The Republic of Adygea Environment (The Handbook of Environmental Chemistry, 106) 3030748472, 9783030748470

Citation preview

The Handbook of Environmental Chemistry 106 Series Editors: Damià Barceló · Andrey G. Kostianoy

Murat K. Bedanokov Sergey A. Lebedev Andrey G. Kostianoy  Editors

The Republic of Adygea Environment

The Handbook of Environmental Chemistry Volume 106 Founding Editor: Otto Hutzinger Series Editors: Damia Barcelo´ • Andrey G. Kostianoy

Editorial Board Members: Jacob de Boer, Philippe Garrigues, Ji-Dong Gu, Kevin C. Jones, Thomas P. Knepper, Abdelazim M. Negm, Alice Newton, Duc Long Nghiem, Sergi Garcia-Segura

In over four decades, The Handbook of Environmental Chemistry has established itself as the premier reference source, providing sound and solid knowledge about environmental topics from a chemical perspective. Written by leading experts with practical experience in the field, the series continues to be essential reading for environmental scientists as well as for environmental managers and decisionmakers in industry, government, agencies and public-interest groups. Two distinguished Series Editors, internationally renowned volume editors as well as a prestigious Editorial Board safeguard publication of volumes according to high scientific standards. Presenting a wide spectrum of viewpoints and approaches in topical volumes, the scope of the series covers topics such as • • • • • • • •

local and global changes of natural environment and climate anthropogenic impact on the environment water, air and soil pollution remediation and waste characterization environmental contaminants biogeochemistry and geoecology chemical reactions and processes chemical and biological transformations as well as physical transport of chemicals in the environment • environmental modeling A particular focus of the series lies on methodological advances in environmental analytical chemistry. The Handbook of Environmental Chemistry is available both in print and online via http://link.springer.com/bookseries/698. Articles are published online as soon as they have been reviewed and approved for publication. Meeting the needs of the scientific community, publication of volumes in subseries has been discontinued to achieve a broader scope for the series as a whole.

The Republic of Adygea Environment

Volume Editors: Murat K. Bedanokov
Sergey A. Lebedev
Andrey G. Kostianoy

With contributions by T. V. Akatova
V. V. Akatov
E. M. Apukhtina
M. K. Ashinova
Y. N. Ashinov
M. K. Bedanokov
R. B. Berzegova
A. R. Bibin
D. Y. Chetyz
S. K. Chich
A. K. Dorgushaova
E. A. Grabenko
G. A. Guk
G. N. Gunina
M. K. Kalashaova
Z. N. Khatko
S. V. Kiseleva
T. B. Kolotiy
L. A. Korinevich
V. N. Korobkov
E. A. Kostianaia
A. G. Kostianoy
P. N. Kravchenko
S. K. Kuizheva
E. M. Kurbanova
I. E. Kurbatova
S. A. Lebedev
I. A. Repina
I. V. Serykh
A. E. Shadge
A. A. Shazzo
S. T. Shefrukova
A. A. Shestakova
O. P. Shevyakova
A. K. Shkhapatsev
E. A. Sirotyuk
V. I. Sychev
I. P. Takh
A. K. Temzokov
R. A. Toroyan
S. A. Trepet
T. P. Varshanina
M. G. Zaretskaya
V. I. Zarubin
S. R. Zhemadukova

Editors Murat K. Bedanokov Maykop State Technological University Maykop, The Republic of Adygea Russia

Sergey A. Lebedev Geophysical Center Russian Academy of Sciences Moscow, Russia Maykop State Technological University Maykop, The Republic of Adygea Russia National Research University of Electronic Technology (MIET) Moscow, Russia

Andrey G. Kostianoy P.P. Shirshov Institute of Oceanology Russian Academy of Sciences Moscow, Russia S.Yu. Witte Moscow University Moscow, Russia

ISSN 1867-979X ISSN 1616-864X (electronic) The Handbook of Environmental Chemistry ISBN 978-3-030-74847-0 ISBN 978-3-030-74849-4 (eBook) https://doi.org/10.1007/978-3-030-74849-4 © 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

Series Editors Prof. Dr. Damia Barcelo´

Prof. Dr. Andrey G. Kostianoy

Department of Environmental Chemistry IDAEA-CSIC C/Jordi Girona 18–26 08034 Barcelona, Spain and Catalan Institute for Water Research (ICRA) H20 Building Scientific and Technological Park of the University of Girona Emili Grahit, 101 17003 Girona, Spain [email protected]

Shirshov Institute of Oceanology Russian Academy of Sciences 36, Nakhimovsky Pr. 117997 Moscow, Russia and S.Yu. Witte Moscow University Moscow, Russia [email protected]

Editorial Board Members Prof. Dr. Jacob de Boer VU University Amsterdam, Amsterdam, The Netherlands

Prof. Dr. Philippe Garrigues Universite´ de Bordeaux, Talence Cedex, France

Prof. Dr. Ji-Dong Gu Guangdong Technion-Israel Institute of Technology, Shantou, Guangdong, China

Prof. Dr. Kevin C. Jones Lancaster University, Lancaster, UK

Prof. Dr. Thomas P. Knepper Hochschule Fresenius, Idstein, Hessen, Germany

Prof. Dr. Abdelazim M. Negm Zagazig University, Zagazig, Egypt

Prof. Dr. Alice Newton University of Algarve, Faro, Portugal

Prof. Dr. Duc Long Nghiem University of Technology Sydney, Broadway, NSW, Australia

Prof. Dr. Sergi Garcia-Segura Arizona State University, Tempe, AZ, USA

Series Preface

With remarkable vision, Prof. Otto Hutzinger initiated The Handbook of Environmental Chemistry in 1980 and became the founding Editor-in-Chief. At that time, environmental chemistry was an emerging field, aiming at a complete description of the Earth’s environment, encompassing the physical, chemical, biological, and geological transformations of chemical substances occurring on a local as well as a global scale. Environmental chemistry was intended to provide an account of the impact of man’s activities on the natural environment by describing observed changes. While a considerable amount of knowledge has been accumulated over the last four decades, as reflected in the more than 150 volumes of The Handbook of Environmental Chemistry, there are still many scientific and policy challenges ahead due to the complexity and interdisciplinary nature of the field. The series will therefore continue to provide compilations of current knowledge. Contributions are written by leading experts with practical experience in their fields. The Handbook of Environmental Chemistry grows with the increases in our scientific understanding, and provides a valuable source not only for scientists but also for environmental managers and decision-makers. Today, the series covers a broad range of environmental topics from a chemical perspective, including methodological advances in environmental analytical chemistry. In recent years, there has been a growing tendency to include subject matter of societal relevance in the broad view of environmental chemistry. Topics include life cycle analysis, environmental management, sustainable development, and socio-economic, legal and even political problems, among others. While these topics are of great importance for the development and acceptance of The Handbook of Environmental Chemistry, the publisher and Editors-in-Chief have decided to keep the handbook essentially a source of information on “hard sciences” with a particular emphasis on chemistry, but also covering biology, geology, hydrology and engineering as applied to environmental sciences. The volumes of the series are written at an advanced level, addressing the needs of both researchers and graduate students, as well as of people outside the field of vii

viii

Series Preface

“pure” chemistry, including those in industry, business, government, research establishments, and public interest groups. It would be very satisfying to see these volumes used as a basis for graduate courses in environmental chemistry. With its high standards of scientific quality and clarity, The Handbook of Environmental Chemistry provides a solid basis from which scientists can share their knowledge on the different aspects of environmental problems, presenting a wide spectrum of viewpoints and approaches. The Handbook of Environmental Chemistry is available both in print and online via www.springerlink.com/content/110354/. Articles are published online as soon as they have been approved for publication. Authors, Volume Editors and Editors-in-Chief are rewarded by the broad acceptance of The Handbook of Environmental Chemistry by the scientific community, from whom suggestions for new topics to the Editors-in-Chief are always very welcome. Damia Barcelo´ Andrey G. Kostianoy Series Editors

Foreword by Head of the Republic of Adygea

Dear friends! A few parts of the Russian Federation are as unique and rich in natural resources as Adygea. Diverse terrain, favorable climate, lakes, and rivers, exceptional diversity of vegetation, and unique natural monuments attract nature lovers from all over Russia and abroad and create positive factors for the development of recreational activities and tourism as a priority area of economic development of the Republic. The new book on the environment of Adygea has become the first scientific publication that comprehensively examines the main components of the environment of our region – its air and water environments, soil and vegetation, mineral resources, climate, and energy sources – in the aspect of modern global transformations considering the results of the latest natural phenomena research methods. Remarkable is the fact that the monograph embodies the results of the surveys of not only the leading scientists of the Republic but also prominent researchers from Moscow and St. Petersburg, highly interested in the study of contemporary issues of the unique natural potential of Adygea. This joint scientific work has resulted in observations and conclusions that are of unassisted value for the preservation of the unique natural and ecological features of the Republic’s territories and their sustainable development, as well as further formation of the tourism cluster of the region. I am sure that the book, published in a foreign publishing house on the eve of the 30th anniversary of our young Republic, will not only be highly appreciated by the specialists but will also attract students of natural science and agricultural

ix

x

Foreword by Head of the Republic of Adygea

specialties, as well as all those who love the amazing nature of our native Adygea and tries to save and increase its wealth for the present and future generations. Maykop, The Republic of Adygea, Russian Federation

Murat K. Kumpilov

Foreword by the President and Rector of Maykop State Technological University

Dear friends! Maykop State Technological University is one of the largest multidisciplinary research and educational institutions of the Southern Federal District of the Russian Federation. A distinctive trait of the university is the training of highly qualified specialists in the field of ecology, environmental management, food and processing industries, agriculture and agricultural engineering, applied informatics and information security for leading research organizations, enterprises and companies of the industrial and technological complex not only in the Republic of Adygea and the South of Russia but also in more than 40 countries. The university has many years of fruitful cooperation with universities and institutes of the Ministry of Science and Higher Education of the Russian Federation, leading institutes of the Russian Academy of Sciences, organizations and enterprises of the Republic of Adygea, leading foreign scientific and educational organizations such as the Albert Ludwig University of Freiburg (Freiburg, Germany), University of Natural Resources and Life Sciences (Vienna, Austria), Institute of Marine Biology (Kotor, Montenegro), Institute of Ecology, Academy of Sciences of Abkhazia (Sukhumi, Abkhazia), University of L’Aquila (L’Aquila, Italy), Lebanese International University and Lebanese University (Beirut, Lebanon), School of Economics and Management of Public Administration (Bratislava, Slovakia), and others. Within the framework of this cooperation, a large number of all-Russian and international seminars, schools, and conferences are held on the interaction of mankind with the biosphere, global and regional climate change, ecology, environmental protection, and environmental management. The International Scientific and Practical Conference “Fundamental and Applied Aspects of Geology, Geophysics and Geoecology Using Modern Information Technologies,” which has been held every 2 years at the university since 2011, has significantly contributed to resolving these issues. The reports and materials of this conference formed the basis of the present scientific book. xi

xii

Foreword by the President and Rector of Maykop State Technological University

We are sure that the book “The Republic of Adygea Environment” will make a considerable contribution to Russian and world science. It will expand the paradigm of research in the field of geoecology in the era of global environmental changes, which will make it possible to effectively use the obtained scientific results and achievements for the benefit of the development of society. We hope that for foreign readers it will be the first step in getting acquainted with the environment of the Republic of Adygea, with the environmental problems of this region and ways to solve them. Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation

President Acad. Aslan K. Tkhakushinov Rector Prof., Dr. Saida K. Kuizheva

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Murat K. Bedanokov, Saida K. Kuizheva, Sergey A. Lebedev, and Andrey G. Kostianoy

1

Physicogeographical Characteristics of the Republic of Adygea . . . . . . Murat K. Bedanokov, Saida K. Chich, Dana Yu. Chetyz, Sergey A. Trepet, Sergey A. Lebedev, and Andrey G. Kostianoy

19

Digital Elevation Model of the Republic of Adygea . . . . . . . . . . . . . . . . Sergey A. Lebedev, Andrey G. Kostianoy, and Pavel N. Kravchenko

57

Spatial-Temporal Geodynamic Model of Adygea . . . . . . . . . . . . . . . . . Tatyana P. Varshanina and Viktor N. Korobkov

85

Development of Exogenous Geological Processes in the Territory of the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey A. Lebedev and Lenina A. Korinevich Ecological Conditions of Soils in the Republic of Adygea . . . . . . . . . . . Sergey A. Lebedev, Galina N. Gunina, Yunus N. Ashinov, and Pavel N. Kravchenko Soil Degradation in the Republic of Adygea Under Exogenous Geological Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey A. Lebedev and Pavel N. Kravchenko Ecological State of Soils of the Republic of Adygea Under High Anthropogenic Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey A. Lebedev, Emiliya A. Sirotyuk, Aslan K. Shkhapatsev, and Pavel N. Kravchenko Maykop City Soil Quality Determination Based on the Analyses of Soil Algae and Cyanobacteria Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . Еmiliya A. Sirotyuk and Saida R. Zhemadukova

113 145

167

185

217

xiii

xiv

Contents

The Republic of Adygea Motor Transport Complex Impact on the Regional Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey A. Lebedev, Galina A. Guk, and Elena M. Apukhtina

237

Spatial Distribution of Heavy Metals Content in the River Belaya Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruben A. Toroyan and Irina P. Takh

271

General Characteristics of the Climate in the Republic of Adygea . . . . Andrey G. Kostianoy, Sergey A. Lebedev, Ilya V. Serykh, Evgeniia A. Kostianaia, and Tatyana P. Varshanina

289

Regional Climate Change in the Republic of Adygea . . . . . . . . . . . . . . Andrey G. Kostianoy, Ilya V. Serykh, Sergey A. Lebedev, Evgeniia A. Kostianaia, and Tatyana P. Varshanina

311

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Irina A. Repina, Anna A. Shestakova, Murat K. Bedanokov, Roza B. Berzegova, and Sergey A. Lebedev

359

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety in the Mountains of the Republic of Adygea . . . . . . . . Murat K. Bedanokov, Roza B. Berzegova, and Saida K. Kuizheva

397

Contemporary Changes of the Vegetation in the Mountainous Adygea as the Reflection of Global Processes . . . . . . . . . . . . . . . . . . . . . . . . . . Valery V. Akatov and Tatyana V. Akatova

413

Seasonal and Interannual Variability of NDVI in the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sergey A. Lebedev, Andrey G. Kostianoy, Pavel N. Kravchenko, and Olga P. Shevyakova

443

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using Remote Sensing Data . . . . . . . . . . . . . . . . . . . . . . . . . Irina E. Kurbatova

461

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in 2015–2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vitaly I. Sychev

497

Seasonal and Interannual Variability of the Krasnodar Reservoir Water Level Based on Satellite Altimetry Data . . . . . . . . . . . . . . . . . . . Sergey A. Lebedev, Olga P. Shevyakova, and Murat K. Bedanokov

525

Renewable Energy Potential in the Republic of Adygea . . . . . . . . . . . . Sofia V. Kiseleva, Lenina A. Korinevich, and Sergey A. Lebedev

545

Contents

xv

Ecological Tourism Development in the Republic of Adygea . . . . . . . . Murat K. Bedanokov, Saida K. Chich, and Dana Yu. Chetyz

573

Shaposhnikov Caucasian State Nature Biosphere Reserve . . . . . . . . . . Aleksey R. Bibin, Sergey A. Trepet, Evgeniy A. Grabenko, and Tatyana V. Akatova

603

Plants and Fungi Species Listed in the Red Book of the Republic of Adygea (2017–2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Еmiliya A. Sirotyuk, Aminet E. Shadge, and Galina N. Gunina

633

Biologically Active Substances from Wild Fruits and Berries at the Piedmont of the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . . Zuret N. Khatko and Tatyana B. Kolotiy

653

Problems of Legal Regulation of Environmental Relations in the Republic of Adygea and Possibilities of Environmental Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marina G. Zaretskaya, Elena M. Kurbanova, Azamat K. Temzokov, Albina A. Shazzo, and Sabina T. Shefrukova

667

Improvement of the Mechanism of the Regional Ecological and Economic System Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Asiyat K. Dorgushaova, Marina K. Ashinova, Saida K. Kuizheva, Vladimir I. Zarubin, and Mareta K. Kalashaova From Regional Studies to International Collaboration . . . . . . . . . . . . . Murat K. Bedanokov, Saida K. Kuizheva, Sergey A. Lebedev, and Andrey G. Kostianoy

681

703

Introduction Murat K. Bedanokov, Saida K. Kuizheva, Sergey A. Lebedev, and Andrey G. Kostianoy

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Geographical Location and Borders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Administrative-Territorial Division . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 History of the Development of the Territory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Science and Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Tourism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 4 5 7 9 9 11 11 13 15 16 17

Abstract This introductory chapter introduces the Republic of Adygea to western readers who are not familiar with the Republic and its beautiful landscapes, history and population, industry and agriculture, transport and tourism, science and education. This is the first book on the environmental issues in the Republic of Adygea in M. K. Bedanokov (*) and S. K. Kuizheva Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected]; [email protected] S. A. Lebedev Geophysical Center, Russian Academy of Sciences, Moscow, Russian Federation Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation National Research University of Electronic Technology (MIET), Moscow, Russian Federation e-mail: [email protected] A. G. Kostianoy P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russian Federation S.Yu. Witte Moscow University, Moscow, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 1–18, DOI 10.1007/698_2021_736, © Springer Nature Switzerland AG 2021, Published online: 25 February 2021

1

2

M. K. Bedanokov et al.

the Western editions. The book is shaped of 28 chapters including the introductory and conclusions chapters, which cover the following fields of science: geography, geology, geodynamics, geoecology, climatology, atmospheric sciences, hydrology, soil sciences, environmental chemistry, botany, zoology, renewable energy, tourism, legal issues, environmental insurance, and economy. The book is written by the consortium of authors from Maykop State Technological University and other universities, research institutes, and state organizations from Maykop, Moscow, St.-Petersburg, Sochi, Nalchik, and Tver. Keywords Agriculture, Education, Environment, Geography, History, Industry, Population, Russian Federation, Science, The Republic of Adygea, Tourism, Transport

1 Introduction The Republic of Adygea is a flowering corner of the Caucasus (Figs. 1 and 2). The land of steppe expanses, forests and mountains, stormy rapid rivers, snow-capped peaks, flowering gardens, golden cornfields, and subalpine meadows. The diversity of nature, the beauty of its plains and mountains, the mild climate, the benevolence and affability of the people distinguish the ancient land of the Adyghe people among the pearls of Russia. So it was made by people living in friendship and mutual understanding. They adorned their native land, generously endowed with nature, the good and glorious fruits of hard-working hands. In the Former USSR, Adygea was very well known for its mountain tourism. Since those times, it is considered to be one of the most popular tourist destinations in the Caucasus in Russia. The mountain landscapes and wildlife here are extraordinarily beautiful. This attracts thousands of hikers, rock climbers, mountaineers, spelunkers, water sportsmen both amateurs and professionals from all over Russia. In 1949, the tourist walking route named «Through the Belorechensky Pass» was Stavropol Krai

44.5° N

no d

ar

B

Republic of Adygea

45.0° N

Kr as

Kr ai

l a c k

44.0° N

S

KarachayCherkess Republic

e

Fig. 1 Location of the Republic of Adygea

a

43.5° N 37° E

38° E

39° E

40° E

41° E

42° E

Introduction

3

Fig. 2 Physical map of the Republic of Adygea [3]

opened as an amateur route; it connected Guzeripl Village, Partizanskaya Polyana (glade), the head of the Belaya River, the Belorechensky Pass, Babuk-Aul Cordon, Solokh-Aul and Dagomys villages, and Sochi City on the Black Sea coast. In 1974, a new hiking route N 825 named «Through Adygea to the Black Sea» was opened. The walking part of the route connected Lago-Naki Tourist Center, the Abadzeshsky Pass, Rubleny (Chopped) and Vodopadny (Waterfall) shelters, the Cherkessky Pass, Babuk-Aul and Solokh-Aul shelters, and the Lazarevskoe Village on the Black Sea coast. During 5 days tourists walked through the Caucasus mountains and descended to the coast of the Black Sea (see Fig. 2 for location). The Republic is also known thanks to a location of the Caucasian State Wildlife Biosphere Reserve (total area of 280 thousand ha), which was organized in 1924 and included in the UNESCO World Heritage List in 1999. It is the largest mountainforest reserve in Europe, the length of which reaches 97 km from west to east, and the width is 50 km from north to south. Unfortunately, the Republic of Adygea is not well known to western readers and scientists. In the Springer database we found only 387 entries with word “Adygea” mentioned in 302 articles, 85 chapters, and 34 conference papers. This was shared between the following disciplines: Life Sciences – 118, Economics – 83, Earth

4

M. K. Bedanokov et al.

Sciences – 46, Biomedicine – 41, Engineering – 29, Mathematics – 18, Political Sciences and International Relations – 12, Environment – 7, etc. [1]. No one book was published yet on the environmental issues in the Republic of Adygea in the Western editions. The idea of the present book was born in 2013 at the International scientificpractical conference “Fundamental and Applied Aspects of Geology, Geophysics and Geoecology Using Modern Information Technologies” which is a bi-annual conference organized by Maykop State Technological University in Maykop, a capital of the Republic of Adygea. Discussion of the book content and building of the author consortium from several universities, research institutes, and state organizations from Maykop, Moscow, St.-Petersburg, Sochi, Nalchik, and Tver took a certain time. Now the book is shaped of 28 chapters including the introductory and conclusions chapters, which cover the following fields of science: geography, geology, geodynamics, geoecology, climatology, atmospheric sciences, hydrology, soil sciences, environmental chemistry, botany, zoology, renewable energy, tourism, legal issues, environmental insurance, and economy.

2 Geographical Location and Borders The Republic of Adygea is located in the southern part of Russia inside the Krasnodar Krai, not far from the Black Sea coast (Fig. 1). It is located in the central part of the Northwestern Caucasus between 45
130 and 43
46’N. and 38
410 and 40
460 E. Parallel 44
30’N divides the Republic almost in half (Figs. 1 and 2). The area of Adygea is 7,790 km2 (0.04% of the territory of the Russian Federation) which is comparable with Cyprus (9,250 km2) or equals to three areas of the state of Luxembourg (2,586 km2) [2]. The distance between the extreme points: northern (45
13’N, 39
380 E) and southern (43
46’N, 40
160 E) is 208 km, western (44
57’N, 38
410 E) and eastern (44
29’N, 40
460 E) is 165 km. The distance from the southern point of the Republic to the equator is 4,848 km [2]. On the map, the territory of the Republic resembles the shape of a large mushroom with an oddly crumpled cap sitting on a thick stalk, in the middle of which flows the Belaya River (Fig. 2). The length of the borders is more than 900 km, one-third passes along water bodies: along the Kuban River, Krasnodar Reservoir, Laba River, and Belaya River (Fig. 2) [2]. More than one-third of the area of Adygea is occupied by specially protected natural areas of various status and categories. The six largest specially protected natural areas are mountain lands, rare in terms of conservation and level of biological diversity (14% of the total area of Adygea), which in December 1999 were included in the UNESCO World Natural Heritage List as part of the “Western Caucasus” nomination [4]. First of all, this is the Kh.G. Shaposhnikov Caucasian State Wildlife Biosphere Reserve (Fig. 3) 91,535 ha of which are located in Adygea [5].

Introduction

5

Fig. 3 Caucasian State Wildlife Biosphere Reserve (photo by S.A. Trepet)

3 Administrative-Territorial Division The territory of the Republic of Adygea is divided into 7 administrative districts: Giaginsky, Koshekhablsky, Krasnogvardeysky, Maykopsky, Takhtamukaysky, Teuchezhsky, and Shovgenovsky; 55 rural administrations in which 226 settlements are located, including 5 urban-type settlements [2, 3]. The settlements are dominated by auls, stanitsa, rural settlements. There are 2 cities of republican significance in the Republic: Maykop City (the capital of the region) and Adygeysk Town (Fig. 4). The republican center is the City of Maykop (Fig. 5), located in the central part of the Republic (44
36’N, 40
060 E) at an altitude of 210–230 m above sea level

6

M. K. Bedanokov et al.

Administrative-Territorial Division Krasnodar 45°

no as Kr

VII

e rR da

V

ir rvo se

IX

VIII III

IV

II I

Labinsk

Maykop Apsheronsk

Tul'sky

VI

Bl a Se ck a

Tuapse

44°

Guzeripl

I - Maykop Republican Urban District II - Adygeysk Republican Urban District III - Giaginsky DistrictI IV - Koshekhabl'sky District V - Krasnogvardeysky District VI - Maykopsky District VII - Takhtamukaysky District VIII - Teuchezhsky District IX - Shovgenovsky District

39°

40°

Fig. 4 Administrative-territorial division of the Republic of Adygea (modified from maps presented in [2])

(northwestern outskirts of the city – 210 m, south-east – 230 m). The city got its name from the Adyghe word “Myekuape” which means the valley of wild apple trees. Maykop is an important industrial, transport, educational, and cultural center of the Republic. Most of the industrial products of the Republic are produced here, most of the research organizations and professional education institutions of the Republic, the editorial offices of the republican mass media are located here. Maykop has direct road and rail links with the center of the Southern Federal District, the city of Rostov-on-Don and its regions, the Black Sea ports: the cities of Novorossiysk, Tuapse, and Sochi. The transport connection between Maykop and the regions of the Republic is carried out through the motorways Maykop City– Krasnodar City, Maykop City–Karachayevsk Town, Maykop City–Ust-Labinsk

Introduction

7

Fig. 5 Central district and park of Maykop City (photo by S.A. Trepet)

Town, Maykop City–Koshekhabl Town, and Maykop City–Guzeripl Village. The capital of Adygea is 1,670 km away from the City of Moscow and 140 km from the City of Krasnodar. The nearest seaports of the cities of Tuapse and Novorossiysk are located 150 and 270 km from the republican center (Fig. 2). The nearest airport is located in the City of Krasnodar.

4 History of the Development of the Territory The acquisition of statehood by the Adyghe people became possible after the radical political transformations held with October Revolution of 1917 in Russia [2]. As a result of the practical realization of the right of peoples to self-determination, the Adyghe people, like other peoples of the North Caucasus, received autonomy. On July 27, 1922, the Presidium of the All-Russian Central Executive Committee made a decision on the formation of the Circassian (Adyghe) Autonomous Region. On August 24, 1922, it was renamed into the Adyghe (Circassian) Autonomous Region, and from August 3, 1928, it was renamed into the Adyghe Autonomous Region of the North Caucasian Krai (Territory) (modern Krasnodar Krai). At the time of its formation, the Adyghe Autonomous Region consisted of five districts, which united 45 settlements (Fig. 6). On January 10, 1934, the Adyghe Autonomous Region became part of the Azov-Black Sea Krai, which included part of the Voronezh and Saratov Regions and the territory of the present Krasnodar Krai and Rostov Region. On April 10, 1936, the City of Maykop, the Giaginsky District, and the Khansky Village of the Maykop District were annexed to the region. As a result, the center of the Adyghe Autonomous Region was moved to the City of Maykop (Fig. 6). On

8

M. K. Bedanokov et al.

Growth of the territory of Republic of Adygea V

Krasnodar 45°

IX

VII VIII III

IV

II I

Labinsk

Maykop Apsheronsk

1922

1936

1940

Tul'sky

VI

1962

Modern Tuapse Administrative-Territorial Division 44°

Guzeripl

I - Maykop Republican Urban District II - Adygeysk Republican Urban District III - Giaginsky DistrictI IV - Koshekhabl'sky District V - Krasnogvardeysky District VI - Maykopsky District VII - Takhtamukaysky District VIII - Teuchezhsky District IX - Shovgenovsky District

39°

40°

Fig. 6 Growth of the territory of the Republic of Adygea

February 21, 1940, the Adyghe Autonomous Region has received the Kuzhora Village of the Tula District of the Krasnodar Krai, and the Maykop District was formed within it (Fig. 6). On April 28, 1962, the territory of the abolished Tula Region of the Krasnodar Krai was annexed to the Maykop District, thereby the territory of the Adyghe Autonomous Region took on its modern appearance (Fig. 6). On October 5, 1990, an extraordinary session of the Adyghe Regional Council of People’s Deputies made a decision to raise the status of Adygea to the level of an independent subject (republic) of the Russian Soviet Federative Socialist Republic (RSFSR) and proclaimed the Adyghe Soviet Socialist Republic. On December 15, 1990, the secession of Adygea from the Krasnodar Krai was legalized by the Second Congress of People’s Deputies of the RSFSR, which amended the Constitution of the RSFSR, according to which the autonomous regions were withdrawn from the territories that they had previously belonged to. On March 23, 1992, the

Introduction

9

Supreme Council of Adygea adopts a law on renaming the Adygea Soviet Socialist Republic into the Republic of Adygea (Adygea). On April 21, 1992, the Congress of People’s Deputies of the Russian Federation introduced the provision on the Republic of Adygea to the Russian Constitution. The amendment entered into force on the date of publication on May 16, 1992.

5 Population 453.4 thousand people live on the territory of the Republic of Adygea, including 85.7 thousand children under 14 years old (as of January 1, 2018), while the urban population is 213.8 thousand people or 47.2% of the population. The rural population is of 239.6 thousand people or 52.8% of the population. The urban population is concentrated mainly in two cities: Maykop (142.0 thousand people) and Adygeisk (12.7 thousand people). The economically active population as of January 1, 2018 amounted to 248.6 thousand people or 54.8% of the total population of the Republic [6]. The dynamics of the demographic situation is shown in Fig. 7. Population growth in recent years occurs only due to migration, with an absolute predominance of pensioners. Currently, 26% of the population of the Republic or every fourth is over the working age. Adygea is a multinational Republic in which representatives of more than 100 nationalities live and work.

6 Industry The industry of the Republic of Adygea is represented by eleven of its branches (Fig. 8). The leading branch of industry is the food industry, the share of which in the total volume of industrial production is more than 50% [6]. The enterprises of the industry produce meat, confectionery, milk, canning, and pasta products, wine, beer, and vodka products, and other types of food products. The presence of forest reserves in the Republic made it possible to create a large furniture, woodworking, pulp and paper industry. The timber industry complex of the Republic of Adygea is based mainly on local raw materials, which gives this industry a priority status. The industry employs 12 large and medium-sized enterprises producing commercial timber, lumber, parquet, sliced veneer, cabinet furniture, soft and bent chairs, corrugated cardboard, and boxes made of it. This industry employs 16% of all workers employed in industrial production [7]. Machinebuilding and metalworking enterprises produce more than 11% of the total industrial output of the Republic.

10

M. K. Bedanokov et al.

453,4

453,4

2016

2017

451,5 449,2

446,4 444,4 442,4 441,0 439,9

2005

2010

2011

2012

2013

2014

2015

(a) 263,3

259,0

258,6 257,8 256,6

256,0 253,7 251,6

248,6

2005

2010

2011

2012

2013

2014

2015

2016

2017

(b) Fig. 7 Dynamics of the demographic situation in the Republic of Adygea: (a) total population (thousand people), (b) economically active population (thousand people) (according to [6])

Introduction

11 Textile and clothing production, footwear production 0,6% Pulp and paper production 13,6%

Manufacture of other non-metallic mineral products 3,1% Manufacture of machinery, vehicles and equipment 2,5% Other types of manufacturing 2,4%

Food production, including drinks and tobacco 60,3%

Metallurgical production and production of hardware 2,8% Wood processing and production of wood products 2,7%

Production of coke and petroleum products, chemical production 11,6%

Production of electrical, electronic and optical equipment 0,4%

Fig. 8 The share of the main industries in the structure of industrial production (according to [6])

7 Agriculture Agriculture is a priority area of economic development in the Republic of Adygea. Favorable soil and climatic conditions predetermine its diversified nature. The largest share in plant growing is occupied by grain and industrial crops, vegetables, fruits, berries (fruit growing). Cattle breeding, sheep breeding, industrial poultry breeding, pedigree horse breeding are developed. The unique climatic conditions of Adygea, especially its foothill zone, contribute to the growth of not only southern (peach, dogwood, quince, pear, grapes), but also subtropical crops (tea). The predominant attention is paid to the cultivation of wheat, barley, corn, and sunflower. In 2017, the Republic produced 613.8 thousand tons of grain (Fig. 9). The largest gross grain harvest was obtained in 2016 – 699.0 thousand tons. From 2010 to 2017 the average grain production in the Republic was 523.7 thousand tons [6].

8 Transport Transport is one of the most important sectors of the economy, since it provides connections between different territories, uniting the economy into a single system. The transport complex of the Republic of Adygea is a combination of different types of transport. Its development reflects the peculiarities of the development of the Republic’s economy. It meets the needs of the economy and the population in

12

M. K. Bedanokov et al.

699,0

638,6 613,8

541,3 509,8

437,4

426,9

323,1 303,6 2005

2010

2011

2012

2013

2014

2015

2016

2017

Fig. 9 Dynamics of grain production in the Republic of Adygea (thousand tons) (according to [6])

transportation, contributes to the formation and development of the territorial division of labor, has a significant impact on the dynamism and efficiency of the socioeconomic development of the region, on the location of production, and the establishment of economic ties with other regions. The transport system of the Republic of Adygea includes rail, road, air, and pipeline modes of transport. Within Adygea, there are passing railway lines, the length of which is 170 km, there are 12 railway stations [2]. Railway transport connects the Republic with the Rostov Region, Krasnodar Krai and Stavropol Krai, and other regions of the North Caucasus. Raw materials and fuel account for about 70% of all transportation [6]. Mineral construction materials, agricultural products, metals, and timber also occupy a significant place. The Republic of Adygea has a developed transport system, which is based on motorways. The highest density of roads (more than 0.2 km/km2) was recorded in Takhtamukaysky, Teuchezhsky, Shovgenovsky, Krasnogvardeysky, Giaginsky Districts and in the urban district of Adygeysk, and the smallest (less than 0.2 km/km2) in the Maykop District, where the Caucasian State Wildlife Biosphere Reserves (UNESCO World Heritage Site) is located (Fig. 10). According to the Adygea Statistical Agency on 1 January 2017, the length of public roads in the Republic was 4,395 km, including those with a hard pavement of 4,052 km [6]. There is also a pipeline transport in Adygea. A gas pipeline was laid from Maykop to Ust-Labinsk Town, Tuapse City, and Armavir City. An oil pipeline runs through the territory of the Republic.

Introduction

13

Highway Provision

Krasnodar 45°

n as Kr

R ar od

e es

rvo

ir

Labinsk

Maykop Apsheronsk

Bl ac k

Tul'sky

Tuapse

Se a

44°

Guzeripl

Highway Provision (km on km2)

< 0,1

0,2 - 0,25

0,1 - 0,2

0,25 - 0,3

Caucasian State Nature Biosphere Reserve

39°

40°

Fig. 10 Provision of public roads in the Republic of Adygea according to data [6]

9 Science and Education The development of the Republic’s education system is a priority and is built taking into account the socio-economic, ethnocultural, demographic, and other characteristics of the region in the interests of the formation of a spiritually rich, physically healthy, socially active personality. In the Republic of Adygea, there are 149 educational organizations and 7 professional educational organizations, in which 51,400 people studied as of September 1, 2018 [6]. In the Republic of Adygea, higher education is represented by 2 Federal State Universities: Maykop State Technological University (https://mkgtu.ru) and Adyghe State University (https://www.adygnet.ru), in which, as of September 01, 2019, 9,100 people studied at ASU and 9,300 people studied at MSTU. Universities are

14

M. K. Bedanokov et al.

actively developing as modern universities, creating ties with the Russian and world scientific and educational community, focusing on new technologies and ideas, using the accumulated potential for active work at the intersection of sciences such as mathematics, biology, chemistry, physics, genetics, economics, sociology, ecology, developing engineering and information, food and agricultural technologies, medical and biotechnology. New areas of research are actively being formed, focused on the existing experience, personnel, and the unique natural potential of Adygea. An innovative educational environment was created with the involvement of well-known scientists and researchers in the organization of the educational process, thereby preparing the conditions for future scientific collaborations. Currently, universities have a sufficiently developed multidisciplinary and multilevel structure for training qualified personnel, which makes it possible to implement educational programs of higher, secondary, and additional professional education. The total number of implemented educational programs of higher education – bachelor’s, master’s, specialist’s, and training programs of higher qualifications – postgraduate programs, is about 170 programs. The research process and innovation activities are carried out on the basis of system integration and interdisciplinary research of university science with the institutions of the Russian Academy of Sciences, a wide range of Russian and foreign partners and potential consumers of the results of research work. Significant scientific results were obtained in the field of research on the biodiversity of the Caucasus; geoecology, nature management, and geographic information systems; nutritional science; technologies for monitoring and predicting the state of the environment, preventing and eliminating its pollution; technologies for the prevention and elimination of natural and man-made emergencies; health preservation; reducing losses from socially significant diseases; immunogenetics; sports physiology; biomechanics; social psychology; ethnic culture, ethnosocial processes; socioeconomic development of the region. Work has begun on the formation of a package of documents for the “Vernadsky” program, which is a project for the creation of regional scientific and educational consortia uniting M.V. Lomonosov Moscow State University, regional universities, academic science, and socially oriented business. The implementation of this and many other projects will increase the level of scientific and innovative activities, develop the academic mobility of teachers and students, and ensure their professional growth. Scientists of the Republic of Adygea, as well as leading scientists of the Russian Academy of Sciences and Russian universities, conduct scientific research in about 100 scientific directions, and their joint results are partially presented in this monograph, including the following aspects: 1. Dominance structure and species richness in plant communities with different models of organization; 2. Sustainable development of mountainous and foothill regions of the Republic of Adygea; 3. Development of low-waste technologies for processing solid household waste in the Republic of Adygea;

Introduction

15

4. Population biology of rare and endangered plants of the Western Caucasus; 5. Study of the geoecology of the environment of the North-West Caucasus and specially protected natural areas; 6. Regional climate change in the Republic of Adygea; 7. Study of the level regime of the Krasnodar Reservoir based on satellite altimetry data, etc.

10

Tourism

Adygea seems to be created for tourists [8]. The magnificent foothills of the Caucasus, the beautiful Lago-Naki Plateau, an abundance of karst caves, snowy mountain peaks, lush alpine meadows, wide steppes, age-old forests, mountain rivers with waterfalls, quiet lakes – this is what an admiring traveler will see who visited Adygea. A favorable combination of unique landscapes (Fig. 11), climate, mineral springs, flora and fauna, exotic natural zones, reserves – all this creates unique conditions for active recreation and treatment [9, 10]. The Lago-Naki Plateau has been attracting skiers and tourists for over a quarter of a century. The snow cover remains here until June. The special terrain on the left bank of the Kurdzhips River makes it possible to create here more than 20 ski tracks, jumps, toboggan runs, and skating rinks. The upper reaches of the Belaya River have long attracted lovers of water travel. Water tourists began to explore this rapid river in the 1970s. The river attracts both fans of rafting of the highest category of difficulty and beginners (Fig. 12). Adygea has long been famous for its picturesque nature, healing mineral springs, kind and hospitable people. It is not surprising that its territory is so rich in

Fig. 11 The mountainous Adygea (photo by S.A. Trepet)

16

M. K. Bedanokov et al.

Fig. 12 Rafting competitions on Belaya River (photo by S.A. Trepet)

sanatoriums, health resorts, dispensaries, and tourist centers, where over many decades millions of people have regained their health, restored physical and spiritual strength.

11

Conclusions

In the last decade, the problems of the state and preservation of non-renewable resources, global climate change, and ecology have acquired particular relevance. All these and other issues of interaction with the biosphere, wildlife were discussed during the International Scientific and Practical Seminar “Environmental Problems of Our Time” organized and conducted by the Faculty of Engineering at the Maykop State Technological University (MSTU). The result of the seminar was the development of further joint scientific research and the signing of agreements on cooperation between MSTU and other scientific and educational organizations, such as M.V. Lomonosov Moscow State University, A.M. Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences, P.P. Shirshov Institute of Oceanology RAS, Space Research Institute RAS, Geophysical Center RAS, Tver State University, Institute of Ecology of Academy of Sciences of Abkhazia, Abkhaz State University, Institute of Marine Biology (Montenegro). When summarizing the results of the seminar, the participants initiated a regular International Scientific and Practical Conference “Fundamental and Applied Aspects of Geology, Geophysics and Geoecology Using Modern Information Technologies” which is organized and held at MSTU every two years. This collective monograph has become a scientific generalization of the most relevant and effective research, tested at the

Introduction

17

discussion platforms of the Maykop Conference over the past decade. The authors of the chapters are mainly scientists from the Maykop State Technological University and colleagues from the collaborative scientific and educational organizations. From the moment of its formation to the present time, the leadership of the Maykop State Technological University pays great attention to fundamental and applied research carried out by the scientists of the University. The organizer and inspirer of this book project was the Maykop State Technological University with the support of the Head of the Republic of Adygea. The team of authors of the monograph hopes that these studies will arouse interest among scientists, talented youth, and the public in Russia and abroad to the problems of studying geology, geophysics, and geoecology in the context of global climate change. The study of these problems, their solution using classical, remote sensing, information, and other innovative methods contributes to the establishment of new contacts for cooperation, the promotion of research, and the dissemination of advanced knowledge in the scientific, educational, and business environment. We also hope that this book will introduce the Republic of Adygea to western readers who are not familiar with the Republic and its beautiful landscapes, its history, and Adygea people. Acknowledgements M.K. Bedanokov was partially supported by the State Project No. 5.9533yu2017/BCh of Maykop State Technological University, approved by the Ministry of Education and Science of Russia, “Study of the Geoecology of the Environment of the North-West Caucasus and Specially Protected Natural Territories.” S.A. Lebedev was supported in the framework of the Geophysical Center RAS budgetary financing, adopted by the Ministry of Science and Higher Education of the Russian Federation. A.G. Kostianoy was partially supported in the framework of the P.P. Shirshov Institute of Oceanology RAS budgetary financing (Project No. 0128-2021-0016). The authors are very grateful to S.A. Trepet for a permission to publish a set of his splendid photos of Adygea.

References 1. SpringerLink (2020). https://link.springer.com/search?query¼Adygea#close. Accessed 28 Apr 2020 2. Buzarov AS, Varshanina TP, Kabayan NV, Krasnopolskiy AV, Krasnopolskaya NV, Kuasheva DA, Melnikova TN, Spesivtsev PA, Khachegogu AY, Shebzukhova EA (1995) Geography of the Republic of Adygea. Adyghe Republican Book Publishing House, Maykop, p 168. (in Russian) 3. The Atlas of the Republic of Adygea (2005) Publishing House “Lev Tolstoy”, Maykop, 79 p. (in Russian) 4. Kovaleva N, Shmidt P, Kovalev V, Butorin A (1998) World natural heritage nomination. UNESCO, Western Caucasus, p 120 5. Bibin AR, Trepet SA, Grabenko EA, Akatova TV (2020) Shaposhnikov Caucasian state wildlife biosphere reserve. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10.1007/698_2020_636 6. Regions of Russia (2018) The main characteristics of the constituent entities of the Russian Federation. Statistical Digest. Rosstat, Moscow, 751 p. (in Russian)

18

M. K. Bedanokov et al.

7. Gisheva SS., Baibekova RA (2014) Economic development of the Adyghe Republic: priorities and guidelines. Bulletin of the Adygea State University. Series 5: Economics 1(138) 8. Recreational Resources of Adygea (1999) Sheujen AK (eds) RIPO “Adygea”, Maykop, 272 p. (in Russian) 9. Bedanokov MK, Chich SK, Chetyz DY, Trepet SA, Lebedev SA, Kostianoy AG (2020) Physicogeographical characteristics of the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi. org/10.1007/698_2020_637 10. Bormotov IV (2009) Mountain Adygea. Polygraph Publishing House, Novosibirsk, p 96. (in Russian)

Physicogeographical Characteristics of the Republic of Adygea Murat K. Bedanokov, Saida K. Chich, Dana Yu. Chetyz, Sergey A. Trepet, Sergey A. Lebedev, and Andrey G. Kostianoy

Contents 1 2 3 4 5 6 7 8 9

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Relief . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Water Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mineral Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geothermal Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Land Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Forest Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Specially Protected Natural Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

20 21 26 26 30 31 32 35 36

M. K. Bedanokov (*) and D. Y. Chetyz Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected]; [email protected] S. K. Chich Federal Budget Health Institution “Hygienic and Epidemiological Center in the Republic of Adygea”, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] S. A. Trepet Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation Kh.G. Shaposhnikov Caucasian State Natural Biosphere Reserve, Sochi, Russian Federation e-mail: [email protected] S. A. Lebedev Geophysical Center, Russian Academy of Sciences, Moscow, Russian Federation Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] A. G. Kostianoy P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russian Federation S.Yu. Witte Moscow University, Moscow, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 19–56, DOI 10.1007/698_2020_637, © Springer Nature Switzerland AG 2020, Published online: 18 July 2020

19

20

M. K. Bedanokov et al.

10 Flora and Fauna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Recreational Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 38 52 53

Abstract The chapter presents the physical and geographical conditions of the Republic of Adygea. Brief information is given on the relief, climate, water, geothermal, land, forest, and mineral resources. Specially protected natural areas, as well as the main species of flora and fauna, are listed. Particular attention is paid to the recreational resources of the Republic, which have great potential. The main problems of environmental pollution are discussed, which, on the one hand, are a consequence of the development of industry and agriculture and, on the other hand, can affect the development of tourism in the Republic. Keywords Climate, Environmental issues, Natural resources, Physical and geographical conditions, Republic of Adygea, Specially protected natural areas, Topography, Tourism

1 Introduction The Republic of Adygea is a region of the Russian Federation located in the central part of the Northwest Caucasus. The Republic belongs to the Southern Federal District. The territory of the Republic is surrounded on all sides by the territory of Krasnodar Krai (Fig. 1). The territory of Adygea occupies the central part of the plain between the Laba River and Afips River and a part of the northern slope of the Greater Caucasus, located in the Belaya River Basin. In the north and northeast, the territory of the Republic is limited by the Kuban River and its tributary, the Laba River, and in the south by the Greater Caucasus Ridge. The area of the Republic is 7,790 km2. The length of its territory from north to south is 208 km (from 45
130 to 43
460 N) and from west to east 165 km (from 38
410 to 40
60 E). The length of the borders is more than 900 km; one third passes through water bodies, along the Kuban River, the Krasnodar Reservoir, the Laba River, and the Belaya River [1, 2]. In this chapter, we briefly present the physical and geographical conditions of the Republic of Adygea: relief, climate, water, geothermal, land, forest, and mineral resources. We provide information on specially protected natural areas, as well as on the main species of flora and fauna. Particular attention is paid to the recreational resources of the Republic, which have great potential. The main problems of environmental pollution are discussed, which, on the one hand, are a consequence of the development of industry and agriculture and, on the other hand, can affect the development of tourism in the Republic.

Physicogeographical Characteristics of the Republic of Adygea

b Ku

Scheme of Greater Caucasus Mountain Ranges

21

an

Ku ba

Krasnodar

n

45°

T

n ra

ai

Pl

r da sno oir Kra serv Re

s

-

Ku

ba

n

n

Natyrbovo Khanskaya

Kuzhorskaya

Labinsk

P 3200

as

tu

Maykop re

R

Apsheronsk

id

2700

2200

1700

Tuapse

44°

1200

Ro R i d g e ck P a s t u r eKamennomostsky La y R te id R i d g e ra ge R o c k y l R id ge G Hamyshki F re ro a nt te Lago-Naki Ra Plateau r Guzeripl ng e L a t e r a l R i d g i

d

g

a Se

e

200 m

Wooded Ridge

R

k ac Bl

700

Tul'sky

ge

39°

e

40°

Fig. 1 The scheme of the Greater Caucasus Mountain Ranges on the territory of the Republic of Adygea delimited by a red line

2 Relief The Republic of Adygea is located on the plains, in the foothills, and in the mountains of the Greater Caucasus. By the relief, Adygea can be divided into three parts: (1) flat, from the latitudinal course of the Kuban River up to the latitude of Khanskaya Stanitsa (a village inside a Cossack host) to Kuzhorskaya Stanitsa to Natyrbovo Village; (2) piedmont, up to the latitude of Kamennomostsky (old name Khadzhokh) Village (up to the Rocky Ridge); and (3) mountainous, to the southern borders of the Republic [1–3] (Fig. 1). The southern part of the Azov-Kuban Plain is called Trans-Kuban Plain, and it is located in a piedmont subsidence and is a lowland that gradually passes into a

22

M. K. Bedanokov et al.

Mountainous Part of the Republic of Adygea

R i d g e

e t u r P a s Kamennomostsky

Gu

Dakhovskaya

Ri au -T ish

F

Az

Khamyshki

r

t R

Lago-Naki Plateau

mt . Pshekish

mt. Oshten

mt. Pshekha-Su

i d

Guzeripl

L

mt. Fisht

a

G

1200

n

mt. Abadzesh

2200

1700

o

Murz Plate ikao au

Nagoy-Chuk Ridge

44° 2700

R i d g e

y

dg e

i ak -N go La ge Rid

'e g or r no Che lateau P 3200

k oR icd g e R a am

r

e

a

t

e

r

mt . Abago

t

mt. Tybga

e

700

R

200 m

Caucasian State Wildlife Biosphere Reserve Reserve

i

d

a

l

g

e

mt. Acheshbok

R

i d

g

e

mt . Chugush

g e

40°

Fig. 2 The mountain part of the Republic of Adygea [5] (with permission from Ecologica Montenegrina)

piedmont high plain. The piedmont zone extends from the Maykop City, lying at an altitude of 230 m above the sea level, up to about Kamennomostsky Village. There are low (up to 300–500 m) sloping ridges, dissected by wide river valleys. Some peaks reach up to 700–900 m (Fig. 1) [1]. The mountainous part of the Republic of Adygea is presented by the system of the Greater, the Front, the Lateral, and the Rocky Ridges of the Greater Caucasus (Figs. 1 and 2). The Skalisty (Rocky) Ridge has a characteristic sharply asymmetric appearance throughout. Its northern slope is gentle and long, while the southern

Physicogeographical Characteristics of the Republic of Adygea

23

slope is steep and short. The top of the ridge is composed of limestones and dolomites, while the lower part is shale clay and sandstone. In its relief, mid-mountain and low-mountain structures are represented, and the following karst forms are widespread: funnels, caves, wells, and mines. The most famous Shidekh Mountain (1,105 m) is located near the village of Dakhovskaya (Dakhovskaya Stanitsa) [4]. The Front Range is located to the north of the Greater Caucasus Range and stretches in the southeastern direction outside Adygea. The width of the Front Range varies between 5 and 15 km. Its length is more than 100 km. At the same time, it has soft and flat topography. The highest point of the Front Range within the Republic of Adygea is Acheshboki Mountain (2,486 m) [1]. Between the Greater Caucasus Ridge and the Front Ridge lies the Lateral Ridge. On the territory of Adygea, it has Pshekish Mountain (2,242 m) and Abago Mountains (2,689 m), which are located in the Caucasus State Wildlife Biosphere Reserve (Fig. 2) [4, 5]. South of Maykop City is the low-mountain Wooded Ridge. The maximum height of the Wooded Ridge in Adygea is 683 m, the most famous being Shakhan Mountain [1]. The Pastur Ridge, located south of the Wooded Ridge and north of the Rocky (Skalisty) Ridge, is a low mountain composed of limestones, schists, and sandstones of the Cretaceous period, with more steep southern and more gentle northern slopes. The significant peak of the Pastur Ridge is Fiziabgo Mountain (992 m) near the village of Pobeda. The relief of the foothills enhances the spatial differentiation of microclimatic conditions [4]. The Greater Caucasian Ridge restricts the territory of the Republic from the south and consists of a system of echelon ridges with different absolute altitudes of 3–25 km in width. The main peaks of the Greater Caucasian Ridge within Adygea are Chugush Mountain (3,238 m) (Fig. 3) and Tybga Mountain (3,064 m) (Fig. 4). These are the highest mountains of the Republic of Adygea; they lie on the territory of the Caucasian State Wildlife Biosphere Reserve [4, 5]. Lago-Naki Plateau (Fig. 5) with the average height of 2,000 m occupies the most part of the mountains of Adygea (Fig. 2). It stretches from the north to the south and from the west to the east for more than 40–45 km and includes the Murzikao Ridge, which comprises Abadzesh Mountain (2,287 m), Kamennoye More (Stone Sea) Ridge, and Nagoy-Chuk Ridge. The mountain group of Fisht Mountain (2,867 m) is a center of the mountainous part of Lago-Naki Plateau which is a part of the Greater Caucasus Range. In the west, Fisht Mountain joins the Pshekha-Su Mountain mass (2,743 m). To the north of Pshekha-Su Mountain, there is the Oshten Mountain mass (2,804 m) (Fig. 6). Major orographic elements located outside the plateau are LagoNaki Ridge, Azish-Tau Ridge, and Chernogor’e (Black Mountain) Plateau [1, 5]. On the Lago-Naki Plateau, 125 karst mines and caves are known [4]. Of these, the deepest cave in Russia, the “Soaring Bird” Cave, is located on the southern array of Fisht Mountain. The total length of the cave is 1,290 m, depth 535 m, area 800 m2, and volume 16,000 m3. The entrance to the cave is located at an altitude of 2,350 m.

24

Fig. 3 Mountain Chugush (photo by S.A. Trepet)

Fig. 4 Mountain Tybga (photo by S.A. Trepet)

M. K. Bedanokov et al.

Physicogeographical Characteristics of the Republic of Adygea

25

Fig. 5 Lago-Naki Plateau (photo by S.A. Trepet)

Fig. 6 Fisht-Oshten Mountain Range (photo by S.A. Trepet)

Fig. 7 Bolshaya (Big) Azishskaya Cave (photo by S.A. Trepet)

On the Azish-Tau Ridge, two karst caves, Bolshaya (Big) Azishskaya Cave (Fig. 7) and Nezhnaya (Tender) Cave, are located. The entrance to the Bolshaya (Big) Azishskaya Cave is located at an altitude of 1,520 m. It has a length of 690 m

26

M. K. Bedanokov et al.

and a volume of more than 11,000 m3. In 1987, 220 m of the cave were equipped for sightseeing. The entrance to the Nezhnaya (Tender) Cave is located on the eastern slope of the Azish-Tau Ridge, near the headwaters of the Mezmay River, at an altitude of 1,400 m above sea level. The dimensions of the cave cavity are as follows: length, 95 m; depth. 7 m; and volume, 530 m3. For visitors, the cave became available only in 1998 [4].

3 Climate The climate in the Republic of Adygea is diverse: in the northern lowland part, it is moderately continental; in the foothills, it is moderately warm humid; and in the mountains, it has a cold climate of high mountains [6]. The total solar radiation entering the territory of the Republic is 4,830–4,914 kJ/cm2 (Fig. 8a). The duration of sunshine reaches 2,200–2,400 h/year. The warm period lasts 9–10 months. A combination of excess heat with a relative lack of moisture is characteristic of the lowland and foothill parts, and in the mountainous part, humidification is excessive. The average air temperature in the plain varies from +24.5
C in summer to 2.4
C in winter and in the mountains from +14
C in summer to 3.8
C in winter (Fig. 8b, c). The average annual rainfall on the plain is 650 mm and in the mountains 1,200 mm (Fig. 8d) [6]. In Maykop City, the climate is mild and humid. The average January temperature is 1.6
C and in July +22.2
C. Precipitation is about 700 mm per year (the largest amount is from April to November). The frost-free period is 180 days [1]. Mountains are a barrier protecting the northern part of the Republic from the Black Sea and significantly weakening its influence. The central and northwestern parts of the Republic are separated from the Black Sea by low mountain ranges; therefore, the influence of the sea is more pronounced in them. The system of ranges prevents the penetration of air masses from the east from the Central Caucasus into the mountain zone of the Republic, while the influence of air masses moving from the north is somewhat enhanced due to their stationation in front of the Greater Caucasus Ridge and exacerbation of fronts. Cold air masses flow into the plains of the Republic from the northeast, in particular, through the Armavir Corridor – the lowered space between the Stavropol Upland and the mountains of the Greater Caucasus [1, 6].

4 Water Resources The Republic of Adygea has large reserves of water resources, which are composed of rivers, lakes, reservoirs, glaciers, and groundwater. The hydrological conditions of the Republic are determined by the features of its relief, geological structure, and climate.

Physicogeographical Characteristics of the Republic of Adygea

27

Average Summer Air Temperature

Total Solar Radiation

Krasnodar

Krasnodar 45°

45° Labinsk

Labinsk

Maykop Apsheronsk

Maykop Apsheronsk

Tul'sky

Tuapse

1175

k

Tuapse Guzeripl

Se a

1200

1225

1250

Bl ac

44°

44°

Bl ac

Tul'sky

1275

10

k

12

Guzeripl

Se a

14

kWh/m2 per year

16

18

20

22

24

Temperature °C

39°

40°

39°

40°

(a)

(b)

Average Winter Air Temperature

Annual Precipitation

Krasnodar

Krasnodar 45°

45° Labinsk

Labinsk

Maykop

Maykop Apsheronsk

Apsheronsk

Tul'sky

Tuapse

Tuapse

k

Guzeripl

Se a

Bl ac k

44°

Bl ac

Tul'sky

Guzeripl

Se a

44° -7

-6

-5

-4

-3

-2

-1

500

0

600

700

800

1000

1200

1400

Precipitation, mm

Temperature °C 39°

(c)

40°

39°

(d)

40°

Fig. 8 Distribution of total solar radiation (kWh/m2 per year) (a), average summer air temperature (
C) (b), average winter air temperature (
C) (c), and annual precipitation (mm) (d) on the territory of the Republic of Adygea based on data from [2, 7]

The hydrographic network of the Republic of Adygea (Fig. 9) belongs to the basin of the Sea of Azov. The average total annual runoff of Adygea rivers is about 10 km3 [1]. The main rivers of Adygea are Laba River, Belaya River, Pshish River, Fars River, Psekups River, Pshekha River, Pshekhashkha River, and Kurdzhips River – all of them are left tributaries of the Kuban River and belong to the class of medium rivers (Table 1). Some rivers of the foothills and the Kuban River in the lower reaches form floodplains, occupying about 30,000 ha (out of 70,000 ha of floodplain lands). The Republic’s territory is crossed by 150 medium and small

28

M. K. Bedanokov et al.

Ku

Rivers and Reservoirs Network of the Republic of Adygea

ban

La b a

Ia

ba Ku

La b

Ul ka

a

afa

45°

n Pse

n

Krasnodar Ps

I

II

Apchas

ora

VI

Apsheronsk s hip dz

Gu

Tsitsa

bs

z’

Dakh

k Sa

h h is Ps

a kh he Ps

Belaya

r Ku

rs Fa

Tul'sky

y hra

Kho d

bs h

sh hi

ir ef

Sh e

Labinsk

VII

Ps

Ps

Ku zh

Maykop

he kh a

ps

Ps

u ek Ps

Khodz’

at

Afip

s

ba

Belorechensk

Chibiy

Sups

-Ub

La

Une

k

in

a hr ek Ch

ga

Ub

rs Fa

Gia

V

a lay

k itu tuk ChChe

IV

Be

rta Ma

s ip Af

III

hi sh

k ac Bl

Tuapse

Se IV V VI VII

Krasnodar Reservoir Tshchikskoye Reservoir Shapsugskoye Reservoir Oktyabr'skoye (Takhtamukayskoye) Reservoir Shendzhiyskoye Reservoir Cheytukskoye Reservoir Maykopskoye Reservoir Kuzhorskoye Reservoir

39°

Be lay a

I Ia II III

Kisha

a

44°

Guzeripl

Caucasian State Wildlife Biosphere Reserve

40°

Fig. 9 Map of the hydrographical network and main water reservoirs of the Republic of Adygea Table 1 Hydrological characteristics of main rivers [1, 2]

River Kuban Laba Belaya Pshish Fars Psekups Pshekha Kurdzhips

Watershed area, km2 57,900 12,500 5,990 1,850 1,450 1,430 2,090 780

Length, km 941 341 277 258 197 146 139 108

Physicogeographical Characteristics of the Republic of Adygea

29

Table 2 Morphometric characteristics of the main reservoirs of the Republic of Adygea [1, 2, 8]

Reservoir Krasnodar (including Tschikskoye) Tschikskoye (separately) Shapsugskoye Shendzhiyskoye Oktyabr’skoye (Takhtamukayskoye) Kuzhorskoye Cheytukskoye Maykopskoye

Morphometric characteristics Volume, Surface, Length, m3 km2 km 2,350 400 46.0

Width, km 9.0

232

76

16.0

5.0

1.0

130 22 15

46 7.8 9.4

9.0 4.0 4.0

8.0 3.6 3.0

3.5 4.0 2.5

1952 1965 1964

2.1 1.5 0.8

0.5 1.5 0.5

1.3 2.6 0.5

2.2 2.5 0.4

2.1 2.1 5.0

1965 1955 1950

Mean depth, m 12.0

Year of construction 1973

rivers flowing down from the Greater Caucasus Ridge and its spurs, 95% of which belong to small streams. The total length of the river network is 9,482 km [1]. Mountain rivers are characterized by high water velocities, lack of ice formation in winter, and low water temperatures in summer. In the central and northern parts of the Republic, rivers are characterized by high turbidity of the waters, which complicates their drinking water use. Most of the rivers of the Republic are fed by atmospheric precipitation by 90%. For Kuban River, Laba River, and Belaya River, the sources of which are located on the Greater Caucasus Ridge, high-altitude snow and glacier feeding is important. There are also more than 100 small lakes, 294 ponds, and 7 water reservoirs that have been created in Adygea to regulate the flow of the Kuban River: Krasnodar Reservoir, Shapsugskoye Reservoir, Shendzhiyskoye Reservoir, Oktyabr’skoye (Takhtamukayskoye) Reservoir, Kuzhorskoye Reservoir, Cheytukskoye Reservoir, and Maykopskoye Reservoir (Table 2) [1, 2, 8]. Lakes of Adygea are small: on the plains, the lakes are floodplain lakes and in the mountain tectonic, glacial-karst, and karst-suffosion lakes. Lakes of karst and glacial genesis are mainly concentrated on the territory of the Caucasian State Wildlife Biosphere Reserve. They are small (0.1–0.3 km2), the depths vary significantly, and the water is fresh and clear. On the Lago-Naki Plateau, karst and glacial-karst lakes are common. The largest glacial-karst lake is Lake Khuko (Fig. 10), located between the Pshekhashkha River and the Shakhe River at an altitude of 1,744 m. “Khuko” in Adyghe language means dolphin. This is due to the fact that from the top of the nearby mountain of the same name, the shape of the lake resembles the shape of a dolphin. The lake has an oval shape and stretches from the southeast to northwest. The banks of the lake are slightly indented. The height of the slopes surrounding the lake ranges from 5 to 100 m. The area of the lake is 27,500 m2 with a length of 260 m and a width of 150 m. Its maximum depth is 10 m. There are no rivers that inflow or outflow the lake, but the water level in it is constant throughout the year [4].

30

M. K. Bedanokov et al.

Fig. 10 Lake Khuko (photo by S.A. Trepet)

The mountain rivers of Adygea are characterized by significant hydropower resources. Currently, the hydropower resources of the Belaya River are used by the Maykop hydroelectric station. The capacity of the hydroelectric power station is 9.4 MW, and the average annual output is 48.4 million kWh [1]. Groundwater plays a significant role in the water balance. The territory of the Republic of Adygea belongs to two artesian basins: the groundwater basin of the Greater Caucasus and the Azov-Kuban Artesian Basin (in the northern part of the Republic). The total water withdrawal from underground sources in the Republic exceeds 50 million m3 per year [1].

5 Mineral Resources The territory of the Republic of Adygea is rich in the following types of mineral raw materials [1]: 1. Fuel and energy resources represented by hydrocarbon raw materials (natural gas, oil, and gas condensate). Two oil and gas condensate fields are currently in operation: Maykopskoye and Koshekhablskoye. 2. Ore minerals are represented by manifestations of nonferrous, ferrous, and noble metals with insignificant resources. These are manifestations of ore mineralization of molybdenum, tungsten, polymetals (lead, zinc, copper), and manganese ores of carbonate and oxidized types.

Physicogeographical Characteristics of the Republic of Adygea

31

3. Nonmetallic minerals occupy the largest share (about 70%) and are represented by a wide variety of species: – Industrial and mining raw materials (ceramic, brick, tiled, expanded clay, technological limestones for the sugar industry, gypsum for various purposes, glauconitic sandstones, phosphorites, zeolites, barite, quartz sand, and dolomites for the glass industry). – Building materials (building gypsum, building sands, boulder-gravel-sand mixtures, building limestones). – Facing and decorative and ornamental stones (red-colored, marbled, and dolomitic limestones, dolomites, gypsum and anhydrites, sandstones, granites, amphibolites, serpentinites, listvinites). 4. Mineral waters of various purposes and composition, healing clay. 5. Fresh groundwater for drinking and industrial purposes and industrial bottling. In the structure of the mineral resource base, the largest share of explored reserves is accounted for by nonmetallic minerals, which makes it possible to extract raw materials for the production of various building materials. The presence of mineral water reserves and healing clays allows to develop the direction of spa and sanatorium recovery and treatment.

6 Geothermal Resources The values of heat flows from the Earth’s interior in the Republic of Adygea range from 40 to 85 mW/m2. In the large central part of the territory, which belongs to the platform part of the West North Caucasus Region, these values reach 60–85 mW/m2. Only in the western part of the Republic, which is located within the East Kuban Depression, the values decrease to their minimum [7, 9, 10]. Increased values of deep heat flows induced by the specifics of the geological structure and development of the Republic of Adygea, combined with changes in the capacities of lithologic-stratigraphic complexes of different thermal conductivities, predetermined the high-temperature character of the subsurface of this territory. The temperature at a depth of 1,000 m varies from 40 to 60
C; at 2,000 m, from 70 to 100
C (Fig. 11); at 3,000 m, from 100 to 140
C; and at 5,000 m, from 140 to 175
C. On the West Kuban Trough territory and in the western regions of the Republic, there is a temperature decrease of 20–40
C in comparison with the central part of Adygea [7]. The deposits discovered so far and the manifestations of thermal waters in the drilled areas indicate that according to the most conservative estimates, almost 50% of the territory of the Republic of Adygea is promising for thermal waters. Hot water here, as a rule, is obtained by spontaneous flow and with a sufficient flow rate for practical use. Thus, the presence of geothermal resources in the territory allows creating and developing alternative energy facilities in the Republic.

