is 1–2 mm), Coarse-grained snow (typical size of ice crystals is 2–5 mm). The above theoretical and empirical models developed in this study are described in detail in the next section.

28

1 Basic Concepts of Microwave Radiometry

1.5.2

Theoretical and Empirical Tools for Studying the Microwave Irradiation of Snow Cover

1.5.2.1

The Theoretical Model

A general approach to assess polarized microwave irradiation characteristics of the scattering layer-rough surface system is based on brightness temperature measurements. The polarization characteristics of the scattering and emission of electromagnetic waves above a rough surface can be evaluated by the solution of the following equation (Chukhlantsev 2006): d cos θ I ðθ, ϕ, zÞ ¼ κbι ðθ, ϕÞ  I ðθ, ϕ, zÞ þ dz Z π  dθ0 sin θ0 Pðθ, ϕ; θ0 , ϕ0 Þ  I ðθ0 , ϕ0 , z0 Þ

Z

2π 0

dϕ0 

, 0 θ π, 0 ϕ

0

2π,

ð1:17Þ

where I ðθ, φ, zÞ is 4  1 vector-column composed of modified Stock’s parameters in bðθ, φ; θ0 , φ0 Þ is the 4  4 matrix of the phase function of unitary the direction(θ, φ), P volume from direction (θ0, φ0) to the direction (θ, φ) and b κо ðθ, φÞ is the relaxation matrix components defined by the scattering amplitude in the forward direction. When heat emission is considered the right part of equation (1.17) is completed byb κп 2k0λT2 ðzÞ, where b κп is the absorption matrixΞ, T is the surface physical temperature, λ is the wavelength, and k0 is the Boltzmann Constant (1.3807  1023, JK1). Equation (1.17) can be considered as an approximation for the microwave irradiation characteristics of parallel-plane snow covers when the attenuation and absorption matrices are diagonal. In this case, radiation transmission can be described by the following equation (Pulliainen et al. 1999; Macelloni et al. 2001): dJ ðz, ϑ, ϕÞ ¼ γJ ðz, ϑ, ϕÞ þ uðz, ϑ, ϕÞ, cos ϑ dz ZZ ωγ uðz, ϑ, ϕÞ ¼ J ðz, ϑ0 , ϕ0 Þ ζ ðϑ, ϕ, ϑ0 , ϕ0 Þ sin ϑ0 dϑ0 dϕ0 þ u0 , 4π

ð1:18Þ ð1:19Þ

where J(z, ϑ, ϕ) is the spectral radial intensity of the radiation flux for a 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 the angle between the ray direction and normal, u0 is the intensity of inherent sources of radiation per unit volume, ζ is the dispersion indicator of unit volume. Simplified model (1.17, 1.18 and 1.19) can be synthesized using two-level representation of the snow layer as shown in Fig. 1.11. Microwave emission of the

1.5 Microwave Polarization Characteristics of Snow

29

Fig. 1.11 Profile layers across the ASSS

atmosphere-snow-soil system (ASSS) is formed depending on absorption and diffraction processes. The ASSS emissivity ability can be described as κ ¼ 1  jρj2

ð1:20Þ

where. ρ¼

1=2 ρSA exp ½2iχz þ ρSS 2π  ε  sin 2 θ , χ ¼ χ 1  iχ 2 ¼ λ 2 ρSA ρSS þ exp ½2iχz

ð1:21Þ

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 ¼ SD is the snow layer thickness. From Eqs. (1.20) and (1.21) we have κ¼ where.

r  ρSS exp ð4χ 2 zÞ  ρSA  2κ1 sin qSA sin ξ r þ κ1 cos ðqSA þ ξÞ

ð1:22Þ

30

1 Basic Concepts of Microwave Radiometry

ξ ¼ qSS þ 2χ1 z; κ1 ¼ 2jρSA j  jρSS j exp ð2χ 2 zÞ; r ¼ 1 þ jρSA j  jρSS j exp ð4χ 2 zÞ; ( )1=2 ( )1=2 ðγ snow  γ atm Þ2 þ ðδsnow  δatm Þ2 ðγ soil  γ snow Þ2 þ ðδsoil  δsnow Þ2 ; ρSS ¼ ρSA ¼ ðγ snow þ γ atm Þ2 þ ðδsnow þ δatm Þ2 ðγ soil þ γ snow Þ2 þ ðδsoil þ δsnow Þ2

ð1:23Þ tgqSA ¼ 8 > >
> :  0 2  00 2 εm þ εm 8 βm > > < am δm ¼ 00 0 A ε m > m  β m εm > :  0 2  00 2 εm þ εm

Am ¼

ð1:24Þ

for horizontal polarization; for vertical polarization; for horizontal polarization; for vertical polarization;

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 ðam þ bm Þ=2; βm ¼ ðam  bm Þ=2; am ¼ b2m þ ε00m ;

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 2 bm ¼ ε0m  sin θ ;εsnow ¼ ε0snow  iε00snow is the dielectric permittivity of the snow cover; εsoil ¼ ε0soil  iε00soil is the dielectric permittivity of the soil; and εatm ¼ ε0atm  iε00atm is the dielectric permittivity of the air with ε0atm ¼ 1 and ε00atm ¼ 0: Sub-index m symbolically representing one of three media: atmosphere, snow and soil. Following Krapivin et al. (2006) the Fresnel coefficients are determined as functions of the snow cover parameters. The dielectric constants of the snow and soil are input parameters to formulae (1.22, 1.23 and 1.24). Considering snow and soil as two-component mixtures of dry matter and water, the dielectric constants can be determined by the following expressions: q
ffiffiffiffiffiffiffiffiffiffi
ffi ffi pffiffiffiffiffiffi pffiffiffiffiffiffiffiffiffiffi pffiffiffiffiffi pffiffiffiffiffiffi
εveg
¼ ρ pffiffiffiffi jεsoil j ¼ ρw εB þ ð1  ρw Þ εW ; P εB þ ð1  ρP Þ εW ;

ð1:25Þ

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. Real and imaginary parts of the snow and soil dielectric constants can be approximated by the following formulae (Engman and Chauhan 1995; Chukhlantsev 2006; Tiuri et al. 1984):

1.5 Microwave Polarization Characteristics of Snow

ε0d ¼ 1 þ 1:7ρ þ 0:7ρ2 ,   ε00d ¼ 0:52ρ þ 0:62ρ2 ε00ice   ε0soil  c1 þ c2 ρB þ c3 ρ2B exp ðc4  c5 =λÞ,   ε00soil  d1 ρB þ d2 ρ2B exp ðd 3  d4 =λÞ,

31

ð1:26Þ

where ε00ice is the imaginary part of the dielectric constant of pure ice (~8  104), ci and di are constants depending on soil type. In whole, expressions (1.25) and (1.26) provide a calculation of snow and soil dielectric constants depending on their characteristics. Thus, following by Krapivin et al. (2006), the brightness temperature of ASSS for uniform media can be represented in the following way:  T bASS ¼

1  ρ2SA

 

    T 3 1  ρ2SS eτ2 þ T 2 ð1  eτ2 Þ 1 þ ρ2SS eτ2 1  ρ2SA ρ2SS exp ð2τ2 Þ

ð1:27Þ

An assessment of the water reserve in a snowpack of a restricted area can be realized based on knowledge about SD and SWE. Different models exist to calculate these snow characteristics (Tsutsui and Maeda 2017; Liu et al. 2017). Here the following approximations are used SD ¼ a(MPDI) + b and SWE ¼ q1MPDI ¼ q2 where the coefficients a, b, q1 and q2 are defined during in-situ experimental measurements. According to (1.27) snow water content (SWC) is assessed depending on the brightness temperature and can finally be calculated using empirical approximations represented by Kelly and Chang (2003) and Clifford (2010).

1.5.2.2

Empirical model of Incident Microwave radiation on Snow Cover

In the isothermal case, the emitting characteristics of the dispersive layer are represented by the value of the emitting coefficient κ that according to Kirchhoff’s law is defined by the following formula (Chukhlantsev 2006) κ ¼ 1  r  q,

ð1:28Þ

where r is the integral reflection coefficient, q is the transmission coefficient of snow layer. The coefficients r and q are defined by means of the radiation transfer equation solution. Analytical solutions of equations (1.18) and (1.19) are known for one-dimensional and anisotropic scattering indicator (Carcolé and Ugalde 2008). In other cases, the numerical solution of the equations (1.18) and (1.19) is needed. In particular, this solution has been defined using the Monte-Carlo method (Macelloni et al. 2001; Chen et al. 2003). However, the snow layer has scatterers within its

32

1 Basic Concepts of Microwave Radiometry

volume coherent interactions that require the use of a radiation transfer theory in the dense medium (Wen et al. 1990). Solution of equation (1.18) when single scattering is considered gives (Chukhlantsev 2006).     κ ¼ ð1  ωÞ 1  eτ sec ϑ , r ¼ ω 1  eτ sec ϑ , q ¼ eτ sec ϑ

ð1:29Þ

where τ ¼ γz is integral attenuation of radiation in the layer under observation in nadir. In the case when the microwave emission of the snow layer is recorded by radiometers of C- and K – bands its brightness temperature can be calculated by means of equation (Macelloni et al. 2001): T bASSS ¼ T snow ð1  r  qÞ þ κsoil T soil q þ T snow ð1  r  qÞð1  κsoil Þq,

ð1:30Þ

where the first term of the right part of the equation characterizes the emission of snow layer, the second term denotes 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 ¼ Tsoil ¼ T, the emission coefficient of the soil-snow layer can be represented by the following expression: T bASSS T  

¼ κsoil 1  1  q2 ½1  ðr 0 þ Rsnow Þq   þ ½1  ðr 0 þ Rsnow Þ  ðr 0 þ Rsnow Þq 1  q2 :

κ¼

ð1:31Þ

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)qν. Krapivin (1996) realized this algorithm and showed that the estimating ν* is a wrong task. Investigations by many authors have revealed that the process of fish migration is accompanied by an external appearance of intentional behaviour. Let us therefore, formulate the law of migration resulting from the general biological principle of adaptation and long-term adaptability: the migration of anchovies, predatory fish and birds obey the principle of complex maximization of the effective nutritive ratio Pi (i¼5,7,8,9), given the maintenance of favourable temperature conditions. In other words, traveling of migrating species takes place at velocities that characterize them in the direction of the maximum inclination of effective feed, given the adherence of temperature constraints. The PCE takes into account the following limitations on preferred temperatures for E9 (σ σ9,max ), E5 (σ σ5,max ) and E7 (σ
σ7,min). Birds migrate without temperature restrictions.

7.5 The Peruvian Current Ecosystem and Ecoinformatics Tools

241

The elements E3, E4 and E6 migrate only in the vertical direction. The diurnal migration regime of the specimen is described by the relation c ¼ ka + kc sin (2πτ)/ 96, where ka is the average diurnal velocity of the active movement of the specimen (0–60 mh1 ), kc is the half amplitude of the diurnal variation in the velocity and τ is the time. As a result the specimen’s position is defined by the following expression: z2 ¼ z1  (w + Δw)Δz/c, where z2 is the layer in which the specimen is situated after the ascent, z1 is the depth where it is originating, w is the velocity of active movement of specimen and Δw is the velocity change.

7.5.3

Results of Simulation Experiments

The model makes it possible to carry out computer experiments tp estimate the PCE dynamics and its response to anthropogenic effects on external conditions. Initial data and model coefficients are determined mainly by studying the results of other authors (Table 7.11). For young and commercial anchovy the temperatures σ

σmax¼289 K and predatory fish σ
σmin¼288K were considered acceptable. The rate of fish migration was determined to be 0.03 ms1, and for birds 0.14 ms1. Real distributions of water temperature in Ω were determined according to the World Resources Institute by means of averaging the measured data during ten years. The thermocline position was described by a binary function (7.48) with δ¼δs in spring, summer and autumn and δ¼δw in winter. In further results, δs¼1o and δw¼300 were found. Other parameters of the model were determined from the literature sources. Specifically, α¼0.013, β¼5105, k2¼0.07, k5¼0.2, k1¼10.7, k4¼0.02, ωi¼0.7–0.95, μ0 i¼0.1μi, μi ¼0.01–0.02, ρ*¼0.1, ν*¼50, k1,10¼k1,13¼0.3, k3,10¼0.005, ρ1¼104, k4,10¼0.5103, δ1¼0.012, k13,0¼0.9, δ2¼δ*¼0.01, ζs¼0.05, z*¼5 m, W*¼0, Δφ¼Δλ¼1o, Δz¼1 m, Δt¼1 day and |v|¼0.2 ms1. The producers’ effort is homogeneous in Ω and is described by the function F (t,φ,λ)¼FoF1(t), where Fo is the factor F1(1970)¼1. The function F is illustrated in Fig. 7.21 where there are some results for model validation. The PCE yield consistent results over a wide scale of variations in model parameters. Figure 7.22 gives the PCE dynamics at the plane of E5E7 under different initial states. This dynamics is kept in other sections of the PCE trajectory. The stable results predicted by the PCE model under GSM control generally agree very well with the field measurements for this case. Here we consider a hypothetical situation and obtain quantitative estimates of PCE survivability. Consequently, some arbitrarily selected scenarios will be analyzed. The results obtained give the facts observed from the expeditions. For example, Figs. 7.23, 7.24, 7.25, and 7.26 give the phenomena described by many authors.

242

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.21 The dynamics of elements E8 (birds), E11 (anchovies) and F (relative fishery production). Curves are model calculations and symbols are mission measurements. Notation: ⋄, fishing efforts; □, anchovy production; Δ, quantity of birds

Fig. 7.22 PCE trajectories at E5 plane (anchovies), x E7 (predatory fish) under different initial conditions

7.5.4

The Effects of Environmental Parameter Variations

7.5.4.1

Temperature Variations

Seasonal and latitudinal temperature variations are known to occur in the PCE area. The natural cycles of such variations have been incorporated into the model by means of the available real data and it is interesting to consider the temperature departures from these model values. Figure 7.23 shows the variation of the biomass

7.5 The Peruvian Current Ecosystem and Ecoinformatics Tools

243

Fig. 7.23 Effect of water temperature variations on predatory fish and anchovy biomass in PCE at 20 S at time t¼100 days. Note: 1. normal temperature regime; 2. the temperature is reduced by 1 K; 3. El Niño regime. The initial data for t¼0 are assumed to be uniform across the region: E1¼200, E2¼400, E3¼80, E6¼E7¼E9¼8, E4¼35, E10¼700, E13¼3.5104, E5¼16, E12¼200, E14¼65 (mgm3), E8¼2 sampleskm2

Fig. 7.24 Estimation of PCE survivability J(t) under temperature variations. The figures in the curves show the water temperature variations under normal conditions (the thick curve). J is measured in relative units

of the anchovy and predatory fish versus temperature. As shown in Fig. 7.23 if the system operates under normal temperature conditions, a clear-cut division is observed between the habitation areas of the anchovy and predatory fish. The maximum concentration of anchovy biomass is observed in this case occurring in the offshore zone of about 1.5–2 wide in longitude, while the biomass of the predatory fish reaches the maximum concentration in the open ocean. This pattern is observed throughout the area and, for the model temperature distributions, appears to be stable 30–40 days later. This pattern collapses as soon as the overall

244

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.25 The vertical structure of PCE as a function of variations in solar radiation E(t,φ,λ,0) under t¼50 days, φ¼12  S, λ¼82  W. The initial conditions are the same as in Fig. 7.24. The parameter R is explained in Fig. 7.26

Fig. 7.26 Relationship between the survivability function J(t) and variations in solar radiation E (t,φ,λ,0) in the scenario: E(t,φ,λ,0)¼R(3.04–0.03φ) (1+0.1sin[2πt/365]), where R¼1 under normal operating conditions of the system

temperature background changes. A reduction in the general temperature level by 1K is due to the fact that anchovy and predator fish populations are increasingly intersecting. Moreover, their total biomass increases by about 10%. During the El Niño periods the effect of division of the ecosystem into the offshore and open-ocean parts disappears completely, accompanied by a decline in total biomass of anchovy and predatory fish. To estimate the survivability of PCE, Fig. 7.24 shows the survivability function J (t
to):

7.5 The Peruvian Current Ecosystem and Ecoinformatics Tools 9 P

J ðt Þ ¼

R

R

i¼1 ðφ, λÞ2Ω z 9 P

R

R

i¼1 ðφ, λÞ2Ω z

245

Ei ðt, φ, λ, uÞdφdλdu ð7:49Þ

E i ðt o , φ, λ, uÞdφdλdu

for various temperature departures from the model value. We will assume that the system is in a living state if the condition J(t)>κJ(0) holds for t
t*, where κ>to. The system, as we say, ‘heals’ over time from the ‘blows’ it has undergone and progresses to the same level of operation. It is different when at a depth z>150 m E12 becomes close or is below a certain critical value E12,min. The system in this case is unable to offset the fluctuations introduced, and at E12>>0.1 mgm3 the system begins to experience the effects of a strong limitation

7.5 The Peruvian Current Ecosystem and Ecoinformatics Tools

247

Fig. 7.27 Dynamics of survivability function J(t) versus time under different initial conditions of water saturation with oxygen (shown in curves in mgm3 )

Fig. 7.28 Dynamics of survivability function J(t) versus time under variations of the rate of photosynthetic oxygen production in percentage deviation from the model value (δ2¼0.01). The thick curve corresponds to the normal conditions

across the area. Such a reduction in nutrient concentration is, for instance, possible in the case of contamination of the bottom sediments with oil products.

7.5.4.5

The Effect of Variations on the Velocity of Vertical Advection

To estimate the turbulent escape of nutrients into layers overlying the thermocline, the model assumes that the velocity of water uprising is 103 cms1. The data obtained point to the fact that, on average, the integrated pattern of distribution of community

248

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.29 Dynamics of the survivability function J(t) versus time for experiments on variation in food spectra Υs (s¼1–6) as given in the text. The thick curve corresponds to the model result

elements is not subject to any significant variation within the velocity range of 3104 to 102 and even 101 cms1 but is observed to be drastically distorted under a higher and, most importantly, under a lower (Jmin for t0.1 μg-atL1 and E14(to,φ,λ,z)>0.5 mlL1. When Ei(to,φ,λ,z)H on Ω and Ei(to,φ,λ,z)¼100 calm3 in the rest area.

7.5.4.7

Investigation of PCE Survivability in Variations in the Trophic Chart

The block – diagram of Fig. 7.20 defines the food chains in the PCE. There is uncertainty in this knowledge. The food range of the jth element is a vector Sj¼(ηji), with ηji¼1 when ith element is consumed by the jth element, and ηji¼0 otherwise. We consider a set of simulation experiments whose results are given in Fig. 7.29. These experiments correspond to the following variations of the food spectra: Here Υk¼||ηji|| is the matrix of the ecosystem food spectrum. Figure 7.29 shows some calculations characterizing the dependence of J(t) on the variations in the trophic graph. We see that the extending food spectra to the Y1 and Y2 scenarios do not practically change the ecosystem dynamics. Another situation arises when correlations are disturbed in the trophic graph. For instance, ‘cutting’ high trophic levels (birds and / or predatory fish) leads the PCE to an unstable state as shown in the Y5 and Y6 scenarios. Therefore, a more detailed investigation of the correlations in the trophic graph is an important task. In particular, it is important to study trophic links for living elements that show predatory relationships. As we see from Fig. 7.29 the consumption of detritus by the bacterioplankton is such an important relationship. In scenarios Y3 and Y4, this consumption does not occur, leading to a reduction in system survivability.

252

7.6 7.6.1

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

New Scenario for Recovering Water Balance of the Aral Sea Water Problems in Central Asia

The variations in the distribution of atmospheric precipitation in vast areas from the steppes of Stavropol and Kalymkia to the Pamirs and Tien Shan are in many respects determined by large-scale spatio-temporal variations of atmospheric moisture fluxes from the basins of Caspian and Aral Seas and Kara-Bogaz-Gol Gulf (KBGG), large reservoirs and accumulators of collector-drainage effluents, saline lands, and other standard evaporators of surface and subsoil waters on the territory of Central Asia. The problem of water balance in Central Asia has been the subject of numerous publications, as it is associated with undesirable phenomena, such as dust and salt storms, floods, droughts, snow avalanches, landslides and mudflows, as well as other hydrological disasters and man-made catastrophes. Also important is the problem of moisture supply to 129 million ha of Central-Asian desert pastures, of which only 48% are used. Finally, there is a slightly studied correlation between the Central Asian water balance and global environmental changes. First of all, rising the Caspian Sea level and Aral Sea shallowness, the degradation of many other unique water bodies, such as Lake Balkhash and KBGG are considered to be related events, is a planetary-scale disaster, as they result in catastrophic consequences for population and the environment of huge neighboring territories. For the Caspian and Central Asian regions, the hydrological parameters of the basins of the Caspian and Aral Seas, other open water bodies and conduits are important sources of replenishment of atmospheric moisture volumes, whose westeastward transport boosting the volumes of the glaciers and snowfields of the Pamirs and Tien Shan, which, on the one hand, increases and stabilizes the flow of the main rivers of the region, creates favourable conditions for the ecological safety of population, prevent dust and salt storms, resolving the urgent problems of the Aral Sea, near-Aral and other regions of Central Asia, but on the other hand, it leads to snow avalanches, landslides, and mudflows. From the viewpoint of the ecological security of the population and prospects for diversified development of the Near-Caspian and Central-Asian regions, the years 1950–1960 were the most favourable. The level of the Caspian Sea ranges from 28 to 28.5 m. It covers an area of 370 to 374 km2. Most of this amount falls in the shallow northern sector of the sea. It is expected that with the elevated level of the Caspian Sea (3 m since 1978), tidal processes may occur whose waves can propagate tens of kilometers from the shoreline, reducing the depth of the depletion of mineralized ground waters, ruining the protective constructions, washing-off to the sea harmful waste and poisonous (sulfur type) production enterprises for the extraction and processing of hydrocarbon raw materials. Preliminary calculations have shown that based on the hydrometeorological situation in the near-Caspian and Central-Asian regions, it is possible in the nearest future:

7.6 New Scenario for Recovering Water Balance of the Aral Sea

253

• to compensate the current rise in Caspian Sea level and decrease due to additional evaporation of the Caspian water masses of the volume of ~60 km3 year1 from specially irrigated saline lands and cavities of the eastern sea coast; • to stimulate the excess (compared to 1960) of precipitation of a total volume of about 110 km3 year1 at rationally selected points of the regions with atmospheric features the forced condensation of vapor and liquid-drop elements. Forced rains (rainmaking technology) can be implemented in the regions of the western and southern Aral Sea (53 m), Lake Sudochye (50 m), the river-bed of Uzboy, the hollows of Sarakamysh (38 m), Kazakhlyshor (28 m), Karashor (25 m), and others, over certain areas of the eastern slope of the watershed Chagrai plateau – northern chink of Ustiurt – western chink of Ustiurt – Kulandag – Kayamdag – the Kopet Kaplankyr ridge. The idea presented is based on the use of GIMS-technology and the following problems appear: • development of a theoretical-information model of the formation of atmospheric water fluxes in the near-Caspian and Central-Asian regions, assessing the potential amount of precipitation in local points with different climate scenarios; • selection of sites for saline lands and hollows in the coastal band of the Caspian Sea useful for irrigation using technology that takes into account the hydrological and economic importance of these landscape elements; • processing and presentation of input information in the form of dynamic electronic spatio-temporal thematic maps, as well as archiving and database of experiments (field and computer ones). In the light of the foregoing, the study carried out in this chapter aimed to adapt GIMS-technology to the conditions of the simulation experiment in the impact zone of the Aral Sea and to look possible ways to exchange environmental dynamics in this zone to restore the features of the sustainable development. For this purpose, it was necessary to develop algorithmic and models that would allow effective control of the hydrophysical and hydrological fields of the impact zone of the Aral Sea under changing level conditions and assess the aqua-geosystem responses to scenarios of anthropogenic forcing on its water balance.

7.6.2

Aral-Caspian Regional Water Cycle Model

The Aral Sea is located in the Turan plain in the centre of the Central-Asian arid zone at a height of 53 m above the World Ocean level, on the borders of the Kara-Kum and Lyzyl-Kum deserts. It acts as a giant evaporator (~60 km3 year1). The basin of the region is over 700 thousand km2 affected by five countries – Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Prior to 1960, the Aral Sea was a sufficiently stable water body in the area 66,459 km2, with centennial oscillations of the water level within 3 m and seasonally within 25 cm. It was

254

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

one of the four largest inland closed salt-water (10 gl1) bodies on the planet. Prior to 1960 the maximum width of the sea basin was 235 km (from northwest to southeast) and 434 km (from southwest to northeast). The depths of the sea were distributed down to 69 m, so that 88% of them were within 30 m. The sea is the deepest in the western sector of the depression. In the southern part of the sea a flat area is located of over 11,000 km2 of the present and former delta of the Amu-Darya River, changing to the south into the sand-hills of the Zaungus Kara-Kum desert. To the east, the Aral Sea borders the desert plane of Kyzyl-Kum with a general slope towards the sea. Here the typical relief is the sand with the large dry beds of the old tributaries of Syr-Darya and Amu-Darya. To the north and northwest the coastline is bordered by the sand-hills of the Mugodjar foothills. The Amu-Darya River has always played an important role for the Aral Sea basin as one of the main components of the water balance. Irrigation measures that began in the 1960s have led to the present poor state of this region with the expected propagation of negative processes beyond its borders, which has prompted many scientists to look for possible ways to prevent them (Krapivin and Phillips 2001). Based on the modeling system EPIC (Environmental Policies and Institutions for Central Asia), Schlüter et al. (2005) developed a model of water regime control in the Amu-Darya impact zone. This model determines the optimization of the irrigation network by calculating the monthly mean of water fluxes control and examining the water needs for the next 15 years. The model was calibrated against the data of the state of the water system of the Amu-Darya zone recorded in 1994 and 1997. The calculations with this model do not take into account all the direct connections and feedbacks in the water balance system of Central Asia are rather symbolic than can serve as a guide for making real decisions. The experience of this modeling and the ideas expressed by the authors make it possible to move to a more complicated model described below. The climatic conditions of the Aral Sea operation are determined by its environments characterized above. Temperature oscillations in the sea zone can reach   78 C. The average January temperature is 14 C, sometimes falling to    33 C. In July the average temperature is +26 C reaching +45 C some years. In general, the climatic situation in the near-Aral zone is variable but not from anthropogenic reasons. For instance, in the period 1951–1960 the inter-annual variability   in surface air temperature ranged between 4 C and 6 C, and in the period 1971–1980 the winter temperature was below normal by 5.5  C. In the following years, there was a trend to transfer the annual temperature regime to the mainland. Table 7.15 gives some indications of mean temperature deviations from the multiannual standards. These values make it possible to prescribe intervals of climatic uncertainty when formulating synoptic scenarios. The sum of the annual precipitation at sea oscillates around 100 mm, while the evaporation is estimated at 1250 mm year1 (i.e., each year a layer of 115 cm evaporates from the sea surface). The temperature regime of the sea itself is  characterized by water temperature variations from 20–25 C in summer to  0.7 C in winter, when a considerable part of the sea surface is covered by ice. As the sea becomes shallow, heating and cooling of the water masses sometimes reach the bottom.

7.6 New Scenario for Recovering Water Balance of the Aral Sea Table 7.15 Deviations of average air temperatures (oC) by season in the Aral Sea region (Krapivin et al. 2019)

Zone Aral Sea Monsyr Kazalinsk Karak Chabankazgan Muinak Chirik-Rabat Kungrat Kosbulak

Spring 1.4 0.6 0.9 0.8 1.1 0.9 0.9 1.4 1.3

Summer 0.4 0.2 0.6 0.2 0.6 0.5 0.5 1.4 0.8

255 Fall 0.1 1.1 1.5 0.8 0.7 0.3 1.0 1.1 0.0

Winter 0.5 1.3 0.1 0.3 1.5 0.6 0.4 0.1 0.0

Between the deserts, the sea is constantly affected by the wind. In the fall and winter, northeast winds blow, bringing cold air masses from Siberia, spring and summer, the southwest wind bring moisture from the Atlantic Ocean, Mediterranean and Caspian Seas. Wind-rose and wind speed are important parameters in analyzing the Aral Sea water balance and should be thoroughly taken into account. Figure 7.30 shows schematically wind-roses above the Aral Sea basin. According to estimates by Bortnik and Chistiayeva (1990), the annual mean wind speed varies depending on the territory within 3–7 ms1. The region of the Aral Sea is characterized by strong variability of wind speed which can steadily reach 30 ms1. For instance, on the west coast, wind speeds are observed more than 50 days a year on average, which is very important for the Aral Sea level recovering scenario considered here. Aral Sea water balance components have been discussed elsewhere (Bortnik and Chistiayeva 1990). Until 1968, when negative trends in the Aral Sea water balance appeared only, Kornakov et al. (1968) gave a comprehensive analysis of its basic constituents. At that time, due to the expansion of irrigated land (increased by 6.5 million ha by 1980), run-of losses in the delta of Amu-Darya reached 9.1 km3year1, and given the intake of irrigation water below in the city of Nukus, these losses amounted to 10.7 km3year1 or 23.3% of the Amu-Darya run-off. Various experts have obtained different estimates, and therefore, in the water balance modeling, the input values are somewhat uncertain. In any case, the estimate of the average multi-year (1934–1960) inflow to the delta of Amu-Darya is close to 47–49 km3 year1. For Syr-Darya it is 15–24 km3 year1. The run-off of other six rivers amounted to 43.8 km3 year1. Prior to 1960, a small portion of the total annual run-off was used for irrigation. Irrigated lands were mainly located in the rivers’ flood plains, which has led to the return of excessive amounts of irrigation water masses to the mother rivers. But in the 1960s, more than 30% of the Amu-Darya run-off was directed to the Main Turkmenian and other shallower canals and to fill-in numerous reservoirs. As a result of the collector-drainage effluents, new unplanned water bodies began to form, whose participation in the water balance of the Aral Sea region manifested itself only via evaporation and their use in hydroeconomic balance, was limited due to substantial mineralization (1.5–12.0 gl1). During the period 1975–1988, after which some stabilization was observed, the total area of lakes and reservoirs in Lower Amu-Darya increased from 483 to 1256 km2.

256

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.30 Characteristic wind directions in the Aral Sea zone and their repeatability (e.g. Bortnik and Chistiayeva 1990)

In Kuksa’s (1994) opinion, a combination of excessive amounts of water with a droughty period and rivers’ shallow between 1974 and 1977 was a stimulus to a catastrophic development of the process of anthropogenic desertification in the Aral Sea region. The river run-off has decreased sharply with increasing evaporation from both the sea basin and adjacent territories. By the end of 1980, the total area of dried water bodies in the Amu-Darya delta alone reached 310 km2. In 1984 the Syr-Darya and Amu-Darya runs-offs were estimated at 4 km3year1 and 28 km3year1, respectively. The process has begun to transform land surfaces with prevailing conversion of hydromorphous, marshy and meadow soils into saline and takyr lands. The area of lakes on the delta plains of the Aral Sea region changed from 400,000 ha in 1960 to 120,000 ha in 1970 and almost vanished by the end of the 20th century. The sea coastline retreated from the position in 1960 by dozens of kilometers. For instance, the former Kulanda fishing settlement is now 35 km from the sea. The characteristic pattern of changes of the elements of the Aral Sea water balance is shown in Tables 7.16 and 7.17 and in Figs 7.31 and 7.32. The worsening of the ecological situation in the Aral Sea impact zone has led, for instance, to Uzbekistan, to an increase of mothers’ and children’s mortality. So, in 1994 the mortality of yet unborn children was 120 out of 100,000 mothers, and the newborns’ mortality was 60 out of 1000. The climatic situation has markedly changed, which has started to tell on the economy of Kazakhstan and Uzbekistan. In many international organizations the Aral Sea problem has been widely discussed. Many experts are concerned about the prolonged unnecessary debate about the global character of the Aral catastrophe, and concrete decisions should be made to save or at least stabilize its level. In addition, the developing ecological and human catastrophes connected with the drying of the Aral Sea will lead to huge economic

7.6 New Scenario for Recovering Water Balance of the Aral Sea

257

and moral losses of the native people making them leave their traditional habitats, since their fields and pastures are ruined by sand-dust storms and poisonous dust. As shown in Fig. 7.31 the Aral Sea consists of four separate lakes. It is known from the history of the Aral Sea that over the past 10,000 years, it has dried up to 8 times and, again, filled. We are now witnessing its ninth drying-out, but it differs from previous ones by a powerful anthropogenic factor manifested through humans’ mismanagement. This mismanagement has proven to be an unusually drastic and large-scale change of river run-off to the Aral Sea as well as a lack of any constructive estimates of the consequences of this change. As a result, only 30 years later, the region-oasis, the richest in natural resources among the Kara-Kum and Kyzyl-Kum desert sands, has become a lifeless desert. During this period the sea Table 7.16 Dynamics of water inflow to the Aral Sea, km3yr1 (Krapivin et al., 2019) Year 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973

Amu-Darya 40.0 37.8 29.2 29.1 29.9 36.5 25.2 33.1 28.6 28.9 55.1 28.7 15.3 15.5 33.4

Syr-Darya 18.3 21.0 – 5.7 10.6 14.9 4.6 9.5 8.6 7.2 17.5 9.8 8.1 6.9 8.9

Total 58.3 58.8 29.2 34.8 40.5 51.4 29.8 42.6 37.2 36.1 72.6 38.5 23.4 22.4 42.3

Year 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988

Amu-Darya 6.2 10.0 10.3 7.2 18.9 10.9 8.3 5.9 0.04 2.3 7.9 2.4 0.4 10.0 16.0

Syr-Darya 1.9 0.6 0.5 0.4 – 2.9 – – – – – – – – 7.0

Total 8.1 10.6 10.8 7.6 18.9 13.8 8.3 5.9 0.04 2.3 7.9 2.4 0.4 10.0 23.0

Table 7.17 Average multi-year values of the Aral Sea water balance for individual periods. Nominators are volumes of water balance components (km3), denominators are water layer (cm)

Period 1911/ 1960 1961/ 1970 1971/ 1980 1981/ 1985 1986/ 1988

Input Речной сток 56.0/ 84.7 43.3/ 68.5 16.7/ 29.3 2.0/4.1 10.8/ 28.0

Осадки 9.1/13.8

Expenditure, evaporation 66.1/100.0

8.0/12.7

65.4/103.5

6.3/11.0

55.2/96.8

7.1/14.7

45.9/96.2

6.2/15.4

47.0/116.3

Water balance 1.0/ 1.5 14.1/ 22.3 32.2/ 56.5 36.8/ 77.4 30.0/ 72.9

Change of level, cm 0.1

Imbalance 1.6

21.0

1.3

57.6

1.1

80.0

2.6

65.6

7.3

258

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.31 Dynamics of the Aral Sea coast and landscape

has lost three fourths of the water volume; its area decreased by more than 50%. More than 33,000 km2 of sea bottom have been exposed and deserted. From this territory, the winds blow up annually more than 100,000 tons of salt and finedispersed dust with admixtures of various chemicals and poisons, raising them to higher atmospheric layers. The curves in Fig. 7.32 demonstrate the correlative dynamics of the main characteristics of Aral Sea level and salinity. Clearly, the development of this dynamics will aggravate the ecological situation in wider areas of adjacent territories. Many experts are looking for a solution to the Aral Sea problem by trying to balance the fluxes of water taken for irrigation and return (Dukhovny and Stulina 2001) and only partially solve the problem by stabilizing the observed situation. At present, the total volume of return water masses is estimated at 36–40 km3. Of these, about 50% are river waters (18–20 km3), with 13% heavily salted. A considerable share of return water is affected through drainage and collector networks, the leakage of which has led to the uncontrolled formation of hundreds of water bodies with a total volume of over 30 km3 and wetlands of dozens of thousands ha. In order to stabilize the Aral water basin’s established water status that resolves many complex ecological problems, it is necessary, to develop an intergovernmental system to regulate the return water masses. A broader view of this problem suggests that

7.6 New Scenario for Recovering Water Balance of the Aral Sea

259

Fig. 7.32 Dynamics of the level (solid curve, m) and salinity (dashed curve, g/ℓ) of the Aral Sea

acceptable solutions can be found, which can alleviate the economic load and coordinate the regional water balance with the global one (Chukhlantsev et al. 2004; Krapivin and Phillips 2001; Kondratyev et al. 2002b, 2004). The project of the annual transport of 27–60 km3 of water masses from the Siberian rivers to Central Asia, which appeared in the 1980s and is still debated at times, proved economically and ecologically problematic (Micklin 2002). Nevertheless, this idea makes some sense for further theoretical studies of such scenarios. One of such scenarios is discussed here. The geographical information systems (GIS) sphere is the most developed part of nature monitoring. GIS combines the computer cartography with databases and remote sensing. GIS elements are the computer network, the database, the data transmission network, and the system that reflects a real situation on the computer’s display. Numerous examples of GIS suggest that GIS-technology provides the control of the state of the object being monitored and serves as an efficient mechanism for combining diverse information about the object. However, GIS-technology has serious limitations when it comes to complex nature monitoring problems that require the construction of a dynamic image of the medium under fragmentary data conditions both in space and time. The main shortcoming of GIS-technology is that it is not oriented towards the flexible forecast of the state of the object being monitored. Such problems will be discussed here. An important step forward in the development of GIS-technology has been made in Kondratyev et al. (2002b) where GIMS-technology has been theoretically substantiated and practically applied. This technology removes many shortcomings of GIS-technology and gives a possibility to synthesize the monitoring systems with

260

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

the forecasting functions. The key unit of GIMS-technology is a mathematical model of the object or process being controlled. It is a combination of empirical and theoretical parts of GIMS-technology that makes it possible to directly evaluate the current and prognostic changes of the examined regional environment. The state of natural objects is characterized by a variety of parameters, including such as the type of soil and vegetation, the water regime of the territory, the salt composition of soils, the depth at which ground waters lie, and much more. In principle, the information required for these parameters can be obtained with varying degrees of reliability from ground observation data, remote sensing, and from GIS databases which contain a-priori information accumulated in recent years. The problem which a decision maker faces consists in obtaining answers to the following questions: 1. Which devices are best to use in ground and remote observations? 2. How to balance the amount of ground measurements and the volume of remote sensing data with its information content and cost taken into account? 3. Which mathematical models of spatio-temporal changes in the natural objects’ parameters should be used for interpolation and extrapolation of the in-situ and remote observations data to reduce the volume (amount) of the latter and, consequently, to reduce the cost of the work as a whole, as well as predict the operation of the observed object? Any sub-system of the environment is considered to be an element of nature interacting through biospheric, climatic, and socio-economic feedbacks with the global NSS. For a particular monitoring object, a model is developed which describes such an interaction and operation of the various levels of spatio-temporal hierarchy of all processes in the environment affecting the state of the object, judging from preliminary estimates. The model incorporates natural and anthropogenic characteristics of a given territory and, in its initial state of development builds on the existing information database. The model’s structure is oriented towards an adaptive regime of use with subsequent episodic corrections of its parameters or units. As a result of the combination of the system of the environmental data collection, the model of the geoecosystem’s operation in a given territory, the computer cartography system and artificial intelligence tools, a single GIMS of the territory is synthesized which provides predictive estimates of the consequences of realization of technogenic projects and other estimates of the geoecoststem’s operation. For the Aral Sea zone, the implementation of GIMS-technology requires a selection of characteristic elements of the natural-anthropogenic system operating in this zone. This process is carried out through a multitude of 2D matrix structures-identifiers, in the symbolic form describing the geographical configuration of the zone, the distribution of soil-plant formations, the dislocation of anthropogenic objects, the location of characteristic synoptic zones, local topography, and transversal waters of the area. For the water balance equations of the Aral Sea region, it is important to consider the elements of land cover whose effect on evaporation and surface runoff is manifested by their characteristics. The identifier A1¼||aij,1|| determines the configuration of the territory, which is taken into account in the model of water balance.

7.6 New Scenario for Recovering Water Balance of the Aral Sea

261

Without breaking the community, assume a fixed geographic grid of the size Δφ in latitude φ and Δλ in longitude λ. Then the identifier А1 in the GIMS database provides a flexible consideration of the sites of the territory in the Aral Sea zone which will be taken into account: aij,1 ¼

1 the site is included into water balance; 0 the site is not included into water balance:

The identifier А2¼||aij2|| specifies the spatial distribution of land covering symbolic elements referring to the water balance components of the territory. Table 7.18 lists the characteristic elements of the zone coverings taken into account by identifier A1.

7.6.3

Remote Sensing Database of the Aral Sea Zone Environment

Regular remote monitoring sessions were carried out in the territory of Central Asia during the period 1972–1990 using microwave, optical and IR sensing methods (Krapivin and Phillips 2001; Chukhlantsev et al. 2004). With simultaneous aerospace and ground observations, regional biogeocenoses studies have been carried out. Radar, radio-thermal, photographic, optoelectronic survey and measurements materials have formed the basis of the remote sensing database which includes information on various characteristics of land covers, hydrometeorological processes, and the atmosphere. In particular, the database contains information about special features of micro- and macro-relief, the type of floristic background, the degree of moistening and salting of soils, the subsurface anomalies (cavities, ground water lenses, etc.), and the atmospheric state. The database includes estimates of the dependencies of reflecting and emitting properties of the surface at different Table 7.18 Determination of the identifier A1 elements

Type of the surface Open lake water Irrigated territory River sector Waterlogged site Dry river bed Tree-brush vegetation Takyr Saline land Steppe Sea sector Sand Reeds Pasture vegetation

Identifier A1 a b с d е f g h t p n m о

262

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

wavelengths on variations in physico-chemical and geographical parameters of environmental elements. These data were used in solving the identification problems based on the algorithms discussed above. In qualitative radar studies of the reflective properties of extensive natural formations, a decoding indicator can be a specific effective scattering surface (ESS). This parameter determines the general background of a radar image of the site and makes it comparatively easy to detect in the image the sites with anomalous reflection properties. However, it is difficult to use the concept of ESS in quantitative comparisons of radar images of various locations in the region or the same territory obtained at different times. For qualitative interpretation of radar images, texture features and spectral image structures, determined by specific local parameters of the respective surface were used. Both of these components have their own statistical characteristics, the first-order statistics of the spectral structure and the texture component described by the multi-dimensional probability density, second-order momentum, and the autocorrelation function, which reflect the interaction between signals’ intensity in adjacent elements. In the latter case, the spatial radius of correlation of the radar image texture is comparable to the resolution of the measuring device and substantially depends on large-scale variations of relief, plants, biomass, and other landscape parameters. Hence, first-order texture statistics can even vary within an image and a class of objects. The microwave measurements have shown the presence of typical spectra of radio-brightness temperature. The radio-brightness spectra with positive first-order values are typical of certain types of ice, water bodies with not deeply located dense algae, young (hot) lava flows and fields, concrete surfaces, and some types of dry soils. Monotonous declining spectra are characteristic of moistened soils, water bodies, rice controls, and others. The alternating spectra values of the first increments are inherent to the multi-layer interference structures, to heterogeneous formations of the peat-bogs type and to the edges of forest fires. The polarization and dispersion characteristics of the thermal field are attributed to significant values in aerospace studies of water bodies, concrete and soil covered takeoff and landing runways, as well as other natural and anthropogenic smooth surface formations. These formations were used as microwave-reference calibrating points. Simultaneous remote and ground-based measurements of the thermal field radiance of saline lands at wavelengths 1.35, 2.25, and 20 cm have shown that a saline land has vast and stable regions (from season to season) with slight variations in radio-brightness temperatures. Along the contour of the saline land, a sharp decrease in the radio-brightness temperature at wavelengths 0.8 and 2.25 cm is observed and its minimum value is in the decimeter interval in the central part of saline land. The Ustiurt plateau was used as a reference; its comparison with the polarization effects makes it possible to reliably classify land covers. The database contains information about radio-brightness contrasts across the whole territory of Central Asia. Elements of territory have been selected as closed systems of water bodies, drainage water accumulators, complexes of man-made and natural lakes, wetted saline lands, and takyrs. Experience in its formation has shown that it is only because of the remote sensing of the flying laboratory that it is possible

7.6 New Scenario for Recovering Water Balance of the Aral Sea

263

to evaluate the moisture content of the atmosphere along the contour of the Caspian – Aral system’s territory. Sequential trace measurements over inland territories allow the distribution of land cover and sub-soil waters level to be determined. Examples of recordings of radio-brightness contrasts from the IL-18 flying laboratory are shown in Figs. 7.33 and 7.34. The Aral Sea water balance has been calculated by many scientists. But these calculations did not take into account the average correlations and estimates for the large territories neighbouring to the Aral Sea hollow. It is apparent that the timedependent character of the climate and the variability of land covers structure require a more detailed description of the water balance equations of the role of a detailed description of the climate parameters and the morphology of the elements involved in water evaporation. The model of the Aral Sea region water balance can be based on a standard model of regional moisture balance for limited territory, as shown schematically in Fig. 6.16. Each area of the Aral Sea zone can have part of the network of rivers, water bodies, and land areas. According to the landscape-hydrological principle, to construct a simulation model in the hydrologic zone it is necessary to select elements connected with the standardization of the floristic background, whose concrete form is determined by micro-relief, type and properties of soil, surface moisture, depth of groundwaters lying, and other factors. Thus, in general, the territory ΩL is characterized by m elements, and the input network has n homogeneous sites. In this way, a closed equilibrium equation system, which serves as a basis for calculating water balance components in the Aral Sea region, is taken into account in the form of the water cycle.

Fig. 7.33 Fragment of the IL-18 flying laboratory record in the saline land of Sor Barsa Kelmes

264

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.34 Fragment of the IL-18 flying laboratory record in the Kaidak saline land (wavelength 1.35 сm)

7.7 7.7.1

Mathematical Modelling and Numerical Experiment in the Geophysical Study of the Aral Sea Region Model for Structured-Functional Analysis of Hydrophysical Fields of the Aral Sea

The experience from the large-scale change of geochemical and hydrological situation in the Aral Sea zone has shown that the problem of suspending desertification and ecological degradation in this region cannot be solved without the creation of a multi-level monitoring system with functions of a forecasting (Krapivin and Phillips 2001; Varotsos and Krapivin 2017). The GIMS-technology suggests an adaptive and sequential analysis of information on the state of the main hydrophysical fields (temperature τ and salinity S) by correcting the simulation model and controlling the collection and processing of monitoring data. Consider a simulation model of the Aral Sea hydrophysical fields (SMASHF). The SMASHF includes information collection units, primary processing and accumulation of monitoring data, simulation of the operation of the Aral Sea aqua-geosystem’s water regime, prediction of its state, estimation of discrepancy between measured and predicted states, decision making on measurements planning, control of hydroeconomic enterprises, service support when operating input and output information. The functional structure of SMASHF is shown schematically in Fig. 7.35. The model of the Aral Sea aqua-geosystem is a key element of the SMASHF which provides, at the expense of structural and parametric change, an adaptation of the monitoring regime. The functional filling of the model is shown in Table 7.19.

7.7 Mathematical Modelling and Numerical Experiment in the Geophysical Study of the. . . 265

The model describes the hydrophysical processes against ΩA in the ice-free period. Currents, convective mixing, river run-off, heat and moisture exchange processes with the atmosphere, turbulent heat exchange, salt, and momentum are simulated. Here, to form the initial conditions for the end of the winter season tw a correction of the hydrophysical parameters of the aqua-geosystem is obtained in the FIC unit, considering the multi-year changes in sea level and other indicators of the state of the water basin ΩA. The calculation of depths and initial fields of salinity S(φ,λ,z,t) and temperature τ(φ,λ,z,t) of sea water are corrected. These procedures take into account the characteristic estimates of parameters indicated by fragments of multi-year measurements at a time tw. The initial field τ(φ,λ,z,0) is prescribed taking into account the geometry of the water masses at that time. The field S(φ,λ,z,tw) is formed considering the division of ΩA into water regions with characteristic homogenous hydrophysical parameters: Sðϕ, λ, z, t w Þ ¼

S1 for ðϕ, λÞ 2 ΩD ; S2 for ðϕ, λÞ 2 ΩA ∖ΩD

where ΩD is the western deep-water sector of the Aral Sea, S1 ¼ (SD  SA)/VD, S2 ¼ (SP  SL)/VP, VD and VP are water volumes in the western deep-water area and the shallow Large Sea, SD and SP are salt supplies in ΩD and ΩA\ΩD, respectively, SA and SL are salt supplies on dry territories adjacent to ΩD and ΩA\ΩD, respectively.

Fig. 7.35 Schematic of information fluxes at SMASHF fixing

266

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Table 7.19 The SMASHF units Unit FIC

MSC MSF MTS MWD SCT RCR

Characteristics of the units’ functions Formation of initial conditions for models for multi-year changes in the hydrophysical characteristics of the Aral Sea. Select steps for digitizing space in latitude and longitude. Determination of the degree of temporal digitization. Model of seasonal changes at Aral Sea level. Models of the formation of the structure of currents in the Aral Sea. Model of the spatio-temporal distribution of water temperature and salinity. Models of water density field formation. Simulation of the process of convective mixing of sea water masses. Provision of the regime of structural and parametric correction of SMASHF units with modeling results taken into account.

The seasonal or intra-annual oscillations of the Aral Sea level are described by the MSC prognostic model which traditionally takes into account the balance of the components of the sea water regime: evaporation, precipitation, and river run-off. The MSC uses the multiannual average values of these water balance components as well as information about the present hydrometeorological situation in the Aral Sea zone. The MSC unit simulates the fluxes in Ω using the method of Shkodova and Kovalev (Bortnik et al. 1994), which enables the evaluation of horizontal components of velocity u(φ,λ,z,t) at any point with geographical coordinates (φ,λ)2ΩA with a random depth value z. The vertical component of velocity is calculated by the continuity equation. It is assumed that the motion of water masses is determined by the tangential wind stress over ΩA, the sea surface slopes caused by negative and positive setups due to wind unevenness considering the effect of coasts and river run-off. The driving force is balanced by the vertical viscosity and friction at the bottom. The coefficient of turbulent viscosity is calculated from the formula v¼0.25aWH/k, where W (uφ, uλ) is the wind speed, H is the depth, k is the wind coefficient, a is the proportionality coefficient. The basic equations of the MSF unit are as follows:   uφ ¼ T φ ðH  zÞ=v þ ðgρo =vÞ  0:5 H 2  z2  ð∂κ=∂φÞ,   uλ ¼ T λ ðH  zÞ=v þ ðgρo =vÞ  0:5 H 2  z2  ð∂κ=∂λÞ,   ∂κ=∂φ ¼ 3T φ =ð2gρo H Þ  3v= gρo H 3  ð∂ψ=∂λÞ,   ∂κ=∂λ ¼ 3T λ =ð2gρo H Þ  3v= gρo H 3  ð∂ψ=∂φÞ, where g is the acceleration of gravity, ρo is the water density averaged over Ω, T(Tφ, Tλ) is the vector of tangential wind stress on the sea surface, κ is the sea level deviation from the surface of the basin, ψ is the function of full flows. The MTS unit simulates the spatio-temporal structure of the distribution of salinity S and sea water temperature τ. The water area ΩA is divided into compartments Ωi ([Ωi¼ΩA), which are internally homogeneous in both S and τ. The heat

7.7 Mathematical Modelling and Numerical Experiment in the Geophysical Study of the. . . 267

and salt transport between Ωi is carried out by the currents and due to gradients’ difference. The boundary-to-atmosphere exchange processes are described by linear relationships taking into account multi-year observational data. The model MWD was used to approximate the vertical variations in the Aral Sea water density ρ(φ,λ, z, t). The SCT unit checks the criterion of stability of water stratification and on this basis carries out a convective mixing of water masses. The stratification is considered stable at ∂ρo/∂z
0. In this case the convective mixing is absent. With ∂ρo/∂z 0:

where Bo is the concentration of the saturated solution, ΔT¼ToT, To is the temperature of the control water, Δv¼vov, vo is the control wind speed, the functions f and F are given in the form of empirical estimates (Fig. 7.40), C10(t) is the scenario describing the pre-history of the inflow of salts from the Caspian Sea; a, b, β, θi, and λi are constant coefficients of the model.

Fig. 7.40 Inflow of salts into KBGG (C8) and their industrial removal (C9)

7.7 Mathematical Modelling and Numerical Experiment in the Geophysical Study of the. . . 277

The wind speed v and temperature T are prescribed as maps corresponding to the steps of the spatial grid (Δφ,Δλ). The temperature values are calculated by the formula: Tw¼A(t)+B(t)Ta, where Ta is the air temperature, Tw is the surface water temperature; the coefficients A and B are determined from: A ðt Þ ¼ B ðt Þ ¼

1:32  0:04 for April‐September, 3:26  0:06 for October‐March, 0:86  0:02 for April‐September, 0:88  0:06 for October‐March,

Equations of water balance (7.34) for the territory Ωg can be written as follows: σ g ∂W=∂t ¼ E  R þ

N  X

 H 4j  H 29j σ gj ,

j¼1

σ gi ∂ci =∂t ¼ H 1i  H 4i þ Bi σ g i þ K i þ N i  H 2i , where σg is the area of the Gulf, σgi is the area of the ith basin of the Gulf in accordance with the grid of digitization. The remaining symbols are given on the scheme in Fig. 7.38.

7.7.4

Parameterization of the Water Balance in the Aral Sea Region

The set of models described above makes it possible to reduce to a single system all fluxes of moisture which can circulate in the Aral Sea area and meet its limits. As shown in Fig. 7.41, a numerical experiment based on this system of models requires a large amount of data. This volume can be reduced by taking into account the numerous correlations suggested by many authors to describe the links between geophysical parameters and the elements of the water balance of the region. A most detailed analysis of such links was given by Bortnik and Chistiayeva (1990). At present, the Aral Sea– is a closed water body without run-off, and the equation of its water balance at each stage is rather simple: H 8 =σ þ H 33 þ U n  H 7  U Φ =σ ¼ ΔA, where Un is the sub-soil inflow of water, UΦ is the filtration of sea water into the coastal bottom, σ is the sea area. In the adaptation of the scheme of total regional water balance (Fig. 7.38) to the Aral Sea region conditions, the constituents Un and UΦ were included into the fluxes H35 and H11 to simplify the requirements to the database and as an insignificant weight compared to other water fluxes.

278

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.41 The main scheme of the water fluxes in the KBGG zone of influence

The role of the underground water expenditure in the water balance of the Aral Sea itself has been discussed elsewhere. From the estimates of Krapivin et al. (2019), there is a direct connection between underground waters and the sea level. They calculated the expenditure of underground waters discharged into the sea and obtained the dependence Unσ ¼ 5.5 + 0.5ΔA (km3 year1), where ΔA is the change of the Aral Sea level in meters. Therefore, for example, by reducing levels by 12 m, groundwater inflow should reach 11.5 km3 year1, which is not the case. Therefore this component in the model discussed here has been taken into account in the form of a scenario, which assumes that the flux Un ¼ const (6.3–7.2 m3 s1). To calculate a part of the flux H8, which refers to the Amu-Darya run-off, we use the equation ΔH8 ¼ 0.52Ya  14.21, where Ya is the volume of the run-off in the kishlak Chatly section. The equation of the sea surface evaporation which is important for accurate evaluation of the water balance has been studied by many authors. So, the abovementioned Goptarev formula is written as: H7 ¼ K(es  ez)Uz, where es is the maximum partial water vapour pressure (hPa) at water (or ice) temperature taken into account; ez is the water vapour partial pressure (hPa) in the atmosphere at a height of 2 m; Uz is the wind speed (ms1) at a height z (m); K ¼ 327.5/(Ln*2  Ln*zo)2; zo is the roughness parameter ( 6104 m); Ln*z ¼ Lnz + az + (az)2/(2  2!) + (az)n/(n  n!). Parameter a is selected from the state of best estimation of the effect of lower stratification temperature on the evaporation rate.