32

M. K. Bedanokov et al.

Temperature Geothermal Waters at a Depth of 2000 m

Krasnodar 45° Labinsk

Maykop Apsheronsk

Tul'sky

Tuapse

44°

Bl ac k

65

70

Guzeripl

Se a

75

80

85

90

95

100

105

°С 39°

40°

Fig. 11 Distribution of temperature of geothermal water (
C) at a depth of 2,000 m in the Republic of Adygea [10]

7 Land Resources Most of the territory of the Republic of Adygea is occupied by agricultural land (42.8%) and forest land (30.6%). The lands of specially protected territories and objects account for 11.9%; the lands of settlements 6.2%; the lands of industry, transport, communications, and other nonagricultural purposes 2.1%; the lands of the water fund 6.2%; and the lands of stock 0.2% of the land fund of the Republic of Adygea (Table 3) (Fig. 12) [11]. In all municipalities of the Republic of Adygea, except for the Maykop District, agricultural lands prevail. More than 80% of agricultural land is located in the Giaginsky District (89.1%), the Shovgenovsky District (84.7%), and the

Municipality Giaginsky District Koshekhablsky District Krasnogvardeysky District Maykopsky District Takhtamukaysky District Teuchezhsky District Shovgenovsky District Maykop City Adygeysk Town Republic of Adygea

44,630

43,612

25,372

39,609

44,173

14,130 1,763 333,436

366,743

46,360

69,797

52,143

28,220 3,239 779,180

Agricultural lands 70,751 49,396

72,552

Total area of lands 79,530 60,596

8,708 1,022 48,088

3,512

3,387

8,827

8,149

4,450

Lands of settlements 5,732 4,301

1,197 394 16,076

273

1,778

1,528

8,085

911

Lands of industry, transport, and other purposes 1,289 621

Table 3 Distribution of land between municipalities of the Republic of Adygea (ha) [11]

0 0 92,920

0

37

1

92,872

0

Lands of specially protected areas 10 0

3,745 51 238,652

3,568

5,702

2,581

212,798

3,984

Forest lands 1,221 5,002

186 9 48,180

617

18,966

8,015

797

18,245

Lands of water fund 116 1,229

254 0 1,828

0

318

36

430

332

Lands of stock 411 47

Physicogeographical Characteristics of the Republic of Adygea 33

34

M. K. Bedanokov et al.

Forest land 30,6%

Agricultural land 42,8%

Lands of specially protected natural territories 11,9%

Water Fund Lands 6,2%

Industrial, transport, communication and other lands 2,1%

Reserve land 0,2%

Fig. 12 Distribution of the Republic of Adygea land fund by categories as of January 2017 (based on data [11]) Table 4 Territorial zoning of lands of the Republic of Adygea according to their suitability for use in agriculture [11] Zone 1 2 3 4 5 6

Territorial zones of the Republic “Especially valuable” “Medium and above average” “Below the average” “Low productive” “Unproductive” Under water

Grain equivalent value >57 44–57 31–44 3, can lead to excessive surface generalization [61]. It was shown in [62] that the use of third-order polynomials for a 5  5 window with a constant grid spacing h in x and y leads to local noise suppression and optimizes the calculation of partial derivatives sensitive to the high-frequency component of the signal. For this method, the root-mean-square errors of the calculation of second-order derivatives are almost five times less than for the Evans method. Moreover, the quadratic errors in the calculation of first-order derivatives are only 10% larger than in the Evans method [59]. However, the developed method provides a higher accuracy of calculating surface curvatures in comparison with the Evans method.

3 Digital Relief Model of the Republic of Adygea The Republic of Adygea is located on the plains, in the foothills and mountains of the Greater Caucasus. The northern part of the Republic is a lowland, gradually turning into a foothill elevated plain. A strip of foothills stretches from the City of Maykop and reaches approximately to the Village of Kamennomostky and is a low gentle slopes, divided by wide river valleys. The highland southern part of the

66

S. A. Lebedev et al. n ba Ku

Republic of Adygea Relief Map

n ba Ku

Krasnodar

Pl

45°

r da sno oir Kra serv Re

a Tr

ns

-

Ku

ba

ai

n

n

Natyrbovo Khanskaya

Kuzhorskaya

Labinsk

P 3200

as

tu

Maykop re

R

id

2700

Apsheronsk

2200

1700

Tuapse

44°

1200

Ro R i d g e ck P a s t u r eKamennomostsky La y R te id R i d g e ra ge R o c k y l R id ge G Hamyshki F re ro a nt te Lago-Naki Ra Plateau r Guzeripl ng e L a t e r a l R i d g i

d

g

a Se

e

200 m

Wooded Ridge

R

k ac Bl

700

Tul'sky

ge

39°

e

40°

Fig. 1 Republic of Adygea relief map based on SRTM 4.0 with spatial resolution 30 m

Republic of Adygea is represented by the system of the Main, Front, Lateral, and Rocky ridges of the Greater Caucasus (Fig. 1) [63–65]. The territory of the Republic of Adygea is characterized by the wide distribution of various genetic types of exogenous geological processes. In the northern part of the Republic, the processes of river lateral erosion, flooding, and processing of the shores of reservoirs were predominantly developed. In the central part of the Republic, river erosion, landslides, ravine, and planar erosion are widespread. In the southern mountainous part of the Republic, landslides, river erosion, landslidetalus processes, mudflows, karsts, kurums, and avalanches develop [66]. Exogenous geological processes (wind and water erosion; flooding, salinization, waterlogging, etc.) have a destructive effect on the soil cover of the Republic [67].

Digital Elevation Model of the Republic of Adygea

67

With such a variety of landforms and the development of exogenous geological processes, the creation of a DEM of the Republic’s territory integrated into the GIS [68] is a necessary condition for: – – – – – –

Geomorphological zoning and landscape science [69] Geological and soil erosion studies [70] Design of the construction of roads and highways [71] Construction of visibility zones for telecommunication and GSM companies Carrying out cadastral valuation of land and urban development Risk assessment of landslides and avalanches and many other tasks

3.1

Digital Elevation Data

The cartographic information bank in the GIS center of the Adygea State University (ASU) has electronic relief maps of the Republic of Adygea and surrounding territories with scales of 1:200,000 (spatial resolution 2 km) and 1:50,000 (500 m), traditionally used in regional analysis, and a set of maps of scale 1:25,000 (250 m) and 1:10,000 (100 m). Digitization was done using Easy Trace7.3 and Easy Trace Pro v.8.X. Electronic map bank was created in ArcGIS 9.1 environment. A total of 69 sheets of maps were digitized in accordance with accepted Russian standards [68]. At Maykop State Technological University (MSTU), SRTM 4.0 data with a spatial resolution of 90 and 30 m were selected as the altitude basis of the digital elevation model for the territory of the Republic of Adygea [21, 22] (Fig. 1). On the map in the northern part of the Republic, the lowlands are clearly visible, turning into a piedmont elevated plain: in the middle part, low gentle ridges, dissected by wide river valleys, and in the southern part, the system of the Main, Forward, Lateral, and Rocky ridges of the Greater Caucasus. In accordance with this, the territory of the Republic can be conditionally divided into three parts (Fig. 2): I. The flat part with the relief height less than 150 m II. The foothill part with the relief height in the range of 150–400 m III. The mountain part with the relief height more than 400 m As a result of this division, approximately equal parts of the territory were obtained. Part I occupies 3,062.4 km2 or 39.31% of the Republic’s area, part II 2,075.8 km2 or 26.65%, and part III 2,651.8 km2 or 34.04%. This separation is necessary for the analysis of topography slopes in the following analysis below. A digital elevation model with 30 m resolution gives a clear picture of the LagoNaki Plateau (Fig. 3). The location of the mountains is clearly visible: Fisht (1), Pshekha-Su (2), Oshten (3), Guzeripl (4), Abadzesh (5), Nagoy-Chuk (6), Messo (7), Uriel (8), Blyam (9), Zub (10), and Nagay-Koshki (11). A comparison of the heights

68

S. A. Lebedev et al.

The Republic of Adygea Territory Division According to Local Relief

Ku

ba

n

Ku ba

Krasnodar

n

45°

r da sno oir Kra serv e R

Natyrbovo Khanskaya

Kuzhorskaya

Labinsk

Maykop Apsheronsk

Tul'sky

Kamennomostsky

Tuapse Hamyshki

k ac Bl

44°

Guzeripl

a Se

I

II 39°

III 40°

Fig. 2 The Republic of Adygea territory division according to local relief: I the flat part with the relief height < 150 m, II the foothill part with the relief height 150–400 m, III the mountain part with the relief height > 400 m

of these mountains from different topographic maps (Table 1) shows that the average difference with the main altitude points in the digital model SRTM 4.0 with a spatial resolution of 90 and 30 m does not exceed 1 m, and the standard deviation is 0.84 m and 1.08 m, respectively. At the same time, the SRTM 4.0 heights with a spatial resolution of 30 m exceed the heights with a spatial resolution of 90 m by an average of 1.08 m, and the standard deviation between these models is 1.83 m.

Digital Elevation Model of the Republic of Adygea

69

8

7

2700 m

6 5 11

2600 m 2500 m 9

44°

2400 m 3 2

2300 m

4

2200 m 10

2100 m 1

2000 m 1900 m

40°

Fig. 3 The relief of Lago-Naki Plateau based on SRTM 4.0 with spatial resolution 30 m. Isohypses (every 100 m) outside the plateau region are shown in gray. 1 Mount Fisht, 2 Mount Pshekha-Su, 3 Mount Oshten, 4 Mount Guzeripl, 5 Mount Abadzesh, 6 Mount Nagoy-Chuk, 7 Mount Messo, 8 Mount Uriel, 9 Mount Blyam, 10 Mount Zub, 11 Mount Nagay-Koshki

3.2

Slope Gradient

Slope gradient (G) is the angle between the horizontal and tangential planes at a given point on the topographic surface: 0sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1
2
2 ∂H ∂H A : G ¼ arctg@ þ ∂x ∂y

ð18Þ

The slope gradient is a nonnegative value, varying from 0 to 90 . The unit is degree. The slope gradient determines the speed of flows moving along the earth’s

NN 1 2 3 4 5 6 7 8 9 10 11

Map scale Spatial resolution Fisht Pshekha-Su Oshten Guzeripl Abadzesh Nagoy-Chuk Messo Uriel Blyam Zub Nagay-Koshki

2,867 2,743 2,804 – 2,287 – – – – – –

Source Atlas of the Republic of Adygea 1:650,000 2,867 – 2,804 – – 2,467 – – – – –

2,867 – 2,804 – – 2,467 – – – – 2,090

Topographic maps 1: 1000,000 1:500,000

Table 1 The heights of the main mountains on the Lago-Naki Plateau according to various sources

2,867 2,743 2,804 – – 2,467 2066 – – – –

1:200,000

2,867.7 2,743.9 2,804.0 2,158.0 2,369.0 2,467.1 2,065.5 2,168.0 2,378.0 – 2,094.0

1:100,000

DEM SRTM 4.0 90 m 30 m 2,868 2,868 2,743 2,744 2,803 2,805 2,156 2,161 2,368 2,370 2,467 2,468 2065 2066 2,169 2,169 2,378 2,378 2,393 2,391 2094 2097

70 S. A. Lebedev et al.

Digital Elevation Model of the Republic of Adygea

71

surface under the influence of gravity. This is a fundamental geomorphometric parameter, which is naturally associated with the following processes and landscape characteristics [45, 46]: 1. Surface runoff and drainage – the steeper the slope, the more intense the surface runoff and less moisture infiltration into the soil stratum. Thus, the slope is of fundamental importance for the regime of moistening soils, especially of the upper layers. 2. Erosion – the intensity of erosion increases exponentially with an increase in slope. This is due to the fact that with an increase in the gradient, the kinetic energy of precipitation remains constant, but transport accelerates in the direction of the foot. As a result, the kinetic energy of the runoff exceeds the kinetic energy of precipitation when the slope passes the 8.5 mark, which contributes to the manifestation of erosion processes. 3. The thickness of the soil profile on the slope naturally changes in accordance with the slope and relative height. As a rule, the soil stratum is smaller in elevated inclined areas due to erosion processes and gravitational movement of the material and gradually increases in the direction of lowered areas with a slight slope. 4. The amount of solar energy also depends on the slope, since it determines the angle of incidence of sunlight on the earth’s surface. An increase in the slope of the surface in the direction of the arrival of sunlight increases the angle of their incidence, which means the amount of energy that the surface receives. This determines the microclimatic features of the site, in particular the temperature, evapotranspiration, and humidity of the upper layers of the soil. 5. The features of the vegetation cover collectively reflect all of the above characteristics, since they directly or indirectly affect such edaphic factors as the water and temperature conditions of the soil, the mechanical composition of the root layer, the content of nutrients, etc. The simplicity of calculation (formula 18) and the information content make the slope gradient the most used indicator in modeling the processes of redistribution of surface and subsurface runoff, erosion, determination of edaphic conditions, indicative mapping in physical geography, and related fields of science. As a rule, indicator values are measured in degrees (it can also be percent or radians) and range from 0 (horizontal plane) to 90 (vertical plane). Figure 4 shows a map of the slopes of the relief of the Republic of Adygea and adjacent territories, calculated using the SRTM 4.0 (30 m) high-altitude basis in the approximation of the topographic surface by a second-order polynomial (formula 10) according to the Evans method [39] taking into account Eqs. (14), (15), (18). As already noted in [57, 58], the Evans method has a higher accuracy compared to the Zevenbergen-Thorne method [48] (Eqs. 1–9) and smooths out noise in the highaltitude DED basis. Complex physical-geographical and landscape studies often use gradations of the slope gradients presented in Tables 2 and 3 [72]. The results of the analysis of the slope gradients of the flat, foothill, and mountain parts of the Republic of Adygea

72

S. A. Lebedev et al.

n ba Ku

Slope Gradient of the Republic of Adygea Relief

Lab a

1

Krasnodar

rs

45°

Be

r da sno oir Kra serv e R

3

afa

Fa

n ba Ku

Ps en

a

ba La

lay

Natyrbovo

Ps ek up

s

s

Pshish

Afip

2

Khanskaya

Kuzhorskaya

Labinsk

a Lab

Apsheronsk

Ku rdz hip s

Pshekha

60°

Maykop Tul'sky

Kamennomostsky

' Khodz

Belaya

50°

40°

30°

Tuapse Hamyshki

44°

20°

k ac Bl

10°

Guzeripl

a Se

0°

39°

40°

Fig. 4 Slope gradient of the Republic of Adygea relief based on SRTM 4.0 with spatial resolution 30 m. 1 Tshchikskoye Reservoir, 2 Shendzhiyskoye Reservoir, 3 Shapsugskoye Reservoir Table 2 Gradation of the slope gradients for the flat part of the Republic of Adygea

Slope gradient 40

Gradation of the slope gradients Flat (sub-horizontal) plains Little sloping plains (very gentle slopes) Sloping plains (gentle slopes) Little sloping slopes Sloping slopes Strong sloping slopes Steep slopes Very steep slopes Cliff slopes

Digital Elevation Model of the Republic of Adygea Table 3 Gradation of the slope gradients for the mountain part of the Republic of Adygea

73

Slope gradient 60

a)

Gradation of slope gradient Flat and almost flat surfaces Gentle slopes Downhill slopes Slopes of medium steepness Steep slopes Very steep slopes Rocky (cliff) slopes

b)

1-3° 45,99%

1-3° 52,02%

3-5° 17,13%

0-1° 17,96%

0-1° 36,92%

7-10° 6,52%

3-5° 9,15%

20-40° 0,27% 10-15° 0,11%

7-10° 0,43%

c)

15-20° 0,74%

5-7° 7,64%

10-15° 3,74%

5-7° 1,37%

10-20° 30,81% 4-10° 24,33% 20-30° 19,67%

0-4° 9,70%

> 60° 0,95%

30-45° 13,56%

45-60° 0,98%

Fig. 5 Slope gradient of the Republic of Adygea relief based on SRTM 4.0 with spatial resolution 30 m according to local relief: (a) 400 m which corresponds to parts I, II, and III on Fig. 2

(Fig. 2) are presented in Fig. 5 in accordance with the gradation according to the data of Tables 2 and 3.

4 Use of Digital Relief Model DEM acts as a significant source of information in assessing the spatial coverage and development potential of agriculture, hazardous exogenous processes, and the construction of highways and other areas of social and infrastructural development of

74

S. A. Lebedev et al.

the region. Below are the results of the first stage of the analysis of slope gradient data in use in various fields.

4.1

Agriculture

An important aspect when placing crops is the slope gradients in terms of land use in agriculture [73]. 88.94% of the total area of the flat part of the territory of the Republic (part I in Fig. 2 – the relief height is less than 150 m) is a flat and slightly inclined plain and has a slope gradient of no more than 3 (Fig. 5a). 9.15% of the flat part of the Republic have the slope gradient of 3–5 , which corresponds to gradation of inclined plains (gentle slopes) (Table 2). This makes almost 98% of this territory suitable for growing most crops. Slope gradients less than 3 has 63.55% of the foothill part of the territory of the Republic of Adygea (part II in Fig. 2 – the relief height lies in the range of 150–400 m), i.e., it is a flat and slightly inclined plain (Fig. 5b). Another 17.13% of the territory have slope gradients of 3–5 and are inclined plains (gentle slopes) (Table 2). Thus, almost 80% of the foothill part of the territory of the Republic of Adygea is suitable for growing most crops. The lands with slope gradients of 5–7 (7.64%) are not very suitable for cultivating row crops (cabbage, potatoes, beets), but are quite suitable for grain crops. Lands with slope gradients of 7–10 make up 6.52% (Fig. 5b) of the total area of this part of the Republic and are suitable only for grass planting. In the mountainous part of the territory of the Republic of Adygea (part III in Fig. 2 – the relief height is more than 400 m), the slope gradients less than 4 have 9.70% of the total area of the territory of this part of the Republic, i.e., it is flat and almost flat surfaces (Fig. 5c). This is mainly the area along the Belaya River in the Dakhovskaya Stanitsa (large Cossack Village) area, located between Kamennomostsky Village and Khamyshki Village (Fig. 4) at an altitude of 480 m. All other areas are much steep and are not suitable for agriculture.

4.2

Analysis of the Hydrological Network

The calculated slope gradient (Fig. 4) is in excellent agreement with the hydrological network of rivers and reservoirs located in the Republic of Adygea and the Krasnodar Territory [65]. The banks of the large Belaya River (below the confluence of the Kurdzhips River) and Laba River have a slope of 1–3 , and the banks of the Pshish River have a slope of 3–5 (Fig. 4). An analysis of the variability of the relief of the Belaya River channel according to SRTM 4.0 with spatial resolution of 30 m (Fig. 6a) shows that the elevation difference from the river mouth to Guzeripl Village is 672 m. There are eight sections along 200 km from the river mouth, where the relief gradient along the channel increases by more than 4 (Fig. 6b). These sites

900

1

2

75 3

4

5

6

7

300

600

a)

0

Elevation along Riverbed(m)

Digital Elevation Model of the Republic of Adygea

60

80 100 120 Distance (km)

140

160

180

200

20

40

60

80 100 120 Distance (km)

140

160

180

200

140

160

180

200

6

40

2

4

b)

40

0

Lateral Slope Gradient on Left-side

30

c)

10

20

Lateral Slope Gradient on Right-side

0

Lateral Slope Gradient along Riverbed (degree)

20

0

Slope Gradient along Riverbed (degree)

0

0

20

40

60

80 100 120 Distance (km)

Fig. 6 Variability of elevation (a), elevation gradient (b), and lateral slope gradient (c) along Belaya River from the mouth to the riverhead based on SRTM 4.0 with spatial resolution 30 m. 1 the confluence of the Pshekha River into the Belaya River (64 km from the river mouth), 2 Khanskaya Stanitsa (82 km), 3 Maykop Hydropower Station (102 km), 4 Nature Monument “Khadzhokhskaya Gorge” (145 km), 5–6 Belaya River Canyon (158–165 km), 7 Guzeripl Village (182 km)

include Maykop Hydropower Station (102 km), Nature Monument “Khadzhokhskaya Gorge” (145 km), and Belaya River Canyon (158–165 km). Upstream from Guzeripl Village (182 km), where the river is exclusively mountainous, more than three such sections are observed (Fig. 6b). DEM is an effective tool for planning of construction of new water reservoirs, which was shown for the Karashor Depression in Turkmenistan which is planned to fill with drainage water and to build the Altyn Asyr Lake [74].

76

4.3

S. A. Lebedev et al.

Exogenous Processes

Exogenous processes, to one degree or another, complicate land development and often take large areas out of use. The influence of the slope gradients extends to the conditions for the development of soil erosion. Soil erosion at a slope gradient of up to 1 is practically absent, and at 3–4 it is already significant. Based on the analysis presented in Fig. 5, it can be said that 88.94% in the flat part and 63.95% in the foothill part of the Republic are not affected by soil erosion. Landslides, in addition to a significant height and steepness of the slopes, are active in the presence of water-resistant horizons of rocks, moisture, washing out the bases with a river. In the conditions of loose rocks, abundant concentrated runoff, and weakly fixed surface, slopes of 3–5 are sufficient for landslides. All these conditions correspond to the banks of the Belaya, Kurdzhips rivers, and their tributaries in the foothills and mountains. Landslide processes are most active on the Belaya River bed section from Khanskaya Stanitsa (82 km from the river mouth) to Tul’sky Village (113 km). The average steepness of the slopes of the left banks of the river is 3.5  1.2 (Fig. 6c), and they are composed of Maykop clay, which is characterized by the ability to significantly swell with excessive moisture and to large volumetric shrinkage upon drying [75]. The slope along the river bed changes sharply from 5.58 to 0.1 , i.e., here the river changes its character and passes from the mountainous part to the foothill part of the Republic (Fig. 6). Upstream of the Belaya River beyond Guzeripl Village (182 km from the river mouth), where the average steepness of the coastal slopes exceeds 17 , mudflows can be observed on treeless coastal sections. The avalanches occur when the thickness of the snow cover is more than 0.3 m, lying on an inclined surface with a steepness of slopes of 25–50 [66]. The last condition corresponds to 23.64% of the total mountainous area of the Republic of Adygea (Fig. 5c). However, taking into account the forest cover and the frequency of heavy snowfall, areas where snow avalanches are likely to fall are located in the southern part of the Republic’s mountainous regions, east of Lago-Naki Plateau [66].

4.4

Planning of Auto Roads

The problem of designing optimal routes for land transport routes, the most important of which are roads, is relevant. The basis for optimizing the design of the road route and providing 3D control of construction equipment is a digital terrain model. It is nothing more than a digital copy of the terrain drawings habitual for developers and builders obtained as a result of engineering and geodetic surveys on the territory of the road construction. Let’s consider the Maykop City-Sochi City road project (1988) through the LagoNaki Plateau (Fig. 7). After the Lago-Naki Plateau became part of the Caucasian State Wildlife Biosphere Reserve in accordance with the Decree of the Government

Digital Elevation Model of the Republic of Adygea

77

8 12

7

6

60°

5 11

45°

13

14 9

44°

30° 3 2

20° 4

10°

10

1

4°

0°

15

40°

Fig. 7 Slope gradient at the Lago-Naki Plateau based on SRTM 4.0 with spatial resolution 30 m. Isohypses (every 100 m) outside the plateau region are shown in gray. 1 Mount Fisht, 2 Mount Pshekha-Su, 3 Mount Oshten, 4 Mount Guzeripl, 5 Mount Abadzesh, 6 Mount Nagoy-Chuk, 7 Mount Messo, 8 Mount Uriel, 9 Mount Blyam, 10 Mount Zub, 11 Mount Nagay-Koshki, 12 Azishsky Pass, 13 Oshtenovsky Pass, 14 Maykopsky Pass, 15 Cherkessky Pass

of the Republic of Adygea dated August 13, 1992, No. 234 “On the transfer of the Lago-Naki Highland Pasture to the Caucasian State Wildlife Biosphere Reserve,” this project was rejected. Below we discuss the landscape characteristics of the 1988 road project. From the Azishsky Pass (2.75 km from the start of the track), the road runs along the Kamennoye More (Stone Sea) Ridge and goes around Nagay-Koshki Mountain (9.6 km). Further, through the Oshtenovsky Pass (13.9 km) and the upper Tsitsa River, head for Maykopsky Pass (26.5 km). Further, the route follows the western slopes of Pshekha-Su, Zub, and Fisht Mountains to Cherkessky Pass (32.6 km). The elevation difference on this route was more than 500 m (Fig. 8a). The maximum altitude of 2,122 m is located on the Oshtenovsky Pass, and the minimum is 1,612 m

S. A. Lebedev et al. 12

1600 1800 2000 2200

Elevation along the Road (m)

78 14

15

5 10 15 20 25

5

10

15 20 Distance (km)

25

30

35

5

10

15 20 Distance (km)

25

30

35

25

30

35

b)

0

Slope Gradient along the Road (degree)

13

a)

0

60

0

c) 40

Lateral Slope Gradient on Left-side

20

Lateral Slope Gradient on Right-side

0

Lateral Slope Gradient along the Road (degree)

11

0

5

10

15 20 Distance (km)

Fig. 8 Variability of elevation (a), elevation gradient (b), and lateral slope gradient (c) along the Maykop City-Sochi City road via Lago-Naki Plateau based on SRTM 4.0 with spatial resolution 30 m. 11 Mount Nagay-Koshki (9.6 km from the beginning of the road), 12 Azishsky Pass (2.75 km), 13 Oshtenovsky Pass (13.9 km), 14 Maykopsky Pass (26.5 km), 15 Cherkessky Pass (32.6 km)

after the Maykopsky Pass. The choice of such a route was determined by the goal to do less damage to the unique natural object of the Lago-Naki Plateau. Over 34 km from the beginning of the road, more than ten sections are observed where the gradient of the relief along the route increases by more than 15 . They are located mainly between the Oshtenovsky Pass and Maykopsky Pass, as well as in the first half of the Maykopsky Pass-Cherkessky Pass section (Fig. 8b). The slope of the relief to the right and left of the road does not exceed 13 on average. However, in the area of Maykopsky Pass (3 km before it and 5 after), there are slopes of more than 20 . Maximum values (over 45 ) are observed closer to the Maykopsky Pass (Fig. 8c).

Digital Elevation Model of the Republic of Adygea

79

In 2019 administrations of Krasnodar Territory and the Republic of Adygea started a construction of a new road Maykop-Chernigovskoe-Dagomys on the coast of the Black Sea across the Caucasus ridges, which will significantly shorten the way from Krasnodar and Maykop cities to Sochi and other resort places on the coast of the Black Sea. Implementation of this road project will significantly benefit from the use of high-resolution DEM of the appropriate regions of the Republic of Adygea and Krasnodar Territory.

5 Conclusions In this chapter we showed the main results on the construction of DEM for the Republic of Adygea which was built on the base of the SRTM 4.0 data with a spatial resolution of 30 m. This is the first DEM of the Republic built on the base of new satellite high-resolution technologies. A comparison with the existing topography maps of the Republic with different spatial resolution has shown that DEM is very accurate, for instance, the difference in heights for the main mountains in the Republic is about 1 m only. Slope gradients have been calculated for the territory of the Republic which allowed to show several possible applications for use in agriculture, analysis of hydrological network and different exogenous processes, and planning of road construction. The creation of a DEM of the Republic of Adygea integrated into the GIS is also necessary for geomorphological zoning and landscape science, geological and soil erosion studies, construction of visibility zones for telecommunication and GSM companies, carrying out cadastral valuation of land and urban development, risk assessment of landslides and avalanches, planning of wind and solar farms, development of mountain tourism and ski resorts, and many other tasks. Acknowledgments S.A. Lebedev (satellite data processing) was supported in the framework of the Geophysical Center RAS budgetary financing, adopted by the Ministry of Science and Higher Education of the Russian Federation, by the project “Intelligent analysis of big data in the tasks of ecology and environmental protection,” carried out within the Competence Center Program of the National Technological Initiative “Center for the Storage and Analysis of Big Data,” supported by the Ministry of Science and Higher Education of the Russian Federation at the Lomonosov Moscow State University and by the Fund of the National Technological Initiative dated December 11, 2018, No. 13/1251/2018. This work employed facilities and data provided by the Shared Research Facility “Analytical Geomagnetic Data Center” of the Geophysical Center of RAS (http://ckp. gcras.ru/). A.G. Kostianoy was partially supported in the framework of the P.P. Shirshov Institute of Oceanology RAS budgetary financing (Project N 149-2019-0004).

References 1. Cayley A (1859) XL. On contour and slope lines. Lond Edinburgh Dublin Philos Mag J Sci 18 (120):264–268. https://doi.org/10.1080/14786445908642760

80

S. A. Lebedev et al.

2. Kirkby MJ, Chorley RJ (1967) Throughflow, overland flow and erosion. Hydrol Sci J 12 (3):5–21. https://doi.org/10.1080/02626666709493533 3. Speight JG (1980) The role of topography in controlling throughflow generation: a discussion. Earth Surf Process 5(2):187–191. https://doi.org/10.1002/esp.3760050209 4. Geiger R, Aron RH, Todhunter P (1995) The climate near the ground.5th edn. Vieweg+Teubner Verlag, p 528. https://doi.org/10.1007/978-3-322-86582-3 5. Franklin J (1995) Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19(4):474–499. https://doi.org/10.1177/ 030913339501900403 6. Penck W (1953) Morphological analysis of land forms: a contribution to physical geology. Macmillan, London, p 429 7. Burbank DW, Anderson RS (2009) Tectonic geomorphology. Wiley, Chichester, p 454 8. Zhou Q, Lees B, Tang GA (2008) Advances in digital terrain analysis. Springer, Berlin, p 462. https://doi.org/10.1007/978-3-540-77800-4 9. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30. https://doi.org/10. 1002/hyp.3360050103 10. Miller CL, Laflamme RA (1958) The digital terrain model – theory and application. Photogramm Eng 24(3):433–442 11. Doyle FJ (1978) Digital terrain models: an overview. Photogramm Eng Remote Sens 44 (12):1481–1485 12. Burrough PA (1986) Principles of geographical information systems for land resources assessment. Clarendon Press, Oxford, p 193 13. Panin AV, Gelman RN (1997) Experience of the GPS technique application to derive detailed digital terrain models. Geod Cartogr 10:22–27. (in Russian) 14. Clark RL, Lee R (1998) Development of topographic maps for precision farming with kinematic GPS. Trans ASAE 41(4):909–916. https://doi.org/10.13031/2013.17247 15. Mikhaylov AP, Chibunichev AG (2016) Photogrammetry. Moscow State University of Geodesy and Cartography, Moscow, p 294. (in Russian) 16. Jensen JR (1995) Issues involving the creation of digital elevation models and terrain corrected orthoimagery using soft-copy photogrammetry. Geocarto Int 10(1):5–21. https://doi.org/10. 1080/10106049509354475 17. Welch R, Jordan T, Lang H, Murakami H (1998) ASTER as a source for topographic data in the late 1990s. IEEE Trans Geosci Remote Sens 36(4):1282–1289. https://doi.org/10.1109/36. 701078 18. Giles PT, Franklin SE (1996) Comparison of derivative topographic surfaces of a DEM generated from stereoscopic SPOT images with field measurements. Photogramm Eng Remote Sens 62(10):1165–1170 19. Toutin T (2004) DTM generation from Ikonos in-track stereo images using a 3D physical model. Photogramm Eng Remote Sens 70(6):695–702. https://doi.org/10.14358/PERS.70.6. 695 20. Zebker HA, Werner CL, Rosen PA, Hensley S (1994) Accuracy of topographic maps derived from ERS-1 interferometric radar. IEEE Trans Geosci Remote Sens 32(4):823–836. https://doi. org/10.1109/36.298010 21. Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission – a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57(4):241–262. https://doi.org/10.1016/S0924-2716(02)00124-7 22. Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodriguez E, Roth L, Seal D, Shaffer S (2007) The shuttle radar topography mission. Rev Geophys 45(2):RG2004. https://doi.org/10.1029/2005RG000183 23. Wehr A, Lohr U (1999) Airborne laser scanning – an introduction and overview. ISPRS J Photogramm Remote Sens 54(2–3):68–82. https://doi.org/10.1016/S0924-2716(99)00011-8

Digital Elevation Model of the Republic of Adygea

81

24. Lloyd CD, Atkinson PM (2006) Deriving ground surface digital elevation models from LiDAR data with geostatistics. Int J Geogr Inf Sci 20(05):535–563. https://doi.org/10.1080/ 13658810600607337 25. Finkl CW, Benedet L, Andrews JL (2005) Interpretation of seabed geomorphology based on spatial analysis of high-density airborne laser bathymetry. J Coast Res 2005(213):501–514. https://doi.org/10.2112/05-756A.1 26. Laguta AA, Pogorelov AV (2018) Peculiarities of Krasnodar water reservoir silting. Evaluation based on the data of bathymetric surveys. Geograph Bull 4(47):54–66. (in Russian) 27. Dixon TH, Naraghi M, McNutt MK, Smith SM (1983) Bathymetric prediction from Seasat altimeter data. J Geophys Res Oceans 88(C3):1563–1571. https://doi.org/10.1029/ JC088iC03p01563 28. Sandwell DT, Smith WHF (2001) Bathymetric estimation. In: Satellite altimetry and earth sciences. Academic Press, San Diego, pp 441–458. https://doi.org/10.1016/S0074-6142(01) 80157-1 29. Khalugin EI, Zhalkovsky EA, Zhdanov ND (1992) Digital maps. Nedra, Moscow, p 415. (in Russian) 30. Eklundh L, Mårtensson U (1995) Rapid generation of digital elevation models from topographic maps. Int J Geogr Inf Syst 9(3):329–340. https://doi.org/10.1080/02693799508902040 31. Miliaresis GC, Argialas DP (1999) Segmentation of physiographic features from the global digital elevation model/GTOPO30. Comput Geosci 25(7):715–728. https://doi.org/10.1016/ S0098-3004(99)00025-4 32. Hastings DA Dunbar PK (1993) Global land one-kilometer base elevation (GLOBE). Digital elevation model, version 1, documentation version 1,0. NGDC key to geophysical records documentation no. 34. National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder, 133 pp 33. Amante C, Eakins BW (2009) ETOPO1 arc-minute global relief model: procedures, data sources and analysis. NOAA technical memorandum NESDIS NGDC-24. National Geophysical Data Center Marine Geology and Geophysics Division, Boulder, p 19 34. Strakhov VN, Strakhov AV, Stepanova IE, Zhalkovskiy EA (2007) About the substitution of the topographic maps for the linear analytical approximations of the earth surface relief. Geod Cartogr 2:21–25. (in Russian) 35. Strakhov VN, Strakhov AV, Stepanova IE, Zhalkovskiy EA (2007) About the substitution of the topographic maps for the linear analytical approximations of the earth surface relief. Geod Cartogr 3:33–38. (in Russian) 36. Wieczorek MA (2015) Gravity and topography of the terrestrial planets. In: Treatise on geophysics, vol 10. 2nd edn. Elsevier, Amsterdam, pp 165–206. https://doi.org/10.1016/ B978-0-444-53802-4.00169-X 37. Carter JR (1988) Digital representations of topographic surfaces. Photogramm Eng Remote Sens 54(11):1577–1580 38. Kumler M (1994) An intensive comparison of triangulated irregular networks (TINs) and digital elevation models (DEMs). Cartographica 31(2):1–99. https://doi.org/10.3138/TM56-74K7QH1T-8575 39. Evans IS (1979) Statistical characterization of altitude matrices by computer. An integrated system of terrain analysis and slope mapping. The final report on Grant DA-ERO-591-73G0040. University of Durham, Durham, 192 pp 40. Shary PA (1995) Land surface in gravity points classification by a complete system of curvatures. Math Geol 27(3):373–390. https://doi.org/10.1007/BF02084608 41. Clarke KC (1988) Scale-based simulation of topographic relief. Am Cartogr 15(2):173–181. https://doi.org/10.1559/152304088783887107 42. Shary PA, Sharaya LS, Mitusov AV (2002) Fundamental quantitative methods of land surface analysis. Geoderma 107(1–2):1–32. https://doi.org/10.1016/S0016-7061(01)00136-7 43. Evans IS (1972) General geomorphometry, derivatives of altitude, and descriptive statistics. In: Chorley RJ (ed) Spatial analysis in geomorphology. Methuen, London, pp 17–90

82

S. A. Lebedev et al.

44. Speight JG (1974) A parametric approach to landform regions. Prog Geomorphol Spec Publ 7:213–230 45. Li Z, Zhu C, Gold C (2004) Digital terrain modeling: principles and methodology. CRC Press, Boca Raton, p 323. https://doi.org/10.1201/9780203357132 46. Florinsky I (2016) Digital terrain analysis in soil science and geology. Academic Press, Cambridge, p 506 47. O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Comput Vision Graph Image Process 28(3):323–344. https://doi.org/10.1016/S0734189X(84)80011-0 48. Martz LW, De Jong E (1988) Catch: a fortran program for measuring catchment area from digital elevation models. Comput Geosci 14(5):627–640. https://doi.org/10.1016/0098-3004 (88)90018-0 49. Freeman TG (1991) Calculating catchment area with divergent flow based on a regular grid. Comput Geosci 17(3):413–422. https://doi.org/10.1016/0098-3004(91)90048-I 50. Quinn PFBJ, Beven K, Chevallier P, Planchon O (1991) The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models. Hydrol Process 5(1):59–79. https://doi.org/10.1002/hyp.3360050106 51. Tarboton DG (1997) A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resour Res 33(2):309–319. https://doi.org/10.1029/ 96WR03137 52. Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surf Process Landf 12(1):47–56. https://doi.org/10.1002/esp.3290120107 53. Skidmore AK (1989) A comparison of techniques for calculating gradient and aspect from a gridded digital elevation model. Int J Geograph Inform Syst 3(4):323–334. https://doi.org/10. 1080/02693798908941519 54. Jones KH (1998) A comparison of algorithms used to compute hill slope as a property of the DEM. Comput Geosci 24(4):315–323. https://doi.org/10.1016/S0098-3004(98)00032-6 55. Zhou Q, Liu X (2004) Analysis of errors of derived slope and aspect related to DEM data properties. Comput Geosci 30(4):369–378. https://doi.org/10.1016/j.cageo.2003.07.005 56. Rodríguez JLG, Suárez MCG (2010) Comparison of mathematical algorithms for determining the slope angle in GIS environment. Aqua-LAC 2(2):78–82 57. Florinsky IV (1998) Accuracy of local topographic variables derived from digital elevation models. Int J Geogr Inf Sci 12(1):47–62. https://doi.org/10.1080/136588198242003 58. Schmidt J, Evans IS, Brinkmann J (2003) Comparison of polynomial models for land surface curvature calculation. Int J Geogr Inf Sci 17(8):797–814. https://doi.org/10.1080/ 13658810310001596058 59. Florinsky IV (2009) Accurate method for derivation of local topographic variables. Geod Cartogr 4:19–23. (in Russian) 60. Florinsky IV (1998) Derivation of topographic variables from a digital elevation model given by a spheroidal trapezoidal grid. Int J Geogr Inf Sci 12(8):829–852. https://doi.org/10.1080/ 136588198241527 61. Wood JD (1996) The geomorphological characterisation of digital elevation models. PhD thesis, University of Leicester, Leicester, 193 pp 62. Florinsky IV (2002) Errors of signal processing in digital terrain modelling. Int J Geogr Inf Sci 16(5):475–501. https://doi.org/10.1080/13658810210129139 63. Buzarov AS, Varshanina TP, Kabayan NV, Krasnopolskiy AV, Krasnopolskaya NV, Kuasheva DA, Melnikova TN, Spesivtsev PA, Khachegogu AY, Shebzukhova EA Geography of the Republic of Adygea. Adyghe Republican Book Publishing House, Maykop, p 200. (in Russian) 64. The Atlas of the Republic of Adygea (2005) Publishing house “Lev Tolstoy”, Maykop, 79 p. (in Russian) 65. Bedanokov MK, Chich SK, Chetyz DY, Trepet SA, Lebedev SA, Kostianoy AG (2020) Physico-geographical characteristics of the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham

Digital Elevation Model of the Republic of Adygea

83

66. Lebedev SA, Korinevich LA (2020) Development of exogenous geological processes in the territory of the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham 67. Lebedev SA, Kravchenko PN (2020) Soil degradation in the Republic of Adygea under exogenous geological processes. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham 68. Khunagov RD, Varshanina TP (2006) Multipurpose national geographic information system of the Republic of Adygea. Vestnik Adygea State Univ 1:262–268. (in Russian) 69. Varshanina TP, Shekhov ZA, Shtelmakh EV, Getmansky MY (2018) The prospects of use of digital technologies for the solution of theoretical problems of landscape study. In: Landscape geography in the 21st century. Publishing House Printing House “Arial”, Simferopol, pp 95–97. (in Russian) 70. Orlov TV, Sadkov SA (2015) Investigation of karst relief in the eastern part of Lago-Naki plateau by LIDAR and high-resolution airborn images. Geoecol Eng Geol Hydrogeol Geocryol 4:365–376. (in Russian) 71. Konstantinov YA, Sinelnikova IE (2018) Organization of engineering and geodetic work in the construction of buildings and structures in the conditions of geodynamic activity of the mountainous part of the Republic of Adygea. In Field of knowledge: questions of the modern stage of development of scientific thought. Individual Entrepreneur Kuzmin Sergey Vladimirovich, Kazan, pp 451–467. (in Russian) 72. Zhuchkova VK, Rakovskaya EM (2004) Methods of comprehensive physical and geographical research. Publishing Center “Academy”, Moscow, p 368. (in Russian) 73. Florinsky IV (1998) Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Prog Phys Geogr 22(1):33–60. https://doi.org/10.1177/ 030913339802200102 74. Kostianoy AG, Lebedev SA (2014) Three-dimensional digital elevation model of Karashor depression and Altyn Asyr Lake. In: The Turkmen Lake Altyn Asyr and water resources in Turkmenistan. The handbook of environmental chemistry, vol 28. Springer, Berlin, pp 177–196. https://doi.org/10.1007/698_2013_238 75. Teslenko PF, Korotkov BS (1967) Effect of arenaceous intercalations in clays on their compaction. Int Geol Rev 9(5):699–701. https://doi.org/10.1080/00206816709474501

Spatial-Temporal Geodynamic Model of Adygea Tatyana P. Varshanina and Viktor N. Korobkov

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Creating Systematic Geodynamic Models: Defining Their Properties and Parameters of the Order of Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Methodology of Creating a Structurally Similar Geodynamic Model of the Territory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Structural Spatial-Temporal Model of the Geodynamic System of the Adygea Territory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Topological Analysis of Adygea Structural Existential Geodynamic Model . . . . . . . 3 Verification of the Structural Existential Model of the Adygea Geodynamic System . . . . 4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

86 91 92 95 102 106 108 112

Abstract The creation of a structurally similar spatial-temporal model of a geodynamic system in the territory of an unlimited area has been developed in this chapter. It offers the parametric measure of the order of geodynamic processes and their forecasting. A structurally similar existential geodynamic model of the territory of the Republic of Adygea has been constructed and verified. Keywords Area concentrators of tectonic tension, Geodynamic processes order parameter, Pointed forecasting of geodynamic processes, Structurally similar spatialtemporal model of geodynamic system

T. P. Varshanina (*) and V. N. Korobkov Adygea State University, Maykop, Russian Federation e-mail: [email protected]; [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 85–112, DOI 10.1007/698_2020_500, © Springer Nature Switzerland AG 2020, Published online: 18 August 2020

85

86

T. P. Varshanina and V. N. Korobkov

1 Introduction The slow tectonic movements of the Earth’s crust lead to changes in earth surface relief. Such changes cause natural and technogenic disasters (breaks in oil and gas pipelines and in irrigation constructions, destruction of industrial and residential buildings) that have a strong impact on individual life activities. Special attention is given to the processes of endo-geodynamics in the mountainous regions located in the suture zones of lithosphere formations of different ranks. In the mountainous plains regions of Adygea, the intensity of the geodynamic processes is illustrated on the map of seismic zoning (Fig. 1). During the twentieth century, epicenters of three earthquakes of magnitude V–VI were recorded in the plains territory of the Republic. These movements are characterized by a pulsation mechanism of compression-tension, which is considered to be the global pattern of all suture zones between colliding plates [1]. As a result of tool measurements, the high mobility and complexity of the mechanisms and the regularity of modern geodynamics of mountainous systems of the Caucasian type have been revealed. Modern geodynamics has a spatial ordering. Modern geodynamics activity maps reflect all known morphological structures of different types, age, and rank, each with its own individual style, intensity, focus, and differentiation of movements, a vast range of block-fault types, and deformation of the Earth’s morphostructures. The revealed regularities of the permanent differentiated movements of multiscale blocks of crust indicate that regional systems of monitoring of endogenic dynamics are necessarily guided by the geomorphological geodynamic schemes of each regional structure. A tool displaying endogeodynamic processes on the Earth’s surface can be proposed as a geodynamic model that is structurally similar to the field of tectonic stresses. Modeling the contiguous field of tectonic tensions includes the area of their concentration, recreating the spatial-temporal differentiation of tectonic movements in modern times and in the historical past. Creating a structural model of the geodynamic system, the global mechanism and spatial-temporal regularities of geodynamic processes in the lithosphere and in the Earth’s crust as formed in tectonophysics, plate tectonics, geotectonics, and morphotectonics were taken into account. Thus, based on analysis of seismic tomography data, a theoretical model of a convection system in the geosphere as the hierarchy of geodynamic systems (GS) was developed in tectonophysics, confirmed by the results of numerical and physical modeling [3]. In this model, the authors pointed out the hierarchy of dynamic cell convection of material and energy flows. The over rank GS–0 functions throughout the mantle and its action are considered to extend to the outer core of the Earth. Rotational tidal forces cause its occurrence. All the layers of the Earth are defined as the sphere of the influence, wherein during rotation the top layers are behind those located below. GS–0 is an axisymmetrical single-cell convection, covering the mantle and outer core of the Earth, the horizontal surface of the northern stream that is directed along

Fig. 1 Seismic zoning of the Republic of Adygea [2]

Spatial-Temporal Geodynamic Model of Adygea 87

88

T. P. Varshanina and V. N. Korobkov

the meridian from the South Pole to the North Pole. The north surface flow corresponds to the counter depth, called the southern counterflow, which is also directed along the meridian from the North Pole to the South Pole. Thus, the authors qualify the GS–0 model as a single-cell convection system. The next on the downward grade of geodynamic systems according to M.A. Goncharov [3] covers two semispherical segments of the mantle: the IndoAtlantic and the Pacific. This system of convection is reconstructed by the computer model “tectonics of floating continents” according to seismotomography and paleoreconstructions of disintegration and changes in the mutual position of the continents. The nature of convection in these cells is defined by two hot superplumes, having as their basis the borders of core and mantle. One of them is located near Africa, in the Indo-Atlantic segment, and is centrifugal; the other is located under the Pacific Ocean and is centripetal. At the top of the mantle, the horizontal flow moves away from the African superplume to the Pacific. Under the upper horizontal flow there is a compensatory countercurrent flow. The axes of these superplumes are antipodal, and the downstream bandwidth, dividing the GS–1 cells, encircles the entire globe in the meridional direction. It is supposed that the horizontal centrifugal stream, compensating for the upward flow of the mantle in the African hemisphere cell, determined the trajectory of the divergence of continents after the breakup of Pangea. The authors of the model came to the conclusions that the geodynamic system of the first grade corresponds to the plume tectonics and the continental drift refers to the influence of the GS–1. To the lower but planetary rank GS–2, Goncharov et al. consider the systems of convection that are responsible for the mechanism of plate tectonics and developed only under the oceans [3]. In support of the statement about the relative independence of GS–2, the following argument is given: lack of seismotomographic continuation of depth spreading zones against distinct seismotomographic expressiveness of the Pacific and African superplumes. The author concludes that spreading and subduction are passive reactions to the drift of continents, which is caused by functioning of GS–0 and GS–1. It is supposed that the continents shield a mantle thermal flow that is directed into the area of the oceans and supports the existence of GS–2. Convective cells of the GS–2 cover the upper mantle and are exposed to ordered convective flows in the lower mantle. The following rank of GS–3 reflects the local system of convection developed in zones of the raised thermal stream: spreading, subduction, and collision. The convection form in these zones is defined by the power of the thermal stream. In the descent of this stream, the zones take the following lines: subduction, collisions, and spreading. The structural picture of the listed zones is defined by the interference of their convection specificities and the specificity of the geobody interactions. The GS–4 rank corresponds to the geodynamic bodies, which are formed by thermofluid convection in the local-scale cells. It is necessary only to emphasize that the duration of cycles of convection in the listed ranks of GS are indicated to be from 1.7 billion years in GS–0 to 1–3 million years in GS–4 [3].

Spatial-Temporal Geodynamic Model of Adygea

89

Thus, there are reasons to claim that geodynamic processes in the Earth’s crust are caused by convective processes in the mantle and lithosphere. The system of convection in a mantle, which is closely connected with rotational tidal forces, forms a hierarchical number of interacting layers and ranks of convective cells, causing a corresponding hierarchical number of tectonic currents in the geospheres. The structures of the highest order of mega-and macroforms of tectonic relief are formed in the geologic environment as a result of the interference of a complex system of multi-scale mantle tectonic currents. The rhythms of tectonic processes in the Earth’s crust generated by mantle flows apparently take millions of years, as confirmed in our research. Specific forms of tectonic relief, that is, the structural and geologic objects that are formed under the conditions of tension, compression, stretching, and shifting, are realized in accordance with the rheological properties of the heterogeneous geologic environment. A schematic diagram of structure formation in a heterogeneous, hierarchically constructed geologic environment was offered by Talitsky [3]. In this scheme, the geologic environment of the Earth’s crust, represented by the system of multi-scale (hierarchical) and heterogeneous structural elements, is responsible for the different types of tectonic loading, the different mechanisms of deformations that cause tension field heterogeneity in the Earth’s crust. During the process of deformations that exhaust the relaxation facilities of the geologic environment, tectonic breaks are formed, and a level of structural organization is formed that is defined as a block. These conclusions are confirmed by empirical data of recent years proving that modern geodynamics possesses a spatial ordering. This order is expressed in precise interrelationships of modern movement fields with morphostructural differentiation of relief in which block-faulting deformation of the Earth’s surface is predominant [1]. The tectonic blocks in the scheme by Talitsky [3] refer to the transitional elements, from the highest order structures of the geologic environment to the structures of the lowest order. The authors have particular interest in this work because the level of the tectonosphere organization and the object of their study is the block structure of the Earth’s crust of the Northwestern Caucasus. Therefore, here we consider the mechanisms of tectonic deformations at the block level as analyzed by V.G. Talitsky in detail. At this level, mobility of the blocks causes the mechanisms of tension relaxation to change, expanding their scale and increasing the effectiveness of the relaxation processes. Deformation of the geologic environment at the block level is caused by the mutual displacement and rotation of the blocks as much as by deformation of the blocks themselves. The general statements about the structured medium of tectonophysics include the observation that the tectonic tension generated by interaction of structures of the highest order within the loaded geologic volumes is distributed unevenly, concentrating on the inhomogeneities of the structures of the lowest orders. The hierarchy of structures of the lowest orders generates, in the geologic environment, a nonuniform field of tension with concentrators of the different sizes and intensity. In the field of

90

T. P. Varshanina and V. N. Korobkov

tension deformation, in the field of concentrators, the tension reaches the limit values earlier than in other parts of the system. The mechanical interaction of blocks, proceeding in conditions of constrained deformation, leads to tension concentration where delayed shifts occur. The inhomogeneity of displacements at the block boundaries also produces the fields of rotary points operating on the blocks. Relaxation of the rising tension is allowed by restructuring. In the presence of such concentrators that can extinguish the tension effectively, for example, redistribution of tension between other concentrators, restructuring is carried out slowly, by change of internal structure and block configuration. If the relaxation capacities of a system are limited, when tension of the breaking point is reached the concentrators are destroyed with the formation of new structural elements that can effectively relax tension. There is a system of cracks and ruptures of various ratios in the concentrator blocks, forming the peculiar structural pictures [3]. Similarly, paragenesis in one case can be formed slowly, in coordination with the movements of blocks, but in another the concentrators are destroyed easily, being followed by seismic effects. When you consider that in addition to the structural parageneses formed during horizontal and vertical movement of blocks along faults, and formed as a result of the rotation of the blocks, apparently the deformable heterogeneous geologic environment results in local areas of a considerable variety of mechanical environments. Considering that structural paragenesis forms at the horizontal and vertical movement of blocks through the fault breaks and as a result of block rotation as well, evidently local sites with considerable variety of mechanical situations appear in the deformable heterogeneous geologic environment. The investigation of structures corresponding to these conditions in a real geologic environment is of undoubted scientific and practical interest, especially in seismically active zones with mastered engineering structures. To predict earthquakes, universal parameter indicators are also required that possess a flexible configuration to display the rheological properties of the geologic environment. Such a parameter is proposed in this research. And so, on the basis of the representations prevailing in tectonophysics, we can make the following conclusions. The interference of the multi-scale and multimillion-year tectonic cycles of tectonic flows, generated in the lithosphere in general and in the Earth’s crust in particular, allows instrumental observation of the permanent movements of a hierarchical system of tectonic plates and blocks, the interaction of which produces an inhomogeneous field of tension. When imposing fields of tensions external to blocks and produced by interaction of blocks, areas of tension concentration are formed, existential structural restructuring, which is determined both by the magnitude of tectonic tension and by the rheological properties of the blocks. Thus, the structural diagram of the Earth’s crust block structure is a necessary foundation for building a systematic geodynamic model, for investigating tectonic stress spatial differentiation, for identifying hub units and studying their behavior,

Spatial-Temporal Geodynamic Model of Adygea

91

predicting their slow and fast movements. Localization of tectonic stress foci and forecasting their behavior are necessary conditions for seismic risk reduction.

2 Creating Systematic Geodynamic Models: Defining Their Properties and Parameters of the Order of Organization The structural diagram of block structure of the Earth’s crust is not only the basis of the investigation of geophysical processes of strong earthquake preparation but is also a means of their prediction on the basis of modern numerical methods and tools of the theory of dynamic systems. The general deployment of the forecasting methodology of dynamic system conditions requires identifying parameters of the system processes, dynamic variables that are nonequilibrium collective order parameters, determining the existential course of the structure formation processes in the hierarchical organization of complex systems. Thus, possessing knowledge of the structural organization of a complex system allows identifying the parameters of system processes that are their productive predictors. Thereof it follows, that to develop technologies for predicting the risk of natural hazards, it is necessary to improve the methods of constructing similar structural models of geospace. The Earth’s geodynamic system is a complex self-organizing system, that is, a dynamic system capable of restoring structure and behavior or modifying them for compensating perturbation actions, adapting to environmental conditions. So, for example, the state of dynamic equilibrium is formed in the Earth’s crust as a result of compensation of the disrupting effects of tectonic movements by the structural rearrangements of the lower-order tectonic units. According to the laws of thermodynamics, this is possible only in open systems, where the substance containing free energy can enter in a larger quantity than is required for compensating the increase of entropy growth from the processes occurring in the system [4]. The energy that goes into the system makes its differentiation possible according to the internal laws of the system organization. Based on geospace system properties, when constructing the structural spatialtemporal geodynamic model of the territory, it is necessary to observe the similarity of its elements in the hierarchical spatial-temporal structure of the constitutive energy flows, the parameters of which are the order parameters of tectonic processes [5, 6]. Interference of multi-scale tectonic movements in the Earth’s body, a hierarchical system of tectonic elements (blocks, lineaments, joints) forms that is structurally similar to an endogenous field of energy, which is associated with tectonic relief produced under conditions of compressive stress, stretching, and slide in an inhomogeneous geologic medium. Thus, an empirically objective geodynamic model of the territory, displaying relative positioning of tectonic elements – blocks, lineaments, and knots – is

92

T. P. Varshanina and V. N. Korobkov

structurally similar to a tectonic energy field. Mutual similarity of the elements of such a geodynamic model and elements of the tectonic energy field structure, first, allows us to determine the parameters interpreting the structure of the generated energy field; and second, reveals their compliance (similarity) to the mode of the tectonic tension, leading to catastrophic consequences. On the one hand, research on the historical dynamics of an endogenous field energy structure on the Earth’s surface focuses on specific geodynamic situations, and on the other hand it focuses on the historical dynamics of seismic events. The authors are certain that this research will allow defining self-similar situations in the course of tectonic tension and seismic manifestations by numerical methods and calculating the corresponding outlook.

2.1

Methodology of Creating a Structurally Similar Geodynamic Model of the Territory

For forecasting geodynamic processes on the basis of the theory of dynamic systems methods when designing a structurally similar spatial-temporal model of the territorial geodynamic system, calculation of the following indicators has been suggested: • Those being a measure of the order parameter of the predicted process • Those being a “collective” measure of several characteristics of the predicted process • Those possessing a low level of uncertainty This method of designing a structural spatial-temporal model of the geodynamic system of the territory includes a complex of popular methods [8, 9]: • • • • • •

Morphostructural regioning Ranging of water currents Creation of polybasic surfaces: alignment surfaces Creation of morphometric maps for geodynamic analysis Analysis of vector fields Creation of a gravitational and dynamic model of the relief [7]

The digitized materials, which include topographic maps of 1:200,000 scale, tectonic and geologic maps, stratigraphic materials, and data from geophysical surveys and remote sensing served as initial materials for the creation of the systemic geodynamic model of the territory of Adygea. On the basis of these materials, a digital model of the relief of the territory [7], maps of orography, of earth cracking, of the type of river networks, order of water currents, and separate waterlines (Fig. 2) has been developed. The method of morphostructural regioning (MSR) [8] allows us to reveal morphotectonic structures in their real borders according to topographic maps and

Spatial-Temporal Geodynamic Model of Adygea

93

Fig. 2 Visual allocation of tectonic blocks, lineaments, and knots (a); identification of cracking (b) [7]

to determine by formalized signs the hierarchy of the tectonic structures that control modern relief: tectonic blocks, lineaments, and knots. According to the MSR method, allocation of modern morphostructures is carried out by the following parameters of the group of informative signs of relief:

94

T. P. Varshanina and V. N. Korobkov

1. Height of the area: height changes reflect spatial differentiation of vertical earth movements 2. Orientation of linear forms of relief: straight sections of riverbeds and other erosive forms, ledges between steps of different heights, changes in the orientation of which mean transition to other types of tectonic tension 3. Type of the pattern of river valleys closely connected with the direction and the steepness of surface bias Blocks in the system of morphostructural regioning represent the territory within which all informative signs of the relief have close values or the values of the signs change gradually. The border of the block is drawn where there is a sharp and essential change (increase in a gradient) of at least one informative sign of relief. Linear zones or morphostructural lineaments 10–40 km wide and 100–1,000 km long are the borders of the block. In the lineament zones, the extent of crushing of crust (small block structure) is higher; their surface has a more level character. In the schemes of MSR, three elements of the modern block structure of crust are noted: blocks of various ranks of uniformity of the territory; borders of blocks, that is, linear zones (lineaments) the rank of which depends on the rank of the blocks limited by them; and morphostructural knots, places of crossing or joining of lineaments of different spaces. As the tectonic blocks controlling the modern relief are noted by typical direction and intensity of tectonic movements, morphostructural regioning of the territories acts as a morphotectonic basis of the geodynamic model. The latest earth movements are revealed in compliance with the method of the morphometric analysis of the tectonic structures [10] as a result of comparative analysis of the maps of basic surfaces constructed with regard to the thalwegs of water currents of one-serial river valleys. This method is based on the following hypothesis: the higher the order of valleys, the older they are, and one-serial valleys have an approximately identical age. The provision of basic surfaces (valleys) of the first and second orders reflects the vertical tectonic movements of a Quaternary age; those of third and fourth orders reflect the total movements of Quaternary and Pliocene age; and higher orders show the algebraic sum of movements for a longer period of time. The difference of provision of basic surfaces indicates the shift of the area on height for the period of time between the formation of valleys of different orders [10]. The power of the layer between basic surfaces indicates the shift of the relief surface for the period that passed between the formation of valleys of different orders. It is known that the vertical movements are characterized mainly by the speed exceeding sedimentation speed, and, therefore, basic surfaces can be considered as isopotential surfaces fixing, with admissible error, provision of the alignment surfaces in various intervals of geologic time. Thereof, maps reflecting the power of the layer between basic surfaces bear information on the speed of vertical movements for the corresponding period of geologic time. Positive differences of heights between basic surfaces correspond to the ascending tectonic movements and negative differences to the descending movements.

Spatial-Temporal Geodynamic Model of Adygea

95

Using the difference of provision of basic surfaces, the power of the differential layer between them is calculated by subtraction of the basic surface of the senior (third, fourth, etc.) order from the surface of the younger order (second, third, etc.). Spatial differentiation of the power of a differential layer is caused by the structure of tectonic tensions in rheological conditions of the crust; that is, it is structurally similar and is displayed by the isolines of the power layer. Because the structure of heights displays the structure of the field of tectonic tensions, the greatest gradient of power of a differential layer between basic surfaces is accepted as a measure of the parameter of the order of tectonic processes. Owing to the interdependence and complement characteristics of the processes of forward movement, the rotation and deformation of elementary volumes of the geologic continuous environment [3], the greatest gradient of power of a differential layer characterizes the direction and relative speed of the movement of tectonic blocks, calculates the turning point of blocks, and reflects their rheological properties and type of tectonic interaction (tension of compression, stretching, shift). Calculation of the greatest gradient of power of a differential layer is made with regard to the isolines of the layer power in the knots of an even uniform lattice with a constant step. So, the structure of the continual field of tectonic tension is calculated. The resultant greatest gradient of the power of a differential layer is calculated from the center of each tectonic block and characterizes its relative speed and the direction of movement.

2.2

Structural Spatial-Temporal Model of the Geodynamic System of the Adygea Territory

The 1:200,000 scale map of the block structure of the area of the Krasnodar territory and the Republic of Adygea displays the hierarchy of the tectonic elements: morphostructural blocks, lineaments, and knots (Figs. 3 and 4). It is well coordinated with data of the neotectonic regioning of the territories, and the history of relief development has been developed and verified. Analysis of the maps of the Republic of Adygea and adjacent territories characterizing the residual relief from the basic to the topographical surface has shown the following results. The map of the residual relief of a rather basic surface of the fourth order (BP-4), a Pliocene (Fig. 5), testifies that in the Pliocene the area of the Kuban Trough intensively decreased. The blocks of the lower part of the Trans-Kuban Plain have not been formed yet in this geologic time. The positive vertical movements in the territory of the Republic are noted from the width of Maykop City. Speed of the vertical movements quickly increases toward the axial part of the Greater Caucasus. In the mountainous part of the Republic with rapid positive vertical movements, borders of the main blocks are formed.

96

T. P. Varshanina and V. N. Korobkov

Fig. 3 Block structure of the area of Krasnodar territory and the Republic of Adygea

The map of the residual relief of the basic surface of the third order (BP-3) (Pliocene–Quaternary period) (Fig. 6) specifies that at this time the positive vertical movements form the blocks of the Trans-Kuban Plain and the width of the Kuban Trough decreases. In comparison with BP-4, rising speed in the axial part of the Greater Caucasus decreases. In the mountainous part, structures of the North Jurassic Depression have been shown. The map of the residual relief of the basic surface of the first order (BP-2) (the Quaternary Period) (Fig. 7) testifies that by this time the blocks of the Trans-Kuban Plain have already been formed. In comparison with BP-3, the rising speed in the axial part of the Greater Caucasus decreases. Crushing of the tectonic blocks of the territory reaches the maximum value. The extensive part of the territory of the TransKuban Plain adjacent to the Azov-Kuban Trough falls. Thus, the existential morphometric analysis allowed a picture of the dynamics of the formation of the tectonic relief in the researched territory for more than 5 million years. The given series of maps of the residual relief testifies to the differentiation of the speed of vertical tectonic movements and to the stability of the decreasing trend in their speed for a Pliocene–Quaternary time.

Fig. 4 Classification of the elements of the morphostructural regioning of the territory of the Republic of Adygea using the method of E.Y. Rantsman and M.P. Glasko [8]

Spatial-Temporal Geodynamic Model of Adygea 97

98

T. P. Varshanina and V. N. Korobkov

Fig. 5 Residual relief layer thickness relative to the 4th order basic surface (Pliocene)

Gradient fields of the greatest power of the differential layer between basic surfaces of two, three, or four orders projected on the modern block structure of the researched territory have been analyzed. The BP 4–3 vector field (according to the power of the differential layer between basic surfaces of BP-4 and BP-3) (Fig. 8) characterizes the direction and relative speed of the horizontal movement of the blocks in the Pliocene. During this period the blocks in the northern part of the researched territory differ in the small relative speed of the horizontal movement, which, in general, was directed to the south toward the axial zone of the Greater Caucasus.

Spatial-Temporal Geodynamic Model of Adygea

99

Fig. 6 Residual relief layer thickness relative to the 3rd order basic surface (Pliocene–Quaternary period)

This tendency in the movement of the blocks remains in the west of the republic to the crustal Akhtyrsky Fault and in the east to the Rocky Ridge. In other territories the field of the tectonic tension is more and more differentiated toward the Greater Caucasus Ridge, testifying to the complex system of the constrained deformations. From the Rocky Ridge to the south, the speed of the horizontal and vertical movement of the blocks increases considerably. The tectonic block of the Lago-Naki Plateau acts as a concentrator of the tectonic tensions because the greatest gradients of the power of the differential layer of all surrounding blocks are directed to this structure. In the area of the North Jurassic Depression and Pshekish-Tyrnauz Fault, a tendency to stretching is noted.

100

T. P. Varshanina and V. N. Korobkov

Fig. 7 Residual relief layer thickness relative to the 1st order basic surface (Quaternary period)

The vector field of the tectonic tensions of BP-3 and BP-2 (according to the power of a differential layer between basic surfaces of BP-2 and BP-3) (Fig. 9) testifies to the preservation of the general tendency of the direction of horizontal movement of the blocks in the flat and mid-mountain part of the territory. But in the field of the North Jurassic Depression, stretching tension is now observed. The compression tension concerning the Lago-Naki Plateau acting as a concentrator of the tectonic tension is becoming prominent. The vector field of tectonic tensions of BP 2–1 (according to the power of the differential layer between basic surfaces of BP-1 and BP-2) (Fig. 10) specifies that at the end of the Quaternary Period the general tendency of the direction of the horizontal movements of the blocks of the researched territory remains stable. In the field of the North Jurassic Depression, a tendency to compression is noted.

Spatial-Temporal Geodynamic Model of Adygea

101

Fig. 8 BP-4 and BP-3: direction and relative speed of the movement of the blocks expressed through the greatest gradient of power of a differential layer

Intensity of the tension of compression from the east blocks increases and is weakened from the western blocks surrounding the Lago-Naki Plateau, which remains the block concentrator of the tectonic tensions. Thus, at the regional level the general nature of the tectonic movements remained invariable for 5 million years as the stable provision of the main lineament, and also permanent decrease in speeds of vertical movements, testifies. At the same time the direction and speed of the movement of each of the blocks undergo changes. Research on the existential differentiation of the calculated continual field of the tectonic tensions within the tectonic blocks (Fig. 11) has shown that from Pliocene to Quaternary time the concentration of the tectonic tensions from the points of their application on the block borders was gradually redistributed, either on the whole block or to the side that is exposed to the greatest tension of the constrained deformation (Fig. 12).

102

T. P. Varshanina and V. N. Korobkov

Fig. 9 BP-3 and BP-2: direction/speed of the movement of the blocks, expressed through the greatest gradient of the capacity of a differential layer

The type of the tectonic tension between the blocks is determined by the direction of the greatest gradient of a differential layer power. The tension of the compression (Fig. 13) is fixed upon convergence and the tension of stretching (Fig. 14) on the divergence of the greatest gradient of power of a differential layer neighboring the breaks dividing the blocks.