7.8 Simulation Experiments and Forecast of the Water Balance Components of the Aral. . . 279

7.8 7.8.1

Simulation Experiments and Forecast of the Water Balance Components of the Aral Sea Hollow Scenario for Potential Directions of Changes in Water Balance Components of the Aral Sea

Figure 7.31 demonstrates schematically the history of the dynamics of the main components of the Aral Sea water balance, and this dynamics is extrapolated under conditions of the preserved trends. However, it is clear that the method of extrapolation of the data of previous years cannot be objective and, the more so, answer to the question about a possibility of the existence of the regimes of influencing the water balance components which would change these trends. The problem of the control of the Aral Sea water balance remains the subject of numerous studies and discussions. In Mciklin’s (2016) opinion, it is impossible to restore the former Aral Sea; it is only possible to stabilize its water regime at levels close to nowadays without large-scale reconstruction of irrigation and drainage systems. In this pessimistic scenario, with the inflow of the Syr-Darya water 3–5 km3 yr1 and regulated water discharge into the Large Sea, with an additional run-off 8–10 km3 yr1 of Amu-Darya water, the Aral Sea level can be maintained at 31–32 m. The model of the Aral Sea water balance developed here allows examining various hypothetical impacts on the water balance of territory Ξ, in order to look for ways of changing it positively by transferring from the present unsatisfactory state to the operating regime, which may be stable and acceptable with economic and hydro-meteorological criteria. One of the ways-out from this critical situation is the reduction in the volume of water taken for irrigation. It is clear that the Kara-Kum canal is impossible to liquidate, as many agricultural regions of Central Asia are connected to it. Nevertheless, the governments of Kazakhstan and Uzbekistan are discussing the possibility of partially reducing the cotton plantations in order to return the required water volumes to the Aral Sea. In general, the bottom of many lowlands on the Caspian east coast has level below the sea. The former Soviet Union planned to direct Caspian waters to the Karagie (Batyr) lowland and builds a 35,000 kW hydroelectric power station. In this case the Karagie lowland could play the role of the Kara-Bogaz-Gol Gulf (32 m). The Karagie lowland (132 m) is located on the Mangyshlak Peninsula about 50 km from the Aktau City. Thus, the use of the lowlands located on the Caspian east coast enables the formation of a water evaporation/precipitation (WEP) scenario in its various implementations: The WEP scenario version 1: Only use Kara-Bogaz Gol as evaporator of the Caspian water. The WEP scenario version 2: In addition to the Kara-Bogas Gol, other natural evaporators of the Caspian waters are used (the area is equal to σE). The WEP scenario version 3: The process of the Caspian waters evaporation from the Kara-Bogas Gol and additional natural evaporators is carried out together with the reduction of river water for irrigation with ξ percent and using a rainfallyielding process with μ percent efficiency.

280

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.42 The results of WEP scenario modeling. Notation: (1) – WEP scenario 1 is used; (2) – WEP scenario 2 is used; (3) – WEP scenario 3 is used for μ¼0% and ξ¼5%; (4) – WEP scenario 3 is used for μ¼90% and ξ¼10%

The simulation results are given in Figs. 7.42 and 7.43. To carry out numerical experiments, we take the region confined to geographical coordinates [41 , 47 ]N and [50 , 70 ]E as the territory Ξ. LetΔφ¼Δλ¼100 . The identifiers {Ai} are supplemented by published data and use the electronic remote sensing database. To formulate a series of scenarios, consider the hypothetical anthropogenic means of controlling water regime. The main goal of the computer experiment is to select a scenario that would provide the optimum steady transfer of the hydrological regime of the territory Ξ to the state by increasing the Caspian Sea level by 14 cm year1 and reducing its level by 1 cmyr1, and restoring the main parameters of the Aral Sea to the level of 1960. An analysis of the data on the dynamics of the Caspian and Aral Sea levels reveals a broken equilibrium in the hydrological regime. The hydro-meteorological situation established by the end of the twentieth century in the territory Ξ cannot be transferred to another equilibrium state without anthropogenic control. The Caspian Sea level can be normalized by increased run-off into other reservoirs. It can be done by forcing withdrawal of water from the Caspian Sea and transporting it to the regions of saline land and depressions on the eastern coast of the Caspian Sea at a lower level than that of the sea (25.7 m). These coastal elements include the sor Dead Kultuk (27 m), the sor Kaidak (31 m), the KBG hollow (32 m), the depressions

7.8 Simulation Experiments and Forecast of the Water Balance Components of the Aral. . . 281

Fig. 7.43 Aral Sea recovery dynamics depending on natural evaporator area and WEP scenario versions: (1) – WEP scenario 2 is used; (2) – WEP scenario 3 is used for μ¼60% and ξ¼0%; (3) – WEP scenario 3 is used for μ¼90% and ξ¼0%; (4) – WEP scenario 3 is used for μ¼60% and ξ¼5%; (5) – WEP scenario 3 is used for μ¼90% and ξ¼5%; (6) – WEP scenario 3 is used for μ¼60% and ξ¼10%; (7) – WEP scenario 3 is used for μ¼90% and ξ¼10%

Karagiye (132 m), Kaundy (57 m), Karyn Aryk (31 m), Chagala-Sor (30 m), and more. The technology of transporting Caspian Sea waters to the Ξ regions is not discussed here. Just note that in many cases open canals are needed for the natural movement of water. Of course, there are additional problems with the stability of environmental parameters. For instance, it is important for KBGG not to violate hydro-chemical processes and bottom relief. Other elements of Ξ should use technologies of the Caspian Sea water transfer, which provide fresh saline lands and accumulation in the coastal depressions of fresh or poorly mineralized water. If the indicated procedure of irrigation is partially or completely carried out, Caspian evaporation increases. The evaporated moisture is transferred to other territories according to the uncontrolled synoptic state. From multi-year data on the wind situation in the western part of Ξ, there are time periods of stable favourable wind-rose. The W, NW, and SW directions are characterized by a high repeatability. Hence, atmospheric transport of the Caspian Sea water to the Aral Sea hollow is possible under a stable regime. The problem is either organizing a forced precipitation of this water or estimating the natural increase in rainfall. In the model the procedure refers to the class of the scenario.

282

7.8.2

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Model Estimation of Aral Sea Water Balance Dynamics in the Case of Preserved Natural-Anthropogenic Situation in the Region

Consider the scenario of realizing the natural trends of the components of the Aral Sea water balance. For this purpose, in addition to the acceptable parameters estimates, we define an anthropogenic constituent flux (H11). The volumes of current and planned water consumption were estimated from the published data and summarized in Table 7.26. Besides, the specification of the evaporation rates from the water surface of the rivers and the transpiration by hydrophilic plants in the flooded zones of the river valleys are important. To reduce possible uncertainties of the evaporation model’s parameters, assume the values H20¼6 km3 year1 for Syr-Darya and H20¼8 km3 year1 for Amu-Darya. The results of the modeling are shown in Figs. 7.44 and 7.45 and in Table 7.26 Calculated level of irretrievable taking of water volumes from the Amu-Datya and Syr-Darya basins, km3/year Zone of the basin Amu-Darya Syr-Darya

Years 1985 33.4 54.0

1990 39.0 57.5

2000 45.0 61.0

2010 45.0 61.0

Fig. 7.44 Forecast of annual sums of precipitation distribution across the Aral-Caspian aquageosystem along the latitudinal sections in 2000 from initial data of 1994 and mean wind situation over the last 5 years. NH degrees are shown in the curves

7.8 Simulation Experiments and Forecast of the Water Balance Components of the Aral. . . 283

Fig. 7.45 Theoretical (solid curves) and measured (o – depth, * – area) estimates of KBGG aquageosystem’s parameters

Table 7.27. It is assumed that one of the wind directions (W, NW or SW) takes place during 80 days with a repeatability of not less than 50%. During the remaining part of the year the wind directions are uniformly distributed. In the Caspian Sea basin, atmospheric moisture flux of 1.3 km3 is formed daily. The artificial evaporators add 0.2 km3 day1 to this flux. As follows from the calculated results, with the persistent westerlies, during the week forced rainfall in the Aral Sea region was equal to the annual volume of rainfall for 1960 and the sea level increased by 0.3 m. In the 80 summer days the volume of the Aral Sea is supplemented with 120 km3 of water, i.e. its level must be increased by 3.3 m. 40% or 50% with a total duration of 80 days a year and more, then the Aral Sea level from 1960 will be reaching 8 or 9 years, respectively. With the wind-rose 60 days long, the indicated result will only be obtained in 12–15 years. In this case, we assume that the repeatability of the east winds above Ξ does not exceed 15%. The contribution of excess atmospheric moisture from the Caspian Sea to river run-off increases about 40 km3 year1, satisfying the relationship 34 < H7 < 50 km3 year1. As shown from Fig. 7.44, the rainfall distribution over the eastern and central parts of area Ξ shows a steady increase of 8% and 12%, respectively, which ensures the Aral Sea level dynamics shown in Fig. 7.42. The positive balance of moisture transport in the eastern boundary of Ξ increases by 4%, which stimulates the increase of river run-off in the Turan lowland. During SW to W and NW winds, the amount of rain in the Aral Sea hollow is unchanged, the eastern winds are mostly neutral or by 4–7% increase in the amount of rain in the Aral Sea zone due to the return of atmospheric moisture. Reported unchanged rain ensures that the irrigation status of arid regions can be adjusted. In particular, in the SE winds, excessive evaporation of Caspian Sea waters can give moisture for the forced irrigation of the arid steppes in Kalmykia and Stavropol Krai.

284

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Table 7.27 Results of model estimation of some components of the Aral Sea water balance in different wind directions and under conditions of the procedure of forced precipitation in the Turan lowland (H8 in km3/yr; H33 in mm/yr) Time from the beginning of the simulation experiment (years) 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Prevailing wind direction NW W H8 H33 H8 H33 H7 38 197 1010 41 188 44 180 991 37 190 70 160 993 55 171 56 174 968 68 183 48 149 1001 50 194 51 187 986 44 189 66 191 999 61 169 61 177 956 63 175 59 163 983 52 166 53 154 979 57 160 49 142 988 55 159 57 138 985 48 144 52 144 987 54 147 55 107 1003 50 133

H7 998 987 869 901 977 983 1015 994 899 908 910 1017 999 976

SW H8 10 12 16 21 18 14 16 12 9 13 17 11 8 12

H33 198 183 160 171 152 188 190 180 171 155 143 140 141 110

H7 1007 1011 1004 1023 1014 989 1003 1004 999 991 1001 973 966 981

Notation: NW North-West, W West, SW South-West

Of course, a question of reliability of all these calculations arises here. The non-linearity of the equations of the models used does not allow the theoretical estimation of the stability and accuracy of simulation models’ results. Also, too many factors have not been taken into account. Therefore, as a confirmation of some degree of reliability, it is possible to compare simulation (theoretical) calculations and estimates of parameters published in the literature. An example of such a comparison is given in Fig. 7.45. It seems that the model reproduces sufficiently the history of the dynamics of some characteristics of the Kara-Bogaz-Gol Gulf. The calculations give rise to the hope that the Aral Sea can be partially restored or stabilized if the necessary measures are taken promptly. A complex approach is possible, including the implementation of the irrigation scenario under consideration in certain territories on the east coast of the Caspian Sea and a large-scale reconstruction of irrigation and drainage systems.

7.8.3

Recommendations on the Monitoring Regime of the Aral Sea Aqua-geosystem

As has been demonstrated by the experience of observations in the area of the State Oceanographic Institute field observations in the Aral Sea region (Bortnik and Chistiayeva 1990; Bortnik et al. 1994) as well as multi-year remote sensing using the

7.8 Simulation Experiments and Forecast of the Water Balance Components of the Aral. . . 285

Fig. 7.46 The block-scheme of the moisture cycle in the aqua-geosystem operating zone with evaporation, evapo-transpiration, leakages, precipitation, and anthropogenic use taken into account

flying laboratories carried out by the Institute of Radioelectronics of the Russian Academy of Sciences and the Institute of Geography of RAS, acquiring operational information on the geophysical and hydrometeorological situation requires significant financial costs. The use of GIMS-technology simplifies the problem of organizing regular monitoring of the indicated region. It is possible due to a coordinated application of the means of observations and mathematical models. In fact, the water balance of the whole territory, whose effect on the Aral Sea is beyond doubt, can be described in a simpler scheme in Fig. 7.46. Then, of course, there are some additional problems with the planning of the measurements. They can be solved by creating a specialized information system whose approximate scheme is shown in Fig. 7.47. This system is used by the monitoring services of the Aral Sea zone and adjacent areas for a specific comparison of the episodic estimates of water balance components with modeling results. This comparison can lead either to corrections of the individual components of water balance model or to additional measurements. Overall, the scheme in Fig. 7.47 can be implemented using regular satellite measurements of the different types of land cover, temperature, moisture content in the atmosphere, wind speed and direction, as well as salinity of the water bodies (Grankov and Milshin 2010). In the present state of the Aral Sea region the satellite monitoring is more efficient. Satellite information, including satellite images, measurements from radiometers and altimeters provides the tracing the dynamics of various characteristics of the Arak Sea. The principal aspects of the problems that arise here are discussed by Ginzburg et al. (2010). The recovery scenario proposed in this chapter to overcome the planet’s greatest environmental disaster deserves attention for its future development.

286

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.47 An approximate scheme of the dialogue regime on the use of the algorithmic layout of the hydrophysical experiment

7.8.4

Reliability of Scenario Implementation

The Aral Sea Basin is covered by the Turan plain in its central and western parts and the mountainous zone to the east. A significant role in the water balance of Central Asia is played by the Kara Kum and Kyzyl Kum Deserts, as well as semi-arid steppes. The sources of Amu Darya and Syr Darya are located in the mountainous zone of the Tien Shan and Pamir. The discrete pixel structure of SMASHF model covers these zones with geographical grid of Δφ¼Δλ¼0.1 . Information on the characteristics of these morphological zones was compiled based on multi-year remote monitoring observations using the IL-18 flying laboratory of the Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences and publications (Bortnik and Chistyaeva 1990; Chen et al. 2013) and dating Aral Sea Basin events starting in the 1950’s to the present (Dukhnovny and Sokolov 2003; Klein et al. 2012). The water delivered by the natural evaporators to the atmosphere partially reaches the mountain glaciers which provide an additional precipitation of 850 mm/yr and the outflow of the river may increase.

7.8 Simulation Experiments and Forecast of the Water Balance Components of the Aral. . . 287

The use of the water balance of the model simulations of Central Asia shows that there is a major solution to the Aral Sea problem. The results obtained from the implementation of the WEP scenario version 3 shows that the Aral Sea volume of the 1960s can be achieved in a visible time due to the 5.4% evaporation of the river inflow into the Caspian Sea using the Kara-Bogaz Gol and other evaporators on the east coastline. In this case, the evaporated water is distributed in the Aral Sea Basin under the influence of the wind fields and the synoptic situations taken into account in the literature sources. In general, the WEP scenario attributes mainly positive changes to all components of the water cycle in Central Asia. For example, surface runoff in rivers is increased by 2.3 km3/yr (H22¼19.14.3 km3/yr) and rivers inflow to groundwater is increased by 1.8 km3/yr (H36¼16.32.2 km3/yr). SMASHF model verification is performed by comparing historical data on Aral Sea volume dynamics and modeling results during 1950–2010, showing a model error of 11–14% depending on the variations of the historic data. Certainly, this precision can be increased when the WBCA model is used in conjunction with the regional climate model and detailed descriptions of the topographic and ecological parameters of the Aral Sea Basin will be assessed. Nevertheless, the SMASHF model shows positive changes in the Central Asia water cycle when the WEP scenario version 3 is implemented. Many elements of the water cycle, as shown in Table 7.2, return to the 1960s. For example, the flow H10 is 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 Aral Sea problem can be solved only in the one case where the governments of five countries of Central Asia can conclude an agreement on the sharing of Aral water basin resources. This cooperation is foreseen by the Multi-Partner Human Security Trust Fund whose programme “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 technologies irrigation systems. The WEP scenario can be a basic tool for decision-making and development of formation strategy for the Aral Sea region. Modeling results of this section show that there is a perspective solution to the Aral Sea problem through the cooperative efforts of the Central Asia countries. The SMASHF model shows that the volume of the Aral Sea in 1960 can be restored over 90–240 years, depending on the WEP scenario versions. Particularly, the use of maximum surface area (about 90 000 km2) of the natural evaporators of the Caspian water and the reduction of river water withdrawal by 10%, as well as the introduction of rain-making technologies allow the Aral Sea recovery during the nearest century. In the other cases, the SMASHF model has longer periods. The weather radars used and other monitoring tools (Krapivin and Shutko 2012; Krapivin et al. 2018a, b) allow for the operational assessment of atmospheric water storage and the choice of the rain-making process status. Such an experience of rain-making processes has been experienced in many countries (Chumchean et al. 2010; Chumchean and Bunthai 2011).

288

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fig. 7.48 Comparison observed and calculated with the help of SMASHF

Certainly, the most important characteristic of the SMASHF is its precision, which is, of course, considered to be the deviations between the observed and calculated characteristics of the Aral Sea, including its volume and extent considered in historical aspects. Figure 7.48 provides information for discussing this question about the reliability of the results provided by SMASHF. The average error of SMASHF in this case is 8.3%.

References Aksenov Y (2016) Arctic pathways of Pacific water: Arctic Ocean Model intercomparison experiments. Journal of Geophysical Research-Oceans 121(1):27–59 Aksenov Y, Coward AC (2001) The Arctic Ocean circulation as simulated in a very high-resolution global ocean model (OCCAM). Annals of Glaciology 33:567–576 AMAP (2009) Radioactivity in the Arctic. Arctic Monitoring and Assessment Programme, Oslo Antcibor I, Eschenbach A, Zubrzycki S, Kutzbach L, Bolshiyanov D, Pfeiffer E-M (2014) Trace metal distribution in pristine permafrost-affected soils of the Lena River delta and its hinterland, northern Siberia, Russia. Biogeosciences 11:1–15 Aota M, Shirasawa K, Krapivin VF, Mkrtchyan FA (1991) The system for data processing in Okhotsk Sea monitoring. In: Proceedings of the sixth international symposium on Okhotsk Sea and Sea Ice, 3–6 February 1991, Mombetsu, Hokkaido, Japan. Okhotsk Sea and Cold Ocean Research Association, Mombetsu, pp 317–318

References

289

Aota M, Shirasawa K, Krapivin VF, Mkrtchyan FA (1992) Simulation model of the Okhotsk Sea geoecosystem. In: Proceedings of the seventh international symposium on Okhotsk Sea and Sea Ice, 2–5 February 1992, Mombetsu, Hokkaido, Japan. Okhotsk Sea & Cold Ocean Research Association, Mombetsu, pp 311–313 Aota M, Shirasawa K, Krapivin VF, Mkrtchyan FA (1993) A project of the Okhotsk Sea GIMS. In: Proceedings of the eigth international symposium on Okhotsk Sea and sea ice, 1–5 February 1993, Mombetsu, Hokkaido, Japan. Okhotsk Sea & Cold Ocean Research Association, Mombetsu, pp 498–500 Belkin IM, Cornillon PC (2004) Surface thermal fronts of the Okhotsk Sea. Physical Oceanography 2(1–2):6–19 Bobylev LP, Kondratyev KY, Johannessen OM (eds) (2003) Arctic environment variability in the context of global change. Springer/Praxis, Chichester Bortnik VN, Chistiayeva SP (1990) The Aral Sea. Hydrometeoizdat, Leningrad. [in Russian] Bortnik VN, Dauletiyarov KZ (1985) Numerical modelling of circulation of the Aral Sea waters (36 pp.). Preprint of Computer Centre, USSR Academy of Sciences, Moscow. [in Russian] Bortnik VN, Lopatina SA, Krapivin VF (1994) A simulation system to study the Aral Sea hydrophysical fields. Meteorology Hydrology 9:102–106. [in Russian] Bras RL (1990) Hydrology. Addison-Wesley, New York Bring A, Fedorova I, Dibike Y, Hinzman L, Mård J, Mernild SH, Prowse T, Semenova O, Stuefer SL, Woo M-K (2016) Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. Journal of Geophysical Research 121(3):621–649 Chen X, Bai J, Luo G, Li J, Li BL (2013) Changes in land use/land cover and ecosystem services in Central Asia during 1990–2009. Current Opinion in Environmental Sustainability 5(1):116–127 Cheng B, Zhao J, Vihma T (2015) Detection of snow and ice thickness from temperature profiles of unmanned ice mass blance buoys. In: Proceedings of the 30th International Symposium on Okhotsk Sea ans Sea Ice, 15–19 February 2015, Mombetsu, Hokkaido, Japan. The Okhotsk Sea & Cold Ocean Research Association, Mombetsu, pp 203–206 Chukhlantsev, A. A., Golovachev, V. P., Krapivin, V. F., & Shutko, A. M. (2004). A remote sensing-based modelling system to study the Aral-Caspian water regime. In Proceedings of the 25th ACRS (Asian Conference on Remote Sensing), 22–26 November 2004, Chiang Mai, Thailand, Vol. 1, pp. 506–511 Chumchean S, Bunthai W (2011) Testing efficacy of rainmaking activities in the northeast of Thailand. Proceedings of the 10th WMO Scientific Conference on Weather Modification, 4–6 October 2011, Bali, Indonezia. WMO, pp. 185–188 Chumchean S, Hanchoowong R, Bunthai W (2010) Comparison between rainmaking and natural rainfall in the northeastern part of Thailand. 5th APHW Conference, Hanoi, Vietnam, 8–10 November 2010 Cousteau JY (1963) The living sea. Harper Collins, Paris Cracknell AP, Krapivin VF, Varotsos CA (eds) (2009) Global climatology and ecodynamics: Anthropogenic changes to planet Earth. Springer/Praxis, Chichester Duarte CM, Agustí S, Wassmann P, Arrieta JM, Alcaraz M, Coello A, Marbà N, Hendriks IE, Holding J, García-Zarandona I, Kritzberg E, Vaqué D (2012) Tipping elements in the Arctic Marine Ecosystem. Ambio 41(1):44–55 Dukhovny V, Sokolov V (2003) Lessons on cooperating building to manage water conflicts in the Aral Sea Basin. Scientific-Information Center of the Interstate Commission for Water Coordination in Central Asia. UNESCO, Paris Dukhovny VA, Stulina G (2001) Strategy of trans-boundary return flow use in the Aral Sea basin. Desalination 139:299–304 Edwards AM, Brindley J (1999) Zooplankton mortality and the dynamical behaviour of plankton population Models. Bulletin of Mathematical Biology 61:303–339 Fernández-Méndez M, Katlein C, Rabe B, Nicolaus M, Peeken I, Bakker K, Flores H, Boetius A (2015) Photosynthetic production in the central Arctic Ocean during the record sea-ice minimum in 2012. Biogeosciences 12:3525–3549

290

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Fisher JA (2011) Atmospheric pollution in the Arctic: sources, transport, and chemical processing. Doctor of Philosophy Dissertation. Harvard University. The Department of Earth and Planetary Sciences, Cambridge, MA Fouest VL, Babin M, Tremblay J-E (2013) The fate of riverine nutrients on Arctic shelves. Biogeosciences 10:3661–3677 Ginzburg AI, Kostianoy AG, Sheremet NA, Kravtsova VI (2010) Satellite monitoring of the Aral Sea region. In: Kostianoy AG, Kosarev AN (eds) The Aral Sea environment, vol XIV. Springer, Berlin, pp 147–179 Grankov AG, Milshin AA (2010) Microwave radiation of the ocean-atmosphere: Boundary heat and dynamic interaction. Springer-Verlag, Berlin Harms IH, Karcher MJ, Dethleff D (2000) Modelling Siberian river runoff – implications for contaminant transport in the Arctic Ocean. Journal of Marine Systems 27:95–115 Herman A (2016) Discrete-element bonded-particle sea ice model DESIgn, version 1.3. Model description and implementation. Geoscientific Model Development 9:1219–1241 Hölemann JA, Schirmacher M, Prange A (2005) Seasonal variability of trace metals in the Lena River and the southeastern Laptev Sea: Impact of the spring freshet. Global Planet Change 48:112–125 Hongpin L, Guanglin L, Weifeng P, Jie S, Qiuwei B (2015) Real-time remote monitoring system for aquaculture water quality. International Journal of Agricultural and Biological Engineering 8 (6):136–143 Hsu SM, Ni C-F, Hung P-F (2002) Assessment of three infiltration formulas based on model fitting on Richards equation. Journal of Hydrologic Engineering 7(5):373–379 Ide K (2018) Situation in the Arctic Region and Japan’s Arctic Policy. In: Proceedings of the 33rd International Symposium on Okhotsk Sea & Polar Oceans 2018, 18–21 February 2018, Mombetsu, Hokkaido, Japan. OSPORA, Mombetsu, pp 1–3 Johannessen OM, Volkov VA, Pettersson LH, Maderich VS, Zheleznyak MJ, Gao Y, Bobylev LP, Stepanov AV, Neelov IA, Tishkov VP, Nielsen SP (2010) Radioactivity and pollution in the Nordic seas and Arctic Region: Observations, modeling, and simulations. Springer/Praxis, Chichester Kaevitser VI, Krapivin VF, Soldatov VY (2013) A new information-modeling technology for monitoring environment in the Okhotsk Sea. In: Proceedings of the 28-th International Symposium on Okhotsk Sea & Sea Ice. 17–21 Fedruary 2013.Mombetsu, Hokkaido, Japan. The Okhotsk Sea & Cold Ocean Research Association, Mombetsu, pp 295–299 Kelley, J. J., & Krapivin, V. F. (2004). Biocomplexity problem related to the Okhotsk Sea fisheries. In Proceedings of the international conference DAS, 27–29 June 2004, Suceava, Romania, pp. 52–57 Kleiber M (1932) Body size and metaboliz. Hilgardia 6:315–332 Klein I, Gessner U, Kuenzer C (2012) Regional land cover mapping and change detection in Central Asia using MODIS time-series. Applied Geography 35(1–2):219–234 Klemas V (2012) Remote sensing of coastal and ocean currents: An overview. Journal of Coastal Research 28(3):576–586 Knies JL, Kingsolver JG (2010) Erroneous Arrhenius: Modified Arrhenius model best explains the temperature dependence of ectotherm fitness. American Naturalist 176(2):227–233 Komuro Y (2014) The impact of surface mixing on the arctic river water distribution and stratification in a Global Ice-Ocean Model. Journal of Climate 27:4359–4370 Kondratyev KY, Krapivin VF (2001) Biocomplexity and global geographic information monitoring. Mapping Sciences and Remote Sensing 38(4):260–271 Kondratyev KY, Grigoryev AA, Varotsos CA (2002a) Environmental disasters: Anthropogenic and natural. Springer/PRAXIS, Chichester Kondratyev KY, Krapivin VF, Phillips GW (2002b) Global environmental change: Modelling and monitoring. Springer-Verlag, Berlin Kondratyev KY, Krapivin VF, Phillips GW (2003a) Arctic Basin pollution dynamics. In: Bobylev LP, Kondratyev K, Johannessen OM (eds) Arctic environment variability in the context of global change. Springer/Praxis, Chichester, pp 309–362

References

291

Kondratyev KY, Krapivin VF, Varotsos CA (2003b) Global carbon cycle and climate change. Springer/PRAXIS, Chichester Kondratyev KY, Krapivin VF, Savinykh VP, Varotsos CA (2004) Global ecodynamics: A multidimensional analysis. Springer-Praxis, Chichester Kondratyev KY, Ivlev LS, Krapivin VF, Varotsos CA (2006) Atmospheric aerosol properties: Formation, processes and impacts. Springer/PRAXIS, Chichester Kornakov VI, Borovets SA, Bostandzhoglo AA (1968) Water balance and forecast for a drop in Aral Sea level. Hydroproject, Tashkent. [in Russian] Krapivin VF (1996) The estimation of the Peruvian current ecosystem by a mathematical model of biosphere. Ecological Modelling 91:1–14 Krapivin VF (2009) Technology for the synthesize of geoecological information-modeling systems (GIMS-technology). Journal of Science & Technology 1:47–55 Krapivin VF (2015) The Okhotsk Sea biocomplexity model. In: Proceedings of the 30th international symposium on Okhotsk Sea & sea ice. 15–19 February 2015. Mombetsu, Hokkaido, Japan. The Okhotsk Sea & Cold Ocean Research Association, Mombetsu, pp 223–226 Krapivin VF, Mkrtchyan FA (2016a) Constructive method for the vegetation microwave monitoring. Journal of Science & Technology 9(8):47–53 Krapivin VF, Mkrtchyan FA (2016b) Spectroellipsometric tools for the water quality diagnostics in the Sea of Okhotsk. In: Proceedings of the 31st international symposium on Okhotsk Sea & sea ice, 21–24 February 2016, Mombetsu, Hokaido, Japan. The Okhotsk Sea & Cold Ocean Research Association (OSCORA), Mombetsy, pp 101–104 Krapivin VF, Phillips GW (2001) A remote sensing based expert system to study the Aral-Caspian aqua geosystem water regime. Remote Sensing of Environment 75:201–215 Krapivin VF, Soldatov VY (2009) Biocomplexity problem related to the Okhotsk Sea ecosystem. In: Proceedings of the 24th international symposium on Okhotsk Sea and sea ice, 15–20 February 2009, Mombetsu, Hokkaido, Japan, The Okhotsk Sea & Cold Ocean Research Association, Mombetsu, pp 143–146 Krapivin VF, Varotsos CA (2007) Globalization and sustainable development. Springer/Praxis, Chichester Krapivin VF, Varotsos CA (2008) Biogeochemical cycles in globalization and sustainable development. Springer/Praxis, Chichester Krapivin VF, Shutko AM (2012) Information technologies for remote monitoring of the environment. Springer/Praxis, Chichester, UK, 498 pp Krapivin VF, Cherepenin VA, Phillips GW, August RA, Pautkin AY, Harper MJ, Tsang FY (1998) An application of modeling technology to the study of radionuclear pollutants and heavy metals dynamics in the Angara-Yenisey river system. Ecological Modelling 111(2–3):121–134 Krapivin VF, Varotsos CA, Soldatov VY (2015a) New ecoinformatics tools in environmental science: Applications and decision-making. Springer, London Krapivin VF, Mkrtchyan FA, Tuyet DV (2015b) Constructive method for the vegetation microwave monitoring. In: Proceedings of the international symposium on engineering ecology, 2–4 December 2015, Moscow. The Russian Sciences Engineering A.S. Popov Society for Radio, Electronics and Communication, Moscov, pp 21–27 Krapivin VF, Varotsos CA, Soldatov VY (2015c) New ecoinformatics tools in environmental science: applications and decision-making. Springer, London, UK, 903 pp Krapivin VF, Mkrtchyan FA, Soldatov VY (2016) An expert system for the Okhotsk Sea investigation. In: Proceedings of the 31st International Symposium on Okhotsk Sea & Sea Ice, 21–24 February 2016, Mombetsu, Hokaido, Japan. The Okhotsk Sea & Cold Ocean Research Association (OSCORA), Mombetsy, pp 304–307 Krapivin VF, Varotsos CA, Nghia BQ (2017a) A modeling system for monitoring water quality in lagoons. Water, Air, & Soil Pollution 228(397):1–12 Krapivin VF, Varotsos CA, Soldatov VY (2017b) The Earth’s population can reach 14 billion in the 23rd century without significant adverse effects on survivability. International Journal of Environmental Research and Public Health 14(8):3–18

292

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Krapivin VF, Varotsos CA, Soldatov VY (2017c) Simulation results from a coupled model of carbon dioxide and methane global cycles. Ecological Modelling 359:69–79 Krapivin VF, Nitu C, Mkrtchyan FA, Soldatov VY, Dobrescu AS (2018a) Information-instrumental tools of microwave and optical environmental monitoring. The Scientific Bulletin of Electrical Engineering Faculty 18(1):11–18 Krapivin VF, Varotsos CA, Marechek SV (2018b) The dependence of the soil microwave attenuation on frequency and water content in different types of vegetation: an empirical model. Water Air Soil Pollution 229(110):1–10 Krapivin VF, Nitu C, Varotsos CA (2019) Microwave remote sensing tools and ecoinformatics. Matrix Rom, Bucharest Kuksa VI (1994) The Southern Seas under conditions of anthropogenic stress. Hydrometeoizdat, St. Petersburg. [in Russian] Kwok R (2010) Satellite remote sensing of sea-ice thickness and kinematics: A review. Journal of Glaciology 56(200):1129–1140 Laurel BJ, Copeman LA (2018) Temperature impacts on Polar cod (Boreogadus saida) during the first year of life. In: Proceedings of the 33-rd International Symposium on Okhotsk Sea & Polar Oceans 2018. 18–21 February 2018, Mombetsu, Hokkaido, Japan. Okhotsk Sea and Polar Oceans Research Association, Mombetsu, pp 4–5 Legendre L, Krapivin VF (1992) Model for vertical structure of phytoplankton community in Arctic regions. In: Proceedings of the Seventh International Symposium on Okhotsk Sea and Sea Ice, 2–5 February 1992, Mombetsu, Hokkaido, Japan. Okhotsk Sea & Cold Ocean Research association, Mombetsu, pp 314–316 Legendre P, Legendre L (1998) Numerical ecology. Elsevier, Amsterdam Libes S (2009) Introduction to marine biochemistry. Elsevier, London Ma J, Hung H, Tian C, Kellenborn R (2011) Revolatilization of persistent organic pollutants in the Arctic induced by climate change. Nature Climate Change 1:255–260 Mangum G, Winkle W (1973) Responses of aquatic invertebrates to declining oxygen conditions. American Zoologist 13(12):529–541 Matoba S, Shiraiwa T, Tsushima A, Sasaki H, Muravyev YD (2011) Records of sea-ice extent and air temperature at the Sea of Okhotsk from an ice core of Mount Ichinsky, Kamchatka. Annals of Glaciology 52(58):44–50 Melentiev VV, Jochannessen OM, Kondratyev KY, Bobilev LP, Tichomirov AI (1998) An experience of satellite-based radiolocation diagnostics of the ice-lake cover: Ecology and history. Research in Earth Space 2:91–101. [in Russian] Mélia DS (2002) A global coupled sea ice–ocean model. Ocean Modelling 4(2):137–172 Michener WK, Baerwald TJ, Firth P, Palmer MA, Rosenberger JL, Sandlin EA, Zimmerman H (2001) Defining and unraveling biocomplexity. BioScience 51(12):1018–1023 Micklin PP (2002) Water in the Aral Sea basin of Central Asia: Cause of conflict and cooperation? Eurasian Geography and Economics 43(7):505–528 Micklin PP (2016) The future Aral Sea: Hope and despair. Environmental Earth Sciences 75 (9):1–15 Mintzer IM (1987) A matter of degrees: the potential for controlling the greenhouse effect. World Resources Institute Research Report No. 15, Washington Mkrtchyan FA, Krapivin VF (2011) GIMS-technology in monitoring marine ecosystems. In: Proceedings of the 26th International Symposium on Okhotsk Sea & Sea Ice. 20–25 February 2011, Mombetsu, Hokkaido, Japan. Okhotsk Sea and Polar Oceans Research Association, Mombetsu, pp 163–166 Mkrtchyan FA, Krapivin VF (2016) About microwave radiometry and spectroellipsometric technologies for monitoring marine ecosystems. In: Abstracts of the PICES annual meeting 2016 “25 Year of PICES: Celebrating the Past, Imagining the Future”, November 2–13, 2016. North Pacific Marine Science Organization, San Diego, pp 276–277 Mkrtchyan, F. A., & Krapivin, V. F. (2017). Application GIMS- technology for the monitoring coastal and marine ecosystems. In North Pacific Marine Science Organization (PICES) Annual Meeting 2017, Abstracts. September 21–30, 2017, Vladivostok, Russia, p. 135

References

293

Nagato Y, Tanaka HL (2012) Global warming trend without the contributions from decadal variability of the Arctic oscillation. Polar Science 6(1):15–22 Nakashima DJ, McLean GK, Thulstrup HD, Castillo R, Rubis JT (2012) Weathering uncertainty: Traditional knowledge for climate change assessment and adaptation. UNESCO/UNU, Paris/ Darwin Nihashi S, Ohshima KI, Tamura T, Fukamachi Y, Saitoh S (2009) Thickness and production of sea ice in the Okhotsk Sea coastal polynyas from AMSR-E. Journal of Geophysical Research 114: C10025 Nikanorov AM, Bryzgalo VA, Kosmenko LS, Reshetnyak OS (2011) The Kolyma River mouth area under present conditions of anthropogenic impact. Russian Meteorology and Hydrology 36 (8):549–558 Nitu C, Krapivin VF, Bruno A (2000) Intelligent techniques in ecology. Printech, Bucharest Nitu C, Krapivin VF, Dobrescu AS (2010) Application of global model to the study of Arctic Basin pollution. Scientific Bulletin of the Electrical Engineering Faculty 1(12):111–114 Ohshima K-I, Martin S (2004) Introduction to special section: Oceanography of the Okhotsk Sea. Journal of Geophysical Research 109(C09S01):1–3 Ohshima K-I, Nihashi S, Hashiya E, Watanabe T (2006) Interannual variability of sea ice area in the Sea of Okhotsk: Importance of surface heat flux in fall. Journal of the Meteorological Society of Japan 84(5):907–919 Ohshima, K. –I., Nakanowatari, T., Nakatsuka, T., Nishioka, J., & Wakatsuchi, M. (2009). Changes in the Sea of Okhotsk due to global warming – Weakening pump function to the North Pacific. PICES Scientific Report No. 36 Osabe T, Fukuda J, Hara T, Ohnishi F, Otsuka N, Saitoh S-I, Sugimoto A, Takahashi M, Tanaka M, Tanaka S, Yasunaga H (2018) Future scenarios for Arctic 2050. In: Proceedings of the 33rd international symposium on Okhotsk Sea & Polar Oceans 2018. 18–21 February 2018. Mombetsu, Hokkaido, Japan. Okhotsk Sea and Polar Oceans Research Association, Mombetsu, pp 117–118 Osipova NA, Stepanova KD, Matveenko IA (2015) Evaluation of metal content in perch of the Ob River basin. Earth and Environmental Science 27:1–5 Proshutinsky AY, Johnson M (2001) Two regimes of the Arctic’s circulation from ocean models with ice and comtaminants. Marine Pollution Bulletin 43(1–6):61–70 Sasaki Y, Asanuma I, Muneyama K, Naito G, Suzuki T (1987a) A simplified micriwave model and its application to the determination of some oceanic environmental parametric values, using multichannel microwave radiometric observations. IEEE Transactions on Geoscience and Remote Sensing GE-25(3):384–392 Sasaki Y, Asanuma I, Muneyama K, Naito G, Suzuki T (1987b) The dependence of sea-surface microwave emission on wind speed, frequency, incidence angle, and polarization over the frequency range from 1 to 40 GHz. IEEE Transactions on Geoscience and Remote Sensing GE-25(2):138–146 Sasaki Y, Asanuma I, Muneyama K, Naito G, Suzuki T (1988) Microwave emission and reflection from the wind-roughened sea surface at 6.7 and GHz. IEEE Transactions on Geoscience and Remote Sensing 26(6):860–868 Schlüter M, Savitsky AG, McKinney DC, Lieth H (2005) Optimizing long-term water allocation in the Amudarya river delta – A water management model for ecological impact assessment. Environmental Modelling & Software 20(5):529–545 Shirasawa K, Krapivin VF, Mkrtchyan FA, Kelley JJ (2017) Biocomplexity problem related to the Okhotsk Sea fisheries. In: Proceedings of the IX international symposium «engineering ecology – 2017». Moscow, 5 – 7 December, 2017. The Moscow Sciences Engineering A.S. Popov Society for Radio, Electronics and Communication, Moscow, pp 75–79 Sorokin YV (1977) The heterotrophic phase of phytoplankton succession in the Japan Sea. Marine Biology 41:107–117 Steehouwer H (2016) Ortec finance scenario approach. ORTeC Finance Scenario Department, Rotterdam

294

7 Space Methods and Monitoring Tools for the Investigation of Aquatic Systems

Steele M, Ermold W, Zhang J (2011) Modeling the formation and fate of the near surface temperature maximum in the Canadian Basin of the Arctic Ocean. Journal of Geophysical Research 116(C11015):1–13 Stohl A (2004) Intercontinental transport of air pollution. Springer, London Stone DP (2015) The changing Arctic environment: The Arctic messenger, vol 374. Cambridge University Press, Cambridge, pp 21–23 Takahashi H, Kasahara M, Kimata F, Miura S, Heki K, Seno T, Kato T, Vasilenko N, Ivaschenko A, Bahtiarov V, Levin V, Gordeev E, Korchagin F, Gerasimenko M (1999) Velocity field of around the Sea of Okhotsk and Sea of Japan regions determined from a new continuous GPS network data. Geophysical Research Letters 26(16):2533–2536 Tateyama K, Inoue J, Hoshino S, Sasaki S, Tanaka Y (2018) Development of a new algorithm to estimate Arctic sea-ice thickness based on Advanced Microwave Scanning Radiometer 2 data. In: Proceedings of the 33rd international symposium on Okhotsk Sea & Polar Oceans 2018. 18–21 February 2018. Mombetsu, Hokkaido, Japan. Okhotsk Sea and Polar Oceans Research Association, Mombetsu, pp 47–52 Varotsos CA, Krapivin VF (2017) A new big data approach based on geoecological informationmodeling system. Big Earth Data 1(1–2):47–63 Varotsos CA, Krapivin VF (2018) Pollution of Arctic waters has reached a critical point: An innovative approach to this problem. Water Air & Soil Pollution 229(11):343/1–343/14 Vinogradov ME, Gitelzon II, Sorokin JI (1970) The vertical structure of a pelagic community in the tropical ocean. Marine Biology 6(4):187–194 Wang D, Hemrichs SM, Guo L (2006) Distributions of nutrients, dissolved organic carbon and carbonhydrates in the western Arctic Ocean. Continental Shelf Research 26(14):1654–1667 Williams M, Eugster W, Rastetter EB, McFadden JP, Chapin FS III (2000) The controls on net ecosystem productivity along an Arctic transect: A model comparison with flux measurements. Global Change Biology 6(1):116–126 Zenkin OV, Leonov AV, Pishchalnik VM, Pokrashenko SA (2009) The use of satellite data to haracterize phytoplankton in Sea of Okhotsk water. Water Resources 36(4):466–477 Zhabin IA, Abrosimova AA, Dubina V, Nekrasov DA (2010) Influence of the Amur River runoff on the hydrological conditions of the Amur Liman and Sakhalin Bay (Sea of Okhotsk) during the spring-summer flood. Russian Meteorology and Hydrology 35(4):295–300 Zhang Y, Lu X, Wang N, Xin M, Geng S, Jia J, Meng G (2016) Heavy metals in aquatic organisms of different trophic levels and their potential human health risk in Bohai Bay, China. Environmental Science and Pollution Research International 23(17):17801–17810 Zweng MM, Boyer TP, Baranova OK, Reagan JR, Dan Seidov D, Smolyar IV (2018) An inventory of Arctic Ocean data in the World Ocean Database. Earth System Science Data 10:677–687

Chapter 8

Microwave Remote Sensing Monitoring and Global Climate Change Problems

8.1

Introduction

The problem of global environmental change is the subject of global ecoinformatics in the context of which information technologies have been developed to ensure the combined use of various data on the past and present state of the Climate-Nature System (CNSS). The creation of a CNSS model based on knowledge and available data, and combined with an adaptive evolutionary concept of geo-information monitoring, which allows for the interconnection of the CNSS model and the global data collection regime, can be considered an important step in global ecoinformatics. As a result, the structure of the CNSS can be optimized to achieve sustainable interaction between nature and human society and to create an international strategy for coordinated use of natural ecosystems. This chapter discusses the development of models of various processes taking place at CNSS as one of the important scientific directions of global ecoinformatics. A global CNSS simulation model was created to be used for the study of global ecodynamics. The CNSS model has a volatility structure that allows different implementation of its components to be considered. It enables the evaluation of the significance of each item for the precision of the parametric description of the CNSS dynamics. The simulation experiments in which the different principal structures of the CNSS model are studied enable us to synthesize the global environmental control system using standard telecommunications media and existing monitoring systems (Varotsos et al. 2018). Of course, the actual internal connections to the CNSS are subject to ecological, economic, social and political laws. That is why the problems of creating a global model that fits perfectly with the real world still cannot be solved even in today’s conditions. A thorough consideration of all the CNSS parameters leads to insurmountable multivariance and information with insurmountable problems. This chapter looks at these questions in all their aspects, showing that the role of biotic regulation in the CNSS model is underestimated. It is evident that the global climate © Springer Nature Switzerland AG 2020 C. A. Varotsos, V. F. Krapivin, Microwave Remote Sensing Tools in Environmental Science, https://doi.org/10.1007/978-3-030-45767-9_8

295

296

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

change problem needs to be addressed, coupled with the numerous direct correlation and feedback that can be achieved through GIMS technology (Krapivin et al. 2015; Varotsos et al. 2018). The World Summit on Sustainable Development “Rio + 10” in Johannesburg in 2002 determined the necessity of substantiation of priorities related to the interaction of society and nature. Other Climate Summits such as Paris – 2015, 2017, 2018; the Katowice Climate Change Conference – 2018 or the International Arctic Forum 2019 in St. Petersburg do not really provide a breakthrough in the overall understanding of the global environmental crisis and its future consequences. The biosphere is a complex unique system and from historical viewpoint, man is an element of it. However, at present, the problem of co-evolution between human society and nature has arisen. The influence of human activity on natural systems has reached a global scale, and it is now possible to make the conditional division between anthropogenic and natural processes. A typical description of this division can be obtained using the tool of system analysis. Usually, there are two interacting systems: human society with technologies, sciences, economics, sociology, agriculture, industry, etc. and nature with climatic, biogeocenotic, biogeochemical, hydrological, geophysical, and other natural processes. Parameterization and investigation of an interaction of these systems is the main objective of the current investigations. More precisely, the principal aim is to assess the biosphere sustainability limits using simulation model technology along with archived data sets of global observations, including those derived from satellites (Krapivin and Shutko 2012). Existing means of gathering information on natural and anthropogenic processes make it possible to create a data set that covers large domains up to the entire biosphere. Remote sensing of environmental monitoring is especially effective here. The purpose of this chapter is to formulate the methods of global database synthesis for the basic model of the nature-society system survivability and to propose a new viewpoint on the global modeling approach. There are a set of global models that describe the interactions within the naturesociety system. However, in these models the study of the nature-society system is limited by simple considerations of the main integrated properties of the system dynamics without grid resolutions in space. Varotsos et al. (2018) have introduced a new type of global modeling technology, based on an adaptive combination of models, algorithms and experiments. Different environmental and anthropogenic processes are properly parameterized using various approaches to synthesize a new global model that describes all aspects of human’s interactions with environmental bodies and their physical, biological and chemical systems. A global model of this type differs from existing models based on a detailed description of the climate system by examining a small set of biospheric components. Unfortunately, studies at the global and regional scale studies on the processes and impacts of global change using this approach have not yet yielded satisfactory results. That is why the global model to be developed in future projects allows the problem of sustainable development to be resolved by examining many socio-economic, ecological, and climate processes. Moreover, traditional approaches to building a global model encounter some difficulties of algorithmic description in relation to these processes, so one has

8.2 Interactive Character of the Global Climate Problems

297

to deal with the uncertainty of information. These approaches to global modeling simply ignore such uncertainty and, consequently, the structure of the resulting model does not adequately reflect actual processes. Future projects will use evolutionary modeling technology to eliminate this drawback by developing a combined model whose structure is adaptable to be background of a biosphere and climate system.

8.2 8.2.1

Interactive Character of the Global Climate Problems Anomalous Situations and Climate

Kosaka and Xie (2013) considering the hiatus in global warming mentioned that despite continued increases in atmospheric GHGs concentrations, annual-mean global temperature has not increased in the twenty-first century. Compared to seasonal norms, in September 2013 the coolest area on the planet was south of South Africa in the southern ocean, where temperatures in the troposphere being about 2.49
C cooler than normal. The warmest area was in the Wilkes Land area of the Eastern Antarctica, where tropospheric temperatures were 5.2
C warmer than seasonal norms. Climate change is manifested on a global as well as a regional scale. One of the important features of climate formation not only on regional but also on a global scale is the considerable variability determined by the internal dynamics of climate system. One of the most substantial factors of internal dynamics is the El Niño/ Southern Oscillation (ENSO) event. One of the recent climate warmings due to ENSO began in October – November 2002 and ended in March–April 2003. However, despite the end of ENSO during the boreal spring period, ENSO-induced warming resulted in regional anomalous rainfall in a wide range of the Pacific Ocean, including the formation of the zone of increased moisture content along the west coast of South America and the region of moisture deficiency in the eastern part of Australia as well as in the south-western sector of the Pacific Ocean. The global mean SAT in 2003 turned out to be close to three maximum values observed during the period since 1880, but below the record level of SAT in 1998. The increase in the global mean SAT in 2003 compared to the average value for 1961–1990 was 0.46
C. According to the data of satellite thermal sensing, the global mean temperature of the middle troposphere in 2003 was the third of the level of warming, compared to the average value for the 1979–1998 period. The annual global average surface temperature anomaly for 2011 was +0.07
C, with the 1981–2010 average as a baseline. 2012 was the ninth warmest year with global average temperature 14.45
C. This is 0.45  0.10
C above the 1961–1990 average. The trend of global average temperature is 0.68
C/Century. August 2013 was the 342nd consecutive month (more than 28 years) with global temperatures well above the twentieth-century average. These data show that the SAT changes with some dispersion relative to some climate trend.

298

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

In general, history regularly represents the temperature anomalies: • 1923/1924 – For a period of 160 days from 31 October 1923 to 7 April 1924, the Western Australian town of Marble Bar set a world record for the most consecutive days above 38
C. • On July 141,954, the thermometer reached 47
C at East St. Louis, Illinois, which remains the record highest temperature for that state. • 1980 – The Dallas/Fort Worth area experienced 42 consecutive days with high temperatures above 38
C, with temperatures reaching 47
C at Wichita Falls, Texas on June 28. • During the summer of 1983 temperatures over 38
C were common across Iowa, Missouri, Illinois, Michigan, Wisconsin, Indiana, Ohio, Minnesota, Nebraska and certain parts of Kentucky; the summer of 1983 remains one of the hottest summers ever recorded in many of the affected states. • 2001 – Newark, New Jersey tied its all-time record high temperature of 41
C with a heat index of over 50
C. • 2003 – In Portugal, temperatures reached as high as 47
C in the south. • The 2006 European heat wave was the second massive heat wave to hit the continent in 4 years, with temperatures rising to 40
C in Paris; temperatures above 32
C have been reported in Ireland, with moderate maritime climate. Temperatures of 35
C were reached in Benelux and Germany (in some areas 38
C, while Great Britain recorded 37
C. • 2006 – Temperatures in some parts of South Dakota exceeded 46
C. California also experienced temperatures that were extremely high, with records ranging from 40
C to 54.4
C. On July 22, the County of Los Angeles recorded its highest temperature ever at 48
C. • 2007 – Bulgaria experienced its hottest year on record, with previously unrecorded temperatures above 45
C. The Indian city of Datia experienced temperatures of 48
C. • 2008 – Alice Springs in Australia‘s Northern Territory recorded ten consecutive days of temperatures above 40
C while the average temperature for that month was 39.8
C. In March, Adelaide, South Australia experienced maximum temperatures of above 35
C for fifteen consecutive days, seven days more than the previous longest stretch of 35
C days. The March heat wave also included eleven consecutive days above 38
C. The heat wave was especially notable because it occurred in March, an autumn month in which Adelaide averages only 2.3 days above 35
C. • 2009 – Adelaide, South Australia was hit by a heat wave with temperatures reaching 40
C for six consecutive days, while many rural areas experienced temperatures ranging in mid 40s
C. Kyancutta on the Eyre Peninsula suffered at least one day at 48
C. In neighbouring Victoria recorded 3 consecutive days over 43
C, and also recorded its highest ever temperature 8 days later in a secondary heatwave, with mercury peaking at 46.4
C. • 2010 – On 26 May, at Mohenjo-daro, Sindh province in Pakistan, a national record of high temperature of 53.5
C was occurred.

8.2 Interactive Character of the Global Climate Problems

299

• In late July and early August 2011, temperatures in Iraq were exceeding 49
C (Baghdad – 52
C). • In May and June 2018 a heat wave affected Pakistan and a significant part of India. Temperatures have reached as high as 48
C and at least 65 people died due to the heat. • In mid-May 2018, the heat wave in Japan came after a major flood that caused over 22,000 hospitalizations and 80 deaths. • According to the European Drought Observatory, most of the areas affected by drought are located across northern and central Europe. Heat waves occurred during 2018 in Europe that practically affected all countries where atmospheric temperatures were significantly high average temperatures. The summer of 2003, in some regions of Western Europe, was one of the warmest summer seasons with heat waves affecting mainly Central and Western Europe. Two abnormal heat waves that occurred in June and July–August (especially the second wave) were especially powerful. Droughts accompanying them have caused forest fires, which covered a considerable part of the territory in the south of France and Portugal in July and August. The summer of 2003 in Western Europe was apparently the hottest during the post-1540 period. The heat wave in France killed 11,000 people. In Germany that summer was the hottest in the twentieth century and (except some areas of north and north-west Germany) the hottest over the whole period of instrumental observations. The most substantial abnormalities that occurred in March 2003 included: 1. extremely intensive precipitation in the mid – Atlantic Ocean, on the southeastern and eastern coasts of the USA; 2. extremely low SAT values and unusual snowfall in the European territory of Russia; 3. 546 tornados in May in the USA, which were unprecedented; 4. a long-term drought in the western USA, where in some regions it was the fourth and fifth year of significant rainfall deficiency; 5. large brush fires in eastern Australia in January and strong forest fires in southern California in October; 6. anomalously intensive precipitation in Western Africa and the Sahel; 7. return to the normal level of precipitation in the Indian sub-continent during the summer monsoon; and 8. close-to-record extent of the “ozone hole” in Antarctica reaching a maximum of 28.2 million km2 in September 2003. The globally averaged temperature for 2012 (14.6
C) marked the tenth warmest year since archiving began in 1880. It also marked the 36th consecutive year with a global temperature above the twentieth century average. The last below-average annual temperature was 1976. Including 2012, all 12 years to date in the twenty-first century (2001–2012) are among the 14 warmest in the 133-year period of record. Only one year (1998) during the twentieth century was warmer than 2012.

300

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Recent years have been marked by increased interest in studying the current climate change in the high latitudes of the Northern and Southern Hemispheres, which is mostly determined by the decision to conduct in 2007–2008 the Third International Polar Year. The major conclusions concerning the Arctic climate diagnostics focused on the analysis of the spatio-temporal variability of the polar climate rather than exaggerated attention to the unfounded simplification of the situation as manifested through a homogeneous anthropogenic enhancement of climate warming in high latitudes. In this context, it is of great interest the new results of the paleoclimatic analysis of an ice core from the “Vostok” station (Krapivin et al. 2019), which demonstrated a negative correlation between changes in CO2 concentration in the atmosphere and air temperature. Paleoclimatic developments are becoming an increasingly realistic way of studying the laws of present climate dynamics (Widmann et al. 2004). Antarctic data discussed in the report (Levinson and Waple 2004), show that the last decade in this region has been anomalously cold. From the late 1970s to the mid-winter of 1990, the sea ice cover extent around the Antarctic continent increased. The monograph of Filatov (2004) dedicated to studies of climate of Karelia can be an example of informational analysis of regional features of climate. New developments in urban climate (Mayers 2004) and the analysis of individual long series of meteorological observations (Alessio et al. 2004; Garcia-Barrón and Pita 2004) have contributed significantly to studies of regional climate change. A new important step in understanding the data of empirical climate diagnostics was the development and application of interactive models of climate system and an ensemble approach to numerical climate modeling. Meteorological events in the world over the last decade have been characterized by extreme weather conditions with significant economic losses and deaths, indicating a fundamental conclusion to the impacts of global climate change. The statistics of recent extreme events characterizes existing climatic trend as a function of anthropogenic and natural factors. This statistics shows an exceptionally large number of record-breaking and destructive heatwaves in many parts of the world (Coumou and Rahmstorf 2012). Table 8.1 shows examples of very high temperatures in the world. These data show no regularity with any trend. There have been years of both rising and decreasing average global temperature. There is an indication that 2019 was the third consecutive year of slight cooling due to the El Niño in 2018/2019. There are many climate models that differ with spatial resolution, input information, conditions of use, and algorithms. In the Java Climate Model change climate, for example, climate change is affected by complex interlinked processes. This interactive model allows you to explore the system and how we can change it, simply by adjusting with your mouse parameters and directly tracking the effect on a variety of plots ranging from socioeconomic drivers to climate impacts. The basic methods of calculations are calibrated to be consistent with the results from the Intergovernmental Panel on Climate Change, which are effectively implemented in the Java language, allowing anyone to access this tool over the internet and explore a

Country/Region Africa Algeria Botswana Tunisia Ghana Western Sahara Asia Hong Kong Iran Japan Kuwait Pakistan North America Canada Cuba Mexico United States Greenland Antarctica All land/ice south of 60
S Antarctica plateau > 2500 m

Data 2 September 1979/5 July 2018 7 January 2016 7 July 1931 26 March 2017 13 July 1961 22 August 2017 29 June 2017 23 July 2018 21 July 2016 26 May 2010 5 July 1937 17 April 1999 28 July 1995 10 July 1913 23 June 1915 30 January 1982 28 December 1989

Temperature

51.3
C 44.0
C 55
C 43.8
C 50.7
C

39.0
C 54
C 41.1
C 54.0
C 53.5
C

45.0
C 38.8
C 52.0
C 56.7
C 30.1
C

19.8
C 7.0
C

Table 8.1 Examples of destructive heat waves in the world

Mainland and adjoining islands South Pole

Country/Region Oceania Wallis and Futuna Australia Marshall Islands Solomon Islands New Zealand Europe Austria Bulgari Cyprus Germany Italy South America Argentina Bolivia Brazil Uruguay Colombia 17.5
C 12.3
C

48.9
C 46.7
C 44.7
C 44.0
C 45.0
C

40.5
C 45.2
C 45.6
C 40.3
C 47.0
C

35.8
C 50.7
C 35.6
C 36.1
C 42.4
C

Temperature

24 March 2015 25 December 2011

11 December 1905 29 October 2010 21 November 2005 20 January 1943 29 December 2015

8 August 2013 5 August 1916 1 August 2010 5 July 2015/7 August 2015 25 June 2007

10 January 2016 January 1960 24 August 2016 1 February 2010 7 February 1973

Data

8.2 Interactive Character of the Global Climate Problems 301

302

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

variety of scenarios and the sensitivity of projections to risk/value assessment and scientific uncertainties. As has been repeatedly emphasized, the interactive components of the present climate system include a broad spectrum of natural and natural-anthropogenic sub-systems and processes, without a complex study of which it is impossible to reliably identify the prevailing trends in climate change. In this context, the most important ones should be listed: • • • • • • • • •

Global water cycle. “Cloud” feedbacks effect. Global carbon cycle. Interaction of water and carbon cycles. Land use and land surface change. Present trends of the GHGs content in the atmosphere and mechanisms of their control. Interaction of climate and land ecosystems’ productivity. Effect of the climate regime shifts on marine ecosystems. Control of natural resources to neutralize the negative consequences of human activity. Socio-economic aspects of ecodynamics and climate and their analysis for optimization of the land use strategy. Interactions between processes in the geosphere and biosphere and their dependence on cosmic impacts.

8.2.2

The Global Carbon Cycle and Its Climatic Implications

Given the analysis of global climate warming and its related exaggerations (Kondratyev et al. 2002b), we will only consider the relevant conceptual circumstances. Increasing the temperature of the climate reduces the solubility of CO2 and therefore weakens the ocean’s absorption of CO2: • Global warming can be followed by an intensified vertical stratification of the ocean, the likely consequences of which are: a reduction in CO2 emissions due to the upwelling and transport of excess carbon to the deeper layers of the ocean as well as changes in bioproductivity. • In the case of shorter time scales, the warming results in increasing rate in heterotrophic respiration on land, but the extent of the impact of this process on long-term CO2 exchange processes remains unclear. Both warming and regional changes in spatial distribution of precipitation and clouds can lead to changes in the structure of the land ecosystems, their geographical distribution and primary production. The overall impact of such forcings should depend on the special features of the regional structures of climate change. The failure of the International COP-6 Conference (Nilsson et al. 2002) held in the Hague in November 2000, at which state representatives signed the U.N. Framework Convention on Climate Change (FCCC), cannot be considered

8.2 Interactive Character of the Global Climate Problems

303

as unexpected since the participants of COP-6 have not been adequately prepared in terms of the strategy lacking the needed scientific substantiation. It turned out that the most important problem of uncertain assessments of reservoirs and GHGs fluxes was almost neglected. After COP-6 the problem has become more acute because of President Bush’s refusal to support the Kyoto Protocol (KP). On the other hand, the Third IPCC Report published in 2001 (Cihlar et al. 2002; IPCC 2001) concludes that the information available enables one to convincingly talk about the GHGs contribution to the formation of climate change and predict climate change more reliably. Some international documents containing analyses of current ideas of climate refer to a consensus with respect to scientific conclusions contained in these documents. This wrongly assumes that the evolution of science is not determined by different views and relevant discussions, but by general agreement and even voting. Apart from the issue of definitions, the issue of uncertain conceptual estimates concerning various aspects of the climate problem remains important. In particular, this refers to the main conclusion of the summary of the IPCC reports (IPCC 2001, 2005, 2007) 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 fifty years is likely to have been due to human activities”. In this context, assessments of the uncertainties of the estimates that serve as a basis for conclusions on climate changes and the measures necessary to prevent them are of key importance (Kerr 2002; Mejer 2001; Newell and Pizer 2002; Reilly et al. 2001). Of special importance is the problem of evaluation of the level of GHGs emissions into the atmosphere connected, above all, with the solution of the global carbon cycle problem. Clearly, without a reliable verification of available estimates of emissions, all discussions with respect to ecological profits of various measures and related costs are of an abstract character (Nilsson et al. 2002). How, for instance, fines can be imposed for failing to comply with KP’s recommendations for reducing GHG emissions if it is impossible to prove that emissions in 2012 will be different from those in 1990? So far, discussions on KP have ignored, in particular, quantitative estimates of the uncertainties of levels of GHGs sinks (this specifically concerns the biosphere). However, the uncertainties in the assessments of CO2 fluxes are rather substantial (exceeding 100% in the conditions of Russia). The estimates of errors in estimating total GHGs fluxes vary to ~5–25%, while the KP levels of GHGs emissions reductions are on average about 5%. An average global situation can be illustrated, for example, by the fact that the uncertainties in the evaluation of GHGs emissions due to energy production systems are approximately equal to those in the estimates of CO2 assimilation by the biosphere and the land (Monnin et al. 2001; Monahan and Dam 2001). In this situation, solving the problems of uncertain estimates (and above all reliable carbon cycle information) and verification are vital. A solution to the verification problem requires an agreement on its mechanisms, which is also very important financially. The results of the simulation modeling show, for instance, that if the 5.2% confidence level for GHGs reduction is raised from 50% to 95%, this entails an increase in the costs on measures to reduce GHGs emissions by a factor of

304

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

3–4. The principal conclusion is that science should recommend measures in the field of ecological policy. In this context the COP-6 failure can be a healing shock (Nilsson et al. 2002). A serious uncertainty of the estimates of possible anthropogenic climate changes and their determining factors has led to the scientific literature and the mass media a hot discussion on these problems (Baliunas 2002; Ellsaesser 2002; Hartmann 2002; Pielke 2002; Pittock 2002; Schönwiese 2002; Wofsy 2001; Wuebbles 2002). So, for example, in an interview with a journalist from “Scientific American” R. Lindzen (Professor of the Massachusetts Polytechnical Institute, USA) stated that he started worrying about speculative discussions on climate change in the summer of 1988, when J. Hansen (Director of the Goddard Institute for Space Studies, New York) said that the global climate warming resulted from growing CO2 concentration due to emissions to the atmosphere of products of the fossil fuel burning. This statement made R. Lindzen clarify that climate modeling is at its initial stage of development and, in particular, there is no consensus on the causes of climate change. At the beginning of 2001 he reported on the problem of climate change at a US government meeting. The fact that the global SAT has increased by about 0.5
C and atmospheric CO2 concentration by about 30% during the last 100 years is far from reflecting any cause-and-effect relation between increasing CO2 concentrations and growing temperatures. R. Lindzen believes that the most reliable estimate of climate sensitivity (SAT increase with a doubling CO2 concentration) is 0.4
C, meaning that there is no basis for worrying about catastrophic changes in the future global climate. According to the IPCC mandate, its task is to prepare an overview of “any climate changes in time, both natural and anthropogenic”. Pielke (2001, 2002) observed, however, that at least two climate-forming factors turned out to be either unreliable or not considered at all: 1. impact on global climate of anthropogenically induced changes in the characteristics of land surface; 2. biospheric impacts of the growth of CO2 concentration in the atmosphere (including the “fertilization effects”). If it is justified that both of these factors exist, the conclusion suggests itself that an agreement of the results of the global climate numerical modeling (in case of mean-global mean-annual SAT) with observations is random. In this connection, Pielke (2002) discussed information that confirms the importance of these two climate-forming factors, and talked about the possibility to check the validity of such a conclusion. For this purpose the data can be used to reflect the impact of anthropogenic changes on land surface characteristics at local, regional, and global climate levels. These changes illustrate the fact that this impact is just as important as the doubling of CO2 concentration in the atmosphere (as well as the increase in other GHGs concentrations). No less important is that the atmospheresurface interaction is characterized by various non-linear feedbacks and therefore it may be impossible to predict climate change for longer than a season. Concerning the potential biological impacts of increasing CO2 concentration, they are manifested through short-term (biophysical), medium-term

8.2 Interactive Character of the Global Climate Problems

305

(biogeochemical), and long-term (biogeographical) impacts of landscape-forming processes on weather and climate. Biophysical impact includes, for instance, the effect of transpiration on the relationship of latent and sensible heat fluxes as surface heat flux components. The biogeochemical impact incorporates the effect of vegetation growth (“fertilization effect”) on evaporating leaf area, on surface albedo, and carbon storage. One of the manifestations of biogeographical impacts is a temporal change in the species composition of vegetation communities. The results of numerical modeling testify to the fact that without taking into account the biophysical/ biogeochemical impacts, assessments of climate change cannot be considered reliable (Bounoua et al. 2002). Further development of climate models should consider, in particular, the following aspects of climate formation: 1. direct and indirect impacts of landscape dynamics through biophysical, biogeochemical and biogeographical processes; 2. consideration of anthropogenic changes in land use at various (local, regional, and global) spatio-temporal scales (Houghton 1999; Houghton et al. 2000; Jepma et al. 2001); 3. assessments of possibilities of climate prediction for a longer term than a season bearing in mind the operation of numerous non-linear feedbacks that determine the atmosphere-surface interaction (Hughes et al. 1999; Joos et al. 1999). As these and other problems have not been resolved, the significance, of the IPCC-2001 Report and US National Report as containing only assessments of global climate sensitivity to changes in some climate-forming factors, is becoming rather limited. During the time-consuming preparations of the three published (2001, 2005, 2007) IPCC Reports, the analysis of climate-forming factors and various feedbacks has deepened with each report (for instance, except GHGs, atmospheric aerosols have also been considered). However, the available complex numerical climate models still cannot be considered adequate from the viewpoint of taking into account every important climate-forming factor. A step forward in the IPCC (2001) was an account of forcings (F) on climate of anthropogenic changes in land use, but limited only to the impact of land use dynamics since 1750, and on the surface albedo. The resulting estimates gave an average F of 0.2 W/m2 with an uncertainty interval of 0  0.4 W/m2. Thus, these estimates are rather uncertain, let alone the sign of F (a more complete consideration of biophysical, biogeochemical, and biogeographical impacts of the evolution of nature’s use of climate suggest that in this case F > 0). In this connection of great importance is the fact that the IPCC-2001 Report considers a new range of possible global warming by 2100 (1.4–5.8
C) based only on numerical modeling data (and therefore will inevitably change in the future). In addition, the problem is that the new range can be directly compared with similar estimates obtained earlier. From the viewpoint of reliability of estimates of future climate changes of great importance is the use in the IPCC-2001 of the term “projections” instead of “predictions”, since the latter means that the factors left out of

306

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

account will not be of importance in the future. It is unacceptable that the latter assumption prevents experts in numerical modeling from stating that climate prediction for 100 years ahead is possible. This conclusion confirms the results of numerical modeling accomplished by Andronova and Schlesinger (2001). The complicated problem of prognostic assessments of climate, and especially selecting the contribution of an anthropogenic component, is illustrated by remaining controversy of analysis of the climatic impact of clouds. Tsushima and Manabe (2001) have analyzed the effect of cloud feedback (CF) on the formation of the annual change of mean global SAT with the use of the ERB satellite measurements data, bearing in mind an assessment of an adequate consideration of CF in numerical climate models by comparing the calculated and observed variations of mean global SAT. It follows from observations that the mean global annual change of SAT is in phase with the annual change of SAT in the Northern Hemisphere, and its amplitude is 3.3 K (this phase-coherence is determined by concentration in the Northern Hemisphere of the continents contributing much to the formation of the amplitude of the SAT annual course). The analysis of the observational data on the ERB components for the period from February 1985 to February 1990 has shown that the mean global estimates of both shortwave and longwave radiative forcings (SWRF and LWRF) depend weakly on the annual course of mean global SAT (the ERB data considered refers only to the range 60oN – 60oS). Thus, the cloud cover dynamics neither intensifies nor reduces the SAT annual change. The considered data on SWRF and optical properties of clouds shows that not only albedo but also cloud amount and cloud top height depend weakly on SAT and, hence, CF does not affect substantially the annual course of mean global SAT. Based on this conclusion, we can say that CF affects negligibly the annual course of mean global SAT. However, this speculative opinion is dangerous due to rather complicated spatial field of SAT. Calculations using three interactive climate models considering the dynamics of cloud microphysical properties, suggested that the cloud top height albedo grows substantially with increasing SAT, which is inconsistent with observation data. This situation reflects prospects for comparing the assessments of the role of CF from the data of numerical modeling and observations from the viewpoint of models validation. Since J. Hansen was one of the principal initiators of apocalyptic prediction of global warming, it is of interest to mark an evolution of his opinion in his recent work (Hansen and Sato 2001), where it was stated that an increase of mean annual mean global SAT from the last 100 years by more than 0.5
C has been, at least partially, due to anthropogenic forcing on climate. The authors calculated the radiative forcing (RF) due to observed growth of GHG concentration. Estimates testify to a considerable level of RF due to methane, which contributed to approximately half of RF due to carbon dioxide: 1.4  0.2 W/m2 including 0.5 W/m2 due to direct and 0.28 W/m2 due to indirect forcing. These results show the need for special attention to CH4 as GHG and to a separate RF account due to methane. New estimates of RF due to tropospheric ozone gave 0.7 W/m2 instead of 0.3–0.4 W/m2,

8.2 Interactive Character of the Global Climate Problems

307

as assumed in the IPCC-2001 Report. Obviously, a range of 0.4–0.8 W/m2 can be assumed for this RF. High uncertainty (due to uncertain input data) calculations of RF due to “black” carbon (soot) gave 0.8  0.4 W/m2. Very important is the RF due to reflecting aerosol of 1.4  0.5 W/m2 with an additional contribution by soil aerosol equal to 0.1  0.2 W/m2. Considerable contributions to total aerosol RF are made by organic aerosol (0.3 W/m2 with an accuracy of a factor of 2) and ammonium sulfate (0.2 W/m2). The sum of all positive components reaches 4.3  0.6 W/m2, that is, it exceeds three times the RF due to CO2, whereas the sum of the negative components is 2.7  0.9 W/m2, and, thus, the resulting RF is 1.6  1.1 W/m2, that is, close to that due to only CO2. Hansen and Sato (2001) performed an analysis of the trends of the various GHGs, which showed that the rates of increase in CO2 concentration grew rapidly during the period from the end of World War II to the mid-1970s, reaching a maximum of about 4% per year. It then declined to a comparatively stable level of concentration growth 1.5 ppm/year. During the same period the rate of CH4 concentration increase grew rapidly, too (from 5 to 15 ppb/year), slowing down markedly after 1980 (the reasons remaining unknown). The total trend of the rate of RF growth due to 13 chlorofluorocarbons (CFC) was characterized by a rapid increase till mid-1980s and subsequent decrease due to reduced CFC emissions in the process of realization of measures stipulated by the Montreal Protocol. Apparently, before 2010 the RF due to CFC should start decreasing. The rate of total RF growth (for a century) reached a maximum of about 5 W/m2. At this rate of growth the level of equivalent RF corresponding to a doubled CO2 concentration should be reached by 2050. However, during the last two decades the rate of the growth of total RF decreased to 3 W/ m2. With an account of possible reduction of GHGs emissions, the global warming during the forthcoming several decades should be +0.15  0.05
C per decade, provided that the level of CO2 emissions will be “frozen”. The remaining considerable uncertainty of the estimates considered is connected, first of all, with the difficulty of the aerosol-induced RF calculations. The G-8 Group of governments decided to organize the World Climate Change Conference, to discuss the changes of climate and the possibility that they are caused by humans. The Conference was held in Moscow between September 29 and October 3, 2003 and was attended by over 2000 participants from more than 100 countries, including scientists, the representatives of governments, the private sector, and non-government organizations. The official goal of the Conference was a “discussion of the natural and anthropogenic factors driving the climate; approaches to reducing anthropogenic emissions; impacts and adaptation measures to on-going climate changes; and hence, to achieve a maximum mutual understanding between scientists, governments, business circles and the public”. It is interesting that before the Conference there was a rather pronounced interest among the media and numerous environmental organizations, but not at its end, when the final conclusions were announced. This was perhaps because this understanding, and expected unanimous support of the Kyoto Protocol have not been achieved. Even the basic questions posed by the Chairman of

308

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

the organizing committee professor Yuri Izrael: “What is really going on this planet – warming or cooling”, and “whether ratifying of the Kyoto Protocol would improve the climate, stabilize it or make it worse”, were not answered. It became also clear that without ratification by Russia the Kyoto Protocol will fall. The climate was changing always since the earth was formed. It is changing now, and will be changing in the future. The alternating warm and cold climatic cycles range from tens to many thousands, and even millions of years, and depend on many interactive climate-forming mechanisms. Since the formation of the atmospheric oxygen hundreds of million years ago the changes in chemical composition of the atmosphere had rather minor influence on climate, with water being its dominant component responsible for most of the “greenhouse effect”. There were periods in the past when concentrations of carbon dioxide, a trace “greenhouse gas” (which is not a pollutant, but a gas of life building all living organisms) were about 10–20 times higher than now, and no catastrophic “run-away” greenhouse effect occurred on the Earth, and glaciers were covering parts of continents and islands. As stated at the Conference by Andrei Illarionov, the chief economic adviser of the Russia’s President, “according to scientific data, in the past 400,000 years a dramatic rise of temperature on Earth occurred every 100,000 years, and this was not in the least linked with man’s activity. In the past millennium considerable changes of temperature were observed also in the 11th, 14th, and 17th centuries”. In the eleventh century the air temperature around the North Atlantic Ocean, in Europe, Asia, South America, Australia, and Antarctica, was about 1.5
C warmer than now. Still earlier, for a long time between 3500 and 6000 years ago, the period of the “Holocene Warming” enjoyed temperature about 2
C higher than now. Illarionov raised ten important questions shattering the shaky edifice of man-made global warming hypothesis. His litany was followed by presentations of numerous Russian and foreign critics of this hypothesis. They did not receive satisfying answers from its proponents. If there is nothing unusual in the current climate changes, why is this enormous attention being paid to climate problems in scientific literature, mass media, and public opinion? Why are such great resources and the future of our civilization itself at hand? The answer to this question is not at all simple. Besides science, it involves politics, business, and industry, a lot of misanthropic ideology, enormous money, and group interests (Indermühle et al. 1999). The World Climate Change Conference was opened by the President of Russia Vladimir Putin who stated that “Even 100 percent compliance with the Kyoto Protocol won’t reverse climate change”. In response to those calling for a quick ratification of the Kyoto Protocol, Putin mentioned in half jokingly: “They often say, either as a joke or seriously, that Russia is a northern country and if temperature gets warmer by 2 or 3 degrees Celsius, it’s not such a bad thing. We could spend less on warm coats, and agricultural experts say grain harvests would increase further”. The President of Russia also said that Moscow would “be reluctant to make decisions on just financial considerations. Our first concern should be the lofty idea and goals we set ourselves and not short-term economic benefits”. “The government is thoroughly considering and studying this issue, studying the entire complex of difficult

8.2 Interactive Character of the Global Climate Problems

309

problems linked with it. The decision will be made after this work has been completed. And, of course, it will take into account the national interests of the Russian Federation”. U.S. President G. Bush rejected the Kyoto Protocol in March 2001, as “fatally flawed”, because: (1) this document does not have an adequate scientific substantiation; (2) since the use of fossil fuels prevails in the energy production, following the Protocol would result in serious negative economic consequences, without any real environmental improvement (decreasing the temperature by 0.2
C in 2100, or switching the increase from the year 2100 to 2106). What President Putin will decide is still unknown, but from what he said at the Moscow Conference, it seems that he is thinking along the same line as the American President, and that probably he will succumb neither to short-term seemingly lucrative proposal of selling spare Russian CO2 emission quotas for about $8 billion per year, nor to the sable rattling during the Conference by the UE Environmental Commissioner Margot Wallström, who warn Russia that it “would lose politically and economically by not ratifying the Kyoto Protocol”. The decision-makers must recognize that limiting CO2 emissions will cause a reduction of the world domestic product, which added up across the whole century represents 1800 trillion dollars. In Eastern Europe and Russia this reduction would reach by 2050, 3–3.5%, and certainly would bring a dramatic rise of joblessness. Andrei Illarionov warned that “The Kyoto Protocol will stymie economic growth. It will doom Russia to poverty, weakness and backwardness”. His words echoed the statement in 1998 by the great British astronomer Sir Fred Hoyle that implementing restrictions in CO2 emissions would be “ruining the world’s industries and so bringing on a situation. . . returning us all to the Dark Ages”. It is clear that the only people who would be affected by abandonment of the Kyoto Protocol would be several thousand people who made a living attending in attractive places conferences on global warming. The most important problems concerning the climate change myths include the following: 1. the observational data do not confirm the presence of uniform “global warming” (this is especially true for the surface temperature in rural regions not influenced by the so-called urban “heat islands” effect, and in the American, Canadian, Russian, Norwegian, and Danish Arctic, the satellite remote sensing results, and balloon measurements); 2. the increase of the atmospheric greenhouse assumed for the supposed doubling of the CO2 concentration in the atmosphere, is about 4 W/m2. But the uncertainties due to the unreliable accounting for the effects of atmospheric aerosols, clouds, and numerous other factors, reach several tens or more than hundred W/m2; 3. the results of numerical climate models that substantiate the “greenhouse global warming” hypothesis, are nothing else but mathematically expressed opinions of their creators on how the climate works; 4. recommendations concerning levels of reduction of emission of GHGs are senseless from the viewpoint of their impact on climate change – they would be utterly inefficient.

310

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

The persistence of concentration in the “greenhouse gases”, as a supposed dominant factor among many other powerful climatic phenomena is a false direction and not only compromises science but may also hamper the socio-economic progress of the developing and industrially developed countries. Presenting these views by numerous scientists at the World Climatic Change Conference, and also a realistic approach to the problem by the Russian government, discussions at the Moscow Conference were so important.

8.2.3

Sources and Sinks of Carbon Dioxide in the Biosphere

Carbon dioxide circulates in the environment among its reservoirs listed in Table 8.2. The carbon fluxes in the form of CO2 can be roughly described by the schemes shown in Figs. 8.1 and 8.2. In general, various carbon compounds form, change, decompose, and among all this diversity, natural and anthropogenic CO2 fluxes are formed in the processes of respiration and decomposition of vegetation and humus, in the burning of carbon-containing substances, in rock weathering, etc. Part of CO2 dissolves in the World Ocean giving carbonic acid and products of its dissociation. Carbon content in its reservoirs and estimates of its fluxes among them are the most important problem of analyzing the global CO2 cycle. Numerous schemes of this cycle drawn from the analysis of global interactions of living organisms and their physical and chemical media, as well as estimates of carbon supplies accumulated over the historical period, serve as a basis for predicting CO2 concentration dynamics in the Earth’s atmosphere, which has been hotly debated over the assessments of the role of CO2 in climate warming (Houghton and Yihmi 2001). An important stage in understanding the processes of the CO2 exchange between biospheric reservoirs is a study of the laws of the development of various ecosystems in pre-industrial epochs, without any anthropogenic factors (Katz 2002). The natural Table 8.2 Data on carbon supplies in the surface vegetation and in the 1-m soil layer (Bolin and Sukumar 2000) Biome Tropical forests Temperate-zone forests Boreal forests Tropical savannahs Temperate-zone meadows Deserts and semi-deserts Tundra Heavily moistened soils Plough soils

Area (106 km2) 17.6 10.4 13.7 22.5 12.5 45.5 9.5 3.5 16.0

Carbon supplies (Gt C) Vegetation Soil 212 216 59 100 88 471 66 264 9 295 8 191 6 121 15 225 3 128

Total 428 159 559 330 304 199 127 240 131

8.2 Interactive Character of the Global Climate Problems

311

Fig. 8.1 The conceptual scheme of the carbon cycle in the environment. (Kondratyev et al. 2003b)

carbon fluxes between the atmosphere, oceans, surface ecosystems, and the inland water bodies are strongly variable in space and time (from year to year and seasonally). Ice core analyses from Greenland and Antarctica have shown reliable variations of atmospheric CO2 in the past. Eight thousand years ago, the CO2 concentration in the atmosphere was 200 ppmv. To be the beginning of the pre-industrial era, this estimate ranged between 275 ppmv and 285 ppmv (10 ppmv). By the year 1985 the concentration of CO2 in the atmosphere had reached ~345 ppmv. But in 1998 was already 366–367 ppmv (Bolin and Sukumar 2000). The total amount of carbon in the atmospheric CO2 is estimated at about 700109 t C. The natural CO2 budget is estimated at ~150109t C emitted annually by respiration and decomposition processes and is assimilated in both land and ocean photosynthesis, as well as in the CO2 dissolve in the World Ocean. Particular emphasis has been placed on the circulation of organic and inorganic carbon in the water domain of the World Ocean, whose mechanisms are closely

312

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Fig. 8.2 Carbon fluxes in the atmosphere-plant-soil system. (Krapivin and Varotsos 2008)

connected with the CO2 partial pressure dynamics in the atmosphere. In some cases, an increase in the atmospheric share of CO2 is followed by an increase in the partial pressure of carbon dioxide on the surface layer of the ocean at about the same rate. In this case a mechanism of interaction of the carbonic acid elements 2 (HCO 3 , CaCO3 , CO3 ) is initiated, which, depending on the relationship of its characteristics, limits or, on the contrary, stimulates the process of either assimilation or emission of CO2 from the ocean. The formation of the carbon flux at the atmosphere-ocean boundary also depends on phytoplankton production, the relationship between organic and inorganic carbon shares, temperature, hydrodynamic and other parameters of the water domain. At a certain level, this flux also depends on the processes of vertical carbon transport with decreasing dead organisms, when they become bottom sediments whose contribution to so-called biological pumping depends on the depth. In this section an attempt has been made to describe these mechanisms at a formal level. Of course, the search for critical factors affecting the difference in partial pressures of atmospheric and oceanic reservoirs of CO2 requires detailed observational studies of the ecological and geophysical processes, operating in the ocean system. In particular, the role of marine organisms in the process of transformation of calcium carbonate and its accumulation, that is, in changing the domain acidity, has been poorly studied. All these processes have different directions and power in the open oceans, estuaries, shallow waters, coastal zones and river deltas. In order to analyze the CO2 dynamics in the biosphere, it is important to take into account the maximum number of reservoirs and fluxes as well as their spatial distribution. It is here that numerous global models of the carbon cycle differ. The current level of these studies does not allow one to answer the principal question

8.3 Anthropogenic Sources of Carbon

313

Table 8.3 CO2 budget on the Earth and its exchange in the biosphere Reservoir Atmosphere Hydrosphere Geosphere Inorganic carbonates Inorganic Noncarbonates Coal, gas, oil Substratum, detritus In the ocean On land Forests

Carbon content as CO2 (109 t C) 697 35,420 18.3106

6.8106 7400

3220 710 10

Component of process in the biosphere Cultivated soils Grass ecosystems Deserts Photosynthesis north of 45


Exchange rate (109 t C/year) 4 1 0.2 15

Human breathing Domestic animals’ breathing Wild animals’ breathing Soil respiration

0.14 0.5 0.4 8.4

Biosphere In the ocean On land

10 124

about the extent of details of the database of carbon supplies and fluxes. Therefore, many authors analyzing the dynamic characteristics of the global CO2 cycle use rather arbitrarily the fragments of databases for the distribution of the carbon sinks and sources (Fung 2000). These fragments are illustrated in Tables 8.3, 8.4, 8.5, 8.6, 8.7 and 8.8.

8.3

Anthropogenic Sources of Carbon

A key component of the global CO2 cycle is its anthropogenic emissions into the environment. The principal problem studied by most of the investigators consists in the assessment of the ability of the biosphere to neutralize an excessive amount of CO2. It is here that all predictions of the consequences of the greenhouse effect are wide open to criticism. All the models of the global CO2 cycle are based on scenarios that describe the dynamics of the extraction and burning of fossil fuels (Rosa and Ribeiro 2001). Here a natural need appears, however, of the models of the energyeconomy system which require a detailed parameterization of the geopolitical structure of the world. So far, among the most widely used models of this type is a model developed by IIASA, in which the globe is divided into nine regions differing in the level of energy consumption per-capita and other parameters. The regional structure is shown in Table 8.3. With this scenario of the socioeconomic structure one can attribute the development strategies of each region to it and assume possible consequences for the environment of future behaviour of the individual regions. Most of the similar scenarios use such indicator as the ratio of acceleration of energy consumption. This parameter varies from 0.2% to 1.5% per year. Various

314

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.4 Estimates of some characteristics of land and ocean ecosystems assuming that the content of carbon in a dry substance constitutes 45% Ecosystem Tropical humid forests Tropical seasonal forests Temperate-zone evergreen forests Temperate-zone deciduous forests Boreal forests Woodland and shrubs Savanna Temperate-zone grass systems Tundra and alpine meadows Desolate dense-shrubs Stones, ice, and sand Cultivated lands Marshes and boggy territories Lakes and rivers All land Open Ocean Upwelling zones Continental shelf Algae and reefs Estuaries The whole ocean The whole biosphere

σ 17.0 7.4 5.0 7.0 12.0 8.0 15.0 9.0 8.0 18.0 24.0 14.0 2.0 2.5 149 332 0.4 26.6 0.6 1.4 361 510

MPP 900 675 585 540 360 270 315 225 65 32 1.5 290 1125 225 324 57 225 162 900 810 69 144

TPP 15.3 5.1 2.9 3.8 4.3 2.2 4.7 2.0 0.5 0.6 0.04 4.1 2.2 0.6 48.3 18.9 0.1 4.3 0.5 1.1 24.9 73.2

MPB 20 16 16 13.5 9.0 2.7 1.8 0.7 0.3 0.3 0.01 0.5 6.8 0.01 5.55 0.0014 0.01 0.005 0.9 0.45 0.0049 1.63

Notation: σ area of the ecosystem (106 km2), MPP mean pure primary production (g C/m2/year), TPP total pure primary production (109 t C/year), MPB mean plants’ biomass (kg C/m2)

combinations are considered when choosing a source of energy among oil, gas, nuclear and solar energy, hydroelectric power stations, and solid wastes burning. Of course, demographic, technical, political, and macroeconomic factors must be taken into account. The size of population in most of the scenarios is projected to increase at a rate that will allow the levels, in 2025 and 2075 to reach 7.9 and 10.5 billion people, respectively. Assuming all of these assumptions in the scenario are true, one can calculate the anthropogenic emissions of CO2 and other GHGs. It is then necessary to determine the total temperature impact ΔTΣ of these gases. The anthropogenic component in the global CO2 cycle causes changes in the reservoirs of the CO2 sink. The biggest changes are connected with urbanization, deformed structures of the soil-plant communities, and hydrospheric pollution. The rates of change in forest mass for pastures and cultivated lands are estimated at 0.05106 km2/year. Dense tropical forests are replaced by plantations at a rate of 105 km2/year. This process increases the rate of desertification (~5104 km2/year), which increases the amount of carbon emitted (~0.1 Gt C/year). The general pattern of the present level of anthropogenic CO2 fluxes has been rather well studied. So, due to the burning of solid and liquid fuels, about 20106 t

8.3 Anthropogenic Sources of Carbon

315

Table 8.5 Quantitative characteristics of the types of land vegetation formations (Bazilevich and Rodin 1967) Type of vegetation formation (see Table 8.7) A C M L F D G R + P U X W E V # S & H B Q Z Y N J T K I 

Formation area (106 km2) 2.55 2.93 2.33 1.55 5.45 5.73 6.60 2.12 7.21 5.75 3.91 3.72 4.29 1.66 2.66 2.08 2.69 1.99 7.16 1.15 3.54 10.4 7.81 9.18 17.1 11.5 0.38 0.9 14.6

Annual production (kg/m2/year) 0.17 0.36 0.38 0.65 0.54 0.63 0.65 0.87 1.25 1.72 0.56 0.74 0.79 1.11 0.38 0.45 0.25 0.35 0.12 0.47 0.76 3.17 2.46 1.42 1.35 0.18 0.18 1.96 0

Phytomass (kg/m2) 0.4 1.9 1.9 3.8 10.0 22.5 23.5 25.0 45.0 43.0 3.8 1.9 1.9 3.8 0.8 0.4 0.2 0.8 0.1 0.8 1.9 60.0 60.0 10.0 0.1 0.4 0.4 45.0 0

Dead organic matter (kg/m2) 1.3 5.6 5.5 9.0 8.1 10.8 14.5 25.1 24.8 22.2 15.0 38.0 33.0 21.0 12.6 12.1 8.1 8.8 1.4 16.9 24.0 21.6 20.5 15.1 2.0 4.9 4.9 21.6 0

CO2 are emitted every year (with the ratio 1:1). The burning of gas fuel contributes to the atmosphere about 4.5106 t CO2. The contribution of the cement industry is estimated at 750103 t CO2. The individual regions and countries are contributing to these fluxes rather non-uniformly. Table 8.9 and Fig. 8.3 give some estimates of such contributions due to biomass burning. Biomass burning in the tropics is one of the main sources of the input of minor gaseous components and aerosol particles into the troposphere (Romashkin et al. 1999). The share of the tropics is about 40% of global land area and about 60% of global primary productivity. The types of vegetation in the tropics are much more diverse compared to other regions. However, at present tropical forests and savannas

316

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.6 Characteristics of the rates of growth of the economic efficiency and population in the world regions following the IIASA scenario (Mintzer 1987). Notation: P annual increment of labour productivity (%), FSU Former Soviet Union

Region Australia Africa Canada and Europe China Latin America Russia and FSU countries Middle Asia USA South-East Asia

P 2.3 1.6 1.6 1.9 1.9 1.3 1.9 1.2 1.8

Size of population (millions) Years 2025 2050 160 150 1600 2200 520 540 1600 1700 720 850 470 500 280 360 290 290 2600 3100

2075 150 2700 540 1700 900 510 410 290 3400

are converted to agricultural lands and pastures at a rate of about 1%/year. This transformation is mainly caused by biomass burning, which strongly affects the chemical composition of the atmosphere and, hence, the climate. During the biomass burning process, huge amounts of non-methane hydrocarbons (NMHC), NOx and many other gas components are released into the atmosphere. As can be seen from the analysis of satellite observations, the share of the tropics constitutes about 70% of the burnt biomass, about half of which is concentrated in Africa, with a maximum biomass burning in the annual course (dry season) observed north of the equator. Savannas and forests in the tropics also emit a great amount of biogenic compounds into the atmosphere. In relation to the extensive fires in savannas and their strong impact on the environment, Nielsen (1999) performed an analysis of special features of the spatio-temporal distribution of fires in the region where the field experiment EXPRESSO (Central Africa) was carried out from the data of AVHRR carried by NOAA satellites for the periods of dry seasons, from November 1994 to December 1997. The fires variability can be described with three characteristics: 1. fire probability at a given point in time; 2. probability of repeated fires at a given point during a certain time period; 3. the spatial extent and the burning savanna temperature affecting the conditions at a given point. Processing of satellite images has shown that a fire is not a random process. The fire probability increases, for instance, with fires occurring in the vicinity of the point considered. A combined analysis of the characteristics of the spatial and temporal variability of fires made it possible to substantiate 12 typical regimes of fires as well as the dependence of the special features on those of the vegetation cover. Although there is no doubt that, as a rule, savanna fires are caused by humans and not by other factors, specific causes of fires as a function of human activity forms remain unclear. From the viewpoint of the temporal variability, it is appropriate to classify fires

8.3 Anthropogenic Sources of Carbon

317

Table 8.7 Identification of the types of soil- plant formations following the classification after Bazilevich and Rodin (1967) Type of the soil-plant formation Arctic deserts and tundras Alpine deserts Tundras Mid-taiga forests Pampas and grass savannahs North-taiga forests South-taiga forests Sub-tropical deserts Sub-tropical and tropical thickets of the grass-toga type Tropical savannahs Solonchaks Forest-tundra Mountain tundra Tropical xerophytic open woodlands Aspen-birch sub-taiga forests Sub-tropical broad-leaved and coniferous forests Alpine and sub-alpine meadows Broad-leaved coniferous forests Sub-boreal and saltwort deserts Tropical deserts Xerophytic open woodlands and shrubs Dry steppes Moderately arid and arid (mountain including) steppes Forest-steppes (meadow steppes) Variably-humid deciduous tropical forests Humid evergreen tropical forests Broad-leaved forests Sub-tropical semi-deserts Sub-boreal and wormwood deserts Mangrove forests Lack of vegetation

Symbol A B C D E F G H I J K L M N O P Q R S T U V W X Y Z + & @ # 

taking into account the beginning of the fires season, the rate of their development, and duration of the fires season. In this context the following types of fires can be identified by a specific dynamics of their development: fast, slow or long. The contribution of fires in savannas is more than 40% with respect to the global level of biomass burning due to which the atmosphere receives minor gaseous components, such as NMHC, carbon monoxide, methane, etc., as well as aerosols. According to the available estimates for the period 1975–1980, 40–70% of savannas were burnt every year, about 60% of such fires took place in Africa. In 1990, about

318

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.8 Distribution of the scales of biomass burning (millions of tons of dry matter per year). Notation: T tropical forests, S savanna, L temperate-zone forests; B boreal forests, Y household fuel, A agricultural wastes, C brown coal Region Tropical America Tropical Africa Tropical Asia Tropical Oceania USA and Canada West Europe Temperate-zone forests Boreal forests Total

Sources T 590 390 280 0 0 0 0 0 1260

S 770 2430 70 420 0 0 0 0 3690

L 0 0 0 0 0 0 224 0 224

B 0 0 0 0 0 0 0 56 56

Y 170 240 850 8 80 40 0 0 1438

A 200 160 990 17 250 170 0 0 2017

C 7.5 9.3 3.3 0 0.5 0.2 0 0 21

2109 t of vegetable biomass were burnt, and as a result, 145 Tg CO reached the atmosphere, which is about 30% compared to the anthropogenic CO emissions. Forest fires are one of the serious impacts on the global carbon cycle. Although forest fires can occur naturally, for example, caused by lightning strokes, nevertheless, the humans’ contribution to their occurrence is constantly growing. The lightning-induced fire effect is only possible if it is hit by permanent wood or, in the case of a delicate forest, the soil covered by moss or litter (Metting et al. 2001; Metzger and Benford 2001). The electrical resistance of standing wood is known to be almost 100 times greater than that of growing trees, and therefore when the lightning strikes a living tree, it does not even burn. Therefore, forest fire risk monitoring gives reliable estimates of the probability of the lightning-caused forest fires. A more complicated problem is to predict the anthropogenic causes of forest fires. More than 90% of forest fires are known to occur in a 10-km zone of populated areas being caused by anthropogenic factors. Hence, the fire load on forests is strongly correlated with the spatial distribution of population density. Of course, the intensity and frequency of occurrence of the fires depend on the climate dryness in a given territory, on forests’ density and their health. A forest fire is dangerous not only because it is a source of pollutants for the atmosphere, but also because their consequences are dangerous. The fires change the forest microclimate, in particular, the illumination and heating of soil intensifies, changing the hydrological regime of the territory. Moreover, in the territory of forest fires the bioproductive ability of biocenosis deteriorates and, hence, the role of this territory in the biogeochemical cycles is changing. It is well known that in a region with dry climate the firedestroyed forests are not naturally restored and the area must be re-forested. Therefore it is important to know the laws of the interaction of forest fire and biocenosis of its territory. For instance, fires in the boreal forests contribute not more than 2% to carbon emissions to the atmosphere, but seriously affect the chemical processes in the high-latitude troposphere and the atmospheric radiative properties. And this can lead to global climatic consequences.

8.3 Anthropogenic Sources of Carbon

319

Table 8.9 Reservoirs and fluxes of carbon as CO2. Reservoirs and fluxes of carbon of CO2 in a model of global carbon dioxide cycle (MGCDC), where its block-scheme is shown in Fig. 8.3 (Krapivin et al. 2017c) CO2 reservoirs and fluxes Carbon Atmosphere Photic layer of the ocean above the thermocline Intermediate photic layer of the ocean under the thermocline Phytoplankton Living elements in the ocean Detritus Deep layer of the ocean Near bottom layer of the ocean Dead organic matter in the soil and peat Land vegetation Permafrost Living organisms on the land Fossil fuels Coal Oil Gas Emission due to burning Vegetation Fossil fuels and cement production Rock weathering Volcanic emanations Assimilation by land vegetation Respiration Plants Human Animal Emissions Decomposed soil humus Plant roots Permafrost thawing Vital functions Human population Animal Vegetation decay Sedimentation to bottom deposits Solution of marine sediments Decomposition of detritus

Identifier in the model

Average estimate of the reservoir (109 t) and flux (109 t/year)

CA CU

650–750 380–520

CI

280–610

CΦ CL CDT CD CS CV CP CLO CB

3 1.5 29 28,500–33,890 5000 1500–3000 550–610 1672 0.5

CC CO CG

4500 500 5000

F8 F28 F4 F5 F6

6.9 4000 0.04 2.7 224.4

F7 F10 F11

50–59.3 0.7 4.1

F9 F15 F2

139.5 56.1 190

F12 F13 F14 F16 F17

0.3 3.1 31.5–50 0.1–0.2 0.1 (continued)

320

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.9 (continued) CO2 reservoirs and fluxes Photic layer Deep layers of the ocean Upwelling with deep water Sinking with surface water due to gravitational sedimentation Photosynthesis Underground sink Surface sink Respiration of living organisms in the ocean Degassing processes Sink to the Earth’s bowels Desorption Sorption

Identifier in the model F22 F18 F19 F20

Average estimate of the reservoir (109 t) and flux (109 t/year) 35 5 45 40

F21 F23 F24 F25

69 0.5 0.5–0.6 25

F1 F26 F27 F3

21.16 1.3 97.08 100.0

Fig. 8.3 The block-diagram of the global biogeochemical cycle of CO2 in the system atmosphereocean-land. The CO2 reservoirs and fluxes are presented in Table 8.9

In general, for different reasons, biomass burning is a complex anthropogenic source of atmospheric pollution and the global impact on the biosphere as a whole (Table 8.8). Estimates obtained by many authors show that the radiative forcing on

8.4 Resources of Biosphere and the Greenhouse Effect

321

climate determined by aerosols from biomass burning is about 1.0 W/m2. In the case of pure scattering aerosols the uncertainty of the estimates is between 0.3 and 2.2 W/m2.

8.4

Resources of Biosphere and the Greenhouse Effect

The dynamics of the global carbon dioxide flux is determined by natural and anthropogenic factors. Natural factors are formed in the process of evolution of the biosphere, and their dynamics depends on the interaction between natural ecosystems. The level of anthropogenic forcing in the global CO2 cycle is determined by the relationship of natural forces with numerous aspects of the humankind development – political, demographic, cultural, religious, economic, etc. All of this diversity of anthropogenic origin in the present world is limited by the resources of the biosphere, which eventually determine these aspects. The tabulated estimates of some resources given to global databases suggest that the omnipotence of the human mind, with its nature-destruction constituent, is limited by many circumstances. Humankind contaminates the environment either by using mineral resources or by changing the planet through changing one ecosystem to another. In both cases, mankind sooner or later contradicts its goals, and therefore the limiting factors exist in the global dynamics of disaster – depletion of resources, worsening of living conditions, etc. A human being, as an element of nature, forming his or her environment, first of all, is interested in the sources of material production – mineral resources. With increasing scientific-technical progress the rate of the mineral resources consumption is constantly increasing, approaching some critical level. Although the late twentieth and early twenty-first centuries are characterized by the broadened spectrum of mineral deposits involved in the industrial operation, nevertheless, no alternative to oil, coal, and gas has been found yet. The increase in energy resources consumption is confirmed by the fact that almost half of the conditional fuel used by humankind (~90109 t) has been consumed in the last 25–30 years. By the end of the twentieth century about 200109 t of mineral deposits had been extracted. There are no reliable estimates of the global supplies of mineral resources. Therefore scenarios of carbon emissions into the atmosphere used by many authors should be considered conditional and questionable (Watson et al. 2000; Houghton and Yihmi 2001). Nevertheless, there is a clear relationship between the volume of CO2 emitted and the national production. This relationship is changing worldwide between developed and developing countries by a factor of 30, which is a considerable reserve in developing an optimal strategy for shaping the profile of the global of CO2 emissions curve in the atmosphere. The World Ocean is one of the poorly mastered sources of mineral resources. Along with getting food products, the humankind is gradually mastering the mineral fuel supplies lying under the bottom of the oceans and, first of all, on the shelf. Oil, gas, and coal are already extracted in large quantities from marine deposits, but of

322

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

course, there are economic and technical limitations. On the whole, from preliminary estimates, in 180 gas basins discovered by geologists in the World Ocean, potential supplies of oil and gas are estimated at 300109 m3 and 1501012 m3, respectively. Thus here are the limiting parameters for atmospheric CO2 emissions scenarios. Human activity in changing land cover is one of the factors in anthropogenic forcing on the global carbon cycle dynamics is difficult to assess. Over the past century, humankind has strongly affected the global distribution of vegetation – one of the important sinks for atmospheric CO2. This is caused by the human activity in engineering, construction, and mining engineering, the creation of new types of land formations, biological re-cultivation, etc. Especially dangerous for the environment are processes of deforestation and desertification. For instance, after the estimate by Watson et al. (2000) by implementing the IPCC scenario of re-forestation, by the year 2050, an additional 60–87 Gt C (70% – tropical forests, 25% – temperate-zone forests, 5% – boreal forests) can be removed from the atmosphere. An important parameter of this scenario is the rate of trees’ growth. For instance, if the forest grows at a rate of 3tC/year/ha, then 1tC can be attributed to the effect of atmospheric CO2 assimilation. In general, the control of land biocenosis can markedly affect the biogeochemical carbon cycle. It is important how this control is realized – planned or spontaneous. Anyhow, humans are creating anthropogenic landscapes on Earth’s surface to improve their habitat. The emergence of artificial seas, recreation zones, cities, and other anthropogenic landscapes reduces the level of natural evolution of land covers. In other words, human activity on changing the landscapes can strongly affect the dynamics of atmospheric CO2.

8.5

The Greenhouse Effect and Global Carbon Cycle

During the last decade the term “greenhouse effect” has been used in numerous publications on the problems of global climate changes on the Earth (Ledley et al. 1999; Friedlingstein et al. 2001; Reid 2001). This term implies a set of simulating results of the effects caused by the climate system and associated with a number of natural and anthropogenic processes. Overall, the term “greenhouse effect” refers to an explanation of changes in atmospheric thermal regime caused by the impacts of some gases on the radiation transfer process. Many gases are characterized by high stability and long residence time in the atmosphere (see Table 8.10). Carbon dioxide is one of them. GHGs are also CH4, N2O, CFC-11 (CCl3F), and CFC-112 (CCl2F2). Their observed growing concentration in the atmosphere is characterized by the following values. From the pre-industrial period to 1997 the CO2 concentration had, on average, increased from 280 ppm to 364 ppm. From the estimates by Ledley et al. (1999) in 1996 the atmosphere received 6.5 GtC due to the fuel combustion and cement industry. From the data in Fan et al. (1998) an additional 1.6  1.0 GtC are emitted to the atmosphere due to land use. Such a high uncertainty of the latter estimate is explained by the poorly studied spatial pattern of land use. Atmospheric

8.5 The Greenhouse Effect and Global Carbon Cycle

323

Table 8.10 Characteristics of the most important GHGs (Mintzer 1987; Ledley et al. 1999) Greenhouse Time life in the gas atmosphere. years Carbon 2–10 dioxide Nitrogen 100–150 oxide Methane 10–11 Chlorofluorocarbons CFC-11, 50–75 CFCl3 CFC-12, 102–111 CF2Cl2

Average concentration 362 ppmv

Speed of the gas concentration growth, % 0,5

308 ppbv

0,25

1815 ppbv

1,0

0.34 ppbv

7,0

0.54 ppbv

7,0

CH4 concentrations increased from 700 ppb in the pre-industrial period to 1721 ppb in 1994 (Houghton et al. 1996). The sources of anthropogenic CH4 connected with fuel combustion give approximately 70–120 mln t CH4/year. At the expense of rice paddies, biomass burning, stockbreeding, and dust-heaps, the atmosphere acquires more than 200–350 mln t CH4/year. Of the non-anthropogenic sources of CO2 and CH4 of special importance are the marsh biogeocenoses. According to Dementjeva (2000) estimates, in 1998 and 1999 the rates of CO2 emissions by marshes during vegetation period were 75.8–216.2 mg CO2 and 94.4–104.5 mg CO2 per square meter per hour, respectively. These figures demonstrate a wide range of seasonal variations of CO2 fluxes from the soil to the atmosphere, which requires a detailed inventory of the land biogeocenosis. Atmospheric N2O concentrations increased from 275 ppb in 1800 to 312 ppb in 1994 (Houghton et al. 1996). Anthropogenic N2O emissions ranged within 3–8 mln t N/year. The principal sources of anthropogenic N2O emissions to the atmosphere were agriculture and industrial acids production. Prior 1950 there were practically no chlorofluorocarbons CFC-11 and CFC-12 in the atmosphere. Then, the use of these components in refrigerators and other devices raised the problem of ozone depletion which led to the Montreal Protocol that provided a substantial reduction of the CFC-11 and CFC-12 content in the atmosphere of the twenty-first century. The stability of some GHGs is estimated from their average lifetime in the atmosphere, which is defined as the time needed to remove 63% of anthropogenic emissions from the atmosphere. It is very difficult to estimate this indicator for CO2 because of different time scales of its fluxes between biospheric reservoirs. Model estimates of CO2 lifetime show that annually 70–85% of anthropogenic CO2 are removed from the atmosphere. Numerous long-term observations in different latitudinal belts show a high level of correlation between temperature and CO2 content. By many authors’ estimates, the largest contribution to this dependence is made by the atmosphere-ocean interaction (Nefedova and Tarko 1993). Although the atmosphere and the ocean are in a

324

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

certain balance with regard to CO2 exchange, nevertheless, this balance remains regularly broken. The most serious reasons for this break are: 1. change of SAT; 2. change of volume of the ocean; 3. change of the regime of the oceanic vertical circulation. In general, these reasons can be characterized, from the viewpoint of their efficiency, by the ratio of their forcing on CO2 concentration in the atmosphere. The first reason introduces about 65% to changes in the CO2 partial pressure in the atmosphere (pa). The remaining 35% relate to the second and third reasons. Quantitatively, this ratio is characterized by an increase 6% of the CO2 partial pressure per every oC of the rising upper ocean layer temperature. The reduction in the ocean volume by 1% is increasing pa by 3% per year. An estimation of the greenhouse effect requires a complex consideration of interactions among processes of energy transformation on Earth. However, there is a certain hierarchy to the significance of the various processes (from astronomical to biological) affecting the climate system on various timescales. But this hierarchy cannot be fixed, as the role of individual processes can vary across a wide range of issues relevant to climate changes. Looking at individual factors simplifies an analysis of its effect on the climate. The level of the greenhouse effect is determined by an excess of surface temperature Ts relative to the effective temperature Teff. The Earth surface temperature Ts depends on emissivity ε. The effective temperature Teff is a function of the emissivity α of the atmosphere-ocean-land system. In general, the values ε and α depend on many factors, including the atmospheric CO2 concentration. There are a lot of simple and complex mathematical models used for the parametric description of these dependencies. Unfortunately, so far, there is no model which would meet the requirements of adequacy and reliably simulates the pre-history of climate trends on Earth. Nevertheless, one can state that the greenhouse effect depends non-linearly on the difference Ts–Teff, that is, the atmospheric transparency, especially in the longwave region. The greater the CO2 content in the atmosphere, the stronger atmospheric attenuation. In the longwave interval 12–18 μm the effect of CO2 on atmospheric transparency is the strongest. Its weakest effect is in the intervals 78, 910. 2.0, 2.7, and 4.3 μm. It is clear that with an increasing partial CO2 pressure in the atmosphere the role of various CO2 bands will increase, and it means that with an intensified CO2 absorption the upward longwave radiation flux will decrease. At the same time, on the Earth surface, the downward longwave flux will increase. From the available estimates a decrease of the upward and increase of the downward fluxes are characterized by the values 2.5 and 1.3 W/ m2, respectively (Ramanathan and Coakley 1978). To assess the level of the greenhouse effect due to CO2, one should be able to predict its concentration taking into account all the feedbacks in the global biogeochemical carbon cycle as well as a correlation of this cycle with the other GHGs cycles. From the estimates by Gale and Freund (2000), the contribution of various gases to the greenhouse effect is: CO2–63.5%, CH4–20.5%, nitrogen oxides – 4.5%,

8.5 The Greenhouse Effect and Global Carbon Cycle Table 8.11 Description of the CO2 global cycle

CO2 flux H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22

325

Processes of the CO2 flux formation Fossil fuels Desorption Sorption Surface soil érosion Volcanic eruptions Photosynthesis in the World Ocean Respiration of the vegetation Vegetation burnings Humus decomposition Anthropogenic activity Vital activity of the World Ocean biota Human vital activity Animal vital activity Vegetation dying off Respiration of the roots Sediments Dissolution of the World Ocean sediments Dead organic matter dissolution Upwelling Dwelling and gravitation sedimentation Surface vegetation photosynthesis River runoff to the World Ocean

CFCs – 11.5%. Temporal changes of various GHGs are one of the most important problems of the global modeling. A complete list of CO2 fluxes on Earth is given in Table 8.11, with independent sub-cycles of CO2 in the World Ocean and on land. The relationship of these components is made through the atmosphere and river run. From the available estimates, the intensity of the mean global CO2 circulation constitutes about 6109 tC/year. Some quantitative characteristics of the CO2 global biogeochemical cycle have been published by many authors (Eliasson et al. 1999; Kelley 1987). The current ratio of the carbon content in various global reservoirs vividly demonstrates the role of each of them in the global CO2 cycle. This ratio is: atmosphere/land/ ocean/geosphere ¼ 1/3/50/10. It follows that the role of the World Ocean in the CO2 cycle exceeds all other reservoirs. The law of carbon flow in the ocean is connected with the carbonate system of the ocean, where carbon is divided into organic and inorganic. Carbon dioxide dissolving in the ocean forms carbonic acid. An equilibrium condition of the reaction of carbonic acid formation depends on the partial pressure of gaseous CO2, concentration of dissolved carbonic acid, temperature, pressure, and salinity of sea water. Carbonic acid gives biocarbonate-ion and carbonate-ion. The latter is the final product of the reaction of dissolved CO2. The concentration of carbonate-ion in the ocean increases till the limit of the calcium carbonate solubility is exceeded.