2.3

Topological Analysis of Adygea Structural Existential Geodynamic Model

When studying the structure and properties of the geodynamic model, use of the methods of topological analysis has been suggested. For example, the topological order of communication can be applied as a measure of durability, and the concept of structural stability in a topological study of distribution of block structures leads to splitting the geodynamic space into a final number of not-blocked structural areas

Spatial-Temporal Geodynamic Model of Adygea

103

Fig. 10 BP-2 and BP-1: the direction/speed of the movement of blocks, capacities of a differential layer expressed through the greatest gradient

Fig. 11 Differentiation of tectonic tensions within the tectonic blocks

104

T. P. Varshanina and V. N. Korobkov

Fig. 12 Results of calculations in the model of historical dynamics of the differentiation of the tectonic tensions in tectonic blocks and breaks: (a) Pliocene; (b) end of the Pliocene – beginning of Quaternary time; (c) Quaternary time

[11]. The combinatory properties of a final topology can be expressed by means of simple operations on its graph. An example of a graph corresponding to the block diagram of the geodynamic model of the territory of the Republic of Adygea is presented in Fig. 15. The edges of the graph have a weight corresponding to the rank of lineaments that they represent. The knots are designated by circles, the size corresponding to their degree. Large knots correspond to more stable circumstances, and small knots are located along active lineaments and in the zones of tectonic instability. The concentration of small unstable knots speaks about the primary process of crushing blocks and the settling down of new morphostructural knots. The graph of the scheme of the geodynamic model of the researched territory with the tops corresponding to their rank allows us to display more and less tectonically

Spatial-Temporal Geodynamic Model of Adygea

105

Fig. 13 Rocky Ridge, the River Belaya lineament: tectonic tension of stretching between the blocks and divergence of the greatest gradient of a differential layer

Fig. 14 The Pshekish–Tyrnauz Fault. The tectonic tension of compression between the blocks: convergence of the greatest gradient of power of a differential layer

stable elements, to calculate automatically its topological characteristics, to carry out expert topological analysis of the model structure, and, using properties of homeomorphism, to forecast its development.

106

T. P. Varshanina and V. N. Korobkov

Fig. 15 Scheme of the geodynamic model of the territory of the Republic of Adygea with tops corresponding to their degree

3 Verification of the Structural Existential Model of the Adygea Geodynamic System For verification of the structural spatial-temporal model of the geodynamic system of the territory, the task of defining the degree of compliance of its parameters to the data of instrumental observation has been set. Analysis of structural compliance of block elements of the geodynamic model of the Republic of Adygea and the tectonic map of the North Caucasus [12] has revealed a high extent of coincidence of borders of structural and material complexes of different eras and geodynamic situations with borders of elements of the block structure of the developed geodynamic model. So, the borders of the Indolo-Kuban marginal trough, the East-Kuban Trough, Laba-Malka Monocline, the Middle zone of the Greater Caucasus increase, the Akhtyrsky deep fault, the Pshekish-Tyrnauz Fault, etc., almost completely coincide with the allocated elements of the block model of the territory that testifies to clear manifestation of the units of the tectonic structure of the territory in the modern relief (Fig. 16). The elements of the block

Spatial-Temporal Geodynamic Model of Adygea

107

Fig. 16 Structural compliance of the elements of the geodynamic model of the territory of Adygea and the tectonic structure of the Northwestern Caucasus (tectonic map [13])

model of the territory correspond to the structure of the convective warm stream field (Fig. 17). A high coefficient of correlation is recorded between instrumental and modeled data of speeds of vertical movements in the studied territory. Generalized data of the spatial differentiation of speeds of vertical tectonic movements on these territories by the results of the repeated leveling during 1925–1992 have been provided in the known schematic maps [12] (Fig. 18). On the digitized schematic maps of Liliyenberg (Fig. 18), grid surfaces have been constructed and the correlation coefficient between instrumental and model values of speeds of vertical movements on each tectonic block has been calculated. The maximum and average values of speeds of vertical movements (in mm/year) have been introduced into the calculations according to the instrumental data and the values corresponding to them calculated for the Quaternary Period according to the power of a differential layer (Table 1) (Fig. 19). Thus, verification of the structural existential model of the geodynamic system of the studied territory has shown a high degree of compliance of its modeled

108

T. P. Varshanina and V. N. Korobkov

Fig. 17 Compliance of the structural elements of the block model of the territory to the convective thermal stream (schematic map of convective thermal stream [14])

parameters to the data of instrumental observation, confirming the possibility of application of the model for monitoring and forecasting tectonic and seismic processes.

4 Conclusion The developed existential geodynamic model should be attributed to structurally similar models as it contains a hierarchy of structural elements, each of which shows mutual similarity of structural units of a field of tectonic energy, displaying its structure and properties. In this way, the degree of uncertainty of dynamic properties of the model decreases and simplifies the calculated forecasts by means of heuristic methods.

Spatial-Temporal Geodynamic Model of Adygea

109

Fig. 18 Cartographic geodynamic models of modern movements of morphostructures of the Caucasus and Transcaucasia (1925–1992) [12]

110

T. P. Varshanina and V. N. Korobkov

Table 1 Correlation coefficient between model and instrumental data of speed of vertical tectonic movements

Model data Average values Maximum values a

Instrumental data (1925–1992) Average values Maximum values 0.78 0.74 t ¼ 2.35%a t ¼ 8.12%a 0.81 0.70 t ¼ 9.30%a t ¼ 8.63%a

Test of significance t at t table ¼ 2,576

It is possible to offer precedence rules for seismic event forecasting: 1. Creation of the dynamic existential model of tectonic behavior on the territory of an unlimited area 2. Determination of areas of concentration of tectonic tensions 3. Space monitoring of height marks in the areas of concentration of tectonic stresses 4. Monitoring the greatest gradient of power of a differential layer in the field of concentration of tectonic tension 5. Monitoring the magnitude of seismic manifestations 6. Pointed forecasting of a place, time, and magnitude of a seismic event Modeling the dynamics of the tectonic movements and estimation of the hierarchy of the tectonic units on the basis of the structurally similar spatial-temporal models are possible in territories of unlimited space. The structurally similar spatialtemporal geodynamic model can be used for various modern methods of obtaining new data: • Investigation of spatial-temporal regularities of tectonic stress fields generated in the Earth’s crust in the hierarchy of blocks of aktuotectonic models and identification of stress concentration points • Creation, in the geoinformation environment of kinematic models, of the processes of blocks of interaction according to algorithms developed in compliance with theoretical provisions of tectonophysics and creating the forecast of their development In practical terms, the model is useful for determining historical trends of tectonic movements and seismic stress concentrators, identifying the points of monitoring earthquake precursors, seismic forecasting, a system optimization tool for geodynamic observations, and monitoring the security of the production infrastructure. The system geodynamic model of the territory is a necessary basis for monitoring adverse and dangerous exogenous processes and defining their relationship with the tectonic behavior of the territory.

Fig. 19 Vertical speeds of tectonic movements on model and instrumental data (a) along the A-B profile (b)

Spatial-Temporal Geodynamic Model of Adygea 111

112

T. P. Varshanina and V. N. Korobkov

References 1. Lilienberg DA (2001) Regularities and mechanisms of recent geodynamics of morphostructures of the Crimea, the Caucasus and the Caspian Sea region/Problems of geomorphology and Geology of the Caucasus and Ciscaucasia. XXIY Plenum of the Geomorphological Commission of the RAS. Kuban State University, Krasnodar, pp 45–72 (in Russian) 2. Martynenko AI (ed) (2005) Atlas of the Republic of Adygea. Closed Joint Stock Company “Associated Cartographic Center – M.”, Maykop, p 79 (in Russian) 3. Goncharov MA, Talitsky VG, Frolova NS (2005) Introduction to tectonophysics. Universitet Knizny Dom, Moscow, p 496. (in Russian) 4. von Bertalanfy L (1968) General system theory foundations, development, applications. George Braziller, New York, p 290 5. Khunagov RD, Varshanina TP (2010) Model and structure of data of the structural approximately similar geospace model, N. 2. Vestnik Adygeiskogo Unviersiteta, Seriya NaturalMathematical and Technical Sciences, pp 93–110 (in Russian) 6. Varshanina TP, Plisenko OA, Solodukhin AA, Korobkov VN (2011) Structurally similar geodynamic model of Krasnodar Region and the Republic of Adygea. Kamerton, Moscow, p 128. (in Russian) 7. Varshanina TP (2009) Object-oriented model of the subsystem relief. Geography and geoecology at the modern stage of interactions of nature and society. The geography and geo-ecology at the present stage of interaction between nature and society. Russian National Conference “Seliverstovskie,” Saint Petersburg, 18–19 November 2009. St. Petersburg State University, pp 146–151 (in Russian) 8. Rantsman EY, Glasko MP (2004) The morphostructural nodes – the places of extreme natural events Media-Press, Moscow, p 224 (in Russian) 9. Philosofov VP (1960) A brief guide to morphometric method of search of the tectonic structures. Saratov University Press, Saratov, p 69 (in Russian) 10. Philosofov VP (1975) Fundamentals of the morphometric method of tectonic searches of structures. Saratov University Press, Saratov, p 232 (in Russian) 11. Hass EC, Plath PJ (1987) King RB (ed) Chemical applications of topology and graph theory. Mir, Moscow, pp 457–471 12. Lilienberg DA, Kaftan VI, Kuznetsov YA, Serebryakova LI (1997) Cartographic model variations of modern tectonic movements morphostructures: Caucasus and Transcaucasia to different eras Geomorphology 4:63–75 (in Russian) 13. Andreyev VK, Baranov GI, Grekov II (1999) Tectonic map of the Northwest Caucasust. Yessentuki, p 104 (in Russian) 14. Grekov II, Oslopov DS, Prutskiy NI (2001) Tectonodynamic structure of the North Caucasus (the experience of the geological interpretation of convective heat flow). No. 1912-B2001. All-Russian Institute of Scientific and Technical Information, Yessentuki, p 108 (in Russian)

Development of Exogenous Geological Processes in the Territory of the Republic of Adygea Sergey A. Lebedev and Lenina A. Korinevich

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Exogenous Geological Processes Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 The Republic of Adygea Territory Exogenous Geological Processes . . . . . . . . . . . . . . . . . . . . . 3.1 Exogenous Geological Processes due to Gravity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Exogenous Geological Processes due to Surface Waters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Exogenous Geological Processes due to Groundwaters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Hazard Exogenous Phenomena on the Republic Territory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

114 114 114 116 129 135 137 141 142

Abstract Exogenous geological processes are variety of geological processes that occur in lithosphere subsurface parts due to external factors impact. The Republic of Adygea geographical position (mountains and plains) determines wide-rage development of exogenous geological processes: water and wind soil erosion; flood, soil salinization, and water logging; reservoir banks destruction; snowslides; mudflows; landslides; karsts, etc. Keywords Creep, Erosion, Exogenous geological processes, Floods, Karst, Landslide, Mudflow, Rockfalls, Scree, Sedimentation, Snowslide S. A. Lebedev (*) Geophysical Center, Russian Academy of Sciences, Moscow, The Republic of Adygea, Russian Federation Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] L. A. Korinevich Department of Mineral Resources of the Southern Federal District, Geology and Licensing Department of the Republic of Adygea, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 113–144, DOI 10.1007/698_2020_558, © Springer Nature Switzerland AG 2020, Published online: 8 September 2020

113

114

S. A. Lebedev and L. A. Korinevich

1 Introduction Geological processes are divided into two interconnected groups: endogenous (from ancient Greek endon – inside, i.e., born inside) and exogenous (from ancient Greek ex – outside, i.e., born outside). Under exogenous geological processes (EGP), we understand the geological processes variety that undergoes in the lithosphere nearsurface parts mainly influenced by the external factors, though endogenous factors play great part in this processes development [1].

2 Exogenous Geological Processes Classification Among existing general genetic EGP classifications, the most convenient from practical point of view and appropriate to require in estimation of process danger is the classification given in the work [2]. It is based on the EGP study systemic approach (Table 1). There are several hierarchical levels: groups, classes, types, and species. The hierarchical ladder can be continued. In case of need the variety of EGP can be allocated. All EGP variety can be united into seven groups according to (Table 1.) I, climatic and biological factors; II, relief energy (gravity); III, surface waters; IV, groundwaters; V, wind; VI, rocks freezing and thawing; VII, underground space development. Classes are distinguished according to the mechanism of the impact of the main agent conditions; types, due to main EGP manifestation forms, and genetic types reflect the specific features of the processes manifestation (Table 1). The work of [3] discusses in detail the exogenous effects on soil of the Republic of Adygea. In this chapter we will consider exogenous geological processes on the Republic of Adygea territory and their hazard manifestations (Figs. 1 and 2).

3 The Republic of Adygea Territory Exogenous Geological Processes Common to the Republic of Adygea territory exogenous geological processes (EGP) are water and wind soil erosion; flooding, soil salinization, and waterlogging; reservoir shoreline erosion, snowslides; mudflows; landslides; karst, etc. (Fig. 1). At the same time, intensity of these processes is different in different districts of the Republic (Fig. 2).

Development of Exogenous Geological Processes in the Territory of the Republic. . .

115

Table 1 General exogenous geological processes classification [2] Group I. Due to climatic and biological factors II. Due to relief energy (gravity)

Class Weathering

Type Areal Linear

Movement without losing contact with the slope or with little loss of it

Landslides

Snowslides

Glaciers

Slope contact losing movement

Creep Rockfalls

Scree III. Due to surface waters

Oceans, seas, and lakes

Reservoirs

Abrasion Thermoabrasion Alongshore sediment motion Shoreline erosion

Water flows

Water logging Erosion

III. Due to groundwaters

Water flows

Thermoerosion Mudflow

IV. Due to groundwaters

Dilution and leaching

Flood Karst

Mechanic carryover

Suffusion

Groundwaters level decrease

Surface subsidence

Species Physical Chemical Biological Debris flow Mud flow Block landslides Flow landslides Rockfall landslides Snowslip Channeled Jumping Mountain (valley) Continental (cover) Rockfall (itself) Rock spalling Rock slide Channel Areal Oceans and tidal seas Tideless seas Lakes Shoreline destruction (rockfalls, scree, landslides) Shoreline erosion Slope Gully River Glacial Rain Snow thaw Dam breaks Volcanic Carbonate Sulfate Saline Suffusion Ground erosion

(continued)

116

S. A. Lebedev and L. A. Korinevich

Table 1 (continued) Group

Class Surface water level increase

Type Flood Salinization Water logging

Weakening and destruction of soil structure connections Clay rock volume increase

Loess subsidence Drift sand Bulking Deflation

V. Due to the wind

Corrosion Accumulation VI. Due to rocks freezing and melting (thawing)

Freezing

Frost bursting Ice

Temperature changes with 0 С transition Thawing

VII. Due to underground space development

Solid mining and tunnels construction Oil and gas mining

3.1

Swelling

Rock glacier Thermokarst Solifluction

Species

High moor Transition swamp Fen soil

True drift sand Pseudo drift sand Deflation Blowing Dune formation Barkhan formation Seasonal Perennial Stream River Mixed Stone rivers Stone seas Fast Slow

Earth surface subsidence and dips Earth surface settling

Exogenous Geological Processes due to Gravity

Exogenous geological processes due to gravity are divided into two classes: rocks mass (ice and snow are considered in this case as rocks) with slope contact or slight contact lose and rocks mass movement with slope contact lose [1]. Landslides, snowslides, and ice are related to the first class. The class of rock movement with slope contact lose includes two genetic types EGP: rockfalls and scree.

3.1.1

Landslides

Landslide processes are consistent changes in composition, condition, and landslide characteristics from the moment of its origin and transportation to another level till

45°

52

53

55

16

26

27

k ac Bl

a Se

1

Apsheronsk

27

27

16

40°

9

9

52

9

26 16 16

1

8

Guzeripl

16

1

15

52

9

53

Tul'sky

Maykop

27

27

55

52

26

Labinsk

52 loess subsidence, gully erosion, landslides, salinization. 53 Landslides, gully erosion, suffusion, deflation. 55 Loess subsidence, suffusion, landslides, deflation.

48 Water logging, salinization.

26 Gully erosion, landslides, deflation, water logging 27 Water logging, suffusion, salinization, solifluction. 41 Water logging, landslides, suffusion, salinization, deflation. 42 Water logging, gully erosion, suffusion, subsidence slopes. 45 Gully erosion, landslides, water logging, salinization, solifluction.

15 Karst, rock falls, snowslides, landslides, talus, solifluction. 16 Karst, scree, mudflows, landslides.

1 Snowslides, rock falls, landslides, mudflows, cryogenic cracking , solifluction. 8 Snowslides, rock falls, landslides, mudflows, cryogenic cracking, solifluction. 9 Rock falls, mudflows

Fig. 1 Modern geological processes on the Republic of Adygea territory map (according to A.P. Karpinsky Russian Research Geological Institute Data)

44°

39°

48

45

Tuapse

15

9

42

41

r da sno oir Kra serv Re

Krasnodar

8

42

41

Shapsugskoye Reservoir

1

48

Exogenous Geological Processes

Development of Exogenous Geological Processes in the Territory of the Republic. . . 117

118

S. A. Lebedev and L. A. Korinevich

Intensity of Exogenous Geological Processes Krasnodar 45°

r da sno oir Kra serv Re

Shapsugskoye Reservoir

Labinsk

Maykop Apsheronsk

Bl ac k 44°

< 3%

Tul'sky

Tuapse

Se a

Guzeripl

3-25% 39°

> 25% 40°

Fig. 2 The Republic of Adygea territory exogenous geological processes intensity manifestation map (according to A.P. Karpinsky Russian Research Geological Institute Data)

total attenuation and exhibit in landslide composing rocks deformation. Landslide process occurs in case of slope unbalance and goes on (sometimes in several stages) till new balance is achieved [4, 5]. The landslide spreading and landslide process character are determined by a complex of engineering and geological location conditions, the most important of which are lithology and geological, geomorphologic, climatic, etc. Landslide is a displacement of rocks down the slope lower hypsometric level, without departing from the base, maintaining contact with the fixed base [6, 7]. Among the main natural landslide trigger causes are rock imbalance with increasing of the slope steepness caused by the river waters undermining; waterlogging slope rocks by precipitation or groundwater; the rock strength weakening due to

Development of Exogenous Geological Processes in the Territory of the Republic. . .

119

weathering; the impact of seismic shocks. Landslides on the Republic of Adygea territory are formed due to the geological structure, topography, climatic, and anthropogenic factors. To this we will relate corresponding paragenetic complexes of exogenous geological processes causing landslide development. Landslides are active in the areas of shale, marl, clay and other impermeable rocks marlstone, and clay and other water-resistant rocks. Different clay formations are considered to be landslide rocks typical that are “creep” characterized. These processes occur on loess mass slopes. The vast majority of landslides are due to groundwater outlet. Current tectonic movement (vertical) impact on landslide processes is not direct and needs such factors like relief, groundwater level, and sea level, causing slow, age-old changes. Most landslides occur at break zones due to high rock fracturing which is one of the signs of their formation. Belorechensky, Giaginsky, Krasnodarsky, Kurdzhipsky, Ust’-Labinsky, Fisht, and Circassian deep faults of the earth’s crust are located on the Republic territory (Fig. 3) [8]. Groundwater effect landslide processes greatly in the case of landslide deformation zone is in groundwater aquifers and, much less, if they compose upperlayer rocks passively involved into landslide. The main and most groundwater effect is the reduction of rock array strength for contact zoning – in fractures and fissility, especially where there are clay material concentrations. Relief affects both direct and indirect landslide intensity and nature. The direct effects are slope steepness, river valley morphology, and thalweg grades. Indirect influence is presented like one of the geographical environment component effects that characterize spatial distribution air circulation, atmospheric precipitation, temperature, surface and groundwaters, and vegetations. The most active landslide manifestations can be observed in mountain and foothill parts. Landslide climatic factors are heat and water regimens that promote landslide formation and activation. They are implemented in a specific weather type. In particular, with average annual precipitation increase, [9] there was noted landslide activation area enlargement on the Maykopsky District territory. At man-made territory development, it is important to estimate the relief stability in this condition, its separate parts, and the possibility of exogenous processes formation, including landslides dangerous to people. The main man-made impact is slope bottom cutting, slope transload and its loosening, artificial watering and rock waterlogging at water conduct effluxion and excess irrigation, explosion and vibrodynamic loading, and mining. There are a lot of landslide classifications due to shifting mechanism, manifestation forms, shifted rocks structure, and other signs. The present classification (Table 1) combines several key features: manifestation form and the movement nature. On these grounds it is advisable to allocate debris flows, mud flows, block landslides, flows landslides, and rockfall landslides. Debris flow a small shifting shallow area mainly of soil and subsoil cover. Their volume does not exceed the first cubic meters. Mud flow – a kind of plastic displacement moist talus deposits, weathering products, or other surface formations on the slopes without a clearly defined depression.

120

S. A. Lebedev and L. A. Korinevich

Deep Breaks of the Crust Krasnodar 45°

r da sno oir K r a s er v Re

3

2 5

10

1

Labinsk

Maykop

Apsheronsk

Tul'sky

4 12

3200

9 Tuapse

2700

2200

44°

Bl ac k

1700

11 Guzeripl

Se a

6

1200

700

200 m

Legend

1

8

7

Number of Deep Break

39°

1 – Akhtyrskaya Suture Zone 2 – Belorechensky 3 – Giaginsky 4 – Dzhubga

40°

5 – Krasnodarsky 6 – Kushchevsky 7 – Kurdzhipsky 8 – Mzymtinsky

9 – Tuapsinsky 10 – Ust’–Labinsky 11 – Fisht 12 – Circassian

Fig. 3 Deep faults of the earth’s crust in the republic of Adygea and Krasnodar Krai territory according to [7]

Flow landslides – plastic motion of earlier shifted landslide masses on wellformed relief shallows. At this landslide type, manifestation the plastic deformation toe boarder (landslide bed) is clearly marked. Landslide flow characteristic feature is the decrease of relative plastic deformation up the section. This landslide-type activation trigger from top, head, slope part, where motion totally shifted rocks structure damage, down the slope structure damage decreases. Block landslides are creeping, usually slow, block-formed rock masses shift down the slope on particular surfaces and form landslide steeps.

Development of Exogenous Geological Processes in the Territory of the Republic. . .

121

Table 2 Landslide sign classification [7] № п\п 1 2 3 4 5

Classification sign Size Displacement mechanism Form in plan Age Geological conditions

Landslide type Small, medium, large, gigantic Sliding, extrusion, floating out, subsidence movement, landslide of rocks, complex landslide Circle, front, gletcher-like, landslide flows, block landslides Ancient, young, modern Bedrock landslides, surface deposit landslides

Rockfall landslides are transition forms of slope rock shifting from landslide to rockfalls, when other motion forms appear: rolling and free falling, along with slipping. This motion type is characterized by complete original structure violation damage and high-speed shifting rocks motion. According to landsliding process mechanism, we highlight sliding, extrusion, floating out, subsidence, flowing, and dilution [10]. According to landslide size, they are divided into small (power has shifted mass of not more than 5 m, small size in terms – a few meters), medium (length and width in the range from tens of meters, power up to 20–30 m), large (length and width in the range from hundreds to several hundred meters, power up to 50–70 m), and giant (length and width in the range from several hundred meters to several kilometers, power up to 100–110 m or more). According to the geological conditions of development, landslides of bedrocks and landslides of surface sediments are distinguished. The sign landslide classification is given in Table 2. Landslides cause hazards due to dynamic shifting rock mass impact, destroying constructions and objects situated on their main truck and in the depositional area. Landslides on the developed territory quite often lead to hazards with extensive material damage and even human victims. Landslide shift destroys roadhouses and industrial buildings, damage pipelines, telephone and electricity networks, block overpasses, and riverbeds. Besides mountain, river slope landslides are fraught with blocking river beds (caused) with landslide masses, that initiates such dangerous for population and economics processes as upstream valley flooding and disastrous mudflow flood when the landslide dam breaks. Landslides can cause considerable damage to agricultural and forest land. Especially dangerous landslides are in mountain areas, where they often destroy entire villages. Man-made landslides on the Republic of Adygea territory are observed along Krasnodar Reservoir coast, where abrasion and slope shortening take place, causing gravity movement – rockfalls and landslides – as well as in the Republic southern part at the sideroad of Kamennomostsky (Khadzhokh) Village, Khamyshki village, at Lago-Naki Plateau. Slope degree, powerful unconsolidated sediments cover, atmospheric precipitation, and constant road clearing trigger new landslide.

122

S. A. Lebedev and L. A. Korinevich

In the Maykopsky District territory, landslide processes occur on Belaya and Kurdzhips Rivers’ banks and their inflows. The increase of damage is noted within the development of Maykop waterproof clay mass in the Belaya River basin. In the clay deposits of detrital molasse, landslides in natural not in artificial undercutting conditions develop as the result of loosened rocks in hypergenesis zone. On the territory of Quaternary molasse deposit, landslides are developed on the Belaya and Kurdzhips Rivers’ valley slopes; their activity is caused only by side erosion. There are block and block – consisting landslides widespread at that area. In Belaya and Kurdzhips Rivers interfluve on 6 30 slope landslides, flows are widespread. In the area of clay sediments, development of Paleogene-Neogene and Creta ceous age (to the west and to the south from Dagestanskaya Stanitsa) rock precipitation (rock watering by precipitation) affects the landslide activity. The block, consistent, block-consistent landslides are spread on this territory. The activation of river shore landslides, water balance of which involves glaciers any way, falls on spring-summer high flood. The man-made landslide manifestations are observed on road cross of Kamennomostsky Village – Khamyshki village, Dakhovskaya Stanitsa – Lago-Naki Plateau (area 1 category Khrebtovy). The slope steep, powerful unconsolidated sediments cover, atmospheric precipitations, and constant road clearing trigger new landslides. In Abadzekhskaya Stanitsa landslides permanently destruct Abadzekhskaya Stanitsa – Kamennomostsky Village road. Investigation of the Maykop group water conduct (first category areas Maykopsky and Khrebtovy) showed to have four landslide areas, causing real water conduct deconstruction danger. First part (area) is situated 850 m to northeast of the main water intake on left slope of Serebryanka River valley. The landslide top width is up to 80 m; the length down the slope is 60 m. Second part is located 300 m from the Bereznin River mouth on the northeast in the place intersection with the water conduit. The landslide length alongside water conduit is 50 m and down the slope – 40 m. Third part is located on the west slope of Pokidin Hillock at the water conduit north turn, 300 m to the northeast from dirt road to Kurdzhipskaya Stanitsa. The landslide length along the water conduit is 175 m and up the slope – 60 m. Fourth part is on the west slope of Belaya and Kurdzhips Rivers’ watershed, opposite the Kurdzhips River bridge (Krasnyy Village). The landslide length alongside the water conduit is 138 m. Landslide activation threats water conduit destruction on these areas. A total of 53.5% of the Republic of Adygea territory is quite hazard the landslide risk level: 35.5%, hazard; 9%, low-hazard; and only 2%, insignificantly hazard [11]. Complex analysis of landslide formation factors allows to allocate three landslide areas on the Republic of Adygea territory (Fig. 4): Great Caucasus foothill area, Northwestern Caucasus area, and West Caucasus area [8, 12]. The landslide area zoning is based on allocation of territorial integrity nature units, within which all landslide processes and phenomena, as well as their formation conditions and

Development of Exogenous Geological Processes in the Territory of the Republic. . .

Scheme of Zoning of Landslide Processes

123

II

Krasnodar 45°

I II

II Labinsk

Maykop Apsheronsk

Tul'sky

2

2

III

Bl ac k

2

Tuapse

1

III

44°

Se a

1 IV

2

V

Guzeripl

1

V

Legend Boundary of Region

V

Number of Region

Boundary of Subregion

1

Number of Subregion

39°

1

40°

I – Azov-Kuban Plain (lowland) Region. IV –Black Sea Cost of Caucasus Region II –Greater Caucasus Foothill Region V – West Caucasus Region III – Northwest Caucasus Region Subregions: Subregions: 1. Varenikovskaya – Neftegorskaya; 1. Krasnopolyanskaya; 2. Khrebtovaya. 2. Otradnenskaya. Fig. 4 The scheme of landslide zoning on the Republic of Adygea and Krasnodar Krai territory [8]

factors, are observed in relation to their individual characteristics, framed by set off territory of particular taxonomic rank.

3.1.2

Creep

Creep (from English creep to creep, crawl) is slow mass motion of surface and in less degree, bedrock sedimentations slope down not disrupting their continuity.

124

S. A. Lebedev and L. A. Korinevich

Creep is considered to be the intermediate phenomenon between landslide and plane denudation. There are deep-earth creeps, when material moves deep inside the earth, and slope creep material moving down the slope. Creep is originated by compaction loose rocks (loess and clay) on depth and the formation decompressed matter at the depth resulting from water thaw and freezing (cryogenic creep), groundwater pumping out, and oil and gas mining (anthropogenic creep). Creep results in formation of saucer-shaped depressions and bare slopes, and there are undulated accumulation of slope-shifted colluvial material (from Latin colluvio accumulation, promiscuous heap). Climatic conditions, vegetations, engineering, and economical human activities play major role in creep development. Forest vegetation destruction (cutting) as well as grass vegetation destruction (overgrazing) decreases structural soil adhesion, their water supersaturation, and – as a result creep activation. This phenomenon can be observed, for example, at cutting areas, where creep prevalence is up to 30 50%. The Republic of Adygea territory is creep-active mainly in foothills and mountain parts to the south from Maykop City. The Belaya River valley is noted to be creepprevalent from 20% to 100%. Precuesta Massivs rockfall-landslide deposits with power sometimes larger than 10 m in Azish-Tau Range district and Kamennoye More (Stone Sea) Ridge (the Belaya River left bank) slowly creep down the slopes.

3.1.3

Snowslides

Snowslide is a fast snow mass motion (movement), accompanied by their overthrow to the slope bottom (foot). We distinguish the following snowslide types: snowslides at all slope surface and channeled and jumping snowslides [13]. Snowslip is called a slipped wide front snow out of strictly boarded beds; the snowslide height is several times larger than the slipped snow stripe. Snowslip does not usually leave traces of their descent are well distinguishable in summer. Therefore, there are difficulties in their mapping. Channeled snowslides move on strictly fixed beds and in unloading sites form snow cones including large amount of rocks and wooden trash. Snowslides moving on narrow, steep areas, where motion gets free fall, are called jumping snowslides. These snowslides fall on the valley bottom with enormous power. Channeled and jumping snowslides leave the fallen timber trace in forest zone. Due to weather conditions, we allocate powder and wet snow snowslides. The majority of snowslide forming (trigger) factors on the Republic of Adygea and the whole Northwestern Caucasus territory are zonal; thus snowslide spread is mainly subject to the altitudinal zone law. The most relief forms promoting snowslide formation are high ridge crests and convex leeward slope side, bearing snow-drift sites, steepy slopes, mountain streams beds and temporal stream, hanging valleys, and steep surface 15–18 and make more snowslide possible. Snowsliding possibility increases up to a certain limit with steep

Development of Exogenous Geological Processes in the Territory of the Republic. . .

Categories of Relief, Promoting the Formation of Snowslide

125

9

9

Krasnodar 45°

9 Labinsk

8

Maykop 8

7

Apsheronsk

3

Tul'sky

7

4 5

2

Bl ac k

Tuapse

2

1

44°

Se a

3

Guzeripl

Categories of Relief

1

1

3

5

7

2

4

6

8

39° I. Relief promoting the formation of powerful snowslide dense net: 1. Alpine highlands and intensive dissected midlands with alpine relief; 2. Intensively dissected midland with sudden relief forms. II. Relief promoting the formation of dense low power snowlside net: 3. lowlands with sudden relief forms, intensively dissected

9

4 2 40°

III. Relief, promoting the formation of a of powerful snowslide sparse net 4. Midlands is average dissected with mild relief form; 5. Midlands with weakly dissected plateau-like watershed IV. Relief, promoting the formation of a sparse low power snowslide net 6. Intense and weak dissected midlands with mild relief forms; 7. The low plateau and plateau with steep slopes of the valleys; 8. High ridged plains, intensively dissected by erosion net and foothill. V. Relief excluding snowslide formation possibility: 9. gentle-hill relief, lowland plains.

Fig. 5 Relief categories causing snowslides formation on the Republic of Adygea territory [14]

degree increase. The work of [14] determines that more than 80% of all Western Caucasus have 25–50 steep slopes. There are three relief categories, promoting snowslide forming, and one excluding their formation (Fig. 5). The most dangerous snowslide are in Alpic highlands and midlands with different degree of differentiation. The main characteristic (peculiarity) of the Republic of Adygea and the whole Northwestern Caucasus is a wide variety of winter snow and snowslide activity. In particular, favorable winter snowslides occur everywhere in rare snowfall zone, and their total amount exceeds several times the average level. The massive snowsliding is assigned mostly to the periods of especially heavy snowfalls at not very low air

126

S. A. Lebedev and L. A. Korinevich

Number of Snowslide per 100 years

Tuapse

44°

Bl a

1

ck

Guzeripl

Se

a

3

5

10

39°

20

30

40°

Fig. 6 Snowslide intensity (density) [14]

temperature (up to 10 С), that is, characteristic of western cyclones [15]. The relief type with the comparatively small percipients amount (500–600 mm per year) does not promote snowslides. Broad-leave forests in middle land part also prevent it. Snowslide zone is located in the south highlands part of the Republic (Fig. 6). Snowslides are possible during intensive snowfalls on the northwestern slopes. The high-rise belt of snowslide distribution is in absolute heights of 1,300 2,000 m limits. According to relief and climate snowslide indicators, these areas have medium and low snowslide danger, as snowslide focus density from 1 to 5 events per 1 linear km and snowslide repeatability is less than 1 per 10 years (Fig. 6). The Belaya River upper basin is located in weak snowland zone (prevalence is 20%). Snowland risk areas are marked only at the most upper Belaya River, on the slopes of such mountain systems as Chugush and Fisht Mountains. There were allocated three snowslide risk areas. During the period of aerial observations (1980 1985), 25 snowslides of average 500 m3 volume were registered. The most powerful snowslides are formed at the woodless highland the Greater Caucasus Range slope zone at 2,000 m height. There were registered long wide snowslide (more than 1 km) swaths, moving down straight to the Belaya River valley, and its right flows bottomward. In this area snowslide prevalence is 50%. The upper Kisha River is in the zone of active snowslide, dated to snowy area with snow cover power on slopes reaches more than 2 m. Here, along with dry winter snowslides associated with heavy snowfalls, giving an increase in snow thickness of more than 20 cm, avalanches of wet snowslides are characteristic, the mass descent of which is observed in March to April. The snowslide risk period in this zone lasts 6–7 months per year. The snowslide prevalence is 70%. Lago-Naki Plateau is the largest ski and rest zone, and up the touristic center in the forest, there are snowslide swaths of crooked forest, large-scale straight line erosion gutters. One more snowslide risk area is the upper Belaya River, near Fisht

Development of Exogenous Geological Processes in the Territory of the Republic. . .

127

Mountain foothills, with touristic path and shelter “Fisht.” The shelter approaches are in snowslide risk zone.

3.1.4

Rockfalls

Rockfalls are sudden fall of rocks downfall with slope contact lose, accompanied by crushing and rocks mixing, and its chaotic accumulation ate the slope bottom. They can be subdivided into breakouts, stonefalls, and rockfalls themselves. Under inrush is usually understood not large in scale mainly rocks masses downfalls from steep slope, that cause no significant slope configuration changes. Stonefalls are falls of separate large stones and rock blocks. Due to its composition, rockfalls are divided into rock (stone), soil, and mixed ones [1]. On the Republic of Adygea territory, the rockfall zone occupies highland zone; on the south of the Republic, most powerful ancient and modern rockfalls are dated to calcareous ridges: to Fisht and Oshten Mountain mass and to Lago-Naki Plateau side parts. The rockfall prevalence here is up to 40 70%. The shelter “Lago-Naki” and touristic center “Lago-Naki” are located in potentially large rockfalls formation area. On the Guzeripl Village – Dakhovskaya Stanitsa (the Belaya River valley) road part at the vertical sledge bottom, consisting of heavy granulated Paleozoic silica sandstone, causes traffic risk. The rockfall processes prevalence here is 40%. Landslide risk area is located in the southwest of Maykop City. Geomorphologically it is the eastern slope of the Tsitsa River watershed and its left inflow, the Bursovaya River. Absolute elevations of the transverse profile of the site range from 480 m in the drive part to 370 m (water cut of the Tsitsa River). Shoreline excess to watershed is 110 m; the territory relief is ancient landslide, hilly depression, up the slope flattened areas and steep (up to 70–80 ) slopes alternate. The rocks are clays with aerolites, sandstone, and siderite interbeds. The sandstone horizon is marked at the bench bottom. The hollow layers fell on the northeast (the fell corner degree is 10–20 ) that composing with eastern slope composition make favorable conditions for landslide formation. Hollow layers fall on northeast. As a result of different-age landslides, the slope is complicated by three to four large landslide blocks that are traced clearly enough on the south area part (the technological unit area). The failure wall length increases from 2–3 m at the landslide top block to 8–10 m at the toe (bottom); subsidence step length changes from 5 to 20 m. At present, the landslide slope is in a weakly active stage. Disruption walls in the upper part are fragmentarily exposed, talus processes are underway, and mediumsized (1.5–2 m wide) landslide blocks are noted. The last significant landslide activation occurred in 2005. One of landslide risk areas is Guzeripl Village – Lago-Naki Plateau road (18–20 km to the west from Guzeripl Village). In the central part of landslide activation area, there is a relatively large block-consistent flow landslide about 150m long and up to 50m wide at the top part. In September, 2009 the landslide

128

S. A. Lebedev and L. A. Korinevich

completely destroyed the 26m long part of the road. The activated landslide is in the previous investigated active area border. Landslides develop actively along road insert in upper and down braes of Guzeripl Village – Guzeripl Nek road. The insert height varies from 8 to 17 m in upper brae, steep 45–60 , and active landslide and creep prevalence up to 70%. Geomorphologically the area presents the south slope of the Kamennoye More (Stone Sea) Ridge. Geologically the area is constructed by Low and Middle Jurassic sediments, presented by argillites, siltstone, sandstone, and limestone. Tectonically the area is located in the zone of modern active earth motions, boarded by Belorechensky Grabena and Fisht Horst. The main landslide activation factor is atmospheric precipitation increase during winter to spring period. Besides weather factors man-made factor also plays an important role (slope trimming cutting at road construction). In Kamennomostsky (Khadzhokh) Village, there is a rockfall-landslide risk area along the Belaya River right bank. In the settlement central part, the river valley is boxlike with steep slopes (more than 60 ), complicated by different-age landslides. The prevalence in the construction area boarders is 50%. The geological composition of the described area is made by Lower Cretaceous clays with limestone, sandstone, siltstone, and marl beds. The Belaya riverside erosion and groundwater draining middle Quaternary watershed on bedrock roof, presented by argillite clays with marl and sandstone beds, can cause rockfalls and landslide activation. To the south of Tul’sky Village on the Belaya River right bank, there is a landslide risk area. The area is an active block-consistent front landslide. The Belaya River bed is the landslide base. Landslides can develop taking bedrock clays within the second terrace above the floodplain. The landslide area has a landslide part width of 15–20 m and area length of 120–150 m. The slope height is 15 m. There is a dirt road running between bluff and the power line. Trigger factors are weather and hydrological factors (the Belaya River slope bottom washing). There is the Krasnooktyabr’sky Village landslide located in the Maykop Hydro Power Station (HPS) area. The Maykop HPS is situated at the floodplain area, about 40 m wide on the Belaya River left bank, in its middle stream opposite to Maykop City. From the south the HPS boarders just to the valley bed slope. There is a large landslide on the slope. The landslide is located to the northeast of Krasnooktyabr’sky Village opposite to Maykop City and extends on almost 600 m to the discharged channel mouth. The landslide is frontal, block-consistent, and long-leaving, of about 300,000 m2 and is formed on ancient landslide slope. New bulging swells up to 3.5 m in height formed in landslide body (trunk); landslide mass is liquefied. Landslides shift in north and northwest directions. The landslide tongue shifts to the Belaya River bed and is actively eroded. The erosion ledge height is 1.3–2.2 m. The concrete floor subsidence in the machine hall of the HPS is observed. The main factors affecting landslide activity on the Krasnooktyabr’sky landslide are climatic, particularly, heavy precipitation. The landslide is located in the zone of active tectonic deformation intersection; landslide slope is laid by loose rocks (sands, loamy sands, loamy soil) and constantly watered due to watershed horizon

Development of Exogenous Geological Processes in the Territory of the Republic. . .

129

unloading at the Belaya River left bank. This landslide stabilization is short-time and only at local landslide areas. Landslide processes develop actively along the Abadzekhskaya Stanitsa – Novosvobodnaya Stanitsa road – that cross the top part of a wide ancient landslide, formed on the right bank of the Mamryuk River valley. The main factors of its formation are geological, tectonic, and meteorological (precipitation). The ancient landslide width is about 600 m, length – 700 m. From 1983 till now, this landslide is noted to have triggering focuses (from 10% to15% of landslide area). The last significant activation took place in spring 2011. In 2013 ancient landslide central part activation in subactive state was observed. The activation area width is up to 400 m, length is 500 m, and total activation area is of 200,000 m2.

3.1.5

Scree

Scree is a continuous rock weathering product demolition on large steeps in the form of separate fine rock debris (from dust to large stones and blocks). There are scree material cones forming at the slope foot (bottom). Scree on their development form can be channel and areal. For the channel scree, material moves on groove or hollows; areal scree doesn’t have clearly distinguished motion ways. In cone talus, not like other genetic types, material sorting by granulometric content is observed: fine material sediment at the head (top) cone part and large debris roll down to the cone (foot) bottom. On the Republic of Adygea territory, most intensive scree processes are developed in the south highland zone, characterized by highest prevalence from 20 to 30%, rarely up to 50%. Scree, formed on erosion ledges, are found on the intensive side slope washing out areas, in sharp narrowing of the bottom of the Belaya River valley, as well as its inflows – the Kisha River and the Sakhray River – on steep slopes of gulches in the newest tectonic structure intersection (crossing). There are often downfalls and constant screeing of rock weathering materials. The scree prevalence in such areas is 30 40%. These scree area activation periods are due to water flow hydrological regime, as well as climatic characteristics that determine weathering processes intensity changes. Engineering in most cases is connected with artificial rock ledge construction that causes sudden damage of natural stress fields in massive rocks and formation of rock anthropogenic rock fracturing. Similar scree processes are observed along the Dakhovskaya Stanitsa – Guzeripl Village road.

3.2

Exogenous Geological Processes due to Surface Waters

The third EGP group due to surface water action is subdivided into two large classes – processes connected to activity of water reservoirs and watercourses. In the EGP class, connected to water reservoir, there are two process types: shoreline

130

S. A. Lebedev and L. A. Korinevich

erosion and reservoir silting, and EGP class connected to watercourses consists of the following processes: erosion, thermoerosion, and mudflow.

3.2.1

Shoreline Erosion

Shoreline erosion in genetic relation as well as abrasion (reservoir bank destruction by wave energy) is a complex process and consists of some elementary processes; the main are rockfalls and landslides, but they differ greatly from the sea shore ones. The main condition characteristic, when shore erosion occurs, is reservoir drawdown (reservoir water drainage), necessary for economics and filling it again in spring; shoreline erosion is a fading process. The Krasnodar reservoir is the largest both in the Republic of Adygea and in the North Caucasus. Its creation changed both the Kuban River water regime and economical activity character at its downstream. Its water provided rice irrigating systems in the Krasnodar Krai and the Republic of Adygea, contributed to some valuable fish species reproduction, and improved the Kuban River navigation, peaks of floods cutting, and hazard flood threat elimination, and reservoir shoreline is a rest zone for local population living around it. Engineering and geological reservoir shoreline survey showed that active abrasion formation here is due to element peculiarities’ natural conditions, dish morphology, and reservoir water regime. The right bank part greatly differs from the left bank. The banks are asymmetric: the right bank is high, steep, and bold, and the left one is low, gentle, and shallow. Reservoir shoreline is laid by loess loams that are characterized by subsidence features up to 4–5 m depth. Active shoreline reformation is mostly due to vehicle and sedimentation accumulation. Abrasion is due to wind; the most erosion effect has large waves, and their intensity decreases in summer due to reservoir level decrease [16]. On the shore located upper Voronezhskaya Village (Fig. 7) with 40–50 m shore ledge, we can observe periodical landslide due to river gradual shore ledge washing. The reservoir left bank characteristic feature is intensive erosion of wide valleys of the Kuban River tributaries. The shoreline is characterized by intensive irregularity, extended flat areas, and a significant amount of cape-like areas. Steep ledges along the reservoir shore are rare and are processed. They are mainly linked to bays in the Pshish, Apchas, Marta, and Psekups River valley wellheads [17]. Along the southeastern bank from Krasnogvardeyskoye Village to the Belaya River mouth, the first above floodplain terrace up to 10 m is traced, and from the Belaya River to hydrosystem dam, there is the second above floodplain terrace of the Kuban River. On the Southeast Krasnodar Reservoir coastline from Krasnogvardeyskoe Village to hydrosystem dam at the abrasion ledge bottom, laid by loose quatrain sedimentation, there are 1.5 m high underwashing, numerous niche, cornices, as well as accumulation of not eroded scree blocks. The destruction impact on the cliff (a steep cliff abrasion, resulting from high bedrock shore destruction by waves) by rockfall processes in this area of investigation was 50–70%

Development of Exogenous Geological Processes in the Territory of the Republic. . .

131

Fig. 7 The scheme of the Krasnodar Reservoir silting for 1973 2012 [20]

3.2.2

Silting

Silting is sedimentation, traction, and suspended load that are brought by the river and its flows directly to the reservoir. One of the most serious problems of the Krasnodar water reservoir during its existence is the process of silting beds by sediments that are carried away from watersheds and/or as a result of shore erosion. The silting speed depends on the regimen of liquid and solid drain, watershed erosion intensity, reservoir exploitation conditions, and others [18]. The annual sediment load of five main rivers (Kuban, Laba, Belaya, Psekups, Pshish) is 6 million tons. In addition to entering solid runoff significant role in the reservoirs silting play the erosion of coasts, which accounted for 15–25% of sedimentation amount. In the period from 1973 to 2005, the reservoir received 255 million m3 of sediments, because this amount has reduced the total reservoir

132

S. A. Lebedev and L. A. Korinevich

capacity (with the value of the normal water level of 35.23 m) by 8.4% of the project [17]. The reservoir useful volume reduction due to filling it with sediments affects its ability to fulfill reclamation and flood control functions and is as important problem as hydrotechnic facilities “aging.” When the reservoir is shallowing, its overfilling with flood waters possibility increases; thus the reservoir volume decrease may be considered as potentially dangerous hydrologic situation that occurs in hazard flood and dam overflow [3, 19]. The other reservoir silting problem is its contamination located on watersheds’ industrial, agricultural, and municipal facilities waste products, which are transported to the reservoir with solid river sediments. The permanent pollutant accumulation and concentration increase leads to severe ecological consequences both for reservoir itself and its users. According to the complex cartographic and remote sensing analyses of the Krasnodar Reservoir during its operation period from 1973 till now, the scheme of silting was composed (Fig. 7). There were allocated five dam formation periods: starting from the Tschikskoye Reservoir initial existence phase and ending with the current state. It should be noted that dam size according to 2009 2012 data is given taking into account the formed shallows, exposed when reservoir drawdowns [20]. The Krasnodar Reservoir water surface area decrease (382 km2 at the normal water level of 32.5 m) from 1973 till 2012 showed that the silting area is about 73 km2 or 19.1% of the reservoir surface area.

3.2.3

Erosion

Erosion is a complex process of rock destruction by flowing water. These are the following erosion types: slope, ravine, and river. Slope erosion is slope soil and topsoil erosion by small water flows. The slope erosion is triggered during heavy rains and snow thawing. Ravine erosion is a deep erosion of loose and coherent rocks by concentrated water flows. River erosion is divided into side and deep erosion. When erosion occurs in frosted rocks, the major role in this process development is played rock thawing. In this case the process is called thermo erosion. Bottom erosion in the Republic of Adygea is widely spread only in south mountain part. Intensive bottom erosion is noted at the upper Tsitsa River. Valley parts with active bottom erosion are registered by the continuous outputs of the riverbed pre-Quaternary rocks. The riverside erosion prevalence in the Republic of Adygea has important, though non-uniform, development. Engineer and geological meaning of this process is quite significant. There are a lot of settlements effected by and located in the possible side erosion area. The bank erosion unsteadiness prevalence analyses in the Republic of Adygea showed extremely insignificant bank erosion development in highlands, where water flow energy is mostly directed on the river bottom dredging. Two main factors effecting space development intensity process are revealed: river drainage density and intensity of meandering of permanent water flows.

Development of Exogenous Geological Processes in the Territory of the Republic. . .

133

The bank erosion speed is determined, mainly, by flow speed and rock erodibility. The highest speed is noted on the Kuban, Laba, Belaya, Pshish, and Psekups Rivers. According to Krasnodar Zonal Hydrometeorological Observation data, the highest North Caucasus river speed is 3–5 m/s. The highest speed was noted on the Belaya River (5.4 m/s). The most eroded rocks are Quaternary rocks accumulated at alluvial terrace; base presence in most cases reduces the shore erosion rate. In the Kuban River valley, the side erosion areas are observed down the Krasnodar Reservoir dam. Here river meandering is limited due to continuous river landslide. Erosion processes are partially developed here, though they occur at many bend tops. The Kuban River bank side erosion prevalence down the Reservoir is of 70%.

3.2.4

Mudflows

Mudflows are sudden short-time mountain flows, highly saturated (compared to usual water flows) with solid materials, originating during rains, intensive snow, and ice thawing as well as during dam and blockage breakouts in river valleys, where there are large volumes of clastic material deposits. By formation peculiarities mudflows are divided into glacial, rain and snow thawing, blockage and dam breaks, and volcanic. Glacial mudflows are connected with glacial; rain mudflows and snow thawing mudflows are formed in small and temperate water flow (stream) valleys; mudflows of breaking genesis are connected with dam and blockage destruction on medial and big water streams (flows); and volcanic mudflow formation of solid and liquefied components is due to volcanic activity. Mudflows can be allocated due to the following signs (characteristics): granulometric content of solid component, water components, and motion regime. According to the prevailing number of major granulometric fractions, the following should be distinguished: stone, mudstone, and mudflows. The mudflow classification by granulometric content [21] shows well the dynamic of mudflow depending on granulometric content. The mudflow types are allocated in this classification according to main granulometric fractions large-scale detrital and clay, as well as more than 2 mm size particle total component. Mudflows are subdivided by the size of large detrital material into four groups: gravel, pebble and boulder content more than 10%; pebble, pebble and boulder content more than 10%, though boulder content is less than 10%; boulder, boulder content more than 10% and large pebble content less than 10%; and block, block content more than 10%. The following mudflow classes with 2 mm larger particle component can be allocated by total content of large clastic material in solid part: (1) 65%. Fine soil part of mudflow solid component by content of clay particle component and plastic characteristics may also be subdivided into four types: (1) clay particle component >35%, (2) loam 35 15%, (3) sandy loam 15 5%, and (4) sand 30%

39°

40°

Fig. 4 Motor transport pollutant emissions in the atmosphere (% of total emissions) in the Republic of Adygea according to information presented in [23]. I – Maykop Republican Urban District, II – Adygeysk Republican Urban District, III – Giaginsky District, IV – Koshekhabl’sky District, V – Krasnogvardeysky District, VI – Maykopsky District, VII – Takhtamukaysky District, VIII – Teuchezhsky District, IX – Shovgenovsky District

2.1.2

Wastewater

Every year, the amount of water used by a person in everyday life, during economic or industrial activity, increases. Accordingly, the amount of polluted wastewater that needs to be diverted from the place of residence of people is increasing as well. Depending on the sources of formation, concentration and composition of impurities, wastewater of enterprises is divided into four types according to information presented in [6]: industrial, surface, domestic, and drainage (Table 2). Discharge of wastewater into the sewerage system. A priority method for wastewater disposal in urban environments is discharging into a central or local sewerage

Ecological State of Soils of the Republic of Adygea Under High. . .

191

Table 2 General characteristics of wastewater Type of wastewater Industrial wastewater

Wastewater category Relatively clean Lightly polluted

Heavily polluted Surface wastewater

Meltwater Rain water Watering

Domestic wastewater

Drainage wastewater

Sources of wastewater formation – Cooling systems – Clean condensates – Hydrotransport – Washing and rinsing products – Dehydration of products and raw materials – Condensates of technological processes – Treatment of solid and gaseous waste – Washing of equipment and vehicles – Washing of industrial premises – Discharge of spent solutions – Facilities for water preparation and wastewater treatment plants (liquid waste) – Territory of enterprises – Roofs of buildings – Territory of enterprises – Roofs of buildings – Cleaning of the territory of enterprises – Bathrooms – Showers – Food blocks – Laundries – Storage sites for solid industrial waste – Solid waste landfills

system. The main advantage of this method is that the sewerage system is equipped with all treatment systems and effectively performs the function of collecting and transporting a large amount of wastewater. Before discharge of wastewater into the sewerage system, it is also treated to the pollution levels established by law. Discharge of wastewater onto the terrain. If it is not possible to discharge wastewater into the sewerage system, then discharging can be arranged directly to the terrain. This drainage system is often used for rain water, which is mechanically treated before discharge. Domestic and industrial wastewater must be mechanically and biologically treated before being diverted to the terrain. Discharge onto the terrain is allowed only after official confirmation of the fact that the concentration of pollutants is below the maximum permissible level. This is necessary for protection of the ecological condition of the soil and water bodies. Discharge of wastewater into water bodies. Currently, industrial enterprises often use the method of discharging wastewater into nearby water bodies. For this, coastal and channel methods of wastewater discharge are used. It is strictly forbidden to discharge wastewater into water bodies, water from which is used for irrigation of agricultural land, for technical and domestic needs. Also, water containing pathogenic bacteria and parasites cannot be discharged into water bodies. The condition of water resources in places of water use of the population used for drinking water supply (category I) and for recreation (category II) has been

192

S. A. Lebedev et al.

-

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 (1) 50,10 35,30 34,90 24,20 24,20 24,22 22,85 21,30 21,44 28,66 28,80 28,37 28,39 25,78 24,42 25,78 25,51 23,15 24,66 (2) 28,63 30,43 24,24 22,00 23,32 23,02 16,21 20,25 21,06 23,44 24,29 23,03 21,37 14,84 16,97 15,07 14,83 14,19 12,88

Fig. 5 Polluted wastewater discharge (1 – million m3, 2 – percent of total water withdrawal) in the Republic Adygea according to the data presented in [7–21]

deteriorating in recent years, both in terms of sanitary-chemical and microbiological indicators. In 2018, the volume of discharge of contaminated wastewater and insufficiently treated water amounted to 24.66 million m3. It increased by 6.5% compared to 2017, and decreased by 14.3% compared to 2010. The maximum discharge of contaminated wastewater and insufficiently treated water was observed in 2000 (50.1 million m3), and the minimum – in 2007 (21.3 million m3). After growing in 2009 by more than 34.6% compared with the minimum of 2007, discharge of contaminated wastewater and insufficiently treated water has been reducing by approximately 0.6 million m3 per year (Fig. 5). The main pollutants of water bodies and soils are enterprises of housing and communal services, which account for more than 70% of the total volume of polluted wastewater discharged (Fig. 6). Currently, these enterprises are not perfect – the level of wear of pipes and equipment is extremely high, and cleaning technologies are outdated. Due to limited funding, modernization of existing wastewater treatment plants is not carried out in a sufficient manner. Water supply systems of the Republic of Adygea have been operating mainly since the 1950s and 1960s, while the wear rate is 67.91%. The highest degree of wear of water supply networks is observed in Krasnogvardeysky District – 93.3%, in Giaginsky District – 89.38%, in Maykop Republican Urban District – 87.8% [24, 25]. There are 96 livestock farms in the Republic in the water protection zone. After treatment, 32 enterprises discharge wastewater into rivers. Although there are 36 treatment facilities here, only 6 of them provide water treatment to the accepted level. Therefore, the rivers receive 5 times more oil products and nitrogen compounds and 2.5 times more phosphorus compounds than their permissible amounts. In addition, 155 million m3 of water from farmland containing mineral fertilizers and pesticides is discharged into the rivers without treatment [26].

Ecological State of Soils of the Republic of Adygea Under High. . .

193

Domestic Wastewater 72,17%

Industrial Wastewater 25,81%

Agricultural Wastewater 2,02%

Fig. 6 Structure of discharge of polluted wastewater in the Republic of Adygea in 2018 according to the data presented in [21]

2.1.3

Industrial and Municipal Solid Waste

The main sources of soil pollution in recent years are municipal and industrial landfills, where waste, hazardous to living organisms, accumulates. According to the origin, waste is divided into two main groups: industrial and municipal solid [27]. Industrial waste is the remnants of raw materials, materials, substances, products, objects formed in the process of production, performance of work (services), that have lost all or part of their original consumer properties. Municipal solid waste is the remains of substances, materials, objects, products, goods that have partially or completely lost their original consumer properties for use for the direct or indirect purpose as a result of physical or moral depreciation in the processes of public or personal consumption, use, or operation. Waste according to the degree of impact on the environment and human health is divided into five hazard classes (Table 3) according to information presented in [27]. The problem of utilization, neutralization, and disposal of production and consumption waste is very relevant for Adygea. Currently, there are practically no enterprises operating on waste-free technology in the Republic. The main way to dispose of waste is to store it. With this method, the soil is exposed to the highest level of pollution by toxic substances. On the territory of the Republic of Adygea, 1.5 million m3 of industrial and municipal solid waste has been accumulated, which is stored in storage facilities, warehouses, landfills, and other sites. The area occupied by places of organized waste accumulation is 152.9 ha.

194

S. A. Lebedev et al.

Table 3 Classification of waste according to the degree of danger to the environment and human life [27] Hazard class I

II

III

IV

IV

Waste characterization Extremely dangerous – The ecological system is irreversibly broken – There is no recovery period Highly hazardous – The ecological system is severely disturbed – The recovery period is at least 30 years after the complete elimination of the source of the harmful impact Moderately hazardous – The ecological system is broken – The recovery period is at least 10 years after reducing the harmful effects from an existing source Low hazard – The ecological system is broken – The self-healing period is at least 3 years Non-hazardous – The ecological system is practically not disturbed

Pollutant characterization Extremely dangerous – Waste contains mercury, mercuric chloride, potassium chromate, antimony trichloride, benzapyrene, arsenic oxide, etc. Highly hazardous – Waste contains copper chloride, nickel chloride, antimony trioxide, lead nitrate, etc.

Moderately hazardous: – Waste contains copper sulfate, copper oxalate, nickel chloride, lead oxide, carbon tetrachloride, etc. Low hazard: – Waste contains manganese sulfate, phosphates, zinc sulfate, zinc chloride

In Adygea, there are 9 registered landfills for the disposal of municipal solid waste with a total area of 37.7 ha, including in Maykop City, Adygeysk Town, Koshekhabl’ Aul, Khakurinokhabl’ Aul, Ponezhukay Aul, Takhtamukay Aul, Tul’sky Village, Giaginskaya Stanisa (large Cossack village), and Krasnogvardeyskoye Village (Fig. 7). On the territory of the Republic of Adygea there are more than 45 facilities for unauthorized disposal of municipal solid waste. None of these facilities meets the hygienic and environmental requirements for the construction, equipment, and maintenance of municipal solid waste landfills (Fig. 7). Of all municipal solid waste storage facilities, only two meet the established hygienic and environmental requirements and are operated by organizations that have licenses for the collection, transportation, processing, disposal, detoxification, and placement of waste of hazard classes I–IV [29]. The territories of five landfills are located in the immediate vicinity of farmland, as a rule, they are separated only by an unpaved road (Khakurinokhabl’ and Ponezhukay Auls, Giaginskaya Stanisa, and Krasnogvardeyskoye Village). Fields are separated from the Maykop landfill by a forest belt. The other landfills are more than 50 m away from farmland [28].

Ecological State of Soils of the Republic of Adygea Under High. . .

195

Garbage Landfills in the Territory of the Republic of Adygea

Krasnodar 45° Belorechensk

Labinsk

Maykop Apsheronsk

Tul'sky

Tuapse

Volume of Solid Garbage (m3) > 1 500 000

44°

Guzeripl

10 000 - 50 000 5 000 - 10 000 1 000 - 5 000

< 100

39°

a Se

100 - 500

Caucasian State Wildlife Biosphere Reserve

k ac Bl

500 - 1 000

40°

Fig. 7 Location of the waste landfills of the Republic of Adygea according to information presented in [28]

Not only municipal solid waste is received at landfills. For example, the landfill of Giaginskaya Stanica receives waste from a yeast plant (spoiled yeast, packaging); food factory (construction waste); dairy plant (feed and liquid waste from the production of cheese). The landfill in Tul’sky Village receives waste from a distillery (broken packaging); “Lotos” woodworking enterprise (sawdust); food factory (broken glassware). The landfill at Ponezhukay Aul is filled up with chicken feather from the nearby Teuchezhskaya poultry farm [28]. However, pathogens of infections and invasions (gastrointestinal, viral, zoonotic and dust infections, helminthiases, as well as infections in which rodents play a certain role) can be transmitted through municipal solid waste. An increase in municipal solid waste leads to such adverse effects as:

196

S. A. Lebedev et al.

-

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 (1) 524,0 423,1 410,5 378,9 379,2 346,9 314,7 436,1 394,4 440,0 430,0 425,0 622,0 738,0 664,0 840,0 617,0 659,0 469,0

Fig. 8 Generation of industrial and municipal waste (1 – thousand tons) in the Republic of Adygea according to the data presented in [7–21]

– air pollution (during waste decomposing into the air, ammonia, hydrogen sulfide, methane, indole, skatole are released); – pollution of open water bodies, groundwater, and other aquifers; – pollution of the soil surface on the territory of human settlements. On average, 520.4 thousand tons of municipal solid and industrial wastes are generated annually in the Republic of Adygea. The maximum volume of municipal solid and industrial waste was observed in 2015 (840 thousand tons), and the minimum – in 2006 (314.7 thousand tons). Compared to 2015, the amount of waste in 2017 decreased by 36.4%. In 2018, 469 thousand tons of waste were produced, i.e. compared to 2017, it decreased by 28.8%. After the increase by more than 62.5% in 2015 compared with the 2006 minimum, generation of industrial and municipal solid waste is reduced by approximately 107.1 thousand tons per year (Fig. 8). More than 99.9% of the total industrial and municipal solid waste accrues to Class IV (85%) and Class V (14.9%) waste, which do not pose a great danger to the environment [29]. The maximum share of total industrial and municipal solid waste (more than 71%) falls upon Maykop Republican Urban District (39.8%), Takhtamukaysky District (19.5%), and Maykopsky District (10%). The other administrative districts account for less than 30% of waste: Giaginsky District – 6.4%, Krasnogvardeysky District – 6.3%, Koshekhabl’sky District – 5.7%, Teuchezhsky District – 4%, Adygeysk Republican Urban District – 3.3%, and Shovgenovsky District – 3% [29] (Fig. 9). This corresponds to the population density in the Republic of Adygea.

2.1.4

Agrochemicals

Agrochemicals include mineral and organic fertilizers, chemical ameliorants and plant protection products, as well as some solid municipal and industrial waste used in agriculture. All of them contain various amounts of macro- and microelements,

Ecological State of Soils of the Republic of Adygea Under High. . .

197

Distribution of industrial and domestic waste Krasnodar 45°

no as Kr

VII

da

erv es rR

V

oir

IX

VIII III

IV

II I

Labinsk

Maykop Apsheronsk

Tul'sky

VI

Bl ac k

Tuapse

Se a

44°

Guzeripl

Amount of industrial and domestic waste (% of total amount)

< 5%

5 - 10%

20 - 25%

25 - 30% 39°

10 - 15%

40°

Fig. 9 Distribution of industrial and municipal waste in the Republic of Adygea by administrative districts according to information presented in [29]. I – Maykop Republican Urban District, II – Adygeysk Republican Urban District, III – Giaginsky District, IV – Koshekhabl’sky District, V – Krasnogvardeysky District, VI – Maykopsky District, VII – Takhtamukaysky District, VIII – Teuchezhsky District, IX – Shovgenovsky District

including a number of toxic substances (Table 4). These values were collected from the data presented in [30]. The misuse of agrochemicals in agriculture and forestry is becoming an increasingly important factor in soil pollution. The harm from the uncontrolled use of pesticides, herbicides, fungicides, defoliants, insecticides, and other substances is widely known. In conventional applications, only a negligible portion of agrochemicals achieves the goal. The vast majority of them pollutes the soil, and most importantly, accumulates in living organisms, sequentially passing through the links of trophic chains.

198

S. A. Lebedev et al.

Table 4 The content of toxic elements in agrochemicals, mg/kg of dry weight [30] Elements Arsenic (As) Cadmium (Cd) Cobalt (Co) Chrome (Cr) Copper (Cu) Mercury (Hg) Manganese (Mn) Molybdenum (Mo) Nickel (Ni) Lead (Pb) Selenium (Se) Zinc (Zn)

Types of fertilizers Phosphoric Nitrogenous 2–1,200 2.2–120.0 0.1–170.0 0.05–8.50 1–12 5.4–12.0 66–245 3.2–19.0 1–300 1–15 0.01–1.20 0.3–2.9 40–2,000 – 0.1–60.0 1–7 7–38 7–34 7–225 2–27 0.5–25.0 – 50–1,450 1–42

Calcareous 0.1–24.0 0.04–0.10 0.4–3.0 10–15 2–125 0.05 40–1,200 0.1–15.0 10–20 20–1,250 0.08–0.10 10–450

Organic 3–25 0.3–0.8 0.3–24.0 5.2–55.0 2–60 0.09–0.20 30–550 0.05–3.00 7.8–30.0 6.6–15.0 2–4 15–250

Pesticides 22–60 – – – 12–50 0.8–42.0 – – – 60 – 1.3–25.0

Mineral Fertilizers Soil pollution is caused not only by the use of pesticides, detergents, and heavy metals, but also by the improper use of conventional mineral fertilizers. The exaggerated or incorrect ratio of the elements of the dose of fertilizers and the unsuccessful timing of their application to the soil determine not only a decrease in their effectiveness, but also a decrease in the quality of agricultural products. If fertilizers are not retained in the soil, they are carried into rivers, lakes, and groundwater, causing eutrophication of water bodies. The increased content of nitrogenous compounds in drinking water causes human diseases. Nitrogenous fertilizers by their interaction with the soil significantly differ from phosphoric and potassium fertilizers. Nitrate forms of nitrogen are not absorbed by the soil; therefore, they can easily be washed out by atmospheric precipitation and irrigation water [31]. Ammonia forms of nitrogen are absorbed by the soil, but after their nitrification they acquire the properties of nitrate fertilizers. Partially, ammonia can be absorbed into the soil unchanged. Non-exchange fixed ammonium is available to plants to a small extent. In addition, nitrogen loss from the soil is possible as a result of its volatilization in the free form or in the form of nitrogen oxides. When nitrogenous fertilizers are applied, the nitrate content in the soil changes dramatically, since fertilizers come with compounds that are most easily absorbed by plants. The dynamics of the nitrate content in the soil to a great extent characterizes its fertility. A very important property of nitrogenous fertilizers, especially ammonia fertilizers, is their ability to mobilize soil reserves, which is of great importance in the zone of chernozem soils. Under the influence of nitrogenous fertilizers, organic compounds of the soil undergo mineralization faster, turning into forms readily available to plants [31].