326

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.12 Biomass and production (dry matter) of some biocenosis Biocenosis Tropical humid forests Tropical seasonal forests Forests of moderate zone: Evergreen Deciduous Arctic forests Bushes Savanna Medous of moderate zone Tundra and mountain vegetation Vegetation of the deserts and semi-deserts Bare deserts (sand, ice and rocks) Cultivated lands Bogs and swamps

Р 2200 1600

В 45,000 35,000

τ 70 80

1300 1200 800 700 900 600 140 90 3 650 3000

35,000 30,000 20,000 6000 4000 1600 600 700 20 1000 15,000

40 40 90 50 50 60 100 100 40 80 40

Notation: P is the primary productivity (g/m2/year); B is the biomass (g/m2); τ is the carbon cycle period (years)

The different classification of CO2 fluxes and sources in Tables 8.9 and 8.11 opens up the possibility to form series of global carbon cycle models depending on the existing environmental databases. Classifications given in Tables 8.9 and 8.11 agree with global carbon cycle models having appropriate block-schemes. Figure 8.3 gives one of them. The corresponding balance equations provide the dynamic characteristics of the carbon cycle. The process of photosynthesis is a source of organic carbon in the ocean. The photosynthesis reaction involves 106 mol CO2, 16 mol nitrate, and 1 mol phosphate. Under the influence of the trophic pyramid of the ocean, the photosynthesis production changes substantially, and as a result, the concentration of inorganic matter is regulated through deposition of calcium carbonate and organic matter oxidation. In this regulation, the process of the vertical mixing of the ocean greatly contributes. The result of all these processes is the CO2 exchange at the ocean-atmosphere border. The CO2 cycle in the atmosphere-plant-soil system has been described in detail by Krapivin et al. (2019). Here are given the basic fluxes of carbon in land ecosystems. Their photosynthetic elements absorb CO2 from the atmosphere and transform it into substratum and humus; 1  2% being washed out in the underground waters and further into the oceans. The spatial structure of these fluxes is determined by the distribution of the types of soil-vegetation formations over the globe. Table 8.12 characterizes the role of various types of biocenosis in the global cycle of CO2. It is clear that an accuracy of the estimates of land vegetation productivity, the reliability and the details of its structural classification are important parameters to specify the scheme and parameters of the global CO2 cycle. The global CO2 balance is defined by numerous functional dependencies applied by various investigators to these balance models. As there is generally, no single

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

327

database available, including adequate descriptions of all processes of CO2 transformation in the ocean and on land, currently all conclusions about catastrophic consequences of the expected warming due to increasing partial pressure of atmospheric CO2 should be assessed as hypothetical. Of course, not all fluxes given in Table 8.11 are equal in their influence on changes in atmospheric CO2 concentrations. But such fluxes as CO2 exchange on the border of the atmosphere with the ocean and land prevail when estimating the atmospheric CO2 gradients. But these fluxes that are still poorly assessed and practically their adequate parameterizations are lacking. Most of the models proceed from the fact that the CO2 flux to the oceanatmosphere border is in proportional to the difference of its partial pressures in these domains. But as shown in Kondratyev et al. (2003b), this flux is more reliably described than the model in which the flux is assumed to be proportional to square root of this difference.

8.6 8.6.1

A Coupled Model of Carbon Dioxide and Methane Global Cycles Introduction

Global CO2 and CH4 cycles have been the subject of many international and national research programs aimed at parameterizing and understanding feedbacks in the climate-biosphere-society system (CBSS). The Global Carbon Project (GCP) provides a comprehensive overview of the global carbon cycle taking into account its biophysical and human components. There are several mathematical models of this cycle by examining the interactions and feedbacks between the various environmental subsystems (Siegenthaler 1993; Doney et al. 2003; Kondratyev et al. 2003a; Ondov et al. 2006; Ebel et al. 2007; Krapivin and Varotsos 2008; Chattopadhyay et al. 2012; Xue et al. 2014; Krapivin et al. 2015). The GCP and relevant publications are intended to implement the various procedures for the accumulation of knowledge about the GHGs sources and sinks. According to existing data, the main CO2 emissions are the cement industry, the burning of the fossil fuels and land use. Unfortunately, this knowledge only provides 80–85% of the global carbon cycle completeness. Such uncertainty also exists in global cycles of other GHGs (Kondratyev and Varotsos 1995). One of the methods to overcome this uncertainty is the development of models of GHGs global cycles, which makes it possible to detect the most critical aspects of these cycles. It is well-known that methane has more than 20 times greater global warming potential than CO2. Many authors attempt to better understand the behavior of methane in the carbon cycle and to develop models of coupled carbon and methane cycles (Panikov and Dedysh 2000; Smemo and Yavitt 2011; Tang et al. 2016). Unfortunately, knowledge about methane’s sources and its involvement in the carbon cycle is still incomplete and contradictory. Nevertheless, a combined

328

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

model of CO2 and methane cycles would be useful to understand the global problem of climate change and to reduce the existing uncertainties. In multiple discussions of this issue the available data were analyzed in detail particularly with regard to the existing contradictions in the study of global ecodynamics (Kondratyev et al. 2003a). Cracknell et al. (2009) grouped the climate change disasters in twelve classes related to environmental stability (including human life) and indicated that only one of them was anthropogenic. Therefore, the coupled model of CO2 and methane cycles must take into account all the causes of environmental changes, namely direct and feedbacks (Cracknell and Varotsos 2011). This is feasible in CBSS, because Krapivin and Varotsos (2008) have shown that CBSS allows the combination of different environmental processes into the unique structure of model components supported by existing global and regional databases. In this regard, Degermendzhi (2009) developed an alternative approach to the biosphere-climate system model taking into account the incompleteness of existing databases. This section discusses the development of a coupled model of СО2 and СН4 cycles, such as CBSS model blocks, where soil-plant formations and oceanic ecosystems exhibit spatial distribution.

8.6.2

Conceptual Scheme of Global Carbon Dioxide and Methane Global Cycles

It is well known that global carbon and methane cycles involve a series of natural and anthropogenic processes that determine their dynamics and have different temporal scales from tens to hundreds and thousands of years. For example, atmospheric CO2 concentration varies significantly throughout the year. The difference between maximum and minimum concentrations of CO2 varies by 10 ppm at the South Pole and 15 ppm in the North Pole. Existing global models of the CO2 do not take into account these seasonal CO2 fluctuations, which are recorded by many measurements taken regularly and span from the South Pole to the Arctic. These fluctuations arise from many causes such as: • carbon accumulates in forests and in the rest of vegetation of the Northern Hemisphere, especially during the summer; • without photosynthesis, the dominant process of CO2 regime is the exhalation of CO2 by bacteria and living organisms; • seasonal fluctuations in CO2 emissions (including permafrost melting) are important. Bearing in mind these conditions it is possible to develop a global model of CO2 and CH4 cycles with a specific structure of their sinks and sources. In this respect, Tables 8.7 and 8.9 show the basic components of these cycles with the spatial heterogeneity of land and ocean ecosystems. In this regard, Figs. 8.3 and 8.4 illustrate the main features of the CO2 and CH4 cycles, while Fig. 8.4 shows the

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

329

Fig. 8.4 Symbolic map of soil-plant formations with a spatial distribution of 4
 5
. The list of symbols and quantitative characteristics of soil-plant formations is given in Table 8.5

structure of a coupled model of carbon dioxide and methane cycles (CMCDMC). As shown in Table 8.9 all carbon reservoirs and fluxes are divided into categories that vary in time scales. It is obvious that CMCDCM describes the carbon dioxide fluxes between its reservoirs. However, existing databases are unable to provide detailed input information on the role of each tree, animal, microorganism, leaf, lake, river, oceanic aquatory, landscape, etc. Therefore, global carbon cycle models are built with specifications characterized by increased complexity (Kondratyev et al. 2003a). Figure 8.3 illustrates an achieved level of complexity of the biogeochemical carbon cycle. The proposed CMCDMC takes into account 30 types of soil-plant formations shown in Fig. 8.4 and Table 8.7. A soil-plant formation occupies the spatial pixel Ξ ¼ [4
 5
] where the atmospheric temperature and solar radiation are uniform in space. The spatial structure of the World Ocean is described by the Tarko’s model (Tarko 2003) which provides for exchange processes in the atmosphere-ocean boundary, taking into account its spatial heterogeneity by separating pelagic and upwelling zones (Krapivin and Varotsos 2016). Seasonal variations in atmospheric CO2 occur only with the introduction of permafrost thawing time. Next, the carbon dioxide fluxes shown in Fig. 8.3 and Table 8.9 are used to synthesize the balanced equations:

330

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

X ∂C As ðφ, λ, t Þ ∂C As ðφ, λ, t Þ ∂C As ðφ, λ, t Þ X þ V φs þ V λs ¼ Fi  Fj ∂t ∂φ ∂λ i2I j2J s

ð8:1Þ

s

where CAs is the carbon reservoir in the s-th pixel of the spatial structure of land and ocean; Is and Js are carbon sources and sinks, respectively; φ is latitude; λ is longitude; t is time; Vs(Vφs,Vλs) is the wind components in the sth pixel. The interaction between the atmosphere and the carbon reservoirs on land and in the ocean is revealed by the carbon fluxes formed by ecological, geophysical and biogeochemical processes, such as photosynthesis, respiration, decomposition, burning, earthquake, soil erosion, etc. Detailed description of these processes are given in Bjorkstrom (1979), Kondratyev et al. (2003b), and Williams and Follows (2011). Some of these are specified through the CMCDMC items listed in Table 8.7. The photosynthesis production of the k-th type of vegetation in the pixel Ξij(φi,λj) at time t is given by: Pk ðφ, λ, t Þ ¼ NPPκ ðφ, λ, t Þ þ Rκ ðφ, λ, t Þ

ð8:2Þ

where the net primary production (NPP) and the respiration R are functions of CO2, precipitation W (mm/year), atmospheric temperature T (
C) and solar radiation E(W/ m2). The CMCDMC item CFCV uses Mintzer (1987) climate model updated by Krapivin et al. (2015) and a global water cycle model by Kondratyev et al. (2002b). Net primary production (NPP) and respiration (R) are calculated using the following models: 8 ρ 1 E ð φÞ μa W CA  Γ > > > > k p k s þ C A þ Γ , ρ 2 þ E ð φÞ , μ b þ W , > >  > < T ðφÞ  T min ðφÞ  max 0, NPPκ ðφ, λ, t Þ ¼ Pκ ðφ, λÞ min T > opt ðφÞ  Tmin ðφÞ > >   > > T ðφÞ  T min ðφÞ > > : exp 0:56  0:42 T opt  T min ðφÞ  2  T ðφÞ ¼T g þ ðT N  T e Þ sin φT  sin 2 φ ,  ψ a T ð φÞ d W , Rκ ðφ, λ, t Þ ¼k B Bκ ðφ, λÞ max 0, , a ψ b þ T ðφÞ d b þ W

9 > > > > > > > = > > > > > > > ;

where kp (3.226) is the photosynthesis rate constant, Γ (5–50) is the photosynthesis compensation constant, ks (930) is the photosynthesis stabilization constant, ρ1 (1.177) and ρ2 (60.538) are empirical constant, reflecting the correlation between E ¼ APAR and NPP; Topt (25
C) is the optimal temperature for photosynthesis; Tmin(κ) is the minimum temperature when the photosynthesis rate is not zero; Tg is the global average temperature; TN is the global average temperature on the pole; Te is the average global temperature in the equator; φT is the latitude at which T (φ) ¼ Tg; μa (4.742), μb (592.357), ψa (1.214), ψb (5.714), da (2.941) are empirical constants. Information about Pκ ðφ, λÞand Bκ ðφ, λÞis given in Table 8.7 and Figs. 8.5 and 8.6.

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

331

Fig. 8.5 Schematic representation of the Pκ distribution (kg/m2/year)

The carbon flux between the atmosphere and living biomass in the pixel Ξij is described by simple expression:     F 6 φi , λ j , t ¼ c6 Pk φi , λ j , t

ð8:3Þ

where c6 ( 0.546) is the coefficient reflecting the efficiency of the photosynthetic response mechanism. The average values of Pk are given in Table 8.7. For the estimation of F6 Bjorkstrom (1979) suggested the following approach: F 6 ¼ að1 þ b ln ½C A =280 cκ

ð8:4Þ

where cκ is the carbon content of the biomass of κ-th type of vegetation; a and b2 [0,1] are empirical coefficients reflecting the temperature dependence of vegetation production. To estimate the average value of Pk the following Lieth’s formula is often used (Lieth 1985):

332

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Fig. 8.6 Schematic map of the phytomass Bκ distribution in land ecosystems (kg/m2)

n o Pk ¼ 3000 min ð1 þ exp ð1:315  0:119T ÞÞ1 , ð1  exp ð0:000664r ÞÞ ð8:5Þ where T is average annual atmosphere temperature (
C), r is the average annual precipitation (mm). In the proposed CMCDMC model the calculation of the F6 flux within each pixel Ξij is achieved by using the above-mentioned relationships, along with the relevant information given in Table 8.7. An alternative way would be to use the models developed earlier by Monsi-Saeki (Hirose 2005) and Sellers et al. (1997). The role of the terrestrial ecosystem in the carbon cycle is described by the NPP that is estimated by means of the datasets of the Normalized Difference Vegetation Index (NDVI), which contributes to the reconstruction of spatial patterns of the NPP, as a key indicator of ecosystem performance and a major component of carbon cycle. The NPP depicts the dependence of the vegetation productivity on climatic factors, such as air temperature, rainfall, sunshine hours, relative humidity, air pressure, global radiation, surface net radiation, and wind speed:         NPP Ξij ¼ PAR Ξij  FPAR Ξij  μ Ξij

ð8:6Þ

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

333

where PAR is the incident photosynthetically active radiation in a certain time period (year), FPAR is the fraction of PAR absorbed by the vegetation canopy, and μ is the actual radiation conversion efficiency. For the estimation of FPAR Wang et al. (2013) proposed the following model:  FPAR ¼

0 when NDVI < 0:075; min f1:163  NDVI  0:04393; 0:9g: when NDVI  0:075:

ð8:7Þ

The carbon cycle in the ocean includes the fluxes formed by the thermohaline circulation, the exchange between the atmosphere and the surface ocean layer, photosynthesis, oxidation processes, and sedimentation. A description of these fluxes is given by Kondratyev et al. (2003a); Krapivin and Varotsos (2008); Tarko (2003). Fluxes F27 and F3 describing the physico-chemical processes of gas exchange in air-water boundary have been studied in detail and described by Kondratyev et al. (2003a). The flux of CO2 between the atmosphere and the ocean is a function of the principal factors that determine the atmosphere-ocean boundary. The flux F3 is described by the following relationship: pffiffiffiffiffi F 3 ¼ ψ ðT L Þ pa =ð1 þ 0, 5pS Þ

ð8:8Þ

where ψ(TL) is the function of the temperature effect on CO2 solubility; pS is the water salinity; TL is the water temperature; pa is the atmospheric pressure. The function ψ can be expressed as follows: ψ ðT L Þ ¼ νðT L ÞωðT L Þðpa  pw Þ

ð8:9Þ

where ν is the kinetic parameter (transfer velocity), ω is the solubility coefficient, pa and pw are CO2 partial pressure in the atmosphere and water, respectively. The fluxes F27 and F3 vary symmetrically, depending on the water acidity pH. A critical pH level is about 8.11. At pH 8.11 the ocean assimilates CO2 and at pH > 8.11 the ocean emits CO2. It should be noted that the role of the Arctic Basin in climate change can be estimated with the help of CMCDMC because of the key role of the Arctic Ocean ecosystem and permafrost zones. The Arctic Basin emits CO2 to the atmosphere as CH4 from permafrost zones including thermokarst lakes (7.1–17.3 TgCH4/year) and peat bogs (10–51 MtCH4 year1). Methane emission rate varies spatially over high latitudes from 4.7 g CH4 m2 year1 in northern Europe to 40.4 g CH4 m2 year1 in Alaska. Methane emissions from terrestrial plants are evaluated at 62–236 Tg CH4 year1 (Christensen et al. 1996; Keppler et al. 2006; Tan and Zhuang 2015). The permafrost carbon cycle deals with the transport of carbon from these sources to terrestrial vegetation and microbes to the atmosphere–, back to vegetation and finally back to permafrost soils through burial and sedimentation due to cryogenic processes. Whole anthropogenic and natural methane sources are characterized by the following percentage distribution: wetlands (22%), coal & oil mining/natural gas

334

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Fig. 8.7 Global methane cycle in the atmosphere-hydrosphere-land system

(19%), enteric fermentation (16%), rice cultivation (12%), biomass burning (8%), landfills (6%), animal waste (5%), sewage treatment (5%), termites (4%), and CH4 hydrates and oceans (3%). Part of this carbon goes into the global carbon cycle (Smith et al. 2010). The CMCDMC takes into account the pixels Ξij in the permafrost zones having partially soil-plant formation defined in Table 8.7 and permafrost section according to Anisimov and Reneva (2006); Smith et al. (2010). Pixels where the permafrost covers all or part of the ground surface occupy about 25% of land area (about 26.9 million km2). The proposed CMCDMC is provided by schemes illustrated in Figs. 8.3 and 8.7 using the block-diagram of Fig. 8.8 and Table 8.13. Undoubtedly, CO2 and CH4 are basic GHGs and thus their cycles mainly determine the climate dynamics. Sources of methane are water soluble areas (22%); zones of oil, gas and coal extraction (19%), fermentation processes (16%); rice raising (12%); biomass burning (8%); landfills (6%); sewage processing (5%); wastes from animal husbandry (5%); termites (4%), and ocean (3%). The average speed of the CH4 formation in each pixel Ξij in the land and in the ocean aquatory Ωsk varies from 0.4  106 t/year in the forest area to 280  106 t/year in rice plantations and from 0.1  106 t/year in the ocean shelf to 6.7  106 t/year in pelagic regions (Panikov and Dedysh 2000; Smemo and Yavitt 2011). Knowing these fluxes contributes to the more precise CO2 radiation potential, which provides the implementation of the scheme shown in Fig. 8.3.

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

335

Fig. 8.8 Coupled model of carbon dioxide and methane cycles (CMCDMC). Notation is given in Table 8.13

Table 8.13 List of the CMCDMC items whose block-schemes are shown in Figs. 8.3, 8.5 and 8.6 Block CSD CMA CFS CFFC ADKR AHIC SS

ACCI CFCV CEFE RRFI IIC

Description of the block Coordination of spatial distribution of Ξij pixels and Ωsk aquatories with the existing databases Control of models and algorithms to parameterize fluxes Fi (i ¼ 1,. . .,28) and Cj ( j ¼ 1,. . .,13). Calculation of fluxes Fi (i ¼ 1,. . .,28) and Cj ( j ¼ 1,. . .,13) taking into account the structure of the atmosphere-land-ocean system. Correction of fluxes Fi (i ¼ 1,. . .,28) and Cj ( j ¼ 1,. . .,13) due to the atmosphere circulation, hydrophysical processes and oceanic currents. Algorithm for the database and knowledge base renovation. Algorithm for the heterogeneous information co-ordination. Synthesis of scenarios considering possible changes in the soil-plant formations (Fig. 8.2), the ocean surface pollution (fluxes F3, F27 and F28) and changes in the used technologies of fossil fuels. An assessment of the climate change indicators after the scenario implementation. Correction of the fluxes Fi (i ¼ 1,. . .,28) and Cj ( j ¼ 1,. . .,13) due to the climate parameter variations. Control of the environmental feedbacks evolution and appropriate correction of the CMCDMC parameters. Recalculation of the radiation forcing indices depending on the Fi/Cj dynamics at corresponding pixels and aquatories. Informational interface and control of the fluxes between the CMCDMC blocks.

It is known that the relation between the radiation potentials of CO2 and CH4 is 1:72, 1:25 and 1:7.6 during 20, 100 and 500 years, respectively. Areas of many sources and sinks of the CH4 occupy only a part of the pixel 4
 5
. Each pixel Ξij can have natural and/or anthropogenic sections with methane fluxes. Block CSD

336

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

forms matrix structures corresponding to spatial distributions of these sections (Bousquet et al. 2006). Therefore, the CMCDMC enables the use of such data for the precise spatial structure of the model. Methane fluxes Ci (i ¼ 1–13) are functions of temperature, geographical coordinates and other environmental characteristics. The most common permafrost zones differ in space where the permafrost thickness typically varies between 100–800 m, 25–100 m, and 10–50 m in the continuous, discontinuous, and sporadic zones, respectively. On the ground, annual temperatures vary between 8 to 13
C in the northernmost zone of continuous permafrost, 3 to 7
C in the discontinuous zone, and 0 to 2
C in the southern sporadic zone. The active layer varies in these zones from several decimeters to a few meters. All this reveals the complexity of methane cycle. Climate change is an important cause of variations in the methane fluxes. In particular, the degradation of the underwater frozen earth and the destruction of hydrates in the Eastern Arctic shelf can significantly alter these fluxes. For example, Panikov and Dedysh (2000) developed a model of methane emission into the atmosphere from marshes covered by snow in Western Siberia, which allows the calculation of CH4 fluxes in pixels where there is permafrost. In this case the flux C8 can be described by the equation: dC8 =dt ¼ ξX ð1  Y Þ=Y

ð8:10Þ

where ξ is the specific speed of microbial population growth with biomass X, Y is the biomass increase per unit of substrate consumed. Microbial biomass X is formed by law: dX=dt ¼ ξX, where ξ ¼ ξmax SQ=ðS þ K S Þ

ð8:11Þ

S is the concentration of substrate catabolism, Q is the function of physiological state, KS is the saturation constant which is numerically equal to this substrate concentration where ξ ¼ 0.5ξmax. Natural wetlands and the rice plantation emit to the atmosphere more than 30% of the methane from all its sources. The flux C3 is described by the following equation:   C 3 ¼ H r f1 ðT s Þf2 ðhÞf3 ðpHÞf4 r p

ð8:12Þ

where Hr is the heterotrophic respiration, Ts is the soil temperature, h is the water surface level, rp is the oxidation-restoration potential, fi (i ¼ 1–4) are the functions that parameterize the methane emission speed. These functions were determined by the following formulas: f1(Ts) ¼ 1 + max{0, 1-exp(0.00556Ts)}, f2(h) ¼ 1.514 (0.256 + 0.412exp{0.3 h}), f3(pH) ¼ 1.888–0.831exp{0.0085pH), f4(rp) ¼ 0.4573. Fluxes C1 and C2 characterize the main sinks of atmospheric methane and are described by its reaction with OH radicals in the atmosphere during daylight (Xu et al. 2007).

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

337

It is worth noting that the following formula facilitates the control of the related model items: C1 þ C2 ¼ ηðC3 þ C12 Þ

ð8:13Þ

where coefficient η (0.5–0.96) indicates the efficiency of climate-based processes (mainly thawing) including η 0.81 for thermokarst lakes and η 0.63 for peat bogs. As permafrost soils that have remained for a long period of the year, a permafrost thawing time is introduced [tb,tf] where tb is the beginning and tf is the end of the permafrost thawing, respectively. In this respect, the following approximation is introduced: C i ¼ Cia exp fγðT s  T a Þg ði ¼ 1, ::, 12Þ

ð8:14Þ

where Cia is the empirical evaluation of the i-th methane flux, Ta is the expected average soil temperature during [tb,tf], γ ( 0.004) is the parameter that determines the permafrost reaction taking into account the soil temperature change. The methane flux C13 is characterized by spatial variance due to its dependence on the methane oxidation rates in various permafrost soils (Smith et al. 2010). The following approach is proposed: C13 ¼ Csoil dT s =T a ð15Þ where d ( 0.01–0.05) is the warming effect coefficient.

8.6.3

Simulation Results

The global coupled model of the two GHGs, carbon dioxide and methane is detailed above. It allows the study of the role of soil-plant formations and oceanic ecosystems in climate change by examining scenarios of anthropogenic impacts on their evolution. Obviously, there is a significant level of uncertainty regarding both environmental data and operational representations of CO2 and CH4 fluxes. Nevertheless, the proposed CMCDMC reliably describes these fluxes considering their maximum possible classification and parameterization. In addition, the CMCDMC allows an assessment to hypothetical situations to land cover changes and World Ocean pollution. The actual CO2 concentration in the atmosphere is given by C A ¼ C A þ ρCCH4 , where ρ is the relation between the radiation potentials of CO2 and CH4. According to current literature, forests play an important role in stabilizing the climate system against atmospheric CO2 capture (Härkönen 2011; Landsberg 2011). The speed of this process depends on the temperature and humidity. Examples of possible hypothetical catastrophic change of forest areas are given in Tables 8.14, 8.15, 8.16 and Figs. 8.9 and 8.10. It is known that land biota behaves as atmospheric

338

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.14 Model estimates of changes in carbon reserves under conditions where coniferous forests in the Northern regions (up to 42
N) get burned in 25% and 50% of forest areas

Years after influence 0 10 20 30 40 50 60 70 80 90 100 200

Deviation in the carbon reserves (GtC) after the 25% scenario implementation ΔCA ΔCS ΔCU ΔCL 37.5 1.1 3.9 0.02 28.6 8.5 7.7 0.7 21.0 11.0 5.4 1.8 16.1 10.9 4.8 2.1 12.1 10.1 3.6 2.6 8.8 8.4 2.9 2.9 6.2 7.5 2.1 3.2 4.1 6.4 1.6 3.3 2.8 5.3 1.2 3.5 1.4 4.4 1.1 3.5 0.5 3.6 0.6 3.4 2.2 0.8 0.2 3.3

Deviation in the carbon reserve (GtC) after the 50% scenario implementation ΔCA ΔCS ΔCU ΔCL 75.1 2.8 7.1 0.04 57.3 17.1 14.0 1.4 40.0 22.1 10.2 3.5 32.3 21.9 9.2 3.5 24.7 20.3 6.9 4.0 17.6 16.9 5.9 5.8 12.5 15.2 4.1 6.5 8.3 12.8 3.2 6.7 5.6 10.7 2.4 6.9 2.9 8.9 2.1 7.1 1.1 7.4 1.3 6.9 4.5 1.7 0.4 6.7

Notation: CS¼CDO + CP, CL ¼ CI + CD + CB Table 8.15 Model-based estimates of carbon reserves when all the forests of the northern latitudes will burn during one year (CS¼CDO + CP, CL ¼ CI + CD + CB) Time after impacts (years) 0 10 20 30 40 50

Deviation in the carbon reserves (Gt) ΔCA ΔCS ΔCU 238.1 7.9 24.9 174.2 31.6 47.9 138.9 67.6 39.2 107.9 90.3 32.2 82.0 64.3 24.1 60.9 56.9 18.4

ΔCL 0.1 4.9 10.0 13.8 16.8 19.1

Time after impacts (years) 60 70 80 90 100 200

Deviation in the carbon reserves (Gt) ΔCA ΔCS ΔCU 44.2 49.1 14.3 33.1 41.6 10.3 20.7 35.0 7.5 12.9 30.1 5.5 7.3 24.7 3.7 12.7 5.9 1.7

ΔCL 20.7 21.9 22.8 23.3 23.6 21.7

Table 8.16 Model-based assessments of changes in the carbon reserves after burning all the tropical forests in one year. (CS¼CDO + CP, CL ¼ CI + CD + CB) Time after impacts (years) 0 10 20 30 40 50

Deviation in the carbon reserves (Gt) ΔCA ΔCS ΔCU 414.2 20.0 41.9 265.3 94.1 74.2 161.5 84.8 48.1 90.6 39.2 27.9 45.4 36.5 15.2 18.3 21.6 7.5

ΔCL 0.2 8.2 15.2 19.1 21.3 21.9

Time after impacts (years) 60 70 80 90 100 200

Deviation in the carbon reserves (Gt) ΔCA ΔCS ΔCU 2.9 12.4 3.0 5.8 7.4 0.5 11.7 4.2 0.9 13.3 2.6 1.7 14.5 1.9 2.1 13.4 2.3 1.9

ΔCL 22.9 22.8 22.6 22.5 21.8 17.7

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

339

Fig. 8.9 The dynamics of CO2 concentration for different scenarios of changing land cover areas: (1) the rates of change in forest area remain for the next 100 years, such as those for the period 1970–2015 (0.45%); (2) forests will be cleared by 2050; (3) by 2050 the area of forests will be reduced by 50%; (4) as in 3 but for a 10% reduction; (5) by 2050 the area of forests will increase by 50%; (6) as in 5 but for an increase of 10%; (7) by 2050 coniferous forests will be reduced by 50%; (8) by 2050 agricultural fields will be increased by 50%

CO2 sink to the dynamics of basic carbon reservoirs. Similar simulation experiments allow the evaluation of the role of all types of soil-plant formations in the dynamics of the atmospheric CO2 concentration. For example, Fig. 8.9 shows the CO2 sequestration by forest areas and its role of the latter in the dynamics of the atmospheric CO2 concentration. As shown in Fig. 8.9, the global forest area of 17.5% (6.825 million km2) can lead to the sequestration of atmospheric CO2 during 2015–2050, which will finally lead to a 21% increase of CO2 concentration by the end of twenty-first century. In contrast, a 10% increase in forest areas over the same period will reduce the CO2 concentration in the atmosphere by 12%. Other scenarios shown in Fig. 8.9 show other dangerous consequences for climate change. As a result, human activity has reached the point where carbon capture of forested areas causes adverse changes in the environment. Moreover, a process of land carbon sequestration, as shown in Table 8.14 and Fig. 8.9, is highly depended on the forest management strategy. Consequently, the scenarios considered herein show that the CMCDMC may be an appropriate tool for evaluating the effects of a

340

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Fig. 8.10 Dependence of CO2 flux F3 on oil spill areas on the ocean surface

scenario aimed at changing forest areas. For example, Table 8.17 shows the F6 flux change depending on the change in vegetation cover. Table 8.18 gives an example of CO2 sinks in Russian territory. The anthropogenic carbon emission to the atmosphere from the Russian territory is assumed to be 410  106 t.C/year in 2015 and will be reduced to 300  106 t.C/year in 2050 remaining stable thereafter. Furthermore, it is assumed that CO2 emission into the atmosphere from forest fires in Russian territory will be equal to 35  106 t.C/year. It should be stressed that the Russian territory plays a significant role in the absorption of atmospheric CO2 due to the boreal forests and water-marsh systems. Unfortunately, the Russian forests suffered by the majority of anthropogenic impacts. Another crucial problem of the global carbon dioxide cycle is connected with the World Ocean pollution that comes from different sources including non-point sources such as runoff and point sources, such as oil or chemical spills (Manizza et al. 2013; Williams and Follows 2011). The pollutants affect F3 and F27 fluxes disturbing the CO2 balance in the atmosphere-ocean boundary. According to Krapivin and Varotsos (2016) these fluxes are significantly different in the pelagic ocean areas and in upwelling zones. In fact, the effects of pollution on gas exchange rates in the atmosphere-ocean interface are varied and this investigation may only be limited to hypothetical consideration. Ocean pollution affects the CO2 exchange coefficient and the light penetration into the water column by limiting the phytoplankton photosynthesis, which disturbs the biological pump in the ocean. It is believed that oil spills and plastics in the ocean may reduce the coefficient ν by 50–70% and the light flux by 20–40%. Figure 8.10 shows the possible consequences of ocean pollution by oil products. Upwelling zones react rapidly to oil spills when

8.6 A Coupled Model of Carbon Dioxide and Methane Global Cycles

341

Table 8.17 The dynamics of CO2 assimilation integral velocities (F6) by vegetation covers during its uniform changes until 2020

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

Future vegetation Forest-tundra Forest-tundra Forest-tundra Mid-taiga forests Mid-taiga forests Mid-taiga forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests Humid evergreen tropical forests

F6 (Future vegetation)/F6 (Present vegetation) Years 2020 2030 2050 2100 2.55 2.12 1.89 2.16 0.94 0.92 0.96 1.12 1.42 1.15 1.01 1.04 1.63 1.44 1.11 1.12 1.98 1.66 1.45 1.33 3.92 4.07 2.84 1.96 3.07 2.55 2.44 1.77 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

153.23

138.4

140.67

791.01

766.43

751.3

766.93

1.42

1.33

1.23

1.25

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

1.09

their area is less than 50% of the upwelling zones. Then CO2 exchange rate begins to decline rapidly in the pelagic zones. This is explained by the difference in the areas of these zones with different photosynthetic processes. Further improvements to the above simulation results require consideration of coupled cycles of carbon dioxide, methane, nitrous oxide, and other GHGs. Advances in modeling of global climate change can be achieved when models such as the CMCDMC include a detailed representation of the surface and deep ocean, including processes of the ice sheet losses or changes in Earth’s albedo linked to evolution of vegetation. Real data on the emissions of aerosols and GHGs to the atmosphere are poorly reliable and their increase can improve the precision of the

342

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.18 The dynamics of CO2 sinks in the Russian territory taking into account the vegetation covers listed in Table 8.4

Soil-plant formation Arctic deserts and tundras Tundras Mountain tundra Forest-tundra South-taiga forests Mid-taiga forests Sub-tropical deserts Broad-leaved coniferous forests Moderately arid and arid steppes Dry steppes Sub-boreal and wormwood deserts Sub-boreal and saltwort deserts Alpine and sub-alpine meadows Total

Rate of СО2 assimilation, 106 tC/year Years 2010 2025 2050 2075 2100 3.1 3.3 5.0 6.7 6.8 4.9 5.4 8.6 10.9 11.2 5.5 5.6 8.6 12.4 12.9 4.2 4.6 5.5 9.2 9.6 15.9 17.6 30.6 43.6 44.2 42.9 47.5 74.6 110.6 111.7 31.4 34.7 53.4 72.2 71.8 6.7 7.4 9.8 13.1 12.6 5.4 6.1 7.5 8.2 8.3 0.8 0.8 0.9 0.9 0.9 2.8 3.1 3.6 3.7 3.7 0.8 0.8 1.0 1.2 1.2 1.7 1.9 2.1 2.2 2.1 133.3 138.8 211.2 294.9 297.0

2125 6.9 12.0 14.2 10.3 45.1 113.6 71.4 12.3 8.5 0.9 3.7 1.2 2.1 302.2

2150 6.9 12.1 13.8 10.4 44.2 109.3 70.5 10.7 7.9 0.8 2.9 1.0 1.8 292.3

CMCDMC. For instance, the large-scale forest fires in Siberia and the Far East in 2019 did not assess their influence on global atmospheric processes. While fires themselves release huge amounts of CO2 into the atmosphere, the dark soot and ash they produce can cover ice and snow and stop them reflecting heat, increasing the risk of permafrost layer could thaw and releasing methane into the atmosphere. It is obvious that it has turned into an ecological disaster.

8.6.4

Conclusions and Discussion of the Results

We showed above that the proposed mathematical tool of the combined model of CO2 and CH4 cycles (CMCDMC) is capable to reliably interpret the role of different environmental parameters in the natural and anthropogenic components of the climate change. This model may be used to investigate the impact of environmental parameters such as temperature or different flow rates on the global radiation balance. The CMCDMC structure does not change if the spatial distribution of land or oceanic ecosystems changes. The location and mechanisms responsible for the carbon and methane sinks in the northern mid-latitude lands are uncertain. For example, the permafrost regions are not sufficiently studied concerning the geochemical and ecological processes taken place in these pixels. Moreover, there are CH4 sources that need detailed study, in particular: landfills, natural gas systems, enteric fermentation, coal mining, manure management, wastewater treatment, petroleum systems, petrochemical production, iron and steel production, etc.

8.7 Microwave Remote Sensing Monitoring and Environmental Problems

343

Therefore, the CMCDMC requires future development mainly to expand its database including the role of cosmic dust fluxes. It is known that about 100–300 tons of cosmic dust goes into the Earth’s atmosphere daily. It is obvious that CMCDMC structure allows it to extend its functions by means of additional items including different tools for parameterizing environmental processes. The most realistic receives information on the characteristics of the permafrost zones by detailed classification of their elements. Certainly, the spatial structures of land ecosystems and oceanic aquatories need to be optimized. Nevertheless, as shown in Table 8.14, the deviation range in the carbon stocks after the burning of the coniferous forests in the northern regions is very high. Following this evolution of the global carbon cycle is changing significantly. Stabilization processes take place over almost 100 or more than 200 years, when 25% or 50% of the coniferous forest areas are burned, respectively. The burning of other forest types leads to the same results. Tropical forests are rapidly revitalizing their environmental functions compared to other forests. As shown in Fig. 8.9 the spectrum of variability of atmospheric CO2 dynamics under different severe scenarios when forest areas are reduced has a wide range. For example, a conservation of the rate of global forest area reduction at the level of 0.45% per annum over the next century may result in atmospheric CO2 concentration reaching 552.5 ppmv to 2150 with a subsequent decrease to 418.3 ppmv in the twenty-third century beginning. Burnt forests restore their functions within the global carbon cycle over 150–170 years. Table 8.17 shows the change in the CO2 assimilation dynamics when forest types are replaced. Implementing such scenarios can have significant environmental implications for the CO2 capture by land vegetation. The results of Table 8.17 show the care of the forest areas, including reforestation and deforestation processes. Table 8.18 shows the key significance of transitional processes at the boundaries between south-taiga forests, mid-taiga forests and forest-tundra where climate changes has major effects. Krapivin et al. (2015) have shown that the global model of the climate-naturesociety system is a unique tool for the reliable assessment of global ecodynamics trends as functions of anthropogenic processes. The CMCDMC may be a block of the global model.

8.7

Microwave Remote Sensing Monitoring and Environmental Problems

The problem of interaction between human society and the environment has acquired over the past two centuries the character has come to the attention of not only ecologists but also politicians from many countries. Initial problems are solved using global models of the climate-nature-society system based on both the theoretical achievements and the instrumental basis of global environmental monitoring.

344

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.19 List of remote sensing tools with their characteristics Tool (sensor, radiometer, measuring system) Advanced Airborne Hyperspectral Imaging Spectrometer (AAHIS) Airborne Imaging Spectrometer (AIS) Compact Airborne Spectrographic Imager (CASI) Compact High Resolution Imaging Spectrograph Sensor (CHRISS) Airborne Ocean Color Imager Spectrometer (AOCI) Wide-angle High-Resolution Line-imager (WhiRL) Airborne Imaging Microwave radiometer (AIMR) Airborne Multichannel Microwave Radiometer (AMMR) Advanced Microwave Precipitation Radiometer (AMPR) Airborne Water Substance Radiometer (AWSR) CRL radar/radiometer Dornier SAR (DO-SAR) Electromagnetic Institute Radiometer (EMIRAD) Electromagnetic Institute SAR (EMISAR) Electronically Steered Thinned Array Radiometer (ESTAR) Microwave Radiometer RADIUS

Used frequencies 440–880 nm

Resolution, sensitivity 3 nm

900–2400 nm 418–926 nm

9.3 nm 2.9 nm

430–860 nm

11 nm

443, 520, 550, 670, 750 and 11,500 nm 595 nm

20 nm 100 nm 2000 nm 20 nm

37 and 90 GHz

3 dB

10, 18.7, 21, 37, and 92 GHz 10.7, 19.35, 37.1, and 85.5 GHz 23.87, 31.65 GHz 9.86; 34.21 GHz 5.3, 9.6, 35 GHz 5, 17, 34 GHz

0.5 K

5.3 GHz 1.4 GHz

22m

0.7, 1.425, 5.475, and 15.2 GHz

0.2–0.5
C 0.1 K 0.5 K 1m 1K

(3–4)
0.5–1.5 K

Table 8.19 briefly presents features for some remote sensing instruments that have produced operational data for global models. The main problem of reliable prognosis of the dynamics of the climate-naturesociety system (CNSS) over at least the next century lies in the deficiency of existing information-modeling technologies. Many climate models as key elements of the forecasting procedure have been making low-precision predictions around the world for a long time. These models are typically generated from mathematical balance equations that use thousands of data points to simulate the transfer of energy and water that takes place at CNSS. As demonstrated by Krapivin et al. (2017a, b) significant role plays the spatial resolution and typical classification of soil-plant formations when making long-term prognosis. Traditional spatial resolutions used for CNSS forecasting are 4
 5
, 1
 1
, and 0.5
 0.5
. Unfortunately, there is no answer to the question which resolution is optimal and is required for reliable forecasts.

8.7 Microwave Remote Sensing Monitoring and Environmental Problems

345

The main negative aspect of almost all global climate models is their chaotic character due to initial factors that cannot be directly tested over a hundred years in the future. Therefore, many authors try to use such versions of climate models that can in principle make such predictions with a reasonable degree of accuracy and when small initial errors lead to small future errors. Such climate models are based on more detailed models of GHGs and on different approximations of temperature effects including climate scenarios (Mintzer 1987; Krapivin et al. 2015; Strassmann and Joos 2018). Forecasting of global climate change requires detailed monitoring of the dynamics of the World Ocean and soil-plant formations. The Global Ocean Observing System (GOOS) provides data about the state of the oceans, including living resources and other information. In addition to the observations made through the other GOOS Ocean Observing Systems there are: • Arctic Regional Ocean Observing System; • Baltic Operational Oceanographic System; • International network to assess the state of marine, coastal and inland-water ecosystems; • European Global Ocean Oberserving System; • International Council for the Exploration of the Seas; • Integrated Marine Observing System (Australia ROOS); • Northwest Shelf Operational Oceanographic System; • North Pacific Marine Science Organization; • Operational Coastal Oceanographic Centre managed by Ifremer; • Western Australian Regional Ocean Observing System; • Regional Observing Systems. Understanding the characteristics of the earth’s surface is an important element in predicting climate change. The Earth Observing System (EOS NASA) provides main information on many biospheric models. A series of artificial satellite missions and scientific instruments on Earth’s orbit provide long-term global observations on the land surface, biosphere, atmosphere, and oceans. This system provides large volumes of data, the effective use of which depends on the use of informationmodeling technologies (Varotsos and Krapivin 2017). Passive microwave satellite observations made using of such as NASA Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Advanced Earth Observing Satellite-II (ADEOS-II), Department of Defense (DoD) WindSat, the National Polar Orbiter Environmental Satellite System (NPOESS) and Conical Microwave Imager Sounder (CMIS) provide information on soil parameters and many geophysical characteristics. The detailed history of passive microwave missions is presented by Sharkov (2003). Future missions are to establish accuracy and reduce quantitative uncertainties in support of global CNSS models.

346

8.8 8.8.1

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Aerosol Radiative Forcing and Climate The Problem with Survey

The unprecedented growth of interest in climate problems observed in recent decades has stimulated both scientific and applied developments, which have greatly enhanced our understanding of the causes of today’s climate change and the laws of paleoclimate. Following Kondratyev et al. (2006) primarily address important aspects related to the aerosol radiative forcing of climate change. They have also promoted the development of scenarios for possible future changes of climate, though it must be stressed that the scenarios are not predictions and that their potential for developing predictions must be assessed as questionable. Unfortunately, the growing interest in the climate is partly explained by the important role played by various speculative exaggerations and revealed apocalyptic ‘predictions’ (e.g., complete melting of Arctic sea ice in the first half of this century). The problems of climate change, formulated as anthropogenic global warming, have been at the heart of geopolitical and global environmental policy. Presidents and prime ministers of several countries now are now discussing whether the Kyoto Protocol (KP) or the latest climate Summits should be considered as a scientifically sound document. The confusion is caused, in particular, to the lack of sufficiently clear and agreed terminology. Ignoring the very complicated concept of climate itself (requiring a separate discussion), we must remember that climate change is defined as anthropogenically. One of the main unresolved problems is the lack of convincing quantitative estimates of the contribution of anthropogenic factors to the formation of global climate, though there is no doubt that there are anthropogenic forcings in the climate. Some international documents containing analyses of current climate ideas refer to a consensus regarding the scientific conclusions contained in these documents. This is wrongly assumed that the evolution of science is not determined by different views and discussions, but by general agreement and even voting. Apart from the issue of definitions, the issue of uncertain conceptual estimates of various aspects to the climate problem remains important. In particular, this refers to the main conclusion of the summary of IPCC-2001, 2007 reports which claim that: “. . . An increasing body of observations gives a collective picture of a warming world and most of the observed warming over the last fifty years is likely to have been due to human activities”. It is regrettable that the former Chairman of IPCC WG-1 Professor J. Houghton in an article in the British newspaper “The Guardian” in 2003, compared the threat of anthropogenic climate changes to weapons of mass destruction and warned the USA against refusing them, support the concept of dangerous, anthropogenic global warming and hence the Kyoto Protocol. No matter how paradoxical it may seem, these claims are in fact made in the light of an increasing understanding of the imperfections of today’s global climate models and their inadequate verification. This makes predictions based on numerical modeling no more than conditional

8.8 Aerosol Radiative Forcing and Climate

347

scenarios. Concerning the USA, we should welcome its huge efforts to support climate studies, which are paying particular attention to improving observational systems and developments in the field of climate problems in general. In 2004, the USA spent 4.5 billion dollars on these problems. President Obama signed in 2009 the “Stimulus Bill” and sent more $26.1 billion to fund climate change programs. From Financial Year 1993 to Financial Year 2014 USA spent $42.49 billion on “climate science”. The statement of the Intergovernmental Group G-8 published on 2 July 2003 (WSSD 2003, 2018) rightly emphasized that in the coming years efforts will focus on three directions: 1. co-ordination of strategies for global observations; 2. provision of a pure, more stable and efficient application of energy; and 3. provision of sustainable agricultural production and preservation of biodiversity. In 2015, 193 United Nations member countries adopted a new sustainable development agenda and a global agreement on climate change (WSSD 2018). Various fora and conferences in recent years have discussed a narrow spectrum of problems, the main outcome of which is not answering the principal question of a coordinated strategy for achieving sustainable development around the world. The Earth’s climate system has indeed changed markedly since the industrial revolution, with some changes of anthropogenic origin. The consequences of climate change are a serious challenge for environmental policy-makers and this alone makes obtaining objective information on climate change, its impacts and possible responses more urgent. To this end, the World Meteorological Organization (WMO) and the UN Environmental Programme in 1988 organized the Intergovernmental Panel on Climate Change (IPCC) divided into three working groups (WG) with spheres of responsibility for: 1. the scientific aspects of climate and its change (WG-I); 2. impacts and adaptation to climate (WG-II); and 3. analysis of the potential for climate change mitigation (WG-III). The IPCC has so far prepared detailed reports (2001, 2005, 2007) as well as several special reports and technical papers. Griggs and Noguer (2002) briefly reviewed the first volume of the IPCC’s Third Assessment Report (TAR) compiled 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 comments. With the participation of delegates from 99 countries and 50 scientists recommended by the leading authors, the final discussion of TAR was held in Shanghai on 17–20 January 2001. A “Summary for decision-makers” has been adopted following detailed discussion by 59 specialists. The analysis of the observational data contained in TAR has led to the conclusion that global climate change is taking place. The IPCC Report (2001) gives a detailed overview of the spatio-temporal variability of the observational data of the concentrations of various GHGs and aerosol in the atmosphere. The adequacy of the

348

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

numerical models has been discussed from the viewpoint of the climate-forming factors and the utility of models to predict climate change in the future. The main conclusion about the anthropogenic impacts on the 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 the prognostic estimates considered in the TAR, both surface air temperature (SAT) increase and sea level rise must take place in the twenty-first century. In characterizing the IPCC data for empirical climate diagnostics, Folland et al. (2002) drew attention to the uncertainty of the definitions of some basic concepts. According to the IPCC terminology, climate changes are statistically significant variations of an average state or its variability, whose stability is maintained 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 as opposed to natural climate change. According to 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 natural (due to internal processes and external forcings) and anthropogenic: it possesses both internal and external variability. As Folland et al. (2002) noted that seven key questions are more important for the diagnostics of observed changes and the climate variability: 1. 2. 3. 4. 5.

How significant is climate warming? Is currently observed warming significant? How fast has the climate changed in the distant past? Has the precipitation and atmospheric water content changed? Are changes in the general circulation of the atmosphere and the oceans taking place? 6. Have climate variability and climate extremes changed? and 7. Are the 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 the climate remains impossible. However, information on numerous meteorological parameters, so important for climate change documentation, detection and attribution, remains insufficient to draw reliable conclusions. This is especially true for global trends in these parameters (e.g., precipitation), which are characterized by high regional variability. Folland et al. (2002) answered some of the questions above. A comparison of the secular change of global average annual sea surface temperature (SST), land surface air temperature (LSAT) and nocturnal marine air temperature (NMAT) for the period 1861–2000 generally revealed some similarities, although warming in the 1980s from the LSAT data turned out to be stronger, and the NMAT data showed a

8.8 Aerosol Radiative Forcing and Climate

349

moderate cooling at the end of the nineteenth century that was not demonstrated by the SST data. The global temperature trend can be interpreted cautiously as equivalent linear warming over 140 years is 0.61
C at a 95% confidence level with an uncertainty range of 0.16
C. Later, in 1901 a warming of 0.57
C was carried out with an uncertainty of 0.17
C. These estimates suggest that by the end of the nineteenth century an average global warming by 0.6
C took place, with estimates ranging into 0.4–0.8
C to a 95% confidence level. Global temperatures in 2018 were 0.83
C warmer than the 1951 to 1980 average. According the most recent data, global temperatures spanning back to December 1, 1978 have increased by an average of 0.13
C each decade. In fact, the global temperature in March 2019 was +0.61F above the seasonal average, the Northern Hemisphere was +0.79 F above the seasonal average, the Southern Hemisphere +0.45 F above, and the tropics +0.74 F above the seasonal average. The spatial structure of the temperature field in the twentieth century was characterized by relatively uniform warming in the tropics and considerable variability in extratropical latitudes. Warming between 1910 and 1945 was initially concentrated in the North Atlantic and adjacent regions. The Northern Hemisphere was characterized by cooling between 1946 and 1975, while the Southern Hemisphere experienced some warming during this period. The temperature rise observed in the decades (1970–2000) generally proves to be quite modern and clearly manifested in the Northern Hemisphere continents in winter and spring. In some regions of the Southern Hemisphere and the Atlantic, however, there was slight cooling throughout the year. A temperature decrease in the North Atlantic between1960 and 1985 was later followed by an opposite trend. Overall, the climate warming over the period of measurements was more uniform in the Southern Hemisphere than in the Northern Hemisphere. In many continents between 1950 and 1993, the temperature rose faster at night than during the day (but this is not reported in coastal regions). The rate of temperature increase varied from 0.1 to 0.2
C/10 years. Biktash (2017) concluded that much of the climate variations can be explained by the effect of the overall effect of soil irradiance and cosmic rays on the state of the lower atmosphere and meteorological parameters. According to aerological observations, the air temperature in the lower and middle troposphere increasing 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 analysis of aerological and satellite information showed that in the period 1979–2000 the temperature trend in the lower troposphere was weak, while near the surface of the Earth proved 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 was 0.13  0.06
C/10 years, which is different from the data for the period 1958–1978, when the average global temperature in the lower troposphere increased more rapidly (0.03
C/10 years) than near the surface. The significant differences between the temperature trends in the lower troposphere and near the surface are likely to be real. So far, these differences cannot be convincingly explained. The climate warming in the Northern Hemisphere

350

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

observed in the twentieth century has been the most substantial over the last 1000 years according to Folland et al. (2002) the observational warming. Particular attention was paid in the IPCC-2001 Report on the predictability of future climatic changes. The chaotic nature of atmospheric dynamics limits longterm weather forecasts to 1 or 2 weeks and prevents a detailed climate change forecast (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 continued growth in GHGs concentrations in the atmosphere. Such scenarios, if credible, may be useful for decision-makers in the field of ecological policy. The basic method for making such scenarios tangible involves the use of numerical climate models that simulate interactive processes in the “atmosphere-ocean-land surface-cryosphere-biosphere” climatic system. As Collinz and Senior (2002) noted, because there are so many such models, a serious difficulty arises in choosing the best model. Since the problem of choice is insoluble, it remains possible to compare climate scenarios resulting from the use of different models. According to the IPCC recommendations, four levels of projection reliability are considered: 1. from reliable to highly probable (in this case there is agreement between the results for most models); 2. most likely (new projections received with latest models); 3. possible (new projections with agreement on a small number of models); and 4. very likely (model results are not certain but changes are of course possible). A major difficulty in making the projections essential is the inability to set agreed predictions on how GHGs concentrations will evolve in the future, making it necessary to take into account a number of different scenarios. The huge thermal inertia of the World Ocean dictates the likehood of delayed climatic impacts of already increased GHGs concentrations. Calculations of the annual average global SAT using the energy-balance climate model with various scenarios of temporal variations in CO2 concentrations resulted in SAT intervals from 2020, 2050, and 2100 to be 0.3–0.9, 07–2.6, and 1.4–5.8
C, respectively. Due to the ocean thermal inertia, delayed warming should manifest itself within 0.1–0.2
C/10 years (such delay can take several decades). The following conclusions can be attributed to the category of projections with the highest reliability (Collinz 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 has a SAT increase (new estimates suggest the conclusion about a weaker manifestation of the aerosol impact);

8.8 Aerosol Radiative Forcing and Climate

351

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 covers extent in the Northern Hemisphere; 6. increase of the average global content of water vapour 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 weakening in sub-tropical latitudes; 8. increase in precipitation intensity (higher 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, accompanied by a shift of the precipitation zones to the east; 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 the daytime maxima); 13. greater reliability of the conclusions on temperature changes than those on precipitation; 14. the attenuation of the thermohaline circulation (THC) that causes a decrease of the warming in the North Atlantic (the effect of the THC dynamics however cannot compensate for warming in West Europe due to the growing concentration of GHGs); and 15. more intense penetration of warming into the ocean depth over high latitudes where vertical mixing is most intense. Concerning estimates with a lower level of reliability, the conclusion (at level 4) of the lack of agreement on the changing frequency of storms at mid- latitudes is of particular interest here, as is a similar lack of agreement on the 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 will allow for the predicting climate change. Allen (2002) discussed the basic conclusions contained in the “Summary for policy-makers” (SPM) of the Third IPCC Report and especially his 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 complements the statement that “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 no one chance in ten chance for the opposite statement to be valid). Clearly, the reality of such statement

352

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

depends on the adequate modeling of the observed climatic variability. The analysis of the results of the relevant calculations using six different models showed that three of the six models reproduce climate variability over time scales of 10–50 years 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 it is unlikely that it can be of only natural origin” (“unlikely” means a probability of three for a contrary conclusion). This conclusion is complemented by 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 20th 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 led to 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. The results of numerical modeling cannot explain the pre-1940 climate warming with only anthropogenic factors taken into account, but are quite adequate considering both natural and anthropogenic impacts (GHGs and sulphate aerosol). As stated in SPM of TAR, “these results. . . do not exclude possibilities of contributions of other forcings”. It is therefore likely 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 according to models, the tropospheric temperature increases more rapidly than near the surface, then the analysis of observational data between 1979 and 2000 reveals that the temperature increase in the free troposphere is slower and may be absent. In evaluating the content of the IPCC-2001 Report, Griggs and Noguer (2002) argued that this report: 1. contains a more complete description of current ideas on known and unknown aspects of the climate system and associated factors; 2. is based on the knowledge of an international group of experts; 3. prepared on the basis of open and professional review; and 4. is based on scientific publications. Unfortunately, none of these statements can be convincingly substantiated. Therefore, the IPCC-2001 Report has been strongly criticized in the scientific literature, the most important of which were discussed by Kondratyev et al. (2006). Many global summits on climate change, global warming and sustainable development that have taken place in recent years have been restricted by common discussions without effective results. The 5th World Summit on Climate Change and Global Warming taken place on February 17–18, 2020 in Amsterdam, the Netherlands, led to progress in resolving existing problems. Certainly, scientific progress in

8.8 Aerosol Radiative Forcing and Climate

353

assessing global climate change is based when the climate effects caused by the addition of GHGs such as carbon dioxide, methane gas and nitrous oxide to the atmosphere are considered. Indeed, environmental science provides governments with reliable information about the consequences of anthropogenic activity, but a decision making is not made.