Ecological State of Soils of the Republic of Adygea Under High. . .

199

Some amount of nutrients, especially nitrogen in the form of nitrates, chlorides, and sulfates, can enter groundwater and rivers. This leads to exceeding the levels of the content of these substances in the water of wells and springs, which can be harmful to people and animals. This also leads to undesirable changes in hydrobiocenoses and damages fisheries. Migration of nutrients from the soil to groundwater in various soil and climatic conditions does not happen in the same way. In addition, it depends on the types, forms, doses, and timing of fertilizers’ application. The harmful potential exposure to nitrogen from fertilizers can be minimized by maximizing its crop use. Thus, it is necessary to ensure that with an increase in the doses of nitrogenous fertilizers, the efficiency of nitrogen utilization by plants increases so that there is not left a large amount of nitrates unused by plants that are not retained by soils and that can be washed out by precipitation from the root layer [31]. Plants can accumulate in their body nitrates, which are in excess in the soil. The yield of plants thus grows, but products turn out to be poisoned. Particularly intensively nitrates accumulate in vegetables and cucurbits. Soluble phosphoric fertilizers introduced into the soil are largely absorbed by the soil and become inaccessible to plants and do not move along the soil profile. It was established that the first culture after applying phosphoric fertilizers uses only 10–30% of Р2О5, and the rest remains in the soil and undergoes all kinds of transformations. For example, in acidic soils, superphosphate phosphorus is mostly converted into iron and aluminum phosphates, whereas in chernozems and in all carbonate soils it is converted into insoluble calcium phosphates. Systematic and prolonged application of phosphoric fertilizers is accompanied by gradual cultivation of soils [31]. It is known that the long-term use of large doses of phosphoric fertilizers can lead to the so-called overphosphating, when the soil is enriched with accessible phosphates and new doses of fertilizers do not have any effect. In this case, an excess of phosphorus in the soil can disrupt the ratio among nutrients and, sometimes, reduce the availability of zinc and iron for plants. For example, in the Krasnodar Territory, on simple carbonate chernozems, with the usual application of Р2О5, corn would unexpectedly sharply reduce productivity. Ways to optimize elemental nutrition of plants had to be found. Soil overphosphating is a certain stage in their cultivation. This is the result of the inevitable process of accumulation of “residual” phosphorus, when fertilizers are applied in an amount exceeding the removal of phosphorus with crops. As a rule, the “residual” phosphorus in fertilizers is more mobile and accessible to plants than natural soil phosphates. With systematic and prolonged fertilization, it is necessary to change the ratio among nutrients, taking into account their residual effect: the dose of phosphorus should be reduced, and the dose of nitrogenous fertilizers (sometimes potassium fertilizers) should be increased [31]. Potassium fertilizers applied to the soil do not remain, as well as phosphorus, unchanged. Part of it is in the soil solution, another part goes into the absorptionexchange state, and yet another part turns into a non-exchangeable form,

200

S. A. Lebedev et al.

inaccessible for plants. Accumulation of available forms of potassium in the soil, as well as their transformation into an inaccessible state as a result of prolonged use of potassium fertilizers, depends mainly on soil properties and weather conditions. For example, in chernozem soils, the amount of available forms of potassium under the influence of fertilizers increases, but to a lesser extent than in soddy-podzolic soils, since in chernozems, potassium from fertilizers is more converted into a non-exchangeable form. In an area with a large amount of precipitation and irrigated cropping, potassium from fertilizers may be washed outside the root layer of the soil. In areas with insufficient moisture, in a hot climate, where the soil is periodically moistened and dries up, there are intense processes of fixing potassium fertilizers in the soil. Under the influence of fixation, potassium from fertilizers is converted into a non-exchangeable state, which is difficult to access for plants. The type of soil minerals and the presence of minerals with a high fixing ability have a great influence on the degree of fixation of potassium by soils. These are clay minerals. Chernozems have a greater ability to fix potassium from fertilizers than soddypodzolic soils [31]. Leaching of the soil caused by the addition of lime or natural carbonates, especially soda, increases fixation of potassium. Fixation of potassium depends on the dose of the fertilizer: with an increase in the dose of the fertilizer applied, the percentage of fixation of potassium decreases. In order to reduce soil fixation of potassium fertilizers, it is recommended to apply potassium fertilizers to a sufficient depth to avoid drying out, and more often to apply them in crop rotation, since soils that are systematically fertilized with potassium, fix it to a weaker extent, when it is added again. However, fixed potassium from fertilizers, being in a non-exchangeable state, also participates in plant nutrition, since over time it can turn into an exchangeable-absorbed state. Thus, the use of mineral fertilizers is a fundamental transformation in the sphere of production as a whole and, most importantly, in agriculture, which has made it possible to radically solve the problem of food and agricultural raw materials on a global scale and in Russia. Without fertilizers, agriculture is now unthinkable. Mineral fertilizers are used for major crops. Nitrogenous fertilizers are added as feeding to fertilize grain crops, while phosphoric fertilizers are added as complex fertilizers when sowing grain and intertilled crops. Currently, in the Republic of Adygea there is a tendency to increase the application of mineral fertilizers by an average of 0.6 thousand tons per year, or 2.6 kg per ha. For example, in 2018 their amount increased by 38.3% compared to 2010. The main part of the increase in mineral fertilizers falls on nitrogenous fertilizers – 0.5 thousand tons per year, which is 78.8% of the total increase. According to [24], the amount of applied organic fertilizers varies significantly from year to year: from 0.1 thousand tons per year in 2010 to 29.9 thousand tons per year in 2015 (Table 5). On average, the application of organic fertilizers has a positive trend – 7 kg/ha per year (Fig. 10).

Ecological State of Soils of the Republic of Adygea Under High. . .

201

Table 5 Variability of mineral and organic fertilizers (thousand tons) in the Republic of Adygea [24] Phosphoric 3.0 2.7 4.1 3.4 3.6 3.2 2.6 3.5 3.2

Potassium 0.2 0.1 0.1 0.2 0.4 0.8 0.8 0.9 1.0

Total 12.0 12.1 12.8 13.9 14.5 15.6 15.3 15.7 16.6

Organic fertilizers 0.1 0.4 0.9 14.1 4.7 29.9 2.14 1.0 19.9

0,10 0,08

65

0,06

60

0,04

55 (1) (2)

2010 50 0,001

2011 50 0,002

2012 53 0,004

2013 58 0,060

2014 60 0,020

2015 65 0,120

2016 64 0,010

2017 66 0,004

2018 70 0,080

0,00

0,02

50

Fertilizers (1) (kg per ha )

45 40

-

Fertilizers (tons per ha)

70

0,12

Mineral fertilizers Nitrogenous 8.8 9.3 8.6 10.3 10.5 11.6 11.9 11.3 12.4

Years 2010 2011 2012 2013 2014 2015 2016 2017 2018

Fig. 10 Application of mineral (1 – kg per ha) and organic (2 – tons per ha) fertilizers in the Republic of Adygea according to the data presented in [7–21]

Pesticides Modern technologies for growing crops include the widespread use of pesticides. According to generalized data, their use prevents crop losses from pests by 5.1–20.7%, from diseases by 13%, and from weeds by 6.8–15.7% [32]. However, uncontrolled use of pesticides has led to environmental pollution [33]. Therefore, they began to be considered not only as a factor in increasing crop yields, but also as a factor disrupting the global cycles of substances in the biosphere. Most pesticides used in growing crops are applied directly to the soil or get there with treated seeds, as well as a result of flushing from the surface of plants with precipitation. Many pesticides can persist in the soil for a long time. Therefore, their concentration in the arable layer gradually increases with prolonged use. Pesticides entering the soil are gradually distributed between its individual phases. Some of them are bound by organic matter and are thus fixed in the form

202

S. A. Lebedev et al.

of stable chemical compounds. Fixation of pesticides in the soil also occurs as a result of their accumulation in the cells and tissues of living organisms. Another part of pesticides passes into the soil solution and therefore spreads very quickly throughout the entire arable layer. Pesticides in the soil are constantly moving from one state to another. The intensity and direction of this process depend on a number of factors – humidity, temperature, gas regime, crops. Pesticides in the soil with downward water flow can be carried into deep groundwater. When examining wells located on the territory of agricultural farms in the United States, 69% of water sources revealed the presence of insecticides and fungicides [34]. Strong pollution of groundwater by plant protection products is also observed in Germany. There, in some cases, their concentration exceeds the established maximum permissible levels by 20 times [35]. Plant protection chemicals are also present in the atmosphere. The air is very contaminated by small-droplet and aerosol spraying of medicine drugs. Small particles of the used solution slowly settle on the plants, which increases the time spent in the air and facilitates transfer to other areas. Part of pesticides contained in the air is absorbed by precipitation and together with it, it enters the soil and water bodies. Accumulation of pesticides in the soil is accompanied by their transition to plants. Moreover, the level of contamination of crops can be significantly higher than the soil on which they are grown. For example, if the soil contains phosphamide in the amount of 1.0 mg/kg, then its concentration in plants is 1.4–6.3 mg/kg. This is significantly higher than the established maximum permissible concentration (1.0 mg/kg) [36]. The range of pesticides used in Adygea has undergone significant changes over the past 5 years. Organochlorine and organophosphorus compounds have been replaced by a new generation of pesticides from the group of sulfonylureas, triazoles, and pyrethroids. The use of mercury-containing preparations has been significantly reduced; fungicides are mainly represented by copper-containing preparations. The consumption rate of such preparations is low and amounts to 5–200 g/ha [37].

2.1.5

Radionuclides

Radioactive contamination of soils is considered as an independent type of degradation, leading to a decrease in their fertility [6]. The problem of studying the behavior of radioactive isotopes in the soil, plants, and in other links of the biological cycle of the matter cycle arose as a result of environmental pollution by radioactive fission products in connection with nuclear weapon tests. Obviously, with the cessation of experimental thermonuclear explosions in the atmosphere, outer space and under water, this problem will remain relevant, as global fallout will still occur for a long time [38]. In addition, with the development of the nuclear industry, it becomes possible to obtain, although in much smaller quantities, radioactive fission products on the Earth’s surface and to have local contamination. Once in the atmosphere and on the Earth’s surface, radioactive

Ecological State of Soils of the Republic of Adygea Under High. . .

203

isotopes are included in the biological cycle of the matter cycle. Radioactive fission products from the environment are accumulated by flora and fauna, which contributes to an increase in their quantity in the biological cycle of matter [6]. Anthropogenic interference with the natural background radiation is: artificial concentrations and redistribution of natural radionuclides; environmental pollution with the latest radioactive metabolites of the nuclear energy origin; production and use of artificial radionuclides and other sources of ionizing radiation in science, medicine, and industry. Soil contamination with radioactive substances can occur through phosphoric mineral fertilizers. Uranium and thorium in the form of impurities are contained in the feedstock (for example, in apatite), which is used in the production of fertilizers and in the processing of raw materials partially passes into fertilizers, and from them into the soil [6].

2.1.6

Heavy Metals

Currently, one of the essential issues of ecology and environmental protection is the study of the effects of pollution of environmental objects, including soils, by chemicals. The environmental and economic damage caused by chemical pollution of soils and agricultural products is enormous. However, even now, the environmental consequences of chemical pollution of soils are not well studied. Knowledge of the effect of chemicals on biological processes in the soil and the mechanisms of soil and plant resistance to pollution should be the basis for developing methods to prevent negative effects of pollution. Heavy metals include more than 40 chemical elements of D.I. Mendeleev’s periodic system, the mass of atoms of which is more than 50 atomic mass units. These are Pb, Zn, Cd, Hg, Cu, Mo, Mn, Ni, Sn, Co, etc. The modern concept of “heavy metals” is not strict, since heavy metals often include non-metal elements, such as As, Se, and sometimes even F, Be, and other elements whose atomic mass is less than 50 atomic mass units [39]. According to modern ecotoxicological data, by hazard level, heavy metals in the soil form the following series: Se > Tl > Sb > Cd > Hg > Ni > Cu > Cr > As > Ba. The list of hazardous elements in the soil should include Tl [40]. Among heavy metals, there are many microelements, which are biologically significant for living organisms. They are necessary and indispensable components of biocatalysts and bioregulators of the most important physiological processes. However, the excessive content of heavy metals in various objects of the biosphere has a depressing and even toxic effect on living organisms. Sources of heavy metals entering the soil are divided into natural (weathering of rocks and minerals, erosion processes, volcanic activity) and technogenic (mining and processing of mineral resources, fuel combustion, impact of automobile vehicles, agriculture, etc.). Agricultural land, in addition to being affected by air pollution, is also contaminated by heavy metals specifically, through the use of pesticides, mineral and organic fertilizers, liming and wastewater [41].

204

2.2

S. A. Lebedev et al.

Interregional Transport of Pollutants

The Republic of Adygea is an internal enclave within the borders of the Krasnodar Territory, i.e. on all sides it borders on only one subject of the Russian Federation. Pollutants emitted into the atmosphere by industrial enterprises of both the Republic of Adygea and the Krasnodar Territory can be carried by air currents over distances reaching hundreds or even thousands of kilometers. At the same time, they accumulate in clouds, are captured by precipitation, and fall on the underlying surface. In addition, particles of impurities settle under the influence of gravity, and are also captured by the surface of the soil due to related chemical reactions. “EuroChem – Belorechensk Mineral Fertilizers,” located at the Krasnodar Territory in close proximity to the borders of the Republic of Adygea, produces sulfuric acid, wet-process phosphoric acid, complex mineral fertilizers: ammophos, liquid complex fertilizers, defluorinated feed phosphates, production of which accompanies the release into the air of hydrochloric and sulfuric acid fumes, sulfur dioxide, elemental sulfur dust, hydrogen sulfide, nitrogen dioxide, ammonia, carbon oxides, aerosols of heavy metals, phosphoric and fluoride compounds. The main solid waste produced by the Belorechensk Mineral Fertilizers plant is phosphogypsum, which covers an area of more than 25 ha and amounts to more than 8 million tons. Phosphogypsum is a dispersed system of finely divided particles distributed in a homogeneous medium. Phosphogypsum contains up to 95% of calcium sulfate dihydrate, 3–4% of phosphoric compounds, 1.5% of impurities (titanium, iron, strontium, fluorine, barium, manganese, chromium, lanthanum, cerium, and other microelements). Solid waste from the chemical plant production is not disposed of. Part of the pollutant emissions enters the soil cover with precipitation. In the zone of influence of the plant, the state of the soil markedly changes; there is a quantitative change in phosphorus and total nitrogen. Emissions from the Belorechensk Mineral Fertilizers plant affect acidity of soils at any time of the year; in the summer, an increase in alkalinity of the soil solution is observed compared to the spring season [26]. The results of studies of the soil samples taken in the area of the chemical plant in Belorechensk and on adjacent land of the Republic of Adygea, used for growing agricultural products, indicate that all the studied soils are acidic (pH 10% – – – – – – – –

40 35 30

200

25

190 (1) (2)

2010 224,0 17,0

2011 212,0 29,7

2013 214,2 27,5

2014 212,9 28,3

2015 205,2 29,6

2016 197,5 34,2

2017 197,5 39,3

2018 197,0 41,6

15

20

180 170 -

Arable area (2) (thousand ha)

210

Arable area (1) (thousand ha)

220

45

230

Year of investigation 2009 2011 2013 2014 2015 2016 2017 2018

Humus content Very low Low 2– Mn (0.007–0.22 mg/dm3) > Cu (0.0009–0.14 mg/dm3) > Zn (0.0013–0.043 mg/ dm3) > Pb (0.00008–0.0025 mg/dm3). Average concentration of dissolved forms of heavy metals in the surface water exceeds the maximum permissible concentration (MPC) in Fe for general-purpose water and amounts to 1.1–5.2 of MPC. The excess of Mn is insignificant. Comparison of the results with a stricter standard for fishery reservoirs [18–20] allowed establishing the variability range of content concentration values: from 1.2 to 27.0 of MPC for Fe (on average, 8–13 of MPC), from 0.7 to 31 of MPC for Mn (on average, 1.2–11 of MPC). Concentration of dissolved forms of zinc is noted only in spring and amounts to 1.3–2.1 of MPC on average. Table 1 shows average (on the gauge station) concentrations of heavy metals content in the Belaya River in dissolved (in mkg/l) and weighed (in mg/kg) forms and physical and chemical indicators (рН, Eh). The numerator shows average arithmetic values of 3–5 parallel definitions and deviations ( – criterion of reliability according to Student (Р < 0.05) has been given), the denominator shows the variability range of the studied indicators. Confidential interval at P ¼ 0.95 made in the dissolved form: Cu, 1.5–69%; Fe, 1–53%; Mn, 0.3–11%; Pb, 1–18%; and Zn, 2–48%. The results of the research in the Belaya River basin show that there is an accurate seasonal distribution of heavy metals with shares of dissolved and weighed forms. Generally heavy metals are concentrated in the suspended substance; the exceptions are sampling points I–IV according to the content of iron in autumn period, II–III according to the content of manganese and III according to the content of lead; zinc migrates mainly in the dissolved form. Excess of iron and manganese according to the standards for fishery reservoirs has been defined on all the river sites, but the greatest concentrations are noted in the middle watercourse, namely, under the town of Maykop (gauge station IV). Field of ferromanganese ores in a pro-layer of bioherms of the top Sarmat (ore formation of the ancient sea basin) is considered to be the reason of such situation [21]. Analysis of the selected samples for the content of heavy metals in the bottom water of the Belaya River has revealed significant differences in the samples of the control gauge stations in relation to the natural background (gauge station I). According to the Student’s t-test, there are significant differences in three of seven gauges by the content of Fe (gauge station II, t ¼ 2.54; gauge station IV, t ¼ 2.24; and gauge station V, t ¼ 2.73) and by the content of Mn in four of seven gauges (significant differences in gauge station II t ¼ 3.30 and gauge station III t ¼ 2.20; highly significant in gauge station V t ¼ 4.49 and close to significance in gauge station VI t ¼ 1.96).

Suspended substance

Studied objects Bethonic water

Mm lim 7:97 7:868:82 þ307 þ219þ354 1374344:0 1, 320:01, 600:0 942:35 25:5310:0 1:270:45 0:622:49 4:851:94 3:269:70 21:135:3 13:6732:75 421:672105:418 96:457818:637 9:9662:492 5:17413:977 0:4670:02 0:0780:833 5:0950:173 3:7096:969 7:2130:245 3:38213:274

Mm lim 7:95 7:088:35 þ253 þ223þ302 453114:0 360:0575:0 15:30:38 12:022:5 1:110:38 0:881:73 6:232:49 2:6814:27 13:23:3 6:2025:55 388:61097:153 194:320784:499 18:2884:572 6:68027:183 0:9300:04 0:2523:494 8:1990:279 6:17910:735 10:3390:351 7:91115:849

Mm lim 8:05 7:988:72 þ62 þ32þ94 814204:0 690:01, 110:0 37:60:94 22:062:0 1:650:58 1:282:11 2:020:81 1:243:43 10:692:7 5:3017:08 449:630112:408 182:7921, 838:781 15:5303:883 36:75740:895 45:8831:973 7:753115:076 6:7960:231 1:87317:248 34:7711:182 22:93260:563

Ministochnik settlement (159 km)

Dakhovskaya stanitsa (106 km) Mm lim 8:11 8:038:82 þ203 þ169þ401 1143286:0 900:01, 250:0 12:50:31 0:8024:5 0:950:33 0:461:09 6:092:43 2:5310:96 14:495:8 6:4823:12 1, 132:121283:030 676:2922, 287:329 36:0909:023 25:24964:551 0:3090:013 0:0490:513 4:8400:165 3:0199:177 10:6130:361 7:55220:321

IV Krasnooktyabr’skiy settlement or Maykop City (173 km) Mm lim 8:32 8:079:06 þ121 þ119þ261 1570393:0 970:02, 310:0 46:80:59 25:070:0 1:580:55 1:012:13 3:991:59 2:745:47 12:745:1 4:5419:96 19:3194:83 3:08242:442 3:4190:855 1:8247:539 2:4240:104 1:4654:932 0:4990:017 0:3560:709 1:6280:055 1:3652:899

Khanskaya stanitsa (192 km)

V

Mm lim 7:87 7:028:46 þ267 þ147þ297 783196:0 480:01, 065:0 33:40:84 11:046:0 1:310:46 0:842:31 4:451:78 2:1511:53 13:815:5 8:6622:80 31:1047:776 19:66953:505 1:6480:412 3:6074:635 1:4360:062 0:7072:431 0:4710:016 0:2410:683 1:8550:063 1:3672:058

Bzhedugkhabl aul (219 km)

VI

Mm lim 8:12 7:968:53 þ176 þ167þ251 2200:55 190:0280:0 31:20:78 0:7044:0 1:810:63 1:012:37 3:141:26 1:576:20 14:625:8 9:5121:44 98:59024:648 92:96799:9823 1:0570:264 0:7504:533 4:0370:176 2:9116:530 0:6580:022 0:3971:215 6:7840:231 3:9369:675

Adamy Aul (275 km)

VII

Remarks: The numerator shows average arithmetic values of 3–5 parallel definitions and deviations ( – criterion of reliability according to Student (Р < 0.05) has been given); the denominator shows the variability range of the studied indicators

Zn

Cu

Pb

Mn

Fe

Zn

Cu

Pb

Mn

Fe

Eh. mB

Parameter element рН

III

II

Gauge stations I Lagerniy kordon (71 km from the river head)

Table 1 Average heavy metals content (mg/dm3) in the Belaya River benthonic water and physical and chemical characteristics for the studied sites

276 R. A. Toroyan and I. P. Takh

Spatial Distribution of Heavy Metals Content in the River Belaya Ecosystem

277

To establish links between heavy metals, рН and oxidation-reduction conditions, correlation coefficients were defined for different seasons. In spring, there is a correlation between the indicators of oxidation-reduction reactions and Zn and Cu content (r ¼ 0.66–0.69) and a very high correlation between Zn and Mn content (r ¼ 0.85). The relationship between iron and manganese is characterized by the average value, with the correlation coefficient of 0.52. In autumn, the highest correlation is noted between Pb, Fe and Cu (r ¼ 0.69–0.73). Average concentrations of heavy metals in the water considerably increase after flowing by the gauge station: Ministochnik settlement (III), Krasnooktyabr’skiy settlement or Maykop City (IV) and Bzhedugkhabl aul (VI). The revealed spatial dynamics of heavy metals content in the river system is probably caused by the presence of sources of their receipt on the studied sites.

4.2

Content of Heavy Metals in Suspended Matter

The average-weighted concentrations of heavy metals in the suspended matter throughout the Belaya River change within a wide range: Fe, from 3.1 to 2,287.3 mg/kg; Mn, from 0.75 to 64.5 mg/kg; Pb, from 0.08 to 115.1 mg/kg; Cu, from 0.24 to 0.7 mg/kg; and Zn from 1.4 to 15.8 mg/kg. The highest concentration of heavy metals is in the middle watercourse due to rainfall flow. During autumn homothermy (uniformity of water temperature in the reservoir depth) in the lower watercourse, there is some increase in the content of heavy metals in suspended matter. This is indicative of desorption and ionic exchange on the surface of suspended matter. This results in transformation of some Fe, Mn and Zn into the dissolved form and adsorption of Cu and Pb on the surface of suspended matter. Increase in the quantity of transported deposits in the spring high water period and in autumn results in overwhelming prevalence of suspended forms of heavy metals in the water. Percentage of dissolved forms of heavy metals in spring and autumn is, respectively, as follows: Fe, 0.1–17% and 0.5–73.5%; Mn, 0.06–17.4% and 0.2–73.4%; Pb, 0.04–7.7% and 0.1–14.8%; and Zn, 0.17–8.14% and 89–99.3%. For the analysis of the condition of water ecosystems, it is preferable to compare the number of suspended particles of heavy metals in a litre of water and their specific concentration in the suspended matter [22, 23]. If both parameters show increased concentration, then it is possible to confirm with certainty the existence of the source of this metal in the studied part of the river. In the Belaya River, such situation was noted 3–5 km below the zone of operation of the Maykop field of geothermal waters and agricultural enterprises (as regards to the content of Pb, Zn and Fe), and 1 km below Maykop to the river mouth in the zones of urbanized and agrarian landscapes (as regards to the content of Fe, Mn, Pb, Cu and Zn). Analysis of spatial distribution of heavy metals in suspended matter in the zones of industrial and agricultural enterprises showed a tendency to accumulate most of heavy metals not near the object (pollution source), but at a certain distance from it (1–3 km).

278

R. A. Toroyan and I. P. Takh

The results of the research have allowed to establish that heavy metals prevail in water environment as part of suspended particles. It is caused by chemical properties of these metals which ions are actively sorbed by natural sorbents. Analysis of the obtained samples for heavy metals content in suspended substance in the studied sites of the Belaya River has shown that there is a strong positive link between the contents of copper and manganese and copper and zinc (r ¼ 0.68–0.95). Dependence between the indicators of water turbidity, acidity and heavy metals concentration is expressed poorly and very poorly (r ¼ 0.08–0.36). This is because suspended substances of smaller fraction are characterized by a high sorption capacity in the conditions of the watercourse high speeds. They present in the waterbed flow for a long time and play defining role in interphase distribution of heavy metals in the system “water-suspended substances.”

4.3

Content of Heavy Metals in Bottom Sediments

Concentration of heavy metals in river bottom sediments usually exceeds their concentration in the water column [24–26]. Bottom sediments in the middle course of the Belaya River are represented by gravelly-pebbly alluvium with sandy filling, in the lower course, and covered by clay and silt sediment. Analysis of the dynamics of mobile forms of the studied heavy metals in river bottom sediments has shown that the average content of elements varies by year and season. However, yearly variations of concentration of heavy metals are much lower than seasonal ones. The content of heavy metals in the top layer of bottom sediments considerably varies: Zn, from 2.6 to 33.2 mg/kg (on average, 11.3 mg/kg per year); Pb, from 1.1 to 14.8 mg/ kg (on average, 4.9 mg/kg per year); Cu, from 0.5 to 7.5 mg/kg (on average, 3.7 mg/ kg per year); Mn, from 25.1 to 140.7 mg/kg (on average, 73.1 mg/kg per year); and Fe, from 276.0 to 2,217.3 mg/kg (on average, 1,172.8 mg/kg per year). Many authors note that the content of heavy metals in bottom sediments depends on many factors. The environmental pH and the sulphate-sulphidic balance considerably influence occurrence forms and the level of content of heavy metals. The balance is determined by oxidation-reduction conditions of bottom sediments [26– 30]. According to Salomons and Stigliani [31], change of these conditions in bottom sediments has two main consequences. First, valence of metals changes, and, second, occurrence forms change (occurrence of heavy metals in poorly soluble or easily soluble forms). At the same time, the influence of oxidation-reduction conditions of bottom sediments on occurrence forms of heavy metals has the same nature for natural waters of any type, irrespective of their chemical composition or hydrological regime [32–36]. The obtained results have shown that heavy metals in almost all studied gauge stations concentrate in the Belaya River bottom sediments in the oxidizing horizon in poorly soluble forms and connected with organic substance. On the river sites around gauge stations III and IV, heavy metals are present in the form of carbonates with transition to sulphidic forms (see Table 2) where the numerator shows average

Pb

Mn

Element Fe

Dakhovskaya stanitsa

Ministochnik settlement

Krasnooktyabr’skiy settlement or Maykop City

Khanskaya stanitsa

IV

V

Adamy aul

VII

III

Bzhedugkhabl aul

VI

II

Khanskaya stanitsa

V

Lagerniy kordon

Krasnooktyabr’skiy settlement or Maykop City

I

Ministochnik settlement

IV

Adamy aul

VII

Dakhovskaya stanitsa

Bzhedugkhabl aul

VI

III

Khanskaya stanitsa

V

II

Krasnooktyabr’skiy settlement or Maykop City

IV

Lagerniy kordon

Ministochnik settlement

III

I

Dakhovskaya stanitsa

II

Gauge station I Lagerniy kordon

рН M lim 7:51 7:507:53 7:52 7:517:53 7:53 7:527:53 7:54 7:527:56 7:95 7:528:31 8:27 8:248:41 8:30 8:168:38 7:51 7:507:53 7:52 7:517:53 7:53 7:527:53 7:54 7:527:56 7:95 7:528:31 8:27 8:248:41 8:30 8:168:38 7:51 7:507:53 7:52 7:517:53 7:53 7:527:53 7:54 7:527:56 7:95 7:528:31

Eh, mB M lim þ229 þ201þ286 þ346 þ155þ367 þ149 þ51þ207 þ167 þ153þ177 þ61 þ49þ119 þ191 þ156þ202 þ167 þ145þ184 þ229 þ201þ286 þ346 þ155þ367 þ149 þ51þ207 þ167 þ153þ177 þ61 þ49þ119 þ191 þ156þ202 þ167 þ145þ184 þ229 þ201þ286 þ346 þ155þ367 þ149 þ51þ207 þ167 þ153þ177 þ61 þ49þ119

Table 2 Heavy metals content (mg/kg of dry weight) in the Belaya River bottom sediments

Mm lim 727:708181:927 704:133761:655 700:444175:111 650:157736:798 691:310172:827 659:114737:214 380:06595:016 339:467418:091 1699:993424:998 1380:6082044:829 1869:014467:253 1740:2942106:907 1870:499467:625 1598:8852006:303 112:86528:216 103:395120:856 101:10225:276 86:567115:620 67:97416:994 65:10572:985 53:09713:274 47:05056:124 91:67422:919 80:10799:294 96:01424:003 76:131106:041 98:96924:742 77:657110:380 9:2660:399 7:92210:670 9:9110:426 8:71511:466 11:7130:504 10:65712:828 7:1760:308 4:7088:513 4:2220:182 3:3505:206

Mm lim 704:281176:070 674:654739:597 711:944177:986 636:304802:751 723:214180:804 683:949768:479 356:68089:170 275:985392:329 1822:666455:667 1484:1302177:994 2096:447524:112 1944:4182217:253 1983:624495:906 1811:8752098:385 57:82614:457 51:52162:118 106:31826:579 88:250140:705 64:66616:167 56:06670:896 60:47915:120 47:39074:141 88:14422:036 73:245100:441 98:28824:572 83:007116:440 101:08525:271 92:029108:572 9:6320:414 8:00811:231 9:3470:402 6:97312:820 12:8910:554 11:17214:839 6:4230:276 4:0647:180 4:3450:187 2:9985:587

Granulometric fraction (depth of selection 0–10 cm), mm 1.0–0.25 2 SD). Both types of events could last a day or more. This methodology was already successfully used for the Black Sea [10], Adriatic coast of Montenegro [11, 12], the Baltic Sea [13], the Barents Sea [14, 15], and the Caspian Sea [16].

Regional Climate Change in the Republic of Adygea

315

3 Climate Change Climate change in the Republic of Adygea was investigated for 1900–2015 on the base of analysis of nine basic meteo parameters (monthly averaged): air temperature at 2 m, max air temperature at 2 m, min air temperature at 2 m, sea level pressure, U (zonal) wind component at 10 m, V (meridional) wind component at 10 m, total cloud cover, total precipitation, and snow depth. For every parameter we present four graphs: (a) seasonal and interannual variability and its linear trend; (b) analysis of anomalies, including anomalies in warm and cold seasons; (c) spectral analysis of anomalies; and (d) wavelet analysis of anomalies. We discuss the most interesting results in Sect. 5 (Figs. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, and 36).

3.1

Air Temperature at 2 m

Fig. 1 Seasonal and interannual variability of monthly average air temperature at 2 m ( C) and its linear trend (blue line) for 1900–2015

316

A. G. Kostianoy et al.

Fig. 2 Interannual variability of monthly average anomalies of air temperature at 2 m ( C) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 3 Energy spectrum of monthly average anomalies of air temperature at 2 m ( C) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

317

Fig. 4 The diagram of the wavelet transformation of monthly average anomalies of air temperature at 2 m ( C) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.2

Max Air Temperature at 2 m

Fig. 5 Seasonal and interannual variability of monthly average maximum air temperature at 2 m ( C) and its linear trend (blue line) for 1900–2015

318

A. G. Kostianoy et al.

Fig. 6 Interannual variability of monthly average anomalies of maximum air temperature at 2 m ( C) (relative to the seasonal variation) smoothed by 2-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 7 Energy spectrum of monthly average anomalies of maximum air temperature at 2 m ( C) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

319

Fig. 8 The diagram of the wavelet transformation of monthly average anomalies of maximum air temperature at 2 m ( C) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.3

Min Air Temperature at 2 m

Fig. 9 Seasonal and interannual variability of monthly average minimum air temperature at 2 m ( C) and its linear trend (blue line) for 1900–2015

320

A. G. Kostianoy et al.

Fig. 10 Interannual variability of monthly average anomalies of minimum air temperature at 2 m ( C) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 11 Energy spectrum of monthly average anomalies of minimum air temperature at 2 m ( C) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

321

Fig. 12 The diagram of the wavelet transformation of monthly average anomalies of minimum air temperature at 2 m ( C) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.4

Sea Level Pressure

Fig. 13 Seasonal and interannual variability of monthly average atmospheric (sea level) pressure (hPa) and its linear trend (blue line) for 1900–2015

322

A. G. Kostianoy et al.

Fig. 14 Interannual variability of monthly average anomalies of atmospheric (sea level) pressure (hPa) (relative to the seasonal variation) smoothed by 2-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 15 Energy spectrum of monthly average anomalies of atmospheric (sea level) pressure (hPa) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

323

Fig. 16 The diagram of the wavelet transformation of monthly average anomalies of atmospheric (sea level) pressure (hPa) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.5

U Wind Component at 10 m

Fig. 17 Seasonal and interannual variability of monthly average U (zonal) wind component at 10 m (m/s) and its linear trend (blue line) for 1900–2015

324

A. G. Kostianoy et al.

Fig. 18 Interannual variability of monthly average anomalies of U (zonal) wind component at 10 m (m/s) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 19 Energy spectrum of monthly average anomalies of U (zonal) wind component at 10 m (m/s) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

325

Fig. 20 The diagram of the wavelet transformation of monthly average anomalies of U (zonal) wind component at 10 m (m/s) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.6

V Wind Component at 10 m

Fig. 21 Seasonal and interannual variability of monthly average V (meridional) wind component at 10 m (m/s) and its linear trend (blue line) for 1900–2015

326

A. G. Kostianoy et al.

Fig. 22 Interannual variability of monthly average anomalies of V (meridional) wind component at 10 m (m/s) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 23 Energy spectrum of monthly average anomalies of V (meridional) wind component at 10 m (m/s) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

327

Fig. 24 The diagram of the wavelet transformation of monthly average anomalies of V (meridional) wind component at 10 m (m/s) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.7

Total Cloud Cover

Fig. 25 Seasonal and interannual variability of monthly average total cloud cover (%) and its linear trend (blue line) for 1900–2015

328

A. G. Kostianoy et al.

Fig. 26 Interannual variability of monthly average anomalies of total cloud cover (%) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 27 Energy spectrum of monthly average anomalies of total cloud cover (%) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

329

Fig. 28 The diagram of the wavelet transformation of monthly average anomalies of total cloud cover (%) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.8

Total Precipitation

Fig. 29 Seasonal and interannual variability of monthly average total atmospheric precipitation (kg/m2) and its linear trend (blue line) for 1900–2015

330

A. G. Kostianoy et al.

Fig. 30 Interannual variability of monthly average anomalies of total atmospheric precipitation (kg/m2) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 31 Energy spectrum of monthly average anomalies of total atmospheric precipitation (kg/m2) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

331

Fig. 32 The diagram of the wavelet transformation of monthly average anomalies of total atmospheric precipitation (kg/m2) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

3.9

Snow Depth

Fig. 33 Seasonal and interannual variability of monthly average snow depth (m) and its linear trend (blue line) for 1900–2015

332

A. G. Kostianoy et al.

Fig. 34 Interannual variability of monthly average anomalies of snow depth (m) (relative to the seasonal variation) smoothed by two-year (orange line) and seven-year (purple line) Butterworth filters. Their linear trend (black line) and the accumulated sum (green line) after removing the linear trend are shown. Average values of anomalies for the warm and cold periods of the year are shown by red and blue dots accordingly

Fig. 35 Energy spectrum of monthly average anomalies of snow depth (m) for the period 1900–2015. A confidence interval from 5% (bottom black line) to 95% (top black line) and a red noise spectrum (red line) are marked

Regional Climate Change in the Republic of Adygea

333

Fig. 36 The diagram of the wavelet transformation of monthly average anomalies of snow depth (m) relative to the seasonal variation without filtering, after preliminary removal of the linear trend and normalization of the data to its standard deviation

4 Extreme Climate Events The territory of Adygea is largely influenced by various unfavorable weather phenomena that affect the development of agricultural crops. The main ones are droughts and dry winds, dust storms, strong winds, hail, frost, and ice crust. Days with dry winds are considered those that have a certain combination of air humidity deficit and strong wind speed. Depending on the various combinations of wind speed and air humidity deficit, weak, medium, intense, and very intense dry winds are distinguished. The total number of days with dry winds in most of the territory of Adygea is 35–75 days per year. Most of them are weak dry winds, which do not bring much harm to agricultural crops and even, to some extent, harden plants against stronger dry winds. Intense and very intense dry winds are observed for 3–5 days. The most dangerous are dry winds, in which the relative humidity of the air drops to 30% or less. The number of days with such air humidity is 30–32 days per year, slightly increasing from north to south due to foehns. Dry winds are most frequent in late July and early August [2].

334

A. G. Kostianoy et al.

On the territory of the Republic there is a high frequency of strong winds with a speed of more than 15 m/s. The average number with strong winds ranges from 13 to 20 days, the maximum in some years reaches 38–68 days per year. Eastern and northeastern winds prevail in most of the Republic. The highest frequency of winds is observed in February and March. Strong winds cause significant damage to agriculture, causing lodging of crops, shedding of fruits, breakage of trees. If strong winds occur during winter or early spring, when the soil is bare, the topsoil dries up quickly, which can cause dust storms. Strong dust storms blow off the top layer of soil, seeds, and even plants. On light soils, the entire top layer down to the parent rock can be blown away. Strong dust storms, which took the character of natural disasters, were noted in the Republic in March–April 1960, in April 1965, in January–February 1969. The dust storm of 1969 has been the worst over the past 60 years [2]. In the warm half of the year, more often in May–June, hail is observed in the Republic, usually accompanied by heavy rainfall, thunderstorms, and sometimes a squall wind. The hail falls in "spots" or stripes, reaching several kilometers in length and thousands of meters in width. It causes significant damage to crops and sometimes can completely destroy crops. In Adygea, the average number of days with hail during the warm period is mainly 1–2 days, and the maximum is 4–10 days. The highest frequency of its fallout is observed in the southeast of the Republic [2]. In mountainous areas, the last spring frosts are observed until the end of the third decade of April, in the rest of the territory until April 10–15. At this time, frosts down to 1 C are observed in 2–3 years. The probability of frost down to 3 C in the second decade of April is once every 5 years. In late April and early May frosts of low intensity up to 1 C are possible, but no more than once every 15–20 years. The first frosts in autumn begin to be observed mainly in the second and third decades of October, but in some years in the third decade of September. At this time, frosts to 1 C are observed no more than 1–2 times in 25 years [2]. In the cold season, dangerous phenomena include a sharp drop in temperature to 30 C and below and snowfalls with an amount of precipitation of 20 mm or more per day. Severe frosts down to 30 C and below, causing damage to gardens and winter crops, occur no more than once every 10 years. Rarely there are long cold winters in Adygea with the sum of negative temperatures over 500 C. Severe winters that cause damage to crops occur 1–2 times every 10 years. The greatest anomalies from average monthly temperatures during severe winters usually occur in February. The most severe winter in the last 70 years was the winter of 1953–54 (see Figs.1, 2, 9, and 10). It was distinguished by its long duration and low temperatures ( 25 C, 28 C in the north of the Republic and up to 20 C, 26 C in its south). The snow cover established in early December persisted throughout the winter and melted only at the end of March. The snow cover in February reached 50–60 cm (see Figs. 33 and 34). Over this winter, the sum of

Regional Climate Change in the Republic of Adygea

335

negative temperatures varied from 600 C to 700 C. In this catastrophic winter, fruit plantations, mainly apple trees, were frozen out of the long cold. Winter crops this year did not suffer, as they were reliably covered by snow. Cold snap is usually preceded by snowfall and the establishment of a small snow cover, which protects winter crops from freezing. Cases of freezing in the Republic of well-developed winter wheat crops are extremely rare. Sharp temperature fluctuations cause significant damage to agricultural crops in winter and early spring. Return cold snaps to 10 C and below are especially dangerous after prolonged thaws, when the sum of average daily temperatures above +5 C will be over +30 C [2]. Extreme climate events in the Republic of Adygea were investigated for 1900–2015 on the base of analysis of 7 basic meteo parameters (daily averaged): air temperature at 2 m, max air temperature at 2 m, min air temperature at 2 m, U (zonal) wind component at 10 m, V (meridional) wind component at 10 m, total precipitation, and snow depth. For every parameter we present six graphs of interannual variability: (a) amplitude of the positive and negative climate extreme events over 1 SD; (b) yearly number of these events over 1 SD; (c) yearly duration of these events over 1 SD; (d) amplitude of the positive and negative climate extreme events over 2 SD; (e) yearly number of these events over 2 SD; and (f) yearly duration of these events over 2 SD. All the anomalies were calculated relative to the average seasonal trend for the period 1900–2015. We considered the events “anomalous” when values of anomalies exceeded one standard deviation and as “extreme” when anomalies exceeded two standard deviations. We discuss the most interesting results in Sect. 5 (Figs. 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, and 50).

4.1

Air Temperature at 2 m

336

A. G. Kostianoy et al.

Fig. 37 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of air temperature at 2 m ( C) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

337

Fig. 38 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of air temperature at 2 m ( C) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo two standard deviation and their linear trends

338

4.2

A. G. Kostianoy et al.

Max Air Temperature at 2 m

Fig. 39 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of maximum air temperature at 2 m ( C) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

339

Fig. 40 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of maximum air temperature at 2 m ( C) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo two standard deviation and their linear trends

340

4.3

A. G. Kostianoy et al.

Min Air Temperature at 2 m

Fig. 41 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of minimum air temperature at 2 m ( C) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

341

Fig. 42 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of minimum air temperature at 2 m ( C) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo two standard deviation and their linear trends

342

4.4

A. G. Kostianoy et al.

U Wind Component at 10 m

Fig. 43 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of U (zonal) wind component at 10 m (m/s) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

343

Fig. 44 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of U (zonal) wind component at 10 m (m/s) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo two standard deviation and their linear trends

344

4.5

A. G. Kostianoy et al.

V Wind Component at 10 m

Fig. 45 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of V (meridional) wind component at 10 m (m/s) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

345

Fig. 46 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of V (meridional) wind component at 10 m (m/s) with positive (red lines) and negative (blue lines) anomalies exceeding by modulo two standard deviation and their linear trends

346

4.6

A. G. Kostianoy et al.

Total Precipitation

Fig. 47 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of total atmospheric precipitation (kg/m2) with positive (red lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

347

Fig. 48 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of total atmospheric precipitation (kg/m2) with positive (red lines) anomalies exceeding by modulo two standard deviation and their linear trends

348

4.7

A. G. Kostianoy et al.

Snow Depth

Fig. 49 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of anomalous events of total snow depth (m) with positive (red lines) anomalies exceeding by modulo one standard deviation and their linear trends

Regional Climate Change in the Republic of Adygea

349

Fig. 50 Annual changes of the average amplitude (top), the number (middle part), and the average duration (lower part) of extreme events of total snow depth (m) with positive (red lines) anomalies exceeding by modulo two standard deviation and their linear trends

350

A. G. Kostianoy et al.

5 Discussion 5.1

Air Temperature

Analysis of seasonal and interannual variability of monthly average air temperature at 2 m showed that from 1900 to 1955 and from 2000 to 2015 there were two periods with relatively warm summers, when air temperature almost yearly reached 24 C (Fig. 1). These periods were separated by a relatively cold summer period of 1955–2000 when air temperature varied between 22 and 24 C. Thus the amplitude of summer temperatures was in the range 2–4 C rarely reaching 26 C after 2005. Winter temperatures showed much larger variability in the range between +6 C (1966) and 7 C (1972). A periodicity of about 20 years can be revealed in winter temperatures. Maximum air temperature at 2 m shows the same interannual behavior with summer temperatures varying between 28 C and 33.5 C after 2005. Winter temperatures varied between +8 C (1966) and 4 C (1972) (Fig. 5). Minimum air temperature at 2 m shows similar interannual variability with summer temperatures varying between 16 C and 21 C after 2005. Winter temperatures varied between +3 C (1966) and 10 C (1950) (Fig. 9). A century averaged air temperature for the Republic of Adygea is of 12 C. A linear trend for 1900–2015 was equal to 0.020 C/10 years. This is 15–25 times less than the recent ones obtained for 1980–2010 for the neighboring Krasnodar Territory (0.469 C/10 years), the Eastern Black Sea (0.387 C/10 years), and Republic of Abkhazia (0.320 C/10 years) [10]. It is interesting that a linear trend of monthly average maximum air temperature at 2 m for 1900–2015 was slightly negative and equal to 0.004 C/10 years (Figs. 5 and 6), while for a minimum air temperature it was positive +0.039 C/10 years (Figs. 9 and 10). It means that summer temperatures did not change at a scale of the past century, while winters became a bit warmer. Varshanina and Mitusov [2] reported the same positive trends for 1902–1990 of 0.010–0.020 C/10 years for Krasnodar, Sochi, and Maykop meteo stations. The period from 1961 to 1988 on the territory of Adygea was characterized by an increase in air temperature, both on the plain (Maykop) and in the mountains (Guzeripl village). However, the intensity of temperature growth is spatially differentiated. On the plain, from the mid-1960s, there was an intense increase in temperature (+0.2 C/10 years), while in the mountains, on the contrary, the intensity of the temperature rise has slightly decreased since the mid-1970s from +0.1 C/10 years to 1980s +0.07 C/10 years) [2]. The analysis of anomalies of monthly average air temperature at 2 m showed that the largest positive and negative anomalies up to +3 C and 3.5 C were observed in the cold period of the year (Fig. 2), which is consistent with what was mentioned above about larger variability in winter months. Since 1993 we observe a steady rise of air temperature for about 2 C. Also, the accumulated sum of anomalies (a green line in Fig. 2) shows periods of general warming from 1900 till 1942, then a cooling to the second half of 1990s, and the next ongoing period of warming. These findings

Regional Climate Change in the Republic of Adygea

351

are confirmed by the analysis of the anomalies of monthly average maximum and minimum air temperature at 2 m (Figs. 6 and 10). A frequency analysis shows three significant peaks at ~2, ~4, and ~40 years (Figs. 3, 7, and 11). A wavelet analysis has confirmed the maximum energy of variability at periods of about 40 years which appeared in mid-1920s for a maximum air temperature (Fig. 8), in mid-1930s for an average air temperature (Fig. 4), and in the beginning of 1940s for the minimum air temperature (Fig. 12), and is persistent till present. The analysis of anomalous and extreme temperature events showed an increase of the amplitude of the positive “anomalous” events (anomalies exceeding by modulo one standard deviation) from 4.4 to 4.7 C and a decrease of negative anomalous events from 5.3 to 5.0 C during 116 years (Fig. 37). The number of such events occurred during a year is of 16-17. The duration of positive anomalous events has increased from 3 to 3.5 days, while the duration of negative anomalous events has decreased by a half of a day (Fig. 37). The amplitude of positive and negative “extreme” temperature events (anomalies exceeding by modulo two standard deviation) is stable and is of 6 C and 8.5 C, respectively (Fig. 38). The number of such strong events has increased from 2 to 3 per year for positive anomalies and has decreased from 6 to 4 for negative ones. Duration of these extreme events shows a little increase for positive anomalies (about 1.5 days on average) and 2 days for negative anomalies (Fig. 38). As concerns maximum air temperature, we observe an increase of positive anomalies from 4.95 C to 5.2 C and a decrease of negative anomalies from 5.7 C to 5.4 C (Fig. 39). The number of positive anomalies has decreased from 20 to 18, and the number of negative events has increased from 19 to 21. Such events last 2–3 days on average (Fig. 39). Extreme temperature events are much larger – from 7 to 8 C for positive temperature events and 9 C for negative events (Fig. 40). The number of extreme positive temperature events has increased from 2.5 to 4 per year, while the number of negative events has decreased from about 7 to 5. These extreme events are quite short, they last from 1.5 to 2 days (Fig. 40). As concerns minimum air temperature, for anomalous events we observe a stable behavior of positive anomalies of about from 4.7 C and a decrease of negative anomalies from 5.5 C to 5.25 C (Fig. 41). The number of positive anomalies has increased from 14 to 18, and the number of negative events has decreased from 16.5 to 15 per year. Such events last from 2.5 to 3.5 days on average (Fig. 41). Extreme temperature events are much larger – from 6.5 C to 7 C for positive temperature events and 8.5 C for negative events (Fig. 42). The number of extreme positive temperature events has increased from 2 to 3 per year, while the number of negative events has decreased from about 5.5 to 4.5. These extreme events are quite short, they last from 1.5 for positive extreme events to 2.5 days for negative extreme events (Fig. 42).

352

5.2

A. G. Kostianoy et al.

Atmospheric Pressure

Atmospheric (sea level) pressure varies between 1,008 and 1,028 hPa with a mean value of about 1,016 hPa and clear positive trend of 0.084 hPa/10 years for 1900–2015 (Fig. 13). Varshanina and Mitusov [2] reported for 1936–1990 a negative trend of 0.17 hPa/10 years for Krasnodar and a positive one of +0.03 hPa for Sochi. Anomalies of atmospheric pressure are larger in the cold period of the year (from 3 to +3 hPa). We observe a steady increase of atmospheric pressure from 1900 to 1935, a decrease of pressure till 1982, an increase of pressure till 1995, its stabilization till 2010, and the next decrease of pressure (Fig. 14). A frequency analysis showed peaks at periods of ~1.2, ~3.6, ~14, and ~40 years (Fig. 15). A wavelet analysis again confirmed the maximum energy of variability at periods between 30 and 40 years which appeared in mid-1940s (Fig. 16).

5.3

Wind Speed

Monthly average zonal component of wind velocity (U) in average is negative and varies between 4 m/s and +3 m/s. This means that the main wind direction is from the east, but with a clear tendency (linear trend of +0.026 m/s per 10 years) to have an equal distribution between east and west winds (Fig. 17). The largest anomalies in wind speed in the range between 1 and +1 m/s are usually observed in the cold period of year (Fig. 18). The accumulated sum of U anomalies corresponds to the behavior of atmospheric pressure (Fig. 14) with the same key years (1935, 1980, 1995, and 2010) (Fig. 18). A frequency analysis showed peaks at periods of ~1.1 and ~8 years (Fig. 19) which is different from the atmospheric pressure. Accordingly, the wavelet analysis did not reveal any significant maximum energy of variability at all periods from 1900 to 2015 (Fig. 20). Monthly average meridional component of wind velocity (V) in average is positive and varies between 2 m/s and +3 m/s. This means that the main wind direction is from the south with no tendency to change (linear trend of +0.002 m/s per 10 years) (Fig. 21). The largest anomalies in wind speed in the range between 0.8 and +0.7 m/s are usually observed in the cold period of year (Fig. 22). The accumulated sum of V anomalies (Fig. 22) does not correspond to the behavior of atmospheric pressure (Fig. 14) and zonal wind speed U (Fig. 18) because the years of V anomalies change are different (1928, 1948, 1992, and 2005) (Fig. 19). A frequency and wavelet analysis did not reveal any significant peaks at all periods from 1900 to 2015 (Figs. 23 and 24). These results are consistent with the analysis of meteo station data for 1902–1990 [2] which showed a dominance of east winds in plain part of the Republic (27% of cases), northeast wind (19%), west and north-west (14%), and calm days of 16%. Wind speed trends are different in the plains and in the mountains. In the plains, there is a decrease in the frequency of the southeast wind from 0.01 to 0.15% per year. In

Regional Climate Change in the Republic of Adygea

353

the mountains south of the Rocky Range, the frequency of the north wind increases by 0.21% per year and the frequency of the northeast wind decreases by 0.2% per year. During the time of meteorological observations on the territory of the NorthWest Caucasus, on the plain, there is a decrease in the average annual wind speed from 0.005 (Belorechensk) to 0.009 m/s per year (Krasnodar). An increase in wind speed is observed only in the western sector of the region (Goryachy Klyuch: +0.006 m/s per year). In accordance with the decrease in the average annual wind speed, an increase in the number of calm days was registered. The increment in the number of calm days ranges from 0.3% per year on the plain part to 1.6% per year in the mountains regions of the Republic of Adygea [2]. The analysis of anomalous and extreme wind events showed that the amplitude of anomalous positive events is of 3.4 m/s and of negative events of 3.2 m/s for zonal component of wind (U) (Fig. 43). The number of negative events was quite stable – 25 per year, while the number of positive events has increased from 25 to 32. Duration of these events was about 2–2.5 days (Fig. 43). The amplitude of extreme wind events was in the range 5–5.5 m/s (Fig. 44). The number of negative events was quite stable – 4 per year, while the number of positive events has increased from 5 to 8. Duration of these events was about 1.5 days (Fig. 44). For meridional component of wind (V), we found for anomalous events, the amplitude of positive events of 3 m/s and for negative ones – of 2.6 m/s (Fig. 45). The number of these events has increased from 24 and 26 days for negative and positive anomalies, accordingly, by 2 days. Duration of these events is of 2 days on average (Fig. 45). As concerns extreme wind events, we found amplitudes of such anomalies of 4.5 m/s; 4–5 events per year for negative anomalies and 9–10 for positive anomalies; and duration of such strong events a bit less than 1.5 days (Fig. 46).

5.4

Cloud Cover

Monthly average total cloud cover is very stable over 1900–2015 period (a linear trend of +0.127%/10 years) with mean value of 50% (Fig. 25). It is interesting that the seasonal range of variability of cloudiness is very large – from 10–20% in summer to 80–90% in winter. The analysis of anomalies shows once more that in the cold period of year there are larger anomalies of the total cloud cover of 8–10% in comparison with the warm period of 6% (Fig. 26). Figure 26 reveals periods of variability of about 30–40 years which is confirmed by a frequency analysis which shows peaks at ~3.6 and ~30 years periods (Fig. 27). A wavelet analysis shows a maximum energy of variability at periods of 40–50 years occurred during 1910–1990 (Fig. 28).

354

5.5

A. G. Kostianoy et al.

Atmospheric Precipitation

Monthly average total precipitation has increased from 1900 to 2015 by 27% (a linear trend of +0.005 kg/m2/10 years) which is a significant value (Fig. 29). In average, anomalies in precipitation are larger in the cold period of year ranging from 0.15 to +0.125 kg/m2 (Fig. 30). In the first 60 years of the period under investigation we observe an accumulation of the sum of precipitation anomalies, then a decrease till 1980, and the subsequent increase from 1987 till 2015 (Fig. 30). A frequency analysis shows two peaks at ~2.4 and ~8 years (Fig. 31). A wavelet analysis did not reveal notable maximums of energy variability at any periods (Fig. 32). The same tendency was reported by Varshanina and Mitusov [2] for 1902–1990 who noted that on the plain and in the low mountains of the North-West Caucasus, there is a tendency to an increase in the amount of precipitation. On the low-lying plain, the precipitation trend is 0.2 mm/year (Ust-Labinsk), 1 mm/year (Krasnodar). The value of the precipitation trend increases from northeast to southwest as the influence of the Black Sea increases. The greatest increase in precipitation is observed in the foothills (Maykop) by 1.7 mm/year and in the low mountains (Kamennomostsky post) by 1.2 mm/year. On the Rocky Ridge separating the low mountains of the Republic from the middle mountains, the sign of the precipitation trend changes. The downward trend in precipitation in the middle mountains of the region has been noted since the second half of the 1960s. In the intermontane basins, in which all mountain observation points of the Republic are located, the intensity of the decrease in the annual amount of precipitation increases with the height of the surrounding ridges ( 0.2 mm/year in Dakhovskaya Stanitsa, 0.3 mm/year in Guzeripl village) [2]. The analysis of anomalous and extreme precipitation events showed that the amplitude of anomalous positive events has increased from 0.67 to 0.8 kg/m2 (Fig. 47). The number of such events has increased significantly from 22 to 31 per year and average duration of such events is of 1.5 days on average (Fig. 47). As concerns extreme precipitation events, the amplitude of them has increased from 1.05 to 1.2 kg/m2 (Fig. 48). The number of such strong events has increased from 10 to 17 per year, and their average duration was of 1.3 days (Fig. 48).

5.6

Snow Depth

Varshanina and Mitusov [2] based on the analysis of snow cover in 1902–1990 showed that snow cover is observed throughout the entire territory of the plain from the first decade of December to the second decade of March. The average height of the snow cover during the winter is 19 cm, the largest value was registered in Krasnodar – 71 cm. A stable snow cover in more than 50% of winters is formed only in the northeast of the territory (in Ust-Labinsk 60% of winters with a stable

Regional Climate Change in the Republic of Adygea

355

snow cover). Snow cover in the foothills is observed from the third decade of November to the third decade of March. A stable snow cover exists from the second decade of December to the second decade of March. The greatest thickness (9 cm) of the snow cover reaches the first decade of February. Snow cover in the mountains up to 2,000 m above sea level is observed from the second decade of October to the third decade of April. The duration of this period increases with height. At altitudes over 1,800 m above sea level some areas of the snowfields remain until June. Above 2,800 m, the snow cover remains round the year. Stable snow cover is observed on average for 55–60 days in the Belaya River Valley and intermontane basins and 180–190 days in the Lagonaki Highlands and the spurs of the Main Caucasian Range [2]. Our present analysis showed that over 1900–2015 there are no changes in the total snow depth (liner trend equals to zero). There were several years when snow depth exceeded 50 cm on average – 1911, 1932, 1954, 1992, and 1993 (Fig. 33). Since that times we observe a steady decrease of snow depth in the Republic. Anomalies of snow depth as large as 10–30 cm occur at periods of 20 and 40 years (Fig. 34) which is confirmed by a frequency analysis (Fig. 35) and wavelet analysis which shows two maximum of energy variability at 20 and 40 years periods (Fig. 36). The analysis of anomalous and extreme snowfall (snow depth) events showed that the amplitude of anomalous positive events is of 12 cm on average, while maximum peaks reached 40–45 cm (Fig. 49). The number of such events was about 2 per year reaching 6–7 cases in some years, and average duration of such events is of 15 days on average (Fig. 49). As concerns extreme snowfall events, the amplitude is a bit large – of 14 cm with a maximum up to 50 cm (Fig. 50). The average number of such strong events is of 1.5 per year, and their average duration is of 6–7 days (Fig. 50).

6 Conclusions The Republic of Adygea, located in the mountain-plain conditions, despite its small area, is distinguished by the multivariance of climatic processes occurring on its territory. Varshanina and Mitusov [2] made an analysis of climate and climate change in the Republic based on meteo station data records for 1902–1990. They noted that it was difficult to make this analysis due to the lack of a representative network of meteo stations and hydroclimatic observations. The territory of Adygea is poorly provided with points of meteorological observations. In 2005 there was only one meteorological station in the Republic – in Maykop City. Therefore, the research on climate change in the Republic [2] was done based on data records from meteorological stations located near its borders and from stations and posts that existed on the territory of the Republic earlier, but closed in different years. The latter include the meteorological stations in Guzeripl, Dakhovskaya, Khamyshki, Kisha, and the posts of Kurgo, Adygea animal farm, Teuchezhkhabl, Giaginskaya,

356

A. G. Kostianoy et al.

Abadzekhskaya, Kamennomostsky, and Zubrovy Park. Today, the Adyghe Center for Hydrometeorology and Environmental Monitoring unites five meteorological stations, one hydrological station, ten hydro, and three agrometeoposts, and although they are equipped with modern equipment, there is an urgent need to increase the number of meteorological stations (including a set of automatic meteo stations) and expand the staff. Our present analysis on the climate change in the Republic of Adygea based on the NOAA-CIRES-DOE Twentieth Century Reanalysis V3 for 1900–2015 showed reliable results which was proved by a comparison with meteo station records analysis provided in [2]. The lack of meteo stations in the Republic makes this type of analysis as alternative to meteo records because it provides a regular in space and time data sets of a large number of meteo parameters, part of them was analyzed in the present study. The ongoing climate change in the Republic of Adygea shows steady slowly warming since 1900 and very intense warming since 1998 (by 2 C till 2015), which could not be detected earlier because the data sets used by Varshanina and Mitusov [2] ended in 1990. A linear trend for the 1900–2015 was equal to 0.020 C/10 years, which is 60 times less than what we observe today in Adygea (1.2 C/10 years) and 15–25 times less than the recent trends obtained for 1980–2010 for the neighboring Krasnodar Territory (0.469 C/10 years), the Eastern Black Sea (0.387 C/10 years), and Republic of Abkhazia (0.320 C/10 years) [10]. Also, it became evident that the ongoing warming in Adygea is caused by significant warming in the cold half of the year, while summer temperatures did not change at a scale of the past century. A sharp increase in air temperature during the past two decades brings the challenge of modern climate change to the fore. Topical problems of future research include monitoring the impact of climate change and its socio-economic consequences on climate-sensitive ecosystems, various sectors of the economy of the Republic and environmental resource use, as well as adaptation of socio-economic systems to bioclimatic, agroclimatic, and unfavorable spatio-temporal features of the climate in the Republic of Adygea. Acknowledgements A.G. Kostianoy was partially supported in the framework of the P.P. Shirshov Institute of Oceanology RAS budgetary financing (Project N 0128-2021-0015). S.A. Lebedev was supported in the framework of the Geophysical Center RAS budgetary financing. T.P. Varshanina was supported in the framework of the "Unified order of the Ministry of Science and Education of the Russian Federation, Federal Agency for Education No. 2005 1.2.04."