8.8.2

Empirical Diagnostics of the Global Climate

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, the climate is characterized by many parameters, such as air temperature and humidity near the Earth’s surface and 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, the empirical analysis of climatic data is usually limited by the results of SAT observations, with datasets 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. It should also be borne in mind that the globally averaged secular trend of SAT values is based, to a large extent, on the use of imperfect observed data of sea surface temperature (SST). The most important (and controversial) conclusion (IPCC 2001) concerning the anthropogenic nature of current climate change is based on analysis of the SAT and SST combined data, that is on the secular trend of mean global average annual surface temperature (GST). In this connection, two questions arise: firstly about the information content of the notion of GST (this problem was formulated by Essex and McKitrick (2002); secondly about 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 ongoing from the perspective of observational techniques. For more than 100 years the SAT has been measured with glass thermometers, but now the settings to protect the thermometers from direct solar radiation and wind have changed repeatedly. This dictates a necessity for filtering out SAT data to provide homogeneous data series. In the period from April to August 2000 at the station of the Nebraska State University, USA (40o83’N; 96o67’W), Hubbard and Lin (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

354

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.20 The ten warmest years characterizing the global combined land and ocean on an average yearly basis

Year 1998 2005 2009 2010 2013

Anomaly,
C 0.64 0.66 0.64 0.70 0.67

Year 2014 2015 2016 2017 2018

Anomaly,
C 0.75 0.91 0.95 0.85 0.79

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 observational data, emphasis should be placed on the analysis of climate variability, where it is important not to take into account the averages but also the moments of higher orders. Unfortunately, no attempts have been made to use this approach. The same approach applies to estimates of the internal correlation of observation series. McKitrick (2002), 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: the rise in the global average SAT over the last 20–30 years is the main basis for concluding on the anthropogenic contribution to the present-day climate changes. During the twenty-first century, the global land and ocean temperature departure from average reached new five times higher (2005, 2010, 2014, 2015, and 2016), with three remaining behind. The years 2015–2017 each had a global temperature departure from the average that was more than 1.0
C (1.8
F) above the 1880–1900 average. Ten warmest years are shown in Table 8.20. Table 8.21 gives a detailed overview of global climate fluctuations.

8.8.3

The Radiative Forcing

Estimates of RF changes contained in IPCC-2001 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 2.42 Wm2, with the following contributions of various MGCs: CO2 (1.46 Wm2), CH4 (0.48 Wm2), halocarbon compounds (0.33 Wm2), N2O (0.15 Wm2). The ozone depletion observed during the last two decades could lead to a negative RF constituting 0.15 Wm2, 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 Wm2. Since the IPCC-1996 Report, RF estimates have changed substantially, not only by purely scattering sulphate aerosol considered above, but also by other types of

8.8 Aerosol Radiative Forcing and Climate

355

Table 8.21 Global and regional anomalies of the surface air temperature Region Global Land

Anomaly,
C

Rank (out of 139 years)

Records Year

Anomaly,
C

+1.12  0.14

Ocean

+0.66  0.16

Land and ocean

+0.79  0.15

Warmest 4th Coolest 136th Warmest 4th Coolest 136th Warmest 4th Coolest 136th

2016 1884 2016 2009 2016 1908

+1.45 0.62 +0.76 0.45 +0.95 0.44

Warmest 5th Coolest 135th Warmest 5th Coolest 135th Warmest 4th Coolest 136th

2016 1884 2015 2008 and 2009 2016 1908

+1.59 0.70 +0.89 0.47 +1.14 0.47

Warmest 4th Coolest 136th Warmest 3th Coolest 137th Warmest 4th Coolest 136th

2015 1917 2016 1911 2016 1911

+1.10 0.64 +0.69 0.43 +0.75 0.44

Northern hemisphere Land +1.18  0.16 Ocean

+0.75  0.16

Land and ocean

+0.92  0.15

Southern hemisphere Land +0.97  0.11 Ocean

+0.58  0.16

Land and ocean

+0.65  0.15

aerosol, especially carbon (soot) characterized by considerable absorption of solar radiation as well as organic, sea-salt, and mineral aerosol. The strong spatiotemporal variability of the aerosol content in the atmosphere and its properties greatly complicate the assessment of the climatic impact of aerosol (Kondratyev 1999; Melnikova and Vasilyev 2004). The new results of the numerical climate modeling have radically changed the understanding of the role of various factors in RF formation. According to Kondratyev et al. (2006), there is an approximate mutual compensation of climate warming due to the growth of CO2 concentration and cooling caused by anthropogenic sulphate aerosol. Under these conditions, anthropogenic emissions of methane (mainly due to rice-fields) and carbon (absorbing) aerosols should play a more important role. Estimates of RF obtained with due regard to GHGs and aerosol are of importance in giving substance to the conclusions concerning the contribution of anthropogenic factors to climate formation. The accuracy of these conclusions, however, is restricted by three factors. One of them is that the interactivity of these factors seriously limits (if not excludes) the possibility to adequately estimate the contributions of individual factors. The second factor, not less important, is that the above calculated estimates refer to average global values and are therefore the results of

356

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

smoothing RF values characterized by strong spatio-temporal variability. Finally, the most complicated problem is the impossibility to reliably assess the RF aerosol given its direct and indirect components. According to estimates in Podgorny and Ramanathan (2001), the value of direct RF at the surface level can be increased to 50 W m2, and Chou et al. (2002) obtained values above 100 W m2 during the forest fires in Indonesia. Vogelmann et al. (2003) estimated RF due to the radiative heat exchange from which shows that during the day near the surface the RF value is usually equal to several W m2. From the data of Pavolonis and Key (2003), the total RF at surface level in the Antarctic ranges between 0.4–50 W m2. Yabe et al. (2003) received an average value 85.4 W/m2 and from Lindsey and Simmon (2003), the RF in the USA is 7–8 W/m2. Weaver (2003) analyzed the possible role of changes of the cloud RF (CRF) at the atmospheric top level, especially in extra-tropical latitudes, as a climate-forming factor whose role consists in regulating the poleward meridional heat transport. The cloud dynamics in extra-tropical 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 (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. Various levels of efficiency can determine a possibility of the existence 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 (70oN – 70oS). Analysis of the results under discussion revealed: 1. the 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;

8.8 Aerosol Radiative Forcing and Climate

357

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); 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 et al. (2003). Markowicz et al. (2003) undertook a study to estimate the aerosol RF due to longwave radiation (radiative heat exchange). Rossow (2003) rightly warned that attempts to isolate and describe a greater number of climatic feedbacks and to quantitatively estimate them using methods proposed earlier, have become confusing and disorienting, since an application of a simple linear theory consisting of many sub-systems, is completely unacceptable. Changes in extra-atmospheric solar radiation are one climate-forming factor that should be taken into account. The contribution of these changes to RF since 1750 could have reached ~20% compared to the contribution of CO2, which is mainly determined by the enhancement of extra-atmospheric insolation in the second half of the twentieth century (of importance is a consideration of the 11-year cycle of insolation). However, the possible mechanisms of enhancement of the climatic impact of solar activity are still far from being understood (Kondratyev et al. 2006). Shamir and Veizer (2003) found out, for instance, a high correlation between intensity of galactic cosmic rays and temperature for the last 500 million years. On this basis, it was concluded that 75% of temperature variability in that period had been determined by the contribution of this factor (this problem has been also considered earlier in Kondratyev (1998)). The reliability of ARF estimates depends on many factors, one being the reliability of information about the aerosol optical thickness (AOT). This information provided through many satellite sensors including multi-spectral thermal videoradiometer MTI, AVHRR, OCTS, POLDER, INDOEX and TOMS instrumentation. The data in Table 8.22 characterize the annual average values of the total content of various types of aerosol. The data in Table 8.23 illustrate the estimates of the contribution of various factors to ARF formation at surface level (SL) and the top of the atmosphere (TOA), as well as shortwave radiation absorbed by the atmosphere (ASWR). Atmospheric aerosol monitoring is performed using satellite remote sensing technologies including radar systems and radiotransluence methods (see Fig. 2.1). Various satellite sensors provide information on the atmospheric aerosol distribution and their characteristics to better quantity the direct and indirect effects of aerosols on the climate. Atmospheric aerosol transfer radiative energy and transform of water vapor into cloud droplets and rain-drops. The effect is taken into account in most general

358

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.22 Annual average values of the total atmospheric content of different types of aerosol in the Northern (NH) and Southern (SH) Hemispheres and around the world

Type of aerosol Natural sulphates SO2 4 OC due to fossil fuels BC due to fossil fuels OC due to biomass burning BC due to biomass burning OC emissions at surface level BC emissions at surface level Natural OC Dust (r < 1 μm) Sea-salt aerosol (r < 1 μm)

Table 8.23 Estimates of the contribution of various factors to the ARF formation and ASWR (W/m2) in the period 5–15 April 2001 in the Eastern Asia (20
–50
N, 100
150
E)

Factor Dust aerosol Sulphates Organic carbon Black carbon Sea salt Internal mixture Longwave RF Total RF (clear sky) Total RF (real cloud conditions)

Anthropogenic sulphates

SO2 4

NH 0.87

SH 0.22

Globe 1.09

0.45

0.42

0.86

0.39 0.08 1.28 0.13 0.49 0.05 0.13 11.11 1.82

0.03 0.01 1.24 0.13 0.52 0.06 0.10 3.57 2.85

0.41 0.09 2.52 0.26 1.02 0.11 0.23 14.68 4.68

SL 9.3 3.6 3.9 4.1 0.4 2.2 3.0 20.5 14.0

ASWR 3.8 0.3 1.7 4.5 0.0 3.5 2.3 11.5 11.0

ATL 5.5 3.3 2.2 0.4 0.4 1.3 0.7 9.0 3.0

circulation models as natural processes linking aerosols with the energy and water cycles which helps to parameterize the direct and indirect effects on the climate. The main problem arising here is the existence of greater uncertainties in estimating climate forcing due to the lack of reliable worldwide measurements. Overcoming existing informational uncertainties through many satellite missions does not solve many problems. The uncertainties remain as a result of assumptions on the shape of the aerosol size distribution and the chemical composition of the aerosol particles. Moreover, solution of the top of the atmosphere radiance equations always brings additional uncertainties (Zhou et al. 2009; Shi et al. 2019; Knobelspiesse and Nag 2018). Aerosol optical properties have been measured since 1970’s using in-situ measurements and remote sensing observations. Lately there has been considerable interest in looking for dependencies between aerosol transport and seasonal atmospheric cycles. Particular attention is paid to the Arctic haze formation as a result of industrial pollution emissions across Europe and Russia, including forest fires. In fact, atmospheric circulation in this region has undergone significant shifts in recent decades in terms of the specific distribution of aerosol types.

8.9 Aerosol Long-Range Transport and Climate

8.9 8.9.1

359

Aerosol Long-Range Transport and Climate Aerosol and Climate

Aerosol structures of various types regulate the balance of the climate system that occurs in the energy exchange between the atmosphere, ocean and land. A balanced concentration of chemicals, gases and aerosols at a distance of 10–15 km from the Earth’s surface is an essential guarantee for sustainable climate dynamics. Systematic information on these concentrations is provided by many satellites around the globe with good spatial and temporal frequency. Unfortunately, existing model tools do not use this data to the fullest extent and thus need further improvements. Certainly, aerosol particles with a diameter between about 0.002 μm to about 100 μm vary greatly in size, source, chemical composition, amount and time and space disorder. An important feature of the aerosol is that it now survives in the atmosphere. This feature plays a key role in understanding the effects on aerosols on climate change. Numerous global satellite products, such as AVHRR, TOMS, MODIS, MISR and SeaWiFS help to estimate the aerosol’s climate impact (see Table 8.24). A comparatively long (up to 2–3 weeks) aerosol particle lifetime in the atmosphere determines the ability of their long-range transport (Kondratyev et al. 2006). The most well-known and studied situations of this kind are dust aerosol (DA) emissions to the atmosphere during dust storms in North Africa and subsequent trans-Atlantic particles transport, with occasional meridional DA fluxes in Western Europe. Another, well-known situation is the long-range DA transport to the north-western sector of Pacific Ocean during dust storms in north-western China and Mongolia (a brief review of the dust-storm problems can be found in the monograph by Kondratyev et al. 2002a). Dust from Sahara desert is transported over long distances to northern regions, including the North Atlantic Ocean, which plays an important role in the global climate system. Dust particles play a significant role as a main contributor to the aerosol indirect effect. The mineral dust from the Sahara across the Atlantic provided to many Caribbean islands and the Amazon Basin affecting the formation of soils. Understanding the key role of aerosol in climate formation has stimulated further studies on long-range transport of aerosol. For example, a new programme ITCTLagrangian-2k4 has been accomplished to study intercontinental transport and chemical transformations of aerosol. Parrish and Law (2003) briefly described the content of the ITCT programme, whose main objective is the study of long-range (intercontinental) transport and the chemical transformations of aerosols and oxidants and their precursors. To obtain observational data for this purpose, it would be worthwhile to use instruments installed on the Lagrangian platform, moving alongwith the air masses under study. However, only “pseudo-Lagrangian” observations are possible using one or more flying laboratories performing multiple soundings with a certain air volume. This is the basic aim of the ITCT programme, the first stage of which consisted of aircraft observations in the North Atlantic region

360

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.24 Satellite platforms and sensors for aerosol retrieval and ground observations Platform, sensor MODIS OMI

TOMS

Producer NASA Terra, Aqua satellites EOS-AURA (NASA)

CALIPSO

Nimbus-7&Earth Probe CALIPSO

AVHRR

NOAA series

GOES

GOES

POLDER

PARASOL

MISR

NASA Terra, Aqua satellites

SeaWiFS (1997–2010)

NASA SeaStar

MERIS

ENVISAT

VIIRS

NPOESS

ATSR, AATSR

ERS-1, ENVISAT

GOME

ERS-2

Short characteristic 36 spectral channels: wavelengths range from 0.405 μm to 14.25 μm. Spatial resolution of 250 and 500 m. The OMI instrument can distinguish between aerosol types, such as smoke, dust, and sulfates, and measures cloud pressure and coverage, which provides data to derive tropospheric ozone TOMS is a NASA satellite instrument for measuring ozone values and sulfur dioxide released in volcanic eruptions. Active sensor-lidar with two wavelengths of 532 nm and 1064 nm. Three-channel IIR of nadir viewing: 8.7, 10.5 and 12.0 mm. It is a space-borne multispectral sensor that measures the reflectance of the Earth in six spectral bands including red, thermal, mid and near-infrared bands. But over time, their spectral ranges have varied. Every satellite of this series views the continental United States, the Pacific and Atlantic Oceans, Central America, South America, and southern Canada. This is a passive optical imaging radiometer and polarime ter instrument for the observation of solar radiation reflected by Earth’s atmosphere, including studies of tropo spheric aerosols, sea surface reflectance, bidirectional reflectance distribution function of land surfaces, and the Earth Radiation Budget It views the Earth with cameras pointed at nine different angles – nadir, 26.1
, 45.6
, 60
and 70.5
. It can distinguish different types of clouds, aerosol particles, and surfaces. SeaWiFS had 8 spectral bands from 412 nm to 865 nm. It collected global data at 4 km resolution, and local data (limited onboard storage and direct broadcast) at 1 km. This instrument is composed of five cameras disposed side by side, each equipped with a push broom spectrometer. Spatial resolution: Ocean: 1040 m  1200 m, land & coast: 260 m  300 m It provides continuity from the MODIS corrected reflectance imagery which was developed to provide naturallooking images by removing gross atmospheric effects such as Rayleigh scattering from the visible bands. ATSR and AATSR are multi-channel imaging radiometers with the principal objective of providing data concerning global Sea Surface Temperature (SST) to the high levels of accuracy and stability required for monitoring and carrying out research into the behaviour of the Earth’s climate. The GOME instrument is a double monochromator which combines a predispose prism and a holographic grating in each of the four optical channels as dispersing elements. (continued)

8.9 Aerosol Long-Range Transport and Climate

361

Table 8.24 (continued) Platform, sensor SCIAMACHY

Producer ENVISAT

Short characteristic SCIAMACHY’s approach for passive atmospheric sounding from space was to measure solar absorption spectra at the top of the atmosphere. SCIAMACHY observed in the wavelength range from 214–2386 nm

in the summer of 2004, focusing on studies of emissions of aerosol precursors and tropospheric ozone in North America, including analysis of their long-range transport and their chemical transformation in the North Atlantic basin, as well as the subsequent impacts on the atmosphere in Western Europe. The key objectives of the ITCT programme include: 1. determination of the potentials of photochemical oxidants and aerosol formation in polluted air masses formed in North America and moving across the Atlantic Ocean to the region of Western Europe; 2. analysis of atmospheric dynamics responsible for the long-range transport of pollutants from the planetary boundary layer to North America; 3. quantitative characteristic of North American pollutants transport to the background atmosphere, their subsequent evolution and impact on climate. Note than ten flying laboratories from different countries were planned to take part in the accomplishment of the ITCT programme. To study the laws of the intercontinental transport of atmospheric pollutants, Stohl et al. (2003a,b) and Stohl (2004) performed a numerical simulation for a period of one year (conditions for year 2000 were considered) for six passive tracers emitted on different continents (to characterize the levels emissions of carbon monoxide CO). Calculations have shown that emissions from the Asian continent are characterized by the fastest vertical propagation, while the tendency to remain within the lower troposphere is typical of the European emissions. European emissions are mainly driven in the Arctic where they contribute more to the formation of Arctic haze. The tracers come from the continent where emissions are made to another continent in the upper troposphere, typically in about four days. After that, the tracers can also appear to be transported in the lower troposphere. With a characteristic lifetime of up to two days, it is proven that local tracers are a dominant component of the atmosphere on all continents except Australia, where the share of “foreign” tracers represents about 20% to the total mass of tracers. Assuming the tracers’ lifetime as 20 days, even on continents with a high level of “home” emissions, the share of tracers from other continents exceeds 50%. Since three regions, where the tracers are transported and where the tracers are slowly “dissolve” require particular attention, further studies should focus on three directions: 1. the winter-time accumulation of tracers from Asia over Indonesia and the Indian Ocean; 2. concentration of Asian tracers in the Middle East in summer, which is maximum;

362

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

3. distribution of tracers coming to the Mediterranean Sea from North America in summer. Simulation of the spatial distribution of dust aerosol (mineral) is a difficult problem due to the episodic nature of DA sources and its long-range transport. Based on observational time-series for 22 years in different locations, but mainly from satellite remote sounding data, Luo et al. (2003) performed comparisons between the observed spatio-temporal variability of aerosol distribution and the results of numerical simulation employing the combined use of the Multi-scale Atmospheric Transport and Chemistry (MATCH) model of aerosol transport in the atmosphere taking into account chemical reactions determining the transformation of its properties and the Dust Entrainment and Deposition (DEAD) model simulating the processes of formation and transformation of dust aerosol (Andersson and Kahnert 2016; Zender et al. 2003). Matsui and Mahowald (2017) considered and used 2-D sectional global aerosol model focused on the mixing of black carbon (BC) and other species (dust, organic aerosol) considering different absorption and scattering properties and cloud condensation nuclei (CCN) and ice nuclei activity (e.g., dust mineralogy, brown carbon). The results of the comparisons revealed a good agreement, albeit with some differences. In order to analyze the reasons of these differences, account was taken of the dependence of the numerical simulation results on the variability of various meteorological input parameters and the choice of the parameterization scheme of the process of particles’ “mobilization”. The discussion of the sensitivity analysis suggested that not far from Australia, the differences between the calculated spatial distributions of the aerosol optical thickness and the observed are explained by the scarcity of wind field data near the surface and at aerosol sources. In the region of Eastern Asia these differences are mainly determined by the insufficiently reliable consideration of meteorological conditions. According to the estimates obtained, total DA emissions to the atmosphere as a result of dust storms are 1654 Tg/year. The most powerful sources of DA emissions are African deserts whose contribution to the total content of DA in the atmosphere is 73%. East Asia contributes most to the formation of the field of aerosol content in the Pacific Ocean in the Northern Hemisphere, while in the Southern Hemisphere, Australia is the main source of aerosol dust. The typical DA lifetime in the atmosphere is approximately six days. Tomasi et al. (2015) rightly note that aerosols are one of the greatest sources of uncertainty in climate model due to their unstable spatial distributions and concentrations that vary over time, introducing stochastic changes in direct radiative forcing effects in the surface-atmosphere system. The existing shape of the main aerosols transport pathways between different regions corresponds in reality to large dispersions. Ground-based and satellite remote sensing measurements of aerosol characteristics significantly overestimate the uncertainties of many climate models (Lenoble et al. 2013; Boucher 2015). Extremely uncertain estimates of the direct and indirect climatic impacts of the aerosol have caused great interest in analyzing the processes determining the effect of the aerosol and clouds on climate formation. These uncertainties are mainly

8.9 Aerosol Long-Range Transport and Climate

363

connected with complicated aerosol-clouds interactions, which depend on cloud droplets’ size distribution, chemical composition and cloud type. The problem is further complicated by changes in the properties of aerosol particles due to their interaction with cloud droplets. Interactions between the aerosol and clouds occur in two ways: through the functioning of aerosol particles as cloud condensation nuclei, and through the inverse impact of clouds on the aerosol leading to changes in aerosol number density, size distribution and chemical composition. Changes in the properties of aerosol particles are caused by the process of coagulation of cloud droplets during their growth due to the addition of substance as a result of oxidation of MGCs such as SO2 taking place in the liquid phase (droplets) as well as changes in aerosol concentration after washing out of particles from the atmosphere and by phoretic processes. The liquid of frozen water particles (hydrometeors) plays a significant role in remote sensing processes when microwave radiative transfer within the troposphere and the lower stratosphere is exposed to high levels of uncertainty in the brightness temperatures. It is a general problem of radiative transfer in an absorbing and scattering media solution that requires new approaches including modeling techniques and tools (Krapivin et al. 2015; Krapivin and Shutko 2012). The information uncertainty also depends on inadequately accurate and complete observations at global scale (Knobelspiesse and Nag 2018).

8.9.2

Aerosol Long-Range Transport

Basic natural aerosol sources are soil dust (1500 Tg/year), volcanic dust (33 Tg/ year), sea salt (1300 Tg/year) and biological debris (55 Tg/year). Anthropogenic aerosol sources are characterized by multiformity and account for 11% (400 Tg/year) of global aerosol volume (3518 Tg/year). Aerosol source areas include mainly African Sahara, Australian and Asian deserts. Aerosols occur both in the troposphere and in the stratosphere, which have important implications for global climate, ecosystem processes, and human health (Hassler et al. 2008; Maoa et al. 2014; Ding et al. 2008). In some cases, distinct dust layers were observed over the trade wind inversion (Bory et al. 2003). Such dust layers could be observed at distances of up to hundreds of kilometers both in the direction of the prevailing transport and in the perpendicular direction. As a rule, no substantial changes in the size of particles (vertical gradients) were observed any except for the uppermost part of the dust-loaded troposphere about 200 m thick. This conclusion also applies to conditions where aerosols are much layered. An analysis of the factors of formation of the vertical profile of DA concentration led to the conclusion that the impact of the particles’ gravitational deposition in the process on their long-range transport was generally not substantial. This, in turn, suggests that the long-range transport across the Atlantic Ocean have not caused any substantial changes in the vertical profile of the DA concentration formed on the African continent. However, the combined

364

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

impact of the processes of large-scale deposition and the convection-induced vertical mixing (together with a differential advection at different levels) results in chaotic changes that are sometimes observed in the DA vertical profile. Many studies have been dedicated to the analysis of sensitivity of the aerosol optical properties to variability in size distribution, chemical composition, and aerosol particles shape. As for the latter factor, it is not as necessary for particles such as hydrated salt particles or derived from biomass burning, but is very important in the case of dust aerosol particles, when considering the shape of particles in their optical properties and, hence, in the estimates of the climatic impact of the aerosol. In this context, Reid et al. (2003) compared data on DA size distribution obtained using various measurement techniques during the PRIDE field experiment conducted in Puerto Rico to study the size distribution of Saharan dust obtained from Africa between 28 June and 24 July 2000. Francis et al. (2019) identified a new mechanism by which dust aerosols travel over long distances across the east side of the North Atlantic Ocean to the Arctic and showed that the total northern 40
N dust load was estimated by the model to be 38 Tg and the dust deposition was estimated to be 1.3 Tg. Observations of the Saharan aerosol arriving in Barbados due to long-range transport across the Atlantic Ocean have been made during 30 years and are a unique series of related long-term observations. Previous processing of observational data showed that during 1960–1980 the surface concentration of the atmospheric aerosol changed 4 times (Doherty et al. 2012). The Saharan dust storms are not the only powerful source of aerosol emissions into the atmosphere from the African continent then transported for long distances. Another substantial source is biomass burning. Fires in the African savannas account for more than two-thirds of the world’s biomass burning in savannas. These fires result in emissions to the atmosphere of various MGCs (including CO2, CO, NOx, SO2, hydrocarbons, halocarbon compounds, oxidized organic compounds, and aerosol). In South Africa, fires in savannas occur mainly in the period of dry season (April – October), when meteorological conditions are characterized by the formation of permanent air masses, south-eastern trade winds, and subtropical belt of high pressure. The presence in the atmosphere of stable layers near 700 and 500 hPa levels limits the vertical motion of the smoke aerosol and MGCs. Pollutants present in the atmosphere in South Africa are transported over long distances, causing an increase in tropospheric ozone in the southern Atlantic Ocean and reaching the Indian Ocean region. Practically all northern regions are reached by aerosol clouds emerging over their sources in the southern regions. Many cases are known where Asian aerosol was registered in Alaska (Cahill 2003). As the meteorological conditions characterizing the NH spring favour long-range eastward transport of air masses, this determines the arrival of polluted air to Asia from Western Europe and even North America. On the other hand, an intense convection over the Asian continent and season-dependent west-eastern transport favor the transport of air masses across the Pacific Ocean. The presence of jet stream which is especially intense near Japan (reaching the rate > 70 m/s) determines the

8.9 Aerosol Long-Range Transport and Climate

365

time of air masses transport between Asia and North America, sometimes several days. Martin et al. (2003) discussed the results of studies on the aerosol long-range transport in the Pacific Ocean that ended in the late winter in the Northern Hemisphere (25 February – 19 April 1999) accomplished by NASA at the PEM West-B field observational experiment to monitor aerosol transport from the Asian continent to the Pacific Ocean (these observations were part of a more general programme of the Global Tropospheric Experiment – GTE). The PEM West-B experiment was accomplished as an addition to earlier PEM West-A mission at another season (14 August – 6 October 1996). Atmospheric data of such MGCs, such as non-methane hydrocarbon and hydrocarbon compounds as well as carbon monoxide were obtained with the instruments carried by the NASA flying laboratories DC-8 and P-3B flying over the Pacific Ocean. To calculate the backward trajectories of air masses along the flight routes, the meteorological information from the European Centre for Middle-Range Weather Forecast was used. The calculations showed that MGCs recorded in the Pacific Ocean, which crossed the ocean during their long-range transport, came from Asia and even from farther western regions, depositing them in the region of sub-tropical Pacific anti-cyclone with the subsequent south-western transport driven by trade winds in the lower troposphere. It took 20–25 days for the particles to reach the western Pacific Ocean near the New Guinea coast. Apparently, similar (“mirrored”) processes of the longrange transport also take place in the Southern Hemisphere, especially with the transport of biomass burning products in South Africa and South America. Field observational experiments accomplished in remote regions of the globe during the last two decades have revealed the existence of a considerable amount of anthropogenic pollutants in the atmosphere of these regions in an environment which was previously considered free of pollutants. This applies in particular to the Arctic and Antarctic as well as the remote regions of the Atlantic and Pacific Ocean. The analysis of aircraft observations revealed large-scale plumes of aerosols and aerosol layers in the free atmosphere formed by industrial pollutants and biomass burning with the subsequent long-range transport of atmospheric pollutants. A detailed analysis of the aircraft observations enabled one to retrieve the pattern of the plume’s evolution as a whole. The plume in the Southern Hemisphere was formed due to accumulation of pollutants near the surface in Africa and South America. In thunderstorm situations the plume moves upwards, but intense emissions of pollutants can also take place due to synoptic-scale motions. Then the plume moves eastward under the influence of the sub-tropical jet stream until its fragmentation occurs caused by storms in the Pacific Ocean. A similar situation is observed in the NH sub-tropics in the PEM- Tropics B period. According to available observational data, long-range transport of dust aerosol and MGCs events took place on the west coast of North America from the deserts of China and Mongolia, from the industrial regions of East Asia and from Siberia, where large-scale forest fires occur. Holzer et al. (2003) analyzed the laws of the long-range transport of dust aerosol and MGCs by estimating the probability density function (PDF) of the time of air masses transport in order to select (filter-out) the

366

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

transport against the background of other factors such as variations of the power of the emission sources and the processes of chemical transformation of DA and MGCs. Unlike the commonly used technique to calculate the backward trajectories of the long-range transport to forecast emission sources, solution of this problem in terms of PDF makes enables a more complete analysis of all possible transport modes taking into account both spatially resolved advection and sub-grid processes. Statistical information on diurnal mean values of air masses was obtained using the MATCH model of the processes of long-range transport with chemical transformation taken into account and with a-priori prescribed meteorological input information. The calculated values of air masses make it possible to estimate the climatic share of the air masses coming from the source region for the transport time (or the age of air masses). On the west coast of North America, this share reaches a maximum with a transport the time of ~8 days in the upper troposphere and 6 days later – close to the surface. Such estimates were obtained when considering DA and pollutants transport, and in case of long-range transport of forest fires products to Siberia, the respective values reached 12–14 days. The analysis of the G variability at a fixed transport time allowed the identification of the DA and MGCs sources and the estimation of the probability of such a transport. So, for instance, one of the events refers to the Pacific North-West (PNW) region with the coordinates (43.8
– 53.3oN)  (115.3
– 124.7oW). The degree of correlation between the G values and the mean wind field in the PNW region makes it possible to reveal the structures of large-scale anomalies corresponding to favourable transport conditions to the PNW region. The Sahara, the deserts of Central and East Asia, are most powerful sources of aerosol dust. Significant contribution to the DA natural formation is made by Taklamakan (western China) and Gobi (north-eastern China and Mongolia) deserts. Pollutants emitted into the atmosphere are subjected to gravitational sedimentation, turbulent mixing, wind-driven transport, and washing out by rain. A set of these impacts determines the behaviour of the polluting cloud, the shape and type of pollutants flow as well as the spatial distribution of aerosol density in a given area. Smoke and other atmospheric aerosols are gravitationally influenced and interact with solar radiation, gases and ions. On the surface layer, this interaction is complemented by different surface effects (vegetation cover, soil, land surface roughness, and sea surface roughness). The role of sedimentation is more substantial in the case of large particles, larger in diameter than sub-micron particles. Small particles sediment much slower compared to their transport by the moving atmosphere and therefore in many models this vertical component is neglected. Note that for the process of sedimentation the diameter of the particles is less important than their density. For instance, soot structures with low efficient density and high aerodynamic cross-section are easily wind-driven and sediments much slower than compact particles of the same mass. The rate of sedimentation of particles with D ¼ 0.1–1 μm averages 0.001 m s1, which is negligibly small compared to the atmospheric transport rate.

8.9 Aerosol Long-Range Transport and Climate

367

In the case of heavily polluted formations one can observe the process of photofluorescence consisting in particles’ lifting due to non-uniform solar heating. However, a possibility of this phenomenon and its characteristics have been poorly studied, therefore in a first approximation, many experts omit it, especially because at time intervals longer than one day, due to the Brownian motion, the irregular heating of the particles is reduced. Finally, such a natural process should be labeled as a coagulation of particles that involves the capture of one particle by another due to different rates of motion. In this case, the particles can either stick together or repel, resulting in varying states of their interactions that determine the shape of the cloud of pollutants and may extend their lifetime in the atmosphere. The washing-out is a very important process of removal of pollutants from the atmosphere. Two situations are possible here. One is connected with a simple capture of particles from the rain droplets, and the other – with the so-called nucleation process. This process is connected with the oversaturated water vapour condensation on the surface of the aerosol particles, which results in the formation of water droplets or ice crystals with subsequent deposition onto the land surface. Therefore one of the ways to specify the model of aerosol dynamics in the atmosphere is to include a water cycle parameterization unit in the different phase states.

8.9.3

Numerical Modeling of the Aerosol Long-Range Transport

8.9.3.1

Relationships Between the Scales of Atmospheric Mixing Processes and the Choice of Models

An experience gained from many studies on the modeling of atmospheric pollutants transport processes dictates the need to classify these processes according to interrelationships between spatial and temporal scales. The need for this classification has been substantiated in many international programmes, such as “Global Changes”, “Global Atmospheric Chemistry” (IGAC), “Modeling of Biogeochemical Global Cycles” (MBGC), and others. The need for and even the necessity of classification of physical processes of atmospheric mixing are dictated by the parameters of the systems for measurements (monitoring) of atmospheric characteristics, the requirements of the simplicity of the models of the aerosol and gases transport in the atmosphere, as well as by limitations of available databases. The interaction of these causes leads to a range of spatial scales, which provides an efficient parameterization of the processes of propagation of atmospheric pollution and conforms to international standards. At present, the GeoDAS standard is the most widespread, with nine levels of spatial resolution ranging from one degree to half a second in longitude and latitude. Seven scales of mapping data are provided in Table 8.25. Data on these scales can be obtained by synthesizing satellite data and national monitoring systems. The latter are important because they specify the data domain and select priorities that are characteristic of a given region. For instance, for

368

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.25 List of scales of cartographic information presentation characteristic of the developed monitoring systems Spatial resolution, km 0,5  0,5 11 55 10  20

Scale 1:1250 1:2500 1:10,000 1:25,000

Spatial resolution 40  40 250  250 500  500

Scale 1:50,000 1:250,000 1:625,000

developed countries operational assessments of air quality in the megapolises and large industrial enterprises are important. For developing countries, the control of trans-boundary transport of pollutants is of prime importance as well as an assessment of the possible change in atmospheric air in connection with the construction of industrial plants. The ratio of scales given in Table 8.25 corresponds to a majority of situations of atmospheric monitoring. Along with the problem of selecting the scales of spatial resolution, there is a problem of their agreement with time scales. This problem is important for the shaping the structure of numerical models that describe the pollutants’ dynamics in the atmosphere. According to preliminary estimates of the International Geosphere-Biosphere Programme, there is a scale of transitions in temporal and spatial measurements between complexity and depth of the hierarchic structure of connections considered in the model. So far, the developed models practically ignored this fact, and therefore, it has often been impossible to apply them to the natural object under study. An agreed-upon discretization scale for natural phenomena to be used then in models proposed by Nitu et al. (2019) allows the classification of natural phenomena taking into account their hierarchic subordination over spatiotemporal scales. This classification is based on the fundamental understanding of the hierarchy in general systems theory. According to this theory, the behaviour of any complicated system is determined by the triple frequencies of its variability, which provides an agreement between coherence and stability of the system. This makes it possible to exclude unnecessary details in the model’s structure according to a prescribed time scale or to establish a minimum time step from spatial scale data. For instance, if the time step in the model is chosen to be equal to one year, it does not make sense to consider such processes as atmospheric turbulence. In other words, in this case the atmosphere can be described with a point model, and all attempts to develop complicated constructions to describe atmospheric motion processes cannot improve the reliability of the model but will increase its complexity. A more strict theoretical substantiation of this approach is a combined description of dynamic processes with different characteristic time scales which result in mathematical description parameters referring to different processes and strongly (by orders of magnitude) differing from each other. This enables one to divide these processes into three groups: • processes referring to a selected time scale;

8.9 Aerosol Long-Range Transport and Climate

369

• processes which, with respect to time scale, can be considered to be in dynamic equilibrium (quasi-stationary) and for which some parameterization can be introduced (fast processes); • processes which, with respect to the chosen time scale, can be considered constant, that is, static (slow processes). As an example, we obtain a numerical model of the biogeochemical cycle of carbon which includes both geological-time-scale processes and fast processes (photosynthesis, respiration). If the aim of the model is to study the dynamics of CO2 content in the atmosphere for several decades, geological processes with characteristic time scales of millions of years should be excluded from consideration and processes should be parameterized with characteristic time scales of days, for instance, processes of living biomass formation in photosynthesis. In general, the problems mentioned have not been solved yet, and there is no constructive mechanism for matching spatial and temporal scales. Each scientist follows his own principle in choosing the model elements for their subsequent implementation. Unfortunately, at this stage there appear unavoidable deviations of the model from reality. Characteristic time intervals of variability for most natural processes are well known. Here are some of them: • • • • • •

deposition processes– minutes, hours; plants’ transpiration – hours, days; plants’ biomass formation– days, months; changes in plants and animals communities – months, years; soil formation – years, centuries; geomorphological processes – centuries, millennia.

Therefore, a systematization of time scales for the processes considered should follow the synthesis of the system of models. As a result, the priorities for their models and units as well as the software structure can be determined. Due to the difficulties to parameterize atmospheric processes, there are many models of atmospheric dynamics. The type of model correlates strongly with the spatial scale. An examination of the models of physical mixing processes together with the chemical processes taking place in the atmosphere is determined by the accuracy level. The developed models, depending on the spatial scale, take into account processes of physical transformation of pollutants from micro-processes in clouds to large-scale atmospheric motions. Depending on this, models are divided into dispersive, Gaussian, Eulerian and Lagrangian. Within this system there is a hierarchy of models taking into account or neglecting the vertical structure of the atmosphere, atmosphere-surface (land, water) interaction, exchange processes between clouds, and vertical air fluxes depending on the relationships between synoptic and physical parameters of the atmosphere. One of the examples of such studies is a series of versions of the ICLIPS model with the spatial resolution 500  500 km and a time step of one year (Integrierte Abschatzung von Klimaschutzstrategien) (Kondratyev et al. 2003b; Toth et al. 2000). A more accurate

370

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Table 8.26 A fragment of the scale for development of the structure of the atmospheric pollution dynamics model Spatial resolution of the model Industrial region, landscape, megapolis, city (up to 50 km)

Large region, district, country (up to 1000 km)

Continent, globe (>1000 km)

Processes recommended for consideration in the model Use of the Gauss-type models. Burning of wastes, deforestation and reconstruction of surface covers, contamination of drinking water and water basins, industrial emissions of aerosol, soil contamination, washing-out of pollutants with rains, production processes, medicobiological assessment of the territory, division of the atmosphere into many levels. Use of Lagrangian and Eulerian models. Large-scale atmospheric circulation with selection of the upper and lower atmosphere, irrigation and other aquatic systems, integral areal sources of biospheric pollution, biogeochemical cycles, erosion, large-scale fires, desertification and swamping, succession of surface covers, river run-off, interactions on shelves. Use of block models. Averaged characteristics of the atmosphere and climate, oceanic circulation, interactions in the system “atmosphere-land-ocean”, biogeochemical cycles, succession of large tracts of forests.

model ECMWF (European Centre for Medium Range Weather Forecast) has a spatial resolution 150  150 km and a time step of 6 h (Gregory et al. 2000). It is impossible to establish an unambiguous connection between the scale of the model and its internal infrastructure without taking into account various characteristics of the model. Therefore the estimates given in Table 8.26 should be considered as recommendations, aimed at the modeler’s purpose and giving him the possibility to assess the feasibility of included into the model of certain components.

8.9.3.2

Interrelationship Between the Types of Models and Aerosol Characteristics

The formation of the atmospheric pollution fields from natural and anthropogenic sources strongly depends on the physical characteristics of the pollutants. Clearly, in order to fully understand the processes of formation and growth of clouds and precipitation, it is necessary to consider all dynamic microphysical interactive processes. These methods are determined by a combination of the physico-chemical parameters of the atmosphere itself and the pollutant components, which are characterized by strongly variable characteristics such as weight, concentration, size, shape, phase state, and electric charge. For instance, the classification of atmospheric aerosol pollution adopted by the US National Oceanic and Atmospheric Administration (NOAA) comprises three basic classes and eight sub-classes. This classification is sufficient for use in models which do not take into account the size of aerosol particles and do not include the ion-level processes. Existing classification of particles by the size covers particles’ diameters from 0.0001 μm to 1 cm. Within

8.9 Aerosol Long-Range Transport and Climate

371

this range, solid particles with a diameter D ¼ 0.0001–1 μm are considered as smoke components, while liquid particles are fog elements. Particles exceeding 1 μm in diameter are interpreted as dust or spray. Depending on the size, the role of the particles in the dynamic processes of atmospheric pollution also changes. Particles with diameter less than 1 μm form smog; tens of μm – clouds and fog, hundreds of μm – haze and drizzle, thousands of μm – rain. This classification simplifies the choice of the model’s structure, if the nature of processes of atmospheric pollution is known. In a more complicated situation, when the size spectrum of aerosol particles is sufficiently wide, the division of pollutants by their physical and chemical characteristics enables one to synthesize the complicated model as a set of hierarchically submitted partial models and thus simplify the procedure of computing its dynamic characteristics of the polluted atmosphere (Alastuey et al. 2004; Kondratyev et al. 2006). The physical characteristics of atmospheric pollutions include also the rate of gravitational deposition, residence in the atmosphere, phase state. Some of the atmospheric gas components such as N2, O2, He, Ne, Ar, Kr, Xe, and H2 have a very long lifetime. The lifetime of CO2, O3, N2O, and CH4 is from several years to decades. Such gases as H2O, NO2, NO, NH3, SO2, H2S, CO, HCl, and I2 live in the atmosphere only several days or weeks. Depending only on this characteristic to describe the dynamics of various gases in the atmosphere, one can choose an adequate model with minimum database requirements. For selecting the type of the polluted atmosphere dynamical model that matters is the size of an aerosol particle. The intervals mentioned cover only partially the possible classification of aerosols. Additional information on the source of pollutant is needed, which further specifies the parametric space of the model. Knowledge of the cause of pollution simplifies the choice of the model type. Of course, the classification and standardization of aerosols and gases can be more detailed. There are, for instance, tens of the types of smoke. The size of the smoke particles can be 1–0.01 μm for the resin smoke, 0.15–0.01 μm for tobacco smoke, etc. Here, in the model, it is necessary to consider micro-processes connected with the motion of these particles. For instance, the run of a particle of carbon smoke during t seconds averages 0.00068 t/D cm. Complementing the monitoring system‘s knowledge base with such dependences is one of the first-priority problems of ecoinformatics. In modeling the scattering of gases and particles in the atmosphere it is important to know the difference between the polluted and the clear atmosphere properties. Also, one should always bear in mind the vertical heterogeneity of the atmosphere. On a global scale, the air quality formation depends on processes at all levels of the atmosphere: the troposphere, stratosphere, thermosphere and ionosphere. For instance, in the problem of the impact of aviation on the atmosphere it is necessary to take into account the interaction between the troposphere and stratosphere. By studying the pollutants fluxes from the surface sources, the motion of the lower atmosphere is first and foremost considered. Of course, here of importance is the spatial scale and, hence, the time interval of the pollutants’ residence in the atmosphere. Data known for clear atmosphere must be used in control simulation experiments.

372

8.9.3.3

8 Microwave Remote Sensing Monitoring and Global Climate Change Problems

Passive and Active Aerosol Transport in the Atmosphere

The final estimation of residence time for a given pollutant in the atmosphere is made using the respective models. This estimation has been given in detail in Brasseur (2017). The meteorological features of pollutants propagation in the atmosphere are described here; scales of transport and scattering of pollutants are analyzed; models that predict the concentration of pollutants are developed; algorithms of parameterization of the processes of clouds and pollutants’ jets formation are simulated; ratios are given to describe the vertical structure of the atmosphere. The components of the Earth’s radiation budget are analyzed and the simplest characteristics of the relationships between pressure, wind, temperature, and humidity are given. The state of the atmosphere is classified as neutral, unstable and stable by the scale of the vertical temperature lapse rate, which greatly simplifies the process of parameterization of the vertical gradients and rates. The scale of atmospheric phenomena is estimated from one second to one month with a spatial scale ranging from 20 km to 1000 km. Within this scale, the processes of transport and scattering of atmospheric pollutants are analyzed from point sources, as well as moving and covering the final territory. In general, a change in the concentration of any pollutant C is described with the following equation: ∂C ðt, φ, λ, hÞ=∂t

!

þ ∇  V C ¼ ∇D  ∇C þ R

ð8:15Þ

 ! where V V ϕ , V λ , V h is the wind speed, φ is the latitude, λ is the longitude, h is the height, t is the time, D is the coefficient of molecular diffusion, R is the change due to atmospheric turbidity, emission, and mixing. The detailed description of the terms of Equation (8.15) requires the analysis of specific processes of atmospheric propagation of pollutants and the construction of respective units of a general model (dynamic, correlative, probabilistic, system, evolutionary, etc.). As examples of such units, we shall consider parameterizations that are successfully used in models ICLIPS, ECMWF, and others. The problems of chemical interaction of atmospheric pollutants are also important, and their consideration in modeling further complicates the study. Therefore most models of pollutants propagation in the atmosphere assume a-priori that all components are mutually neutral. However, in some cases a parameterization of the processes of chemical conversion of pollutants is possible due to the use of statistical characteristics of chemical reactions or by describing the laws of phase transitions. In particular, a simple model of SO2 conversion into H2SO4 turned out to be sufficiently effective (Kondratyev et al. 2004). d½H2 SO4 =dt  d½SO4 =dt ¼ W½SO2

where W ¼ 0.1% day1 in the daytime and 0.01% day1 at night.

8.9 Aerosol Long-Range Transport and Climate

373

Numerous models have been developed to simulate the process of sedimentation of pollutants. So, for instance, Sinik et al. (1985) proposed several parameterizations for the coefficient of aerosol washing out from the atmosphere: r ¼ 104 I 1=2 ; r ¼ θ  I a ; r ¼ C1 dC=dt; r ¼ 3, 3  104 I 0,9 ; where I ¼ RR/(24 N ) is the rain rate (mm/hr), RR is the precipitation amount per month (mm), N is the number of days precipitation, θ and а are parameters. The following diffusion equation is widely used:  ∂  ∂   ∂C ∂C ∂C ∂C ∂  þ Vφ þ Vλ þ Vh ¼ V φC  V λC  V h C ð8:16Þ ∂t ∂φ ∂λ ∂h ∂φ ∂λ ∂h Assuming in (8.16) that an advection prevails over diffusion in the direction h,  that is, ∂ V h C =∂h < þcooperative behavior; bij ¼ antagonistic relationships; > : 0 indifferent behavior: Many theories have been dedicated to studies of the laws of interactions of complicated systems of various origins. In the asymmetric case considered here, it is the survival of the H system and an attempt to find a way to assess the future dynamics of the N system. According to Krapivin et al. (2019), the reflexive behavior of H will help humankind, eventually, find a behavior pattern “able to weight profits and danger, to understand principal limitations of our capabilities, and to feel new threats in due time”. As Chernavsky (2004) observes, man is adaptable, and knowledge of this synergetic capability will in the future make it possible to

400

9 Global Climate Monitoring with Microwave Measurements

describe the H[N system bearing in mind all social peculiarities of their variability, observing the boundaries of the integrated mentality of human society. The mechanisms of self-organization and self-regulation of natural systems determine the complexity of this method (Krapivin et al. 2019). Of course, the deep semantic and philosophical concepts of personal architecture, which had to be taken into account when designing the model of vitality, at the current level of formal description of the intellect remain beyond the feasibility of the present ecoinformatics.

9.3

Natural Disasters Risk

The concept of the extent of natural disasters includes geographical, spatial, temporal, ecological, economic, and human factors, each with a specific scale. From a historical point of view, this concept had undergone many changes that are nowadays acquiring the complex form of its components. In terms of the present understanding of natural disasters, their extent was previously determined by the information available or the data retrieved for catastrophic events. It is clear that in the historical past the level of unfavourable natural phenomena was higher than today. As numerous excavations have shown in the past, volcanic eruptions were one of the most destructive types of natural disasters. In areas of active volcanic activity, geologists find traces of settlements and cities buried under thick layers of ash, pumice, and lava. So, during archaeological excavations carried out by scientists of the Koeln University in eastern Germany, in the area of Lake Laahersee in the Noiwieder hollow, early settlements of people (11 thousand years old) were found under the 15-m layer of lava. Another example of a terrible natural drama is the disappearance of two Italian cities of Pompeii and Herculanum in 79 A. D. as a result of the Somma (Vesuvius) volcano eruption. History has retained information about many tragic events on all continents, when not only large settlements but also civilizations had disappeared (Grigoryev and Kondratyev (2001)). From the objective point of view, the development and scale of a dangerous natural phenomenon depends on natural background conditions, which can either hinder or facilitate the spread of the event and, hence, reduce or enhance its environmental impact. The amount of victims depends on the level of development of the society, which is manifested through the developed system of prediction, warning and prevention of possible natural disasters. In fact, the matter concerns the formation of a multitude of factors that can be considered as the natural and social precursors of a natural disaster. The scale assessment of a natural disaster depends on the human response to that disaster. For instance, the danger of a tropical cyclone is determined by a combined impact of all its elements – wind, rain, stormy surges, and waves. The wind speed in a tropical cyclone can exceed 250 km/h, covering a bandwidth of 40–800 km wide. At this wind speed, buildings collapse, communications fade, and plants are destroyed. As a result, a tropical cyclone can either cause human victims or injure people. During a tropical cyclone the rainfall can reach 2500 mm and cause flooding. An important factor is a stormy surge – the rise of

9.3 Natural Disasters Risk

401

Table 9.2 Possible precursors of a natural disaster {xi} Natural disaster Volcanic eruption Earthquake Flood Tropical cyclone Dust storm Landslide, mudflow Forest fire

Precursors of natural disaster Amplitude of tectonic shifts, surface temperature, a change in the composition of gas emissions, SO2 content. Ground waters level, amplitude of surface fluctuation River level dynamics, air temperature, precipitation variability, depth of snow cover. Wind velocity, atmospheric pressure, ocean surface temperature, air temperature variability, wind shear, ozone hole. Albedo, wind velocity. Changes in relief and landscape, rain rate, surface and deep porous water pressure. Temperature, rain rate, soil moisture, forest age.

seawater above the average ocean level of up to 7 m and higher, which leads to a rapid flooding of low areas of the shore. Finally, the combination of wind - stormy surge leads to high - wave propagation, which destroys beaches, agricultural land, construction and buildings in the coastal zone. The cyclone’s body itself usually moves at a speed not exceeding 24 km/h increasing at 80 km/h as the cyclone moves from its centre. The giant waves accompany the cyclone have great destructive power. The scale and size of damage from tropical cyclones can be judged from the data presented in Table 9.2. It is not always possible to evaluate the damage from tropical cyclones. In many countries, especially in the agricultural sector, the financial losses in most cases cannot be estimated due to the lack of statistical services and skilled personnel. As a whole, therefore, there are no statistics on the effects of natural disasters on a globalscale and therefore it is impossible to estimate their scale in many regions. The service of evaluation of the consequences of natural disasters is best developed in the USA and other industrial countries, with the developed infrastructure of geoinformation monitoring system. This system consists of various levels of observation of regions of possible occurrence of hurricanes, whose location is well known. The space images of tropical hurricanes (Fig. 9.1) show that their origins are well seen in the optical and IR spectral periods. Multi-year observations of tropical cyclones in the US provide information on their parameters and disasters. Here we have to mention hurricane “Camilla” that flew over USA soil in 1969 and it was one of the most destructive natural disasters. The lives of 248 people were lost, and more than 8000 people were injured. The economic damage constituted 1.4 billion dollars. The 10-m “Long snake” hurricane waves that hit the Japanese islands in mid-December 2004 took away 62 lives, sank many ships standing at the berths of the ports, and caused numerous destructions in the coastal zone. Hurricane “Isabelle” in late September 2003 devastated a large area on the east coast of USA, destroying more than 360 thousand buildings with damages estimated at 5 billion dollars. Forty people lost their lives. Overall, the year 2003 for US territory is marked by a sharp increase in natural disasters. During this year, the tropical hurricanes took away 68 lives, and total financial loss was 5.89

402

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.1 Photos showing the origin of cyclones (from NASA and NOAA libraries) taken in 2018 from US satellites. Left photo of a Helene hurricane taken in September 2018, Cape Verde Islands. Right photo of typhoon Jelawat (Caloy) taken in March 2018, Manila Islands

billion dollars (the insurance payments was 2.43 billion dollars). Many springsummer hurricanes have broken records in both the meteorological and insurance sectors. Hurricanes caused landslides and landslips in Texas and North Carolina, a heavy snow storm covered the northern states, and forest fires took place in the southern regions of California. Due to these events, damage from natural disasters insurance exceeded its highest level in 1994. Only two severe winter storms caused damage estimated at 500 million dollars, in addition to the record snowfall in northeast cities causing serious problems for populations and municipal services. So, in March 2003, snowfall in Alabama, Denver, and Georgia reached 81 cm, and in Colorado snow cover was 220 cm high. Severe frost in late January blocked river transport systems in the north-east USA. In Alabama and Georgia, on February 16–18, 2003, a heavy hail occurred with hailstones up to 7.5 cm in diameter. Every year, tropical hurricanes annihilate large forests, which can have global consequences, as forests are sinking for excessive CO2 emissions from the atmosphere. Hurricanes receive carbon from the forests and thus affect the global heat balance. The scale of this impact from McNulty (2002) estimates for USA territory alone represents 20 Tg C/year of carbon withdrawn from circulation. Most hurricanes affect the south-eastern coast of USA, which is covered by 55% of forests. A hurricane can take about 10% of annual forest production, which is about 1 billion dollars recalculated for the price of timber obtained. McNulty (2002) analyzed data for 1900–1996 on carbon losses due to the impact of hurricanes on the forest systems in the US territory and concluded that hurricanes convert huge amounts of live biomass into dead organics, whether used for construction either burns or decomposition with the participation of microorganisms. As a result, a considerable part of the carbon returns to the atmosphere. Thus, hurricanes are an important factor in the long-term climate impact. Of course, hurricanes promote rejuvenation of forests, but it takes 15–20 years for the destroyed canopy to grow, and this means that during this period the sink of atmospheric CO2 reduces in a given area. Large-scale natural disasters also include floods that frequently follow the tropical hurricanes as well as occur in rivers’ bottom lands during prolonged and heavy rains

9.3 Natural Disasters Risk

403

or rapid snow melting. One of the characteristic examples of a powerful flood is a situation that took place in September 2000 in central Japan due to heavy rains caused by typhoon “Saomai”. The resulting flood paralyzed the automobile project “Toyota”, eight people died, and about 500 thousand people were evacuated. The city of Nagoya was flooded. The central part of the Honshu Island was left without electric power. Passenger trains’ traffic was stopped. Many highways were blocked for a long time. Almost simultaneously, typhoon “Maemi” swept through Japan and the Okhotsk Seas, which, already weakened, reached Kamchatka and at a speed of 20 m/s caused minor damage to the Yelisovsky, Ust’-Bolsheretsky regions and Petropavlovsk-Kamchatsky cities. Several events that took place in 2002 are characteristic examples of the consequences of typhoons and storms. In August, in Majorca, the rain that fell for 3 hours reached 224 mm, which caused several landslides and led to heavy mud flows. On August12, in Dresden, rainfall reached 154 mm during 1 day, and on the night of September 8–9, in the south of France, the 36-h rainfall reached 670 mm. These examples show that extreme situations are accidental, and their forecasting is the task of the system of predicting random processes. This task becomes complicated when several phenomena are combined, as, such as in July in Tajikistan. Here, after several days of fall, there was an earthquake and a hurricane. As a result, more than 700 homes and a number of administrative buildings were destroyed, and there was no electricity in many residential areas. As far as spatial coverage is concerned, a flood is an extreme natural event that happens in most cases in lands adjacent to river beds or in dry climate zones with prevailing storms. Floods are characterized by a high level of damage. Such extreme river overflows are possible, when water flows begin destroying bridges, buildings, machinery, and swamping roads, changing the environmental relief. Floods occur in practically all regions of the globe. In any case they cause many problems to the population. So, in India, the July 2004 flood in the north-eastern part of the country destroyed many villages, leaving homeless 35 thousand people, and washing away a large number of bulldozers working on the bank of the overflowing river. As a result, 40 people perished. The flood in Russia which took place simultaneously in Kuban, caused damage to the Krasnoyarsk Region, which is estimated at about 200 million rubles. The August 17, 2004 flood in Great Britain, which resulted from a 6-cm rainfall in the Boscastle area, has created a street river that has withdrawn cars, homes and wagons. During the July 2004 flood in southwest China, in two districts of Yun-nan Province, 11 people were killed, 6 people were seriously injured, and 34 people missing. The flood caused muddy flows and landslides that seriously affected more than 2000 farm homes, with damage estimated at 33.7 million dollars. Rainfall on July 31 through August 262,002 caused major floods across Europe affecting areas of Austria, the Czech Republic, Germany, the Russian Federation, Romania, Italy, Spain, and Slovakia. As result of this event total economic losses were more than EUR 15 bn (including Germany - 9.2, Austria - 2.9, Czech Republic - 2.3) and 100 people were lost. The unpredictability and existence of floods is confirmed by the flood of 9 January 2005 caused by the most powerful hurricane in northern Europe and Scandinavia. In