References 1. Kostianoy AG, Lebedev SA, Serykh IV, Kostianaia EA, Varshanina TP (2021) General characteristics of the climate in the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10. 1007/698_2021_735

Regional Climate Change in the Republic of Adygea

357

2. Varshanina TP, Mitusov DV (2005) Climatic resources of landscapes of the Republic of Adygea. Publishing House of Adygea State University, Maykop, p 237. (in Russian) 3. The Atlas of the Republic of Adygea (2005) Publishing House “Lev Tolstoy”, Maykop, 79 p. (in Russian) 4. IPCC (2013) In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds.) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, and New York, 1535 pp 5. Kattsov VM, Semenov SM (2014) Second Roshydromet assessment report on climate change and its consequences in the Russian Federation, Moscow, “Planeta”, 1018 pp. (in Russian) 6. Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ, Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P, Brönnimann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY, Jones PD, Kruk M, Kruger AC, Marshall GJ, Maugeri M, Mok HY, Nordli Ø, Ross TF, Trigo RM, Wang XL, Woodruff SD, Worley SJ (2011) The twentieth century reanalysis project. Q J R Meteorol Soc 137:1–28. https://doi.org/10.1002/qj.776 7. Giese BS, Seidel HF, Compo GP, Sardeshmukh PD (2016) An ensemble of ocean reanalyses for 1815-2013 with sparse observational input. J Geophys Res Oceans 121:6891–6910. https:// doi.org/10.1002/2016JC012079 8. Slivinski LC, Compo GP, Whitaker JS, Sardeshmukh PD, Giese BS, McColl C, Allan R, Yin X, Vose R, Titchner H, Kennedy J, Spencer LJ, Ashcroft L, Brönnimann S, Brunet M, Camuffo D, Cornes R, Cram TA, Crouthamel R, Domínguez-Castro F, Freeman JE, Gergis J, Hawkins E, Jones PD, Jourdain S, Kaplan A, Kubota H, Le Blancq F, Lee T, Lorrey A, Luterbacher J, Maugeri M, Mock CJ, Moore GK, Przybylak R, Pudmenzky C, Reason C, Slonosky VC, Smith C, Tinz B, Trewin B, Valente MA, Wang XL, Wilkinson C, Wood K, Wyszyński P (2019) Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system. Q J R Meteorol Soc. https://doi.org/10.1002/qj.3598 9. Torrence DC, Compo GP (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79:61–78 10. Kostianoy AG, Serykh IV, Ekba YA, Kravchenko PN (2017) Climate variability of extreme air temperature events in the Eastern Black Sea. Ecol Montenegrina 14:21–29 11. Kostianoy AG, Serykh IV, Kostianaia EA, Mitrovic´ L, Ivanov M (2017) Regional climate change in the Boka Kotorska Bay. In: Joksimovich A, Djurovic M, Semenov AV, Zonn IS, Kostianoy AG (eds) The Boka Kotorska Bay environment. Springer, Cham, pp 473–493 12. Kostianoy AG, Serykh IV, Kostianaia EA (2018) Climate change in the Lake Skadar region. In: Pesic V, Karaman G, Kostianoy AG (eds) The Skadar/Shkodra Lake environment. Springer, Cham, pp 63–88. https://doi.org/10.1007/698_2018_350 13. Serykh IV, Kostianoy AG (2019) About the climatic changes in the temperature of the Baltic Sea. Fundamental Appl Hydrophys 12(3):5–12 14. Serykh IV, Kostianoy AG (2019) Seasonal and interannual variability of the Barents Sea temperature. Ecol Montenegrina 25:1–13 15. Serykh IV, Kostianoy AG (2021) On climatic changes in the temperature of the Barents Sea and their possible causes. In: Lisitsyn AP (ed.) The Barents Sea system, pp 166–179 (in Russian) 16. Serykh IV, Kostianoy AG (2020) The links of climate change in the Caspian Sea to the Atlantic and Pacific Oceans. Russian Meteorol Hydrol 45(6):430–437

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief of Adygea Irina А. Repina, Anna А. Shestakova, Murat K. Bedanokov, Roza B. Berzegova, and Sergey A. Lebedev

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Atmospheric Boundary Layer Over Mountains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Orographic Winds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Thermal Winds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Valley Winds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Novorossiysk Bora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Stratified Boundary Layers in Highlands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Impact of Reservoirs on the Climate of the Territory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

360 362 366 366 371 373 380 383 385 385

I. А. Repina (*) Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russian Federation Moscow Center for Fundamental and Applied Mathematics, Moscow, Russian Federation Research Computing Center of Lomonosov Moscow State University, Moscow, Russian Federation e-mail: [email protected] A. А. Shestakova Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russian Federation M. K. Bedanokov and R. B. Berzegova Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation S. A. Lebedev Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation Geophysical Center, Russian Academy of Sciences, Moscow, Russian Federation National Research University of Electronic Technology (MIET), Moscow, Russian Federation Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 359–396, DOI 10.1007/698_2021_733, © Springer Nature Switzerland AG 2021, Published online: 25 February 2021

359

360

I. А. Repina et al.

Abstract The main part of the territory of the Republic of Adygea is located in the mountain-valley relief. The state of the atmosphere over complex relief is controlled by synoptic-scale processes, mesoscale circulations caused by thermal effects, and turbulent processes. The chapter explores the key features of the structure and dynamics of the atmospheric boundary layer over mountainous terrain. The main features of the mountain-valley circulation, the causes and characteristics of slope and valley winds are presented. Special attention is paid to the Novorossiysk bora and exchange processes over reservoirs. The territory of the Republic of Adygea is unique in terms of the variety of landscapes and can become a good testing ground for a comprehensive study of the features of complex wind circulation and validation of models from eddy-resolving to regional numerical models of weather forecasting. This will contribute not only to improving the quality of weather forecasts and dangerous meteorological phenomena, but also to improving the ecological state of the region through more competent planning of economic activities, taking into account the landscape and climatic features of the region. Keywords Katabatic and anabatic winds, Mountain-valley circulation, Novorossiysk bora, Reservoirs, Stable and convective boundary layer, The Republic of Adygea, Valley winds

1 Introduction The relief of the territory of Adygea is complex and very heterogeneous [1, 2]. The Republic has high mountain ranges, plateau systems, separated from each other by plain and low-lying territories. A nearby coastal zone of the Black Sea interacts with the mountain systems of the Republic that leads to the formation of strong coastal wind circulations, in particular, bora. The landscape of the territory is also very diverse, characterized by different types of soil and vegetation. All this creates certain difficulties for numerical modeling of the evolution of atmospheric processes. Regional atmospheric models, due to a number of simplifications, primarily in representing the processes of interaction between the atmosphere and the surface, and insufficient spatial resolution, cannot sufficiently take into account the local features of the relief and landscape of Adygea and adequately describe the structure of hydrometeorological fields. Despite the fact that the boundary layer of the atmosphere has a small thickness compared to the total height of the atmosphere (even for an unstable state, the height of the boundary layer of the atmosphere is only 1–2 km), its role in atmospheric processes is extremely large. In addition to the fact that the airflow is inhibited in the boundary layer, the vertical gradients of temperature and moisture are strongest here, the vertical variability of the concentrations of various gases and impurities is great. The most intense processes of energy and moisture exchange between the earth’s (water) surface and the atmosphere occur precisely in its boundary layer. Various

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

361

types of pollution are also transported through the boundary layer, which is extremely important for assessing the ecological state of the atmosphere and surface. The interaction of atmospheric airflow with the surface of the earth or water (underlying surface) leads, as a rule, to the occurrence of turbulence, i.e. chaotic vortex air movement. Turbulence is a powerful mechanism for energy transfer and the transfer of various impurities, exceeding the intensity of molecular transfer by more than an order of magnitude. As a result, turbulent flows of various substances (impulse, heat, water vapor, gases, and aerosol components) arise in the boundary layer of the atmosphere. The Earth receives the vast majority of its energy from the Sun. But the atmosphere absorbs only a small part of this energy, since it is practically transparent to sunlight, and most of the radiant energy falls on the underlying surface. The energy absorbed by the surface is transferred to the free atmosphere through a thin layer adjacent to it (the boundary layer), and turbulence is the main method of this transfer. The study of exchange processes between the Earth’s surface and the atmosphere plays an important role in meteorology and in many related disciplines such as climatology, hydrology, glaciology, biology, and agricultural sciences. Exchange is the transfer of the properties of the medium through its border, and it can occur either at the border of the medium (for example, the exchange of the surface atmosphere) or through an imaginary surface inside the medium (for example, between the boundary layer of the atmosphere and the free atmosphere), while the flow of matter or energy is the rate of exchange per unit area. Exchange processes on the Earth’s surface and in the Atmospheric Boundary Layer (ABL) are presented in models of numerical weather and climate forecast using empirically tuned parameterization schemes [3]. It is the uncertainties of these parameterizations that limit the accuracy of weather and climate forecasts, especially in the surface layer of the atmosphere [4, 5]. At the same time, small-scale processes characterizing energy and mass transfer, which are not clearly represented in climatic or regional models with a resolution from 1 to 100 km, play a key role in the “atmosphere-surface” interaction system. These processes include turbulent mixing in the atmosphere; microphysics of clouds and aerosols; radiation transport in the atmosphere; turbulent exchange of momentum, heat, and matter in the surface–atmosphere system; heat transfer in soil; the influence of topography on the dynamics of the ABL. It is the insufficiently reliable parameterizations of small-scale phenomena that are the main cause of errors in climate and regional models. To develop parameterizations of small-scale turbulent processes in the ABL, certain methods and concepts are used, for example, Reynolds averaging, scaling based on dimension analysis, various similarity theories [6]. This approach works well on flat and homogeneous terrain, but in the case of mountainous terrain, it is not always applicable [7]. Passing over an orographically uneven territory, the airflow is transformed, forming phenomena covering all scales of atmospheric movements. Mountains affect the atmospheric flow, changing the synoptic-scale advection processes and generating planetary waves [8], they form organized mesoscale structures [9] and introduce changes in microscale turbulent mixing [10, 11]. The spatial scale of mesoscale phenomena arising from the orographic impact is determined by the

I. А. Repina et al.

362

shape of the relief and varies from 102 m for a stream over an isolated slope to 106 m when flowing around a mountain ridge. The corresponding time scales vary from seconds (for bursts of turbulence in stably stratified flows) to tens of minutes (with the formation of convective circulations) and several days (frontal formations when crossing large mountain ranges). Some weather phenomena in the mountains, such as breeze and mountain-valley circulation, have a pronounced diurnal variation [12]. In addition to orography, a variety of types of vegetation, soil cover, the degree of soil moisture [13], and the presence of water bodies [14] also have a significant effect on metabolic processes in mountainous areas. For the physical description of the structure and dynamics of the ABL over the mountainous terrain, it is necessary to synthesize three approaches: first, the use of the results of short-term target measurement campaigns and network observations, including remote sensing data, allowing to study the entire complex of atmospheric processes over the mountainous area [10, 15–20]. Second, it is necessary to use high-resolution regional numerical models [21], using appropriate parameterizations of subgrid processes, as well as initial and boundary conditions, which include the multiscale variability of the atmosphere over and near mountains. Third, special attention should be paid to interactions of different scales, for example, between synoptic, meso- and microscale processes.

2 Atmospheric Boundary Layer Over Mountains The atmospheric boundary layer (ABL) is usually the part of the troposphere that is directly influenced by the earth’s surface and reacts to surface influences with a time scale of about an hour or less [5]. The most important factor in the temporal and spatial variability of the ABL is the energy balance of the surface. It includes radiation balance, sensible and latent heat fluxes in the atmosphere, and heat flux through the soil (Fig. 1a). Heat and moisture fluxes (sensible and latent heat) are usually estimated from special so-called micrometeorological measurements at a certain height above the surface using the pulsation (eddy covariance) or, less often,

a)

b)

Fig. 1 The surface energy balance (a) The components of the budget are Rn (net radiation), H (sensible heat flux), LE (latent heat flux), and G (ground heat flux). (b) For complex terrain. The experimental characterization of the energy balance refers to a sampling volume. In particular, advection effects are generally neglected but can be important over heterogeneous terrain

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

363

profile method based on the gradient ratios of the similarity theory [23]. That is, in fact, the experimentally determined energy balance characterizes the balance in a certain layer of the atmosphere, but not on the surface (Fig. 1b), therefore, when calculating the energy balance, additional variables are used to denote the accumulation of energy in a certain control volume [24]. At the same time, measurements show that the heat balance does not close even over plain and flat terrain under stationary weather conditions [25]. In most experiments, the total energy of solar radiation and heat fluxes through the surface (ice, snow, or soil surface) turns out to be greater than the energy carried away by turbulent sensible and latent heat fluxes obtained from pulsation (EC) measurements [26]. But numerical models for forecasting weather and climate postulate a closure of the heat balance. The daily variability of the incoming solar radiation and, accordingly, the energy balance of the surface leads to alternating periods of heating and cooling in the lower layers of the atmosphere. In the case of a flat and homogeneous topography, the influence of stratification and the action of buoyancy forces enhance the turbulent exchange during the day and suppress it at night [5]. In 1946 A.M. Obukhov defined a universal scale for metabolic processes in the u3 surface layer of the atmosphere [27] L ¼    , which connects the buoyancy   parameter

g T0

κ

g T0

H cp ρ

, dynamic velocity u, and turbulent sensible heat flux H. Here, g is

the acceleration of gravity, T0 surface air temperature, cp heat capacity of air, ρ air density, κ Karman’s constant. The scale L can be interpreted as a height proportional to the height of the dynamic sublayer, in which the effect of stratification is insignificant [28]. The introduction of this scale logically led to the development of a theory for calculating the statistical characteristics of atmospheric turbulence [29] – the Monin–Obukhov similarity theory (MOST). Based on Buckingham’s π-theorem [30], Moninand Obukhov establishedthat  the dimensionless profiles of z ∂U the mean temperature Tz ∂T and wind speed u ∂z are functions of the same ∂z three parameters, which are included in the scale L, and the height z, that is, they depend on only one dimensionless variable z=L . This theory became the basis of modern micrometeorology and the development of experimental equipment for the study of atmospheric turbulence [23]. First of all, it might be applicable under stationary meteorological conditions and with the existence of a layer of constant fluxes (a layer in which vertical gradients of heat and momentum fluxes are practically absent) in the surface layer covering approximately 10% of the atmospheric boundary layer [31]. In other terms, the use of MOST is limited to the lower atmosphere over a homogeneous surface, in which the stability parameter is |z/ L|  1  2 [5, 28, 31, 32]. The stationarity and uniformity requirements can also be formulated as follows: 1. The terrain is flat and the underlying surface is sufficiently homogeneous, so that the wind speed and temperature fields are uniform horizontally;

364

I. А. Repina et al.

2. During time intervals in which the natural diurnal variation of the weather is hardly noticeable, the wind speed and temperature fields are statistically stationary. Under these conditions, the statistical characteristics of meteorological fields can only depend on the height of the measurements. Despite the idealization of the MOST conditions, this theory, together with Kolmogorov’s theory of the existence of an inertial interval in the spectra of the velocity components, is widely used to estimate the momentum, heat and moisture fluxes, the transport of impurities in the surface layer, and in particular, in weather and climate forecast models over land as well as sea. In the presence of horizontal and vertical gradients of statistical moments of meteorological quantities (in particular, fluxes), the use of MOST should lead to errors, the level of which has not been sufficiently studied to date. The last decades have seen significant progress in the development of systems and methods for measuring the characteristics of atmospheric turbulence: new instruments are being developed, methods of correction and data quality control are being improved. This made it possible to obtain high-quality experimental data on turbulent processes in the ABL, as well as to determine the limits of applicability of MOST under various background conditions. In the case of extended landscapes with large elements of heterogeneity (mountain-valley relief [33–36], forest [37, 38], urban development [39, 40], sea ice [41, 42] coastal zones [43–46]) studies show that under certain conditions at a qualitative level, MOST is applicable. But in most cases, the observations were carried out on one meteorological mast with one or more measurement levels. Expansion of the experiment to several multilevel stations provides information on the significant spatial variability of turbulence over a nonhomogeneous relief [35, 45]. This can also be the cause of the above-mentioned non-closure of the heat balance [25, 47, 48]. In conditions of complex orography, the following factors can lead to non-closure of the heat balance: horizontal heterogeneity [10, 197], horizontal and vertical advection [49, 50], the effect of surface inclination on the radiation balance [33, 51], the absence of a layer of constant fluxes [52, 53], and others. As an example, Fig. 2 shows the daily cycle of surface heat balance measured in a mountain valley in Western Caucasus (Vladikavkaz town). The difference between the total energy of solar radiation and heat flux through the surface and the total energy of turbulent and fluxes of sensible and latent heat in the figure significantly exceeds the uncertainty that can be attributed to measurement errors, which is usually associated with the divergence of the balance with a uniform topography. In this case, vertical advection may be the cause, but it is necessary to take into account other components of the heat balance, which, as a rule, are not measured (for example, horizontal advection and horizontal flow divergence). In the case of complex orography and spatial heterogeneity of the underlying surface, the daily cycle of metabolic processes in the ABL is influenced not only by the intensification and suppression of turbulence, but also by the baroclinicity arising from non-uniform heating or cooling, which leads to organized air movements, such as, for example, breezes. In mountainous areas, breeze circulation occurs due to

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

600

Rn

H+LE+G

365

D

500

W/m2

400 300 200 100 0 0:00 -100

4:00

8:00

12:00

16:00

20:00

Local time

Fig. 2 Daily cycle of the energy balance components at the mountain valley (Vladikavkaz town) in summer time in cloudiness condition. D is the disbalance

thermal contrasts on the scale of a single slope, valley, or an entire mountain range. With a weak synoptic flow, the surface pressure gradients quasi-hydrostatically depend on temperature disturbances in the overlying atmosphere. Consequently, thermal wind circulations usually have their maximum intensity near the surface, where pressure gradients are the strongest. These wind currents, in particular slope katabatic and valley winds, under good weather conditions are an important characteristic of the dynamics of the atmosphere in mountainous regions and, together with turbulent mixing, control exchange processes. Another difference between ABL over flat and homogeneous terrain and over mountains is that due to strong horizontal gradients of meteorological parameters, vertical exchange processes cease to be decisive, and horizontal transport becomes significant. Another understudied problem is the unsteadiness and anisotropy of turbulence over the mountain landscape [37, 54]. Difficulties in the applicability of MOST over heterogeneous landscapes are associated, first of all, with the fact that nonlocal processes caused by the heterogeneity of the generation of turbulent motions and various mesoscale circulations are added to the local turbulent mixing caused by high-frequency turbulence. Under certain conditions, MOST is also applicable over a nonhomogeneous surface, but a more generalized approach is required to calculate the characteristics of atmospheric turbulence in this case, which may include, among other things, classical MOST as a special case [55, 56]. The implementation of this approach is possible with the inclusion of new independent dimensionless groups in the similarity functions [45] or the introduction of new empirical scales [38]. Since anisotropy in a complex landscape can be related to the features of the relief [57], its systematic analysis can help determine the properties of turbulence depending on the properties of the relief. This can be used to generalize MOST for complex landscapes, including to determine new scales. The use of local universal functions has been proposed as an alternative [33, 58], but this is a much less efficient approach

I. А. Repina et al.

366

than the extension of MOST [59]. For this reason, the use of local scaling in numerical models would be inappropriate. In addition to limiting the applicability of the similarity theory, horizontal heterogeneity in mountainous areas also leads to significant small-scale variability of turbulence characteristics [19, 60, 61], especially in the case of stable boundary layers.

3 Orographic Winds 3.1

Thermal Winds

The wind regime of the territory of Adygea obeys the orography of the area [62, 63] and is very variable. So, the winds of the northern and southeastern directions prevail in the village of Dakhovskaya, while the north, northeast, south, and southwest winds prevail in the settlement of Guzeripl located in the valley of the Belaya River. A characteristic feature of the wind regime in the mountain and foothill zone is the presence of mountain-valley winds. Mountain-valley circulation is one of the main processes that form the wind regime and the ecology of the air basin in the foothill and mountain regions of Adygea. The circulation patterns are associated with solar heating and radiation cooling of the underlying surface; in this case, not only local winds are formed, but also inversion layers that prevent the vertical removal of pollutants. Mountain-valley winds are most pronounced on warm, clear summer nights. This phenomenon is divided into three independent processes: 1. slope winds on detached mountains or mountain ridges (during the day – up the slope, at night – down); they lead to daytime condensation; 2. compensatory winds between high plateaus and lowlands; 3. basically mountain-valley winds that usually accompanied by slope winds, in which a closed circulation with countercurrent flow occurs at heights. In case 2, there is no compensatory high-altitude current, and the leveling is provided by a semidiurnal alternation of winds [64]. The layer of the atmosphere in high mountains, swept by a mountain or valley wind, extends from the bottom of the valleys for 1 km or more, often to the crests of ridges; a reverse wind (counter current) is detected higher. In the lower mountains, mountain-valley winds are limited to layers above the very slopes of the mountains (slope winds). Mountainvalley winds have a significant effect on the daily variation of humidity, cloudiness, and precipitation. In particular, they contribute to daytime cloud formation and precipitation over mountains and clearings at night. Also, the development and intensity of mountain-valley circulation are influenced by: – geographical location of mountain ranges (latitudinal–sublatitudinal, meridional– submeridional);

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

367

– the difference in elevation, the location of the main (large) ridges and valleys in relation to moisture-carrying streams; – the nature of the manifestation of altitudinal zonality; – the complexity of the orography of the area, the dissection of the relief, the contrasts of the elevation difference, the complexity of the territorial distribution of ridges and spurs; – synoptic processes and types of atmospheric circulation in the region; – a large amplitude of temperatures during the day and a significant temperature difference during the year; – wind regime in the surface layer of the atmosphere, in the lower troposphere (altitude up to 3 km); – the openness or isolation of the mountain valley, the isolation of slopes and valleys. The mountain wind speed depends on the steepness of the slope, as well as on the width and depth of the valley. It is small, but in some cases it can reach 10 m/s. During the day, the valley wind blows from the mouth of the valley up the valley and up the mountain slopes. At night, the mountain wind blows down the slopes and down the valley towards the plain [65]. It is possible to indicate at least two independently acting reasons for the occurrence of mountain-valley winds. One of them is the daytime rise or night lowering of air along the mountain slopes (the winds of the slopes). The other creates a general air transport up the valley during the day and down at night, i.e. mountain-valley winds in the full sense of the word. During the day, the surface of the mountain slopes is warmer than the surrounding air. Therefore, the air in the immediate vicinity of the slope heats up more than the air located farther from the slope, and a horizontal temperature gradient is established in the atmosphere, directed from the slope into the free atmosphere. In calm conditions in the surface layer, daily cycles with phases of mountain (katabatic runoff) and valley (anabatic rise) winds are formed, which in the higher layers of the atmosphere close with countercurrents and form night and day circulation cells. In the intervals between these main phases, transient regimes associated with sunrise and sunset are realized. In addition, a slow process of cold air lens accumulation and regional sublatitudinal wind blowing from the southwest to the northeast is observed at night (Fig. 3). It should be emphasized that this daily cycle can vary depending on the background conditions of the atmosphere. For example, at the stage of the emergence of a young anticyclone, episodes are possible when local circulation is suppressed by the incoming air currents. In addition, the patterns of mountain-valley circulation strongly depend on the season. So, in summer, it is more pronounced due to large temperature drops: all phases are more clearly traced and higher airflow velocities are observed. In winter, the mountain wind takes longer of the day due to the shortened daylight hours and low intensity of solar radiation. At this time, the cold lens dissolves more slowly, and the wind speed rarely exceeds 1–2 m/s due to the smaller temperature difference between the snow-covered valleys and mountains.

368

I. А. Repina et al.

Fig. 3 Mountain breeze and valley breeze at day and night time

Such a qualitative description of the process, of course, does not reflect a detailed picture of the wind regime. In reality, the wind field in the lower atmosphere is highly variable both in space and in time. This is due to the fact that the mountain ranges are heavily rugged by gorges, numerous slopes are sanctified by the sun in different ways, and, moreover, their temperature regime strongly depends on the properties of the underlying surface. As a result, meso air currents and slope flows, interacting in a complex way, form a mixed picture of the wind field. The complex wind regime poses serious problems for researchers in solving problems of air pollution and developing strategies for air protection measures. Slope and barrier effects in local air circulation are integrally expressed in the structure and in the

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

369

subannual dynamics of the local climate [66]. According to the available statistical data [67], regular differences in the structure of weather types on opposite slopes both along the vertical profile and according to the seasons of the year are recorded with confidence in the distribution of the frequency of frosty (), non-frosty (+) weather and with the transition through 0 С (). The local air circulation (foehn, mountain-valley winds) reaches the maximum effect at the foot of the southern slopes in winter (it is no accident that foehn is called the “eater of snow” in the Alps). By summer, these differences decrease, and in autumn they grow again. Up the profile, the inter-slope differences in the climate structure are gradually smoothed out in all seasons of the year and practically disappear in the highlands. The descending air gradually heats up adiabatically with a foehn, and its relative humidity decreases; these values reach their maximum at the foot of the slopes, where wind speeds can reach storm force [68]. Slope winds react very quickly (on a time scale of several minutes) to changes in apparent heat flow at the surface and are almost always turbulent, with turbulence over slopes mainly due to buoyancy during the day and wind shear at night. Night runoff (katabatic, i.e. downslope) winds have been studied much more extensively than daytime anabatic (upslope) winds. The upslope flows are more difficult to measure and model because they extend to greater altitudes above the ground and contain a wider range of motion scales due to convective impact. But a quantitative assessment of the vertical transport allows checking and, possibly, improving the existing parameterizations of the near-surface and boundary layers of the atmosphere, which should be based on the decomposition of the vertical transport into a turbulent component and a component of advective circulation on the slope [69]. The thermodynamic effect of thermal slope circulations can also affect dynamic atmospheric processes in mountainous areas, for example, leading to orographic blocking [70]. The katabatic flow is very narrow, usually a few tens of meters, with the maximum wind speed just a few meters from the surface. And the application of the Monin–Obukhov similarity theory (which is usually used to calculate turbulent flows of heat and moisture from the profiles of wind speed, temperature, and humidity) is limited from below 1 to 2 m from the surface. The first cohesive theory of slope winds was developed by the German scientist J. Gann (1879) and it remained leading for decades, although it caused numerous disputes. The classical theory of katabatic winds was developed in the middle of the twentieth century [71– 73]. But the main properties of the movement of a wind flow along a heated or cooled slope are described by Prandtl’s model [74, 75]. The classical Prandtl model is a system of two equations: the equations of motion and the equations of heat flow. The main disadvantages of this model are: the constancy of the coefficients of vertical turbulent mixing, the stationarity of the model equations, the setting of the boundary condition at an infinite height. Back in the late 1940s, A.S. Monin generalized Prandtl’s theory, but, in contrast to him, showed the dependence of the wind speed on the steepness of the slopes and took into account the effect of surface roughness [76]. L.I. Gutman came to roughly

370

I. А. Repina et al.

the same conclusions [72]. In the joint work of Monin and Gutman, a more complex problem of a temperature-heterogeneous slope surface was considered [77]. Subsequently, the classical Prandtl model was developed and expanded, taking into account the Coriolis force [78–81] and the change with height of the coefficients of viscosity and thermal conductivity [82–84]. Some works also took into account the effect of thermal nonhomogeneity of the surface, specified either as a gradual or abrupt change in the buoyancy flux [82, 85–89]. Verification (complication) of this model while maintaining the mathematical approach was also proposed in [78, 90]. The model included the effects of slope curvature [91], changes in heat fluxes along the slope [85], daily variability of surface temperature [92], and turbulent mixing in the form of changes in the viscosity coefficient [83, 93, 94]. But none of these extensions completely removes all the limitations of the model. The simulation results are quite different from observations [69] and show significant errors in determining the temperature maximum in the runoff air current [83]. The applicability of the Prandtl model in the case of anabatic winds sharply decreases under conditions of weak stratification due to convective mixing [69, 92, 95]. An alternative approach for modeling slope winds is represented by integral stationary models using the non-hydrostatic approximation [96]. In the non-hydrostatic two-dimensional model, the air is assumed to be incompressible and the Boussinesq approximation is taken [97]. This approach works well for simple slope geometries and in the absence of stratification [98–100]. First of all, such models reproduce conditions external to the slope wind layer and do not show turbulent processes inside the flow. As a result, neither Prandtl-type models nor integral models offer an acceptable solution to the problem of estimating heat and mass fluxes caused by katabatic and anabatic winds [101, 102]. For this, a good solution is to use vortex resolving (LES) modeling [95, 103]. In particular, it is used to determine the influence of the inclination angle on the profiles of the first and second turbulent moments, to study the effect of surface roughness on turbulent flows in a katabatic wind [104]. But there are still open questions that can be solved only with the use and development of methods for direct numerical simulation of turbulent processes. First of all, this is the determination of the conditions under which coherent turbulent structures (bars, plumes) are formed in the anabatic flow due to convective processes, which leads to enhanced mixing of the wind slope flow and the surface layer of the atmosphere in the valley [102]. The second question is the reaction of the turbulent flow structure to the spatial variability of the surface and atmosphere structure above the flow layer. There is practically no modeling of the structure of slope wind flows in the morning and evening hours, in particular, the change in wind direction at different heights, which arises due to the development of small-scale coherent structures [105]. And, finally, it is not known what leads to the formation of thermal plumes that periodically appear over the tops of the mountains, and what role the upslope flows play in their formation [106]. Also, eddy-resolving modeling of katabatic and anabatic flows on mountain slopes can be used to determine new scales in the Monin–Obukhov similarity theory, which will expand its application for modeling ABL in mountainous areas [107].

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

371

The transition of the airflow through the ridge, taking into account all daily periodic processes, can be modeled using a fairly simple linear theory [108]. This model expands the well-known theory of continuously stratified, steady, hydrostatic, frictionless flow over a mountain by presenting daily heating as an exponentially decreasing function with height. In the highlands, the upward shift of this function causes a local excess of the heating rate relative to the environment at the same height. Stationary linear solutions of this model give gravitational waves, which fade away due to interference with other internal waves. Modifications of this model reproduce the daytime convective boundary layer and diurnal variations by including the lower boundary condition of the model [108] into a two-layer linear, timevarying and dissipative model [109, 110], which fairly realistically reproduces heat and wind circulations. This approach is the most promising for representing subgrid heat circulations in numerical weather prediction models.

3.2

Valley Winds

Winds develop along the valley as a result of the greater amplitude of daily temperature fluctuations in the valley atmosphere compared to the atmosphere above the plain [96]. However, they can also arise if the daytime temperature range in the valley is not much greater than on the neighboring plain, because the pressure field on the surface is hydrostatically affected by heating anomalies in the upper layers [111–113] (Fig. 4). Valley winds can also occur due to temperature differences between air columns located at different points in the valley [114]. In valleys with steep slopes running from west to east, crosswinds can develop blowing from a shaded slope to a sunlit slope. In the climate of the Caucasus, valley winds develop mainly in summer and mainly on sunny days.

Fig. 4 Valley wind formation mechanism. dT is valley-plain air temperature differences, dP is valley-plain atmospheric pressure differences. (a) During the day, the atmosphere in the valley is warmer than that over the plain. (b) During the night is the opposite situation

372

I. А. Repina et al.

Valley winds develop on a longer time scale compared to slope winds. The pressure gradient, to which the upflow reacts, is established in a few hours. It usually takes about 6 h to reach a more or less stable state [115]. The surface wind itself reacts to changes in the pressure gradient on a time scale of 30–45 min [114, 116, 117]. The large amplitude of daily temperature fluctuations in the valleys is primarily associated with the influence of topography. The increase in the daytime temperature range is caused by the fact that due to the mountains surrounding the valley, the volume of the atmosphere below the specified control height is less above the valley than above the plain. Consequently, the temperature in the valley will rise faster (provided that the valley’s atmosphere can be considered as a closed thermodynamic system) with the same heat input per unit surface area. The importance of the valley volume effect in comparison with other possible mechanisms of increasing the daily temperature amplitude has been studied in detail using numerical simulations [98, 111, 112, 118–120]. However, it should be considered that this is true only in the case of a “pure” valley thermal wind. Indeed, the effect of the valley volume without other thermal effects in a homogeneous valley surface is the main one in the formation of its thermal regime and, consequently, the valley wind. But in the transitional time of day, horizontal and vertical heat advection can also play an important role. In the evening and morning hours, slope winds are compensated by vertical air movements in the valley. In these cases, the existing horizontal advection promotes local heating or cooling of the valley [111, 115, 121]. Modern models do not sufficiently reproduce the intensity of wind circulation over mountain valleys. This is due to the incomplete accounting of feedbacks arising in the mountain–valley system. It is especially difficult to take into account the influence of wind circulation systems that arise over the lakes in the valley [14] and urban areas [122–125]. It is known that vast bodies of water (lakes, seas) generate breeze circulation due to the temperature difference above the water surface and land. If the dimensions of the water surface are relatively small (small lakes, large rivers), local circulation may occur, affecting the area, the dimensions of which are comparable to the dimensions of the water surface that gave rise to it. The urban heat island also contributes to the circulation processes, which leads to the appearance of breeze winds, i.e. the rise of warm air over the urbanized territory and the flow of colder air from the surrounding area into the city. There is speculation that the latter can either amplify or destroy orographic breezes. One of the approaches to assessing the intensity of the valley wind and associated exchange processes is to use simple linear and nonlinear models [126–128]. These models are capable of predicting the temporal evolution of the wind, but their results are very sensitive to input parameters and are applicable only to simple valley geometry. An alternative method consists in studying the limit of the quasi-stationary regime of the valley wind system [115] and introducing additional scales that determine the characteristics of exchange processes depending on the geometry and size of the valley, atmospheric stratification, and radiation balance. In contrast to simple dynamic models, this approach is applicable to complex geometry of valleys. Much of modern knowledge about valley winds has been obtained as a result of experimental studies of atmospheric circulation in several valleys of middle

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

373

latitudes, for example, in the valley of the River Inn in the Austrian Alps [96, 129]. Measurements in other regions often reveal unexpected phenomena, such as the recent discovery that the daytime range of surface temperatures in the Adige Valley in the Italian Alps during valley winds is no greater than in the adjacent plain of the Po River [113]. From the point of view of organizing new observations, the mountain valleys of the Republic of Adygea are a promising experimental ground. Observations should be based not only on ground, but also on aircraft measurements [14, 130, 131], as well as remote sensing data [143].

3.3

Novorossiysk Bora

The processes occurring on the territory of Adygea under certain synoptic conditions lead to the formation of local coastal winds, which are called the Novorossiysk bora. This is a meteorological phenomenon, expressed primarily in a sharp increase in wind speed on the leeward side of the coastal ridges in the regions of Novorossiysk and Gelendzhik. Also, this phenomenon is expressed in a sharp change in the course of temperature, air humidity and a strong gust of wind is characteristic of the bora. The Novorossiysk bora arises as a result of the interaction of the oncoming largescale northeastern flow and the relief, while the maximum bora velocities are observed on the leeward slope of the coastal ridge and at its foot, where the port City of Novorossiysk and the resort City of Gelendzhik are located. Such winds are most dangerous for sea vessels [132], as well as for aviation [133]. There are known cases of deaths of ships and people during the bora and material damage is estimated at millions of dollars to the infrastructure of Novorossiysk and port downtime [134– 137]. It is the Novorossiysk bora that is the reason that the North Caucasus region occupies one of the “leading” positions in terms of weather risk in Russia and the first place in terms of the frequency of dangerous weather phenomena in the European territory of the country. The first detailed analysis of the synoptic conditions for the occurrence of bora, as well as a classification of the types of this phenomenon, is given in an extensive monograph, eds A.M. Gusev [136], although there are earlier works devoted to this phenomenon [138–141]. Much attention has traditionally been paid to the issues of synoptic analysis [142] and bora forecast [143]. Along with the statistical and synoptic analysis, analytical models describing this phenomenon have been proposed [144], as well as forecast methods based on the physical-empirical connections of the leeward speed enhancement with the large-scale state of the atmosphere [145]. The results of numerical modeling using modern mesoscale atmospheric models are presented in recent works [145–150]. The observations of bora are carried out at the meteorological stations of the Roshydromet network located in Novorossiysk and Gelendzhik, as well as at the anemometric network of the Novorossiysk Port (Fig. 5a). Also the short-term (no more than six months) winter observations were carried out within the expeditions of the Department of Meteorology and Climatology of Lomonosov Moscow

374

I. А. Repina et al.

Fig. 5 (a) A schematic map of the relief in the area of the Novorossiysk bora occurrence with the designation of the meteorological stations location (blue punsons – stations of the anemometric network of the Novorossiysk Port, red dots – automatic meteorological stations (AMS) of expeditions of the Department of Meteorology and Climatology of Moscow State University in 2011–2014; in the photographs (b-d) depicts meteorological instruments of the Department of Meteorology: (b) a sonar IRS (sodar), (c) a gradient mast, (c) AWS Davis Vantage Pro). The gray arrow shows the direction of the incoming flow

State University in 2011–2014 (head is Toropov P.A., Associate Professor of the Department) using an expanded network of automatic weather stations (Fig. 5a, d). In addition, within the expeditions, measurements of the characteristics of the bora in the surface layer were carried out using a 10-m gradient mast (Fig. 5c), as well as vertical sounding of the boundary layer using a sonar IRS (sodar) (Fig. 5b). Earlier (in July 2005) measurements during the bora were carried out by Obukhov Institute of Atmospheric Physics (head of the expedition is Repina I.A., head of the Laboratory of Atmospheric and Ocean Interactions) [151], using the temperature geometry tool MTP-5. All these data made it possible to estimate the intensity of turbulent mixing and the applicability of the logarithmic approximation of the change in wind speed with height at bora. In addition, the characteristic temperature stratification in the surface and boundary layers was assessed and the magnitude of the vertical and horizontal speed on the axis of the bora stream was determined. Also a valuable source of information about bora is the data of satellite sounding of the atmosphere, which make it possible to study the spatial structure of the phenomenon [137, 145, 153–155].

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

375

A strong northeastern wind on the Black Sea Coast of the Caucasus can be observed in the section from Anapa to Tuapse (the stream is completely blocked by mountains in the Sochi area), but in the form of a bora it is realized only in the area of Novorossiysk and Gelendzhik. The Markotkhsky and Main Caucasian ridges (Fig. 5a) are stretched from north-west to south-east (that is, perpendicular to the northeast stream), their height reaches 500–600 m in the Novorossiysk region and 600–700 m in the Gelendzhik region. The leeward slope of the Markotkhsky ridge is significantly (3 times) steeper than the windward one. In the area where the bora appears, most of the windward slopes are covered with woody vegetation, while on the leeward slopes the forest is sparse, and low-growing vegetation prevails. There are Sapun and Doob mountains (maximum height about 400 m) on the leeward side of the coast. According to Novorossiysk station, bora blows on average 46 days a year, 15–20 days a year wind speeds reach storm force, exceeding 20 m/s, and 5 days a year reach hurricane speed (more than 33 m/s). A catastrophic bora is observed approximately once every 20 years, accompanied by wind gusts of more than 45 m/s, which leads to significant destruction, human casualties and almost completely paralyzes the operation of the Novorossiysk Port. The maximum wind speeds correspond to a wind direction of 30 to 60 . Bora can be observed at any time of the year, including in summer [151], but its greatest frequency and intensity occur in the cold season. On average, there are 18 days with bora in the winter months, 12 days in the fall, and 11 days in the spring. The duration of the bora is on average about 1.5 days, but in some cases it can reach 8 days. In general, bora is characterized by very low daily variability of wind speed and temperature values. Thus, on average, the daily amplitude of the wind speed in bora is only 0.2 m/s, while on ordinary days of the cold season it is about 1 m/s. The absence of the nighttime maximum speed confirms that bora does not belong to katabatic winds. The daily amplitude of air temperature in bora also decreases and is, on average, 2  C. It is common to distinguish the anticyclonic and cyclonic types of the Novorossiysk bora. Anticyclonic bora develops during ultrapolar processes, on the southern periphery of cold anticyclones (Siberian, Arctic, or Scandinavian) (see the typical pressure distribution during anticyclonic bora in Fig. 6) and it corresponds to low cloudy weather and especially strong winds (by analogy with the “white” Adriatic bora [155]). At the same time, moisture condensation occurs on the windward slope and above the summit in most cases (visually, this can be seen by the formation of a foehn bar over the ridge, the local name is “beard”), and on the leeward side moisture evaporates due to heating of the air when it descends. The cyclonic bora that develops in the rear parts of the Mediterranean and Atlantic cyclones, in the cold front zone, is accompanied by continuous cloudiness and precipitation (by analogy with the “black” Adriatic bora) [155]. The average height of the tropopause in the bora is about 10.5 km, that is, the phenomenon most often takes place amid the invasion of moderate continental air mass, the surface characteristics of which in winter are close to the arctic.

I. А. Repina et al.

376

1030

48°N

H

1025

46°N

1020

1015 1010

44°N

1005

42°N

L

40°N

32°E

34°E

36°E

38°E

40°E

42°E

44°E

Relief height, m 50

280 510 740 970 1200 1430 1660 1890 2120 2350 2580 2810 3000

Fig. 6 Schematic map of a typical distribution of a large-scale pressure field (contours, interval 5 hPa) for Novorossiysk bora (composite of pressure maps of the 12 strongest bora episodes over the past 40 years). The color shows the height of the relief. The red triangle shows the location of the Novorossiysk weather station

The oncoming flow in the bora is characterized by the presence of an elevated temperature inversion with a lower boundary at heights of about 500 m and with an average thickness of about 700 m. Most often, the inversion is formed due to anticyclonic air subsidence. At the same heights, a low-tropospheric jet stream is also observed in the oncoming stream, i.e. a local maximum of the wind speed in the lower troposphere, which often occurs at the periphery of the anticyclone. The average speed of the low-tropospheric jet stream is 15–20 m/s, less often up to 25 m/s. Most often, in the oncoming stream, the wind reverses (changes in direction) with height, on average, at the heights of the middle troposphere [150]. Bora usually ceases rapidly in the open sea and its spatial scale along the flow direction is 10–20 km [146]. Satellite data and mesoscale modeling results indicate the appearance on the leeward side of the ridges of typical bora jet bands (high wind speed regions elongated in the direction of the flow) which alternate with wind calm regions [146]. Their length along the stream can reach 150 km [152]. The maximum wind speed during bora is observed in the eastern part of the Tsemesskaya Bay (Fig. 7) and in the Novorossiysk Port. The wind speed decreases

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

377

Fig. 7 Map of wind speed (with color and feathers, m/s) during bora on January 26, 2012 at 23:00 (local time) according to the results of numerical simulation using the WRF-ARW model (the results of a numerical experiment from [149]). Isolines show the relief (100 m interval)

in Gelendzhik. This is due to an increase in the height of the Caucasian and Markotkhsky ridges from west to east, which leads to an increase in the blocking of the lower layers of the flowing stream by mountains in the Gelendzhik region. Within the bora, the wind speed directly at the foot of the leeward slope is higher than at other points. With a distance from the leeward slope, the wind speed decreases rapidly [146, 151], by 15–20% at a distance of about 5 km from the ridge compared to the speed at the foot (Fig. 7.8). This distribution of wind speed is consistent with the results of observations of similar phenomena in other regions [156, 157]. The minimum wind speed is observed on the windward slope of the Caucasus Range (Fig. 3). As an incoming stream approaches the ridges, its stagnation in the lower layers intensifies. On the windward slope, the speed decreases (2–4 times) compared to the speed of the oncoming stream (in Krymsk town). For example, according to the AMC measurements on the windward slope of the Main Caucasian Ridge in February 2013, the wind speed did not exceed 1 m/s when several episodes of bora were observed. In the leeward area, low wind speeds during bora are usually observed in the wind shadow of Mount Doob and on the windward slope of Mount Sapun (Fig. 7).

378

I. А. Repina et al.

Fig. 8 Wind speed variation during bora on January 26–28, 2012 according to the data of anemometric sensors installed in the Novorossiysk port at the foot of the leeward slope and at a distance of about 5 km from it (time discreteness of measurements 2 min)

The greatest fluctuations in wind speed (and other meteorological values) and gustiness are observed directly at the foot of the ridge. Various episodes of the bora are characterized by wind speed changes from 5 to 25 m/s within 1 h (Fig. 8). The main cause of flow turbulization and gustiness in such winds is considered to be the Kelvin–Helmholtz instability [158] associated with a velocity shear at the boundary between the lower layer of high velocities (the bora layer) and the overlying layers with a relatively low velocity. The Novorossiysk bora is always accompanied by a decrease in temperature, since the adiabatic heating of air when it descends along the leeward slope is small (due to the low height of the ridges) compared to the advection of cold air from the northeast. At the same time, there is a difference in potential temperature between the windward and leeward sides, since part of the cold air, the kinetic energy of which is not enough to flow around the ridge from above, is blocked by mountains. The occurrence of blockage of the flow by mountains is favored by the synoptic situation in which the bora develops, since the flow has a north or northeast direction, and the inflowing air is cold (and therefore heavy) and is usually limited from above by a raised inversion. As the bora develops with increasing of the oncoming flow velocity and/or weakening of stratification in the lower layer, the temperature difference (and hence blocking) usually decreases. The temperature difference in the Gelendzhik area is much greater (approximately 2–3 times) than in the Novorossiysk area, which is associated with the higher height of the ridges in the Gelendzhik area with the same parameters of the oncoming flow [150]. Also, significant disturbances of the pressure field are observed during bora, arising simultaneously due to processes of synoptic and meso scales. Observational data of atmospheric pressure in the Novorossiysk bora in the study area indicate a significant increase in the pressure drop between the windward and leeward sides

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

379

during the bora. This is primarily due not to an increase in pressure on the windward side, but due to its fall on the leeward slope (i.e. in Novorossiysk and Gelendzhik) [150]. According to observations at the expanded network of stations of the Department of Meteorology and Climatology of Moscow State University in 2013–2014 the pressure from the windward slope to the leeward slope in the bora falls on average by 15 Pa per kilometer, which is more than 10 times the average horizontal baric gradient and is comparable to the pressure drop in tropical cyclones. Leeward waves (gravitational waves spreading horizontally) can be observed with bora, which are visually recorded along extended cloud beds, located parallel to the ridges on the leeward side, at approximately equal distance from each other. Clouds form at the crests of leeward waves when the air reaches saturation. Leeward waves also appear in the simulation results [147, 148]. Bora also affects oceanic processes. Usually, the height of sea waves in bora increases with an increase in the wind speed from the coast towards the open sea [159]. The combination of waves and negative air temperatures leads to icing of ships due to the rapid freezing of drops blown off by the wind from the wave crests. For example, in [134] data are given on the freezing of about 40 cm of ice for 6 h, which led to the capsizing of the vessel. Also, the upper layer of water is driven off and the deep waters rise to the surface during bora [132, 152]. The Novorossiysk bora, like the Adriatic bora, the Alpine foehn, and chinooks, belongs to the class of downslope windstorms. There are several hypotheses for the occurrence of leeward storms. Initially, this phenomenon was attributed to the type of katabatic winds [136, 157]; however, the wind amplification during the katabatic process is too small and does not correspond to the observed one [155]. At present, theoretical approaches are widely used to describe the phenomenon, based on simplified models of the dynamic interaction of an oncoming wind flow with relief irregularities. The wave approach to the description of the mechanism of occurrence of leeward storms is based on the use of two-dimensional models of a stratified fluid, in which the intensification of wind near the leeward slope is due to the intensification of internal gravity waves (IGW) over mountains [160]. In nonlinear models it deals with the effects of IGW collapse [161] and features of the shape of the streamlined relief (including the asymmetry of the windward and leeward slopes) [162– 165]. Within the hydraulic approach, the solution of the flow problem is based on the equations of the theory of shallow water, while the increase in the wind speed behind the obstacle is associated with the transition of the oncoming flow from subcritical (slow and deep flow, with a maximum velocity at the top of the ridge) to supercritical state (fast and shallow flow) with the appearance of an analogue of a hydraulic jump in the atmosphere behind the leeward slope [166]. A hydraulic jump is an abrupt (turbulent) change in the height of the free surface (or the thickness of the fluid layer). The analysis of the conditions in an undisturbed atmosphere in front of the mountains shows that the main properties of the oncoming flow in bora can be approximately taken into account within a single-layer stationary hydraulic flow

380

I. А. Repina et al.

model. The observed elevated inversion layer is a natural analogue of the free interface when using the hydraulic approach [167]. The data from observations of wind speed across the ridge also support the hydraulic theory. An increase in wind speed from the top of the ridge to its foot, simultaneously with the minimum speed on the windward slope, means that the flow regime is transformed from subcritical to supercritical over the ridge, which is also confirmed by the results of numerical simulations [145]. This flow behavior during leeward storms was first described in [168]. Conversely, the prevalence of stable stratification in the atmosphere creates favorable conditions for the emergence and spreading of internal gravity waves [169]. The presence of wind reversal in the oncoming flow under certain conditions can lead to the capture of wave energy in the lower layers and wave resonance, which will affect the disturbances of meteorological fields near the ground and will contribute to an increase in leeward velocity. In most cases, the conditions of the oncoming flow in the Novorossiysk bora, which can be expressed in terms of dimensionless numbers, in this case the Froude number Fr ¼ U/Nh (U and N are the wind speed and Brunt–Väisälä frequency in the oncoming flow, h is the height of the mountain range), will contribute to the appearance of nonlinear wave effects, i.e. breaking waves. Typical structures in the atmosphere are manifestations of wave and hydraulic mechanisms, namely the collapse of internal waves and a hydraulic jump are difficult to detect from observational data, but they are revealed in numerical modeling of various episodes of the Novorossiysk bora [145, 147, 148], and usually simultaneously, which indicates a significantly mixed mechanism of the formation of this phenomenon.

4 Stratified Boundary Layers in Highlands It is common to distinguish three main states of the atmospheric boundary layer: stable, unstable (convective), and neutral. The stable layer usually forms at night, when the surface air is greatly cooled by radiation from the surface. The stable layer is characterized by a small thickness (300–600 m) and a low level of turbulence, and, consequently, a low transfer rate. But the most interesting is the unstable, convective boundary layer (CBL), which usually develops over land in the daytime due to heating of the underlying surface due to solar radiation. Such a layer can also form when a cold air mass flows onto a warm surface. The structure of a homogeneous convective ABL above a homogeneous surface is a multilayer formation that includes a surface layer, a mixing layer, and a traction carpet, and a different modeling method is applied for each layer. Due to the interaction between different streams (synoptic stream, slope wind, valley wind), the CBL over the mountains has a completely different structure [22]. In mountainous areas, wind circulation plays a decisive role in the structure, length, and vertical and horizontal variability of the CBL.

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

381

In particular, the convective processes in the valleys are significantly affected by the horizontal advection of cold air arising from the valley winds blowing up the valley, and if the surface layer in the valley is stably stratified, the vertical advection of warm air arises to compensate for the rising winds [170–173]. The transport of near-surface air by thermal winds to mountain ranges and then up slopes [174] is often referred to as topographic or mountain blowout. The anabatic winds over flat terrain are random in contrast to the ones in highlands that are tied to prominent orographic features. Thermal convergence increases vertical transport just above mountain ridges [175], possibly also initiating wet convection [176–178]. As a result, pollutants and moisture can be transported vertically over 1,000 m above mountain peaks [179, 180]. Advection of turbulence to the traction carpet around the ridge [98, 118, 173] can also contribute to a decrease in the stability of the atmosphere, thereby enhancing vertical mixing. Besides vertical exchange, heat transfer circulations also affect horizontal exchange. For example, at the foot of the coastal mountains, sea breezes and valley winds often enhance horizontal transport of moist air [181, 182]. An increase in the concentration of water vapor and aerosols is possible over mountain ranges [179], which leads to horizontal gradients and an increase in their horizontal transport. Combined with horizontal mean wind advection (which can be caused by either synoptic conditions or mesoscale processes), these horizontal gradients are responsible for advective ventilation [179, 180]. Advective ventilation generates warm, uplifted mixed layers, which often have a strong inversion at the base [183]. Because of their thermal structure, elevated intermixed layers can significantly affect the height of the surface intermixed layer above the surrounding lower terrain or valley. Signs of layering are often found in the atmosphere over a complex orography [184], while surface mixed layers are thin in valleys [10, 118, 185] or are absent altogether [172] in mountain valleys. The cross-cutting impact of thermal breezes on the structure of the ABL near the mountains makes it difficult to determine its vertical extent. The upper border of the ABL is sharply defined on flat terrain. It coincides with the mixing height and depends, first of all, on the thermal structure of the atmosphere and on the intensity of turbulent exchange (determined, for example, by the Richardson volume number) [186]. But this scheme is not suitable for representing the ABL over the mountains [121, 171, 185]. The multi-layered structure of the daytime atmosphere over mountains is often revealed during observations and simulations. Lidar observations, which are increasingly used to determine the vertical extent of the ABL, often reveal elevated aerosol layers over mountainous areas. The upper boundary of these aerosol layers does not always coincide with the boundary of the CBL determined using thermodynamic profiles and can be a source of confusion when determining the appropriate mixing height over mountainous terrain. Since the quantification of vertical exchange is partly dependent on an estimate of the vertical extent of mixing and transport processes, more research is needed to determine the appropriate scale of transport and mixing heights. Until now, the main attention in studying the daytime boundary layer over mountains has been paid to the temporal and spatial evolution of the upper boundary

382

I. А. Repina et al.

of the CBL, as is customary in traditional studies of the boundary layer. But if there is still some information about the dynamics of the CBL in the valleys, there are very few observations of its structure on the slopes and tops of the mountains. It was found that important factors determining its dynamics are atmospheric stratification, synoptic wind speed, vertical and horizontal scales of orography, and the presence of valleys. Developed SBL are less subordinate to the relief than low ones, and the upper border of the CBL is subject to greater variability over orographic objects with a large horizontal extent [187]. Observations and models give different answers to the question of whether advection leads to a change in the height of the CBL over the mountains. Consequently, here, complex studies are also needed with the involvement of data from experimental polygons and numerical modeling. Only on their basis, it is possible to define clearly the atmospheric boundary layer over the mountains eliminating the confusion caused by the intersection of various atmospheric processes. At night, radiation cooling of the surface and stabilization of the atmosphere over a complex orography maintains the spreading of internal gravity waves and causes drainage flows that can lead to the formation of cold air lenses. A stable drainage flows can form in valleys with a sloping bottom and with an increasing cross-sectional area down the valley [188, 189]. Under these conditions, the flow, moving down the valley, is characterized by a maximum wind speed directly above the ground and turbulence is maintained due to the formation of wind shear, the scale of the largest eddies in the flow can reach 100 m or more, the stratification is weak and, probably, the turbulence is only slightly anisotropic. In contrast, valleys with flat bottom or with blocking obstacles downstream allow the formation of strong surface inversions and areas of cold air [190, 191]. The time scale of nighttime cooling in such valleys is usually shorter than in the plain [192]. The formation of areas of cold air is facilitated by certain geometric properties of orography (for example, altitude), but their intensity and stability depend on several meteorological factors, mainly determined on a synoptic scale (the presence of cold and dry air mass, weak wind, and cloudless sky) [193]. Areas of cold air separate the near-surface layer from the overlying flow, which can be synoptic or orographic. Stratification in such areas reduces the intensity of turbulence, but even under these conditions, weak turbulent mixing is possible [194]. Brief mixing can occur when wind shear over the upper boundary of the cold region becomes sufficient to cause the Richardson number (Ri) to become subcritical. But when this happens, the wind shear is reduced and turbulence is suppressed again. Although the origin of these sporadic mixing events due to wind shear is qualitatively well studied, their quantitative description (for example, for parameterization purposes) remains an unresolved problem. Slope wind flows are almost always highly turbulized and rarely stably stratified. But, despite this, the maximum wind at a low altitude leads to the fact that the standard theories of a turbulent boundary layer in such a flow do not work [35, 195]. Therefore, in a stably stratified ABL, slope jets significantly affect its stability. That is, difficult terrain leads to the destabilization of a steady flow.

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

383

The study of the dynamics of the ABL over the mountains shows that the interaction of the atmosphere and the surface is influenced by weather processes that develop on different spatial and temporal scales. And often the transfer of energy between movements of different scales is controlled by the existence of stable layers of different genesis. These layers can be terrestrial or elevated and, if stratification is strong, can be associated with temperature inversions. Stable layers in mountain valleys determine the relationship between the valley atmosphere and the overlying free atmosphere. Some of the processes that create stable layers over complex orography also occur in flat and uniform terrain (e.g. radiation cooling, decrease in the ABL height). Other processes are typical only for ABL over mountainous terrain. Resistant layers suppress turbulence, resist mixing by wind shear, inhibit vertical exchange and control the spreading of gravity waves, and protect the valley from synoptic currents when located below the ridge. Naturally, this has serious implications for air quality in mountain valleys, especially when populated areas are located there [130]. Ground stable layers control periodic turbulent exchange in the entire boundary layer, suppressing the occurrence of shear turbulence. Raised stable layers block heat and moisture under them and thus, in combination with horizontal advection, can contribute to the occurrence of rapid convective phenomena (thunderstorms, squalls). In addition, wind shear through stable layers can contribute to the occurrence of organized convection and the development of prolonged storm events [196].

5 Impact of Reservoirs on the Climate of the Territory In addition to the mountain-valley relief, water bodies, including artificially formed ones, have a significant impact on metabolic processes [197]. Reservoirs have become an integral part of the life and development of various countries as sources of water supply and irrigation, energy and shipping facilities, and fisheries. However, in addition to the obvious economic value, they have also become a source of problems that the residents of territories remote from the sea did not face before. This is a change in the wind regime, and an increased moisture content of the atmosphere, which leads to fog, ice, and frost. Large reservoirs of water have changed the surrounding climate, and these changes continue to occur, often causing extreme weather events. The wind speed increases and the direction of the wind changes and winds such as breezes arise. Reservoirs affect cloudiness and precipitation, causing phenomena similar to lake snowfalls. An extremely unfavorable consequence of the creation of large reservoirs, caused by a change in the thermal regime, is a non-freezing polynya in the tail bay leading to fog formation and to an increase in the level of air pollution. In addition, reservoirs are sources of greenhouse gas emissions, especially methane and carbon dioxide, which contribute to global climate change [198, 199]. The radiation balance changes over the water area of large reservoirs, the air temperature in the territories adjacent to the reservoir decreases in spring and in the

384

I. А. Repina et al.

first half of summer (cooling effect) and increases in the second half of summer and autumn (warming effect). There is a shift in the dates of the transition of air temperatures through the main gradations, the absolute and relative air humidity increases. Within the Republic of Adygea, seven main reservoirs are distinguished: Krasnodar, Shapsug, Shendzhiy, Oktyabrsk, Chetuk, Kuzhorsk, Maykop, which have different areas of water lines (from 0.4 to 402 km2) and water volumes (from 0.8 to 2,350 mln m3). Geographically, the reservoirs are located on the lowland, with the exception of two foothills (Kuzhorsk, Maykop). Basins of reservoirs by genesis are valley-type, blocked by dams (Krasnodar, Shapsug, Maykop, Kuzhorsk, Chetuk) and off-stream storage reservoirs, created in places of groundwater outlet (Shendzhiy, Oktyabrsk). All reservoirs are seasonal by the nature of the runoff. The total area of artificial reservoirs was approximately 4–5% of the area of Adygea [200]. Krasnodar, Shapsug, Maykop reservoirs have an average degree of level fluctuation (3–10 m) in the level regime. The thermal regime of the reservoirs is characterized by heterogeneity: the northern ones are colder than the southern ones (Maykop, Kuzhorsk), ice formations are observed in the winter season (30–35 days). The main transformative processes of reservoirs are: abrasion, landslides, water erosion, accumulation, bog formation. The Krasnodar Reservoir is the largest in the North Caucasus and is an integrated water management facility. About 90% of its water area is located on the left bank of the Kuban River, on the lands of the Republic of Adygea. The reservoir regulates the entire river flow of the left tributaries of the Kuban in order to protect the underlying floodplain lands from flooding with floodwaters. The Krasnodar Reservoir had a negative environmental impact on the adjacent territories of the Republic of Adygea. Gradually, climate changes occurred in the zone of influence of the reservoir in the direction of increasing air humidity: summer became cooler, winters softer. The appearance of a small “sea” near the city has led to an increase in the number of fogs by 5–10%, which periodically interfere with the operation of the Krasnodar Airport. The Maykop Reservoir is the smallest in the Republic of Adygea that was commissioned in June 1950 on the Belaya River near the City of Maykop. It is designed to limit intraday flow regulation and ensure water intake for the operation of the Maykop hydroelectric power station and it is also a source of drinking water supply for the capital of the Republic. Monitoring of the reservoir siltation showed that the Maykop Reservoir was subjected to intensive siltation from the first days of its operation. Up to 70% of its total volume and more than 20% of its useful volume were lost by the fourth year of operation of the Maykop Reservoir. 1.84 million m3 of sediment has settled in the reservoir. The total volume of the reservoir decreased from 2.76 to 0.40 million m3 (i.e. by almost 90%) turning it into a shallow pool. And this makes the reservoir a possible source of methane release into the atmosphere. Currently, it is planned to amend the current legislation of the Russian Federation that regulates greenhouse gas emissions and offsetting the carbon footprint of economic entities. And here the role of reservoirs will be given special attention. The methane content in reservoirs depends, on the one hand, on the ratio of its flux from bottom sediments, direct formation in water, input from the water

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

385

catchment surface, including tributaries and industrial and domestic wastewater and, on the other hand, on oxidation methane in water and its emissions into the atmosphere [201–203]. An additional source of carbon dioxide is blue-green algae, the bloom of which is becoming a serious problem for the quality of water in artificial reservoirs [204, 205]. The content of dissolved gases in water is influenced by both natural factors and, first of all, climatic and hydrological conditions that control the seasonal and daily dynamics of physicochemical and biochemical processes, and anthropogenic impact, superimposed on natural factors and processes [206, 207]. Since methane is a product of anaerobic decomposition of organic matter, and an increase in water temperature intensifies the activity of microorganisms, its emission from reservoirs depends on this indicator [208]. An important factor is the depth of the reservoir: more methane enters the atmosphere from shallow-water parts of the water area than from deep-water ones. But the role of the reservoirs of Adygea as a source of greenhouse gases has not yet been assessed.

6 Conclusion Turbulent processes, mesoscale circulations caused by thermal impact, and processes of a synoptic scale determine the structure of the atmospheric boundary layer over a territory with a heterogeneous surface, including mountainous relief. The presented examples of mountain-valley wind circulations and strong coastal winds forming on the slopes show the multiscale and complexity of modeling of these processes. The territory of the Republic of Adygea is unique in terms of the variety of landscapes and can become a good testing ground for a comprehensive study of the features of complex wind circulation and validation of models from eddy-resolving to regional numerical models of weather forecasting. This will contribute not only to improving the quality of weather forecasts and dangerous meteorological phenomena, but also to improving the ecological state of the region through more competent planning of economic activities, taking into account the landscape and climatic features of the region. Acknowledgements This work was supported by the RFBR grant 20-05-00834 A.