404

9 Global Climate Monitoring with Microwave Measurements

Germany, England and Sweden 14 people perished and dozens were injured or vanished. Hurricane winds (30 m/s) have caused Sweden and Latvia to suffer large electricity losses from destroyed power lines. In the Estonian city of Piarnu, water rose 2.8 m above zero level, resulting in nearly 25% of urban flooding. The hurricane took place over the Pskov, Leningrad and Kaliningrad regions of Russia. Thus, on January 9, 2005 due to the threat of flooding, several subway stations were closed in St. Petersburg. In addition, boilers-works in the Petrograd region were closed and pre-measured in some other regions of the city. Further studies will show whether this flood was the result of the earthquake of 26 December 2004 in the so-called “danger zone” or was an independent event in the environmental dynamics. Flooding as an extreme natural phenomenon can evolve slowly, gradually changing the structure of the environment of a limited region, and with the global climate warming, large-scale floods of large territories are possible. As an example, the Caspian Sea level could rise by about 3 m, over several decades, and as a result, more than 400 thousand ha of coastal territories have become useless for agriculture, leading to 6 billion dollars in financial losses and more than 100 thousand people were damaged. Earthquakes are accompanied by powerful destructive factors which lead to horizontal shift of the land surface layers, and cause a tsunami at sea. As a result, residential homes are destroyed and people perish. Large-scale earthquakes are felt in large areas reaching an area of over than four million km2. The earthquake magnitude and the corresponding destruction on this area are determined by the type of the ground. Magnitude scales are used to measure the scale of an earthquake. So, in Japan the 7-magnitude scale is used, but the Richter scale is more widely used (Richter 1969). This scale is determined by the formula: log E ¼ 11.4 + 1.5 M, where E is the total released energy, M is the magnitude corresponding to the amplitude of the horizontal shifting. According to this dependence, each subsequent unit of Richter scale means that the released energy is by a factor of 31.6 greater than that corresponding to the previous unit of scale. Hence, the most powerful earthquakes are those of magnitude 7 and higher, notably: the 1906 earthquakes in San-Francisco (magnitude 8.25), Tokyo in 1923 (8.1), Asam in 1950 (8.6), Alaska (8.4–8.6), and the larger earthquake in Gobi-Altai on 4 December 1957 (magnitude 11). In November 2004 alone, there were 4 earthquakes of magnitude 7 on the Richter scale: two in Indonesia (11 November – 7.5, and 26 November – 7.2), on the west coast of New Zealand (7.1) and on the west coast of Columbia (15 November – 7.2). The number of victims depends on the population density and the measures taken. So, the Gobi-Altai earthquake mentioned above was felt over the area more than five million km2, including the entire territory of Mongolia, southern Buriatiya, the Yakutsk and Chita oblasts, and the northern provinces of China, but due to a low population density, victims were not numerous. Worldwide, from the US Geological Survey (USGS) data, about 20 thousand earthquakes are recorded every year, 18 of which are of 7.0–7.9 magnitude and one earthquake of 8.0 R. In the USA, 39 states are at risk of earthquakes. A perfect and Advanced National Seismic System (ANSS) has been developed in the USA, with 6 thousand sensors, within 5–10-min. Warning time. The USGS, as part of the

9.3 Natural Disasters Risk

405

implementation of the National Earthquake Hazards Reduction Programme (NEHRP), started in 2001, and installed more than 300 sensors in many large cities, such as San Francisco, Seattle, Anchorage, Las Vegas, and Memphis, in order to increase the feasibility of timely recording of an earthquake and improve the operational risk warning of the population. Therefore, a number of earthquakes in the USA in 2003 did not cause considerable damage. Among them are the 9 April earthquake north-east of Alabama (magnitude 4.6), the 6 June earthquake in western part of Kentucky (4.0), the 9 September earthquake in Virginia (4.5), the earthquake of November17th in the Aleut Islands, at a distance of 2220 km from Anchorage. The Sumatra-Andaman and Nias Island earthquakes on 26 December 2004 and 28 March 2005 with magnitudes 9.0–9.3 and 8.2–8.7 respectively were greater in 40 years. The Sumatra-Andaman earthquake was the second largest earthquake in the instrumental record and the third most fatal earthquake ever. It was equivalent to a 100-gigaton bomb. Its energy at 4.3  1018 generated a tsunami that traveled to Antarctica, the east and west coasts of America, and the Arctic Ocean (Bilham 2005). One of the peculiar features of earthquakes and volcanic eruptions is their ability to cause secondary natural processes, such as landslides, landslips, mud flows, tsunami, and floods. The cascade nature of earthquakes often occurs in densely populated areas in the form of fires, gas explosions, and other indirect factors (floods, shock currents). The lava flowing beneath the snow- or ice-covered mountain slopes causes muddy flows and floods. The power flow of hot mud, can gradually be transformed into an avalanche and then, with further melting snow and ice, transformed into a powerful water flow which, breaks out of the gorges, gaining destructive power. One of the most feared natural disasters is the tsunami. The tsunami is giant waves reaching near the shore a height of 10–30 m and move with a tremendous speed. The waves appear in the ocean at the epicenter of an earthquake. An example is the event of November 5, 1952 that occurred in the Kuril Islands. A huge ocean wave completely covered the Paramushir Island which totally flooded the city of Yuzhnokurilsk. It took several minutes for everyone to be killed and huge damage was caused. A 30 m high tsunami that fell in many Southeast Asia countries on December 26, 2004 caused about 232 thousand deaths in Indonesia, Thailand, India, Bangladesh and other coastal countries of the Indian Ocean basin. The tsunami was triggered by an earthquake with magnitude 9 on the Richter scale in the eastern part of the Indian Ocean. The warning about the danger issued by the Pacific Centre of tsunami warning located not far from Honolulu (the Hawaii) did not reach them due to the lack of a coordinated warning system in these countries. The following events are examples of major tsunami disasters and deaths. On April 1, 1946 as a result of a 7.3 magnitude earthquake near the Aleut Islands, the resulting tsunami wave caused 159 deaths in Hawaii and caused an estimated damage of more than 26 million dollars. The earthquake of magnitude 8.2 southeast of Kamchatka on November 5, 1950 caused a huge tsunami wave that broke off over the northern Pacific Ocean at a tremendous speed. On March 9, 1957, an earthquake of magnitude 8.3 struck the Aleut Islands. The resulting tsunami wave 23 m high fell

406

9 Global Climate Monitoring with Microwave Measurements

Table 9.3 Characteristics of the Fujita-Pearson scale Level of scale F0

Intensity Storm

Wind speed, km/h 40–72

F1

Moderate tornado

F2

Substantial tornado

113–157

F3

Strong tornado Devastating tornado Incredible tornado

158–206

Inconceivable tornado

319–379

F4 F5

F6

73–112

207–260 261–318

Type of danger Some damages to chimneys, broken tree branches, uprooted trees, signs torn from buildings. Lowest wind speed of the early hurricane. Blows off the roofs, pools down houses from foundation or turns then over, moves the cars, can destroy separate or attached garages. High level of danger. Blows off the roofs, destroys mobile dwellings, overturns box garages, breaks or uproots big trees, takes away light-weight objects. It destroys roofs and walls of stationary constructions, overturns trains, and uproots the trees. It destroys solid buildings, takes away houses with poor foundations, cars, and large objects. It throws houses off foundations and takes them away destroying, moved cars at distances up to 100 m, breaks trees, heavy damages to steel, concrete-fastened strictures. Such winds are amazing. Their destructions are difficult to distinguish from those of tornados of the type F4 and F5. The emerging vortex exhibit a huge power over a small area, can take large volumes of water and transport them over long distances.

to the islands Umnak and Kanai with tremendous destructive force. An 8.3 magnitude earthquake near Chili caused a tsunami wave that reached Hawaii, taking away 61 lives and destroying 537 houses and damages exceeding 23 million dollars. In Hawaii the 14-m wave on November 29, 1975, after an earthquake of magnitude 7.2 caused 4.1 million dollars in damage. Finally, the earthquake of magnitude 7 in the northern sector of the Bismarck Sea on July 17, 1998 caused a tsunami wave that killed 2202 people and destroyed homes of 10 thousand people in coastal regions. Such disasters can be caused by tornados (whirlwinds) which usually occur suddenly and affect a confined area for a short time. The length of the tornado stroke is on average about 25 km with the coverage band not exceeding 400 m. The whirlwind occurrence is connected with thunder clouds in the presence of a sharp contrast of temperature, humidity, density, and other parameters of air fluxes. Usually, a whirlwind appears in the zone of the contact of cool and dry air masses in the surface air layer. As a result, high winds begin to blow in a narrow transition zone leading to a vortex. The destructions caused by tornados are terrific. The tornados destroy buildings, uproot trees, and lift into the air, as if sucking up by a huge pump, cars, fragments of buildings and anything heavy, including humans. To evaluate possible effects of tornados, the Fujita-Pearson scale is usually used (Table 9.3), which, despite its subjectivity, helps to range tornados by the level of their risk. Tornado statistics in

9.3 Natural Disasters Risk

407

the US show that about 69% of moderate (weak) tornados occur over US territory with duration of 1 to 10 minutes. Fatalities do not exceed 5% of all victims of tornados. More powerful tornados occur in 29% of cases and last more than 20 minutes, with 30% of fatal effects. Horrified and savage tornados occur in 2% of cases where human victims make up 65% of the victims of all tornados. They destroy everything within an hour and more. From the 2003 data, only during first 10 days of May over the territory of the USA there happened 412 tornados at a wind speed of 330–420 km/h. In South Dakota and Mississippi, on 4 May, 94 tornados were recorded. Many of them covered a band up to 450 m wide at a distance of 24 km. Despite the precautionary measures taken, the damage amounted to 7 billion dollars. Serious damage is usually caused by forest fires, which can be of both natural and anthropogenic origin. Their spatial scale can be judged by the number of annually recorded wild fires. During the last years there were recorded 12,300 fires in 2000, 84,079 fires in 2001, 88,458 fires in 2002, and 29,634 fires in 2003. Accordingly, during this period, forests were burned over the territory of 3.4 million ha in 2000, 1.4 million ha in 2001, 2.8 million ha in 2002, and 4.1 million ha in 2003. Every year, over the territory of Russia, from 12 to 37 thousand forest fires are eliminated from 0.4 to 4.0 million ha of forests losses, amounting to hundreds of millions of dollars. Regardless of the causes, a forest fire usually spreads rapidly causing a largescale disaster. So, in January 2002 wild forest fires on the outskirts of Sidney (Australia) destroyed many national parks and came very close to the city. Forests burn to the ground over the area of 0.5 million ha, and the atmosphere over the southeastern coast of Australia was filled with smoke. In 2003, in the south of California (USA), unusually severe forest fires were recorded in late October. In total, forest fires over the area 1.62 million ha amounted to 60 thousand. In 2002, 90 thousand forest fires were recorded over the area 2.92 million ha. On average, during 1993–2003 across the US, 100 thousand forest fires burned 1.74 million ha each year. A very powerful forest fire in the US was recorded in June 2002 south of Denver (Colorado State). The fire covered an area of 36 thousand ha. The extinction of forest fires requires a great number of special fire-prevention means based on land and in the air. A characteristic example of the resulting situations is the annual forest fires and the struggle against them on the territory of Russia. One of the causes of forest fires is the increase in air temperature leading to the drying up of waste and the creation of favourable conditions for the spread of fire. The most dangerous are periods of arid seasons with a prolonged deficit of soil moisture, and shallow rivers and water basins. Such situations often occur in Africa, Australia, Central America, and Asia. For instance, during the 2002 drought in southern India, more than a thousand people died. In the China Province Anhoi, in the summer of 2003 there was recorded a drought, the heaviest in the last 25 years, resulting from the combined high temperature (41.3
C) and rainfall reduced by 81.7%. In particular, in the vicinity of Huangshan, the deficit of soil moisture was recorded over the area of 43 thousand ha of cultivated land (89% of all area under crops). Drought often destroys many rice controls in Indonesia. At the higher northern latitudes, droughts were recorded in Bashkiria, where they occur in

408

9 Global Climate Monitoring with Microwave Measurements

combination of several agro-meteorological phenomena, the most important of which is the intrusion of arctic air masses containing low moisture. The spatial scale of the impact of forest fires on climate can be indirectly manifested through the change of the gas exchange in the atmosphere-soil system in the zones of permafrost. Fire-induced soil heating can lead to enhanced respiration and thus favour enhancement of post-fire CO2 release to the atmosphere over the time periods of 10 years (Krapivin et al. 2019). In this connection, it should be noted that the high-latitude ecosystems cover 22% of land surface and contain about 40% of world soil carbon. A considerable part belongs to the permafrost zone whose dynamics is determined by the cycles of degradation (thermocarst formation) and aggradation. These cycles are closely linked to forest fires, which mainly disturb the boreal forests. In recent decades, in the boreal and arctic regions of Canada and Alaska, soil temperature has risen by 1.5
C, including permafrost zones. As a result, there have been changes in the rate of nitrogen fixation, moss growth, the depth of the organic layer and soil drainage. As mentioned above, the scale of natural disasters increases both in frequency of occurrence and in the damage caused. For instance, there have been reports of losses and damage in recent years. Note that, for instance, in 2003, on a global scale, natural disasters took away more than 50 thousand lives, while in 2002 these losses amounted to 11 thousand people. Due to the heat in Europe and the earthquake in Iran more than 20 thousand people have died. During 2002–2003 economic losses totaled 55 and 60 billion dollars, respectively, with tornadoes, heating, forest fires and floods in Asia and Europe respectively. The colossal typhoon destroyed South Korea on September 12, 2003. Financial losses amounted US$3.7 bn (insured - US $0.27 bn). It should also be noted that the unstable character of the time of occurrence of natural disasters alternated with seemingly incompatible phenomena. For instance, in India, Pakistan and Bangladesh in May–June the heat reached 50
C, and from June to September there was a chain of heavy floods. It should be noted that a comprehensive assessment of the scale of natural disasters and their consequences should take into account the statistics of economic losses. Unfortunately, this statistics is made not in all countries. Knowledge of the distribution of damages by the types of natural disasters is important for insurance companies. For instance, for the USA, in 2003 the distribution of economic (insurance) losses was as follows: tropical cyclones – 38 (49)%; earthquakes and volcanic eruptions – 19 (16)%; floods – 16 (3)%; thunderstorms – 13 (16)%; snow storms – 11 (10)%; and other events – 3 (6)%. The ratio of financial losses to insurance payments for damages caused by natural disasters during the last decades in the USA has decreased from 6 in 1950 to 2.4 in 2003, which demonstrates the efficiency of the insurance strategy. A new category of possible natural cataclysms may be the source of space (Goldner 2002). Specialists studying planets and astrophysics believe that during the last 6 thousand years there were ~ 30 large-scale natural disasters caused by collisions of our planet with cosmic objects. These statements are based on archaeological and paleontological data, and various models and morphological information are used here. The spatial scale of some similar disasters is estimated at 105–106

9.3 Natural Disasters Risk

409

Mt. In this connection, there are speculations about the origin of many formations on the Earth’s surface due to such disasters. Among them is the Caspian Sea. The problem of the collision of the Earth with cosmic bodies has been discussed earlier in connection with observations of the motion of comets and prediction of their orbits. In the eighteenth century, astronomers could reliably assess the possibility of such collisions. The paradox was that were no predictions of large-scale earthquakes, such as the November 1, 1755 earthquake, which completely destroyed the city of Lisbon (Kendrick 1957). At the same time, mathematicians reliably predicted the motion of the Galley comet, convinced of their accuracy when the comet appeared in 1835, 1910, and 1986. Any time in the future of the Earth there may be a cataclysm caused by collision with a large cosmic body. When it happens, it will be a possible ordinary planetary event which apparently had taken place in the past and created conditions for the origin and development of life. Such a hypothesis is being discussed by many scientists who believe that the collision of the Earth with a comet or an asteroid will become in the future a crucial moment in the evolution, ruining our civilization and originating a new one, as had been in the past. Therefore the study of the history of disasters and an extension of the base of their knowledge is a necessary element of solution of the problems such as: • development and improvement of the strategy of the man-nature interaction ensuring sustainable development; • analysis of the knowledge of the pre-history of natural disasters and the search for technologies to use this knowledge to assess future trends in the development of humankind making it possible to reveal the ultimate loads on nature; • understanding of the role and location of the NSS human component in space. These and similar problems can be resolved using data for the last 6 thousand years of the present human civilization available in archaeology and paleontology. The role of informatics should be manifested here by overcoming a high level of uncertainty at the time of the origin of natural disasters by using new highperformance forecasting. Particularly important is the problem of predicting the ecological consequences of natural disasters, which can gradually manifest themselves in decades in the form of reduced ecosystems productivity, altering the structure of soil water balance, and broken vital environmental parameters. In other words, when developing the technology of assessing the spatial scale of natural disasters, it is necessary to take into account a set of criteria: medico-biological, economic, social, botanical, soil, zoological, and geodynamical (Krapivin et al. 2019). One of the elements of this technology is the class of natural disasters and the application of knowledge from different sciences, especially those with perspective technologies of future use. One of the urgent problems that emerge on the eve of future environmental changes is to understand the role of climate change in the processes of genetic modification (Hinchliffe et al. 2002). Similar problems are now hotly discussed by scientists in different spheres of knowledge. Morris et al. (2003) considered and compared many models describing the environmental change, emphasized their limits, and considered an important problem, such as the ratio of

410

9 Global Climate Monitoring with Microwave Measurements

temporal and spatial scales, with due regard in these models to the impact on the environment due to anthropogenic application of natural resources, water, soil, and atmosphere, as well as attempted to assess the role of technologies and economy in shaping these impacts. Blowers and Hinchliffe (2003) focused their analysis on revealing correlations between changes of the environment and technical, economic, and political responses to these changes, as well as setting numerous problems concerning the existing uncertainties and risk of those interactions. Hardy (2003) classified the potential issues of climate change for the planet as a whole and for the population, in particular, dwelling upon possible perspectives for developing a society – nature relationship. Several efforts have been made since long ago to develop tools for reliable earthquakes prediction. An example is presented just below. The presence of aliovalent impurities in ionic solids results in the formation of extrinsic defects (e.g. vacancies, interstitials) a significant portion of which are placed near the impurities, thus forming electric dipoles relaxation time depends on pressure (stress). Varotsos and Alexopoulos (1984a, b) showed that when the pressure reaches a critical value, a cooperative orientation of these dipoles may occur, which results in the emission of a transient electric signal. This may happen before an earthquake (critical point) since the stress gradually increases in the focal region before the rupture. Along this direction, a detailed experimentation started in Greece in 1981, which showed that actually transient variations of the electric field of the Earth are observed before the occurrence of earthquakes (Varotsos 2005 and references therein). These signals are termed Seismic Electric Signals, SES (of the so called VAN earthquake prediction method; VAN comes from the initials of Varotsos, Alexopoulos and Nomikos). The results have been published in a series of more than 150 papers during the last 35 years in refereed journals and arouse a great interest in the international scientific community. The main properties of SES could be summarized as follows: First, the SES amplitude is interrelated with the magnitude of the impending earthquake. This interrelation is in fact a power-law which corroborates that the approach of a system to a critical point (second order phase transition) is accompanied by fractal structure, thus being in accordance with the original SES generation mechanism proposed by Varotsos and Alexopoulos (1984a, b). Second, SES cannot be observed at all points of the Earth’s surface but only at certain points called “sensitive points”. Each sensitive station enables the collection of SES only from a restricted number of seismic areas (“selectivity effect”). A map showing the seismic areas that emit SES detectable at a given station is called “selectivity map of this station”. This allows the determination of the epicenter of an impending earthquake. Third, at epicentral distances of the order of 100 km, the SES electric field precedes markedly (~1 s) the time-derivative of the relevant magnetic field variations. This finds applications in the determination of the epicenter of the impending earthquake as well as in the distinction of true SES from “noise” emitted from manmade sources. The physical properties of SES can be theoretically explained, if we take into account the aforementioned SES generation mechanism together with the existence of inhomogeneities in the Solid Earth’s Crust. The SES collection from the real time VAN

9.3 Natural Disasters Risk

411

telemetric network (which nowadays consists of 9 measuring stations in Greece) enables the estimation of the three parameters: time, epicenter and magnitude of the impending mainshock. These predictions, when the expected magnitude is 6 units or larger, are submitted for publication in scientific journals before the earthquake occurrence. In these cases SES activities are recorded (a SES activity comprises several SES recorded within a short time and appears approximately a few months before a major earthquake). A new concept of time, termed natural time, was introduced by Varotsos et al. (2002). The results of the new analysis showed that novel dynamical features hidden behind time series in complex systems can emerge upon analyzing them in the new time domain of natural time, which conforms to the desire to reduce uncertainty and extract signal information as much as possible. The analysis in natural time enables the study of the dynamical evolution of a complex system and identifies when the system approaches a critical point. Hence, natural time plays a key role in predicting impending catastrophic events in general (Varotsos et al. 2011 and references therein). Data analysis in natural time have appeared to date in diverse fields, including Biology, Earth Sciences (Geophysics, Seismology), Environmental Sciences, Physics and Cardiology. In Earth Sciences, the SES activities exhibit scale invariance over five orders of magnitude, which agrees with the original proposal that SES are governed by critical dynamics. The natural time analysis also showed that all the measured SES activities are characterized by very strong memory and fall on a universal curve. As for the SES distinction from similar looking “noise”, this is achieved upon employing modern tools of Statistical Physics (detrended fluctuation analysis, wavelet analysis etc.), but applied to the natural time domain. In Seismology, natural time enables the determination of the occurrence time of an impending major earthquake since, as mentioned, it can identify when a complex system approaches a critical point. Since the detection of an SES activity signifies that the system enters in the critical regime, the small earthquakes that occur after the SES detection are analyzed in natural time. It was found that the variance of natural time becomes equal to 0.070 (which manifests the approach to the critical point) a few hours to one week before the main shock. For example, the 6.9 earthquake on 14 February 2008, which is the strongest earthquake that occurred in Greece during the last 35 years, was publicly announced as imminent on February 10, 2008 along with the identification of its epicenter and magnitude. Upon studying in natural time the fluctuations of the parameter of seismicity when a natural time window of constant length (i.e., comprising a constant number of events) is sliding event by event through a seismic catalog, the following is found: When this number is comparable with the number of earthquakes occurring within a few months (which is on average equal to the lead time of SES activities), the fluctuations of seismicity parameter exhibit a minimum a couple of months before a major earthquake. This fact has been repeatedly confirmed before major earthquakes in various seismic regions (California, Greece, Japan) which supports the aforementioned model for SES generation. In addition, this criticality model foresees that the initiation of an SES activity should be accompanied by the simultaneous appearance of other precursory phenomena, e.g., Earth’s surface displacements with specific orientations of the horizontal azimuths, termed GPS azimuths. This has been recently confirmed

412

9 Global Climate Monitoring with Microwave Measurements

(Varotsos et al. 2019) by observed phenomena before the giant Tohoku earthquake of magnitude MW9.0 on 11 March 2011 in Japan. In the following we focus on the occurrence of the M8.2 earthquake on 7 September 2017, which is Mexico’s largest earthquake in more than a century. This earthquake struck the Mexico’s Chiapas state. It left dozens dead and destroyed or severely damaged the homes of 2.3 million or more. Most big Mexico’s earthquakes occur right along the interface between subducting Cocos plate and North American plate. But in this case the earthquake occurred within the Cocos plate itself. Seismologists say this type of faulting would not produce such large earthquakes and this is why characterized it (in several scientific journals including Science and Nature) as an “extremely strange” event and largely “unexpected”. Well before this earthquake occurrence, however, two researchers from Mexico (RamirezRojas and Flores-Marquez) employed natural time analysis and studied Mexico’s seismicity in six tectonic regions (the selection of which was based on tectonic and geological grounds) including the Chiapas region. This study showed that the probability for the occurrence of a large earthquake was the highest in the Chiapas region (where the above mentioned M8.2 occurred) compared to the five other tectonic regions. This conclusion was further strengthened in Sarlis et al.(2018) jointly published with Ramirez-Rojas and Flores-Marquez, in which was shown that two key properties of seismicity were obeyed in Chiapas region supporting the conclusion that the occurrence of an extreme event in this region should not be considered unexpected. In the same paper, the entropy change ΔS under time reversal of the seismicity during the 6-year period 2012–2017 in the Chiapas region was studied by using a sliding natural time window comprising a number of events comparable with that occurring on average within the lead time of SES activities. It was found that the quantity ΔS exhibited a clear minimum ΔSmin on 14 June 2017, thus signaling that a major event was impending there, as actually happened almost three months later with the occurrence of the M8.2 earthquake on 7 September 2017. This finding is strikingly similar with the minimum ΔSmin of seismicity found before the giant Tohoku MW9.0 earthquake in 2011 in Japan. In a later study Sarlis et al. (2019) found that the temporal correlations between the earthquake magnitudes had different behavior before and after the ΔSmin observation, i.e., while before ΔSmin they exhibited anticorrelation (almost close to random behavior), they turned to long-range correlations after ΔSmin.

9.4

Spatial and Temporal Characteristics of Natural Disasters

The spatial and temporal distribution of each type of natural disasters is well known and varies with time insignificantly. Nevertheless, in connection with the recently growing number of extreme natural phenomena, zones of extreme danger can shift. For example, a record increase in extreme weather conditions in 2004 in Europe. So,

9.4 Spatial and Temporal Characteristics of Natural Disasters Table 9.4 Worldwide earthquakes 1990–2018

Year 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Magnitude 5–5.9 6–6.9 1617 109 1457 96 1498 166 1426 137 1542 146 1318 183 1222 149 1113 120 979 117 1104 116 1505 146 1361 121 1341 127 1358 140 1515 141 1693 140 1712 142 2074 178 1948 168 2057 144 2209 150 2481 185 1523 108 1595 123 1729 143 1565 127 1696 130 1455 104 1601 112

413

7–7.9 18 16 13 12 11 18 14 16 11 18 14 15 13 14 14 10 9 14 12 16 23 19 12 17 11 18 16 6 14

8.0+ 0 0 0 0 2 2 1 0 1 0 1 1 0 1 2 1 2 4 0 1 1 1 2 2 1 1 0 1 1

Deaths 52,056 3210 3920 10,096 1634 7980 589 3069 9430 22,662 231 21,357 1685 33,819 298,101 87,992 6605 708 88,708 1790 226,050 21,942 689 1572 756 9624 1339 1232 3155

Table 9.5 Number of tornadoes in the USA from 2009 to 2018 Years Number of tornadoes

2009 1156

2010 1282

2011 1691

2012 938

2013 906

2014 886

2015 1177

2016 976

2017 1429

2018 1154

in June, in southern France, the average temperature exceeded the threshold value of 40
, which is above the normal by 5–7
. In Switzerland, June has been the hottest month in the last 250 years. Many regions of India where the temperature exceeded the by 5
average normal, suffered unprecedented heat. In the US, in 2004 there were a record number of tornados (1819 events). May alone amount of 562 events. In 2018 there are 1154 tornado incidents. Thunderstorms have flooded Sri Lanka causing large-scale floods and landslides. For instance, the data of Tables 9.4 and 9.5 characterize to some extent the growing number of one of the most dangerous natural disasters – earthquakes and tornadoes. The growing heterogeneity of the occurrence of natural disasters, both in space and time, is explained by the increasing anthropogenic component and the natural

414

9 Global Climate Monitoring with Microwave Measurements

trend of the climate system. The problem of their relationship has been repeatedly discussed in the literature (Grigoryev and Kondratyev (2001)). The authors note that the regional features of the consequences of natural disasters are characterized by clear indicators of their territorial density. The lowest density of natural disasters (one disaster per 470 thousand km2) falls in North America. The average value of this indicator is maintained by Africa, Europe, and South America, where there is one disaster per 270, 240, and 230 thousand km2, respectively. The regions of the Central America and Caribbean basin, Asia with Australia, and Oceania are characterized by the highest territorial density of natural disasters (one disaster per 150, 120, and 80 thousand km2, respectively). Thus in the last 40 years, taking into account this indicator of the territorial density of natural disasters, it can be concluded that the territories of Australia, Asia, the Caribbean basin, Oceania, and Central America are most affected by natural disasters. But this conclusion does not correspond to the territorial distribution of the scale of damage from natural disasters, since it does not reflect their dependence on the state of the economy and other factors of the society’s development. As mentioned above, the spectrum and spatial distribution of natural disasters have been established in the process of the Earth’s evolution. However, looking ahead, one should note that with the civilization development both spatial distribution and the character of natural disasters can change over time. Many scientists believe that principal changes will take place in the future. Here are some of the possible changes: • Earthquakes and floods, even over several decades, will kill tens of thousands of people in developing countries, and developed countries will continue to suffer large-scale financial losses and violate progress in many spheres of life. • Epidemics, despite the development of medicine, will, as usual, prevent the introduction of a healthy lifestyle due to emergence of new kinds of diseases, which may be caused by genetic engineering. • Aggression of people living on the territory of other people as well as the possible intrusion of living beings from other planets in the future can create a precursor to colonization and the principal change in the way of life of population on Earth. A reduction of traditional supplies of biological food and mineral resources can cause a change in species which can be fed by solar energy or some chemical elements, many of which are in the World Ocean, for instance, deuterium. • The impact of cosmic bodies on Earth can cause in the future a sharp global climate change, which will lead to the global catastrophe. A comet or asteroid with a diameter of several km is able to devastate huge areas; either by direct forcing or by fires, tsunami, and other extreme phenomena, as well as in case of a change in orbit, life on the Earth can be stopped. The probability of such an event is negligible. • An approach of the Earth to a super-new star can kill any living being on the surface because of its high radiation. But that cannot occur before several million years have passed.

9.5 Environmental Impacts of Natural Disasters

415

• Global glaciation could happen in the next 10 thousand years as an alternative to the expected climate warming. • Change of the Earth’s magnetic field by changing poles can eliminate the ozone layer and cause thereby irreversible changes in the biosphere. • Anthropogenic disasters are expanding as new kinds of the impact on the environment and human society emerge. They will include deviations in the social and cultural spheres, in science and engineering. Bioterrorism will enhance, robot interaction problems will arise, and nano-technologies will change the structure of the energy balance of the planet, increasing the efficiency of solar energy absorption from today’s 10% to 50% in the future.

9.5

Environmental Impacts of Natural Disasters

The natural cycles of varying time scales are followed by the inevitable destructive forcings to the environment, which, in principle, can overthrow back the current civilization. Of course, today’s civilization has the power and knowledge to successfully withstand such an issue of a possible global natural disaster. But, nevertheless, there is a danger of collisions with large meteorites, powerful volcanic eruptions and earthquakes, when a drastic climate change with the resulting change of the environment may occur, which will create conditions unfavourable and, maybe, impossible for the existence of living organisms. Among the numerous scenarios for the development of such situations in the future, one should study those which that allow people’s survival, if they can find a way to withstand possible threats. • The problem of humankind survival is too complex to be resolved based only on a model. Here both the philosophical and medical aspects are also important without understanding which strategies and behavior of people are impossible to identify. For many decades scientists have questioned the nature of biospheric evolution, the shaping of specific features of individuals and their communities, and the definition of the concept of biosphere. But there are still no constructive solutions to how the biosphere evolves. Along with the biotic and physicochemical processes, it is necessary to take into account psychological, ethical, religious, linguistic, cultural and political peculiarities of the population of a given region. In these spheres, the positions of people are often diametrically opposed, and hence, difficult to formulate the goal H for the system H. Some people want to see the world green and not be destroyed by anthropogenic processes, but for others comfortable living conditions are a top priority. The compromise between these aspects of the goal H is often impossible, and therefore the final choice is made by one of the sides regardless of the other’s wishes. But natural disasters are involved in this option, which may allow a compromise between the indicated aspects of the goal H

416

9 Global Climate Monitoring with Microwave Measurements

• Natural disasters have played a decisive role in certain periods of the history of life on Earth. Only in today’s world has man become one of the determinants, though he has not been able to overcome many troubles caused by natural disasters and even to eliminate the causes of their origin. The role of natural disasters in changing social structure and even in political life is not always understood. • Nature gives people even less opportunities to solve the strategic problems of improving their lives. It requires creation of planetary strategy of interaction with the environment. In other words, the global problem of NSS sustainable development becoming urgent every year. At present, there are practically no unpopulated territories on the globe without cities and settlements, without energy systems, and without pollutants emitted into the atmosphere and oceans. The growing need for natural resources is a cause for concern about their depletion and a possible future situation when forests are destroyed, land will be unsuitable for agriculture and water resources will be useless. In this situation people will starve and die of various epidemics. From this point of view, the current concept of natural disaster changes its meaning. • Speaking about the habitat of living beings, it should be borne in mind that the environment is constantly changing as a result of the matter-energy exchange between its components. As a result of these changes, the earlier connection between the elements of this system is broken, which can be the cause of the origin of natural disasters in soils where they have not happened before. Therefore the level of interaction between man and the natural-geographical domain has a historical character in many respects. It is clear that the historical aspect of the nature-society interaction is strictly geographically linked to special features of both the natural and socio-economic conditions of a given region. For instance, the Near-East countries are located in the zone of arid climate, and are characterized by a social and cultural way of thinking that differs from Europe or America, which explains the different perception of extreme natural phenomena. For this region, limited water resources, nomadic lifestyles, the desire to build irrigation systems, etc., they are normal As a result, the adaptability of the population of the countries of the Near and the Middle East to environmental conditions is characterized by a sustained balance between the possibilities of the environment and the needs of society, which has been achieved by the creation of a particular social class. • Talking about the relationships between natural and anthropogenic processes under the conditions of the possible occurrence of extreme environmental situation, it is necessary to take into account that a human, in contrast to animal, is a unity of natural and social elements, and this means that human exchange with the environment is socially dependent and is carried out non-biologically. Therefore the humans’ habitat is determined by the strategy of intentional activity and perception is subjective in many respects. But Earth’s main resources for sustaining life in its present form are limited and therefore are common properties and should not be changed by a single region.

9.6 Role of Natural Disasters in the Climate/Biosphere System Evolution

9.6

417

Role of Natural Disasters in the Climate/Biosphere System Evolution

One of the numerous factors of the development of evolutionary processes in the biosphere-climate system is the sudden changes of the environmental characteristics that cause stress for living organisms with a possible fatal outcome. In different periods of evolution the scale and significance of some factors had changed. Two international ICSU programmes “Dark nature: rapid natural change and human responses” (started in 2004) and “The role of Holocene environmental catastrophes in human history” (2003–2007) deal with studies of the role of catastrophic processes taking place during 11,500 years, in the development of the current civilization. A longer period of evolution of the planetary system has been considered by Condie (2005) who has analyzed the interaction of different components of the planet for the last four million years, selecting those that have affected the history of land ecosystems, oceans, and the atmosphere. The present approach is characterized by a trend towards increasing natural disasters of anthropogenic origin, such as floods, forest and peat-bog fires, deforestation, desertification, and epidemics. Of course, it is not always possible to distinguish between the causes of a natural disaster. But one thing is apparent, that the present ecodynamics is followed by an increase in extreme situations in the environment. For instance, at the turn of 2000/ 2001, the ratio of human victims from natural disasters reached 17,400/25000 despite the attempts of many countries to take preventive measures to protect population. Maximum economic losses during the last years were recorded in 1995 (180 billion dollars) and in 2004 (220 billion dollars). The most dismal years of natural disasters were 1998 and 2004 that resulted in 50,000 and 232,000 deaths. The contribution of various types of catastrophes to human losses and economic damage varies from year to year, but maximum victims result from earthquakes, floods, winter storms, and tsunamis. For instance, of the 700 natural disasters recorded in 1998, winter storms and floods constituted 240 and 170 cases, respectively, and their economic damage constituted 85% of all losses. In Europe, in mid-November 1998, more than 215 people froze to death. • Significance and scale of critical situations in the environment depending on the state of numerous NSS components. As a rule, elemental phenomena are assessed in the context of the resulting damage for human life and economic activity. It is well known, however, that on the other hand, elemental phenomena are an important factor of ecodynamics on local-to-global scales. As Lindenmayer et al. (2004) note, for many processes that determine the ecosystems’ dynamics, natural disasters are of key importance as factors that regulate such processes. Therefore Wright and Erickson (2003) discussed the problem of taking into account the impact of natural disasters on the problems of numerical modeling of environmental changes. Special attention has recently been paid to forest fires, which are an important component of ecodynamics, are mainly of anthropogenic origin and cause material damage and human losses.

418

9 Global Climate Monitoring with Microwave Measurements

• Forest fires occur regularly in different regions of the world act as a factor in the ecosystems’ dynamics, manifested by the fire-induced emissions to the atmosphere of GHGs and aerosols. According to available estimates, about 30% of tropospheric ozone, carbon monoxide, and carbon dioxide in the atmosphere are determined by the contribution of forest fires. Aerosol emissions to the atmosphere connected with forest fires can substantially affect the microphysical and optical characteristics of cloud cover, leading to climate change. Satellite observations on Indonesia have shown, for instance, that the presence of smoke in the atmosphere due to the continuing fires has led to a precipitation reduction, which has favoured the further development of fires. In this context, Ji and Stocker (2002) performed a statistical processing of the satellite TRMM-data (on tropical rainfalls) and the TOMS (total ozone mapping spectrometer) data for the aerosol index (AI) for the period from January 1998 to December 2001 in order to analyze the laws of the annual change, intra-seasonal and inter-annual variability of the quantity of forest fires on global scales. In the period considered there was a clearly expressed annual change of forests in South-Eastern Asia with a maximum in March and in Africa, North and South America – in August. The analysis of the data also revealed an inter-annual variability of forest fires in Indonesia and Central America, correlating with the El Niño / Southern Oscillation (ENSO) cycle in 1998–1999. In 1998, the boreal forests burned in large areas of Russia and North America. The fires covered an area of about 4.8 million ha in boreal forests of Canada and USA and 2.1 million ha in Russia. • A distinct correlation is observed between variability of the aerosol content in the global atmosphere and above-mentioned variation of frequency and intensity of forest fires. One exception is the region of South-Western Australia where intense fires recorded from TRMM-data were not followed by smoke layers formation (from TOMS-data). With the Australian region excluded, the correlation coefficient between the amount of fires and AI (from TOMS-data) is 0.55. Statistical analysis of the data using calculations of the empirical orthogonal functions (EOF) revealed a contrast between the Northern and Southern Hemispheres, as well as an inter-continental aerosol transfer resulting from fires in Africa and America. Statistical analysis data show fluctuations during the 25–60 day period that overlap with the annual variation in the amount of fires and the aerosol content. A similarity has been detected between the intra-seasonal variability of the quantity of fires and dynamics of the Julian-Madden oscillation (Krapivin et al. 2019). • As McConnel (2004) rightly observed, the reason why some forests are destroyed, degraded and lost species diversity, while other forests remain in good shape and even extend their range. There is no doubt that the population growth, the further expansion of market relations and the intensive development of the various economic infrastructures are important factors in the observed deforestation. Factors resisting this process are nature protection measures, which promote the forests preservation. Eventually, the forest cover dynamics is determined by a complicated and interactive set of factors such as biogeophysical

9.6 Role of Natural Disasters in the Climate/Biosphere System Evolution

•

•

•

•

419

processes, the growth of population density, market relations, various forcings (forest fires including), and institutional microstructures. Forest fires affect the formation of global carbon cycle. Actually, the global scales of forest fires have recently become equivalent in the territory of Australia. Almost 40% of global CO2 is emitted to the atmosphere; 90% of forest fires are of anthropogenic origin. It means that the inherent balance of natural factors is strongly violated and the laws of natural evolution are subject to strong forcing. Other natural phenomena affecting the environmental dynamics include volcanic eruptions, dust storms, and thunderstorms. One of the consequences of a largescale volcanic eruption is heavy atmospheric pollution, which can lead to climate change in some regions or even on a global scale. So, in particular, aerosols connected in many respects with emissions of sulphur dioxide during the Pinatubo eruption in 1991, led to a global temperature decrease in the lower atmosphere by about 0.5
C in 1992. It was also noted that 2 years later the level of global ozone content decreased by 4% compared to the previous 12-year period. And though during the last century there has been no drastic climate change due to volcanic eruptions, even on the historical aspect such situations have occurred. 73,500 years ago, due to the Toba volcano eruption on the Island Sumatra, global atmospheric temperature decreased by 3–5
C. The area of glaciers has increased, and the size of population on Earth has decreased (Oppenheimer 1996). Thus the 600 volcanoes existing now on Earth can constitute a global threat to the population, since they can seriously change the climate and further affect the NSS development. Dust storms taking place in many regions of the Earth play a similar-to-volcanoes role in the environmental change. Dust spreads over long distances that negatively affect plants and soils and causes heavy pollution of the lower layers of the atmosphere for extended periods of time. In the regions with thin vegetation cover and a dry climate, dust storms remove the fertile soil layer, damaging the plants. Dust storms occur periodically, for instance, in the plains of the central and western states of the USA during heavy hurricanes. Sometimes they last for several days, increasing the dust to 1.5–1.8 km and even to 5–6 km and transport it over hundreds and even thousands of kilometers towards the Atlantic Ocean. This is a destruction of soil-vegetation cover in steppes and partially wooded steppes in agricultural-intensive areas associated with catastrophic dust storms. Along with short-term forcings and an apparent damage, dust storms form a regular component of global ecodynamics under the scales of decades and even centuries. Thunderstorms play a special role in the environmental change. The lightnings taking place in practically all latitudes affect the process of photochemical reactions in the atmosphere and are the factor of fire risk. From estimates available, there are, on average, 1800 thunderstorms at any given time on Earth, followed by 200 lightnings per hour (or 3.3 strikes per minute). From satellite observations, the global mean frequency of lightnings is 22–65 lightnings per second. Observations from the satellite Microlab-1 gave 44  5 lightnings per second, which corresponds to an occurrence on Earth of 1.4 billion lightnings per

420

9 Global Climate Monitoring with Microwave Measurements

year. The use of Microlab-1 data made it possible to draw global maps of the frequency of lightnings in different seasons. Analysis of these maps showed that lightnings take place mainly over land, and the ratio between their quantity over land and over the ocean is on average 10:1. About 78% of lightnings fall in the latitudinal band 30oN – 70oS. The most intensive is the all-the-year-round regime of lightnings in the Congo basin where the frequency of lightnings (in Rwanda) averages 80 lightnings km2 year1, which corresponds to conditions of the central region of Florida. The all-the-year-round intensive regime of lightnings is characteristic of the central part of the Atlantic Ocean and the western region of the Pacific Ocean, where under the influence of cold air advection over the warm surface of the ocean the atmosphere becomes unstable. A lightning in the eastern sector of the tropical Pacific Ocean and in the Indian Ocean, where the atmosphere is warmer, does not occur as often. The maximum frequency of lightnings in the Northern Hemisphere falls in summer, whereas a semi-annual cycle of lightnings is observed in the tropics. • In the northern regions, an important constituent of the evolution control mechanism is the severe frosts whose impact on vegetation cover depends on plants’ hardiness. Jönsson et al. (2004), with the Norwegian spruce Picea abies as an example, studied the response of boreal forests to temperature variations. It Sudden changes in temperature have been shown to cause changes in wood density, and with the expected climate change vegetation cover can also change.

9.7

Reality and Expected Changes of the Environment

All the above-said makes one able to re-analyze the observed regularity of transformation of the living beings’ habitat and draw a conclusion about its limitations. As many specialists have pointed out, the frequency of occurrence of natural disasters is increasing, and this suggests an idea of approaching an unknown threshold of acceptable anthropogenic impacts on the environment beyond which humans’ life may be impossible. The main conclusion drawn by the scientists is that the accumulated data on various environmental parameters cannot be considered complete and adequate for global ecodynamics studies. Environmental health depends on many related factors and processes, which cannot be estimated reliably with the available knowledge. Humans introduce noise into processes taking place in nature and do not know how much they change not only the dynamics of these processes but also the laws of their formation. It is enough to note the fact, for instance, that global primary production varies from year to year, following the climatic noise. This change from different estimates is between 5% and 10%. For instance, the primary production of coniferous forests in the south-eastern US in July–October was on average for 5 days, changing to an amplitude of about 0.55 gCm2 day1 on average 6.26 gCm2 day1. This variability for broad-leaf forests increases with the shift from middle to high latitudes (10
E – 40
E, 52oN – 70oN). Coniferous forests

9.7 Reality and Expected Changes of the Environment

421

respond more slowly to climatic variations at high latitudes than at low latitudes. Unfortunately, such dependences have been poorly studied, but those that determine the accuracy of estimates of the sink of atmospheric carbon in the vegetation cover of the planet, especially in coniferous Siberian forests. In recent years, attention has grown to studies of climate change in Siberia and in the Far East of Russia as a factor determining the change in global carbon cycle. In this region the temperature has increased by 0.5–0.9
C during the last decade. It has been noted that the trends of average atmospheric temperatures increase from north to south. Also, a re-distribution of precipitation is observed with increasing in the cold period and with a slight decrease in some local areas in the warm season. Thus, during the 30 years in the basin of the river Amur, precipitation in the cold period increased by 35%, and the annual sum of precipitation increased by 12.3%. The irregularity of these changes in space also manifests itself in seawater temperature. For instance, during the 100 years the water temperature near Vladivostok increased by 0.64
C and in Nakhodka it decreased by 0.27
C. Such changes are affecting the estimation of the CO2 sink in the World Ocean, especially in coastal upwelling zones. Thus, along the west coast of the northern sector of the Pacific Ocean during the upwelling season, the CO2 sink represents 5% (0.5 PgC) of the total sink in the Pacific Ocean, while the area of this zone represents 25% (0.7  106 km2) of the entire coast (0–200 m) of the ocean and < 2% of the northern Pacific Ocean (14oN – 50oN) where the upwelling season represents 30% of the year. Hales et al. (2005) assessed the power of the biological pump that pumps out excess CO2 from the atmosphere in the upwelling zone of the US Pacific coastline. This zone covers 25% of the USA shelf area. It has been shown that the prevailing low CO2 concentrations in the ocean water during the upwelling season this zone becomes a CO2 sink for the following reasons: (i) The rising water masses are rich in nutrients. (ii) The operation of the oceanic carbonate system changes sharply. (iii) The rising water masses are moderately warmed. The gas exchange flux H C3 at the atmosphere – water interface is determined from the formula:   H C3 ¼ 0:79 þ 0:0062U 310 K CO2 ΔPCO2 where KCO2 is CO2 solubility (molm3 atm1), ΔPCO2 is the sea-air PCO2 difference (atm.), PCO2 is the CO2 partial pressure (atm.), and U10 is hourly mean of the wind speed at 10 ms1. The average value of H C3 in the studied zone was equal to 20 mmol m2 day1, which is about 15 times greater than the global mean rate of CO2 assimilation by the World Ocean estimated at 1.3 mmol m2 day1 (2 PgCyear1). The total CO2 assimilation for May – August, when the upwelling process on the Pacific coastline of the USA prevails, constituted 2 mol m2 (5% of the annual mean sink of CO2 in this zone estimated at 40  1012 molyear1).

422

9 Global Climate Monitoring with Microwave Measurements

Hales et al. (2005) rightly concluded that similar studies in the zones of upwelling would make it possible to specify the spatial distribution of CO2 sinks in the World Ocean and to assess thereby the risk level of anthropogenic carbon emissions. These studies will also enable one to understand the role of tropical cyclones in the formation of fluxes H C2 and H C3 . As mentioned above, hurricanes affect local gas exchange rates at the atmosphere-ocean interface by altering the thermal and physical structure of the upper layer of the ocean. From the estimates of Perrie et al. (2004), for instance, hurricane Gustav that occurred on 10–12 September 2002 over New England at a speed of 48 ms1 caused a linear increase in local CO2 flux from the atmosphere to the ocean by up to 2.1 mmolm2 hr.1. In general, the impact of hurricanes on fluxes H C2 and H C3 (Table 8.9) has been poorly studied. The water bodies of the North Atlantic where hurricanes are frequent events, creating the zone of upwelling by decreasing CO2 partial pressure in water to 60 μ atm, introduce, of course, a substantial change in the ratio H C2 /H C3 , but the magnitude of this contribution is unknown. Discussions on distinguishing between the roles of land biota and that of the World Ocean in stabilization of climate through correlations and feedbacks in the energy system of the planet remain without constructive answers. Of course, one of the principal problems of global ecodynamics is the assessment of the response of vegetative communities to climate change. It can be added that changes in vegetation productivity depending on climatic oscillations are characterized by temporal delays specific to the types of vegetation cover. For instance, tropical vegetation responds to climate change with a delay of about 50 years. Besides, the configuration of soilplant formations itself is determined in many respects by the climatic situation. For example, rainy tropical forests only survive in soils where the dry period lasts no more than 4 months. In particular, in the northwest of Brazil, because of the semidesert character of climate with a dry period of about 8 months, the rainforest is not growing. This means that the duration of the dry season is some threshold magnitude that controls the climate-vegetation relationship. Unfortunately, there are no reliable answers to the question of whether this relationship is reversible, i.e., whether plants can return to their former climatic situation through their change. The answer to this question is important in connection to the anthropogenic change of land covers. For example, in recent decades in the Kazakhstan, whose ninth place in the world, land cover has changed substantially, which can be shown by climate change in the region where global catastrophe with drought in Aral Sea is growing. Recently observed anomalous climate change is an integral manifestation of the impact of many factors such as urbanization, deforestation, atmospheric and hydrospheric pollution, decrease of biodiversity, and intrusion of foreign elements to the ecosystems. The following phenomena are observed in many global regions: • • • • •

anomalous temperature maxima and growing quantity of unusually hot days; anomalously heavy precipitation; decreasing number of cold periods and a decrease of the total time with frosts; smoothing the amplitude of the diurnal change of temperature; the summertime drying-up of the continents;

9.7 Reality and Expected Changes of the Environment

423

• increase of the maximum speed of tropical cyclones; • increase in average and maximum rainfall during tropical cyclones. A sufficiently convincing analysis of the present state of studies in the field of global change has been carried out in studies of Victor (2001, 2004). The author draws attention to the complexity of the problem of global change and the presence of substantial uncertainties in the available scientific ideas, which determine a strong contradiction of the whole problem. Especially this contradiction is manifested via making international decisions, such as the Kyoto Protocol. As an example, we can take the US decision in 2001 to withdraw from the Kyoto Protocol, after which the criticism of the paradigm of global warming propagandized by many politicians and scientists has drastically intensified. In the US, three alternatives have been proposed for elaborating an appropriate eco-strategy, coupled with the desperation of seeking a compromise on this example: • Moderate precautionary measures including the support to scientific developments, the programme of voluntary reduction of GHGs emissions to the atmosphere, and the rejection of the obligation imposed by the international agreement on such emissions. • Development of a new international “success” agreement in the Kyoto Protocol, which should foresee realistic measures for the USA and the participation of developing countries in reducing GHGs emissions as well as the creation of the global “waste trade” system. • Encouraging the market of new technologies, which will provide low levels of GHGs emissions in the USA and other countries, especially in developing ones. These alternatives cover six directions: • Scientific analysis of the causes and effects of climate change, including measures to support additional developments. • Adaptation to climate change. • Strategies of the control of GHGs emissions. • Investments in the development of new technologies. • Coordination and cooperation of efforts with key developing countries. • Informing the population. The 10-year strategic programme CCSP for climate change studies will promote the development of these guidelines. In this connection, as Victor (2004) notes, some uncertainties in climate science must first be overcome, including: • Inadequate and incomplete consideration of climatic feedbacks (especially regarding the role of clouds). • Poorly studied carbon cycle formation processes and corresponding feedbacks (including the “fertilization” effect). • Imperfection of climate models (despite the considerable progress made in this area) and their application is limited mainly by the consideration of such a climate parameter as temperature (note also that model verification is of particular importance).

424

9 Global Climate Monitoring with Microwave Measurements

• The possibility of sudden and even catastrophic climate changes like those of the past is ignored. • Poorly studied socio-economic aspects of climate change problems (the main problem here is to obtain quantitative estimates).

9.8

The Current Needs on Ecological-Climatic Modelling

Journalist Gabriel Popkin (Washington) recently has published interesting article on one of the major global problems of the Earth’s System and how it is solved in the DOE through the development of global climate models. Indeed, this problem is more complex and ambiguous and is subsequently represented in this section. Many scientists from different countries try to synthesize global models for the simulation of separate processes in the climate-nature-society system whose fate is not yet defined. The purpose of many International Environmental Programs is to assess the real changes in the climate-biosphere-society system that have occurred in the past, observing them now and possibly in the future. The existing results of such assessments are very contradictory starting with the Club of Rome models and ending with the models mentioned by Gabriel Popkin. The most popular comments on existing views about the Earth’s System fate concern global ecological catastrophe as a result of overpopulation and pollution. Unfortunately, the nearest prospect of this problematic construction solution is uncertainty. With development of the civilization, the main problem of people’s living conditions is becoming more and more real due to the scarcity of energy resources and the increasing risk of natural disasters. In practice, existing global climate models cannot forecast the natural disaster with high probability and reliability. The development of constructive tools for forecasting natural disasters requires a knowledge base of the correlations between survivability, biocomplexity and evolution of the natural environment that are possible with the formal conjugate description of biological, geochemical, geophysical and anthropogenic processes occurring in spatial and temporal hierarchy of scales. To solve these tasks, the methods and tools used to diagnose local, regional and global changes in the Earth’s System are developed. Satellites and in-situ measurement systems play a key role in this regard; a method of radio-eclipsing method between them is the most effective tool for the vertical monitoring of the atmosphere. Existing global climate models do not actually use 10–15% of monitoring data based on theoretical descriptions of the atmosphere-ocean-land interfaces. Today there are two practically independent directions in the global modeling of the Earth’s System and its subsystems: 1. modeling Earth’s land biosphere when detailed parameterization is performed to produce green vegetation and water balance according to climate change and anthropogenic factors; and

9.8 The Current Needs on Ecological-Climatic Modelling

425

2. global climate modeling, including investigation of climate system sensitivity and response to solar variability, volcanic eruption, anthropogenic and natural emissions of GHG’s and aerosols. Many global biosphere models are known. A simple biosphere model was compiled at the Center for Ocean-Land-Atmosphere Interactions at the University of Maryland (USA). This model calculates a surface albedo as a function of vegetation layer evolution, which can be directly relevant to the global climate model. An important advantage of this model is the use of simple configurations of different biosphere processes that allow minimal input parameters. A specific approach to biosphere modeling was proposed in the Institute of Biophysics of the Russian Academy of Sciences (Krasnoyarsk). This approach is based on the use of small-scale models of biosphere processes that are associated with positive, negative and uncertain effects and opens a new direction in biosphere ecology that states a hierarchical principle for describing the ecosystem structure. The International Institute for Applied Systems Analysis (Luxemburg) has developed a series of climate and biosphere models that address limited global problems such as agroecological and hydro-economic tasks of sustainable development, global optimization of harmonized development of world food system. The compilation of global climate models is usually based on the formulation of differential equations describing the solar radiation pathways in the atmosphereocean-land system, balance of energy/heat and mass motion by winds, and the effects of surface covers on the Earth’s energy balance. This variety of climatic system components generates many uncertainties about different parameters and model factors and the relationships between individual components of the global system. Therefore, climate scenarios are widely used as components of Earth’ System models. Such an approach allows many uncertainties to be overcome in the global model structure and its contributors. But this approach does not help to increase the reliability of the modeling results, particularly, in the context of forecasting a climate for a hundred years in the future. The positive results of overcoming of uncertainties in the global modeling process were achieved at the Hadley Centre for Climate Prediction and Research whose major aims are parametrical descriptions of physical, chemical and biological processes within the climate system. Most of the uncertainties and difficulties associated with them arise when the global climate model covers the Arctic and Antarctic latitudes. The Arctic Climate Modeling Program of the Geophysical Institute of University of Alaska, the Bjerknes Centre for Climate Research at the University of Bergen, and the Norwegian Meteorological Institute are engaged in the arctic climate analysis. It is associated with the analysis of ice fields and forecasting sea ice and ocean conditions taking into consideration the surrounding Atlantic and Pacific areas. Constant attention to the Arctic Basin modeling problems is provided by the Nansen Environmental and Remote Sensing Center where polar oceanography and arctic climate dynamics are studied. Arctic water pollution is the main problem of the world’s population survivability. No one can assess the sensitivity at the global

426

9 Global Climate Monitoring with Microwave Measurements

environmental level to Arctic ecosystem change and how this change is possible. One of pollution source of the arctic waters is the trans-boundary transport of contaminants. The problems that arise here are being studied at the University of Athens. Arctic sources of river water pollution from Russian, North American and North European territories are characterized by contradictory elements that make it difficult to model Arctic processes in the global model of the Earth system. The above-mentioned reasons and comments show on the extent of investigations in the context of the problem of global sustainable development and the role of global modeling in such research. Gabriel Popkin points to the Energy Exascale Earth System Model as a possible effective project to parameterize climate-energy system, including Arctic latitudes in combination with energy, water, and land-use. Unfortunately, not all global modeling research is coordinated in the context of the sole International Center, although the Intergovernmental Panel on Climate Change is claiming this role, but its reports are of limited importance. It is evident that the development and implementation of effective technology for ecological safety assessment on a global scale is possible within the framework of the International Center for Global Monitoring of Geo-Ecological Information (ICGGM) which enables the implementation of collaborative development mechanisms for sustainable development of nature and Humanity. Only then can global environmental monitoring be optimized by providing a balanced composition of the global environmental database. Department of Environmental Physics and Meteorology of the University of Athens and Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences proposed new approach to the solution of global environmental problems using existing biosphere models and monitoring data processing algorithms as separate items of Geoecological Information-Modeling System (GIMS) key section of which is remote sensing evaluation of global model parameters. Basic principles of the GIMS include integration, unification and coordination of environmental databases, optimization of monitoring regime, coordination and compatibility of information sources, using the unified classification, coding and formats for environmental data. The GIMS has adapted the function to the specific structure of biosphere, climate and social media specific for given spatial resolution accepted for the realization of simulation experiment. As a result the GIMS controls global model of performance using quality criterion based on the indicators such as biocomplexity, survivability, Human Development Index, Food Production Index, and Living Planet Index. The global model structure has a series of items describing separate environmental and anthropogenic processes. Each item exchanges by information with others items only through its inputs and outputs which provides a possibility to change or add items without reconstruction of others items. The ICGGM equipped with GIMS as operating environment with items realizing separate models of processes in the Earth’s System could make the global modeling and forecasting efforts of this system more effective. International global intergovernmental fora such as the 2015 United Nations Climate Change Conference in Paris are restricted by agreements having a mainly declarative character. There exists positive example of Caribbean Crisis when American and Russian scientists based

9.9 Satellite Observations of Climate-Nature-Society System Dynamics

427

on the global climate model have shown catastrophic consequences of possible nuclear war. The ICGGM could consolidate the efforts of many scientists and organizations to predict the Earth’s future computational system to help make an environmental strategy decision in the near future. For each country there will easily be to overcome uncertainties of sustainable development as its final goal. Finally, mankind is in a situation where it is necessary to solve environmental problems and many other global issues in order to survive. Separate country cannot solve arising problems here.