References 1. Bedanokov MK, Berzegova RB, Kuizheva SK (2020) Atmospheric disturbances in the flow of the mountains and the problem of flight safety in the mountains of the republic of Adygea. In: Bedanokov M, Lebedev SA, Kostianoy A (eds) The Republic of Adygea environment. Springer, Berlin. https://doi.org/10.1007/698_2020_494

386

I. А. Repina et al.

2. Mihajleva N.Y. (2013) Social'no-ekonomicheskie i ekologicheskie problemy gornyh territorij Severnogo Kavkaza. Izdatelstvo Severo-Osetinskogo gosudarstvenogo universiteta imeni K.L. Hetagurova, Vladikavkaz. 251. (in Russian) 3. Baklanov AA, Grisogono B, Bornstein R, Mahrt L, Zilitinkevich SS, Taylor P, Larsen SE, Rotach MW, Fernando HJS (2011) The nature, theory, and modeling of atmospheric planetary boundary layers. Bull Am Meteorol Soc 92(2):123–128. https://doi.org/10.1175/ 2010BAMS2797.1 4. Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land–atmosphere coupling and climate change in Europe. Nature 443(7108):205–209. https://doi.org/10.1038/nature05095 5. Tomasi E, Giovannini L, Zardi D, de Franceschi M (2017) Optimization of Noah and Noah_MP WRF land surface schemes in snow-melting conditions over complex terrain. Monthly Weather Rev 145(12):4727–4745. https://doi.org/10.1175/MWR-D-16-0408.1 6. Stull RB (1988) An introduction to boundary layer meteorology. Kluwer Academic Publishers, Dordrecht, p 670. https://doi.org/10.1007/978-94-009 7. Rotach MW, Gohm A, Lang MN, Leukauf D, Stiperski I, Wagner JS (2015) On the vertical exchange of heat, mass, and momentum over complex, mountainous terrain. Front Earth Sci 3:1–14. https://doi.org/10.3389/feart.2015.00076 8. Smith RB (1979) The influence of mountains on the atmosphere. Adv Geophys 21:87–230. https://doi.org/10.1016/S0065-2687(08)60262-9 9. Markowski P, Richardson Y (2010) Mesoscale meteorology in midlatitudes. Wiley, Hoboken, p 430. https://doi.org/10.1002/9780470682104 10. Rotach MW, Zardi D (2007) On the boundary-layer structure over highly complex terrain: key findings from MAP. Q J R Meteorol Soc 133(625):937–948. https://doi.org/10.1002/qj.71 11. De Franceschi M, Zardi D, Tagliazucca M, Tampieri F (2009) Analysis of second-order moments in surface layer turbulence in an Alpine valley. Q J R Meteorol Soc 135 (644):1750–1765. https://doi.org/10.1002/qj.506 12. Zardi D, Whiteman D (2013) Diurnal mountain wind systems. In: Chow F, De Wekker S, Snyder B (eds) Mountain weather research and forecasting: recent progress and current challenges. Springer, Dordrecht, pp 35–119. https://doi.org/10.1007/978-94-007-4098-3_2 13. Schicker I, Arias DA, Seibert P (2016) Influences of updated land-use datasets on WRF simulations for two Austrian regions. Meteorog Atmos Phys 128(3):279–301. https://doi. org/10.1007/s00703-015-0416-y 14. Laiti L, Zardi D, de Franceschi M, Rampanelli G, Giovannini L (2014) Analysis of the diurnal development of a lake-valley circulation in the Alps based on airborne and surface measurements. Atmos Chem Phys 14(18):9771–9786. https://doi.org/10.5194/acp-14-9771-2014 15. Ducrocq V, Braud I, Davolio S, Ferretti R, Flamant C, Jansa A, Kalthoff N, Richard E, Taupier-Letage I, Ayral PA, Belamari S, Berne A, Borga M, Boudevillain B, Bock O, Boichard JL, Bouin MN, Bousquet O, Bouvier C, Chiggiato J, Cimini D, Corsmeier U, Coppola L, Cocquerez P, Defer E, Delanoë J, Di Girolamo P, Doerenbecher A, Drobinski P, Dufournet Y, Fourrié N, Gourley JJ, Labatut L, Lambert D, Le Coz J, Marzano FS, Molinié G, Montani A, Nord G, Nuret M, Ramage K, Rison W, Roussot O, Said F, Schwarzenboeck A, Testor P, Van Baelen J, Vincendon B, Aran M, Tamayo J (2014) HyMeX-SOP1: the field campaign dedicated to heavy precipitation and flash flooding in the northwestern Mediterranean. Bull Am Meteorol Soc 95(7):1083–1100. https://doi.org/10.1175/BAMS-D-12-00244.1 16. Barry RG (1992) Mountain weather and climate. Cambridge University Press, Cambridge, p 506 17. Chow F, DeWekker S, Snyder B (eds) (2013) Mountain weather research and forecasting: recent progress and current challenges. Springer, Heidelberg, p 760. https://doi.org/10.1007/ 978-94-007-4098-3 18. Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography: the Innsbruck box (i-box). Bull Am Meteorol Soc 98(4):787–805. https://doi.org/10.1175/BAMS-D-15-00246.1

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

387

19. Medeiros LE, Fitzjarrald DR (2014) Stable boundary layer in complex terrain. Part I: linking fluxes and intermittency to an average stability index. J Appl Meteorol Climatol 53 (9):2196–2215. https://doi.org/10.1175/JAMC-D-13-0345.1 20. Emeis S, Kalthoff N, Adler B, Pardyjak E, Paci A, Junkermann W (2018) High-resolution observations of transport and exchange processes in mountainous terrain. Atmos 9(12):457. https://doi.org/10.3390/atmos9120457 21. Chow FK, Schär C, Ban N, Lundquist KA, Schlemmer L, Shi X (2019) Crossing multiple gray zones in the transition from mesoscale to microscale simulation over complex terrain. Atmos 10(5):274. https://doi.org/10.3390/atmos10050274 22. Serafin S, Adler B, Cuxart J, De Wekker SF, Gohm A, Grisogono B, Kalthoff N, Kirshbaum DJ, Rotach MW, Schmidli J, Stiperski I (2018) Exchange processes in the atmospheric boundary layer over mountainous terrain. Atmos 9(3):102. https://doi.org/10.3390/ atmos9030102 23. Foken T (2017) Micrometeorology, 2nd edn. Springer, Berlin, p 362. https://doi.org/10.1007/ 978-3-642-25440-6 24. Cuxart J, Conangla L, Jiménez MA (2015) Evaluation of the surface energy budget equation with experimental data and the ECMWF model in the Ebro Valley. J Geophys Res Atmos 120 (3):1008–1022. https://doi.org/10.1002/2014JD022296 25. Foken T (2008) The energy balance closure problem: an overview. Ecol Appl 18 (6):1351–1367. https://doi.org/10.1890/06-0922.1 26. Stoy PC, Mauder M, Foken T, Marcolla B, Boegh E, Ibrom A, Arain MA, Arneth A, Aurela M, Bernhofer C, Cescatti A, Dellwik E, Duce P, Gianelle D, Gorsel E, Kiely G, Knohl A, Margolis H, McCaughey H, Merbold L, Montagnani L, Papale D, Reichstein M, Saunders M, Serrano-Ortiz P, Sottocornola M, Spano D, Vaccari F, Varlagin A (2013) A datadriven analysis of energy balance closure across FLUXNET research sites: the role of landscape scale heterogeneity. Agric For Meteorol 171-172:137–152. https://doi.org/10. 1016/j.agrformet.2012.11.004 27. Obukhov AM (1946) Turbulence in a temperature-inhomogeneous atmosphere. Trudy Instituta Teoreticheskoy Geofiziki Akademii nauk SSSR 1:95–115. (in Russian) 28. Monin AS, Yaglom AM (1965) Statistical fluid mechanics, volume I-II. Nauka, Moscow, p 640. (in Russian) 29. Monin AS, Obukhov AM (1954) Basic laws of turbulent mixing in the atmospheric surface layer. Trudy Geofizicheskogo Instituta Akademii nauk SSSR 24(151):163–187. (in Russian) 30. Kantha LH, Clayson CA (2000) Small scale processes in geophysical fluid flows. Academic Press, San Diego, p 750 31. Kaimal JC, Finnigan JJ (1994) Atmospheric boundary layer flows: their structure and measurement. Oxford University Press, Oxford, p 304. https://doi.org/10.1007/BF00712396 32. Wyngaard JC (2010) Turbulence in the atmosphere. Cambridge University Press, New York, p 393. https://doi.org/10.1017/CBO9780511840524 33. Nadeau DF, Pardyjak ER, Higgins CW, Parlange MB (2013) Similarity scaling over a steep alpine slope. Bound-Layer Meteorol 147(3):401–419. https://doi.org/10.1007/s10546-0129787-5 34. Babic K, Rotach MW, Klaic ZB (2016) Evaluation of local similarity theory in the wintertime nocturnal boundary layer over heterogeneous surface. Agric For Meteorol 228-229:164–179. https://doi.org/10.1016/j.agrformet.2016.07.002 35. Grachev AA, Leo LS, Di Sabatino S, Fernando HJS, Pardyjak ER, Fairall CW (2016) Structure of turbulence in katabatic flows below and above the wind-speed maximum. Bound-Layer Meteorol 159(3):469–494. https://doi.org/10.1007/s10546-015-0034-8 36. Stiperski I, Rotach MW (2016) On the measurement of turbulence over complex mountainous terrain. Bound-Layer Meteorol 159(1):97–121. https://doi.org/10.1007/s10546-015-0103-z 37. Babic N, Vecenaj Z, De Wekker SF (2016) Flux–variance similarity in complex terrain and its sensitivity to different methods of treating non-stationarity. Bound-Layer Meteorol 159 (1):123–145. https://doi.org/10.1007/s10546-015-0110-0

388

I. А. Repina et al.

38. Barskov KV, Glazunov AV, Repina IA, Stepanenko VM, Lykossov VN, Mammarella I (2018) On the applicability of similarity theory for the stable atmospheric boundary layer over complex terrain. Izvestiya Atmos Oceanic Phys 54(5):462–471. https://doi.org/10.1134/ S0001433818050031 39. Quan L, Hu F (2009) Relationship between turbulent flux and variance in the urban canopy. Meteorog Atmos Phys 104(1–2):29–36. https://doi.org/10.1007/s00703-008-0012-5 40. Wood CR, Lacser A, Barlow JF, Padhra A, Belcher SE, Nemitz E, Helfter C, Famulari D, Grimmond CSB (2010) Turbulent flow at 190 m height above London during 2006–2008: a climatology and the applicability of similarity theory. Bound-Layer Meteorol 137(1):77–96. https://doi.org/10.1007/s10546-010-9516-x 41. Grachev AA, Andreas EL, Fairall CW, Guest PS, Persson POG (2015) Similarity theory based on the Dougherty–Ozmidov length scale. Q J R Meteorol Soc 141(690):1845–1856. https:// doi.org/10.1002/qj.2488 42. Rodrigo JS, Anderson PS (2013) Investigation of the stable atmospheric boundary layer at Halley Antarctica. Bound-Layer Meteorol 148(3):517–539. https://doi.org/10.1007/s10546013-9831-0 43. Zhao Z, Gao Z, Li D, Bi X, Liu C, Liao F (2013) Scalar flux–gradient relationships under unstable conditions over water in coastal regions. Bound-Layer Meteorol 148(3):495–516. https://doi.org/10.1007/s10546-013-9829-7 44. Kral ST, Sjöblom A, Nygard T (2014) Observations of summer turbulent surface fluxes in a high Arctic fjord. Q J R Meteorol Soc 140(679):666–675. https://doi.org/10.1002/qj.2167 45. Grachev AA, Leo LS, Fernando HJ, Fairall CW, Creegan E, Blomquist BW, Christman AJ, Hocut CM (2018) Air–sea/land interaction in the coastal zone. Bound-Layer Meteorol 167 (2):181–210. https://doi.org/10.1007/s10546-017-0326-2 46. Figueroa-Espinoza B, Salles P (2014) Local Monin–Obukhov similarity in heterogeneous terrain. Atmos Sci Lett 15(4):299–306. https://doi.org/10.1002/asl2.503 47. Panin GN, Bernhofer C (2008) Parametrization of turbulent fluxes over inhomogeneous landscapes. Izvestiya Atmos Oceanic Phys 44(6):701–716. https://doi.org/10.1134/ S0001433808060030 48. Leuning R, van Gorsel E, Massman WJ, Isaac PR (2012) Reflections on the surface energy imbalance problem. Agric For Meteorol 156:65–74. https://doi.org/10.1016/j.agrformet.2011. 12.002 49. Rotach MW, Andretta M, Calanca P, Weigel A, Weiss A (2008) Boundary layer characteristics and turbulent exchange mechanisms in highly complex terrain. Acta Geophys 56 (1):194–219. https://doi.org/10.2478/s11600-007-0043-1 50. Cuxart J, Wrenger B, Martínez-Villagrasa D, Reuder J, Jonassen MO, Jiménez MA, Lothon M, Lohou F, Hartogensis O, Dünnermann J, Conangla L, Garai A (2016) Estimation of the advection effects induced by surface heterogeneities in the surface energy budget. Atmos Chem Phys 16:9489–9504. https://doi.org/10.5194/acp-16-9489-2016 51. Wohlfahrt G, Albin H, Niedrist G, Scholz K, Enrico T, Peng Z (2016) On the energy balance closure and net radiation in complex terrain. Agric For Meteorol 226-227:37–49. https://doi. org/10.1016/j.agrformet.2016.05.012 52. Barskov K, Stepanenko V, Repina I, Artamonov A, Gavrikov A (2019) Two regimes of turbulent fluxes above a frozen small Lake surrounded by Forest. Bound-Layer Meteorol 173 (3):311–320. https://doi.org/10.1007/s10546-019-00469-w 53. Rotach MW, Calanca P, Graziani G, Gurtz J, Steyn DG, Vogt R, Andretta M, Christen A, Cieslik S, Connolly R, De Wekker SFJ, Galmarini S, Kadygrov EN, Kadygrov V, Miller E, Neininger B, Rucker M, van Gorsel E, Weber H, Weiss A, Zappa M (2004) Turbulence structure and exchange processes in an Alpine Valley: the Riviera project. Bull Am Meteorol Soc 85(9):1367–1386. https://doi.org/10.1175/BAMS-85-9-1367 54. Lampert A, Pätzold F, Jiménez MA, Lobitz L, Martin S, Lohmann G, Canut G, Legain D, Bange J, Martínez-Villagrasa D, Cuxart J (2016) A study of local turbulence and anisotropy

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

389

during the afternoon and evening transition with an unmanned aerial system and mesoscale simulation. Atmos Chem Phys 16(12):8009–8021. https://doi.org/10.5194/acp-16-8009-2016 55. Johansson C, Smedman A-S, Högström U, Brasseur JG, Khanna S (2001) Critical test of the validity of Monin–Obukhov similarity during convective conditions. J Atmos Sci 58 (12):1549–1566. https://doi.org/10.1175/1520-0469(2001)058%3C1549:CTOTVO%3E2.0. CO;2 56. Wilson JD (2008) Monin-Obukhov functions for standard deviations of velocity. Bound-Layer Meteorol 129(3):353–369. https://doi.org/10.1007/s10546-008-9319-5 57. Carruthers DJ, Hunt JCR (1990) Fluid mechanics of airflow over hills: turbulence, fluxes, and waves in the boundary layer. In: Blumen W (ed) Atmospheric processes over complex terrain. American Meteorological Society, Boston, pp 83–107. https://doi.org/10.1007/978-1-93570425-6_5 58. Moraes OLL, Acevedo OC, Degrazia GA, Anfossi D, da Silva R, Anabor V (2005) Surface layer turbulence parameters over a complex terrain. Atmos Environ 39(17):3103–3112. https://doi.org/10.1016/j.atmosenv.2005.01.046 59. Sfyri E, Rotach M, Stiperski I, Bosveld F, Obleitner F, Lehner M (2018) Scalar-flux similarity in the layer near the surface over mountainous terrain. Bound-Layer Meteorol 169(1):11–46. https://doi.org/10.1007/s10546-018-0365-3 60. Stiperski I, Calaf M, Rotach MW (2019) Scaling, anisotropy, and complexity in near-surface atmospheric turbulence. J Geophys Res Atmos 124(3):1428–1448. https://doi.org/10.1029/ 2018JD029383 61. Medeiros LE, Fitzjarrald DR (2015) Stable boundary layer in complex terrain. Part II: geometrical and sheltering effects on mixing. J Appl Meteorol Climatol 54(1):170–188. https://doi.org/10.1175/JAMC-D-13-0346.1 62. Karchagina LP, Mamsirov NI, Tuguz RK, Kostina NE (2013) The adaptive-landscape system of agriculture foothill zone of the Republic of Adygea. Magarin O.G, Maikop, p 86. (in Russian) 63. The Atlas of the Republic of Adygea (2005) Publishing House “Lev Tolstoy”, Maykop, 79 pp. (in Russian) 64. Burman EA (1969) Local winds. Gidrometeoizdat, Leningrad, p 341. (in Russian) 65. Gutman LH (1969) Introduction to the nonlinear theory of mesometeorological processes. Gidrometeoizdat, Leningrad, p 295. (in Russian) 66. Gasanov SS (2009) Structural analysis of high-altitude beitof north caucasus region geosystems, Izvestiya Dagestanskogo gosudarstvennogo pedagogicheskogo universiteta. Yestestvennyye i tochnyye nauki 1(6): 96–103. (in Russian) 67. Danilova NA (1982) Climatorecreational resources of the North Caucasus. Materialy meteorologicheskikh issledovaniy 5. (in Russian) 68. Poltraus BV (1972) Foehn of West Caucasus. Meteorol Gidrol 7:57–65. (in Russian) 69. Noppel H, Fiedler F (2002) Mesoscale heat transport over complex terrain by slope winds–A conceptual model and numerical simulations. Bound-Layer Meteorol 104(1):73–97. https:// doi.org/10.1023/A:1015556228119 70. Kirshbaum DJ (2017) On upstream blocking over heated mountain ridges. Q J R Meteorol Soc 143(702):53–68. https://doi.org/10.1002/qj.2945 71. Ball FK (1956) The theory of strong katabatic winds. Aust J Phys 9(3):373–386. https://doi. org/10.1071/PH560373 72. Gutman LN (1983) On the theory of the katabatic slope wind. Tellus A 35(3):213–218. https:// doi.org/10.1111/j.1600-0870.1983.tb00198.x 73. Manins PC, Sawford BL (1979) A model of katabatic winds. J Atmos Sci 36(4):619–630. https://doi.org/10.1175/1520-0469(1979)036%3C0619:AMOKW%3E2.0.CO;2 74. Prandtl L (1942) Führer durch die Strömungslehre, Vieweg und Sohn. Braunschweig, p 375 75. Haiden T (2003) On the pressure field in the slope wind layer. J Atmos Sci 60(13):1632–1635. https://doi.org/10.1175/1520-0469(2003)60%3C1632:OTPFIT%3E2.0.CO;2

390

I. А. Repina et al.

76. Monin AS (1948) Model of wind on slope. Trudy Tsentral'nogo instituta prognozov Glavnogo upravleniya Gidrometeorologicheskoy sluzhby 8:35. (in Russian) 77. Gutman LN, Monin AS (1950) About local winds in mountainous areas. Trudy Tsentral'nogo instituta prognozov Glavnogo upravleniya Gidrometeorologicheskoy sluzhby 21(48). (in Russian) 78. Gutman LN, Malbakhov VM (1964) On the theory of katabatic winds of Antarctic. Meteorologicheskiye Issledovaniya 9:150–155. (In Russian) 79. Lykosov VN, Gutman LN (1972) Turbulent boundary layer over an inclined underlying surface. Izvestiya Akademii nauk SSSR Atmos Oceanic Phys 8(8):799–809. (In Russian) 80. Stiperski I, Kavcic I, Grisogono B, Durran DR (2007) Including Coriolis effects in the Prandtl model for katabatic flow. Q J R Meteorol Soc 133(622):101–106. https://doi.org/10.1002/ qj.19 81. Shapiro A, Fedorovich E (2008) Coriolis effects in homogeneous and inhomogeneous katabatic flows. Q J R Meteorol Soc 134(631):353–370. https://doi.org/10.1002/qj.217 82. Chernyshev RV, Stepanenko VM, Repina IA (2018) Modelling of katabatic flow on ice slope over Kongsvegen glacier. Protsessy v geosredakh 3(16):1062–1069. (in Russian) 83. Grisogono B, Oerlemans J (2001) Katabatic flow: analytic solution for gradually varying eddy diffusivities. J Atmos Sci 58(21):3349–3354. https://doi.org/10.1175/1520-0469(2001)058% 3C3349:KFASFG%3E2.0.CO;2 84. Grisogono B, Oerlemans J (2002) Justifying the WKB approximation in pure katabatic flows. Tellus A Dyn Meteorol Oceanogr 54(5):453–462. https://doi.org/10.3402/tellusa.v54i5.12166 85. Egger J (1981) On the linear two-dimensional theory of thermally induced slope winds. Beitr Phys Atmos 54(4):465–481 86. Shapiro A, Fedorovich E (2007) Katabatic flow along a differentially cooled sloping surface. J Fluid Mech 571:149–175. https://doi.org/10.1017/S0022112006003302 87. Shapiro A, Burkholder B, Fedorovich E (2012) Analytical and numerical investigation of two-dimensional katabatic flow resulting from local surface cooling. Bound-Layer Meteorol 145(1):249–272. https://doi.org/10.1007/s10546-011-9685-2 88. Kondo H (1984) The difference of the slope wind between day and night. J Meteorol Soc Jpn Ser II 62(2):224–233. https://doi.org/10.2151/jmsj1965.62.2_224 89. Burkholder B, Shapiro A, Fedorovich E (2009) Katabatic flow induced by a cross-slope band of surface cooling. Acta Geophys 57(4):923–949. https://doi.org/10.2478/s11600-009-0025-6 90. Fedorovich E, Shapiro A (2017) Oscillations in Prandtl slope flow started from rest. Q J R Meteorol Soc 143(703):670–677. https://doi.org/10.1002/qj.2955 91. Zammett RJ, Fowler AC (2007) Katabatic winds on ice sheets: a refinement of the Prandtl model. J Atmos Sci 64(7):2707–2716. https://doi.org/10.1175/JAS3960.1 92. Zardi D, Serafin S (2015) An analytic solution for time-periodic thermally driven slope flows. Q J R Meteorol Soc 141(690):1968–1974. https://doi.org/10.1002/qj.2485 93. Ye ZJ, Segal M, Pielke RA (1987) Effects of atmospheric thermal stability and slope steepness on the development of daytime thermally induced upslope flow. J Atmos Sci 44 (22):3341–3354. https://doi.org/10.1175/1520-0469(1987)044%3C3341:EOATSA%3E2.0. CO;2 94. Giometto MG, Grandi R, Fang J, Monkewitz PA, Parlange MB (2017) Katabatic flow: a closed-form solution with spatially-varying eddy diffusivities. Bound-Layer Meteorol 162 (2):307–317. https://doi.org/10.1007/s10546-016-0196-z 95. Schumann U (1990) Large-eddy simulation of the up-slope boundary layer. Q J R Meteorol Soc 116(493):637–670. https://doi.org/10.1002/qj.49711649307 96. Vergeiner I, Dreiseitl E (1987) Valley winds and slope winds – observations and elementary thoughts. Meteorog Atmos Phys 36(1–4):264–286. https://doi.org/10.1007/BF01045154 97. Enger L, Korasin D, Yang X (1993) A numerical study of boundary-layer dynamics in a mountain valley. Bound-Layer Meteorol 66(4):357–394. https://doi.org/10.1007/BF00712729 98. Schmidli J (2013) Daytime heat transfer processes over mountainous terrain. J Atmos Sci 70 (12):4041–4066. https://doi.org/10.1175/JAS-D-13-083.1

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

391

99. Kuwagata T, Kondo J (1989) Observation and modeling of thermally induced upslope flow. Bound-Layer Meteorol 49(3):265–293. https://doi.org/10.1007/BF00120973 100. Leukauf D, Gohm A, Rotach MW (2016) Quantifying horizontal and vertical tracer mass fluxes in an idealized valley during daytime. Atmos Chem Phys 16(20):13049–13066. https:// doi.org/10.5194/acp-16-13049-2016 101. Kamenetsky ES, Radiono AA (1999) Aerodynamics of mountain valleys with varying cross sections. Bound-Layer Meteorol 91(2):191–197. https://doi.org/10.1023/A:1001863929405 102. Stiperski I, Holtslag AA, Lehner M, Hoch SW, Whiteman CD (2020) On the turbulence structure of deep katabatic flows on a gentle mesoscale slope. Q J R Meteorol Soc 146 (728):1206–1231. https://doi.org/10.1002/qj.3734 103. Giometto MG, Katul GG, Fang J, Parlange MB (2017) Direct numerical simulation of turbulent slope flows up to Grashof number Gr¼2.1x1011. J Fluid Mech 829:589–620. https://doi.org/10.1017/jfm.2017.372 104. Axelsen S, Dop H (2009) Large-eddy simulation of katabatic winds. Part 1: comparison with observations. Acta Geophys 57:803–836. https://doi.org/10.2478/s11600-009-0041-6 105. Leukauf D, Gohm A, Rotach MW, Wagner JS (2015) The impact of the temperature inversion breakup on the exchange of heat and mass in an idealized valley: sensitivity to the radiative forcing. J Appl Meteorol Climatol 54(11):2199–2216. https://doi.org/10.1175/JAMC-D-150091.1 106. Hocut C, Liberzon D, Fernando H (2015) Separation of upslope flow over a uniform slope. J Fluid Mech 775:266–287. https://doi.org/10.1017/jfm.2015.298 107. Khanna S, Brasseur JG (1997) Analysis of Monin–Obukhov similarity from large-eddy simulation. J Fluid Mech 345:251–286. https://doi.org/10.1017/S0022112097006277 108. Crook NA, Tucker DF (2005) Flow over heated terrain. Part I: linear theory and idealized numerical simulations. Mon Weather Rev 133(9):2552–2564. https://doi.org/10.1175/ MWR2964.1 109. Kirshbaum DJ (2013) On thermally forced circulations over heated terrain. J Atmos Sci 70 (6):1690–1709. https://doi.org/10.1175/JAS-D-12-0199.1 110. Kirshbaum DJ, Wang CC (2014) Boundary layer updrafts driven by airflow over heated terrain. J Atmos Sci 71(4):1425–1442. https://doi.org/10.1175/JAS-D-13-0287.1 111. Serafin S, Zardi D (2011) Daytime development of the boundary layer over a plain and in a valley under fair weather conditions: a comparison by means of idealized numerical simulations. J Atmos Sci 68(9):2128–2141. https://doi.org/10.1175/2011JAS3610.1 112. Schmidli J, Rotunno R (2012) Influence of the valley surroundings on valley wind dynamics. J Atmos Sci 69(2):561–577. https://doi.org/10.1175/JAS-D-11-0129.1 113. Giovannini L, Laiti L, Serafin S, Zardi D (2017) The thermally driven diurnal wind system of the Adige Valley in the Italian Alps. Q J R Meteorol Soc 143(707):2389–2402. https://doi.org/ 10.1002/qj.3092 114. Whiteman CD (2000) Mountain meteorology. Fundamentals and applications. Oxford University Press, Oxford, p 355 115. Schmidli J, Rotunno R (2015) The quasi-steady state of the valley wind system. Front Earth Sci 3:79. https://doi.org/10.3389/feart.2015.00079 116. Nickus U, Vergeiner I (1984) The thermal structure of the Inn Valley atmosphere. Arch Meteorol Geophys Bioclimatol Ser A 33(2–3):199–215. https://doi.org/10.1007/BF02257725 117. Wagner JS, Gohm A, Rotach MW (2015) Influence of along-valley terrain heterogeneity on exchange processes over idealized valleys. Atmos Chem Phys 15(12):6589–6603. https://doi. org/10.5194/acp-15-6589-2015 118. Rampanelli G, Zardi D, Rotunno R (2004) Mechanisms of up-valley winds. J Atmos Sci 61 (24):3097–3111. https://doi.org/10.1175/JAS-3354.1 119. Schmidli J, Rotunno R (2010) Mechanisms of along-valley winds and heat exchange over mountainous terrain. J Atmos Sci 67(9):3033–3047. https://doi.org/10.1175/2010JAS3473.1

392

I. А. Repina et al.

120. Wagner JS, Gohm A, Rotach MW (2015) The impact of valley geometry on daytime thermally driven flows and vertical transport processes. Q J R Meteorol Soc 141(690):1780–1794. https://doi.org/10.1002/qj.2481 121. Weigel AP, Chow FK, Rotach MW, Street RL, Xue M (2006) High-resolution large-eddy simulations of flow in a steep alpine valley. Part II: flow structure and heat budgets. J Appl Meteorol Climatol 45(1):87–107. https://doi.org/10.1175/JAM2323.1 122. Giovannini L, Zardi D, de Franceschi M (2011) Analysis of the urban thermal fingerprint of the city of Trento in the Alps. J Appl Meteorol Climatol 50(5):1145–1162. https://doi.org/10. 1175/2010JAMC2613.1 123. Giovannini L, Zardi D, de Franceschi M, Chen F (2014) Numerical simulations of boundarylayer processes and urban-induced alterations in an alpine valley. Int J Climatol 34 (4):1111–1131. https://doi.org/10.1002/joc.3750 124. Rendón AM, Salazar JF, Palacio CA, Wirth V, Brötz B (2014) Effects of urbanization on the temperature inversion breakup in a mountain valley with implications for air quality. J Appl Meteorol Climatol 53(4):840–858. https://doi.org/10.1175/JAMC-D-13-0165.1 125. Rendón AM, Salazar JF, Palacio CA, Wirth V (2015) Temperature inversion breakup with impacts on air quality in urban valleys influenced by topographic shading. J Appl Meteorol Climatol 54(2):302–321. https://doi.org/10.1175/JAMC-D-14-0111.1 126. Vergeiner I (1987) An elementary valley wind model. Meteorog Atmos Phys 36 (1–4):255–263. https://doi.org/10.1007/BF01045153 127. Egger J (1987) Simple models of the valley-plain circulation part II: flow resolving model. Meteorog Atmos Phys 36(1–4):243–254. https://doi.org/10.1007/BF01045152 128. Egger J (1990) Thermally forced flows: theory. In: Atmospheric processes over complex terrain. American Meteorological Society, Boston, pp 43–58. https://doi.org/10.1007/978-1935704-25-6_3 129. Zängl G (2004) A reexamination of the valley wind system in the alpine Inn Valley with numerical simulations. Meteorog Atmos Phys 87(4):241–256. https://doi.org/10.1007/ s00703-003-0056-5 130. Gohm A, Harnisch F, Vergeiner J, Obleitner F, Schnitzhofer R, Hansel A, Fix A, Neininger B, Emeis S, Schäfer K (2009) Air pollution transport in an alpine valley: results from airborne and ground-based observations. Bound-Layer Meteorol 131(3):441–463. https://doi.org/10.1007/ s10546-009-9371-9 131. Laiti L, Zardi D, de Franceschi M, Rampanelli G (2013) Residual kriging analysis of airborne measurements: application to the mapping of atmospheric boundary-layer thermal structures in a mountain valley. Atmos Sci Lett 14(2):79–85. https://doi.org/10.1002/asl2.420 132. Mikhailov VI, Zorin VY, Kapochkina MB (2015) On the vulnerability of the Russian fleet and high risks for cargo flows through the ports of Anapa, Novorossiysk, Tuapse. Sci J 4(1 (9)):31–34. https://doi.org/10.15587/2313-8416.2015.40916. (in Russian) 133. Vasiliev AA (1965) Rump of helicopters on the Black Sea coast of the Caucasus with boratype winds. Trudy Tsentral'nogo instituta prognozov Glavnogo upravleniya Gidrometeorologicheskoy sluzhby 146:11–20. (in Russian) 134. Sukhanov SI, Druzhevskiy SA (2005) Analysis of synoptic conditions of the Novorossiysk bora in December 2002. Navigatsiya i gidrografiya 20-21:87–97. (in Russian) 135. Shigin VV (2006) Victims of the Novorossisk fighting. Voyenno-istoricheskiy zhurnal 10:65–67. (in Russian) 136. Gusev A (1959) Novorossiysk bora. Trudy Morskogo gidrofizicheskogo instituta Akademii nauk SSSR 14:140. (in Russian) 137. Ivanov AY, Antonyuk AY (2013) Anomalously strong bora events over the Black Sea studied using satellite imagery. Issledovaniye Zemli iz kosmosa 1:32–43. https://doi.org/10.7868/ S0205961413010028. (in Russian) 138. Wrangel FF (1876) Novorossiysk bora and its theory. Tipografiya Yu.G. Reno, Nikolayev, 17 pp. (in Russian)

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

393

139. Arndt AO (1913) About the Novorossiysk bora. Zapiski po gidrografii 36:267–277. (in Russian) 140. But IV (1938) Novorossiysk bora. (in Russian) 141. Korostelev NA (1904) Novorossiysk bora. Zapiski Imperatorskoy Akademii nauk 15(2). (in Russian) 142. Semenov EK, Sokolikhina NN, Sokolikhina EV (2013) Meteorological and synoptic aspects of the formation and evolution of the Novorossiysk bora. Russ Meteorol Hydrol 38 (10):661–668. https://doi.org/10.3103/S1068373913100026 143. Masterskikh MA, Bel'skaya NN, Mineyeva MN (1973) Forecast of the frontal Novorossiysk bora and other similar physical and geographical areas (methodological instructions). Gidrometizdat, Moscow, p 20. (in Russian) 144. Gutman LN, Frankl FI (1960) Bora hydrodynamic model. Akademii nauk SSSR 30(3):23–37. (in Russian) 145. Blinov DV, Perov VL, Peskov BE, Rivin GS (2013) Extreme bora of February 7-8, 2012, in the area of Novorossiysk and its forecast with the COSMO-Ru model, Vestnik Moskovskogo Universiteta. Seria 5, Geografia 4, pp 36–43. (in Russian) 146. Gavrikov AV, Ivanov AY (2015) Anomalously strong bora over the Black Sea: observations from space and numerical modeling. Izvestiya Atmos Oceanic Phys 51(5):546–556. https:// doi.org/10.1134/S0001433815050059 147. Efimov VV, Barabanov VS (2013) Simulation of bora in Novorossiysk. Russ Meteorol Hydrol 38(3):171–176. https://doi.org/10.3103/S1068373913030059 148. Efimov VV, Barabanov VS (2013) Gustiness of the Novorossiysk bora. Russ Meteorol Hydrol 38(12):840–845. https://doi.org/10.3103/S1068373913120054 149. Toropov PA, Shestakova AA (2014) Quality assessment of Novorossiysk bora simulation by the WRF-ARW model. Russ Meteorol Hydrol 39(7):458–467. https://doi.org/10.3103/ S1068373914070048 150. Shestakova AA, Moiseenko KB, Toropov PA (2015) Hydrodynamic aspects of the Novorossiysk bora episodes in 2012–2013. Izvestiya Atmos Oceanic Phys 51(5):534–545. https://doi.org/10.1134/S0001433815040118 151. Repina IA (2008) “Wind, wind – all over God's light ...” On the nature of katabatic winds, Pririoda 5, 36–43. (in Russian) 152. Ivanov AY (2008) Bora in Novorossiysk: a look from space. Issledovaniye Zemli iz kosmosa 2:68–83. (in Russian) 153. Alpers W, Ivanov AY, Dagestad KF (2010) Observation of local wind fields and cyclonic atmospheric eddies over the eastern Black Sea using Envisat synthetic aperture radar images. Issledovaniye Zemli iz kosmosa 5:46–58 154. Alpers W, Ivanov A, Horstmann J (2009) Observations of bora events over the Adriatic Sea and Black Sea by spaceborne synthetic aperture radar. Mon Weather Rev 137(3):1150–1161. https://doi.org/10.1175/2008MWR2563.1 155. Grisogono B, Belusic D (2009) A review of recent advances in understanding the meso and microscale properties of the severe Bora wind. Tellus A Dyna Meteorol Oceanogr 61(1):1–16. https://doi.org/10.1111/j.1600-0870.2007.00369.x 156. Lepri P, Vecenaj Z, Kozmar H, Grisogono B (2017) Bora wind characteristics for engineering applications. Wind Struct 24:579–611. https://doi.org/10.12989/was.2017.24.6.579 157. Yoshino M (1976) Local wind bora. University of Tokyo Press, Tokyo, p 289 158. Belušić D, Žagar M, Grisogono B (2007) Numerical simulation of pulsations in the bora wind. Q J R Meteorol Soc 133(627):1371–1388. https://doi.org/10.1002/qj.129 159. Toropov PA, Myslenkov SA, Shestakova AA (2012) Numerical simulation of Novorossiysk bora and related wind waves using the WRF-ARW and SWAN models. Russ J Earth Sci 12(6): ES6001. https://doi.org/10.2205/2012ES000524 160. Klemp JB, Lilly DK (1975) The dynamics of wave-induced downslope winds. J Atmos Sci 32 (2):320–339. https://doi.org/10.1175/1520-0469(1975)032%3C0320:TDOWID%3E2.0.CO;2

394

I. А. Repina et al.

161. Peltier WR, Clark TL (1983) Nonlinear mountain waves in two and three spatial dimensions. Q J R Meteorol Soc 109(461):527–548. https://doi.org/10.1002/qj.49710946106 162. Kozhevnikov VN (1999) Perturbations of the atmosphere when flowing around mountains. Nauchnyy mir, Moscow, p 159. (in Russian) 163. Berzegova RB, Kozhevnikov VN, Bedanokov MK, Repina IA (2019). Energy modeling of Novorossiysk bora. In: IOP conference series: earth and environmental science. Institute of Physics Publishing, 231(1). https://doi.org/10.1088/1755-1315/231/1/012010 164. Kozhevnikov VN, Berzegova RB, Bedanokov MK (2020) Modeling of the Novorossiysk bora. Part 1. Atmospheric disturbances over the mountains of Novorossiysk. Russ J Earth Sci 20:ES1001. https://doi.org/10.2205/2019ES000684 165. Kozhevnikov VN, Berzegova RB, Bedanokov MK (2020) Modeling of the Novorossiysk bora. Part 2. Energetics of the atmosphere at the Novorossiysk bora. Russ J Earth Sci 20: ES1002. https://doi.org/10.2205/2019ES000685 166. Long RR (1954) Some aspects of the flow of stratified fluids: II. Experiments with a two-fluid system. Tellus 6(2):97–115. https://doi.org/10.1111/j.2153-3490.1954.tb01100.x 167. Klemp JB, Durran DR (1987) Numerical modelling of bora winds. Meteorog Atmos Phys 36 (1–4):215–227. https://doi.org/10.1007/BF01045150 168. Durran DR (1986) Another look at downslope windstorms. Part I: The development of analogs to supercritical flow in an infinitely deep, continuously stratified fluid. J Atmos Sci 43 (21):2527–2543. https://doi.org/10.1175/1520-0469(1986)043%3C2527:ALADWP%3E2.0. CO;2 169. Gill AE (1982) Atmosphere-Ocean dynamics. Academic Press, San Diego, p 680 170. Steinacker R (1984) Area-height distribution of a valley and its relation to the valley wind. Beiträge zur Physik der Atmosphäre 57(1):64–71 171. Weigel AP, Rotach MW (2004) Flow structure and turbulence characteristics of the daytime atmosphere in a steep and narrow Alpine valley. Q J R Meteorol Soc 130(602):2605–2627. https://doi.org/10.1256/qj.03.214 172. Khodayar S, Kalthoff N, Fiebig-Wittmaack M, Kohler M (2008) Evolution of the atmospheric boundary-layer structure of an arid Andes valley. Meteorog Atmos Phys 99(3–4):181–198. https://doi.org/10.1007/s00703-007-0274-3 173. Serafin S, Zardi D (2010) Daytime heat transfer processes related to slope flows and turbulent convection in an idealized mountain valley. J Atmos Sci 67(11):3739–3756. https://doi.org/10. 1175/2010JAS3428.1 174. Fast JD, Zhong S (1998) Meteorological factors associated with inhomogeneous ozone concentrations within the Mexico City basin. J Geophys Res Atmos 103 (D15):18927–18946. https://doi.org/10.1029/98JD01725 175. Lu R, Turco RP (1994) Air pollutant transport in a coastal environment. Part I: two-dimensional simulations of sea-breeze and mountain effects. J Atmos Sci 51 (15):2285–2308. https://doi.org/10.1175/1520-0469(1994)051%3C2285:APTIAC%3E2.0. CO;2 176. Kalthoff N, Adler B, Wieser A, Kohler M, Träumner K, Handwerker J, Corsmeier U, Khodayar S, Lambert D, Kopmann A, Kunka N, Dick G, Ramatschi M, Wickert J, Kottmeier C (2013) KITcube–a mobile observation platform for convection studies deployed during HyMeX. Meteorol Z 22(6):633–647. https://doi.org/10.1127/0941-2948/2013/0542 177. Panosetti D, Böing S, Schlemmer L, Schmidli J (2016) Idealized large-eddy and convectionresolving simulations of moist convection over mountainous terrain. J Atmos Sci 73 (10):4021–4041. https://doi.org/10.1175/JAS-D-15-0341.1 178. Langhans W, Schmidli J, Fuhrer O, Bieri S, Schär C (2013) Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloudresolving resolutions. J Appl Meteorol Climatol 52(6):1490–1510. https://doi.org/10.1175/ JAMC-D-12-0167.1

Dynamics of the Atmospheric Boundary Layer in the Mountain-Valley Relief. . .

395

179. Adler B, Kalthoff N (2014) Multi-scale transport processes observed in the boundary layer over a mountainous island. Bound-Layer Meteorol 153(3):515–537. https://doi.org/10.1007/ s10546-014-9957-8 180. Lang MN, Gohm A, Wagner JS (2015) The impact of embedded valleys on daytime pollution transport over a mountain range. Atmos Chem Phys 15(12):11981–11998. https://doi.org/10. 5194/acp-15-11981-2015 181. Adler B, Kalthoff N, Kohler M, Handwerker J, Wieser A, Corsmeier U, Kottmeier C, Lambert D, Bock O (2016) The variability of water vapour and pre-convective conditions over the mountainous island of Corsica. Q J R Meteorol Soc 142:335–346. https://doi.org/10. 1002/qj.2545 182. De Wekker SFJ, Godwin KS, Emmitt GD, Greco S (2012) Airborne Doppler lidar measurements of valley flows in complex coastal terrain. J Appl Meteorol Climatol 51(8):1558–1574. https://doi.org/10.1175/JAMC-D-10-05034.1 183. Arritt RW, Wilczak JM, Young GS (1992) Observations and numerical modeling of an elevated mixed layer. Mon Weather Rev 120(12):2869–2880. https://doi.org/10.1175/15200493(1992)120%3C2869:OANMOA%3E2.0.CO;2 184. Wagner JS, Gohm A, Rotach MW (2014) The impact of horizontal model grid resolution on the boundary layer structure over an idealized valley. Mon Weather Rev 142(9):3446–3465. https://doi.org/10.1175/MWR-D-14-00002.1 185. De Wekker SFJ, Steyn DG, Fast JD, Rotach MW, Zhong S (2005) The performance of RAMS in representing the convective boundary layer structure in a very steep valley. Environ Fluid Mech 5(1–2):35–62. https://doi.org/10.1007/s10652-005-8396-y 186. Zilitinkevich SS (2013) Atmospheric turbulence and planetary boundary layers. FIZMATLIT, Moscow, p 256. (in Russian) 187. Kossmann M, Vögtlin R, Corsmeier U, Vogel B, Fiedler F, Binder HJ, Kalthoff N, Beyrich F (1998) Aspects of the convective boundary layer structure over complex terrain. Atmos Environ 32(7):1323–1348. https://doi.org/10.1016/S1352-2310(97)00271-9 188. Arduini G, Staquet C, Chemel C (2016) Interactions between the nighttime valley-wind system and a developing cold-air pool. Bound-Layer Meteorol 161(1):49–72. https://doi.org/ 10.1007/s10546-016-0155-8 189. Arduini G, Chemel C, Staquet C (2017) Energetics of deep alpine valleys in pooling and draining configurations. J Atmos Sci 74(7):2105–2124. https://doi.org/10.1175/JAS-D-160139.1 190. Cuxart J (2008) Nocturnal basin low-level jets: an integrated study. Acta Geophys 56 (1):100–113. https://doi.org/10.2478/s11600-007-0042-2 191. Martínez D, Jiménez MA, Cuxart J, Mahrt L (2010) Heterogeneous nocturnal cooling in a large basin under very stable conditions. Bound-Layer Meteorol 137(1):97–113. https://doi. org/10.1007/s10546-010-9522-z 192. De Wekker SFJ, Whiteman CD (2006) On the time scale of nocturnal boundary layer cooling in valleys and basins and over plains. J Appl Meteorol Climatol 45(6):813–820. https://doi. org/10.1175/JAM2378.1 193. Dorninger M, Whiteman CD, Bica B, Eisenbach S, Pospichal B, Steinacker R (2011) Meteorological events affecting cold-air pools in a small basin. J Appl Meteorol Climatol 50 (11):2223–2234. https://doi.org/10.1175/2011JAMC2681.1 194. Cuxart J, Jiménez MA (2007) Mixing processes in a nocturnal low-level jet: an LES study. J Atmos Sci 64(5):1666–1679. https://doi.org/10.1175/JAS3903.1 195. Banta RM, Pichugina YL, Newsom RK (2003) Relationship between low-level jet properties and turbulence kinetic energy in the nocturnal stable boundary layer. J Atmos Sci 60 (20):2549–2555. https://doi.org/10.1175/1520-0469(2003)060%3C2549:RBLJPA%3E2.0. CO;2 196. Scheffknecht P, Serafin S, Grubišic V (2017) A long-lived supercell over mountainous terrain. Q J R Meteorol Soc 143(709):2973–2986. https://doi.org/10.1002/qj.3127

396

I. А. Repina et al.

197. Williamson CE, Saros JE, Vincent WF, Smold JP (2009) Lakes and reservoirs as sentinels, integrators, and regulators of climate change. Limnol Oceanogr 54(6part2):2273–2282. https://doi.org/10.4319/lo.2009.54.6_part_2.2273 198. Tremblay A, Varfalvy L, Roehm C, Garneau M (2005) Greenhouse gas emissions: fluxes and processes, hydroelectric reservoirs and natural environments. In: Environmental science series. Springer, New York, p 732. https://doi.org/10.1007/978-3-540-26643-3 199. Cole JJ, Prairie YT, Caraco NF, McDowell WH, Tranvik LJ, Striegl RG, Duarte CM, Kortelainen P, Downing JA, Middelburg JJ, Melack J (2007) Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10(1):172–185. https://doi.org/10.1007/s10021-006-9013-8 200. Melnikova TN (2015) Monitoring of reservoirs in the Republic of Adygea. Int J Exp Educ 10 (Part 1):53–54. (in Russian) 201. Fedorov YA, Tambieva NS, Garkusha DN, Khoroshevskaya VO (2007) Methane in aquatic ecosystems Rostov-na-Donu. ROSTIZDAT, Moscow, 330 pp. (in Russian) 202. Lima I, Ramos F, Bambace L, Rosa R (2008) Methane emissions from large dams as renewable energy resources: a developing nation perspective. Mitig Adapt Strateg Glob Chang 13(2):193–206. https://doi.org/10.1007/s11027-007-9086-5 203. Louis VL, Kelly CA, Duchemin E, Rudd JWM, Rosenberg DM (2000) Reservoir surfaces as sources of greenhouse gases to the atmosphere: a global estimate. Bioscience 50(9):766–775. https://doi.org/10.1641/0006-3568(2000)050[0766:RSASOG]2.0.CO;2 204. Gavrilovsky DV (2015) Ecological problems of the Tsimlyansk reservoir, Novaya nauka: Teoreticheskiy i prakticheskiy vzglyad, (6 part 2), pp 17–19. (in Russian) 205. Zaytseva NV (2014) The problem of development of blue-green algae in the Votkinsky and Izhevsk reservoirs. Modern scientific researches and innovations 6. (in Russian) 206. Grechushnikova MG, Repina IA, Stepanenko VM, Kazantsev VS, Artamonov AY, Lomov VA (2019) Methane emission from the surface of the Mozhaisk Valley-type reservoir. Geogr Nat Resour 40(3):247–255. https://doi.org/10.1134/S1875372819030077 207. Grechushnicova MG, Repina IA, Stepanenko VM, Kazantsev VS, Artamonov AY, Varentsov MI, Lomova DV, Molkov AA, Kapustin IA (2018) Spatio-temporal change of methane distribution and emission in reservoirs with different water residence coefficient. Izvestiya Russkogo Geograficheskogo Obshestva 150(5):14–33. (in Russian) 208. Rosa LP, Dos Santos MA, Matvienko B, Dos Santos EO, Sikar E (2004) Greenhouse gas emissions from hydroelectric reservoirs in tropical regions. Clim Chang 66(1–2):9–21. https:// doi.org/10.1023/B:CLIM.0000043158.52222.ee

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety in the Mountains of the Republic of Adygea Murat K. Bedanokov, Roza B. Berzegova, and Saida K. Kuizheva

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Geographical Features of the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Theoretical Model of Mountain Air Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Airflow Disturbances Over the Mountains of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Flight Safety Problem Over the Mountains of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

398 400 402 404 407 410 411

Abstract This chapter considers airflow around mountain systems as a mesoscale atmospheric phenomenon. It presents a nonlinear stationary dimensional theoretical model of the airflow of the Northwest Caucasus Mountains, taking into account characteristics of a real mountain terrain. The chapter further discusses the results of the calculations of the speed field of the airflow and general regularities of the origin and the scale of the rotary-wave deformation of the airflow over the mountains. The flow over the mountain greatly smoothed, and the rotor area completely disappeared. Flight safety indicators over the mountains of the Republic of Adygea for two types of aircrafts (light-engine and speed) were calculated on the basis of the obtained data. In certain conditions, flights for both high-speed and single-engine aircrafts can be considered dangerous. Keywords Atmospheric physics, Flight safety, Flow over mountain systems, Hydrodynamics, Internal gravity waves, Rotary-wave deformation, The Republic of Adygea

M. K. Bedanokov (*), R. B. Berzegova, and S. K. Kuizheva Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected]; [email protected]; [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 397–412, DOI 10.1007/698_2020_494, © Springer Nature Switzerland AG 2020, Published online: 29 August 2020

397

398

M. K. Bedanokov et al.

1 Introduction Among meteorological phenomena that have a significant impact on the operation of aircraft, atmospheric turbulence causing intense aircraft bumpiness is one of the most dangerous. The main reason for air current turbulence is contrast in wind and temperature fields originating in the atmosphere [1]. These contrasts create the following processes: – – – –

Airflow friction against the Earth surface Deformation of air currents by orographic obstacles Uneven heating of different parts of the underlying surface Cloud formation processes, which cause condensation heat separation and change the character of temperature and wind fields – Interaction of air masses of different properties, at the borders of which horizontal gradients of temperature and wind are very pronounced – Presence of atmospheric inversion layers where internal gravity waves can occur that lose stability under certain conditions All the processes listed above can operate simultaneously in the same or opposite direction and thereby increase or decrease the degree of atmospheric turbulence. When turbulence is classified, the reasons for its occurrence are usually taken into account, i.e., orographic (mechanical) turbulence, thermal (convection) turbulence, and dynamic turbulence. Orographic turbulence depends on the wind speed at the Earth surface, terrain unevenness, as well as on mutual disposition of the wind and ridge direction. Airflow gets deformed during the flow around mountain obstacles. The degree and nature of this deformation depend on the nature of the oncoming flow – its speed, direction, and temperature stratification, as well as on the ridge shape and dimensions [1]. Interaction between a moving air stream and terrain unevenness refers to mediumscale or local atmospheric processes. Atmospheric disturbances under consideration are of a wave nature. This is because the atmosphere is steadily stratified with respect to rapid vertical displacements of its particles and terrain unevenness affects the airflow as a driving force in an elastic medium. The resulting waves are an example of internal gravity waves. In case of mountain airflow, atmospheric disturbances can be classified as internal gravity waves of an orographic nature [1]. They are characterized by spatial asymmetry, because they are observed only over the mountains and downstream of them. This is why they are usually called lee or mountain waves [2]. In the recommendations of the International Civil Aviation Organization, strong mountain waves are included in the list of stormy weather events along the flight route. Among the meteorological phenomena associated with features of mountain areas such natural phenomena as foehn is designated (German – Föhn, foehn; Latin – favonius – warm west wind) – the wind blowing down from the mountains and bringing significant, and sometimes very sharp temperature rise and relative humidity decrease [3]. It occurs when the air flow passes through a mountain ridge of a considerable height (1,000 m). When the air flow meets a mountain barrier on its

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . .

399

way, it rises up its windward slopes. Rising air along the mountain is cooled to a condense level with a rate of about 1
C per 100 m and higher – for the value of the moist adiabatic gradient. Condensed moisture forms clouds and falls in the form of precipitation, so by the end of lifting, the air loses a significant amount of moisture. Crossing the mountain range, the air begins to descend down and is heated according to dry adiabatic law, i.e., approximately by 1
C for every 100 m of descent. If we assume that the ascending air is cooled by an average of 0.6
every 100 m of lifting, when crossing over a 1-km-height ridge, the air is cooled down by 6
during lifting and is warmed by 10
during descending and thus will have a temperature for 2
higher than the original one. If the ridge is of 2 km height, the air temperature will increase by 8
. Foehn duration varies from a few hours up to 20–30 h, and the horizontal scale of this phenomenon ranges from several tens up to 100–500 km. From the point of view of physics, there is a natural phenomenon called “bora” that is similar to foehn. In both cases air topping over the mountains in many respects is similar to two-dimensional flow processes that are discussed in this chapter, and in both cases the air during the descent along the leeward slopes is adiabatically heated. Spatial and nonsteady effects, as well as anticipating synoptic conditions, play a very important role in these two phenomena. Bora occurs when the cold air meets relatively low mountain ridges that border a sea or a large lake [4, 5]. The Novorossiysk Bora, the most known in Russia, occurs on the southern slope of the Northwestern Caucasus in the Novorossiysk City area at the coast of the Black Sea [6]. Thus the wind peaked on February 7–8, 2015, night, and the air temperature dropped to 19
C. Coast stations recorded the speed of 52 m/s, after which there was a power outage. Raid ship stations recorded wind speed of 60 m/s. The bora swept a vast area in the northeastern part of the Black Sea (up to 200–300 km), and in the open sea, the wind speed ranged from 10–14 m/s to 18–24 m/s. Studies of air flow problems have been going on for many decades. Success is linked with works of Russian and foreign scientists [7–21]. The first works [7, 9, 11, 22, 23] contained theoretical models based on the assumption about the smallness of the occurring disturbances (linear models) and using idealized obstacle forms. Theory and field observations carried out in mountainous regions showed great airflow disturbances in the flow around mountains [12]. Vertical displacement of air particles is comparable to mountain heights and may even significantly exceed them. Wave energy of orographic disturbances is comparable to kinetic energy of incident flotation. It often exceeds the energy spent to overcome surface friction, which is taken into account in usual schemes of weather forecast. For this reason, we now see the development of nonlinear models that do not take into account assumptions of small perturbation [2]. With the development of numerical methods and computer technology, it is now possible to more widely use the nonlinear approach in solving problems of airflow over obstacles. This allowed for taking into account the shape of real mountains as a source of disturbances in the capacity of the lower boundary condition and obtaining numerical estimates for specific geographical areas [2, 12, 14, 19, 24].

400

M. K. Bedanokov et al.

2 Geographical Features of the Republic of Adygea The Republic of Adygea is located on the plains, in the foothills, and in the mountains of the Greater Caucasus. By the relief nature, Adygea can be divided into three parts: flat, from the latitudinal course of the Kuban River up to the latitude of Khanskaya Stanitsa (village inside a Cossack host) – Kuzhorskaya Stanitsa – Natyrbovo Village; piedmont, up to the latitude of Kamennomostsky (old name Khadzhokh) Village (up to the Rocky Ridge); and mountainous, up to the southern borders of the Republic [25, 26] (Fig. 1.). The southern part of the Azov-Kuban plain is called Trans-Kuban plain, and it is located in a piedmont subsidence and is a lowland that gradually passes into a piedmont high plain. The piedmont zone extends from the Maykop City, lying at

Fig. 1 The scheme of the Greater Caucasus mountain ranges on the territory of the Republic of Adygea and Krasnodar territory

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . .

401

an altitude of 230 m above the sea level and up to about Kamennomostsky Village. There are low (up to 300–500 m) sloping ridges, dissected by wide river valleys. Some peaks reach up to 700–900 m. The mountainous part of the Republic of Adygea is presented by the system of the Greater, the Front, the Lateral, and the Rocky Ridges of the Greater Caucasus (Fig. 2). The Greater Caucasian Ridge restricts the territory of the Republic from the south and consists of a system of echelon ridges with different absolute altitudes of 3–25 km in width. The main peaks of the Greater Caucasian Ridge within Adygea

Fig. 2 The mountain part of the Republic of Adygea with Lago-Naki Plateau. The dashed line shows the cross section of the terrain through Mount Fisht for model calculations

402

M. K. Bedanokov et al.

are Chugush (3,238 m) and Tybga (3,064 m). These are the highest mountains of the Republic of Adygea; they lie on the territory of the Caucasian State Wildlife Biosphere Reserve. Lago-Naki Plateau with the average height of 2,000 m occupies the most part of the mountains of Adygea (Fig. 2). It stretches from the north to the south and from the west to the east for more than 40–45 km and includes the Murzikao Ridge that comprises Abadzesh Mountain (2,287 m), Kamennoye More (Stone Sea) Ridge, and Nagoy-Chuk Ridge. The mountain group of Fisht Mount (2,867 m) is a center of the mountainous part of Lago-Naki Plateau which is a part of the Greater Caucasus Range. In the west Fisht Mountain joins the Pshekha-Su mountain mass (2,743 m). To the north of Pshekha-Su Mountain, there is the Oshten mountain mass (2,804 m). Major orographic elements located outside the plateau are Lago-Naki Ridge, Azish-Tau (Azishtau) Ridge, and Chernogor’e Plateau. The Front Range is located to the north of the Greater Caucasus Range and stretches in the southeastern direction outside Adygea. The width of the Front Range varies between 5 and 15 km. Its length is more than 100 km. At the same time, it has soft and flat topography. The highest point of the Front Range within the Republic of Adygea is Acheshbok Mountain (2,486 m). Between the Greater Caucasus Ridge and the Front Ridge lies the Lateral Ridge. On the territory of Adygea, it has Pshekish (2,242 m) and Abago Mountains (2,689 m), which are located in the Caucasus State Wildlife Biosphere Reserve. The wind regime of the Republic of Adygea depends on the orographic terrain. For example, in Dakhovskaya Stanitsa, winds of the northern and southeastern directions prevail. In Guzeripl village, which is situated in the Belaya river valley, there are mostly winds of the northern, northeastern, southern, and southwestern directions. A characteristic feature of the wind regime in mountain and foothill zones is the presence of mountain-valley winds. In the Republic of Adygea, there is a high frequency of strong winds with the speed of more than 15 m/s. The average number of days with strong winds is between 13 and 20 days, and the maximum number of them in some years can vary between 36 and 68. The highest frequency of winds is recorded in early spring, specifically in February and March [25].

3 Theoretical Model of Mountain Air Flow A hydrodynamic model of orographic atmospheric disturbances based on a nonlinear three-layer analytical model that takes into account layered ruptures of stability has been developed in Maykop State Technological University and is explained in details in the works [27, 28]. The lower layer in the model presents the troposphere; the middle layer, the lower stratosphere; and the upper one (of unlimited height), the upper atmosphere.

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . .

403

The solution of the problem of orographic air flow disturbances in this model reduces to the solution of the Helmholtz equation for the disturbances of the stream function in the three selected layers: ∇2 ψ 0 þ χ 2 ψ 0 ¼ 0, χ ¼

gð γ a  γ Þ N 2π ¼ , N2 ¼ , Tc U λc

ð1Þ

where N is the Brunt-Väisälä frequency, U wind speed in impinging stream, λс the Lyra scale [7], γ a dry adiabatic gradient, γ vertical gradient of temperature reduction in the impinging stream, Tc a characteristic temperature, g the acceleration of gravity, and ∇2 the Laplace operator. In this approach, the layers differ in equation coefficient magnitude or in the known Lyra scale magnitude: λc ¼ 2π

U N

ð2Þ

The connection of ψ 0 disturbances with ψ complete current function and its value in the impingement flow is determined by the formulas: ψ 0 ¼ ψ  ψ, ψ ¼ Uz

ð3Þ

The horizontal and vertical components of the flow velocities u and w and T temperature disturbances are defined by the correlations: u¼

∂ψ ∂ψ ψ0 , w¼ , T 0 ¼ ðγ a  γ Þ U ∂z ∂x

ð4Þ

Correlations (1)–(4) are the solution of the model problem. ψ 0 disturbances are considered to be small, and the speed and the temperature gradient are not dependent on the height in the impingement undisturbed flow U ¼ Const, γ ¼ Const [2, 20, 23]. In the course of search for solution, it’s been assumed that disturbances decay at z growth, and in particular they decay rapidly toward an incident flotation. The height of any streamline was specified in the form of h(x) ¼ z + G(x), where z is the height of the streamline in an undisturbed incident flotation. Then, sliding conditions ψ ðx, hðxÞÞ ψ 0 ðx, z þ GðxÞÞ ¼ z or ¼ G ð xÞ U U

ð5Þ

must be met along any streamline due to stationarity and incompressibility. The general solution of the problems (1)–(4) is a linear combination of x axis shifted ψ 00 solutions with Bi weights [2, 7]:

404

M. K. Bedanokov et al.

ψ 0 ¼ Uk 1

N X

Bi ψ 00 ðr i , φi Þ,

i¼1

h

i12

r i ¼ ð x þ a i Þ 2 þ z 2 , φi ¼ ψ 00 ¼

19 X

x þ ai z arcsin , r x þ a j i ij

f0 ðr m , ϕ Þ, r m ¼ b0m ψ m 0

m¼1

ψe0 0 ¼ 0, 25N 1 ðkr i Þ sin ϕi þ π 1

1 X v¼1

2πr m , λc

2v J 2v ðkr i Þ sin 2vϕi , ð2vÞ2  1

where Nn, Jn1 are Neumann and Bessel functions, respectively. Δai phase change is constant and equals Δai ¼ ai + 1  ai ¼ 0.1125λc. To search the Bi solution coefficients, a program was used, based on iterative procedure of gradual selection of coefficient data, while boundary condition (5) was not satisfied with required accuracy for G(x) function prescribed on the Earth [2, 19]. On the basis of methodology developed in [2, 29], the function of G(x) kind was found from the geographical map. In the following calculations, the relief shape was reproduced with an accuracy of not lower than 5 m.

4 Airflow Disturbances Over the Mountains of Adygea The theoretical model [20, 21, 27, 28], developed in the Maykop State Technological University, was used to investigate airflow disturbances in the flow around the Greater Caucasus Range in the Republic of Adygea. The selected area of the Greater Caucasian Ridge (Mount Fisht) has two features: a sufficient probability of lee wave formation and a rather good justification for the use of a two-dimensional theoretical model. The study began with the identification of the heights along the middle of the Greater Caucasus Range. This closely matches the direction from the northwest to the southeast. The airflow around the mountains, perpendicular to it, passes in the direction from the southwest to the northeast (shown as the dashed line in Fig. 2). On the basis of the numerical terrain model of the Republic of Adygea [26], the desired two-dimensional terrain was calculated [2, 27, 28, 30]. For this, real data of incident flotation in the Greater Caucasus Range area for August 2001 were used. They were obtained from nearby weather stations (Maykop City, Guzeripl Village, Dakhovskaya Stanitsa, Shuntuk Steading, Dondukovskaya Stanitsa) and by radiosonde stations (Tuapse and Rostov-on-Don), located practically on the windward and leeward side of the Greater Caucasian Ridge.

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . .

405

Heights of lower and upper boundaries of a stable layer in the incident flotation H1 and H2were defined first. Then the γ j magnitude was averaged layer wise. Using the temperature gradient values, layer-wise values of λj scales were calculated. Analysis of the airflow around Mount Fisht included several model scenarios. This chapter presents the results of three model scenarios (Table 1). For simplicity, the temperature drop gradient was considered the same at all altitudes N ¼ Const and γ in the expression (1). Therefore, the values of wind speed in the U incident flotation were determined by λj value. As a result, we managed to consider the main part of the range of changes defining the parameters and to trace their effects on the airflow disturbance. The calculation results have shown that for the model scenario I at wind speed U ¼ 15 m/s (Fig. 3), the disturbances are the most intense over the ridge crests, where they are characterized by a zone with rotors and extended portions of vertical movements. Lee wave disturbances are strong enough just above and below this zone. The core of the rotor zone, where disturbances are especially large, is located in a region similar to a rectangle. The total length of the rotor zone downwind is more than the length of its nucleus and is close enough in value to the extent of the lee part of the terrain (downwind from the main top). The area of maximum amplitudes is located in the windward side of the rotor zone. These amplitudes are several times higher than the maximum height of the mountains. This phenomenon may depend on the size of the Lyra scale, shape, and height of the mountains. Table 1 Model parameters for assumed model scenarios Model scenario I II III

H1, km 10 10 10

H2, km 18 18 18

U, m/s 15 19 22

λ1, km 7.5013 9.5016 11.0019

λ2, km 4.3910 5.5619 6.4401

Fig. 3 Pattern of the Mount Fisht airflow for the model scenario I (U ¼ 15 m/s)

λ3, km 4.4823 5.6776 6.5740

406

M. K. Bedanokov et al.

Such substantial disturbances suggest that a part of rotor disturbances obtained in the calculations can be transformed into high-intensity turbulent zones in the nature. If there is a lack of moisture, these areas will manifest in clouds. Meteorologists call this phenomenon “clear-air turbulence” [31]. Figure 4 clearly shows that when the Lyra scale was zoomed (model scenario II – U ¼ 19 m/s), lee wave disturbances slightly increased in length along the wind, but little changed in amplitude. The rotary zone changed and moved downstream. The dimensions of the main part of this zone along the vertical increased approximately twice. The model scenario III (U ¼ 22 m/s) is accompanied by sharp transformation of the rotor zone (Fig. 5). Closed vortices disappear; there is no purely vertical and especially backward motion. The flow over the mountain greatly smoothed, and the rotor area completely disappeared. The presented results show a clear dependence of the properties of disturbances on the values of the Lyra scale. With their increase, rapid “smoothing” of disturbances is observed. As a result, leeward waves increase their length and decrease in amplitude, and the rotor zones degenerate. The streamline trajectories presented in Figs. 3, 4, and 5 show that the vertical borders of the rotor zones are difficult to transfer into a three-dimensional pattern, as they are made for one two-dimensional cross section and may vary considerably for other cross sections taken along the ridge. The obtained calculation results enable us to establish some qualitative patterns: the rotor zone core is located significantly higher than λ1=2, and with magnification of the Lyra scale, its height increases, and the vertical extent of rotary zones decreases. All these data expand and clarify considerably the previous understanding of the rotor zone location [17].

Fig. 4 Pattern of the Mount Fisht airflow for the model scenario II (U ¼ 19 m/s)

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . .

407

Fig. 5 Pattern of the Mount Fisht airflow for the model scenario III (U ¼ 22 m/s)

5 Flight Safety Problem Over the Mountains of Adygea Scientists have known about the flight danger in mountainous areas for a long time, and relevant data are used in practice when laying regular air routes. However, these studies rarely take into account the fact that aviation security depends essentially on the state of the atmosphere and on orographic features of the mountains. Even less often theoretical calculations are linked to the stability of flights of real airplanes [14, 32]. The aircraft falls into a “special situation,” which is understood as a set of conditions that lead to reduction in flight safety due to environmental influences. Various hazard situations can occur in different meteorological processes: in conditions of intense turbulence, near fronts, near the surface layer, characterized by a strong wind speed shift, and so on [32]. One of the first attempts to take into account all of these factors was made in the research [19]. This study analyzed flight safety in specific mountain regions, such as the North Ural mountain mass, the Crimea peninsula mountains, the Kuznetsky Alatau mountains, the Dzhugdzhur mountains, and the Karatau mountains. The results of the model calculations presented above can be used to determine the degree of danger of flights over the mountainous region of the Republic of Adygea (Lago-Naki Plateau, in particular). The decrease in the safety level in a steady horizontal flight in the zone of atmospheric disturbances may be due to two factors. The first one is the loss of stability due to a sharp increase in the angle of attack of the aircraft wing. This increase may exceed the maximum permissible value, which will cause the wing stall. As a result, the aircraft will start to “fall down,” that is, it will start to unpredictably fall and rotate. The exit out of this situation is only possible by

408

M. K. Bedanokov et al.

reducing the angle of attack. The second factor is acceleration that occurs when an aircraft gets into a zone of abrupt change in the wind speed, especially of its vertical component. Loads on the load-carrying structures of an aircraft increase; passengers feel uncomfortable [32]. Changes in wing incidence Δα and vertical accelerationΔn can be estimated by the formulas [2, 32]: Δα ¼

w w  , Δn ¼ βVw V þu V

where u, w are horizontal and vertical wind speed components in the atmosphere, V is the aircraft speed in zero wind conditions, and βis the empirical coefficient, which depends on aircraft technical characteristics and air density ρ in incident floatation in the following manner: β ¼ qρ

ð6Þ

Safety indicators for Lago-Naki Plateau were calculated separately for high-speed and light aircrafts for the following variations of the incident floatation properties (Table 2). What is more, coefficient q(6) in the formula is equal to 2.3  105 m s2/kg for high-speed aircrafts and 103 m s2/kg for light-engine ones [19]. The atmospheric layers above the troposphere were not considered. In the calculations, the field of perturbations of the trajectories of motion obtained from the flow simulation and the speed field data were used. Calculations were carried out for rotor zones, as the highest values of the vertical speed were forecasted specifically for them. Below are our calculations for the model scenario I (Table 1), that is, for the case when the largest disturbances may be expected by visual assessment (Fig. 3). Two variants of flights at an altitude of 7 and 5 km were considered (Table 2). Let’s consider high-speed aircraft flight at an altitude of 7 km. The first column of Table 2 gives a number of sequentially encountered and the most prominent values of vertical velocity. The other columns show corresponding changes in the angle of attack and vertical acceleration. The table shows that the theory predicts a slight turbulence and low hazard changes in the angle of attack. In case of flight at the altitude of 5 km, the situation worsens for both high-speed and single-engine ones. Acceleration for both ones increases slightly, and bumping should not come out of the category of weak. But change in the angle of attack is more significant. These changes double at high-altitude flight and reach 2.7
for high-speed aircrafts. This may be considered as entering a dangerous situation. For a single-engine aircraft, change in the angle of attack can considerably exceed 6 and even 8
, which is more than the maximum value of the maximum permissible increase in the angle of attack [32]. The situation can be considered as reaching the critical level. However, the obtained data on Δα and Δn do not cover the whole problem of flight safety assessment over mountains. Let us consider in more detail the character

Aircraft types High-speed Height of 7 km Vertical wind velocity component w, m/s 2.5 1.55 2.1

Angle of attack Δα, degree 1.15 0.76 0.99

Vertical load factor Δn 0.35 0.28 0.29

High-speed Height of 5 km Vertical wind velocity component w, m/s 6.25 6.10 5.15 Angle of attack Δα, degree 2.71 2.64 2.25

Vertical load factor Δn 0.43 0.41 0.34

Table 2 Variation in the angle of attack and vertical load factor for flights at 5 and 7 km over Mt. Fisht

Vertical wind velocity component w, m/s 6.25 6.10 5.15

Single-engine Angle of attack Δα, degree 8.50 8.30 7.0

Vertical load factor Δn 0.46 0.42 0.36

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . . 409

410

M. K. Bedanokov et al.

of movements in the rotor zone in Fig. 3. There are four rotors in the trajectory field, i.e., there are areas where both speed components abruptly change. At 4.5 km, there is one rotor formation and the air therein rotates counterclockwise. During a horizontal flight through such area, the plane will be affected by vertical movements of the atmosphere, which rapidly reverse sign. According to the instructions, when flying in such conditions, a pilot must use manual control, which means that his skills should be really advanced. The worst situation seems to be the case when an airplane, after falling into the descending stream, unexpectedly meets the ascending one. In this case, a pilot focused on the need to increase the angle of attack in the zone of descending air stream may miss the moment when the maneuver should be reversed. Because of this, he may not notice the excess of the permissible change of the angle of attack. At an altitude of 5 km (Fig. 3), the most critical area appears to be the one from the first rotor to the second one. At an altitude of 4.5 km, the most critical area is the area in front of the rotor between the ascending and descending branches of the streamline with a height of 5 km in incidence flotation. The duration of a flight in these dangerous areas may be up to 1 s [2]. This research analyzed the risks during the horizontal flight when a pilot does not perform maneuvers to change the height. In the case of such maneuvers, the risk of achieving the critical angle of attack may considerably increase. The presented method of determining the danger level of flying over the mountains of the Republic of Adygea is currently used in the Adygea Republican Center of Hydrometeorology and Environmental Monitoring.

6 Conclusion This research studied atmospheric disturbances in the flow of the mountains of the Republic of Adygea. For this purpose, a hydrodynamic model of orographic disturbances of airflow was developed. This was done on the basis of the nonlinear three-layer analytical model that takes into account layered gaps of stability. At the same time, it takes into account the vertical and horizontal infinity of the atmosphere. Two-dimensional characteristics of the real mountains were determined from a geographical map and used in the calculations with high accuracy. Model calculations were used to obtain motion trajectories and speed disturbance fields in the troposphere for the real range of the Lyra scale values in the atmosphere. Flight safety over the considered mountainous region was assessed for two types of aircrafts using the obtained disturbances. The location of high-risk areas in the space over the mountains and the way they depend on the properties of incident flotation were determined. It is shown that in certain parts of the space, this danger can be critical. The danger level of orographic disturbances near the land surface for the mountains of the Republic of Adygea was assessed for the first time.

Atmospheric Disturbances in the Mountain Flow and the Problem of Flight Safety. . .

411

Acknowledgments The study was funded by the Russian Foundation for Basic Research Project No. 16-35-50120 mol_nr and the State Task N 5.9533.2017/BCh for the implementation of the project “Study of the geoecology of the environment of the North-West Caucasus and specially protected natural territories.”