9.9

Satellite Observations of Climate-Nature-Society System Dynamics

Knowledge of the Earth’s vegetation, especially forest ecosystems, is currently one of priority themes for global climate change. The vegetation covers play significant role in the greenhouse effect control and food production. Existing satellite monitoring of forested areas and agriculture fields provides the big data clouds processing that is realized with the GIMS technology (Varotsos and Krapivin 2017; Krapivin and Shutko 2012; Nitu et al. 2000a, b). A variety of the relationships between society and vegetation covers complicates reliable assessments of the consequents from anthropogenic impacts on vegetation covers. These assessments are provided by GIMS technology when different scenarios are considered (Krapivin et al. 2015a). It is evident that the present situation of these interactions of population with forests leads to significant consequences for global environmental changes with negative habitat characteristics. The land biota and forests particularly is one of the sinks for atmospheric CO2. Unfortunately, the available data and knowledge of the processes of plants’ respiration suggest only roughly integrated CO2 flux estimates in vegetation cover. Currently, the role of the plant in the assimilation of atmospheric CO2 changes sharply over 24 hours and is a complex function of such environmental factors as temperature, illumination, and air humidity. Nevertheless, attempts to parameterize the vegetation functions made in GIMS makes it possible to assess the role of all types of the soil-plant formations in CO2 assimilation. The relation of the role of different ecosystems to carbon formation in the biosphere defines the speed and direction of changes in regional meteorological situations and global climate. The precision of the assessment of these changes depends on the reliability of land ecosystem inventories. This is justified by the considerable dispersion of the evaluations of carbon storage in vegetation of various types giving the opportunity to talk about the importance of more precise classification of land ecosystems. Krapivin et al. (2015a) illustrates the influence of afforestation/deforestation processes on the carbon storage under the FAO-2000 anthropogenic scenario, where the forest is defined as an area of at least 0.5 ha and is covered by trees

428

9 Global Climate Monitoring with Microwave Measurements

with height of more than 5 m which it occupies more than 10% of area (FAO 2001). Deforestation is the removal of a forest or stand of trees where the land is thereafter converted to non-forest use and the crone area is less than 10% of the total area. Afforestation is the establishment of a forest or stand of trees in an area where there was no forest. It is noted that the natural expansion of forest is also considered by FAO-2000 scenario as afforestation. Finally, direct artificial planting of trees in the area where the forest had moved earlier is considered as afforestation. Modern population biology has developed a scientific basis for the creation of mathematical models of vegetation dynamics according to environmental parameters, including climate change. Its foundation is the consideration of the land ecosystems with their features that define their ranges. The main role of environmental dynamics belongs to the forest biocenosis. Existing mathematical models of the forest ecosystems usually take into account their spatial inhomogeneity reflecting species diversity, age group and genetic structure. Of particular interest is the fact that forest ecosystems play an important role in global environmental change. A forest ecosystem is a natural woodland unit consisting of all plants, animals and micro-organisms (biotic) in an area functioning together with all of non-living physical (abiotic) environmental factors. The set of forest organisms (trees, shrubs, herbs, bacteria, fungi, and animals, including humans) along with their environmental substrate (ambient air, soil, water, organic debris, and rocks) were studied by many authors (Gyde 2012; Silver 1998; Sudarshana et al. 2012). Forests and woodlands occupy about 31–38% of the Earth’s surface, and are more productive and have greater biodiversity than other types of terrestrial vegetation. Forests grow in a wide range of climates, from tropical rain forests to cold arctic mountain slopes, and from arid inland mountains to windy raining coastlines. The type of forest in a given place results from a variety of factors, including frequency and type of disturbances, seed sources, soils, slope and aspect, climate, seasonal patterns of rainfall, insects and pathogens, and history of human influence. The total forest area of the planet is just over 4 billion hectares, which corresponds to an average of 0.6 ha per capita. The five most forest-rich countries (the Russian Federation, Brazil, Canada, the United States of America and China) account for more than half of the total forest area. Ten countries or areas have no forest and an additional 54 have forest on less than 10% of their total land area. Existing climate models consider the vegetation covers as the CO2 sinks in the framework of spatial structure of discrete pixels with spatial resolution Δφ by the latitude and Δλ by the longitude (Waring and Running 2007; Burkhart and Tomê 2012). These parameters of digital pixel structure are usually coordinated with the spatial resolutions of the satellites whose information is used. As a rule, climate models are based on vegetation models paying particular attention to forest models. Krapivin et al. (2019) characterize a set of existing forest ecosystem models and propose a concept for the synthesis of biological models of the multi-species different-age forests. This concept is based on the following positions:

9.9 Satellite Observations of Climate-Nature-Society System Dynamics

429

• basic bioecological parameters of species change during ontogenesis and are given for each age-specific position of the modeling species; • available photosynthetic active radiation is a basic backbone factor for the formation and evolution of the forest ecosystems within the boundaries of moderate climate regions; • growth rates of trees depend on the mutual positions of species and other groups in the space, features of the light regime, moisture availability, and the mineral salt elements; • species death (group of species) is the result of natural aging, long-term shortage of living resources and anthropogenic impacts; • number of species appearing as a result of natural recovery depends on the germs growth conditions and the ability to recover; • spatial structure of individual trees and their groups can be represented as a collection of rectangular parallelepipeds. The forest ecosystem is an important component of the global continuity of the soil-plant formations on Earth. Therefore, the synthesis of models, which could describe the forests evolution, is the principal stage in which the model of the global climate-nature-society system is created. In particular, forest ecosystems play an important economic role in human life defining regional economic opportunities. In addition to wood products, regional forests provide many more services to human society. Forests are of great recreational value especially for the tourism sector, provide clean groundwater, have a positive, mediating effect on the regional climate and protect against erosion and relax winds (Nilsson et al. 2011). A variety of types of forest ecosystems that operate under varying environmental conditions are forcing researchers to look for tools to coordinate these varieties. Many types of multifunction types, operating on different types of graphical media, allow the development and desire of telecommunications. Hasenauer (2006) discusses a modeling technology for forest ecosystems and distinguishes three main directions: • growth and productivity models; • succession models and • biogeochemical models. Dynamic forest ecosystem models help understand the life cycle of nutrient and carbon in forests, as well as to predict the effects of climate change and tree growth (Campbell et al. 2009). Indeed, more complex forest models combine to produce what is needed to coordinate spatio-temporal scales. Figure 9.2 illustrates this coordination. Earth’s hydrosphere accounts for more than 71% of the Earth’s surface and plays a significant role in the global climate as a regulator of greenhouse effect. The main problem nowadays, is that humans are drastically impacting the hydrosphere and will continue to be due only to the needs of the population. It is known that the oceans today absorb about one-third of the CO2 released into the atmosphere by burning of fossil fuels. In this connection, the perspectives of the impact of

430

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.2 Scheme for coordinating spatial and temporal scales when a forest ecosystem model is synthesized

hydrosphere on global climate change are evaluated by many experts with alternative opinions (Lomborg 2001). Some of them believe that the current opinion on global warming has not been proven due to existing uncertainties in both global monitoring data and climate modeling results. Unfortunately, alternatives to climate change have several convincing grounds. The prevailing view on climate change in the scientific community sounds like “scientific consensus on climate change”. Promoting the assessment of the reliability of the role of the World Ocean in global climate change requires a more detailed description of the production and hydrology processes occurring in oceanic geoecosystems. Biological processes play a significant role in the oceanic carbon cycle. Phytoplankton absorbs nutrients and dissolved CO2 from oceanic waters as a result of the creation of organic matter that partially enters the food chains or bottom sediments. The problem of changing climate/society coupling and risk assessment is given significant attention with particular regard to human survivability (Krapivin et al. 2017a, b). Indeed, humanity has come to terms when it is necessary to make important and decisive alternative strategy on how to interact with the environment. The speculation about the global sustainability and self-organization of the climatenature-society system (CNSS) with increasing frequency is being discussed by many experts paying particular attention to the use of energy resources. Evidently, the humankind now has leaved the unstable trajectory of its development. Nature responds to the strategy of society in an appropriate way, by expanding a spectrum of inverse responses and extending powerful negative environmental processes such as tropical cyclones, earthquakes and volcano eruptions (Kondratyev et al. 2006). It is obvious that reliable assessment and prognosis of the CNSS evolution requires a sophisticated global monitoring system that is accompanied by modern mathematical methods. The main GIMS structure corresponds to this purpose and is oriented towards coupling the global unique environmental database and GMNSS. GIMS enables to combine the knowledge from different sciences with the ability to

9.9 Satellite Observations of Climate-Nature-Society System Dynamics

431

manage this knowledge. The development and implementation of GIMS-technology allows the future to solve basic problems: • Prognosis of the start time and dangerous levels of natural disasters, safety situations and technological disasters. • Controlling accidents and disasters in their dynamics, including complex meteorological conditions and providing decision-making information. • Emergency and disaster assessment of cities, towns, rural areas and aquatic ecosystems. • Deliver objectives to conservation teams while implementing search procedures. The ICGGM should consist of three structural units corresponding to the main directions of ecological activity: ecosociology, ecoinformatics and ecotechnology. Each of them, according to its own problems, can be divided into several sections: 1. Ecosociology: • study of the impact of human society on nature and the interactions in the system human society – nature; • analysis and prediction in the system human society – nature; • nature-protection measures; • decision-making and interaction with powerful structures. 2. Ecoinformatics: • recording, storage, transmittance, analysis, synthesis, modelling and presentation of information on the state of the environment; • reception and analysis of the space-derived information; • aero-survey with a mobile aircraft-group; • ground observations with the network of sensors and ground stations; • control of human health; • Modeling and maintenance of the ecomodel in the region; • information-calculation center. 3. Ecotechnologies: • creation of technologies and technical means with minimal impacts on both nature and the human organism. • ecoanalysis of industrial enterprises; • ecoanalysis of the extractive industry; • monitoring compliance with technologies; • expertise of technologies and technical decisions. • Ecosociology problems include: • analysis of the social structures affecting their nature, motives, goals, methods and technical means; • analysis of the necessity and adequacy of the anthropogenic impacts of social structure on nature; • analysis of the optimal distribution of industrial and rural infra-structure and dwellings in nature;

432

9 Global Climate Monitoring with Microwave Measurements

• development of the eco-protection measures taking into account socioregional, socio-national and socio-global interests. The problem of ecoinformatics involves obtaining of diagnostic, prognostic and practical information about the state of nature and its changes due to anthropogenic and natural forcings. The information obtained should reflect the following: • quantitative estimation of the state of nature and natural resources; • quantitative estimation of anthropogenic impact and detection of harmful and surplus production; • the current status and maintenance of regional (in some cases – regional) simulation models of interactions in the “production –medium”, “agriculture – medium” and “dwelling –medium” systems; • the current state and maintenance of the regional ecomodel; • the current state of affairs of ecomodel in the context of national and international cooperation; • the current state that makes it possible, based on the simulation ecomodel, to build prognostic schemes for the impact of harmful and surplus production on the nature of the region; • the current state used for prognostic modeling of nature changes in the extraction of natural resources; • the current state used, on the basis of simulations and prognostic models, to draw up recommendations on the development or reduction of production and dwellings in the region; The ecoinformation monitoring system is constructed by a 3-level scheme, including satellite and ground observations as well as air-borne observations. The integrated operating scheme consists of the following main components: • the complex observation system (data collection) which includes cosmic, air-borne and ground segments, ensuring regular in-situ and remote measurements of environmental parameters; • reception/transmittance sub-systems of data based on space- and ground-based media using satellite, microwave, wire and fiber-optic communication channels; • primary data processing sub-systems from various observing systems; • sub-systems of data accumulation and systematization (databases); • sub-systems groups to solve applied (thematic) monitoring problems. With their goal and information-technical characteristics, systems of space-borne observations fell into the following classes: (a) routine detailed observations systems from space-borne platforms using multizonal and topographic survey data. (b) Direct observation systems divided into sub-classes: • systems of planned survey on the problems of global direct control of the state of the atmosphere and the Earth’s surface with low spatial resolution of continuous (quasi-continuous) observations using space-borne platforms on

9.9 Satellite Observations of Climate-Nature-Society System Dynamics

433

geostantionary orbits and regular observations using space-borne platforms on high-elliptic and circumpolar orbits; • systems of detailed observations operating on continuous (in high-elliptic or circular orbits) and regular (at mid-altitude circular orbits) regimes, with the high or middle spatial resolution, providing data on selected objects or regions on the Earth’s surface; At present, most of the spacecraft’s middle-orbit instruments both understand the both sub-problems. In the aeronautical observation system, the organization of aircraft identification that detects regions with complex ecological conditions requires an aeronautical detector capable of transporting experimental equipment and operating on both pilot and remote controls. The regional ecomodel and its operational maintenance problems require an efficient information-calculation network. It should be based on the distributed network of modern computing media with corresponding software. The regional ecological monitoring system should be the focus of ecological expertise in territorial management. The ecological monitoring system will provide every citizen of the region with access to environmental information (generally available sites or pages on any global network). Based on this knowledge, a man will be able to make sound decisions about many vital problems in both the social and personal areas. The ecotechnological problem includes: • assessment of the actual and potential danger of functional productions connected with emissions of substances harmful to the environment and the human organism; • assessment of technological processes and detection of the cycles of processes emitting these substances; • correction of technological processes with the parameters of end- products maintained but reduced harmful impacts on the environment and the human organism; • development of equipment for corrected technological process; • instructions for staff working in new conditions and with new equipment; • qualified expertise in the technological processes and equipment they carry out. to assessing the dynamics of the climate-nature system and society, we can conclude that significant progress can be made in seeking ways to achieve global sustainable development through a systematic approach to multifunctional climate-monitoring. As mentioned earlier, the main problem arising when collecting and analyzing environmental big data is the level of uncertainty in such data (Chen and Zhang 2016; Shi et al. 2018). Uncertainty-based spatial big data reduces the reliability of GIMS-based modeling predictive models. Unfortunately, sources of environmental uncertainty data arise from every stage of environmental monitoring, including ground observation and airborne methods. It is certain that GIMS technology will help overcome the limitations arising from the global analysis of environmental and social processes in the climate-nature-society system (CNSS). Satellite platforms

434

9 Global Climate Monitoring with Microwave Measurements

Table 9.6 Some of platforms and sensors used to derive environmental properties from space Launch 1975 1979 1997 1997 1998 1999

Platform GOES-1 ~ 12 NOAA-6 ~ 12 TRMM OrbView-2 SPOT-4 TERRA

2001 2001 2002

PROBA Odin ENVISAT

2002 2004

MSG-1 AURA

2004 2006 2013 2014

PARASOL CALIPSO Proba-V Global precipitation measurement Deep space climate observatory Jason-3

2015 2016

Instrument VISSR AVHRR VISR SeaWiFS POAM-3 MODIS MISR CHRIS OSIRIS AATSR MERIS SCIAMACHY SEVIRI OMI HIRDLS POLER-3 CALIOP OIP GPM, DPR

Wavelengths (μm) 0.65–12.5 0.58–12.0 0.63–12.0 0.412–0.865 0.354–1.018 0.4–14.4 0.45–0.87 0.4–1.05 0.274–0.810 0.55–12.0 0.4–1.05 0.24–2.4 0.6–13.4 0.27–0.5 6–18 0.44–0.91 0.532–1.064 447 ~ 1650 nm 10–85.5, 10–183 GHz

PlasMag, EPIC, NISTAR DORIS, AMR, CARMEN-3

0.2 ~ 100, 317 ~ 779 nm 1 ~ 20 Hz; 5.3, 13.575, 18.7, 23.8, 34 GHz,

produced aim to reduce this information uncertainty. Table 9.6 lists some of them. Table 9.7 lists separate passive microwave missions. Satellite systems belong to a limited number of countries. Therefore, the availability of satellite monitoring data is a responsibility of each country’s satellite systems in space. Existing geostatic and polar satellites are currently available in the weather to provide a better understanding of climate change and its effects on the nature / society system. Many scientific structures in the United States, Europe, the Russian Federation, India and China are trying to develop information-modeling tools which could help to evaluate and to predict the consequences of anthropogenic scenarios and determine their optimal structure of satellite observing system. Let’s mention a lot of satellite systems that provide useful information for solving environmental problems. For example, the Geostationary Operational Environmental Satellite Networks (GOES-13-17) series provide images and atmospheric measurements of the weather, ocean and Earth’s environment, real-time mapping of total lightning activity, and improved monitoring of solar activity and space weather. The purpose of Indian National Satellites (INSAT) partly consists in the supporting of meteorological Earth observation and cyclone warning dissemination service. FengYun’s Chinese Geostatistical Meteorological Satellite Series plays an important role

9.9 Satellite Observations of Climate-Nature-Society System Dynamics

435

Table 9.7 Fragments of the history of passive microwave satellite missions Launch 1962 1968 1970

1972

Spacecraft Mariner-2 Cosmos243 Cosmos384 Nimbus-5

Instrument – –

Frequency (GHz) 15.8, 22.2 3.5, 8.7, 22.2, 37

Spatial resolution 1300 km 15–50 km

ESMR

19.35

29 km

NEMS

22.35, 31.40, 53.65, 54.90, 58.80 13.9 1.41 37

29 km

37

20  43 km

H2O vapour and precipitation. The same as Nimbus-5

50.3, 53.7, 55.0, 57.9

110 km

Temperature profile.

6.6, 10.69, 18.0, 21.0, 37.0 6.6, 10.69, 18.0, 21.0, 37.0 0.42, 2.5

95  148 km 70  109 km 43  68 km 36  56 km 18  27 km 95  148 km 70  109 km 43  68 km 36  56 km 18  27 km 56 km

Sea temperature, wind speed, snow cover, soil moisture.

6.925, 10.65, 18.7, 23.8, 36.5, 89.0

5.4 ~ 56 km

1973

Skylab

S-193 S-194 –

1974

Meteor

1975

Nimbus-6

1978 1979 1978

TirosN NOAA-5 NOAA-6 Nimbus-7

1978

Seasat

SMMR

1979

Salyut-6

KRT-10

2004

Aqua

AMSR-E

ESMR SCAMS MSU

SMMR

16 km 115 km 40  60 km

Function Temperature, H2O vapour H2O vapour and liquid, sea ice concentration, sea temperature. Rain and H2O vapour; firn and ice concentration and classification. Temperature profile, H2O vapour and liquid, firn and ice classification, snow cover.

Soil moisture.

Sea temperature, wind speed, snow cover, soil moisture.

Thermal map of soil and sea surface. Information about the Earth’s water cycle: evaporation, water vapor, clouds, precipitation, soil moisture, sea and land ice, snow cover.

in daily weather forecasting, detecting and evaluating spring dust storms and heavy rainstorms, controlling fires and snow and ice in the mountains. Meteorological system with Electro Geostationary Operational Meteorological Satellite (GOMS) allows:

436

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.3 Synthetic structure of CNSS sustainable development

• real time to capture visible and infrared images of the Earth’s surface and cloud cover; • continuous observation of various atmospheric processes; • detection of hazardous natural phenomena; and • determination of sea surface temperature and wind velocity/directions; The Global Observing System (GOS) is an important result of the international collaboration of the last 60 years that has begun to support global climate investigations. Existing separate satellite missions provide multiform information on various elements of the global CNSS, which gives confidence to in future successful prognostic assessments of CNSS evolution and will determine the global strategy for sustainable development. Microwave sensors only provide brightness temperature at a given frequency which can be used to estimate different parameters of the Earth’s surface. The reliability and accuracy of these estimates are determined by the algorithms and models used to process brightness temperature time series. GIMS-technology proposes a constructive approach to the solution of the emerging tasks. A schematic of this approach is represented in Fig. 9.3. GIMS-technology allows the reconstruction of information provided by satellite and land-based observational systems that are characterized as time episodic and fragments in space using

9.10

The Earth’s Population Survivability

437

spatial-temporal interpolation and models. One of the main GIMS function is to assess the environmental status of the predetermined forecasting diagnostics of environmental changes (due to anthropogenic impacts) and to analyse the evolution of environmental processes, taking into account the anthropogenic scenarios.

9.10

The Earth’s Population Survivability

9.10.1 Short Description of the Problem The origin of the twenty-first century does not practically relax, but aggravate the problem of sustainable development of human society. If human society was on the threshold of nuclear war in the middle of twentieth century, when there was a Caribbean Crisis, then the global climate- nature-society system (CNSS) is now in a state of crisis for a number of reasons, including the most significant (Krapivin et al. 2017a): • Early growth of the global population compared to the increase in productivity of agricultural and natural ecosystems leads to a decrease in food volume per capita. The food-deficit is a field in many regions. Food per person stops with time and hungry people is expected to grow. • The environment response to anthropogenic intervention to the natural cycles is tracked by the intensification of natural disasters, including the occurrence of new incurable diseases. • Global climate change, due to the disruption of GHGs and water resources cycles, is leading to a change in the spatial distribution of water resources, including drinking water. • The development of new powerful weapons and platforms for their operational production practically all contexts concerning the distortion of the information environment contributes additional uncertainties to the problem of human population survivability. • Intensification of both international and regional conflicts is followed by dramatic changes in the globalization and decentralization processes which does not encourage the improvement of the living conditions for the population. • One of the possible causes of ecological catastrophe can be threatened by insectpollinators that can occur as an ecological consequence of mobile communication means including mobile phones. Thereby, strained global relations put under the humanity the survivability problem solution, which is impossible to solve on a regional scale. It is necessary to develop a constructive information technology that allows the complex description of the global ecological, demographic, socio-economic and climatic processes taking place in the climate-nature-society system (CNSS). It provides the opportunity to look for constructive strategies for the CNSS survivability, taking into account existing assessments and forecasts of environmental resources. A

438

9 Global Climate Monitoring with Microwave Measurements

cornerstone of the concept of sustainable co-existence of nature and humans is the convention that all countries must seek out balanced strategies for the evolution of the biosphere-population system taking into account biosphere reserves. The global population in its tendency to poverty reduction is realizing that biosphere reserves are exhausting. Therefore, the complex goals of the global population are research and monitoring related to conservation and sustainability. In this problem, there are many investigations based on global models (Kondratyev et al. 2004; Krapivin and Varotsos 2007, 2008; Krapivin et al. 2005, 2015a). These and other investigations of global environmental processes are based on different models of the present view of the CNSS structure. Many of them have a virtual character based on the philosophyideology of the world state. The efficient constructive approach of the global environmental model 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). Research has developed a mathematical approach to the global environmental model and provides simulation experiments with global environmental processes, including assessments of the effects of anthropogenic impacts on biosphere ecosystems (Krapivin 1993; Sellers et al. 1996; Degermendzhi 2009; Krapivin and Kelley 2009). The difference between the Club of Rome models and other models lies mainly in the methodology principles (Saavedra-Rivano 1979): • The authors of the Club of Rome’s models focused primarily on global economic processes that link them to separate environmental processes and choosing the demographic block as a main element of the global model. • Moisseev’s (1979) starting point was biosphere research where he looked at the human element as a biosphere element and the demographic and economic processes that were examined only in the context of a systematic analysis of global ecological evolution. Today’s socio-economic theories of sustainable development are far from Moisseev’s ideas and certainly from Vernadsky’s noosphere theory (Vernadsky 1944). Numerous indicators such as Happy Planet Index (HPI), Human development Index (HDI), Food Production Index (FPI), Gross Domestic Product (GDP) and others undoubtedly help assess the development tendencies in specific CNSS section but make difficulties for the complex evaluation of the CNSS evolution. It is only possible using a global model providing the opportunity to take into account the maximum number of direct and indirect couplings present in CNSS. The tendency to improve global models is characterized by efforts to improve their accuracy and reduce information requirements. The complexity of organized reality at the same time hampers this approach of improvement and brings a set of constraints associated with chaotic environmental processes and multidimensional problem. It is an office for many researchers worldwide (Degermengzhi 2009; Krapivin et al. 2019). Indeed, each global model is individual in nature, examining and focusing on a limited range of environmental processes and elements. Krapivin et al. (2015b) proposed a new approach to global model synthesis based on the use of high-level tools for the use of separate functions related to the description of

9.10

The Earth’s Population Survivability

439

processes in CNSS. The Geo-Ecological Information-Information Modeling System (GIMS), whose architecture is based on the combined use of GIS technologies and modeling tools, has been developed. This paragraph taking into account existing global models describing different processes in the CNSS, proposes the use of GIMS as a universal tool for the complex parameterization of the most important global processes to search sustainable state between nature and human society. Starting from the traditional concept and civilized sense of processes at CNSS, the architecture of the global GIMS/CNSS model was created to demonstrate an integral scheme of direct and indirect relationships between these processes. GIMS/CNSS is composed of the operation of many items, each with individual task or field of action. GIMS/CNSS components operate autonomously to represent part of the desired functionality.

9.10.2 General Description of the Survivability Model An essential aspect of assessing human survivability is the ecological situation of the Earth natural evolution, which determines food production and other conditions for the population survivability. Certainly, the level of self-organization and the structure of the CNSS itself depend on the many factors of the nature-population co-evolution as elements of the biosphere. Therefore, the synthesis of the CNSS model is possible only on the basis of the synergetic approach that dictates the form and structure of the GIMS/CNSS. The GIMS plays a management role by providing co-ordination between CNSS items and extending their functionality. Following this approach the basic components of GIMS/CNSS are defined as the core of information kernel on ecological, geophysical, hydrological, biocenotic and demographic processes occurring at different regions of the globe. The Earth’s surface Ξ is divided into the World Ocean ΞO and land ΞL (Ξ ¼ ΞL[ΞO). The land surface ΞL is covered by a geographical grid with discrete steps of Δφi and Δλj by latitude and longitude, respectively, so that all processes within the pixel ΞLij ¼ {(φ,λ): φi  φ  φi + Δφi; λj  λ  λj + Δλj} are considered uniform and are parameterized by the point models. Each pixel area σij ¼ χφχλΔφiΔλj is occupied by soil-plant formation (r1th part), agricultural vegetation (r2th part), hydrophysical objects (r3th part), and anthropogenic objects ((1-r1-r2-r3)th part), where χφ (111 km) and χλ (¼111.3Cosφ) is number of the kilometers to a degree of latitude and longitude, respectively. In the World Ocean case, three latitudinal zones are separated: equatorial zone ΞO1 ¼ {(φ,λ): φ2[0
N,30
N][[0
S,30
S]; 0
 λ  360
}, temperate latitudes ΞO2 ¼ {(φ,λ): φ2[30
N,60
N][[30
S,60
S]; 0
 λ  360
} and the Arctic and Antarctic zone ΞO3 ¼ {(φ,λ): φ2[60
N,90
N][[60
S,90
S]; 0
 λ  360
}. Pelagic ΞO1P and upwelling ΞO1U aquatories are selected in the ΞO1 zone to differ in productivity and gas exchange rate at the air-water boundary. Figure 3.4 and Table 3.2 represent the GIMS/CNSS block structure synthesized by taking into account the components and parameters of the global biogeosystem

440

9 Global Climate Monitoring with Microwave Measurements

which is controlled by geoinformation monitoring systems. The GIMS/CNSS spatial structure is defined by the available data and knowledge bases. The simplest version of the point model takes place when the World Ocean and the land are considered as the only element of the planet. Spatial heterogeneity is realized by the various forms of global space sampling. The basic form of spatial digitization is the selection of a uniform grid Δφ  Δλ. GIMS allows for a different spatial grid for each CNSS model item supporting the ability to embed ΞLij pixels. This kind of spatial structure of the biosphere allows the model to be adapted to the heterogeneity of the databases and to perform simulation experiments with the realization of the individual regions. Depending on the particularities of the physical process under consideration, a regional structure can be identified by climatic and geographical zones, continents, natural biomass and socio-administrative structures. For example, Krapivin (1993) divided the land biosphere into pixels with sizes Δφ ¼ 4о and Δλ ¼ 5о. Biogeocenotic processes are studied according to Δφ ¼ Δλ ¼ 0.5о (Sellers et al. 1996), socio-economic processes are usually represented by three or nine regions according to the country development status (Krapivin et al. 2017b), atmospheric processes in the biogeochemical cycles of long-living elements are approximated by point models (Nitu et al. 2004, 2013), the functioning of oceanic ecosystems is represented by heterogeneous spatial structure, including pixels ΞOij of shelf zone and pelagic zones of the four oceans (Kondratyev et al. 2002). Tarko (2003) developed the Moscow Global Biosphere Model where the World Ocean is represented by the upper quasi-uniform and deep layers separately for four latitudinal zones to the north and south aquatories. The GIMS allows the combined use of these parameterizations. The GIMS/CNSS data listed in Table 3.2 performs calculations of energy and matter flows between spatial pixels of the biosphere taking into account its components. GIMS/CNSS stability is provided by the information channels that connect the operating components so that changing or modifying the component does not affect other components. The GSA element provides parametric identification for pixel components, including soil-plant formations, pollutant sources, water ecosystems and population. As a result, matrix structures are formed as spatial identifiers of CNSS elements. The AHIS component solves the task of evaluating the survivability levels for the population based on available indicators. One of them is the survivability indicator: J ðt Þ ¼

8 < X 1 σ



R1 ði, j, t Þ σ ij r1 1Φ :ði, jÞ2Ξ RΦ ði, j, t0 Þ L

þ

R2 ði, j, t Þ r2 2Φ RΦ ði, j, t0 Þ



þ

R3 ði, j, tÞ r3 3Φ RΦ ði, j, t0 Þ

þ

3 X s¼1

σ Os

9 =

RP ðs, tÞ RP ðs, t 0 Þ;

ð9:1Þ where

9.10

The Earth’s Population Survivability

441

  C A ðtÞ  ΓðiÞ aE Eði, t Þ aW W ði, j, t Þ , , , R RkΦ ði, j, tÞ ¼ R k ð i, j Þ min a ð i, t Þ þ M kΦ ði, j, t Þ C T Φ bC þ CA ðt Þ þ ΓðiÞ bE þ E ði, tÞ bW þ W ði, j, tÞ    T ði, tÞ  T min ðκ Þ T ði, tÞ  T min ðκ Þ RT ði, tÞ ¼ max 0, exp aT  bT , T opt ðκ Þ  T min ðκ Þ T opt ðκ Þ  T min ðκÞ

  ρ T ði, t Þ d W ði, j, t Þ M kΦ ði, j, t Þ ¼ K B Φ k ði, jÞ max 0, T , a , dT þ T ði, t Þ d b þ W ði, j, t Þ   T ði, t Þ ¼ T g ðt Þ þ ðT N ðt Þ  T e ðt ÞÞ sin 2 ϕT  sin 2 ð4iÞ ,

RP ðs, t Þ ¼ R s P min fΥ0 ðT W Þ, Υ1 ðE Þ, Υ2 ðnÞ, Υ3 ðPÞg, s ¼ 1, 2, 3; ,       TW TW E E Υ0 ðT W Þ ¼ exp θW 1  exp θE 1  , Υ1 ðEÞ ¼ , E max E max T Wopt T Wopt    θ n   nðs, t Þ Pðs, t Þ , Υ3 ðPÞ ¼ 1  exp γ P , Υ2 ðnÞ ¼ 1  exp γ n nðs, t 0 Þ Pðs, t 0 Þ Γ is the photosynthesis compensation constant (varies from 5 at equator to 50 at pole), CA is CO2 content in the atmosphere (ppmv), E is solar radiation (W/m2), W is precipitation (mm/year), TN and Te are global temperatures I n the pole and equator, respectively (
C); Tg is the global average temperature (
C); Tmin and Topt are the critical and optimal temperatures for the photosynthesis (
C), respectively φT is the latitude at which T(i,t) ¼ Tg; Emax is the solar radiation corresponding maximal photosynthesis; n is the content of the biogenic salts (mg/m2); P is the phytoplankton biomass (mg/m2); aC (3.226), bC (930.03, aE (1.177), bE (60.538), aW (4.742), bW (592.357), aT (0.56), bT (0.42), ρT (1.214), dT (5.714), da (0.0267), db (208.333), θW (0.21), θE (0.25), θn (0.6), γn (0.1), and γP (0.25) are adaptation coefficients providing the coordination between the model and pre-history trends of CO2, global temperature and population size; σ ¼ 510.1  106 km2; t0(2015) is the starting time when global average production is assessed by R Φ(2015) ¼ 48.7 PgC/year and 2 R P (2015) ¼ 56.2 PgC/year. Under this R 1 P ¼ 0:049PgC=day in ΞO3, RP ¼ 3 0:033PgC=day in ΞO1, and RP ¼ 0:072PgC=day in ΞO3 (Krapivin et al. 2017b). The indicator J(t) is an integral characteristic of the CNSS complexity reflecting the individuality of its structure and its evolution at time t. According to the laws of natural evolution, the decrease or increase in J(t) will reflect the CNSS ability to survive. Moreover, the decrease J(t) corresponds to the negative perturbation of biogeochemical cycles that intensify the resource-depletion processes and shift the vector of energy exchange between the core functions of the CNSS. In particular, the decrease in J(t) leads to a decrease in total food stocks, which may be reflected by the food production index (FDI) which is a function of climate, scientific-technical progress and economic factors. CM provides the calculation of the spatial distribution of the mean annual temperature of the atmosphere based on the simple model of climate used by Mintzer (1987) and modified by Krapivin et al. (2015a):

442

9 Global Climate Monitoring with Microwave Measurements

ΔT g ¼ ΔT CO2 þ ΔT N2O þ ΔT CH4 þ ΔT O3 þ ΔTCFC11 þ ΔTCFC12 , T ðφÞ   ð9:2Þ ¼ T g þ γ sin 2 φT ‐ sin 2 φ , where γ is the difference of atmospheric temperatures between the pole and the equator, φT is the latitude, where T(φ) ¼ Tg, h i ΔT CO2 ¼ ‐0:677 þ 3:019 ln ½C А ðt Þ=C А ðt Þ , ΔT N2O ¼ 0:057 N2 Oðt Þ1=2 ‐N2 Oðt Þ1=2 , h i ΔT CH4 ¼ 0:019 CH4 ðt Þ1=2 ‐CH4 ðt Þ1=2 , ΔT O3 ¼ 0:7½O3 ðt Þ‐O3 ðt Þ =15, ΔTCFC11 ¼ 0:14½CFC11ðt Þ‐CFC11ðt Þ , ΔTCFC12 ¼ 0:16½CFC12ðt Þ‐CFC12ðt Þ : ð9:3Þ The value of t is identified by the year 1980, when the GHGs concentration were known (CO2 337.7 ppmv; N2O 270 ppb; CH4 722 ppb; CFC11 167.99 ppb; CFC12 307.75 ppb). The items CMCM, GNCM and GCOO calculate concentrations of CА(t), N2O(t), CH4(t), O3(t) using corresponding models and CFC11(t), and CFC12 (t) taking into account the data provided by Butler and Montzka (2016). Item DM realizes a model of population dynamics G(i,j,t) taking into account the environmental factors: dGði, j, t Þ=dt ¼ RG ði, j, t Þ‐M G ði, j, t Þ,

ð9:4Þ

where RG and MG are the indicators of birth rate and mortality, respectively. Birth rate and mortality are mainly functions of food supply and environmental characteristics. A detailed description of these functions is given by Kondratyev et al. (2004). According to Kondratyev et al. (2004) functions RG(i,j,t) and MG(i,j,t) in (4) can be formalized by the following equations: RG ði, j, t Þ ¼ μB Gði, j, t Þ, M G ði, j, t Þ ¼ μd Gω ði, j, t Þ,

ð9:5Þ

where μB and μd are the coefficients characterizing a birth rate and mortality, respectively; ω is the index of the population density influence on mortality. These coefficients are functions of environmental and anthropogenic characteristics: μB ¼ ρminfμ1 ð1‐HDIÞ þ μ2 HDI; μ1 ð1‐HPIÞ þ μ2 HPI; μ1 exp ½‐ ξ1 FPI=FPIðt 0 Þ þ μ2 ½1‐ exp f‐ξ1 FPI=FPIðt 0 Þg; μ1 exp ½‐ξ2 GDP=GDPðt 0 Þ þ μ2 ½1‐  exp f‐ξ2 GDP=GDPðt 0 Þg; μ1 exp ½‐ξ3 V G þ μ2 ð1‐ exp ½‐ξ3 V G Þg, ð9:6Þ μd ¼ βminfη1 ð1‐HDIÞ þ η2 HDI; η1 ð1‐HPIÞ þ η2 HPI; η1 exp ½‐ χ1 FPI=FPIðt 0 Þ þ η2 ½1‐ exp f‐χ1 FPI=FPIðt 0 Þg; η1 exp ½‐χ2 GDP=GDPðt 0 Þ þ η2 ½1‐ exp f‐χ2 GDP=GDPðt 0 Þ g; η1 exp ½‐χ3 V G þ η2 ð1‐ exp ½‐χ3 V G Þg, ð9:7Þ

9.10

The Earth’s Population Survivability

443

where μ1 and μ2 are coefficients of maximum and minimum birth rates, respectively; η1 and η2 are maximum and minimum mortalities, respectively; ρ, β, χ1, χ2, χ3,ξ1,ξ2 and ξ3 are adaptation coefficients; VG is the efficient amount of food defined as the weighed sum of the components of the personal food spectrum calculated by the UEM, PMAA, MWEL, and PMTM items. In the common case we have: V G ðt Þ ¼ 8 2 ) < X 3 X X 1 2 3 4 σ r d R ði, j, t Þ þ r 2 d2 RΦ i, j, tÞ þ þr3 d3 RΦ ði, j, tÞ þ d4 RP ðs, t Þ = σ ij Gði, j, t Þ :ði, jÞ2Ξ ij 1 1 Φ s¼1 ði, jÞ2Ξ L

L

where d1 (0.023), d2 (0.65), d3 (0.11) and d4 (0.013) are the coefficients that determine the contribution of the natural vegetation, agricultural plants, land water systems and oceans, respectively to the population food spectrum. Each pixel Ξij is characterized by a biocomplexity level and is evolved in food production as a limited area that can establish different biomes, ecosystems and anthropogenic areas. To determine the typical description of the spatial structure of the CNSS, three socio-economic groups of countries are selected to be represented by corresponding areas of the land ΞL: ΞLD denotes the area occupied by countries with HDI2[0.85,1]), area ΞLM is occupied by the countries with transitional economics (HDI2(0.65,0.85)) and ΞLG corresponds to the territory of developing countries (HDI2[0,0.65]. Social costs, economic development, food insecurity, and environmental disruption in each area are presented with different intensity. Food supply is carried out by the sources available: • Agricultural technologies are the main food producers that can promote food and nutrition safety. Global agriculture provides 2940 kcal per person at present with forecast up to 3050 to 2030. Existing protein support is assessed by 60 g per day per person when the medical norm is 70 g. The total protein deficit is assessed at 10 to 25 million tones. About more than half of world’s population (7.5 billions) suffers from protein deficiency (Debertin 2012). • The second major source of food is fishing and fish-cultivation in natural lakes and reservoirs. In 2016 each person consumed about 22 kg of fish production. Currently, the ecosystems of the World Ocean and the seas provide about 20% of the global population demand for proteins of animal origin. Principally, oceanic biomass is assessed from 150 thousand of animal species and 10 thousand aquatic plants with a total weight of about 35 billion tones which is sufficient for the survival of 35 billion people (Lucas and Southgate 2012). • Natural plants and forests in the first place can be thought as hypothetical food sources, including wild animals and edible plants, hazelnuts, etc. The further development of the food industry and corresponding science allows for the expansion of primary natural biomass for food production. As shown in Figs. 9.4, 9.5 and 9.6, the general trend of food production in the various countries is characterized by a steady increase in the rate of food production. In practice, in the early 21 century, most countries have achieved comparable levels

444

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.4 Food production indices in major countries

Fig. 9.5 Comparison of food production indices for developed and developing countries

of food production. But the problem of individual food distribution has not been resolved. This problem is quite complex and relates to the socio-economic and cultural-ideological space, of which can be distinguished depending on the ideology and traditional conception of social justice, which are searched by means of various indicators (Krapivin et al. 2017b). According to the results of Figs. 9.7 and 9.8, the CNSS space indicator has many uncertainties that may be linked to existing causes of the non-uniform distribution of vital resources. Under the condition of the peaceful coexistence, the problem of population survivability comes from the food provision to those who must seek the dependencies of the global distribution of food and water supplies on the path of globalization.

9.10

The Earth’s Population Survivability

445

Fig. 9.6 Comparison of food production indices in developed country and weakly developed countries

Fig. 9.7 Birth rate and mortality dependence on the Human Development Index adopted by countries

9.10.3 A Scenario-Based Prognosis The GIMS/CNSS allows for the simulation of different environmental situations using information and data that determine specific characteristics of the land surface, the distribution of soil-plant formations and hydrosphere. The land surface is covered by a discrete number of land cover types represented in Fig. 8.4, Tables 8.7 and 9.8. Table 9.8 extends the parametric space of the soil-plant formations. The numerical values of the GIMS/CNSS parameters are given in Table 9.9. Certainly,

446

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.8 An interdependence of the Food Production Index (FPI) and Human Development Index (HDI) for different countries

these parameters can change over time but not significantly. To take into account of this aspect, these parameters change for the regions. It is clear that the precision of the forecast can only be assessed after many years and decades. Nevertheless, a set of ideas and assumptions put into the GIMS/CNSS structure determine a complete picture of the world and are the mechanisms for constructively describing the direct and inverse relationships in which the CNSS survivability is determined by the criterion (9.1). The biocomplexity of the environment determines exactly the level of food supply for the world population. As shown in Fig. 8.4a contribution of nature to this maintenance has a non-uniform spatial distribution. The corresponding modern spatial distribution is specific to agricultural and fishery products. GIMS/CNSS items that calculate average regional temperature (CM) and simulate regional hydrological balance (RHCM) allow estimation of surface vegetation production (item BMSPF) depending on temperature and precipitation. A part of such estimates is given in Table 9.10. We should consider that survivability level J(t) is the most important for each region. The GIMS/CNSS is a comprehensive overview of the population dynamics of the world’s pixel structure, taking into account the respective interactions between the biosphere and the climate system. Undoubtedly, the GIMS / CNSS implementation proposed here does not close the description of organized complexity reality, but it improves the structure of existing global models and provides a more accurate calculation of population dynamics.

9.10

The Earth’s Population Survivability

447

Table 9.8 Quantitative characteristics of the types of land vegetation formations Indicator and type of soil-plant formation A – Arctic deserts and tundra C – tundra M – Mountain tundra L – Forest tundra F – North-taiga forests D –Mid-taiga forests G – South-taiga forests R – Broad-leaved coniferous forests +  Broad-leaved forests P – Sub-tropical broad-leaved and coniferous forests U – Xerophytic open woodlands and shrubs X – Forest-steppes (meadow steppes) W – Moderately arid and arid (mountain including) steppes E – Pampas and grass savannas V – Dry steppes # – Mangrove forests S – Sub-boreal and saltwort deserts & – Sub-tropical semi-deserts H – Sub-tropical deserts B – Alpine deserts Q – Alpine and sub-alpine meadows Z – Humid evergreen tropical forests Y – Variably-humid deciduous tropical forests N – Tropical xerophytic open woodlands J – Tropical savannas T – Tropical deserts K – Saline lands I – Sub-tropical and tropical grass-tree thickets of the tugai type – Lack of vegetation

σS 2.55 2.93 2.33 1.55 5.45 5.73 6.60 2.12 7.21 5.75 3.91 3.72 4.29 1.66 2.66 2.08 2.69 1.99 7.16 1.15 3.54 10.4 7.81 9.18 17.1 13.52 0.38 0.9

R 1 Φ 0.17 0.36 0.38 0.65 0.54 0.63 0.65 0.87 1.25 1.72 0.56 0.74 0.79 1.11 0.38 0.45 0.25 0.35 0.12 0.47 0.76 3.17 2.46 1.42 1.35 0.18 0.18 1.96

Φ 0.4 1.9 1.9 3.8 10.0 22.5 23.5 25.0 45.0 43.0 3.8 1.9 1.9 3.8 0.8 0.4 0.2 0.8 0.1 0.8 1.9 60.0 60.0 10.0 0.1 0.4 45.0 45.0

Tmin 5 5 3 5 5 5 5 1 1 0 0 2 2 5 5 5 5 5 5 3 3 5 5 5 5 5 4 4

Topt 40 40 35 40 40 40 40 43 43 43 43 43 43 45 45 45 50 30 45 10 10 50 50 50 45 45 50 45

14.6

0

0





2 Notation: σS is the biome area (mln km2), R 1 Φ is the annual increment of plants (kg/m /year), Φ is 2 the phytomass (kg/m ). Tmin and Topt are minimal and optimal temperatures for the photosynthesis, respectively (
C)

The internal resources for each region are determined by the level of Gross Domestic Product (GDP) and its distribution by the strategic objectives. The curves of Fig. 9.9 show the dependence of the system survivability on the distribution of investments and demonstrate the standard of living of the population according to the distribution of GDP by the economic sectors that are correct over the nearest limited time period. Overall, the GIMS/CNSS allow the assessment of population dynamics under these assumptions. Let’s look at some of them. Figure 9.10 represents such evaluations in the context of the following assumptions (SP scenario scientific progress):

448 Table 9.9 Coefficients of the GIMS/CNSS for the land surface

9 Global Climate Monitoring with Microwave Measurements Coefficient ρ, year1 β, year1 η1 η2 ξ1 ξ2 ξ3 χ1 χ2 χ3 μ1 μ2 γ,
C ω

Region ΞLD 1.19 1.21 0.01 0.003 0.031 0.012 0.006 0.035 0.014 0.003 0.02 0.005 34 0.56

Region ΞLM 1.26 1.23 0.011 0.005 0.027 0.011 0.005 0.032 0.012 0.002 0.03 0.009 34 0.61

Region ΞLG 1.32 1.25 0.014 0.009 0.025 0.009 0.004 0.031 0.011 0.001 0.04 0.012 34 0.67

• the problems arising from the limitation of energy sources will be overcome up to 2050; • the emissions of GHGs will increase by 10% to 2050 compared to 2015 and then begin to decrease uniformly to 2200 up to 5%; • agricultural technologies for the productivity increase by 100% to 2050 and by 200% to the end of twenty-second century will be production; • the replacement speed of the forest ecosystems by the plough-land is reduced by 10 times to 2050 compared to 2015 and then the forested pixels are not disturbed; and • the contribution of the World Ocean resources to food production will increase from 1% in 2015 to 5% in 2050 and then increase uniformly to 10% in 2200. As it follows from the results of Fig. 9.10, the population size may reach 14.9 billion at the beginning of the twenty-third century with the tendency for small growth. The percentage distribution of the population by regions will change in the direction of the increase of 6.9% in the developing countries. The contributions of the ΞLD and ΞLM regions to population growth decreased by 2.1% and 4.8%, respectively. These changes are related to the different birth and mortality rates in Eqs. (9.6) and (9.7) as functions of the Community situation and food supply, as well as the climatic parameters. Figure 9.11 shows some of these characteristics in their dynamics up to 2215. It is seen that prior assumptions about the dynamics of different anthropogenic impacts on the environment play a significant role in the dynamics of all the CNSS components. Unfortunately, these cases only take place as specific scenarios. The implementation of the RCP8.5 scenario (a scenario of comparatively high GHGs emissions, Li and Mao 2016) results in an increase in CO2 concentration up to 800 ppm in the twenty-third century, starting with achieving maximum surface temperature rise of almost 3%.
C. On the other hand, a fairly realistic RCP2.6 scenario (exploring the possibility of keeping global mean temperature below

Precipitation, WΞ (mm/year) 3130 2880 2630 2380 2130 1880 1630 1380 1130 880 630 380 130

0.19 0.21 0.28 0.39 0.14

0.18 0.26 0.28 0.29 0.41 0.32

0.31 0.32 0.42 0.53 0.54 0.31

0.39 0.41 0.43 0.52 0.57 0.69 0.22

Atmospheric temperature, TΞ (
С) 14 10 6 2

0.62 0.73 0.77 0.83 0.89 0.66 0.24

2

1.63 1.34 1.16 1.05 0.92 0.91 0.64 0.24

6

10 3.39 3.27 3.09 2.85 2.57 2.38 2.04 1.75 1.66 1.53 0.92 0.67 0.24

14 3.49 3.36 3.27 2.93 2.69 2.38 2.14 1.91 1.84 1.43 0.85 0.57 0.24

18 3.68 3.47 3.31 3.09 2.67 2.43 2.12 1.95 1.92 1.33 0.84 0.56 0.23

22 3.81 3.63 3.44 3.12 2.94 2.55 2.26 2.13 1.84 1.36 0.73 0.55 0.14

26 3.92 3.73 3.54 3.22 2.91 2.62 2.35 2.18 1.83 1.27 0.72 0.43 0.13

30 4.01 3.82 3.65 3.33 3.03 2.74 2.42 2.09 1.75 1.24 0.71 0.42 0.11

Table 9.10 The dependence of annual vegetation production RΦ(TΞ ,WΞ ) (kg/m2/year) on average annual temperature (TΞ ) and full of precipitations (WΞ )

9.10 The Earth’s Population Survivability 449

450

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.9 Survivability indicator according to the distribution of GDP by agriculture (solid lines) and industry (dashed lines). The numbers to the right of the curves show investment time periods: 1–25 years, 2–50 years, 3–75 years, and 4–100 years

2
C, Wayne 2013) leads to corresponding levels of 520 ppm for CO2 and 0.8
C for change of temperature in the twenty-second century with the following decrease of these levels. Therefore, a more accurate forecast requires a detailed analysis by experts of the current trends in the socio-economic developments of the different regions. However, even these hypothetical scenarios provide information to consider possible safe ways of population growth when survival is maintained for a long time. Figure 9.12 shows a dynamics of essential factors that form the evolution process of the society development. The birth rate coefficients μB for the regions ΞLD, ΞLM and ΞLG change from 0.0115, 0.0177 and 0.0267 in 2015 to 0.005, 0.0098 and 0.0191 in 2200, respectively. Under this, the birth rate coefficients of the ΞLD and ΞLM regions will decrease uniformly over time and the birth rate coefficient will reach a maximum value 0.034 in the ΞLG region in 2060 and then decrease. The mortality coefficients μd are similarly modified from the ΞLD, ΞLM and ΞLG regions from 0.0107, 0.0138 and 0.0175 in 2015 to 0.0121, 0.0153 and 0.0211 in 2200, respectively.

9.10

The Earth’s Population Survivability

451

Fig. 9.10 Population dynamics on a global and regional scale

9.10.4 Looking to the Future The proposed version of the global geoecological information-modeling system provides tools for studying and assessing the limiting anthropogenic impacts on the biosphere and allows for understanding its reactions and identifying the exclusion zone for potential human activity. Under this, GIMS/CNSS provides the ability to detect regional ecological reactions to the effects identified in the limited number of spatial pixels. Undoubtedly, GIMS/CNSS reflects the limited range of feedbacks on CNSS with emphasis on ecological interactions. GIMS/CNSS enables it to modernize its structure through additional items that parameterize the socioeconomic and biotic feedbacks to the global climatic system. The model of global environmental processes based on GIMS-technology differs substantially from other global models in the ability of evolutionary adaptation to prehistory using CNSS state information indicators. Of course, the adjustment process and the selection of information indicators need additional research. The results of this study show that survivability problem will not be critical in the next two centuries depending on population growth. It is possible in the context of weighed decisions on the impact on biosphere processes, including through shifting forests classes, pollution of the hydrosphere and imbalances in the scarce resources. Restrictions on the availability of food production resources emerged at the end of

452

9 Global Climate Monitoring with Microwave Measurements

Fig. 9.11 The dynamics of climatic factors (CO2 concentration and temperature are represented by solid and dashed lines, respectively). A comparison of the implementation of the SP scenario with the results of the RCP8.5 and RCP2.6 scenarios (Riahi et al. 2011)

twenty-first century, when, as Fig. 9.12 shows, the global NPP was slowly declining due to climate change and changes in regional hydrological balances. Particularly, increasing temperature in tropical latitudes causes the water content in the soil to decrease due to evaporation leading to a NPP decrease. On the contrary, in the northern pixels, increasing temperature leads to a prolongation of the growing season by about 16–20 days in the twenty-second century starting at the NPP growing by 9–12%. These negative and positive feedbacks are non-uniformly distributed by the pixels. As a result, the food production dynamics depicted in Fig. 9.12 shows that exporting surplus food stocks of the ΞLG region to other regions is only possible by the end of twenty-first century, as the human population expands the effectiveness of such strategies as expanding the land area used for agriculture, the extension of fisheries, and the increase of agricultural productivity. Current trends in regional population growth suggest that meeting food demands is unlikely to occur unless human society seeks sustainable interactions with nature. Estimates of food production are approximate and may be more accurate when spatial digitization of land and oceans is, for example, 0.5
C  0.5
C or less. It is well known that variations in net primary production in ocean vary from 1800 g/m2/year in

9.10

The Earth’s Population Survivability

453

Fig. 9.12 A dynamics of vital factors (area food supply, birth rate, and mortality are represented by broken, solid, and chain lines, respectively). The regional identifiers are placed in the curves

estuaries to 50 g/m2/year in the open ocean. Fluctuations in biomass and biomass production of land vegetation also have a wide range. This case is an additional reserve to make the results of the global model more accurate. Of course, the GIMS / CNSS model allows for a more detailed description of the soil-plant formations shown in Fig. 9.9 taking into account existing fluctuations in their areas and productivity as well as the specification of agricultural ecosystems. Further expansion and specialization of global and regional environmental databases is needed. As reported by Krapivin et al. (2019) that eco-information is the science of information in ecology and environmental science. This book demonstrates different definitions, semantic structures, algorithms, methods, and models that contribute to the parameterization of various environmental processes in the nature-society system. One of the main goals of ecoinformatics is the development of new effective information technologies for the detection and prediction of environmental factors that are important for human life. Indeed, eco-information is a multidisciplinary study of ecology, GIS, mathematical modeling, and computer science. Ecoinformatics promotes the new exploitation and integration of ecological data and knowledge through state-of-the-state simulation technology. Ecoinformatics and

454

9 Global Climate Monitoring with Microwave Measurements

GIMS are state-of-the-art technologies that transform the way data is collected and analyzed. The field of ecoinformatics provides concepts, methods and models to guide the management and analysis of ecological data with particular emphasis on exploring the co-occurrence of organisms and their relation to environmental conditions and taxonomic characteristics. In addition, ecoinformatics is developing new information technologies for future research on a global scale, with a particular emphasis on major environmental problems related to the survivability of human society. The evolution of the Nature-society system depends on the numerous economic, social, natural and technological processes taking place both in the regions and in the world. The combined research of these processes is a key issue for its sustainable development. An effective tool for resolving the regional and global problem of sustainable development will be based on the combined use of knowledge from different sciences. One of these tools is the GIMS technology described in the above chapters. It is obvious that the creation of universal technology for the global model of the nature-society system is unlikely. Unfortunately, the knowledge accumulated by humanity does not cover all the problems of sustainable global development. Many of them have demonstrated the absence of constructive algorithms for existing large data processing. This weakness mainly lays in the “dimension of decline”, i.e. the inability to take into account the interactions between all the subsystems of the nature-society system. The authors of this book are confident that GIMS technology enables: • optimizing data flows from different sources to effectively evaluate and predict the future of climate-nature-society in the distant future. • defining the role and location of satellite monitoring systems to solve sustainable development problems both in the near and in the distant future. • to control the process of sustainable development that refers to a way of human development in which the use of resources aims to meet human needs while preserving the environment so that these needs are met not only by present but also by future generations. • co-ordinate the main categories of sustainability, including nature, economy, society and prosperity, seeking the best possible coordination among them within the responsibility of international organizations within the United Nations.

References Abrahamson DE (1989) Challenge of global warming. Island Press, Washington Bilham R (2005) A flying start, then a slow slip. Science 308:1126–1127 Blowers A, Hinchliffe S (2003) Environmental responses. John Wiley & Sons, London Burkhart HE, Tomê M (2012) Modeling forest trees and stands. Springer Netherlands. Butler JH, Montzka SA (2016) The NOAA annual greenhouse gas index (AGGI). NOAA Earth System Research Laboratory, Boulder. http://www.erst.noaa.gov/gmd/aggi.html

References

455

Campbell JL, Rustad L, Boyer EW, Christopher S (2009) Consequences of climate change for biogeochemical cycling in forests of northeastern North America. Can J For Res 39:264–284 Chen X, Zhang X (2016) How environmental uncertainty moderated of relative advantage and perceived credibility on the adoption of mobile health services by Chinese organizations in the big data era. Int J Telemed Appl 2016:1–11. https://doi.org/10.1155/2016/3618402 Chernavsky DS (ed) (2004) Recognition, autodiagnostics, thinking: synergetics and human science. Radiotekhnika Publ, Moscow. [in Russian] Condie KC (2005) Earth as an evolving planetary system. Elsevier Academic, Burlington, MA Debertin DL (2012) Agricultural production economics. Macmillan Publishing Company, London Degermendzhi AG (2009) New directions in biophysical ecology. In: Cracknell AP, Krapivin VF, Varotsos CA (eds) Global climatology and Ecodynamics. Springer/Praxis, Chichester, pp 379–396 FAO (2001) Global forest resources assessment 2000. Main Report. FAO Forestry Paper, Rome, 140 Field CB, Raupach MR (eds) (2004) Global carbon cycle: integrating humans, climate, and the natural world. Island Press, Washington Field JG, Hempel G, Summerhayer CP (eds) (2002) Oceans 2020: science trends and the challenge of sustainability. Island Press, Washington Forrester JW (1971) World dynamics. Wright - Allen Press, Cambridge, MA. Gardner JS (2002) Natural hazards risk in the Kullu District, Himachal Pradesh, India. Geogr Rev 92:172–177 Goldner J (2002) Messages from space. Michael Wiese Production, Suite Grigoryev AA, Kondratyev KYA (2001) Ecological catastrophes. The St.-Petersburg Scientific Centre of RAS, St.-Petersburg. [in Russian] Gyde LH (2012) Definitions of Forest, deforestation, afforestation, and reforestation. Forest Information Services, Gainesville Hales B, Takahashi T, Bandstra L (2005) Atmospheric CO2 uptake by a coastal upwelling system. Glob Biogeochem Cycles 19(GB1009):1–11 Hardy JT (2003) Climate change. Wiley, Washington Hasenauer H (2006) Sustainable Forest management: growth models for Europe. Springer, Berlin Hinchliffe S, Blowers A, Freeland J (2002) Understanding environmental issues. Wiley, London Ji Y, Stocker E (2002) Seasonal, intraseasonal, and interannual variability of global land fires and their effects on atmospheric aerosol distribution. J Geophys Res 107(D23):4697 Jönsson AM, Linderson M-L, Stjernquist I, Scglyter P, Bärring L (2004) Climate change and the effect of temperature backlashes causing frost damage in Picea abies. Glob Planet Chang 44 (1–4):195–207 Kendrick TD (1957) The Lisbon earthquake. Lippincott, Philadelphia Kondratyev KYA, Krapivin VF, Phillips GW (2002) Global environmental change: modelling and monitoring. Springer, Berlin Kondratyev KYA, Krapivin VF, Savinykh VP, Varotsos CA (2004) Global ecodynamics: a multidimensional analysis. Springer-Praxis, Chichester Kondratyev KYA, Krapivin VF, Varotsos CA (2006) Natural disasters as interactive components of global ecodynamics. Springer/Praxis, Chichester Krapivin VF (1993) Mathematical model for global ecological investigations. Ecol Model 67 (204):103–127 Krapivin VF, Kelley JJ (2009) Model-based method for the assessment of global change in a naturesociety system. In: Cracknell AP, Krapivin VF, Varotsos CA (eds) Problems of global climatology and ecodynamics. Springer/Praxis, Chichester, pp 133–184 Krapivin VF, Shutko AM (2012) Information technologies for remote monitoring of the environment. Springer/Praxis, Chichester Krapivin VF, Varotsos CA (2007) Globalization and sustainable development. Springer/Praxis, Chichester

456

9 Global Climate Monitoring with Microwave Measurements

Krapivin VF, Varotsos CA (2008) Biogeochemical cycles in globalization and sustainable development. Springer/Praxis, Chichester Krapivin VF, Mkrtchyan FA, Ivanov MV (2005) Expert system for the operative environmental diagnostics. The 26th Asian conference on remote sensing (ACRS2005). Hanoi. 7–11 November 2005. Vietnam. Asian Association on Remote Sensing – 26th Asian conference on remote sensing and 2nd Asian space conference, ACRS 2005, 3, pp 1967–1975. Krapivin VF, Varotsos CA, Soldatov VY (2015a) New ecoinformatics tools in environmental science: applications and decision-making. Springer, London Krapivin VF, Varotsos CA, Soldatov VY (2015b) New ecoinformatics tools in environmental science: applications and decision-making. Springer, London Krapivin VF, Varotsos CA, Soldatov VY (2017a) The Earth’s population can reach 14 billion in the 23rd century without significant adverse effects on survivability. Int J Environ Res Public Health 14(8):3–18 Krapivin VF, Varotsos CA, Nghia BQ (2017b) A modeling system for monitoring water quality in lagoons. Water Air Soil Pollut 228(397):1–12 Krapivin VF, Nitu C, Varotsos CA (2019) Microwave remote sensing tools and ecoinformatics. Matrix Rom, Bucharest Li J, Mao J (2016) Changes in the boreal summer intraseasonal oscillation projected by the CNRMCM5 model under the RCP 8.5 scenario. Clim Dyn 47(12):3713–3736 Lindenmayer DB, Foster DR, Franklin JF, Hunter ML, Noss RF, Schmiegelow FA, Perry D (2004) Salvage harvesting policies after natural disturbance. Science 303(5662):1303 Lomborg B (2001) The skeptical environmentalist – measuring the real state of the world. Cambridge University Press, Cambridge Lucas JS, Southgate PC (2012) Aquaculture: farming aquatic animals and plants. Wiley, New York Maarten KA (2006) The impacts of climate change on the risk of natural disasters. Disasters 30 (1):5–18 McConnel WJ (2004) Forest cover change – tales of the unexpected. Global Change Newsl 57:8–11 McNulty SG (2002) Hurricane impacts on US forest carbon sequestration. Environ Pollut 116:S17– S25 Milne A (2004) Doomsday: the science of catastrophic events. Praeger Publisher, Westport Moisseev NN (1979) Mathematics produces an experiment. Science Publ, Moscow. [in Russian] Monin AS, Shishkov YA (1991) The warming dilemmas in the 20th century. In: Monin AS (ed) Man and Chaos. Hydrometeoizdat, St. Petersburg, pp 47–49. [in Russian] Morris D, Freeland J, Hinchcliffe S, Smith S (2003) Changing environments. Wiley, London Nilsson K, Sangster M, Gallis C, Hartig T, de Vries S, Seeland K, Schipperijn J (eds) (2011) Forests, trees and human health. Springer, Dordrecht Nitu C, Krapivin VF, Bruno A (2000a) Intelligent techniques in ecology. Printech, Bucharest Nitu C, Krapivin VF, Bruno A (2000b) System modelling in ecology. Printech, Bucharest Nitu C, Krapivin VF, Pruteanu E (2004) Ecoinformatics: intelligent systems in ecology. – magic print. Onesti, Bucharest Nitu C, Krapivin VF, Soldatov VY (2013) Information-modeling technology for environmental investigations. Matrix ROM, Bucharest Oppenheimer C (1996) Volcanism. Geography 81(1):65–81 Perrie W, Ren X, Zhang W, Long Z (2004) Simulation of extratropical Hurricane Gustav using a coupled atmosphere-ocean-sea spray model. Geophys Res Lett 31(3):L03110 Riahi K, Krey V, Rao S, Chirkov V, Fischer G, Kolp P, Kindermann G, Nakicenovic N, Rafai P (2011) RCP-8.5: exploring the consequence of high emission trajectories. Clim Chang 109(33). https://doi.org/10.1007/s10584-011-0149-y Richter CF (1969) Earthquakes. Nat Hist 78:37–45 Saavedra-Rivano N (1979) A critical analysis of the Mesarovic-Pestel world model. Appl Math Model 3(5):384–390 Sarlis NV, Skordas ES, Varotsos PA, Ramírez-Rojas A, Flores-Márquez EL (2018) Natural time analysis: on the deadly Mexico M8.2 earthquake on 7 September 2017. Physicol A 506:625–634

References

457

Sarlis NV, Skordas ES, Varotsos PA, Ramírez-Rojas A, Flores-Márquez EL (2019) Investigation of the temporal correlations between earthquake magnitudes before the Mexico M8.2 earthquake on 7 September 2017. Physicol A 517:475–483 Sellers PJ, Randall DA, Collotz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Bounoua L (1996) A revised land surface parametrization (SiB2) for atmospheric GCMs. Part 1: model formulation. J Clim 9(4):676–705 Shi W, Zhang A, Zhou X, Min Z (2018) Challenges and prospects of uncertainties in spatial big data analysis. Ann Am Assoc Geogr 108(6):1513–1520 Silver D (1998) Tropical rain Forest. McGraw-Hill, New York Sudarshana P, Nageswara M, Soneji JR (2012) Tropical forests. InTech, New York Tarko AM (2003) Analysis of global and regional changes in biogeochemical carbon cycle: a spatially distributed model. Interim report, IR-03-041. IIASA, Laxenburg Varotsos P (2005) The physics of seismic electric signals. TerraPub, Tokyo Varotsos P, Alexopoulos K (1984a) Physical properties of the variations of the electric field of the earth preceding earthquakes, I. Tectonophysics 110:73–98 Varotsos P, Alexopoulos K (1984b) Physical properties of the variations of the electric field of the earth preceding earthquakes. II. Determination of epicenter and magnitude. Tectonophysics 110:99–125 Varotsos CA, Krapivin VF (2017) A new big data approach based on geoecological informationmodeling system. Big Earth Data 1(1–2):47–63 Varotsos PA, Sarlis NV, Skordas ES (2002) Long-range correlations in the electric signals that precede rupture. Phys Rev E 66:011902 Varotsos PA, Sarlis NV, Skordas ES (2011) Natural time analysis: the new view of time. Precursory seismic electric signals, earthquakes and other complex time series. Springer, Berlin/Heidelberg Varotsos PA, Sarlis NV, Skordas ES (2019) Phenomena preceding major earthquakes interconnected through a physical model. Ann Geophys 37:315–324 Vernadsky VI (1944) Several words about the noosphere. Successes Present Biol 18(2):49–93. [in Russian] Victor DG (2001) The collapse of the Kyoto Protokol and the struggle to slow global warming. Princeton Univ. Press, Princeton Victor DG (2004) Climate change. debating America’s policy options. The Council of Foreign Relations, New York Walker G (2003) Snowball earth: the story of the great global catastrophe that spawned life as we know it. Crown Publishers, New York Waring R, Running S (2007) Forest ecosystems. Analysis at multiple scales. Academic Press, Orlando Wayne GP (2013) The beginner’s guide to representative concentration pathways. Sceptical Science, p 24 Wright EL, Erickson JD (2003) Incorporating catastrophes into integrated assessment: science, impacts, and adaptation. Clim Chang 57:265–286

Index

A Absorption coefficients, 14, 17, 50, 56, 57, 91, 173, 174, 177, 188, 190, 192 fields, 48, 57, 60, 62, 196 frequencies, 38, 55, 57 lines, 3, 47, 55–57 spectra, 55, 63, 147 zones, 47, 173, 174, 196 Adaptive technology, 221, 296 Agricultural fields, 16, 19, 24, 105, 128, 133, 134, 339, 437 wastes, 318 Agriculture, 16, 26, 123, 129, 130, 136, 138, 165, 166, 170, 178, 296, 323, 404, 416, 427, 432, 443, 450, 452 Alaska, 170, 188, 333, 364, 404, 408, 425 Albedo, 4, 16, 26, 28, 167, 202, 203, 216, 305, 306, 341, 350, 401, 425 Algorithms, v–vii, 4, 7, 16, 19, 34, 38, 46, 51, 57, 64–84, 86, 100, 102, 104–108, 123, 131, 146–148, 151, 152, 155, 174, 185, 196, 198, 220, 240, 262, 268, 273, 274, 296, 300, 335, 372, 426, 436, 453, 454 Anomalies, 2, 47, 173–178, 239, 297, 298, 354–357, 366 Antarctic, 106, 300, 356, 365, 425, 439 Antennas, 3, 7, 18–20, 24, 25, 48, 60, 86, 93, 94, 150–152 Anthropogenic carbon emissions, 318, 340, 422

components, 54, 195, 219, 232, 282, 296, 306, 313, 314, 317, 342, 356, 370, 413, 417, 439, 448 impacts, 54, 93, 121, 131, 203, 209, 211, 212, 217, 218, 230, 232, 233, 239, 253, 271, 300, 304–307, 314, 318, 320, 322, 337, 340, 342, 347, 348, 352, 355, 410, 417, 420, 427, 429, 431, 432, 437, 438, 448, 451 processes, 54, 103, 122, 206, 211, 212, 217, 218, 231, 232, 256, 260, 281, 296, 310, 314, 321, 322, 339, 342, 343, 363, 409, 415–417, 420, 424, 426, 427, 437 sections, 203, 231, 334, 335, 426 Approximations, 7, 15, 16, 28, 31, 32, 50, 58, 66, 67, 77, 112, 127, 133, 137, 151, 155, 200, 238, 337, 345, 367, 375, 378, 379, 382, 385 Aral Sea, vi, 252–288, 422 Arctic air masses, 408 basins, 27, 196, 201–218, 333, 407, 425 climate, 196, 219, 225, 300, 336, 428 desert and tundra, 317, 341, 342, 447 ecosystems, 195, 214, 216, 218, 408, 428 ocean, 106, 202, 205, 210, 212, 214, 219, 333, 334, 345, 405, 425 regions, 202, 218, 219, 345, 408 waters, 27, 195, 202, 203, 205, 206, 208, 211–214, 216–219, 225, 407, 425, 426, 428 Asia, 164, 170, 301, 308, 316, 318, 358, 361, 362, 364–366, 405, 407, 408, 414, 418

© Springer Nature Switzerland AG 2020 C. A. Varotsos, V. F. Krapivin, Microwave Remote Sensing Tools in Environmental Science, https://doi.org/10.1007/978-3-030-45767-9

459

460 Assessments, v, 2, 3, 14, 15, 26, 31, 38, 52, 93–95, 107, 115, 123–129, 135, 136, 141, 148–150, 173, 179, 181, 187, 202–204, 208, 219, 220, 225, 233, 287, 302, 303, 305, 306, 310, 313, 335, 337, 338, 343, 347, 355, 368, 370, 400, 408, 422, 424, 426, 427, 430, 431, 433, 436–438, 447 Atlantic Ocean, 171, 172, 206, 208, 255, 299, 308, 359–361, 363, 364, 419, 420 Atmosphere, vi, 1, 3–5, 8, 16–18, 29, 30, 45–48, 50, 54, 55, 57, 58, 60–63, 95, 103, 104, 109, 111, 112, 114, 115, 123, 126, 130, 145, 148, 164, 167, 169–172, 184, 185, 188, 196, 197, 202, 203, 205, 234, 236, 239, 246, 261, 263, 265, 275, 278, 285, 286, 300, 302–304, 308–313, 315–319, 321–327, 330–333, 335–337, 339–343, 345, 347–351, 353–374, 377, 379, 380, 383–386, 398, 402, 407, 408, 410, 416–424, 429, 432, 441 Atmospheric boundary layer, 170, 171, 361, 382, 383 chemistry, 169–172, 362, 367 concentrations, 55, 297, 304, 308, 323, 327, 328, 350, 354 gases, 45, 55–58, 63, 103, 164, 308, 321, 322, 366, 367, 371 layers, 34, 50, 57, 103, 171, 207, 258, 312, 354, 365, 366, 373, 375, 380–383, 385, 398, 440 liquid water, 1 optical depths, 16, 55, 56 processes, 3, 34, 54, 55, 61, 64, 171, 203, 209, 287, 312, 318, 322, 327–329, 337, 343, 348, 350, 362, 366–370, 372, 374, 376, 385, 420, 427, 436, 440 temperatures, 3, 16, 34, 39, 55, 57, 63, 111, 216, 245, 297, 299, 304, 308, 329, 330, 333, 337, 372, 381, 419, 421, 442 transmittance, 8, 16, 113, 115 Attenuation, 3, 6, 7, 19, 28, 32, 34, 39, 99, 100, 107, 112, 145–159, 178, 184, 186, 188, 234, 324, 351 Australia, 297–299, 301, 308, 316, 345, 361, 362, 407, 414, 418, 419

B Balance equations, 102, 221, 223, 235, 260, 263, 268, 270, 326, 344 Big data, vii, 101–103, 106, 123, 131, 196, 201–222, 225, 230, 232, 395, 427, 433

Index Biocomplexity, 203, 212–214, 216, 218, 219, 224–225, 227, 228, 232, 424, 426, 443, 446 Biogenic salts, 205, 441 Biogeochemical models, 429 Biosphere, 100, 102, 121, 122, 167–169, 204, 216, 217, 245, 296, 297, 302, 303, 310–314, 320–322, 345, 398, 415, 417–420, 424–427, 438–440, 446, 451 Biospheric components, 296 evolution, 415 impacts, 296, 304 models, 296, 323, 345, 370, 415 pollutions, 370 reservoirs, 310, 323 Boreal forests, 114, 167–170, 172, 310, 314, 318, 322, 340, 408, 418, 420 Brightness temperatures, 2, 7–10, 15–17, 27, 28, 31, 32, 34, 37–39, 49, 51, 56, 61, 62, 65, 67, 84, 111, 113, 115–117, 125, 132, 136, 140–142, 173, 177, 179, 180, 185–188, 190–192, 198–200, 363, 436

C Canada, 168, 170, 172, 188, 301, 316, 318, 360, 408, 418, 428 Carbon aerosols, 307, 317, 341, 354, 355, 362, 365, 371, 418 cycles, 106, 168, 172, 303, 308, 310, 311, 314, 318, 319, 321, 324–343, 369, 418, 423, 429 dioxide, 63, 106, 306, 308, 310–313, 319, 321, 322, 325, 327–343, 353, 418 monoxide, 317, 361, 365, 418 reserves, 321, 338 sinks, 6, 99, 167, 172, 303, 310–314, 322, 327, 328, 330, 339, 340, 342, 402, 421, 422 Catabolism, 336 Central Asia, 252–254, 259, 261, 262, 279, 286, 287 Circulation, 3, 5, 107, 167, 202, 203, 205, 212, 231, 233, 311, 324, 325, 333, 335, 348, 351, 356–358, 370, 402 Climate changes, 3–5, 26, 27, 36, 93, 123, 147, 163, 167, 171, 173, 195, 196, 202, 205, 208, 211, 219, 220, 224, 225, 227, 228, 230, 231, 245, 287, 295–386, 395, 396, 408,

Index 409, 414, 415, 418–424, 426–430, 434, 437, 452 models, 105, 147, 216, 219, 231, 245, 253, 287, 300, 305, 306, 309, 324, 327, 330, 344–346, 348, 350–352, 358, 362, 409, 423–426, 441, 446 variability, 170, 172, 233, 263, 297, 300, 347–349, 351, 354–356, 395, 418, 420, 425 warmings, 123, 167, 297, 300, 302–305, 308–310, 327, 346, 348–353, 355, 404, 415, 430 Clouds, 4, 5, 18, 45, 46, 48, 55, 57, 58, 103, 106, 126, 196, 217, 219, 221, 225, 230, 302, 306, 309, 353, 356–358, 360, 362–364, 366, 367, 369–372, 374, 375, 377, 378, 381, 382, 406, 418, 423, 427, 435, 436 Communities, 141, 149, 166, 207, 219, 246, 247, 261, 287, 305, 314, 369, 410, 415, 422, 430, 448 Complexity, 4, 17, 20, 108, 122, 123, 329, 336, 368, 400, 423, 438, 441, 446 Concentrations, 11, 23, 24, 30, 48, 49, 55–57, 63, 110, 123, 125, 130, 164, 167, 169–173, 186, 196, 198, 202, 205, 207, 209–211, 214, 217, 220, 232, 233, 236, 238, 243, 245–247, 249, 250, 274–276, 300, 304, 306, 307, 309–311, 322–326, 328, 336, 337, 339, 343, 347, 350, 351, 355, 359, 361–364, 370, 372, 375–377, 380, 381, 383–385, 395, 398, 421, 435, 442, 448, 452 Continental shelf, 203, 314 Continents, 54, 164, 165, 298, 300, 306, 308, 349, 351, 361, 363–365, 370, 400, 422, 440 Controls, vii, 2, 3, 5, 19, 25, 46, 54, 60, 61, 84, 86, 88, 99, 106, 124, 128, 130, 131, 163–165, 169, 170, 173, 177, 178, 181, 188, 195, 198, 200, 204, 241, 253, 254, 259, 262, 264, 274, 276, 279, 280, 295, 302, 322, 335, 337, 368, 371, 384, 396, 407, 420, 422, 423, 426, 427, 431–433, 454 Correlations, 4, 6, 27, 37, 55, 58, 61, 67, 83, 84, 89, 93, 106, 108, 111, 127, 135, 147, 168, 190, 196, 198, 230, 234, 239, 251, 252, 262, 263, 277, 296, 300, 323, 324, 330, 354, 356, 357, 366, 398, 410, 412, 418, 422, 424 Cultivated lands, 314, 326, 407

461 D Deserts, vi, 19, 23, 51, 114, 126, 139, 252–255, 257, 286, 310, 313, 317, 326, 341, 342, 359, 362, 363, 365, 366, 396, 447 Dielectric constants, 10–12, 14, 30, 31, 65, 93, 110, 132, 148, 157, 190, 200 Dispersions, 28, 46, 60, 67, 68, 84, 86–88, 178, 190, 213, 262, 297, 362, 375, 377, 382, 427 Distributions, 5–7, 50, 54, 60–64, 83, 84, 99, 100, 103, 108, 112, 128, 134, 142, 146, 148, 153, 157, 158, 163, 172, 173, 204, 208, 211–214, 216, 218, 219, 228, 231, 232, 234, 239, 241, 243, 245, 247, 248, 252, 260, 261, 263, 266, 267, 271, 274, 282, 283, 302, 312, 313, 316, 318, 322, 326, 328, 329, 331–333, 335, 336, 342, 356–358, 360, 362–364, 366, 374, 375, 382, 385, 399, 408, 412, 414, 422, 431, 437, 441, 444–448, 450 Diversity, 105, 157, 166, 220, 310, 321, 418, 428 Doppler effect, 62, 95

E Earthquakes, 93, 330, 397, 401, 403–406, 408–415, 417, 430 Ecodynamics, vii, 122, 165–172, 302, 417 Ecoinformatics, v, vii, 69, 173, 233–251, 295, 371, 400, 431, 432, 453, 454 Ecological activity, 431 Ecology, vii, 47, 138, 425, 453 Ecosociology, 431 Ecosystems, 2, 4–6, 19, 102, 106, 122, 139, 165–167, 169, 171, 172, 195, 203, 205, 207, 208, 211–214, 216–233, 236, 244, 250, 251, 295, 302, 310, 311, 313, 314, 321, 326, 328, 332, 333, 337, 342, 343, 345, 363, 397, 409, 417, 418, 422, 425–431, 437, 438, 440, 443, 448, 453 Ecotechnologies, 431 Electromagnetic waves, 1, 6, 8, 28, 45, 47, 61–63, 99, 109, 127, 141, 145, 147–152, 156, 157, 173, 175, 178, 190 Emission models, 49, 50, 132, 197, 205, 336, 366, 374 sources, 50, 55, 60, 170, 205, 366, 374, 384, 385 speed, 174, 336, 385

462 Emissivity atlas, 14 coefficients, 13–15, 34–36, 39, 49, 89, 112, 141, 178, 180, 185, 186, 188–190 databases, 14, 36, 115 products, 14, 125 Environment, v–vii, 4, 8, 11, 17, 46–48, 51–54, 60, 68, 89, 93, 100, 102, 104, 106, 121, 122, 124, 128, 145, 150, 173, 177, 186, 188, 195, 196, 198, 200–205, 208, 211, 214, 217, 218, 225, 229, 231, 238, 246, 252, 254, 260–263, 310, 311, 313, 316, 321, 322, 339, 343, 365, 384, 395, 397, 398, 404, 410, 415–417, 420–424, 426, 430, 431, 433, 434, 437, 446, 448, 454 Environmental acquisition system, 7, 100, 103 characteristics, 8, 20, 107, 336, 386, 417, 442 conditions, 6, 21, 99, 233, 416, 429, 454 databases, 326, 426, 430, 453 elements, 45, 116, 262 parameters, 6, 18, 46, 47, 50, 82, 84–88, 93, 99, 112, 115, 198, 223, 225, 232, 242, 245–247, 249, 251, 281, 342, 397, 409, 420, 428, 432 problems, 2, 4, 6, 20, 51, 108, 201, 343–345, 427, 434, 454 processes, v, 104, 328, 343, 430, 437, 438, 451, 453 science, 353, 453 Equations, 16, 17, 28, 31, 32, 34, 38, 50, 63–79, 81, 82, 84, 85, 88, 91, 92, 108, 112, 117, 146, 155, 172, 179, 204, 206, 207, 211, 221, 223, 225, 229, 234, 237, 238, 245, 246, 263, 266, 273, 275, 277, 278, 284, 329, 336, 358, 372, 373, 376, 377, 379, 382–384, 425, 442, 448 Erosion, 330, 370, 429 Estuaries, 195, 312, 314, 452 Evolutionary dynamics, 204 Extinction coefficient, 16, 28, 155

F Forcings, 122, 202, 253, 280, 302, 305, 306, 321, 322, 324, 335, 346, 348, 352, 358, 398, 414, 415, 419, 429, 432 Forest ecosystem, 165–167, 172, 427–430, 448 fires, 53, 106, 107, 122, 136, 138, 163–192, 262, 299, 318, 340, 342, 356,

Index 358, 365, 366, 396, 397, 402, 407, 408, 417–419 models, 112, 115, 145–147, 158, 163, 167, 173, 177, 178, 180, 186, 192, 338, 341, 428–430 Formations, v, 1, 4, 39, 46, 55, 60, 62, 81, 99, 102–107, 129, 133, 137, 145, 148, 153, 165, 167, 171, 172, 177, 185, 187–192, 196, 204–206, 208, 219, 253, 258, 260, 262, 266, 279, 287, 297, 303, 305, 306, 308, 312, 315, 317, 322, 325, 326, 328, 329, 334, 335, 337, 339, 342, 344–346, 355–359, 361–364, 366, 367, 369–372, 374, 375, 400, 408–410, 418–420, 422, 423, 427, 429, 439, 440, 445, 447, 453 Fossil fuels, 170, 304, 309, 313, 319, 325, 327, 335, 358, 429 Fragmentation, 365 Frequencies, 1, 2, 8–12, 14, 18, 19, 22, 25–27, 35, 37–39, 45, 46, 50, 56–58, 60–63, 89, 91, 112, 114–116, 127, 135, 145, 146, 148, 150–158, 165–167, 172, 198, 199, 201, 318, 344, 351, 356, 359, 368, 395, 408, 418–420, 428, 430, 435, 436 Functions, vi, 1, 2, 4, 6, 11–14, 28, 30, 45, 49, 50, 54, 58, 60, 64–67, 69, 70, 73–77, 79, 82, 85, 91–93, 100, 102–104, 106, 108, 110, 113, 114, 121, 124–133, 135, 139, 146, 151, 155, 157, 163, 173, 174, 177, 178, 180, 181, 188, 189, 197, 198, 200, 203–207, 209, 213, 214, 216, 217, 224, 225, 228, 231, 236, 237, 240, 241, 244, 246–248, 250, 260, 262, 264, 266, 267, 271, 276, 300, 316, 319, 324, 330, 333, 336, 343, 360, 365, 374, 377, 380, 381, 383–386, 395, 399, 418, 425–427, 435, 437, 438, 441, 442, 448

G Geoinformation monitoring, 46, 81, 102, 129–131, 173, 401, 440 Geo-risk, 93–95 GIMS technology, 6, 17, 20, 23, 62, 63, 68, 100, 107, 108, 129, 132, 134, 147, 173, 204, 219, 221, 253, 259, 260, 264, 268, 285, 296, 427, 431, 433, 436, 451, 454 GIS technology, 205, 259, 439 Global carbon cycles, 6, 99, 167, 302–310, 312, 318, 322–327, 329, 332, 333, 343, 419, 421, 430

Index climate, 4, 5, 26, 27, 36, 93, 102, 104, 123, 167, 170, 172, 173, 177, 195, 202, 205, 208, 211, 216, 221, 230, 231, 233, 239, 295–386, 395, 396, 402, 404, 414, 415, 418, 419, 421, 422, 424–430, 436, 437, 446, 452 data, 3–6, 14, 24, 58, 93, 102, 103, 115, 122, 167, 170, 177, 195, 203, 216, 295–297, 300, 304–306, 308, 327, 328, 337, 341, 344, 345, 347–349, 352–354, 356, 359, 360, 362, 365, 367, 371, 402, 420, 424, 426, 430, 440, 454 ecodynamics, 4, 122, 123, 165–172, 295, 328, 343, 419, 420, 422 environmental problems, 4, 5, 426, 427, 454 models, 6, 7, 14, 100, 103, 106, 121, 123, 172, 177, 203–205, 208, 217, 221, 231, 260, 295, 296, 312, 313, 319, 326–343, 345, 350, 359, 367, 376, 425–427, 433, 438–441, 451, 453, 454 perspectives, 5, 100, 429 scales, 3, 54, 58, 103, 121, 167, 169, 170, 213, 296, 297, 328, 329, 363, 367, 371, 376, 402, 408, 415, 418, 419, 424, 426, 437, 451, 454 trends, 167, 173, 203, 205, 213, 221, 307, 343, 348, 349, 421, 441, 450, 452 vegetation covers, 3, 7, 100, 419, 421, 422, 427 warming, 202, 232, 297, 302, 306, 307, 309, 327, 348, 423 Greenhouse effects, 4, 6, 56, 99, 147, 164, 202, 308, 309, 313, 321–327, 354, 427, 429 gases, 5, 56, 102, 173, 202, 231, 324 Greenland, 202, 216, 301, 311

H Heat balances, 268, 402 emissions, 28, 46, 402 losses, 190, 402, 408 transport, 65, 202, 267, 356 Human society, 93, 218, 295, 296, 343, 397–400, 410, 415, 430, 431, 437, 439, 452, 454 survivability, 430, 439 Hydrocarbons, 2, 202, 205, 210–212, 215, 232, 252, 316, 364, 365 Hydrological processes, 2, 103, 206 Hydrosphere, 313, 429, 445, 451

463 I Ice covers, 2, 27, 38, 39, 48, 145, 201, 219, 220, 300, 342, 351, 425, 435 layers, 48, 145, 219, 342 Illumination, 126, 205, 222, 236, 237, 245–246, 318, 427 Incident angle, 16, 180 Indicators, 28, 31, 106, 108, 114, 132, 139, 141, 179, 180, 185, 187, 192, 211, 213, 215, 219, 224–225, 227, 228, 237, 262, 265, 271, 313, 323, 332, 335, 395, 399, 414, 426, 438, 440–442, 444, 447, 450, 451 Information, v, vi, 1, 4, 5, 19, 20, 24–26, 38, 52, 53, 63, 64, 81–83, 88, 93, 102, 103, 105, 107, 108, 122, 128–131, 133, 136, 145, 147, 148, 150, 163, 164, 180, 181, 188, 195, 196, 198, 203–206, 215, 217, 219, 253, 259–262, 264–266, 268, 271, 274, 285, 286, 288, 295–297, 300, 303, 304, 329, 330, 332, 335, 343, 345, 347–349, 352, 353, 356, 357, 359, 363, 365, 366, 368, 371, 374–376, 385, 395, 396, 400, 401, 408, 411, 426, 428, 431–440, 445, 450, 451, 453, 454 Intensities, 1, 7, 9, 28, 34, 48–50, 57–60, 93, 125, 150, 166, 173, 175, 177, 179, 180, 185, 186, 196, 209, 212, 228, 234, 262, 318, 325, 351, 357, 396, 399, 406, 418, 443 Interferometry, 93–95 Intergovernmental panel on climate change (IPCC), 303–305, 322, 346–348, 350, 351, 353 Ionosphere, 45, 47, 57, 62, 126, 371

K Knowledge, xv, 6, 7, 12, 14, 27, 31, 38, 45, 46, 48, 49, 61, 100, 102, 112, 113, 115, 121, 130, 132, 133, 136, 139, 145, 147, 148, 159, 171, 173, 177, 178, 182, 188, 191, 198, 200, 213, 251, 295, 327, 335, 352, 371, 376, 385, 386, 395, 399, 408, 409, 415, 420, 424, 427, 430, 431, 433, 440, 453, 454 Kyoto Protocol (KP), 303, 307–309, 346, 423

L Lagoons, 66, 274 Landscapes, 3, 4, 9, 105, 135, 138, 165, 166, 177–179, 253, 258, 262, 305, 322, 329, 370, 401 Landslides, 93, 136, 252, 396, 397, 401–403, 405, 413

464 Land surfaces, 1, 3–5, 7–17, 23, 46, 48, 49, 52, 100, 102, 114, 123, 125, 148, 167, 168, 173, 177, 182, 183, 256, 302, 304, 345, 348, 360, 366, 367, 373, 404, 408, 439, 445, 448 Leaf area, 4, 147, 167, 179, 184, 305

M Mankind, 321, 427 Matrix identifiers, 204, 268, 385 Meteorological conditions, 126, 150, 181, 362, 364, 431 Methane cycles, 334, 336 emission, 333, 336 fluxes, 329, 334–337, 343 sources, 327, 328, 333–336, 342 Microprocessor, 86, 88 Microwave emission, 1, 7–17, 28, 32, 34, 59, 62, 108, 132, 142, 174 methods, 2, 3, 6, 27, 45–49, 54, 55, 62, 99–117, 124, 134, 159, 174, 175, 433 models, 6, 15–17, 27, 28, 31–33, 37, 38, 63, 89, 100, 107–113, 115, 132, 133, 146–148, 151, 157, 159, 174, 178–179, 200, 201 monitoring, 2, 3, 6, 7, 9, 14, 22, 27, 37, 38, 47–51, 54–64, 84, 87, 89–92, 99–117, 124, 128, 129, 131–135, 147, 173, 174, 178–192, 196–201, 343–345, 395–454 radiation, 1–3, 7, 8, 13, 15–17, 24, 28, 31–33, 39, 47, 49, 55, 58–60, 100, 124–126, 147, 173, 175, 188, 196, 200, 201 radiometers, 1–3, 8, 21, 22, 24–26, 32, 38, 46, 52, 57, 60, 111, 112, 124, 126, 128, 132–135, 169, 173, 174, 181, 344 sensors, 2, 3, 12, 16, 21, 22, 55, 84–88, 131, 135, 395 Model validation, 241 Moisture contents, 10, 16, 23, 49, 110, 125, 126, 130, 132, 133, 136–138, 153, 155, 157, 169, 173, 177–179, 263, 285, 297 levels, 16, 129, 130, 132, 169, 179, 252, 255, 263, 265, 283 profiles, 16, 17, 19, 129, 132, 133, 136, 137, 187 soils, 2, 10, 14–19, 23, 48, 49, 110, 121–142, 173, 177, 179, 202, 263, 268, 351, 407

Index Monitoring systems, v–vii, 7, 45, 46, 54, 55, 57, 62, 63, 68, 81, 86, 93, 100–102, 105, 107, 129–132, 158, 159, 173, 177, 180, 181, 196, 259, 264, 267, 295, 367, 371, 374, 396, 401, 430, 432, 433, 440, 454

N Nitrates, 141, 210, 229, 326 Northern Africa, 359, 364, 407, 418 Atlantic, 349 hemisphere, 169, 170, 300, 306, 328, 349, 351, 358, 362, 365, 418, 420 regions, 203, 338, 343, 359, 364, 420 Nutrients, 171, 172, 205, 207, 218, 222, 224–226, 229, 233, 234, 236, 239, 246–249, 421, 429, 430

O Observations, vi, 2–4, 6, 14, 16, 27, 32, 38, 46–48, 58, 65, 66, 82, 93, 94, 99, 104, 106, 107, 124, 125, 127, 131, 141, 148, 150, 155, 157, 166, 170, 173–180, 182, 185, 186, 190, 196–199, 218–220, 260, 261, 284–286, 296, 299, 300, 303, 304, 306, 316, 323, 345–347, 349, 352–354, 356–360, 363–365, 379, 401, 409, 412, 418, 419, 427–437 Okhotsk Sea, 218–233, 403 Oxygen cycles, 106, 229 deficit, 224, 229, 232, 246 limiting coefficient, 223 saturation, 222, 232, 246, 247 uptake coefficient, 224 Pacific Ocean, 83, 195, 205, 225, 231, 297, 359, 362, 364, 365, 405, 420, 421 Parameters, 1, 4, 6, 8, 15–18, 21–23, 25, 27, 28, 30, 38, 45–48, 50, 52–55, 58–64, 67, 82–88, 93, 95, 99, 100, 103, 107, 108, 110, 112–115, 122, 124, 126, 127, 131, 137, 145–149, 152, 157–159, 172, 178, 179, 184, 185, 196–198, 200, 206, 209, 210, 216, 217, 223, 225, 231, 232, 240–252, 255, 260, 262, 263, 265, 267, 269, 273, 274, 276–278, 280–284, 287, 295, 300, 312, 313, 322, 326, 333, 335, 342, 348, 353, 362, 367–370, 373–375, 377, 379–382, 385, 386, 398, 406, 411, 420, 423, 425, 426, 428, 429, 432, 433, 436, 439, 445, 446, 448

Index Peats, 53, 169, 173, 187–192, 319, 333, 337 Permafrost melting, 328 thawing, 329, 337 zones, 333, 336, 343, 408 Peruvian current ecosystem (PCE), 233–251 Phosphates, 229, 234, 326 Plantations, 279, 314, 334, 336 Platforms, 3–5, 17–26, 95, 187, 359–361, 432–434, 437 Polarizations, 1, 7, 8, 15, 16, 18, 19, 22, 25–39, 46, 58–61, 114, 132, 188, 197, 198, 262 Polar Year, 203, 300 Pollutants, 1, 23, 24, 54, 55, 64, 106, 200, 201, 205–209, 211, 214, 216, 218, 236, 239, 308, 318, 340, 361, 364–379, 385, 416, 440 Precision, 2, 7, 15, 17, 20, 22, 40, 60, 66, 100, 112, 124, 133, 134, 139, 148, 152, 175, 198, 212, 216, 233, 251, 287, 288, 295, 341, 427, 446 Predictions, 3, 63, 123, 131, 180, 185, 196, 264, 267, 305, 306, 313, 344–346, 350, 397, 400, 409–411, 425, 431, 453 Probability, 164, 262, 316, 318, 352, 365, 366, 412, 414, 424 Productivity, 2, 5, 105, 130, 139, 166, 195, 208, 212, 218, 219, 222, 224, 232, 233, 237, 248, 249, 302, 315, 316, 326, 332, 409, 422, 429, 437, 439, 448, 452, 453 Propagation, 16, 46, 47, 55, 60, 63, 64, 100, 109, 145, 146, 149, 152, 157–159, 163, 165, 186, 231, 254, 361, 367, 372, 374, 376, 377, 381, 383, 401 Protections, 106, 353, 418

R Radiation intensities, 7, 15, 58 Radiative forcings, 99, 169, 306, 320, 346–358, 362 Radiobrightness temperatures, 9, 17, 51, 58, 174, 175, 177, 185, 186, 196, 198 Radiometers, 2, 3, 8, 17, 18, 20, 23–25, 33, 34, 38, 49, 53, 65, 67, 83, 84, 86, 88, 125, 133–135, 137, 173, 285, 344, 360 Radiometric data, 2, 24, 27, 46, 48, 130, 141, 145, 150 measurements, 2, 3, 24, 33, 50, 60, 87, 130, 132, 141 methods, 46, 48, 141, 187 models, 141 observations, 27, 48, 173–178

465 risks, 124–129 technologies, 46, 53, 150 tools, 2, 145 Rayleigh-Jeans law, 65 Regions, v, 2, 17, 26, 34, 45, 54, 55, 58, 60, 62, 65, 70, 77, 82, 83, 106, 129, 131, 134, 139, 164–167, 169–171, 173, 181, 183–185, 187, 198, 202, 203, 209, 218, 219, 221, 224, 231–233, 240, 243, 246, 252–256, 260, 262–285, 287, 297, 299–301, 309, 313, 315, 316, 318, 324, 334, 338, 342, 343, 349–351, 355, 356, 358, 359, 361, 362, 364–367, 370, 376, 377, 384, 395–397, 399, 401–404, 406, 408, 410–416, 418–422, 429, 431–433, 437, 439, 440, 446–448, 450, 452, 454 Relationships, 4, 5, 7, 15, 19, 85, 89, 91, 100, 113, 149, 173, 182, 184, 213–216, 225, 234, 244, 251, 267, 268, 271, 283, 305, 312, 321, 325, 332, 333, 367–370, 372, 376, 378, 379, 385, 398, 399, 410, 414, 416, 422, 425, 427, 439, 446 Relaxation, 12, 28, 46, 60, 62, 63, 174, 197, 410 Remote methods, 4, 7, 17, 50–52, 55, 83, 122, 131, 133, 184, 261, 296, 357 monitoring, 3–7, 19, 45, 47, 50, 93, 105, 106, 122, 128, 131, 141, 142, 145–159, 195, 219, 259, 261, 285, 286, 295–386, 426 sensing, 2, 3, 5–7, 14, 17, 19, 20, 22–26, 38, 45–47, 51–55, 77, 81, 99, 100, 106, 107, 111, 121–142, 145–159, 163, 164, 181–185, 191, 220, 259–263, 280, 284, 295–386, 396, 425, 426 Reservoirs, 4, 51, 55, 130, 172, 198, 252, 255, 272, 280, 303, 310, 312–314, 319, 320, 325, 329, 330, 339, 443 Respiration, 141, 167–169, 172, 223, 228, 302, 310, 311, 313, 319, 320, 325, 330, 336, 369, 408, 427 Runoffs, 27, 103, 202, 203, 205, 207–211, 225, 229, 231, 260, 272, 287, 325, 340

S Salinity, 14, 15, 23, 49, 53, 65, 125, 126, 128, 130, 132, 135, 139, 141, 157, 196–199, 205, 209, 226, 231, 232, 258, 259, 264–266, 270, 285, 325, 333 Salts, 24, 49, 125, 130, 132, 205, 207, 209, 222, 226, 229, 246, 252, 258, 260, 265, 267, 274–276, 358, 363, 364, 429

466 Satellite imagery, 131 missions, 25, 135, 345, 358, 434–436 observations, 38, 177, 185, 218, 219, 316, 345, 357, 419, 427–437 Saturation, 237, 246, 249, 336 Savannahs, 310, 317 Scales, v, vi, xv, 9, 51, 54, 55, 61, 64, 106, 107, 163–165, 167, 178, 214, 216, 225, 236, 241, 297, 302, 305, 318, 323, 348, 352, 367–372, 376–379, 385, 396, 400, 401, 404–411, 414, 417, 419, 429, 430 Scanning radiometers, 3, 18, 135, 170, 345 Scenarios, 106, 108, 123, 205, 216, 217, 225, 227, 231, 240, 241, 244, 251–263, 274, 276, 278–282, 284–288, 302, 313, 314, 316, 321, 322, 335, 337–341, 343, 345, 346, 350, 385, 415, 425, 427, 428, 434, 437, 447, 448, 450, 452 Screening effect, 145–159, 176, 177, 186 Sensitivity, 2, 7, 15–17, 19, 22, 25, 38, 49, 51, 64, 65, 114, 122, 125–127, 131, 173, 174, 198–200, 202, 217, 245, 249–251, 302, 304, 305, 344, 354, 362, 364, 425 Siberia, 164, 166, 255, 336, 342, 365, 366, 421 Signals, 2, 26, 47, 48, 50, 51, 58, 62, 63, 86, 88, 93–95, 113, 114, 124, 126–128, 146, 150, 157, 159, 262, 410, 411 Simulation, vii experiments, 104, 106, 108, 131, 205, 208–217, 221, 225, 241, 246, 250, 251, 253, 267, 269, 273, 274, 279–288, 295, 339, 371, 426, 438, 440 models, 64, 102, 103, 108, 215–217, 239, 246, 251, 253, 263, 264, 267, 284, 295, 296, 362, 383, 385, 426, 432, 438, 440, 453 Soil densities, 10, 11, 15, 17, 49, 125, 126, 130, 132, 136, 163, 318 hydrological regimes, 18, 124, 125, 318 moisture, 2, 10, 14–19, 21, 23, 49, 51, 110, 121–142, 163, 177, 179, 202, 268, 273, 351, 401, 407, 435 parameters, 2, 14–16, 30, 48–50, 110, 111, 125–127, 130, 133–135, 141, 146, 179, 260, 268, 337, 345, 349, 397, 401, 409 radiation, 15, 49, 125–127, 132, 133, 176, 180 surfaces, 8, 11, 16, 17, 21, 24, 26, 36, 39, 49, 105, 111, 113, 124–128, 135, 137, 139, 141, 167, 168, 176, 179, 187, 262, 268, 271, 310, 435

Index Solar energy, 205, 245, 314, 414, 415 South Africa, 171, 297, 364, 365 South America, 15, 164, 233, 240, 297, 301, 308, 360, 365, 396, 414, 418 South-China Sea, 66 Spatial images, 5, 62, 94, 142, 204, 262, 316 resolutions, 3, 5, 14, 18, 24, 25, 33, 38, 48, 93, 103, 123, 126, 128, 129, 150, 159, 204, 221, 262, 300, 344, 360, 367–369, 426, 428, 432, 433 Spectrum, 1, 10, 45, 58, 64, 146, 152, 175, 176, 179, 186, 198, 209, 234, 236, 251, 302, 321, 343, 347, 371, 414, 430, 443 Structural scheme, 86 Structures, vii, 5, 7, 20, 21, 27, 32, 54, 62, 81, 83, 86, 100, 102–104, 106, 107, 112, 114, 122, 127, 130, 141, 146, 147, 150, 151, 157, 159, 166, 167, 173, 174, 178, 188, 190, 203–205, 207, 216, 217, 219–221, 224, 225, 231, 240, 244, 245, 248, 260, 262–264, 266–268, 272, 274, 286, 295, 297, 302, 313, 314, 326, 328–330, 335, 336, 342, 343, 349, 356, 359, 366, 368–373, 383–385, 395, 398, 399, 404, 409, 410, 415, 416, 422, 425, 426, 428–431, 434, 436, 438–441, 443, 446, 451, 453 Substrate, 336, 428 Subsurface anomalies, 261 dielectric irregularities, 52 moistening, 261 peat fires, 188 sensing, 19 soils, 19, 186–188, 261 water level, 132 waters, 17, 132, 142, 261 Successions, 166, 168, 172, 370, 429 Summer, 114, 158, 164, 172, 202, 219, 220, 231, 241, 254, 255, 283, 298, 299, 304, 328, 351, 361, 362, 374, 395, 407, 420 Surface albedo, 305, 425 conditions, 13, 23, 45, 61, 127, 175, 219, 220, 227, 233, 254, 274, 420 emissivities, 8, 9, 13–16, 39, 49, 114, 115, 125, 179, 188, 324 irregularities, 93 processes, 3, 4, 27, 93, 102, 125, 130, 196, 208, 220, 231, 261, 275, 287, 305, 310, 312, 322, 324, 333, 341, 362, 367, 370, 371, 377, 383, 398, 419, 425, 439

Index properties, 4, 11, 14, 15, 48, 49, 135, 147, 198, 200, 219, 261–263, 353, 371, 383, 410 reflectivity, 7, 8, 111, 127, 199 roughness, 3, 4, 15, 16, 61, 84, 93, 111, 114, 126, 127, 136, 167, 188, 196, 198, 278, 366, 380 sensing, 1, 3, 17–19, 25, 45, 93, 111, 124–126, 128, 142, 147, 148, 167, 261, 297, 309 temperatures, 1, 2, 7, 8, 13, 28, 48, 49, 53, 84, 115, 125, 126, 135, 167, 173, 179, 187, 188, 196–200, 219, 227, 230–232, 240, 254, 271, 277, 278, 297, 309, 312, 324, 332, 336, 348, 349, 352, 353, 360, 373, 378, 383, 398, 401, 406, 408, 436, 448 types, 4, 9, 49, 125, 148, 180, 185, 188, 200, 208, 261, 263, 268, 271, 322, 357, 359, 373, 385, 404, 445 vibrations, 94 Survivability, 93, 106, 108, 121, 122, 212–219, 222, 224–225, 227, 228, 232, 241, 243–248, 251, 296, 424–426, 430, 437–454 Systematization, 149, 369, 432

T Technologies, vi, 2–7, 21, 38, 45–95, 99–102, 107, 108, 123, 124, 147, 164, 174, 203, 217, 221, 253, 259, 281, 287, 295–297, 335, 344, 345, 357, 397, 399, 409, 410, 423, 426, 429, 431, 437, 443, 448, 453, 454 Transmittance, 8, 16, 113, 115, 431, 432 Trends, 130, 131, 170, 212, 217, 218, 228, 231, 232, 254, 255, 274, 279, 282, 297, 300, 302, 307, 324, 348, 349, 352–354, 409, 414, 417, 443 Tropical Africa, 15, 171, 316, 418 America, 318 cyclones, 104, 195, 351, 400, 401, 408, 422, 423, 430 deserts, 447 forests, 158, 163, 165, 167, 169–171, 314, 315, 317, 318, 322, 338, 341, 343, 402, 418, 422, 428, 447, 451 humid forests, 314, 326 Oceania, 318 savannahs, 310, 317 seasonal forests, 314, 326

467 Troposphere, 63, 169–172, 297, 315, 318, 349, 350, 352, 357, 361, 363, 365, 366, 371 Tundra, 112, 310, 314, 317, 326, 341, 342, 447

U Upwelling ecosystems, 106, 207, 250 zones, 248, 251, 329, 340, 421, 422, 439

V Validation, 3, 146, 218, 233, 306 Vegetation biomass, 23, 111, 125–127, 130, 135, 139, 145, 155, 159, 172, 315, 323, 331, 334, 453 canopies, 7, 47, 48, 100, 105, 107, 112, 115, 128, 145–148, 153, 157–159, 179, 180, 182, 184, 333 covers, 1, 3, 6, 11, 15, 18, 19, 25, 26, 47, 48, 99, 100, 105, 107–113, 115, 125, 126, 128, 130, 135, 139, 145–151, 159, 167, 169, 172, 180, 316, 322, 334, 340–342, 397, 420, 422, 427, 428 indices, 4, 19, 112, 114, 115, 147, 155, 178, 181, 182, 184, 332 layers, 6, 19, 47, 99, 103, 105, 112, 129, 145, 146, 148, 152, 157, 159, 178, 179, 310, 419, 425 media, 6, 100, 112, 146–150, 156, 178, 310 screening effect, 145–159 temperatures, 1, 15, 16, 105, 125, 130, 157, 168, 172, 178, 181, 331, 420, 421, 427, 446, 447, 449, 452 types, 6, 99, 105, 107, 148–152, 156–159, 178, 181, 315, 317, 326, 330, 331, 422, 427, 447 water contents, 15, 16, 111, 114, 125, 129, 135, 145, 147, 148, 151, 155–157, 159, 178, 179, 452 Velocities, 25, 55, 177, 204, 207, 217, 236, 240, 241, 247–249, 266, 275, 333, 341, 381, 401, 436 Verification, 129, 146, 217, 218, 287, 303, 346, 356, 423 Vertical advection, 217, 247–249 Vietnam, 66, 134 Volcanic eruptions, 93, 325, 352, 360, 397, 400, 401, 405, 408, 415, 419, 425 Volterra integral equation, 77 operators, 70

468 W Water balances, 27, 107, 133, 148, 216, 252–263, 266, 268–274, 277–288, 340, 409, 416, 424, 452 objects, 1, 15, 25, 45, 94, 125, 130, 135–142, 145, 260, 268, 270, 397 oxygen, 3, 47, 229, 231, 232, 237, 239, 246, 247, 308, 398 salinity, 15, 65, 125, 197, 205, 209, 226, 230, 333 Wavelengths, vi, 2, 8, 9, 13–15, 17–20, 22, 26–28, 32, 36–38, 46–51, 55–58, 62, 65,

Index 67, 84, 89, 91, 92, 94, 110, 111, 117, 125–127, 132–134, 136, 142, 145, 149, 156, 173–175, 177, 178, 180, 181, 184, 186–191, 196, 197, 199, 200, 261, 262, 264, 360, 361, 434 Wildfires, 123, 138, 163–172, 176, 178–192, 395, 396 Winter, 114, 158, 168, 172, 202, 219, 220, 228, 231, 241, 254, 255, 265, 349, 350, 365, 396, 397, 402, 417