References 1. Wyngaard JC (2010) Turbulence in the atmosphere. Cambridge University Press, Cambridge, p 406 2. Kozhevnikov VN (1999) Atmospheric disturbances over mountains. Nauchny Mir, Moscow, p 160. (in Russian) 3. Boorman EA (1969) Local winds. Gidrometeoizdat, Leningrad, p 341. (in Russian) 4. Jiang Q, Doyle JD (2005) Wave breaking induced surface wakes and jets observed during a bora event. Geophys Res Lett 32(17):1201–1207. https://doi.org/10.1029/2005GL022398 5. Grisogono B, Belusic D (2008) A review of recent advances in understanding the meso- and microscale of the severe Bora winds. Tellus 61(1):1–16. https://doi.org/10.1111/j.1600-0870. 2008.00369.x 6. Toropov PA, Myslenkov SA, Samsonov TE (2013) Numerical modeling of bora in Novorossiysk and associated wind waves. Vestnik Moskovskogo Unviersiteta, Seriya Geografiya 2:38–46. (in Russian) 7. Lyra G (1943) Theorie der stationaren Leewellenstromung in freien atmosphare. Z Angew Math Mech 23(1):1–28. https://doi.org/10.1002/zamm.19430230102 8. Dorodnitsyn AA (1950) Effect of the earth’s surface relief on airflows. Proc Central Inst Forecasts 21(48):3–25. (in Russian) 9. Long RR (1955) Some aspects of the flow of stratified fluids: III. Continuous density gradients. Tellus 7(3):341–357. https://doi.org/10.1111/j.2153-3490.1955.tb01171.x 10. Kibel IA (1957) Introduction to hydrodynamic methods of short-term weather prediction. Gostekhizdat, Moscow, p 375. (in Russian) 11. Queney P, Corby G, Gerbier N, Koschmieder H, Zierep J (1960) In: MA Alaka (ed) The airflow over mountains. WMO technical note no 34, 135 p 12. Kozhevnikov VN (1963) A single nonlinear problem of the orographic disturbance of stratified air flow. Bull (Izv) Acad Sci USSR Geophys Ser 7:1108–1116. (in Russian) 13. Davis RE (1969) The two-dimensional flow of a stratified fluid over an obstacle. J Fluid Mech 36(1):127–143. https://doi.org/10.1017/S0022112069001558 14. Kozhevnikov VN, Kozoderov VV (1970) Theoretical picture of flow over Crimean range in Yalta area. Izv Atmos Ocean Phys 6(10):979–988. (in Russian) 15. Granberg IG (1979) Numerical modeling of airflow over mountain. Izv Atmos Ocean Phys 15(12):867–872. (in Russian) 16. Lilly DK, Klemp JB (1979) The effects of terrain shape on nonlinear hydrostatic mountain waves. J Fluid Mech 95(2):241–261. https://doi.org/10.1017/S0022112079001452 17. Smith RB (1979) The influence of mountains on the atmosphere. Adv Geophys 21:87–230. https://doi.org/10.1016/S0065-2687(08)60262-9 18. Scorer RS (1978) Environmental aerodynamics. Halsted Press, Ultimo, p 488 19. Kozhevnikov VN, Pavlenko AP (1993) Atmospheric disturbances over mountains and flight safety. Izv Atmos Ocean Phys 29(3):283–295. (in Russian) 20. Kozhevnikov VN, Bedanokov MK (1993) Nonlinear multi-layer model for flow over mountains of arbitrary shape. Izv Atmos Ocean Phys 29(6):780–792. (in Russian) 21. Kozhevnikov VN, Bedanokov MK (1998) Wave disturbances over the Crimean mountains: theory and observations. Izv Atmos Ocean Phys 34(4):491–500. (in Russian)

412

M. K. Bedanokov et al.

22. Demetrashvili DI (1979) Nonstationary problem of meso-scale processes in the free atmosphere over orographically inhomogeneous Earth’s surface. Izv Atmos Ocean Phys 15(7):699–709. (in Russian) 23. Rontu L (1986) A finite-amplitude mountain wave model. Report no 26, Department of Meteorology University of Helsinki, Helsinki, 41 p 24. Kozhevnikov VN (2004) Unperturbed nature of the free flow upstream of a mountain range. Izv Atmos Ocean Phys 40(1):21–34. (in Russian) 25. Buzarov AS, Varshanina TP, Kabayan NV, Krasnopolsky AV, Krasnopolskaya NV, Kuasheva DA, Melnikova TN, Spesivzev PA, Khachegogu AE, Shebzuhova EА (2001) The Republic of Adygea geography. Republic of Adygea, Maykop, 200 p. (in Russian) 26. Lebedev SA, Kostianoy AG (2020) Three-dimensional topographic model of the Republic of Adygea. In: Bedanokov M, Lebedev S, Kostianoy A (eds) The Republic of Adygea environment. Springer, Cham 27. Bedanokov MK, Kobleva RB, Mirzova OD, Mirzova SD (2008) Nonlinear modeling of flow around an arbitrary profile of the mountains in Kislovodsk area. New Technol 5:67–73. (in Russian) 28. Bedanokov MK, Berzegova RB (2015) Air flow in stability rupture and orographic disturbances in the stratosphere. In: Proceedings of the III International scientific-practical conference “Applied aspects of geology, geophysics and geo-ecology, using modern information technologies”, Maykop, Russia. May, 11–14, 2015. Kucherenko V.O., Maykop, pp 44–43. (in Russian) 29. Kozhevnikov VN, Losev AS (1982) On the construction of flow model at exact fulfillment of the boundary conditions on the cylinder profile. Vestnik Moskovskogo Unviersiteta, Seriya. Phys Astron 23(5):43–50. (in Russian) 30. Bedanokov MK, Kobleva RB (2009) Influence of orographical perturbation on ozone reallocation in an aerosphere around Kislovodsk. Vestnik Adygeiskogo Universiteta, Seriya Natural-Mathematical and Technical Sciences 1, 37–44. (in Russian) 31. Astapenko PD, Baranov AM, Shvarev IM (1985) Aviation meteorology. Transport, Moscow, p 262. (in Russian) 32. Nikolaev LF (1990) Aerodynamics and flight dynamics of transport aircrafts. Transport, Moscow, p 392. (in Russian)

Contemporary Changes of the Vegetation in the Mountainous Adygea as the Reflection of Global Processes Valery V. Akatov and Tatyana V. Akatova

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Brief Characteristic of the Mountain Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Climatogenic Changes of the Altitudinal Limits of Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Litvinov’s Birch (Betula litwinowii) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Nordman’s Fir (Abies nordmanniana) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Broad-Leaved Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Post-Grazing Recovery of High Mountain Meadows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Invasion of Habitats by Alien Plant Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Adventive Component of the Flora of the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . 5.2 Сhanges of the Upper Distribution Limit of Alien Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Invasibility of Plant Communities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Species Richness of Plant Communities Dominated by Alien Species . . . . . . . . . . . . . . 6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

414 414 415 416 417 419 420 423 423 425 429 430 433 434

Abstract Contemporary changes of plant communities of the mountain areas of the Republic of Adygea which can be considered as global have been characterized. They include climatogenic changes of altitudinal distribution borders of trees species; recovery of mountain meadows after the termination of a long pasture; distribution of the adventive (alien, non-native) plants; and their influence on the native species in the region.

V. V. Akatov (*) Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] T. V. Akatova Caucasian State Wildlife Biosphere Reserve, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 413–442, DOI 10.1007/698_2020_493, © Springer Nature Switzerland AG 2020, Published online: 23 September 2020

413

414

V. V. Akatov and T. V. Akatova

Keywords Alien species, Altitudinal limits, Climate change, Dominants, Pasture digression, Recovery succession

1 Introduction The complex geological history, strongly dismembered relief, and zonality of the climate caused a high cenotic diversity of the vegetation of the mountainous part of Adygea. Under the influence of numerous natural and anthropogenic factors, and also as the result of autogenous processes, it changes constantly. Some contemporary changes of plant communities of the region connected with the human activity or its termination and can be considered as global: the reaction of tree species to the global climate change, restoration of high mountain meadows after the termination of long pastures, and expansion of the adventive (alien, non-native) species of plants. In this chapter they will be characterized using the synthesis of the data published before and some new information.

2 Brief Characteristic of the Mountain Vegetation The mountainous part of the Republic of Adygea includes practically all range of the main types of plant communities, characteristic to the Western Caucasus. The subnival vegetation is fragmentary expressed at mountain tops and ridge crests more than 3,000 m above sea level. It is formed by open aggregations of lichens, mosses, and some vascular plants. The alpine belt is located in the height range of 2,300–2,800 m above sea level. Its landscape is characterized by a combination of stony and grassy slopes, rock streams and screes, glaciers, and snowfields. The most widespread plant communities are rock plant aggregations, alpine short-grass meadows, lichen heaths, mats, and meadows with Geranium gymnocaulon (snow bed communities). Heights of 1,700–2,300 m above sea level correspond to the subalpine belt. The middle- and high-grass meadows and thickets of evergreen bush – Rhododendron caucasicum – are characteristic to it. Communities of fens are quite often formed on the place of the lakes [1, 2]. At 2,000–2,100 m above sea level, mountain meadows are replaced by subalpine birch, pine, and beech forests (dominant species Betula litwinowii, Pinus hamata, and Fagus orientalis). The upper and middle mountain forests are dominated by Abies nordmanniana and Fagus orientalis and low mountain forests – Quercus robur, Q. petraea, and Carpinus betulus. The subalpine birch and beech stands are formed by a small number of species: Betula litwinowii, Sorbus aucuparia, Salix caprea, and Acer trautvetteri. The height of trunks makes 5–6 m with a diameter of 12–15 cm. The density of kroner seldom

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

415

exceeds 0.5 [3]. The strip of the fir and beech forests is located lower (600–1,800 m above sea level). At the height of 1,500–1,800 m, Acer trautvetteri, Sorbus aucuparia, and Betula litwinowii act as the accompanying breeds. At the lower levels, they are replaced by species of broad-leaved trees: Acer pseudoplatanus, A. platanoides, Tilia caucasica, Ulmus glabra, etc. [4, 5]. The strip of the broad-leaved woods is presented mainly by beech and oak formations. The beech woods are widespread within the limits of 400–600 m above sea level, woods where Quercus petraea dominates with 200–1,000 m and Q. robur with lower than 400 m. The stands include Fraxinus excelsior, Tilia caucasica, Ulmus glabra, Pyrus caucasica, etc. Riparian forests are presented by the pure and mixed forest stands with the domination of Alnus incana, A. glutinosa, Populus alba, P. nigra, and Salix alba and participation of Acer campestre, Carpinus betulus, Ulmus glabra, etc. [6, 7].

3 Climatogenic Changes of the Altitudinal Limits of Trees There are evidences that modern climate warming has already been changing the borders of separate species of plants and communities in general, including forests [8–13]. The Caucasus is located on the border of moderate and subtropical climate zones, moderate damp European, and dry Asian regions, and therefore it is characterized by the extremely various climatic conditions [14]. For the last 40 years, the increase of average and maximum temperatures in summer and autumn months has been observed [15]. According to the scenario considered by V.D. Panov [16], summer and winter temperatures will become approximately 2 and 4 C higher, respectively, by the year 2050. If so, it can be expected that altitudinal vegetation belts in this region will be displaced upward, with the timberline shifting approximately 200–300 m higher. The tendency of the changes of high borders of tree species was defined by the analysis of the age structure of their populations, diameter, and vitality of individuals [8, 17, 18]. If tree species have a distinct tendency toward expansion to higher (lower) elevations, their populations at the altitudinal limits should consist of tree with a small diameter, less than 25–30 years of age, and of high vitality. If the upper (lower) distribution limit has a tendency to descend (to rise), uppermost (lowermost) trees should be of considerable diameter and age but of poor vitality. Finally, if the distribution limits of these species remain unchanged, trees growing at these limits should have different age, but relatively small diameter. Researches were conducted in 2005–2011 on several massifs within the Caucasian State Wildlife Biosphere Reserve. Betula litwinowii, Abies nordmanniana, Acer platanoides, A. рseudoplatanus, and Ulmus glabra were chosen as the objects of research. The natural (climatic) timberline (1,810–2,300 m), formed by crooked birch forests, is regarded as the upper distribution limit of birch and fir, the boundary of middle mountain and upper mountain beech-fir forests (1,300–1,700 m) – broadleaved tree species (Fig. 1). The condition of the fir populations was analyzed on the

416

V. V. Akatov and T. V. Akatova

Fig. 1 The high mountain forests. The Belaya River head, the mount of Fisht (photo by Akatov V.V.)

lower limit of distribution of this species too. For this purpose the woods of the Belaya riverbed at 450–700 m above sea level were surveyed. Factual data were collected along transects extending from the upper (lower) tree limits down (or up) the slope, to the elevation where the values of test parameters stabilized. The main results of these researches were stated in a number of publications [19–21].

3.1

Litvinov’s Birch (Betula litwinowii)

The age structure of Betula litwinowii was analyzed along four transects. On all transects, the maximum stem diameters showed a distinct tendency to decrease upslope. The distance from the upper distribution limit to the level at which stabilization of the maximum stem diameters was observed varied from 6 to 25 m. Trees growing at a distance of up to 3.5–5 m from the upper distribution limit were no older than 15–20 years, with the age of such trees at distance of 5 and 12 m increasing to 30 and 40 years, respectively. Therefore, it appears that upper distribution limit of Betula litwinowii has shifted upslope slightly (several meters) over the past 30 years (Fig. 2).

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . . 50

160

417

- Diameter

20 30 Age, year

80

0

0

10

40

Diameter, mm

120

40

- Age

0

3

6 9 Distance to treeline, m

12

15

Fig. 2 Changes in maximum stem diameter in Betula litwinowii trees and age of these trees with distance from the limit of species distribution down the slopes of Abago Mountain [20]

3.2

Nordman’s Fir (Abies nordmanniana)

In the research area, Nordman’s fir grows in the range of heights from 500 to 2,000 m above sea level, that is, 200 m lower or higher than the border of forest stands where this species acts as a dominant or subdominant, forming the belt of dark coniferous and mixed woods. The results of the researches showed that, despite some features, the condition of the populations of this species on the upper and lower limits of distribution is similar (Fig. 3). In particular, at both boundaries, the maximum age of the individuals makes about 60 years that testifies to their stability, at least during half a century. Besides, both on upper and lower borders of fir distribution, there is practically no individuals of this species younger than 20 years of age. Considering that this species is fastidious to soil humidity, it’s been assumed that it is connected with the increase in the duration of the dry periods in a warm season in the last two decades. So, according to Onishchenko et al. [22], the existence of the drought summer periods from 1990 to 2000 in the Teberdinsky State Wildlife Biosphere Reserve (the Karachay-Cherkess Republic) became a norm. Such situation was registered on “Dzhuga” meteorological station of the Caucasian State Wildlife Biosphere Reserve (2,041 m) in 1984, 1994, 1999, 2000, and 2001 [23]. This explanation coordinates with the results of the research of a radial gain of fir trunks at its high boundaries [24]. They testify that the values of this indicator are defined not only by air temperature but by the amount of precipitation. Similar results were obtained in Central Asia on other coniferous breed – Picea schrenkiana [25]. According to the authors, the gain of forest stands of this species, both on the upper and lower borders of its distribution, depends more on the amount of precipitation, than on the

V. V. Akatov and T. V. Akatova 100

418

- Minimum Age

(a)

60 40 0

20

Age, year

80

- Maximum Age

1825

1850

1875

1900 1925 Elevation, m

1950

200

1800

1975

2000

2025

- Minimum Age

(b)

120 80 0

40

Age, year

160

- Maximum Age

475

500

525

550

575 600 Elevation, m

625

650

675

700

Fig. 3 Changes of the minimum and maximum age of the individuals of Abies nordmanniana along the high-hill profiles on the upper (a) and lower (b) limits of distribution of this species [21]

temperatures of the warm season. Holtmeier and Broll [26] noticed similar problems with the renewal of Abies lasiocarpa and Picea engelmannii on the upper bound of their distribution in the Rocky Mountain National Park (Colorado, USA). It was considerable in rather warm and snowy years of 1940–1970; however in the 1990s, after the dry period, the authors found only single individuals of 10–20 years old. They draw a conclusion that renewal processes of these species on the upper bound of their distribution were more sensitive to the changes in the humidity of climate, than to its warming.

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

419

Thus, the results show that climate change on the Western Caucasus after 1990 has not led to changes of altitudinal limits of Abies nordmanniana yet, but has created prerequisites to their possible retreat.

3.3

Broad-Leaved Trees

Signs of increase on 15–75 m above sea level of the upper limit of the distribution of Acer platanoides, A. pseudoplatanus, and Ulmus glabra have been revealed. However, this tendency isn’t universal, and on some massif the border of distribution of these species remains stable (Table 1). Table 1 Height of the upper bound of distribution of broad-leaved species of trees and arrangement of individuals of more than 30 years of age on mountain mass [20] Profile Mountain number mass Acer platanoides 1 Azish-Tau Ridge 2 Abago Mountain 3 Abago Ridge 4 Pshekish Mountain 5 Pshekish Mountain 6 Solontsovyi Ridge Ulmus glabra 7 Azish-Tau Ridge 8 Pshekish Mountain 9 Abago Mountain 10 Abago Ridge Acer pseudoplatanus 11 Solontsovyi Ridge 12 Solontsovyi Ridge 13 Port Arthur Ridge 14 Abago Ridge

Height of distribution above sea level (m) Individuals of more than Upper limit of 30 years of age distribution

Difference of heights (m)

1,685

1,685

0

1,510

1,560

50

1,450

1,525

75

1,530

1,545

15

1,600

1,665

65

1,630

1,630

0

1,770

1,770

0

1,490

1,545

55

1,490

1,510

20

1,420

1,420

0

1,500

1,550

50

1,560

1,560

0

1,615

1,640

35

1,370

1,420

50

420

V. V. Akatov and T. V. Akatova

Let’s pay attention to the fact that despite a large number of works from the different regions of the world testifying to the increase in the number of undergrowth of different tree species on the upper and northern limits of distribution in the last 20–50 years and its moving on earlier non-forest sites, cases of lack of such tendency are also not rare [13, 27–30]. In general opinion, it speaks about complex nature of link between the climate change at the global level and altitude and/or spatial areas of wood plants. The results given above are one more argument in favor of this conclusion.

4 Post-Grazing Recovery of High Mountain Meadows Pastures appeared several million years ago under the influence of large herbivores. About 4,000–11,000 years ago, their area was considerably expanded by a man, and they became one of the most widespread types of ecosystems and a pasture of domestic animals – one of the main ways of using lands [31–34]. However, in the last decades, economic pressure led to the refusal of a pasture in many regions of the world, including Russia [31, 35–45]. In the countries of Eastern Europe, it is related to the economic crisis after the destruction of socialist economy, in the countries of Western Europe and other regions of the world – with gradual industrialization, market orientation, and urbanization [39, 46–48]. As the result, in the last 20–50 years, recovery successions were widely adopted on pastures of many regions. As follows from the publications, the general direction of such processes doesn’t differ much in different types of plant communities. At the first stage, the share of annuals, low and badly consumed by animals species, reduces and, on the contrary, the share of high perennial plants and primary dominants increases, as well as the total canopy cover and productivity of communities [31, 34, 42–44, 49]. Then, steppes are gradually replaced by shrub communities and meadows, except for the ones located above the timberline, by forests [37, 42–44, 46, 50, 51]. The species diversity of the communities at the first stages of the recovery succession either increases (mainly in productive steppe and Mediterranean communities) or decreases (mainly in highly productive meadows due to the increase of the abundance of dominants) [34, 41, 45, 48, 49, 52–57], and then in the process of accumulation of litter and increase in density of shrubs and trees, it only decreases [39, 45, 46, 48, 58]. The speed of рost-grazing successions depends on many factors, including the type of vegetation, soil conditions, amount of precipitation, history of pasture, and duration of rest period [42, 59, 60]. Therefore, data on this parameter from different regions of the world differ significantly. So, in some works the conclusion is drawn about the high speed of the recovery processes, at least, at their beginning [39, 40, 42, 53], and in others about minor changes in the first 10–25 years [41, 45, 60, 61] and acceleration in the next years [45, 61]. There is an opinion that complete natural recovery of strongly brought down pastures will take decades [31, 39, 50, 62].

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

421

One of the results of post-pasturable successions is almost universal increase in the area of the woods and reduction of the area of pasturable communities (in Western Europe for 12% for the last 20 years [63]). And for a number of reasons, this process is considered as adverse. First of all, it is due to possible decrease in the species diversity of regions, because extensively used pastures belong to the species-rich types of plant communities [33, 36–38, 43, 46, 48, 51, 58, 60, 64]. Homogenization of landscapes, growth of probability of emergence of natural fires, and distribution of alien plants are considered to refer to other negative consequences of the recovery successions [48, 51, 60]. In the historical past, cattle was pastured on all highland massifs of the Western Caucasus. However, in the second half of the twentieth century (from 1951 to 1993), the flank of ridges of Lago-Naki Plateau formed by limestone (Murzikao Ridge, 2,300 m; Nagoy-Chuk Ridge, 2,400 m; Lago-Naki Plateau, 2,000 m; etc.) (Fig. 4) was used mainly for these purposes within the Republic of Adygea. The pasture, quite often excessive and unsystematic, led to transformation of the structure and composition of mountain meadows, decreasing their productivity [65, 66]. After the return of the high mountainous part of the upland in 1992 to the Caucasian State Wildlife Biosphere Reserve, cattle pasture was ceased on its most part. To evaluate the character and speed of post-pasturable changes on Lago-Naki Plateau in 1989–1995, a series of sites of alpine heaths and subalpine meadows, undisturbed and moderately disturbed by pasture, was described. In 2010, disturbed

Fig. 4 High mountain meadows on Lago-Naki Plateau (photo by Akatov V.V.)

422

V. V. Akatov and T. V. Akatova

sites were redescribed. Changes in coverage and frequency of plant species and species richness of communities for period between descriptions were analyzed [65–67]. On the sites, not available for pasture, the cover of subalpine middle-grass meadows varies from 95 to 100%, the species richness averages 47.1 species per 16 m2, and the dominant species are Calamagrostis arundinacea and Festuca woronowii (F. varia). In 1994 and 1995, pasturable communities were characterized by the closed grass stand, lowered species richness (on average 34 species per 16 m2), and domination of species resistant to pasture (Bromopsis variegata, Agrostis planifolia, Trifolium ambiguum). Considerable part in the formation of communities belongs to successional (Prunella vulgaris, Elytrigia repens, etc.), resistant to pasture (Veratrum lobelianum, Anemone fasciculata, Aconitum nasutum, Betonica macrantha, Carduus laciniatus, and some others), and short-grass species more characteristic for the alpine communities (Carex tristis, Campanula tridentata, Gentiana djimilensis, Potentilla gelida, etc.). According to Shiffers [1], the abundance of alpine species in the communities of the subalpine middle-grass meadows of the North Caucasus is one of the signs of pasture digression. For the last 15–20 years of “rest,” the subalpine communities disturbed earlier have changed mainly toward the pre-pasture state. Most plant species resistant to pasture or characteristic for the short-grass communities have decreased in frequency, and some species characteristic for natural subalpine meadows have increased it. On the other hand, climax dominants of the subalpine meadows have restored only on some sites, and species richness of communities has decreased even more (on average to 23 species per 16 m2). Thus these communities do not return to their previous state, and we can assume that the recovery of grazed subalpine vegetation requires considerably more long time periods. And due to the ambiguity of the origin of subalpine meadows as the result of overgrowing of stony substrate or decrease in the upper bound of the forest [1, 68], it is difficult to predict the outcome of this process. Communities of the alpine heaths and meadows at the time of the first investigations (1989–1992) were in rather good state due to their high natural resistance to pasture [1] and remoteness from cattle settlement. They were characterized by high coverage of climax dominants (Festuca ovina, Carex huetiana, and C. tristis) and high species richness (on average 29 species of vascular plants per 16 m2). However, on some sites the high coverage of plants resistant to pasture was observed: Alchemilla caucasica, Anthemis marschalliana, and Antennaria caucasica. Post-pasturable changes in the alpine communities were limited to the increase of frequency of species more characteristic for the communities of subalpine belt: Anemone fasciculata, Betonica macrantha, Leontodon hispidus, Festuca woronowii, Rhinanthus minor, and Pedicularis condensata. We shouldn’t exclude that strengthening positions of these species on the described sites of the alpine communities can be connected not with the termination of pasture, but with climate changes which are observed in the Western Caucasus. Concrete information on the climate changes of the alpine vegetation is rather seldom. It is based on the results of comparison of data on the species diversity of floristic complexes of tens of mountain tops of the Alps periodically surveyed during

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

423

1835–1995 [69–71]. Seventy percent of them are marked with the increase in number of species, including the last several decades. Similar information was found on other mountain systems, for example, the Scandinavia [72–74] and the Carpathians [75]. In recent years some evidences of change of the alpine plant communities due to the climate warming were obtained on permanent plots in the Western Caucasus [76]. In all these works, contemporary climatic processes are considered an important factor of the revealed changes, but there is also another point of view [77]. According to Michelsen et al. [74], when interpreting the results of such observations, none of possible factors should be excluded from the analysis. In particular, sharp decrease in the intensity of livestock grazing can cause changes in plant communities similar to climatic [74, 76]. Therefore it needs additional researches of these processes.

5 Invasion of Habitats by Alien Plant Species Despite the fact that invasion of adventive (i.e., alien, non-native) species to plant communities has reached a global scale, certain facts suggest acceleration of this process during the last decades [78–81]. This is associated with continuous destruction of natural landscapes, appearance of ever more new alien plant species in the regions, end of the lag phase for the species earlier brought to new areas, rapid expansion of their secondary distribution ranges, and adaptation to new habitats [78, 82–84]. Recently, the expansion of alien species has been ascribed to climate warming [11, 82, 84, 85]. There is an opinion that due to global warming, alpine areas may become more available for colonization of adventives species [70]. Many studies show that the non-native plants can affect the community structure (species diversity and composition) and ecosystem processes (nitrogen cycling, hydrology, fire regime, etc.). Therefore, the invasion of habitats by alien species may pose a serious threat to managed and natural ecosystems [86–89].

5.1

Adventive Component of the Flora of the Republic of Adygea

Nowadays according to our data, in the natural and disturbed habitats of the mountainous part of the Republic of Adygea grow 64 adventive species of plants from 52 genera and 32 families. The greatest number of these species belongs to Asteraceae family (15), followed by Poaceae (6), Amaranthaceae (4), Euphorbiaceae (3), and Fabaceae (3). Other families are presented by one or two species. Concerning life-forms, 46 non-native species (or 72% of the total) are herbaceous plants (65% of them are monocarps: annuals, more rare biennials), 15 species (23%) are trees, and only 3 species (5%) are shrubs and woody vines (Amorpha fruticosa, Parthenocissus quinquefolia, and Vitis labrusca).

424

V. V. Akatov and T. V. Akatova

More than half of alien species of plants originated from North America (38 species, or 59%); 9 species (14%) originated from East Asia, the Himalayas; 8 species (12%) are from Western Europe and the Mediterranean; and an insignificant part of species come from Southwest or Central Asia, and Africa, or their origin is unknown. About 60% of species have dropped out of the culture – ergasiophytes. Majority of them are perennials. Other alien species are unintentionally introduced weeds – xenophytes. These species are mainly monocarps. Forty-two non-native species (66%) are the plants of the disturbed and аnthropogenic habitats; 17 species (27%) are plants which are taking root into natural communities, but settling mainly on the disturbed sites; and 5 (7%) are species which have taken root into natural communities. The most common non-native species are Erigeron annuus, Erigeron canadensis, Bidens frondosa, Amaranthus retroflexus, Ambrosia artemisiifolia, Juncus tenuis, Morus alba, Morus nigra, Oxalis stricta, Xanthium strumarium, Amorpha fruticosa, Robinia pseudoacacia, and others. Some alien species are not widespread in the mountainous part of Adygea, but in some areas they can dominate, forming extensive almost pure thickets (Asclepias syriaca, Helianthus tuberosus, Solidago canadensis, Galinsoga ciliata, etc.) (Fig. 5). Alien hydrophyte – Elodea canadensis – has a similar distribution pattern in Adygea, marked only in some waters, but in high abundance. Many non-native species occur very rarely, for

Fig. 5 Thickets of Galinsoga ciliata by the roadside at an altitude of 580 m above sea level (Photo by Akatov V.V.)

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

425

example, Aquilegia vulgaris, Acorus calamus, Hibiscus trionum, Oenothera glazioviana, Rudbeckia hirta, Elaeagnus angustifolia, Quercus rubra, Narcissus poeticus, Hemerocallis fulva, and Amaranthus caudatus. All of them are ergasiophytes [90–92]. The great majority (86%) of the non-native species grows at the low elevations. Here, except for ruderal and segetal communities, floodplain forests, river shoals, fallow and pasture lands, forest edges and glades, roadsides, and artificial lakes are their main habitats. In particular, in disturbed low mountain floodplain forests, it recorded 18 alien species (Robinia pseudoacacia, Acer negundo, Amorpha fruticosa, Parthenocissus quinquefolia, Solidago canadensis, Ambrosia artemisiifolia, Bidens frondosa, and others); in the communities of fallow lands, there are also 18 (Ambrosia artemisiifolia, Erigeron canadensis, E. annuus, Asclepias syriaca, and others). Open plant communities of river shoals include a slightly smaller number of non-native species (14): Erigeron canadensis, E. annuus, Bidens frondosa, Ambrosia artemisiifolia, Xanthium californicum, Oenothera biennis, Galinsoga parviflora, Populus deltoides, and others. In waters it recorded only two species: Elodea сanadensis and Acorus calamus. The number and abundance of non-native species strongly decreased with altitude (Fig. 6). Twenty-six of them (41%) reach 600 m elevation (the boundary of low and middle mountain forests); nine alien species (14%) reach 1,500–1,800 m (zone of upper mountain forests), and only one – Matricaria matricarioides – reached 2,000 m (above timberline). This species was found in Lago-Naki Plateau on greatly disturbed grazing subalpine meadows. It should be noted that a significant portion of middle and upper mountain forest zones of the Republic of Adygea is included in the Caucasian State Wildlife Biosphere Reserve. On its territory within Republic of Adygea, 25 non-native species of plants have been revealed [91]. They grow mainly on ruderal habitats of the cordons and its neighborhoods (Ambrosia artemisiifolia, Bidens frondosa, Amaranthus retroflexus, Helianthus tuberosus, Galinsoga ciliata, etc.). Only Erigeron annuus, E. canadensis, and Juncus tenuis have been observed outside. The most common is Erigeron annuus, which grows along roads and trails, in the plant communities of forest glades, windfalls, and river shallows.

5.2

Сhanges of the Upper Distribution Limit of Alien Plants

The direction and rate of change of the boundaries of the area of alien organisms are usually evaluated by direct methods, i.e., through direct long-term observations of the distribution of species, as well as the analysis of collection samples and publications for different years [84]. Using this approach, we have examined the road from the Guzeripl village to the “Yavorovaya” glade (in 2008, 2011, and 2013) three times (650–1,711 m above sea level), intended for the forest export and recreational purposes. In 2008, this road was dirt and almost impassable for the most types of motor vehicles; after the reconstruction in 2011, it has an asphalt

426

V. V. Akatov and T. V. Akatova

Matricaria matricarioides Erigeron annuus Ambrosia artemisiifolia Robinia pseudoacacia Oxalis stricta Oenothera biennis Xanthium strumarium Bidens frondosa Erigeron canadensis Juncus tenuis Amaranthus retroflexus Galinsoga parviflora Galinsoga ciliata Amaranthus deflexus Ailanthus altissima Helianthus tuberosus Xanthium californicum Solidago сanadensis Morus nigra Amorpha fruticosa Morus alba Gleditsia triacanthos Acer negundo Parthenocissus quinquefolia Euphorbia maculata Acalypha australis Abutilon theophrasti Digitaria ischaemum Paspalum dilatatum Elodea canadensis Populus deltoides Fraxinus pennsylvanica Sorghum halepense Juglans nigra Juglans regia Asclepias syriaca 0

200

400

600

800 1000 1200 1400 1600 1800 2000

Elevation, m

Fig. 6 Upper distribution limit of some alien plants on the territory of the Republic of Adygea

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

427

surface. Observations have shown that during 3 years of road reconstruction (from 2008 to 2011), Erigeron annuus rose from 1,690 m above sea level to 1,711 m, i.e., for 21 m; Ambrosia artemisiifolia for 590 m (up to 1,711 m); Bidens frondosa for 632 m (up to 1,582 m); and Erigeron canadensis for 550 m (up to 1,500 m). Besides, some species appeared that were not observed in 2008: Oxalis stricta, Oenothera biennis, Xanthium sp., and also undergrowth Robinia pseudoacacia (up to 1,525 m above sea level). In 2013, individuals’ number of locust increased more than ten times; one of the trees (at an altitude of 1,396 m) reached the reproductive stage of development – flowering and fruiting – and 1–2year-old undergrowth moved to a height of 1,708 m (Fig. 7). Since direct observation is not always possible, indirect methods involving the analysis of the status of their populations in different parts of the area can be used in the process of study of the dynamics of distribution of non-native plant species. At present this approach is widely used to assess the reaction of wood plants to global warming, but in most cases the native species have been the object of study [8–10, 12]. This method was used to estimate dynamic trends of the populations of the two most common in the region alien species of trees – Robinia pseudoacacia and Acer negundo in riverine and floodplain forests of the Belaya River [93]. The results showed that the upper limit of Robinia pseudoacacia in these forests, located at an altitude of 600 m above sea level, has been stable over the past decades (the age

Fig. 7 Robinia pseudoacacia at the verge of mountain road at an altitude of 1,500 m above sea level (photo by Akatov V.V.)

428

V. V. Akatov and T. V. Akatova

Table 2 Age structure of the populations of Robinia pseudoacacia and Acer negundo in the riverine forests of the Belaya River village [93] Abs. height Number of individuals Robinia pseudoacacia 73, 161 64 190–240 104 307, 352 47 405, 465 55 492, 561 26 Acer negundо 73, 161 59 194 27 307 36 452 26 460 27 a

Age classes 1–10 11–20

21–30

>30

Max. age

37.5a 55.8 78.7 38.2 76.9

43.8 35.6 21.3 45.5 19.2

10.9 6.7 – 7.3 –

6.3 1.9 – 9.1 3.9

52 32 20 43 74

59.3 77.8 63.9 50.0 96.3

30.5 22.2 33.3 50.0 3.7

8.5 – 2.8 – –

1.7 – – – –

33 18 23 19 20

Share of individuals in age class (%)

of some trees on this range reaches is more than 70 years) (Table 2). Above 700 m the Belaya River Valley is located within the protected natural area (the Caucasian State Wildlife Biosphere Reserve), and therefore the penetration of this species into the mountain gorges of the rivers is likely hampered by the lack of anthropogenic disturbances and low probability of importation of seeds. It was also found that at all the levels 80–100% of studied species of Robinia pseudoacacia are under 20 years old. This proves that the expansion of this species in the valley of the Belaya River occurs mainly through the development of the occupied altitudinal interval, mostly in the last 20 years. However, in recent years, as shown above, it is combined with its rise high up in the mountains up to 1,700 m above sea level due to the intensification of road construction. Acer negundo in the riverine and floodplain forests of the Belaya River grows up to the height of 460 m above sea level, up to the height of 300 m, it is common, above – it is found in the form of small groups of individuals. At all altitudes its populations include primarily or only individuals under 20 years of age (Table 2). Accordingly, we can assume that on the most areas of riverside forests of the Belaya River, Acer negundo has appeared recently, and at the same time. Remarkably, factors of intensification of the dissemination of alien species of trees in the past 20 years have been revealed in dry steppes of the northern Black Sea area (Ukraine). According to B. Sudnik-Wójcikowska and co-authors [80], the maximum age of the individuals in the artificial plantations of Elaeagnus angustifolia and Robinia pseudoacacia in the dry steppes of the northern Black Sea area (Ukraine) was 47 years old. In the case of abandoned cultivated fields, the oldest trees were estimated to be 19–20 years old, on solonetz soils 19–22 years old, and in the desert steppe 17–18 years old. The authors point to several possible reasons for this phenomenon: (1) crisis of agricultural production, accompanied by the

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

429

increase in the area of unused land, including vacant lots, pools, and abandoned pasture; (2) climate change; and (3) way out of lag phase. None of these reasons can be completely rejected when explaining the present increase in the number of Robinia pseudoacacia and Acer negundo in the Western Caucasus.

5.3

Invasibility of Plant Communities

The question as to why some communities are saturated with non-native species to a higher degree than others is of both scientific and practical interest and has been actively discussed by ecologists in recent years. In particular, according to [85, 86], alien species most successfully invade the cenoses with periodically unutilized resources (the hypothesis of fluctuating resources). This hypothesis is favored by a considerable saturation with alien species of frequently disturbed communities independently of the region, such as segetal and ruderal communities or communities of fallow lands, lawns, trails, and roadsides [94–96]. Another hypothesis was proposed by Elton [97]. This hypothesis emphasizes the species richness of cenoses. According to it, the higher the species richness, the higher is the degree of resource utilization, and the lower is the probability for adventive species to invade the corresponding cenoses [97]. This hypothesis has been tested in numerous studies; however, the obtained results are contradictory [78, 98]. In general, the environmental conditions favorable for native species have most frequently appeared also favorable for alien species [78, 96, 99–102]. Recently, the species pool of communities has been ever more frequently mentioned as a factor of invasibility [101, 103–106]. Its size may depend on both local environmental conditions and regional-scale processes, such as the rate of speciation, history of communities, and degrees of their isolation [107]. It has been shown that the larger the species pool size of communities, the lower their saturation with non-native species [104–106]. There is also the opinion that the degree of saturation with adventive species in local areas of plant communities depends on the number of such species that can grow in the corresponding region under certain conditions and supply these areas with their diaspores [78, 108]. In particular, species with R-population strategy have stronger capacities for transcontinental seed dispersal and, hence, play a leading role in plant cover adventization in new regions. Since most of these species in their native growing regions are involved in the initial stages of secondary successions, they mainly invade frequently disturbed phytocenoses [95]. Finally, the invasibility of plant communities may be determined by the balance between the competitive abilities of non-native and native species, which can differ in different types of communities [78]. In particular, on the one hand, many papers emphasize the competitive advantages of the former as compared with the latter [109–112]. On the other hand, it is emphasized that the advantage of non-native species is rarely of a universal character and the result of competition between the non-native and native species to a considerable degree depends on particular circumstances [78, 113].

430

V. V. Akatov and T. V. Akatova

Our results are consistent with some from these hypotheses [114–118]. In particular, they demonstrated that the greatest numbers of adventive species usually grow in periodically disturbed open communities (segetal, on roadsides, river shoals, etc.). Moreover, they showed that the total abundance of alien species in plant communities is negatively correlated with the size of their species pool and positively with their initial species richness and the number of alien species that can grow in these communities. In forest stands the contribution of first two factors to the variation of abundance of invading species was 35% and herbaceous communities 30% – the combined contribution of all three factors reached 60%. The relative competitiveness in different herbaceous communities was evaluated for the two groups of adventive plant species distinguished according to the way of their transfer: xenophytes and ergasiophytes. Results showed that in the communities of fallow lands, fields of annual crops, and river shoals of the middle mountain zone, the xenophytes display a considerably higher competitive ability as compared with the native species. However, as for the communities of the steppe meadows and forest glades, their advantages are poorly pronounced, whereas native species are more competitive as compared with xenophytes on low mountain river shoals. The species of another group – ergasiophytes – are the most competitive in fallow lands, where they frequently become dominant; however, in fields of annual crops, they evidently yield to the native species, and in the communities of glades, steppe meadows, and river shoals, these species are rare. Thus, our results indicated a different relative competitiveness of adventive species toward indigenous species in different herbaceous communities. Consequently, one may assume that this factor plays an essential role in the level of resistance of plant communities to the invasions of such species, which may be compared with the role of their cenotic structures [119].

5.4

Species Richness of Plant Communities Dominated by Alien Species

There are a number of evidences of the significant influence of alien species of animals, pathogenic fungi, and microorganisms on the species diversity and composition of natural communities [120, 121]. But the understanding of the consequences of the introduction of non-native plants into natural and seminatural cenoses for aboriginal species is vague. In particular, in some works, examples are given of displacement of aboriginal species by adventive ones, for example, of populations of aboriginal species of the genus Bidens by North American beggar ticks Bidens frondosa [109, 110, 112]. In other publications, in contrast, it is shown that invasions of non-native species take place without displacement of native species [95, 122, 123]. The process of adventization of the vegetation often results in replacement of native dominants by adventive ones. There are at least two reasons that can lead to the decrease in the species richness of plant communities. Firstly, alien

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

431

species of plants can be stronger competitors as compared to native species dominating in such habitats and reach a higher abundance and higher level of dominance. The higher the level of dominance, the lesser resources are available for accompanying species and the lower their possible abundance and species richness of the community [124–126]. Secondly, the role of some alien species in the ecosystem formation (by selective use of mineral resources, changing of the light regime, physical and chemical properties of soils, allelopathy, etc.) can hinder the growth of certain aboriginal species in the cenoses that are common for such habitats [127–129]. As a result, the communities with the predominance of alien plants can contain a fewer number of species than the initial ones even if the abundance of native dominants in the initial communities was high as well. The bioecological features of alien species and the complex, not yet completely understood nature of dominance make it difficult to predict probable consequences of the replacement of native by alien dominants. This follows from the results of field studies, which show that alien dominants can have a significant effect on the species richness and composition of phytocenoses [128, 130–132] and, on the other hand, that such an effect may be absent [129, 133, 134]. In the Western Caucasus, the alien species of plants often become dominant in tree and shrub layers of riparian and floodplain forests and in the communities of fallow lands. In the course of field studies (2007–2010), we revealed and described plots of riparian forests where the tree layer was dominated by alien species Robinia pseudoacacia or Acer negundo and shrub layer – by alien species Amorpha fruticosa and plots of fallow lands with the predominance of different adventives herbaceous species (Erigeron annuus, Solidago canadensis, Ambrosia artemisiifolia, Asclepias syriaca, etc.). Data on invaded communities were compared with those on similar communities dominated by native species growing in analogous habitats (taken as reference communities). The patterns of species abundance distribution in plots (communities) dominated by alien and native species were compared by plotting and analyzing average logarithmic rank – abundance diagrams for the groups of plots, with the X axis showing the abundance rank (the most abundant species is given rank 1, the second most abundant is 2, etc.) and the Y axis showing the average abundance of corresponding species on a log scale [119, 135]. The results show that in forest areas where Acer negundo and Amorpha fruticosa prevailed in the tree and shrub layers, the level of dominance was higher, while species richness and average density of associated species were lower than in reference areas. The relative abundance of Robinia pseudoacacia in the stands of riparian forests is close to the relative abundance of native dominants in corresponding reference areas, and differences in species richness between the invaded and reference communities are also insignificant [119] (Fig. 8). The fallow communities with more abundant alien dominants are characterized by lesser number of native species and higher number of adventive ones. However, the relations between all these characteristics are poor. Adventive dominants do not have also a significant specific effect on the species composition of fallow communities [135].

100

V. V. Akatov and T. V. Akatova

10 1 0.1

3

4

100

2

5 6 Species Rank

7

8

9

10

- Dominance of Foreign Species

(b)

- Dominance of Foreign Species

0.1

1

10

- Dominance of Native Species

0.01

Log 10 of the Number of Individuals

- Dominance of Foreign Species - Dominance of Native Species

1

2

3

4

5 6 Species Rank

7

8

9

10

100

1

- Dominance of Foreign Species

(c)

- Dominance of Foreign Species

0.1

1

10

- Dominance of Native Species

0.01

Log 10 of the Number of Individuals

- Dominance of Foreign Species

(a)

0.01

Log 10 of the Number of Individuals

432

1

2

3

4

5 6 Species Rank

7

8

9

10

Fig. 8 Logarithmic rank – abundance diagrams for species recorded in the tree and shrub layers of riparian forests in plots dominated by (a) Robinia pseudoacacia, (b) Acer negundo, or

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

433

6 Conclusions Over the past 30 years in the mountainous regions of Adygea, there has been a slight upward shift of the boundary of distribution of Betula litwinowii (timberline) and some deciduous species of trees (Acer platanoides, Ulmus glabra, and Acer pseudoplatanus) as a result of global warming. However, this trend is not universal, and in some mountain ranges, the borders of distribution of these species remain stable. Climatic changes have not yet led to the change in altitude area of Abies nordmanniana, but have created prerequisites to reduce it in the future, both at the upper and lower limits. Long grazing on Lago-Naki Plateau has led to the significant transformation of the communities of subalpine meadows. For the last 15–20 years of “rest,” the subalpine communities have changed mainly toward the pre-pasture state. Most plant species resistant to pasture have decreased the frequency, and some species characteristic for natural subalpine meadows have increased it. But climax dominants of the subalpine meadows have restored only on some sites, and species richness of communities has decreased. Post-pasture changes in alpine plant communities are confined by the growth of the occurrence of a number of species more typical for the communities of the subalpine zone. Strengthening their position may be due to the recovering processes, as well as climate changes. Currently, in the Republic of Adygea, 64 non-native plant species are recorded. The highest number is observed at the low elevations, but some of them grow in the middle and upper mountain forests zones, and only one alien species was found above timberline. Higher up in the mountains, non-native species move along river shoals and roadsides, and in recent years this process has accelerated, as well as the growth of their number within the occupied altitudinal interval. The greatest numbers of adventive species grow in periodically disturbed habitats. Saturation of the plant communities with these species is largely related to their local species richness and the size of the species pool of native and non-native species. The relative competitiveness of non-native species relative to native species is not the same in different types of communities. In many parts of the plant communities, where alien species have become dominant, species richness has decreased. Acknowledgments The research was done in the framework of the Russian Foundation for Basic Research Grant N 20-04-00364.

 ⁄ Fig. 8 (continued) (c) Amorpha fruticosa and in reference plots dominated by native species [119]. Dark circles, dashed lines areas with domination of foreign species; solid lines native species

434

V. V. Akatov and T. V. Akatova

References 1. Shiffers EV (1953) The vegetation of the North Caucasus and its natural feeding grounds. Izdatel’stvo AN SSSR, Moscow, 400 pp (in Russian) 2. Onipchenko VG, Thompson K (2002) In: Thompson K (ed) Alpine vegetation of the Teberda Reserve, the Northwestern Caucasus: Die Alpine Vegetation Des Teberda Reservates, Nordwest-Kaukasus. Geobotanisches Institut der ETH, Stiftung Rübel, Zürich, 168 pp 3. Golgofskaya KY (1967) Vegetation cover of the upper limit forest land in Caucasian reserve. Botanicheskij zhurnal 52(2):202–214 (in Russian) 4. Orlov AY (1951) Dark coniferous forests of the Northern Caucasus. Izdatel’stvo AN SSSR, Moscow, 254 pp (in Russian) 5. Golgofskaya KY (1967) Types of the beechen and fir woods in the basin of the Belaya river and their classification. Trudy Kavkazskogo zapovednika (9):157–284 (in Russian) 6. Orlov YA (1953) Beech forests of the North-Western Caucasus. Broad-leaved forests of the North-Western Caucasus. Izdatel’stvo AN SSSR, Moscow, pp 244–381 (in Russian) 7. Grudzinskaya IA (1953) Broad-leaved woods in the foothills of the Northwest Caucasus. Broad-leaved woods of the Northwest Caucasus. Izdatel’stvo AN SSSR, Moscow, pp 5–187 (in Russian) 8. Gorchakovsky PL, Shiyatov SG (1985) Phytoindication of the environment conditions and natural processes in highlands. Nauka, Moscow, 208 pp (in Russian) 9. Kronberg BI, Watt MJ (2000) The precariousness of north American boreal forest. Environ Monit Assess 62(3):261–272. https://doi.org/10.1023/A:1006201904039 10. Shugart HH, French NHF, Kasischke ES, Slawski JJ, Dull CW, Shuchman RA, Mwangi J (2001) Detection of vegetation change using reconnaissance imagery. Glob Change Biol 7(3):247–252. https://doi.org/10.1046/j.1365-2486.2001.00379.x 11. Kullman L (2002) Rapid recent range-margin rise of tree and shrub species in the Swedish Scandes. J Ecol 90(1):68–77. https://doi.org/10.1046/j.0022-0477.2001.00630.x 12. Shiyatov SG, Terent’ev MM, Fomin VV (2005) Spatiotemporal dynamics of forest-tundra communities in the Polar Urals. Russ J Ecol 36(2):69–75. https://doi.org/10.1007/s11184-0050051-9 13. Kapralov DS, Shiyatov SG, Moiseev PA, Fomin VV (2006) Changes in the composition, structure, and altitudinal distribution of low forests at the upper limit of their growth in the Northern Ural Mountains. Russ J Ecol 37(6):367–372. https://doi.org/10.1134/ S1067413606060014 14. Gvozdetskiy NA (1963) Caucasus. Essay on the nature. Gosudarstvennoe izdatel’stvo geograficheskoi literatury, Moscow, 264 pp (in Russian) 15. Kostianoy AG, Serykh IV, Lebedev SA, Kostianaia EA (2020) Regional climate change in Republic of Adygea. In: Bedanokov M, Lebedev S, Kostianoy A (eds) The Republic of Adygea Environment. Springer, Cham 16. Panov VD (2000) Climatic conditions and ecological status of the highland areas of Karachaevo-Circassian Republic, vol 3: Evaluation of ecological condition in highland and foothill ecosystems of Caucasus. Izdatel’stvo “Kavkazskij kraj”, Stavropol, pp 53–62 (in Russian) 17. Alexandrova VD (1964) Dynamics of vegetation. Field geobotany. Nauka, Мoscow, pp 300–432 (in Russian) 18. Leac WB, Craber RE (1974) A method for detecting migration of forest vegetation. Ecology 55(6):1425–1427. https://doi.org/10.2307/1935472 19. Akatov PV, Akatov VV (2010) Trends of changes in the upper limits of Eagle Claw Maple distribution in the Northwestern Caucasus. Lesovedenie 5:12–19 (in Russian) 20. Akatov PV (2012) The change of the upper limits of trees extending on the Western Caucasus. LAP Lambert Academic Publishing, Saarbrücken, 100 pp (in Russian)

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

435

21. Akatov VV, Akatov PV, Maiyorov SV (2013) Trends in Altitude Area of Nordmann fir in the Western Caucasus (Basin of Belaya River) in the Relation with the Global Warming Issue. Izvestija RAN, Serija geograficheskaja 2:104–114 (in Russian) 22. Onischenko VV, Salpagarov DS, Salpagarov AD (2000) Some results of integrated environmental studies of the highlands of North-Western Caucasus directed to strengthening the role of the association in formulating the regional policy of nature management and environment protection, vol 3: Evaluation of ecological condition in highland and foothill ecosystems of Caucasus. Izdatel’stvo “Kavkazskij kraj”, Stavropol, pp 11–28 (in Russian) 23. Zhivotov AD (2008) The dynamics of meteorological parameters in the territory of Caucasian Biosphere Reserve (1985–2005). Proc Caucasian State Wildlife Biosphere Res 18:6–22 (in Russian) 24. Grabenko EA (2013) Variability of forest vegetation in the conditions of the reserve mode on the Western Caucasus. PhD thesis abstract, Moscow, 26 pp (in Russian) 25. Solomina ON, Maksimova OE (2010) Study of three-rings of spruce at the timberline as a source of Paleoclimatic data for Tien Shan Mountains (Kyrgiztan Republic). Izvestija RAN, Serija geograficheskaja 6:54–66 (in Russian) 26. Holtmeier F-K, Broll G (2005) Sensitivity and response of northern hemisphere altitudinal and polar treelines to environmental change at landscape and local scales. Glob Ecol Biogeogr 14(5):395–410. https://doi.org/10.1111/j.1466-822X.2005.00168.x 27. Walther G-R (2003) Plants in a warmer world. Perspect Plant Ecol Evol Syst 6(3):169–185. https://doi.org/10.1078/1433-8319-00076 28. Kullman L (2004) Tree-limit landscape evolution at the southern fringe of the Swedish Scandes (Dalarna province) – Holocene and 20th century perspectives. Fennia-Int J Geogr 182(2):73–94 29. Harsch MA, Hulme PE, McGlone MS, Duncan RP (2009) Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol Lett 12(10):1040–1049. https:// doi.org/10.1111/j.1461-0248.2009.01355.x 30. Callaghan TV, Velichko AA, Borisova OK (2010) Tundra in changing climate. Bulletin of the Russian Academy of Sciences. Geography 4:17–27 (in Russian) 31. Shilov MP, Urazov IR (1984) Meadows protection. Publishing House of the Ivanovo State University, Ivanovo, 95 pp (in Russian) 32. Frank DA, McNaughton SJ, Tracy BF (1998) The ecology of the earth’s grazing ecosystems. Bioscience 48(7):513–521. https://doi.org/10.2307/1313313 33. Cingolani AM, Renison D, Tecco PA, Gurvich DE, Cabido M (2008) Predicting cover types in a mountain range with long evolutionary grazing history: a GIS approach. J Biogeogr 35(3):538–551. https://doi.org/10.1111/j.1365-2699.2007.01807.x 34. Deng L, Sweeney S, Shangguan Z-P (2013) Grassland responses to grazing disturbance: plant diversity changes with grazing intensity in a desert steppe. Grass Forage Sci 69(3):524–533. https://doi.org/10.1111/gfs.12065 35. Austin D, Urness PJ, Riggs RA (1986) Vegetal change in the absence of livestock grazing, Mountain Brush Zone, Utah. J Range Manag 39(6):514–517. https://doi.org/10.2307/3898761 36. Peco B, Sánchez AM, Azcárate FM (2006) Abandonment in grazing systems: consequences for vegetation and soil. Agric Ecosyst Environ 113(1–4):284–294. https://doi.org/10.1016/j. agee.2005.09.017 37. Gellrich M, Baur P, Koch B, Zimmermann NE (2007) Agricultural land abandonment and natural forest re-growth in the Swiss mountains: a spatially explicit economic analysis. Agric Ecosyst Environ 118(1–4):93–108. https://doi.org/10.1016/j.agee.2006.05.001 38. Başnou C, Pino J, Šmilauer P (2009) Effect of grazing on grasslands in the Western Romanian Сarpathians depends on the bedrock type. Preslia 81(2):91–104 39. Kandalova GT, Lysanova GI (2010) Rehabilitation of steppe pastures of Khakassia. Geogr Nat Resour 31(4):356–361. https://doi.org/10.1016/j.gnr.2010.11.009

436

V. V. Akatov and T. V. Akatova

40. Golodets C, Kigel J, Sternberg M (2010) Recovery of plant species composition and ecosystem function after cessation of grazing in a Mediterranean grassland. Plant Soil 329(1):365–378. https://doi.org/10.1007/s11104-009-0164-1 41. Vassilev K, Pedashenko H, Nikolov SC, Apostolova I, Dengler J (2011) Effect of land abandonment on the vegetation of upland semi-natural grasslands in the Western Balkan Mts., Bulgaria. Plant Biosyst 145(3):654–665. https://doi.org/10.1080/11263504.2011. 601337 42. Mirkin BM, Naumova LG (2012) Modern condition of the basic concepts of the science of vegetation. AN RB, Guilem, Ufa, 488 pp (in Russian) 43. Setten G, Austrheim G (2012) Changes in land use and landscape dynamics in mountains of northern Europe: challenges for science, management and conservation. Int J Biodivers Sci Ecosyst Serv Manag 8(4):287–291. https://doi.org/10.1080/21513732.2012.738094 44. Navarro LM, Pereira HM (2012) Rewilding abandoned landscapes in Europe. Ecosystems 15(6):900–912. https://doi.org/10.1007/s10021-012-9558-7 45. Pornaro C, Schneider MK, Macolino S (2013) Plant species loss due to forest succession in Alpine pastures depends on site conditions and observation scale. Biol Conserv 161:213–222. https://doi.org/10.1016/j.biocon.2013.02.019 46. MacDonald D, Crabtree JR, Wiesinger G, Dax T, Stamou N, Fleury P, Lazpita JG, Gibon A (2000) Agricultural abandonment in mountain areas of Europe: environmental consequences and policy response. J Environ Manag 59(1):47–69. https://doi.org/10.1006/jema.1999.0335 47. Baumann M, Kuemmerle T, Elbakidze M, Ozdogan M, Radeloff VC, Keuler NS, Prishchepov AV, Kruhlov I, Hostert P (2011) Patterns and drivers of post-socialist farmland abandonment in Western Ukraine. Land Use Policy 28(3):552–562. https://doi.org/10.1016/j. landusepol.2010.11.003 48. Plieninger T, Hui C, Gaertner M, Huntsinger L (2014) The impact of land abandonment on species richness and abundance in the Mediterranean вasin: a meta-analysis. PLoS One 9(5):e98355. https://doi.org/10.1371/journal.pone.0098355 49. Yunusbaev UB, Bashirov EV, Musina LB (2001) Peculiarities of pasture digression and reestablishment of the Trans-Urals steppes. In: Chibilev AA et al (eds) Modern dynamics of ecosystems’ components in the desert-steppe regions of Russia. Academy of Agricultural Sciences, Moscow, pp 75–83 (in Russian) 50. Gorcharuk LG, Semagina RN (1985) The influence of economic activity on the highland meadows of the Western Caucasus. Ecological researches in the Caucasian Biosphere Reserve. Publishing House of the Rostov State University, Rostov, pp 130–145 (in Russian) 51. Barrio IC, Bueno CG, Nagy L, Palacio S, Grau O, Munilla I, García MB, Garcia-Cervigón AI, Gartzia M, Gazol A, Lara-Romero C, Anadon-Rosell A, Ninot JM, Chocarro C, Alados CL, Fillat F, Zamora R (2013) Alpine ecology in the Iberian Peninsula: what do we know, and what do we need to learn? Mt Res Dev 33(4):437–442. https://doi.org/10.1659/MRD-JOURNALD-13-00052.1 52. Kononov VN (1960) Natural forage lands of the basin of the upper reaches of the Kuban, the issues of their protection and economic use. In: Materials of study the flora and vegetation of the highlands, vol 5: Problems of botany. Publishing House of the Academy of Sciences of the USSR, Moscow, pp 140–148 (in Russian) 53. Persson S (1984) Vegetation development after the exclusion of grazing cattle in a meadow area in the south of Sweden. Vegetatio 55(2):65–92. https://doi.org/10.1007/BF00037330 54. Zhang W (1998) Changes in species diversity and canopy cover in steppe vegetation in Inner Mongolia under protection from grazing. Biodivers Conserv 7(10):1365–1381. https://doi.org/ 10.1023/A:1008852017493 55. Schultz NL, Morgan JW, Lunt ID (2011) Effects of grazing exclusion on plant species richness and phytomass accumulation vary across a regional productivity gradient. J Veg Sci 22(1):130–142. https://doi.org/10.1111/j.1654-1103.2010.01235.x

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

437

56. Wu J, Zhang X, Shen Z, Shi P, Yu C, Song M, Li X (2012) Species richness and diversity of alpine grasslands on the Northern Tibetan Plateau: effects of grazing exclusion and growing season precipitation. J Resour Ecol 3(3):236–242. https://doi.org/10.5814/j.issn.1674-764x. 2012.03.006 57. Catorci A, Cesaretti S, Gatti R (2013) Effect of long-term abandonment and spring grazing on floristic and functional composition of dry grasslands in a Central Apennine farmland. Pol J Ecol 61(3):505–518 58. Miller GR, Geddes C, Mardon DK (1999) Response of the alpine gentian Gentiana nivalis L. to protection from grazing by sheep. Biol Conserv 87(3):311–318. https://doi.org/10.1016/ S0006-3207(98)00076-7 59. Rabotnov TA (1984) Grassland ecology. Publishing House of MSU, Мoscow, 320 pp (in Russian) 60. Ewans R (2004) Effects of removing grazing from native grasslands in the eastern South Island of New Zealand: a literature review. DOC Science Internal Series 168. Department of Conservation, Wellington, 25 pp 61. Valone TJ, Sauter P (2005) Effects of long-term cattle exclosure on vegetation and rodents at a desertified arid grassland site. J Arid Environ 61(1):161–170. https://doi.org/10.1016/j. jaridenv.2004.07.011 62. Gadzhiev VD (1979) Recovery peculiarities of communities in the highlands of the Caucasus. Problems of botany, vol 14(1). Flora and vegetation of the highlands. Nauka, Novosibirsk, 1979, pp 178–182 (in Russian) 63. Soussana J-F, Duru M (2007) Grassland science in Europe facing new challenges: biodiversity and global environmental change. CAB Rev Perspect Agric Vet Sci Nutr Nat Resour 72(1):1–11 64. Metera E, Sakowski T, Słoniewski K, Romanowicz B (2010) Grazing as a tool to maintain biodiversity of grassland – a review. Anim Sci Paper Rep 28(4):315–334 65. Akatov VV, Akatova TV (1991) About the modern state of phytogene pool of alpine meadows and lichen heathlands on highland pastures of the Western Caucasus. In: Mountainous ecosystems under the man influence, Proceedings of the Mountain Geophysical Institute, vol 84, pp 114–124 (in Russian) 66. Akatov VV, Akatova TV, Glushkov VD (2002) The influence of the cattle pasture on the specific structure and richness of subalpine phytocenosis of Lago-Naki plateau (The Western Caucasus). Proc Caucasian State Wildlife Biosphere Res 16:310–317 (in Russian) 67. Akatov VV, Akatova TV (2012) Changes of phytocenosis in highland meadows and heathlands of Lago-Naki plateau (the West Caucasus) for the last 15–20 years. Veg Russ 21:3–12 (in Russian) 68. Tumadzhanov II (1963) Experience of the fractional geobotanical zoning of the Northern slope of the Caucasus mountains (on the example of Karachay). Metsniereba, Tbilisi, 240 pp (in Russian) 69. Grabherr G, Gottfried M, Pauli H (2001) Long-term monitoring of mountain peaks in the Alps. In: Burga CA, Kratochwil A (eds) Biomonitoring: general and applied aspects on regional and global scales (Tasks for vegetation science 35). Kluwer, Dordrecht, pp 153–177. https://doi. org/10.1007/978-94-015-9686-2_10 70. Pauli H, Gottfried M, Grabherr G (2003) Effects of climate change on the alpine and nival vegetation of the Alps. J Mt Ecol 7:9–12 71. Burga CA, Frauenfelder R, Ruffet J, Hoelzle M, Kääb A (2004) Vegetation on Alpine rock glacier surfaces: a contribution to abundance and dynamics on extreme plant habitats. Flora 199(6):505–515. https://doi.org/10.1078/0367-2530-00179 72. Klanderud K, Birks HJB (2003) Recent increases in species richness and shifts in altitudinal distributions of Norwegian mountain plants. Holocene 13(1):1–6. https://doi.org/10.1191/ 0959683603hl589ft

438

V. V. Akatov and T. V. Akatova

73. Odland A, Høitomt T, Olsen SL (2010) Increasing vascular plant richness on 13 high mountain summits in southern Norway since early 1970s. Arct Antarct Alp Res 42(4):458–470. https:// doi.org/10.1657/1938-4246-42.4.458 74. Michelsen O, Syverhuset AO, Pedersen B, Holten JI (2011) The impact of climate change on recent vegetation changes on Dovrefjell, Norway. Diversity 3(1):91–111. https://doi.org/10. 3390/d3010091 75. Kobiv YY (2009) Global climate change as a threat to the species biodiversity in the highmountain zone of the Ukrainian Carpathians. Ukr Bot J 66(4):451–465 76. Elumeeva TG, Onipchenko VG, Egorov AV, Khubiev AB, Tekeev DK, Soudzilovskaia NA, Cornelissen JHC (2013) Long-term vegetation dynamic in the Northwestern Caucasus: which communities are more affected by upward shifts of plant species? Alp Bot 123(2):77–85. https://doi.org/10.1007/s00035-013-0122-7 77. Kammer PM, Schöb C, Choler P (2007) Increasing species richness on mountain summits: upward migration due to anthropogenic climate change or re-colonisation? J Veg Sci 18(2):301–306. https://doi.org/10.1111/j.1654-1103.2007.tb02541.x 78. Richardson DM, Pyšek P (2006) Plant invasions: merging the concepts of species invasiveness and community invisibility. Prog Phys Geogr 30(3):409–431. https://doi.org/10.1191/ 0309133306pp490pr 79. Lambdon PW, Pyšek P, Basnou C, Arianoutsou M, Essl F, Hejda M, Jarošík V, Pergl J, Winter M, Anastasiu P, Andriopoulos P, Bazos I, Brundu G, Celesti-Grapow L, Chassot P, Delipetrou P, Josefsson M, Kark S, Klotz S, Kokkoris Y, Kühn I, Marchante H, Perglová I, Pino J, Vilà M, Zikos A, Roy D, Hulme PE (2008) Alien flora of Europe: species diversity, temporal trends, geographical patterns and research needs. Preslia 80(2):101–149 80. Sudnik-Wójcikowska B, Moysiyenko I, Slim PA, Moraczewski IR (2009) Impact of the invasive species Elaeagnus angustifolia L. on vegetation in Pontic desert steppe zone (Southern Ukraine). Pol J Ecol 57(2):269–281 81. Borisova EA (2010) Invasive plant species distribution un the Upper Volga basin. Russ J Biol Invasions 3(4):2–9 (in Russian) 82. Mirkin BM, Naumova LG (2002) The vegetation adventivisation through perspective of modern ecological ideas. J Gen Biol 63(6):500–508 (in Russian) 83. Qian H, Ricklefs RE (2006) The role of exotic species in homogenizing the North American flora. Ecol Lett 9(12):1293–1298. https://doi.org/10.1111/j.1461-0248.2006.00982.x 84. Vinogradova YK, Maiyorov SR, Khorun LV (2009) The black book of flora of the central Russia (Alien plant species in ecosystems of Central Russia). GEOS, Moscow, 494 pp (in Russian) 85. Davis MA, Thompson K, Grime JP (2005) Invasibility: the local mechanism driving community assembly and species diversity. Ecography 28(5):696–704. https://doi.org/10. 1111/j.2005.0906-7590.04205.x 86. Davis MA, Grime JP, Thompson K (2000) Fluctuating resources in plant communities: a general theory of invisibility. J Ecol 88(3):528–536. https://doi.org/10.1046/j.1365-2745. 2000.00473.x 87. Levine JM, Vila M, DʼAntonio CM, Dukes JS, Grigulis K, Lavorel S (2003) Mechanisms underlying the impacts of exotic plant invasion. Proc R Soc Lond B 270:775–781. https://doi. org/10.1098/rspb.2003.2327 88. Schwartz EA (2004) Conservation of biodiversity: communities and ecosystems. Association of Scientific Editions KMK, Moscow, 111 pp (in Russian) 89. Sharma GP, Singh JS, Raghubanshi AS (2005) Plant invasions: emerging trends and future implications. Curr Sci 88(5):726–734 90. Akatova TV, Akatov VV (2007) Recreation and distribution of adventitious species of vascular plants in the North-Western Caucasus. Materials of the 2-nd International (Russian-German) scientific-practical conference. Prospects of the development of especially protected natural territories and tourism in the Northern Caucasus, Maykop, pp 253–255 (in Russian)

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

439

91. Akatova TV, Akatov VV (2013) The expansion of adventitious sorts of plants in the Caucasian Reserve. Proc Caucasian State Wildlife Biosphere Reserve 20:84–110 (in Russian) 92. Akatova TV, Akatov VV, Eskina TG, Zagurnaya YS (2009) On extensivity of some adventitious species of grassy plants on the Western Caucasus. Ecol Bull North Caucasus 5(2):41–50 (in Russian) 93. Akatov VV, Akatova TV, Grabenko ЕА (2014) The changes of upper bound extension of Black Locust and Maple in the valley of the Belaya river (The Western Caucasus). Forest Sci 1:21–33 (in Russian) 94. Rejmánek M (1989) Invasibility of plant communities. In: Drake JA, Mooney HA, di Castri F, Groves RH, Kruger FJ, Rejmanek M, Williamson M (eds) Biological invasions: a global perspective. SCOPE Report No 37. Wiley, New York, pp 369–388 95. Rejmánek M, Richardson DM, Pyšek P (2005) Plant invasions and invisibility of plant communities. In: van der Maarel E, Franklin J (eds) Vegetation ecology. Blackwell, Oxford, pp 332–355. https://doi.org/10.1002/9781118452592.ch13 96. Pyšek P, Chytrý M, Jarošík V (2010) Habitats and land use as determinants of plant invasions in the temperate zone of Europe. In: Perrings C, Mooney H, Williamson M (eds) Bioinvasions and globalization. Ecology, economics, management, and policy. Oxford University Press, Oxford, pp 66–79. https://doi.org/10.1093/acprof:oso/9780199560158.003.0006 97. Elton CS (1958) The ecology of invasions by animals and plants. Methuen, London, 181 pp 98. Stohlgren TJ (2002) Beyond theories of plant invasions: lessons from natural landscapes. Comments Theor Biol 7(6):355–379. https://doi.org/10.1080/08948550290022385 99. Sax DF, Brown JH (2000) The paradox of invasion. Glob Ecol Biogeogr 9(5):363–371. https://doi.org/10.1046/j.1365-2699.2000.00217.x 100. Brown RL, Peet RK (2003) Diversity and invasibility of Southern Appalachian plant communities. Ecology 84(1):32–39. https://doi.org/10.1890/0012-9658(2003)084[0032: DAIOSA]2.0.CO;2 101. Gilbert B, Lechowicz MJ (2005) Invasibility and abiotic gradients: the positive correlation between native and exotic plant diversity. Ecology 86(7):1848–1855. https://doi.org/10.1890/ 04-09997 102. Stohlgren TJ, Barnett DT, Jarnevich CS et al (2008) The myth of plant species saturation. Ecol Lett 11(4):313–326. https://doi.org/10.1111/j.1461-0248.2008.01153.x 103. Moore JL, Mouquet N, Lawton JH, Loreau M (2001) Coexistence, saturation and invasion resistance in simulated plant assemblages. Oikos 94(2):303–314. https://doi.org/10.1034/j. 1600-0706.2001.940211.x 104. Smith MD, Knapp AK (2001) Size of the local species pool determines invasibility of a C4-dominated grassland. Oikos 92(1):55–61. https://doi.org/10.1034/j.1600-0706.2001. 920107.x 105. Herben T (2005) Species pool size and invasibility of island communities: a null model of sampling effects. Ecol Lett 8(9):909–917. https://doi.org/10.1111/j.1461-0248.2005.00790.x 106. Daehler CC (2006) Invisibility of tropical islands by introduced plants: partitioning the influence of isolation and propagule pressure. Preslia 78(4):389–404 107. Ricklefs RE (1987) Community diversity: relative roles of local and regional processes. Science 235(4785):167–171. https://doi.org/10.1126/science.235.4785.167 108. Meekins JF, McCarthy BC (2001) Effect of environmental variation on the invasive success of a nonindigenous forest herb. Ecol Appl 11(5):1336–1348. https://doi.org/10.2307/3060924 109. Vinogradova YK (2003) Laboratory investigation of the plant invasions (by the example of Genus Bidens). In Problems of study the adventitious and seminatural flora in the regions of the Commonwealth of Independent States (CIS). Botanical Garden of Moscow State University, Moscow, pp 31–33 (in Russian) 110. Vinogradova YK, Vozna LI (2008) Invasibility of natural phytocenosises and competitive relationships between local and invasive species. In Biodiversity: problems and prospects of preservation. Penza State Pedagogical University, Penza, V.G. Belinsky, pp 17–19 (in Russian)

440

V. V. Akatov and T. V. Akatova

111. Ovcharenko AA, Zolotukhin AI (2003) On the distribution of Acer negundo L. and Fraxinus pennsilvanica Marsh. In the floodplain forests of the steppe zone of Balashov area (Saratov region). In Problems of study the adventitious and seminatural flora in the regions of the Commonwealth of Independent States (CIS). Botanical garden of Moscow State University, Moscow, pp 75–76 (in Russian) 112. Vasilieva NV, Papchenkov VG (2011) Mechanisms of influence invasion Bidens frondosa L. on native bur-marigold species. Russ J Biol Invasions 4(1):15–22 (in Russian) 113. Daehler CC (2003) Performance, comparisons of co-occurring native and alien invasive plants: implications for conservation and restoration. Annu Rev Ecol Evol Syst 34:183–211. https://doi.org/10.1146/annurev.ecolsys.34.011802.132403 114. Akatov VV, Akatova TV (2010) Saturation and invasion resistance of non interactive plant communities. Russ J Ecol 41(3):229–236. https://doi.org/10.1134/S1067413610030069 115. Akatov VV, Akatova TV (2013) Species pool, species richness, density compensation effect, and invasibility of plant communities. Russ J Biol Invasions 4(1):1–11. https://doi.org/10. 1134/S2075111713010025 116. Akatov VV, Akatova TV, Chefranov SG, Shadzhe AЕ (2009) The level of complete integration and the potential of plant communities invisibility: a hypothesis of correlation of the species funds. Biol Bull Rev 70(4):328–340 (in Russian) 117. Akatov VV, Akatova TV, Eskina TG (2010) Factors of variation in the number of adventive species in herbaceous communities of the Western Caucasus. Russ J Ecol 41(5):386–392. https://doi.org/10.1134/S1067413610050048 118. Akatov VV, Akatova TV, Eskina TG, Zagurnaya YS (2012) Relative competitiveness of adventive plant species in herbaceous communities of the Western Caucasus. Russ J Biol Invasions 3(4):235–242. https://doi.org/10.1134/S2075111712040029 119. Akatov VV, Akatova TV, Shadzhe AE (2012) Species richness of tree and shrub layers in riparian forests of the Western Caucasus dominated by alien species. Russ J Ecol 43(4):294–301. https://doi.org/10.1134/S1067413612040030 120. Nikolaev I (1979) Implications of unforeseen anthropogenic dispersal of aquatic fauna and flora. Ecological forecasting. Nauka, Moscow, pp 76–93 (in Russian) 121. Neronov VM, Lushekina AA (2001) Alien species and the conservation of biological diversity. Biol Bull Rev 121(1):121–128 (in Russian) 122. Ricklefs RE, Schluter D (1993) Species diversity: regional and historical influences. In: Ricklefs RE, Schluter D (eds) Species diversity in ecological communities: historical and geographical perspectives. University of Chicago Press, Chicago, pp 350–362 123. Sax DF, Gaines SD (2003) Species diversity: from global decreases to local increases. Trends Ecol Evol 18(11):561–566. https://doi.org/10.1016/S0169-5347(03)00224-6 124. Mirkin BM, Naumova LG (1998) Science of vegetation (the history and current state of the main concepts). Guilem, Ufa, p 413 (in Russian) 125. McKane RB, Johnson LC, Shaver GR, Nadelhoffer KJ, Rastetter EB, Fry B, Giblin AE, Kielland K, Kwlatkowski BL, Laundre JA, Murray G (2002) Resource-based niches provide a basis for plant species diversity and dominance in arctic tundra. Nature 415:68–71. https://doi. org/10.1038/415068a 126. Kunte K (2008) Competition and species diversity: removal of dominant species: increases diversity in Costa Rican butterfly communities. Oikos 117:69–76. https://doi.org/10.1111/j. 2007.0030-1299.16125.x 127. Callaway RM, Ridenour WM (2004) Novel weapons: invasive success and the evolution of increased competitive ability. Front Ecol Environ 2(8):433–436. https://doi.org/10.1890/15409295(2004)002[0436:NWISAT]2.0.CO;2 128. Reinhart KO, Greene E, Callaway RM (2005) Effects of Acer platanoides invasion on understory plant communities and tree regeneration in the Rocky Mountains. Ecography 28(5):573–582. https://doi.org/10.1111/j.2005.0906-7590.04166.x

Contemporary Changes of the Vegetation in the Mountainous Adygea as the. . .

441

129. Hulme PE, Bremner ET (2006) Assessing the impact of Impatiens glandulifera on riparian habitats: partitioning diversity components following species removal. J Appl Ecol 43(1):43–50. https://doi.org/10.1111/j.1365-2664.2005.01102.x 130. Meiners SJ, Pickett STA, Cadenasso ML (2001) Effects of plant invasions on the species richness of abandoned agricultural land. Ecography 24(6):633–644. https://doi.org/10.1111/j. 1600-0587.2001.tb00525.x 131. Silliman BR, Bertness MD (2004) Shoreline development drives invasion of Phragmites australis and the loss of plant diversity on New England salt marshes. Conserv Biol 18(5):1424–1434. https://doi.org/10.1111/j.1523-1739.2004.00112.x 132. Hejda M, Pysek P, Jaroík V (2009) Impact of invasive plants on the species richness, diversity and composition of invaded communities. J Ecol 97(3):393–403. https://doi.org/10.1111/j. 1365-2745.2009.01480.x 133. Houlahan JE, Findlay CS (2004) Effect of invasive plant species on temperate wetland plant diversity. Conserv Biol 18(4):1132–1138. https://doi.org/10.1111/j.1523-1739.2004.00391.x 134. Sagoff M (2005) Do non-native species threaten the natural environment? J Agric Environ Ethics 18(3):215–236. https://doi.org/10.1007/s10806-005-1500-y 135. Eskina TG, Akatov VV, Akatova TV (2012) Composition and species richness of fallow plant communities with predominance of adventive species (Belaya river basin, Western Caucasus). Russ J Biol Invasions 3(3):180–187. https://doi.org/10.1134/S2075111712030022

Seasonal and Interannual Variability of NDVI in the Republic of Adygea Sergey A. Lebedev, Andrey G. Kostianoy, Pavel N. Kravchenko, and Olga P. Shevyakova

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Spectral Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Vegetation Cover in the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 NDVI Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Seasonal and Interannual Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

444 445 447 449 451 457 458

Abstract Determination of the natural productivity of lands and permanent monitoring of their use require qualitatively new technical support and the use of more

S. A. Lebedev (*) Geophysical Center, Russian Academy of Sciences, Moscow, Russian Federation Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation Big Data Storage and Analysis Center at the Lomonosov Moscow State University Center for Digital Economy, Moscow, Russian Federation National Research University of Electronic Technology (MIET), Moscow, Russian Federation e-mail: [email protected] A. G. Kostianoy P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russian Federation S.Yu. Witte Moscow University, Moscow, Russian Federation e-mail: [email protected] P. N. Kravchenko Tver State University, Tver, Russian Federation e-mail: [email protected] O. P. Shevyakova Maykop State Technological University, Maykop, The Republic of Adygea, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 443–460, DOI 10.1007/698_2021_742, © Springer Nature Switzerland AG 2021, Published online: 25 February 2021

443

444

S. A. Lebedev et al.

advanced methods of operational assessment of the state of lands, determination of the quality of vegetation cover and the yield of crops grown. The article discusses various approaches to solving this problem. The focus is made on the analysis of the spatial, seasonal, and interannual variability of the Normalized Difference Vegetation Index (NDVI) on the territory of the Republic of Adygea. Keywords Interannual variability, Land cover, NDVI, Productivity of lands, Republic of Adygea, Satellite remote sensing, Seasonal variability, Vegetation cover

1 Introduction Agricultural production in the Republic of Adygea is the main branch of the economy, which predetermines the economic potential of the region, employment of most of the population and their standard of living [1, 2]. This area of production has a significant impact not only on the economy, but also on the entire surrounding nature. Taking care of natural resources and their rational use requires permanent monitoring of all territories where one or another type of human economic activity is carried out. A comprehensive assessment of land and water resources can apply not only to areas used and potentially suitable for agricultural production [3], but also to specially protected natural areas, where there is a need for the rehabilitation of disturbed lands [4]. The extensive use of agricultural land, which has become widespread in the Republic of Adygea, as in many regions of Russia in the past 20 years, often leads to soil degradation, the development of erosion processes [5], and in some places to the onset of the desert. A striking example of such a phenomenon is the development of desertification in some areas of the Astrakhan and Volgograd regions, in the Republics of Kalmykia, Dagestan, and Khakassia, where semi-deserts prone to wind and water erosion have arisen in place of previously productive natural pastures [6]. A complete inventory of land in use and the identification of lands suitable for agricultural production, a comprehensive assessment of soil and vegetation cover, an assessment of hydrological conditions and permanent monitoring of nature management in large areas are currently impossible without the use of new technical means. These currently include remote sensing of the Earth from special spacecraft (satellites) and aircrafts, which make it possible to survey huge territories and small areas of the Earth in periodic mode with different frequencies [7, 8]. Earth remote sensing (ERS) methods are currently widely used in the agroindustrial complex of many countries of the world (Russia, USA, Canada, EU countries, India, Japan, etc.). The most famous example of existing agricultural monitoring systems is the MARS project (The Monitoring of Agriculture with Remote Sensing), implemented by the European Commission’s Joint Research Center for agricultural land monitoring [9]. Remote sensing data and the corresponding software used by this Center allow determining the areas of land and crops, the condition of plants, and the yield of agricultural crops. The results of

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

445

remote sensing data processing are used to predict yields of various crops and the potential degree of market filling. All this makes it possible to develop measures to stabilize the level of profitability of agricultural producers through the use of a flexible system of prices, quotas, and export–import relations, to adjust tax policy [10]. Russia is developing a national Earth remote sensing space system for monitoring agricultural lands. The work is carried out within the framework of the State Program for the Development of Agriculture and Regulation of Agricultural Products, Raw Materials and Food Markets (2013–2020), approved by the Government Decree N 717 of July 14, 2012.

2 Spectral Indices

Visible Spectrum

Near-Infrared

Short-Wavelength Infrared

40 30 20 0

10

Reflectance (%)

50

60

A characteristic feature of vegetation and its condition is spectral reflectivity, characterized by large differences in the reflection of radiation at different wavelengths (Fig. 1). Knowledge of the relationship between the structure and state of vegetation and its spectral reflectivity makes it possible to use remote sensing data for mapping and identifying vegetation types and their stress state. However, it is difficult and very expensive to obtain a full spectral characteristic by the ERS method. ERS devices that exist today have several tens of channels or spectral bands, which are used for measurements [12], operating in the wavelength range of electromagnetic radiation from 0.2 μm (ultraviolet radiation) to 3 m (ultrashort radio waves). The section of optical wavelengths (0.2–1 μm) includes the ultraviolet range (0.29–0.40 μm), visible (0.40–0.75 μm), and infrared (0.75–100 μm) subranges. In turn, the infrared radiation range is usually further

0.5

1.0

1.5 Wavelength(µm)

2.0

Fig. 1 Spectral reflectivity of vascular plants (Source: modified from [11])

2.5

446

S. A. Lebedev et al.

subdivided into near 0.75–1.3 μm, average 1.3–3.5 μm, and far (thermal) 3.5–100 μm. However, they lack the spectral range and accuracy for the classification of objects and vegetation, which can only be provided by hyperspectral sensors [13]. The ability of hyperspectral sensors to measure in hundreds of spectral bands increases the cost of measurements from spacecraft and the complexity of dealing with the vast amount of data acquired. For this reason, in modern environmental monitoring systems based on remote sensing data, the so-called index images are created. Based on the combination of brightness values in certain spectral bands, which are informative for highlighting the object under study, the spectral index is calculated, which allows to select the object under study or assess its condition. Spectral indices used for studying and assessing the state of vegetation are commonly referred to as vegetation indices. Currently, there are about 160 variants of vegetation indices [14]. They are selected experimentally (empirically) based on the known features of the curves of the spectral reflectivity of vegetation and soils. The most detailed vegetation indices are considered in [15]. In this paper, a number of fundamental answers to the concept of vegetation index and a detailed analytical review with a detailed classification of types of vegetation indexes are given. The calculation of most of the vegetation indices is based on the two most stable (independent of other factors) sections of the spectral reflectance curve of plants (Fig. 1). The red zone of the spectrum (0.62–0.75 μm) accounts for the maximum absorption of solar radiation by chlorophyll, and the near infrared zone (0.75–1.3 μm) has the maximum energy reflection by the cellular structure of the leaf. Thus, high photosynthetic activity (associated, as a rule, with a large phytomass of vegetation) leads to lower coefficients of reflectance in the red zone of the spectrum and higher values in the near infrared. As is well known, the ratio of these indicators to each other makes it possible to clearly distinguish vegetation from other natural objects. The use of not a simple ratio, but a normalized difference between the minimum and maximum reflections increases the measurement accuracy, reduces the influence of such phenomena as differences in the image illumination, clouds, haze, absorption of radiation by the atmosphere, etc. Normalized Difference Vegetation Index (NDVI) is a simple quantitative measure of the amount of photosynthetically active biomass (commonly referred to as the vegetation index). It is one of the most common and used indices for solving problems using quantitative estimates of vegetation cover and various natural and artificial objects (Table 1) [16, 17]. NDVI is calculated by the following formula: NDVI ¼

NIR  RED NIR þ RED

where NIR is reflection in the near infrared band of spectrum, RED is reflection in the red band of spectrum. NDVI is measured between 1 and +1, which is very convenient for classification of land and vegetation cover. In general, negative values of NDVI (values approaching 1) correspond to water; values close to zero

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

447

Table 1 NDVI values for different natural and artificial objects Type of object Dense vegetation Sparse vegetation Open soil Clouds Snow and ice Water Artificial materials (concrete, asphalt)

Reflection in the red band of the spectrum 0.1 0.1 0.25 0.25 0.375 0.02 0.3

Reflection in the infrared band of spectrum 0.5 0.3 0.3 0.25 0.35 0.01 0.1

NDVI value 0.7 0.5 0.025 0 0.05 0.25 0.5

(0.1 to +0.1) correspond to barren areas of rock, sand, or snow; low positive values (approximately 0.2 to 0.4) represent shrub and grassland; and high values indicate temperate and tropical rainforests with high level of vegetation. As a rule, a scaled scale is used for tasks related to vegetation mapping. The following formula is used for this scaling: NDVIscaled ¼ 100  ðNDVI þ 1Þ:

3 Vegetation Cover in the Republic of Adygea A distinctive feature of the vegetation of the Republic of Adygea is its latitudinal and belt distribution (Fig. 2) [18]. In the flat part, the steppe and forest-steppe zones are widespread. With the rise in the mountains, they are naturally replaced by a mountain-forest belt, subalpine and alpine meadows, subnival vegetation of rocks and talus. The steppe zone occupies a relatively narrow strip of the Trans-Kuban inclined plain. At present, almost all of the steppes are plowed up and occupied by agricultural landscapes. Cereals and other agricultural crops are grown in the fields. Typical steppe plants are found only in hard-to-reach places. In areas with wetter soils, shortrhizome grasses are included in the herbage. The forest-steppe occupies the flat part of the interfluve of Belaya and Laba Rivers. It arose, probably, as a result of cutting down oak forests, which in the first half of the nineteenth century covered the entire left bank of the Kuban River. At present, the steppe areas are almost completely plowed up. Forest vegetation of the forest-steppe zone is confined to low areas of the terrain and river valleys. In composition, they are mixed broadleaf. They have a rich grass cover. Floodplains of rivers are accompanied by floodplain forests, flooded meadows, and areas of meadow-bog vegetation. The floodplain forests of the left tributaries of the Kuban are extremely diverse. The forests of the flooded part of the floodplain are

448

S. A. Lebedev et al.

Schematic Map of the Republic of Adygea Vegetation Cover

2

Krasnodar 45°

r da sno oir Kra serv Re

1 4

3 2

2 4 4 Khanskaya

2

Natyrbovo

Kuzhorskaya

Labinsk

Maykop 6

5

Apsheronsk

5

Tul'sky

6

4

3

6 7

6

Kamennomostsky

6

k ac Bl

Tuapse

a Se

Hamyshki

8

44°

9

8

Guzeripl

1

2

3

4

5

6

7

8

9

39°

9

40°

Fig. 2 Schematic Map of the Republic of Adygea Vegetation Cover based on data from [18] 1. Plavni and meadow-bog complexes, plowed in places 2. Floodplain meadows, partly with trees and shrubs, plowed in places 3. Foothill steppe meadows and meadow steppes, mostly plowed 4. Deforested gentle slopes and plumes with rare groups of trees and shrubs 5. Oak forests dominated by summer oak 6. Oak forests dominated by winter oak 7. Broad-leaved forests dominated by beech 8. Fir, fir-spruce forests 9. Subalpine meadows, mostly in combination with thickets of rhododendron, with crooked forests

characterized by the variegated composition of the trees and sparse grass cover. The foothill-forest-steppe latitudinal zone to the south of Maykop City is gradually transforming into landscapes of high-altitude zones. The forest belt is divided into low mountain (300–500 m above sea level) and medium (1,000–2,000 m), high mountain (from 1,000–1,200 to 1,800–2,000 m)

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

449

forests. At an altitude of 450–500 m, oak forests give way to a belt of beech forests. Rock oak and Caucasian hornbeam also grow. In the middle mountains, beech-fir and fir forests are widespread. The eastern spruce penetrates into the alpine dark coniferous forests – this is the western edge of its range. The height of 1,700–2,000 m on the border of mountain forests and subalpine meadows is framed by a narrow strip of birch and beech crooked forests. Subalpine meadows (1,800–2,500 m) are very diverse in composition. Here, on the border with the forest, tall grasses develop – a community of giant grasses reaching 2–3 m in height. The Alpine belt stretches from 2,000–2,400 m to 2,600–2,800 m. The subnival and nival belts begin above 2,900–3,000 m. There are only spots of lichens, mosses, specific rocks, and talus plants.

3.1

NDVI Variability

Traditionally, land cover mapping has been based on field survey data at key sites and extrapolation of the results to sites with similar physical and geographical characteristics. However, the combination of ground-based studies of vegetation cover with the analysis of remote sensing data makes it possible to create maps with a higher accuracy than using only one field study [19]. This made it possible to create maps of the distribution of forest communities in the Western Caucasus with the domination of certain types of trees, the modern boundary of continuous forests, as well as the location and shape of deforested areas within their boundaries, which may be of independent interest to ecologists, in particular, as a basis for monitoring forest vegetation and environmental studies of various profiles. An analysis of the variability of the NDVI index, calculated from individual images of the Operational Land Imager (OLI) of the Landsat-8 satellite for 18 April 2020 and 6 October 2018, showed the correspondence of the field survey data and the remote sensing data. The NDVI index was calculated from the data of Band 4 RED (0.64–0.67 μm) and Band 5 NIR (0.85–0.88 μm) with a spatial resolution of 30 m (Figs. 3 and 4). In the spring (image on April 18, 2020) (Fig. 3) fields sown with winter crops and located in the interfluve of the Belaya and Laba rivers (Giaginsky District, Koshekhabl’sky District, Krasnogvardeysky District, and Shovgenovsky District), the value of the NDVI index changes on average within 0.6–0.8 for winter fields, 0.4–0.6 – for early germination, and 0.1–0.4 – for irrigated fields and rice paddies. In the Adygeysk Republican Urban District, Takhtamukaysky District, and Teuchezhsky District, uncultivated fields (fallow) and rice paddies (located mainly near the downstream of the Krasnodar Reservoir), the NDVI index varies on average within 0.1–0.4. In the Maykopsky District, the maximum value of the NDVI index above 0.75 is observed in the zone of oak and broad-leaved forests (Fig. 2) due to the strengthening of deciduous cover. In the coniferous forest zone the NDVI is of 0.2–0.4. Snow cover is still observed on Lago-Naki Plateau at this time, but vegetation is already appearing on the alpine meadows.

450

S. A. Lebedev et al.

Fig. 3 NDVI on 18 April 2020 at 30 m spatial resolution for the territory of the Republic of Adygea (white line) and adjacent regions by OLI Landsat-8

In autumn (satellite image on 6 October 2018) (Fig. 4), when the entire crop harvest has already been harvested, in the entire plain and foothill parts of the Republic of Adygea, the value of the NDVI index varies on average within 0.1–0.5. In the zone of oak and broad-leaved forests (Fig. 2), the value of the NDVI index varies on average within the range of 0.4–0.5. Compared to spring, no significant changes were observed in the NDVI values in the coniferous forest zone. Snow cover is still observed on Lago-Naki Plateau at this time; however, due to the decrease in the meadow activity, areas of open rocks appear.

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

451

Fig. 4 NDVI on 6 October 2018 at 30 m spatial resolution for the territory of the Republic of Adygea (white line) and adjacent regions by OLI Landsat-8

3.2

Seasonal and Interannual Variability

To analyze the seasonal and interannual variability of the NDVI index, the territory of the Republic of Adygea in accordance with the Digital Elevation Model [20] was divided into three parts (Fig. 5): I – the flat part with the relief height less than 150 m; II – the foothill part with the relief height in the range of 150–400 m; III – the mountain part with the relief height more than 400 m. For the study, the data of the PROBA-V (PROBA-Vegetation) project were selected, which combined the ERS data in the visible range of the SPOT (Satellite Pour l’Observation de la Terre) satellites from 1999 to the present [21]. We used NDVI data with a spatial resolution

452

S. A. Lebedev et al.

The Republic of Adygea Territory Division According to Local Relief

b Ku

an

Ku ba

Krasnodar

n

45°

r da sno oir Kra serv Re

Natyrbovo Khanskaya

Kuzhorskaya

Labinsk

Maykop Apsheronsk

Tul'sky

Kamennomostsky

Tuapse Hamyshki

k ac Bl

44°

Guzeripl

a Se

I

II

III

39°

40°

Fig. 5 The Republic of Adygea territory division according to local relief: I – the flat part with the relief height less than 150 m, II – the foothill part with the relief height in the range of 150–400 m, III – the mountain part with the relief height more than 400 m

of 300 m. The analysis showed that for all parts of the territory of the Republic of Adygea, NDVI index variability has a pronounced seasonal character (Figs. 6 and 7).

3.2.1

Flat Part of the Republic

In the flat part of the Republic, the average long-term minimum value of the NDVI index of 0.3360 is observed in winter (Fig. 6) (Table 2). This is due to the fact that this region is agricultural (over 88% of the area [21]), where at this time of the year most of the fields and rice paddies are fallow, i.e. plowed up.

453

0.9 0.8 0.7 0.6 0.5

I - Flat Part (h < 150 m) II - Foothill Part (h 150-400 m) III - Mountain Part (h > 400 m)

0.3

0.4

Normalized Difference Vegetation Index (NDVI)

1.0

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

Jan

Feb

Mar

Apr

May

Jun Jul Date (year)

Aug

Sep

Oct

Nov

Dec

Fig. 6 Seasonal variability of the NDVI index for different regions of the Republic of Adygea

The average air temperature in winter (December–February) in this region is 2.9 С. The duration of the winter period is about 53 days: on average, from December 24 to February 15, which is characterized by significant and prolonged thaws [22]. For this reason, the NDVI index in winter (December–February) varies within the range 0.2095–0.5663 (Fig. 7a) (Table 2). In the period from 1999 to 2019, the minimum value of the NDVI index of 0.2095 was observed in February 2012, and the maximum value of 0.5663 in December 2003. The winter of 2011–2012 was one of the coldest in the region of the Black and Caspian seas. Daytime temperatures dropped to 13 C, and nighttime temperatures dropped to 19 C. Winter of 2002–2003 was one of the warmest winters for the period from 1999 to 2019. The absence of snow cover and warm winter allowed winter crops (the main agricultural crop of this region) to develop more actively and have a large phytomass, as indicated by the high value of the NDVI index. From February to May, the NDVI index grows rapidly at a rate of 0.1304 per month and reaches an average long-term maximum value of the NDVI of 0.7220 in June (Fig. 6) (Table 2). The average air temperature in summer (July–August) is +22.9 С [22]. Favorable weather conditions stimulate active growth of crop biomass. In summer (June–August) the NDVI index changes within the range of 0.5201–0.7695 (Table 2). For the period from 1999 to 2019, the minimum value of the NDVI index of 0.5201 was observed in August 2001, and the maximum value of 0.7695 in June 2008. The summer of 2001 over the period from 1999 to 2019 was drier, which led to the lowest value of the NDVI index. In the summer of 2008, the air temperature varied within the range of 21–36 С during the day and 19–31 С at night, which, together with precipitation (378 mm), allowed agricultural crops to actively gain phytomass. From July to November, the NDVI falls slowly at a rate of 0.0524 per month and reaches an average multi-year minimum in winter (Fig. 6) (Table 2).

a)

S. A. Lebedev et al. 1.0

454

0.8 0.6 0.4 0.2

Normalized Difference Vegetation Index (NDVI)

+0.0021 per year

b)

2002

2004

2006

2008 2010 2012 Date (year)

2014

2016

2018

2020

1.0

2000

0.8 0.6 0.4 0.2

Normalized Difference Vegetation Index (NDVI)

+0.0022 per year

1.0 0.4

2002

2004

2006

2008 2010 2012 Date (year)

2014

2016

2018

2020

0.6

0.8

c)

Normalized Difference Vegetation Index (NDVI)

2000

0.2

+0.0021 per year

2000

2002

2004

2006

2008 2010 2012 Date (year)

2014

2016

2018

2020

Fig. 7 Temporal variability of the NDVI index for different regions of the Republic of Adygea: (a) the flat part with the relief height less than 150 m, (b) the foothill part with the relief height in the range of 150–400 m, (c) the mountain part with the relief height more than 400 m

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

Flat part (relief height 400 m) Minimum Average Maximum 0.3669 0.4205 0.5173 0.3056 0.4087 0.4645 0.3299 0.4271 0.5096 0.4807 0.5575 0.6623 0.7594 0.8185 0.8690 0.8414 0.9069 0.9458 0.8829 0.9208 0.9445 0.8553 0.9115 0.9357 0.8200 0.8735 0.9155 0.6410 0.7382 0.8081 0.5099 0.5790 0.6739 0.3630 0.5072 0.5841

Table 2 Extreme and average monthly average values of the NDVI index for different regions of the Republic of Adygea

Seasonal and Interannual Variability of NDVI in the Republic of Adygea 455

456

S. A. Lebedev et al.

The interannual variability of the NDVI is positive and is of +0.0021 per year (Fig. 7a).

3.2.2

Foothill Part of the Republic

In the foothill part, the average long-term minimum value of the NDVI index of 0.3882 is also observed in winter (Fig. 6) (Table 2). However, it is higher than in the flat part of the Republic, because in addition to agricultural land and pastures (about 80% of the area [21]), there are thickets of perennial shrubs and deciduous forests (Fig. 2). The average air temperature in winter (December–February) in this region is 2.4 С. The duration of the winter period is about 69 days: on average, from November 30 to February 8, which is also characterized by significant and prolonged thaws [22]. In winter (December–February), the NDVI varies within 0.2901–0.6018 (Fig. 7b) (Table 2). In the period from 1999 to 2019, the lowest value of the NDVI index of 0.2901 was observed in February 2000, despite the fact that the winter of 1999–2000 was one of the warmest in the region. Daytime temperatures dropped to 5 C, and nighttime temperatures dropped to 10 C. The maximum value of the NDVI index 0.6018 was observed in December 2003. Winter 2003–2004 was also one of the warmest winters for the period from 1999 to 2019. The absence of snow cover and the warm winter made it possible for winter crops to develop more actively and to increase phytomass. From February to May, the NDVI grows rapidly at a rate of 0.14 per month and reaches an average multi-year maximum value of the NDVI of 0.8101 in June (Fig. 6) (Table 2). The average air temperature in summer (July–August) is +22.3 С [22]. Favorable weather conditions stimulate active growth of the biomass of crops and deciduous forests. In summer (June–August) the NDVI index changes within 0.6252–0.8502 (Table 2). For the period from 1999 to 2019, the minimum value of the NDVI index of 0.6252 was observed in August 2001, and the maximum value was 0.8502 in June 2012. As already noted, the summer of 2001 was drier over the period from 1999 to 2019, which also led to the lowest value of the NDVI index as in the flat part of the Republic. In the summer of 2012, the air temperature varied within 19–36 С during the day and 16–29 С at night, which, together with precipitation (352 mm), allowed agricultural crops and broadleaf trees to actively gain phytomass. From June to November, the NDVI falls slowly at a rate of 0.0529 per month and reaches an average multi-year low in winter (Fig. 6) (Table 2). Interannual variability of the NDVI index is also positive and amounts to +0.0022 per year (Fig. 7b), which is slightly higher than in the flat part of the Republic.

3.2.3

Mountain Part of the Republic

In the mountainous part, the average long-term minimum value of the NDVI index of 0.0487 is also observed in winter (Fig. 6) (Table 2). However, it is higher than in

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

457

other parts of the Republic, because it is dominated by oak, broad-leaved, fir and fir-spruce forests, as well as thickets of perennial shrubs along river floodplains (Fig. 2). At a distance from Maykop City (210 m above sea level) to Guzeripl (760 m above sea level), the average monthly temperature in winter decreases from 1.7 С to 2.2 С [22]. In winter (December–February) the NDVI index varies within the range of 0.3056–0.5841 (Fig. 7b) (Table 2). In the period from 1999 to 2019, the minimum value of the NDVI index of 0.3056 was observed in January 2000, despite the fact that the winter of 1999–2000 was one of the warmest in the region. Daytime temperatures dropped to 9 C, and nighttime temperatures dropped to 21 C. The maximum value of the NDVI index of 0.5841 was observed in December 2012. From February to June, the NDVI grows rapidly at a rate of 0.17 per month and reaches an average long-term maximum value of the NDVI of 0.9208 in July (Fig. 6) (Table 2). The average air temperature in summer (July–August) is +19.2 С [22]. Favorable weather conditions stimulate the active growth of the biomass of forests located on the slopes of low mountains. In summer (June–August), the NDVI index changes within 0.8414–0.9458 (Table 2). For the period from 1999 to 2019, the minimum value of the NDVI index of 0.8414 was observed in June 2001, and the maximum value was 0.9458 in June 2012. From June to November, the NDVI index slowly falls at a rate of 0.1103 per month and reaches an average multi-year minimum in winter (Fig. 6) (Table 2). Interannual variability of the NDVI index is also positive and amounts to +0.0021 per year (Fig. 7c), as well as in the flat part of the Republic.

4 Conclusions Normalized Difference Vegetation Index (NDVI) derived from satellite remote sensing data was used to investigate spatial and temporal variability of vegetation cover in the Republic of Adygea. It seems that this is the first research in this field of science applied for the territory of the Republic. Spatial variability of NDVI was shown on two examples taken for spring (18 April 2020) and autumn (6 October 2018) based on OLI Landsat-8 satellite imagery of high spatial resolution (30 m). It was possible to detect difference in the vegetation cover related with agricultural activities (fields of winter crops, irrigated fields and rice paddies, uncultivated fields) and natural land cover (oak and broad-leaved forests, the coniferous forest, alpine meadows, snow cover, water bodies, open rocks), and establish their relationship with the value of the NDVI index. The seasonal and interannual (1999–2019) variability of the NDVI index for the territory of the Republic of Adygea was investigated separately for three parts of the Republic: (1) the flat part with the relief height less than 150 m; (2) the foothill part with the relief height in the range of 150–400 m; and (3) the mountain part with the relief height more than 400 m. For this study, the SPOT satellite data of the PROBA-

458

S. A. Lebedev et al.

V (PROBA-Vegetation) Project were selected with a spatial resolution of 300 m. The analysis showed that for all parts of the territory of the Republic of Adygea, NDVI index variability has a pronounced seasonal character related with both natural seasonal variability of vegetation cover and seasonal variability in the agriculture activities. Peculiarities of interannual variability of NDVI (maximum and minimum values in different seasons) are related with known cases of mild or cold winters, and dry/hot or wet summers occurred in 1999–2019. In general, all three parts of the Republic show a positive trend in the NDVI variability of 0.0021 per year (or an increase by about 10% during the past 20 years) which is likely to be explained by warming of regional climate which favors good state and progressive development of vegetation cover. Future research on NDVI for the Republic of Adygea is very promising and should be accomplished in close collaboration with specialists in agriculture, forestry, botany and other experts in land and vegetation cover. Practical applications should be focused on the elaboration of agricultural monitoring system, which will include high resolution Earth remote sensing methods, for the agro-industrial complex in the Republic of Adygea. This will allow determining the areas of land and crops, the condition of plants, and the yield of agricultural crops. Prediction of yields of various crops will be of great importance for the Government of the Republic in order to control the potential degree of market filling and develop measures to stabilize the level of profitability of agricultural producers. Acknowledgments S.A. Lebedev (satellite data processing) was supported in the framework of the Geophysical Center RAS budgetary financing and by RFBR, MOST (China), and DST (India) according to the research project № 19-55-80021. S.A. Lebedev (calculation of seasonal and interannual variability of NDVI) was supported in the framework by the project “Intelligent analysis of big data in the tasks of ecology and environmental protection,” carried out within the Competence Center Program of the National Technological Initiative “Center for the Storage and Analysis of Big Data,” and supported by the Ministry of Science and Higher Education of the Russian Federation at the Lomonosov Moscow State University and by the Fund of the National Technological Initiative dated December 11, 2018, No. 13/1251/2018. S.A. Lebedev and O.P. Shevyakova were partially supported by the State Project No. 5.9533yu2017/BCh of Maykop State Technological University, approved by the Ministry of Education and Science of Russia. A.G. Kostianoy was partially supported in the framework of the P.P. Shirshov Institute of Oceanology RAS budgetary financing (Project No. 0128-2021-0016).

References 1. Bedanokov MK, Kuizheva SK, Lebedev SA, Kostianoy AG (2021) Introduction. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10.1007/698_2021_736 2. Bedanokov MK, Shambaleva GV (2003) Forecasts of the development of agricultural production in the Republic of Adygea. Izvestiya vysshikh uchebnykh zavedeniy Severo-Kavkazskiy region Obshchestvennyye nauki (2):85–87. (in Russian)

Seasonal and Interannual Variability of NDVI in the Republic of Adygea

459

3. Lebedev SA, Gunina GN, Ashinov YN, Kravchenko PN (2020) Ecological conditions of soils in the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10.1007/698_2020_640 4. Bibin AR, Trepet SA, Grabenko EA, Akatova TV (2020) Shaposhnikov Caucasian state wildlife biosphere reserve. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10.1007/698_2020_636 5. Lebedev SA, Sirotyuk EA, Shkhapatsev AK, Kravchenko PN (2021) Ecological state of soils of the Republic of Adygea under high anthropogenic load. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10. 1007/698_2021_741 6. Trofimov IA, Shamsutdinov ZS, Trofimova LS, Shamsutdinova EZ, Yakovleva EP, Orlovskyi NS (2010) The problem of desertification in Russia. Zemledelie 7:7–9. (in Russian) 7. Baghdadi N, Zribi M (2016) Land surface remote sensing in agriculture and Forest. ISTE Press/ Elsevier. 496 pp. https://doi.org/10.1016/C2015-0-01224-2 8. Bartalev S, Egorov V, Zharko V, Loupian E, Plotnikov D, Khvostikov S, Shabanov N (2016) Land cover mapping over Russia using earth observation data. Space Research Institute of Russian Academy of Sciences, Moscow. 208 p. (in Russian) 9. King D, Jones RJA, Thomasson AJ (1995) European land information systems for agroenvironmental monitoring, European Commission, 285 p 10. Baruth B, Royer A, Klisch A, Genovese G (2008) The use of remote sensing within the MARS crop yield monitoring system of the European Commission. Proceedings of the 21st congress of the international society for photogrammetry and remote sensing, vol 27(B8), pp 935–940 11. Govender M, Chetty K, Bulcock H (2007) A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA 33(2):145–151. https://doi.org/ 10.4314/wsa.v33i2.49049 12. Lavrova OY, Kostianoy AG, Lebedev SA, Mityagina VI, Ginzburg AI, Sheremet NA (2011) Complex satellite monitoring of the Russian seas. Space Research Institute of Russian Academy of Sciences, Moscow. 480 p. (in Russian) 13. Khan MJ, Khan HS, Yousaf A, Khurshid K, Abbas A (2018) Modern trends in hyperspectral image analysis: a review. IEEE Access 6:14118–14129. https://doi.org/10.1109/ACCESS. 2018.2812999 14. Xue J, Su B (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sens:1353691. 17 p. https://doi.org/10.1155/2017/1353691 15. Ray TW (1994) A FAQ on vegetation in remote sensing. Div. of Geological and Planetary Sciences California Institute of Technology, California, 18 pp 16. Horning N, Robinson JA, Sterling EJ, Turner W, Spector S (2010) Remote sensing for ecology and conservation: a handbook of techniques. New York, Oxford University Press, Oxford 17. Justice CO, Holben BN, Gwynne MD (1986) Monitoring east African vegetation using AVHRR data. Int J Remote Sens 7(11):1453–1474. https://doi.org/10.1080/ 01431168608948945 18. Buzarov AS, Varshanina TP, Kabayan NV, Krasnopolskiy AV, Krasnopolskaya NV, Kuasheva DA, Melnikova TN, Spesivtsev PA, Khachegogu AY, Shebzukhova EA (2001) Geography of the Republic of Adygea. Adyghe Republican Book Publishing House, Maykop. 200 p (in Russian) 19. Akatov PV, Varshanina TP (2013) Modern border of the forests on the West Caucasus (Krasnodar Region and Adygea Republic). Ekologicheskiy vestnik Severnogo Kavkaza 9 (3):86–88. (in Russian) 20. Lebedev SA, Kostianoy AG, Kravchenko PN (2020) Digital elevation model of the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10.1007/698_2020_656 21. Dierckx W, Sterckx S, Benhadj I, Livens S, Duhoux G, Van Achteren T, Francois M, Mellab K, Saint G (2014) PROBA-V mission for global vegetation monitoring: standard products and image quality. Int J Remote Sens 35(7):2589–2614. https://doi.org/10.1080/01431161.2014. 883097

460

S. A. Lebedev et al.

22. Kostianoy AG, Lebedev SA, Serykh IV, Kostianaia EA, Varshanina TP (2021) General characteristics of the climate in the Republic of Adygea. In: Bedanokov MK, Lebedev SA, Kostianoy AG (eds) The Republic of Adygea environment. Springer, Cham. https://doi.org/10. 1007/698_2021_735

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using Remote Sensing Data Irina E. Kurbatova

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methods and Approaches to the Organization of Integrated Reservoir Monitoring . . . . . . . 2.1 Geo-system Approach to the Organization of Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Monitoring Information Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cartographic and Aerospace Unit for the Krasnodar Reservoir Transformation Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Stages of the Reservoir Formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Ecological Effects of Silting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Impact of the Belaya River Catchment on the Krasnodar Reservoir Pollution . . . . . . . 4.1 Assessment of the Ecological Condition of the River Basin . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Ecological-Geographical Mapping the Belaya River Basin . . . . . . . . . . . . . . . . . . . . . . . . . 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

462 465 465 466 469 469 477 483 483 489 492 493

Abstract The structure of geo-ecological monitoring the main water bodies of the Republic of Adygea (the Krasnodar Reservoir and its tributary the Belaya River) is developed. The stages of formation of the reservoir from the inception to the present are described. The causes of the catastrophic silting the reservoir divided into two parts by an extensive crosspiece formed by the Belaya River sediments are considered. Impossibility of conducting ground surveys is compensated by using highresolution satellite data. Conclusions on a significant reduction of the useful volume of reservoir and the risk of its overflow during catastrophic floods are presented. A cartographical analysis of an anthropogenic load on the Belaya River watershed was carried out, and the qualitative composition of pollutants carried away by the river

I. E. Kurbatova (*) Institute of Water Problems, Russian Academy of Sciences, Moscow, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 461–496, DOI 10.1007/698_2020_641, © Springer Nature Switzerland AG 2020, Published online: 9 December 2020

461

462

I. E. Kurbatova

into the reservoir and accumulated in the array of silt sediments was assessed (in conditions of a lack of network observations). Keywords Environmental problems, Krasnodar water reservoir, Riverbed reservoirs, Side tributaries, Silting, Space monitoring, Watershed

1 Introduction The North Caucasus region, in particular the Republic of Adygea, combines areas with various climatic, landscape, and hydrological conditions. Uneven distribution of annual precipitation (from 300 mm in the flat areas to 2,750 mm in the mountains at about 3,000 m altitude) largely determines uneven distribution of the hydrographic network. Its density varies from 0.05–0.3 km/km2 in the Ciscaucasia to 1.68–1.90 km/km2 in high mountain regions [1]. The presence of excess and insufficient moistening zones in the region has significant impact on the specificity of economic activities, which led to the creation of a large number of reservoirs in arid regions, the main purpose of which is irrigation, watering, water supply, and fishery. On the northern slope of the Greater Caucasus, 55 reservoirs with a total water mirror area of 1,047 km2 were built [1]. According to their genesis, they are divided into three main groups: channel, bulk, and lake reservoirs. The biggest of them are channel reservoirs, created on large- and medium-sized rivers. Their formation is the most cardinal human intervention into the natural life of the river and surrounding areas. Channel reservoirs differ in the size of flooded areas as well as large proportion of shallow water areas. They are among the most dynamic water bodies of artificial origin since the runoff regulation causes sharp fluctuations in water volumes and levels, resulting in significant displacement of coastline in flat areas and formation of shallow drained areas. At some reservoirs, the area of the water mirror can vary by two times or more, as, for example, at the Rybinsk Reservoir [2]. The larger the reservoir is, the greater its impact on the environment is, and, consequently, the harder the negative consequences and the more complex set of the environmental protection measures are. Environmental problems associated with building, formation, and long-term operation of reservoirs are the most often considered, and only in some cases, environmental problems of the reservoir itself provoked by adverse natural and anthropogenic factors of the impact of the main river catchment areas and reservoir side tributaries are paid attention. One of the serious problems accompanying reservoirs throughout the entire period of their existence are the processes of bed silting with sediments taken from the river watershed areas and/or received by processing river banks. Silting rate depends on the regimes of liquid and solid runoff, intensity of erosion processes in

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

463

Fig. 1 Current state of the Shapsug Reservoir: (a) view from space (archive Google Earth); (b) view from the helicopter: deflated reservoir is presented in the foreground; the Kuban River is visible in the background (photo from https://catcher.fish/enciklopedia/vodoemy/ufo/shapsugskoevodohranilishhe/)

the catchment, reservoir operation conditions, and so forth. The greatest silting is observed in the reservoirs of the southern mountainous and foothill regions of Transcaucasia, the North Caucasus, the Carpathians, and Central Asia [3]. Reducing the useful reservoir volume as a result of its filling with sediments affects the full implementation of its land reclamation and flood protection functions and is no less important problem than the “aging” of hydraulic structures. As the reservoir shallowing increases, the likelihood of its overflow with flood waters increases too, so the loss of reservoir tanks may be considered as a potentially dangerous hydrological situation, the occurrence of which is possible during catastrophic floods and overflows of water over the dam. One of the striking examples of silting and subsequent descent is the Shapsug Reservoir located on the territory of Adygea near Krasnodar City. The Shapsug Reservoir is the first large body decommissioned from the lower Kuban River water complex operation. The reservoir was built in 1939–1952 with the aim to regulate the flood runoff of the Afips River, as well as more efficient using water resources in various sectors of the national economy. Initially the area of the reservoir water surface was 46 km2, the length was 9 km, the width was 8 km, and the average depth was 3.5 m. By the end of the service age set by the normative regulatory documents (50 years), hydro-technical structures came to emergency condition, and the reservoir bed silted up and overgrew with moisture-loving and woody vegetation. There was the violation of the basic functionalities of the reservoir such as reducing the flood protection of the lower Kuban River and decline of areas of rice irrigation systems and other agricultural lands. This led to negative economic and social consequences: flooding of vast areas, water logging and flooding of settlements, and worsening living standards of part of the population (Fig. 1). Therefore in 2002 the reservoir was lowered and taken out of service for reconstruction [4]. The first phase of the reconstruction lasted from 2007 to 2018. During this time, part of the dam was overhauled and emergency spillway structure was built. The second stage of reconstruction is planned to be completed by 2026 [5].

464

I. E. Kurbatova

Another problem that arises when silting reservoirs is their pollution by waste of industrial, agricultural, municipal production, disposed in the catchment area, transferred to the receiving reservoir with solid river suspensions. The permanent accumulation and increasing concentration of pollutants lead to severe environmental consequences both for the reservoir itself and for its water users. Therefore, for reservoirs subjected to intensive silting, it is necessary to organize regular monitoring changes of their hydrographic, hydro-chemical, and morphometric characteristics. The sharp cut-back of the hydro-meteorological observation network of the Federal Service for Hydrometeorology and Environmental Monitoring in Russian river basins over the past two decades has reduced the reliability of hydrological forecasts and significantly complicated the work on the operational regulation of water management systems and timely emergency forecasting [6]. So, in the Kuban River basin, the total number of the hydrological network stations was 121 in 1960 and 54 in 2000. At the Belaya River, the major tributary of the Kuban River and the main river of Adygea, there were eight stationary hydrological stations, which were distributed in high-altitude areas in the following way: three stations were situated at the altitude below 500 m; two stations were from 500 to 1,000 m; another two ones were located between 1,000 and 1,500 m; and one station was situated in the altitude range from 1,500 to 2,000 m. On average, in the Republic of Adygea, one hydrological observation station accounted for 986 km2. It should be noted that observations on small rivers of the foothill territory and in the highlands are the smallest [7]. By 2011, the number of stations in the Belaya River basin increased to 10. More frequent occurrence of catastrophic floods with serious negative consequences for society in recent years (flooding in the Kuban Region in winter and summer 2002; heavy flooding on the Black Sea coast in August 2002 [8]; in Krymsk and Novorossiysk in 2012; in Sochi, Adler, and Tuapse in 2013–2019; etc.) requires attracting the attention of specialists to the hydro-technical structure condition, clogging of riverbeds, etc. Under the condition of the lack of ground-based observations, it is very relevant to look for new alternative ways to obtain information for a comprehensive assessment of the current state of channel reservoirs aged several decades. Such methods include earth remote sensing and mapping its surface. Identification of the nature and extent of human impact on the river catchment areas is no less important for preliminary qualitative assessment of the receiving reservoir pollution since the bulk of contaminants from these watersheds is transported by liquid and solid river runoff and accumulated in the reservoir.

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

465

2 Methods and Approaches to the Organization of Integrated Reservoir Monitoring 2.1

Geo-system Approach to the Organization of Monitoring

Mutual influence of the reservoir and the environment is diverse and unequal in different geographical regions, in the upper and lower pools of water engineering systems, in different parts of the reservoir. These effects may be permanent or temporary and positive or negative. Therefore, one of the most urgent and practically significant tasks of nature management is the development of comprehensive studies of reservoirs, which should be based on the study of the relationship of internal and external factors in the formation of the “catchment-reservoir” system. Such research would be appropriate with the help of geo-environmental monitoring. According to Gilbert F. White [9, 10], the main objectives of the monitoring are, on the one hand, improving basic knowledge on natural systems and, on the other, introducing new methods of using small amounts of information to describe large systems. As a result, creation and development of various accurate and reliable problem-oriented models based on the understanding of the basic nature processes will allow carrying out wide analysis of the different natural system conditions using relatively small network of observation points. Such formulation of the problem, in our opinion, is fully consistent with the concept and the main objectives of the integrated geo-ecological monitoring water reservoirs. At that, the study of the reservoirs should be carried out on the basis of geo-system approach, which considers the reservoir not only as pond of anthropogenic origin within the main system “catchment-main river-receiving reservoir” but also as independent sub-structure in which it is receiving water body for the side tributaries with their watersheds. At the same time, this system should be considered as integral economic pattern. The ecological condition of the reservoir along with the influence of the direct water consumers is associated the scale of economic transformations of natural landscapes (or their constituents) of the catchment area. However, in environmental and water management practice, analysis of the spatial expansion of pollution sources and estimates of their load on water resources of the receiving reservoir are made very rarely. Meanwhile, the elicitation of the consistent patterns of anthropogenic load distribution over the catchment area and obtaining its qualitative and quantitative estimates help to identify the causes of the deterioration of the reservoir condition and development of the optimum program of activities for its restoration. The monitoring structure, the range of tasks, the set of information blocks, and objects of observation depend on the characteristics of the reservoir including its geographical and elevation position, morphometric characteristics, types of use, regulation, volume of the main and lateral tributaries, and the extent of anthropogenic impact on the catchment. The main tasks of geo-ecological monitoring reservoirs are inventory, control, and tracking of the various components of “catchmentwatercourse-reservoir” system for the subsequent elicitation of their

466

I. E. Kurbatova

interconnections and ecological condition in order to solve economic, hydroecological, and environmental problems [11] (see Fig. 2). Thus, observations under monitoring should include not only water bodies but also the surrounding catchment area when considering natural and anthropogenic components separately.

2.2

Monitoring Information Support

The basis of the monitoring information support is composed of three groups of sources [12]. The informational significance of each of them is determined by the tasks of monitoring, the solution of which requires priority using information of certain types: • Field data of stationary ground ranges, observation points located on a watercourse, catchment, in the coastal and open parts of the receiving reservoir (river and estuary hydro-meteorological and hydro-chemical stationary and mobile posts), as well as the results of various seasonal field surveys • Fund cartographic materials (topographic, bathymetric, navigation, specialized) • Remote sensing data of different scales and types of shooting, containing new information on hydrological, hydro-biological, and other features of rivers and water reservoirs, the natural environment watershed condition, and the extent of anthropogenic impact, providing the possibility of spatial-temporal extrapolation of local observations Noting the extreme sparseness (both spatially and temporally) of the network of stationary hydrological, hydro-chemical, hydro-biological observations on the water and water object conditions, the researchers turn to cartographic materials and remote sensing data in order to obtain the necessary information by means of environmental interpretation of the information included in them [13]. When studying the initial stage of reservoir formation, the mass building of which in Russia dates from 1930 to 1940 and from 1950 to 1960, fund cartographic materials are of particular value. They allow restoring information on the natural features (topography, vegetation, soils) and the economic use of lands of the intended flooding zone, assessing the coast condition and the dynamics of the coastline of the reservoir in the first and subsequent years of its formation. In the future, changes in the water area and coastal natural-territorial complexes can be traced using the archival fund of space images, which covers the interval of the last 40 years. The earliest archival satellite images (from the late 1960s of the last century) differ from modern satellite images in low quality, small scale, and low resolution. However, they are valuable documentary material for identifying large-scale dynamic processes. In recent years, actively used space shooting data of high and ultra-high spatial resolution from efficiently operating satellites such as RapidEye, QuickBird, WorldView-1, WorldView-2, WorldView-3, and GeoEye have been considered to be important sources of information [14, 15]. The latest equipment installed on these satellites having 5–8 high-resolution spectral channels (0.46–6.5 m) is characterized

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

467

Main concepts of integrated geo-ecological monitoring

Adoption of the “catchment-watercoursereceiving reservoir” as holistic geo-ecological formation

Using landscape-basin approach to the study of the relationships between the environment and the reservoir

Production of information support for the research based on integration of Cartography, Remote Sensing and Geoinformatics

Main objectives

Inventory, assessment of the condition and functional integrity of ecosystems, elicitation of negative changes

Identification of the causes of changes and evaluation of their consequences, forecast, definition of correctional measures

Providing the state environmental and water management authorities with reliable and up-to-date information

Main activity courses

Control and observation for the reservoir ecosystem as a specific water body of anthropogenic origin with complicated regime of level fluctuations

Observation, analysis and forecast of the impact of natural and anthropogenic factors of the environment on the reservoir (assessment of the watershed effect on the reservoir formation)

Observation, analysis, forecast of changes of the environment under the influence of water factor (the impact of the reservoir on the formation of the adjacent territory)

The monitoring observation objects

Water area with the division into hydrographic areas and sections (main and boundary reaches, drawdown zones, backwater, deep- and shallowwater areas of lower and upper pools), zones of river solid inflow

Coastal area with zoning shore types to determine the dominant processes of coast formation, elicitation of conflict situations, ecotone release

Lateral inflow watersheds with allocation of river network and lands with different patterns of use to assess the impact of anthropogenic and natural watershed factors on the water quality of the reservoir, the development of erosion processes, successions of coastal ecosystems

Objects of direct and indirect negative impact of the reservoir on the nature and economy of the adjacent areas such as areas of flooding, waterlogging, and changing soil and vegetation cover

Fig. 2 Geo-system approach to the organization of integrated monitoring channel reservoirs (author I.E. Kurbatova)

468

I. E. Kurbatova

by high productivity, wide survey band (about 20 km), high precision of gridding, multispectral characteristics, and high frequency of repeated shooting. Utilizing the obtained information that is able to satisfy different requests of numerous users can significantly expand the range of solvable tasks, improve classification of the land and water surface characteristics, and quickly (with frequency of 1–2 days) monitor the emergence and development of dangerous situations of natural or anthropogenic nature. This information can provide the basis for organizing space monitoring water bodies due to the regular systematic accumulation of diverse materials in database of homogeneous and comparable in quality data, simultaneous for vast water areas and adjacent land, which is almost impossible with ground-based surveys. The obtained satellite information can be effectively used when observing coast reformation, as well as when mapping the effects of natural disasters (floods, landslides, fires, etc.). One of the most significant and relevant directions of using satellite data is the identification of the actual distribution of river sediment over the reservoir, depending on the regime and volume of solid runoff. The identification of the areas of spreading suspension from these data is based on differences between the optical densities of the images of more transparent water and turbid river runoff. Water surface areas where water contains large amount of solid suspensions have the high reflectivity in the range of 0.5–0.6 μm and, therefore, look in the photo image lighter than areas with lower concentration of fine-dispersed suspensions. This information combined with the results of observations in the nearest river gauge at different shooting dates allows estimating the extent and rate of silting reservoir. The obtained information can be used to solve different water management problems, in particular, to produce forecast evaluation of the reservoir carrying capacity under conditions of its intensive siltation. The rationale for choosing spacecraft, survey equipment, spatial coverage, shooting frequency, and its scale depends on the specifics of the geo-systems of the main river basins, their tributaries and receiving reservoirs of different ranks, their structure, dynamics, and functioning in dependence on natural and anthropogenic features (Tables 1 and 2) [16]. Decoding results confirmed by ground-based observation data provide reliable information on spatial and temporal changes of hydro-ecological characteristics at any point of the water area and the coastal zone of the reservoir and are important information component in the cartographic modeling the processes under study. Joint analysis of cartographic materials and remote sensing data for the entire period of the artificial reservoir existence will allow not only tracking the occurred changes but also working out forecast for its further development, taking into account the rate of morphometric characteristic changes.

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

469

Table 1 Space information requirements for studying and mapping geo-system components [16] (reused with permission from Current Problems in Remote Sensing of the Earth from Space) Sizes of monitoring objects Area, km2 Basins Small
2,000 Medium 2000–5,000 Large 5,000 Receiving reservoirs Medium 10–100 Large 100–1,000 Very large 1,000 Reservoirs Medium Large Very large The largest

20–100 100–500 500–5,000  5,000

Scale level of problems

Shooting parameters Spectral bands, Shooting μm periodicity

Optimal mapping scale

Local Regional Interregional

0.4–0.7 0.7–1.3 8–12

Once a season

1:200,000 1:500,000 1:1000,000

Local Regional Interregional

0.4–0.5 0.5–0.6 0.7–1.3 8–12

Once a month in warm season

1:50,000 1:100,000 1:200,000 1:1000,000

Local Regional Interregional Global

0.4–0.5 0.5–0.6 0.7–1.3 8–12

Once a month in warm season, as well as during filling and discharge

1:50,000 1:100,000 1:200,000 1:1000,000

3 Cartographic and Aerospace Unit for the Krasnodar Reservoir Transformation Monitoring 3.1

Stages of the Reservoir Formation

The Kuban River is one of the largest rivers in the North Caucasus, its length is 906 km, and the catchment area is 57,900 km2 [1]. The territory of the Kuban watershed is presented by a variety of landscapes: from coastal lowland to alpine nival ones. The high extent of anthropogenic development of the territory is due to favorable agro-climatic conditions, presence of minerals, and recreational significance. The anthropogenic disturbance of this area is very significant, especially in the middle and lower current of the Kuban, and the natural river channel network is greatly changed by economic activity. Large-scale use of the Kuban waters for irrigation and water supply started in the 1930s of the twentieth century, and the most significant works to increase impressments of water for household needs were carried out in the 1950s–1980s [17]. As a whole, 5 retaining waterworks, 4 large reservoirs, 36 reservoirs with a volume from 1 to 10 million m3, and also about 600 ponds were built in the Kuban and its tributaries [18]. The need to regulate the Kuban waters was also due to the complex water regime of the river: in addition to the summer (the highest) flood, the river has on average about 6–7 another floods per year, during which the amplitude of water-level fluctuations near the City of Krasnodar could reach 5 m [19]. In particular, five reservoirs were created in

Coming sediments from river catchments, their accumulation in the coastal zone, forming deltas Watersheds Plowing water protection zone, increase of ravines, deforestation in catchment area, industrial pollution

Assessing the impact of natural- territorial complexes of catchment on the reservoir

Detailed Local

Local Regional

Local Regional

Observation and zoning coast types according to risk level and processing intensity Monitoring siltation and reservoir volume change

Shores reformation (collapse, accumulation, erosion)

Detailed Local

Level of monitoring

Detailed Local

Elicitation of conflict situations in water areas and on shores

Objectives of monitoring

Shallowing, eutrophication, pollution

Phenomena and processes Reservoirs Flooding, swamping shores

1–2 times in warm season

5–6 times in warm season

1–2 times during period of level rise 4–5 times during period of discharge 1–2 times in warm season

Observation periodicity

Resource-DК, Ikonos-2, OrbView-3, QuickBird, IRS-PS, EROS, GeoEye-1, SPOT-5, HRG, IRS-RAN, ALOS, Formosat-2 (RAN), RapidEye, WorldView-2

HRG, IRS-RAN, ALOS, Formosat-2 (RAN), RapidEye, WorldView-2, Resource-DК

Resource-DК, Ikonos-2, OrbView-3, QuickBird, IRS-PS, SPOT-5, EROS, GeoEye-1, WorldView-2 Resource-DК, Ikonos-2, OrbView-3, QuickBird, IRS-PS, EROS, GeoEye-1, SPOT-5, WorldView-2 HRG, IRS-RAN, ALOS, Formosat-2 (RAN), RapidEye, HRVIR, Formosat-2(MS), HRG

Name of spacecraft

0.4–0.7 0.7–1.1

0.4–0.5 0.5–0.6

0.5–0.7 0.7–1.1

0.4–0.6 0.7–1.1

0.5–0.6 0.7–1.1

2–5 5–10

5–10 10–20

5–10 10–20

2–5 5–10

2–5 5–10

Characteristics of imaging equipment Spectral band, Resolution, μm m

Table 2 Main objectives and levels of monitoring in the study of negative processes in reservoirs and their catchments [16] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

470 I. E. Kurbatova

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

471

Fig. 3 The Kuban River basin [20]

Adygea, Krasnodar, Shapsug, Takhtamukay, Shenjiy, and Maykop reservoirs, allowing more rational using surface water resources, mainly for retention, accumulation, storage, and optimal redistribution of water according to the seasons for irrigation of crops, providing fish hatcheries, etc. The Krasnodar water reservoir built in 1968–1973 is the largest channel reservoir not only in the Kuban River basin but also all over the North Caucasus (Fig. 3). It is located in the middle of the riverbed between Voronezhskaya Cossack village and Krasnodar City. The following design characteristics of the reservoir were proposed: the area of water mirror has to be about 400 km2 (397.8 according to some sources), the maximum length is 46 km, the width is 8.6 km, the full volume is 2,349.3 million m3, the useful volume is 2.2 billion m3, and the retaining water level at the dam is 25 m [18]. There was a plan to solve a whole complex of problems when creating the reservoir: to carry out inter-season runoff hydrograph alignment and eliminate catastrophic floods in the lower current of the Kuban; to provide water for irrigation systems; to improve shipping conditions, to adjust reproduction of valuable fish species; and to improve water supply of the population and recreation conditions. At the same time, the negative aspects of reservoir existence began to appear in addition to the positive effect of its formation. In particular, as a result of the reservoir creation, the lower parts of the Belaya, Pshish, Marta, Apchas, and Psekups Rivers were flooded. Some tributaries of these rivers began to fall directly into the water reservoir (Filtruk, Tuapcha, Dysh Rivers). In the mouths of these rivers, bays were

472

I. E. Kurbatova

formed of estuary type. The Belaya River was the exception; the outlying delta began to form in its mouth. The rise of the delta subsequently led to very negative situation in the region, threatening the existence of the Krasnodar Reservoir and sharply worsening the condition of adjacent lands. Studying the specifics of the Krasnodar Reservoir transformation and assessing the impact of the Belaya River and its catchment on the water body were carried out by the author in frame of the integrated monitoring of the “watershed-water current-reservoir” geo-system based on the experience of sharing use of remote sensing, cartographical, and hydrological data, where the reservoir was considered as a receiving one. The main objectives of the study were: 1. To determine the extent of the reservoir bed silting at different stages of its formation 2. To assess the impact of the Belaya River catchment on the ecological condition of the reservoir As noted above, the use of cartographic materials and remote sensing data when studying the region is due to the high information content of these data and possibility of simultaneous continuous visual coverage of vast areas of the reservoir and the Belaya River catchment under lack of network ground observations on the condition of their waters [11]. To trace dynamics of the Krasnodar Reservoir shore formation, topographic maps for 1962–1996 and remote sensing data for different days of 1984–2013 were used. Thus, more than 40-year period of the reservoir existence was provided with initial materials. In this period the following stages could be highlighted: 1. The preliminary stage is the creation of the Tshchik Reservoir (1940–1941). 2. The initial stage is the formation of the Krasnodar Reservoir shores (1973–1984). 3. The intermediate stage is the moving forward of the Belaya River delta into the water area of the reservoir (1984–1993). 4. The current stage is the reduction of the normal retaining level (NRL) by 0.9 m, dividing the reservoir by crosspiece from the sediments of the Belaya River into two independent reservoirs (1993–2017).

3.1.1

Preliminary Stage

The history of the creation of the Krasnodar Reservoir goes back to the years 1939–1941 when the Tshchik Reservoir was built on the site of the former Tshchik floodplains (Fig. 4a, b) with the aim of partially eliminating floods in the Kuban and Belaya, redistributing runoff, and preserving navigation on one of the estuary channels of the Belaya River during low-water period. The reservoir as a separate water body existed from 1941 to 1973. Its characteristics during this period were as follows: initial capacity was 350 million m3, water mirror area was 80 km2, dam length was 32 km, and dam height was 3.5–7.5 m. In the 1960s of the last century, it was decided to build a new reservoir and to incorporate Tshchik one into its borders.

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

473

Fig. 4 Background of the Krasnodar Reservoir formation. Tshchik Reservoir: fragment of map of 1947 (a); fragment of map of 1962 (b); the projected boundaries of zones of flooding territory when filling the Krasnodar Reservoir (c) [21] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

The contours of the future bed of the Krasnodar Reservoir are presented in Fig. 4c. The map clearly shows visible tributaries of the Kuban River, forests, settlements, and transport network. According to different sources, the water submerged from 20 to 26 settlements, many social and cultural facilities, and 35 thousand hectares of arable land; more than 13 thousand people were resettled; and 16 thousand hectares of forest were cut down.

3.1.2

Initial and Intermediate Stages of Shore Formation

At the confluence of the Belaya River into the reservoir, extensive delta began to form from sediment transported by the river, the average volume of which was 74 kg/s [1]. Already in this period fixing the above-water part of the delta with hygrophilous vegetation and shrubs began. The satellite image in the infrared (IR) range of 1984 (Fig. 5a) shows emerging above-water part of the Belaya River delta (1) and deeply embedded bays in the

474

I. E. Kurbatova

Fig. 5 Formation of the Belaya River delta after 10 and 20 years later on the beginning of filling the reservoir: archive satellite image of 1984 (a); topographic map of 1995 (b). The dotted line shows the flooded channel of the Kuban River [21] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

mouths of shallow rivers (2, 3). On results of topographical survey, the delta area was about 9 km2 by 1986 (Fig. 5b). It should be noted that such delta configuration was displayed on the maps until 1996, although by that time there occurred very significant changes of its size.

3.1.3

The Current Stage of Shore Formation

The sharp intensification of the water body silting processes was due to the decline of the marks of the Krasnodar Reservoir normal retaining level (NRL) by 0.9 m (from 33.65 to 32.75 m abs.), carried out in 1993 in accordance with the “Agreement of the Republic of Adygea and the Krasnodar Territory on the temporary operation mode of the Krasnodar Reservoir” to reduce the negative impact of the reservoir on the adjacent territory of Adygea. This led to the reduction of the reservoir water mirror area from 400 to 382 km2 and to the decrease of its volume from 2.200 to 1.928 million m3 [18]. As a result of measures on reducing the level, the outlying delta of the Belaya River increased sharply in area and after few years completely cut off the waters of the reservoir, dividing it into two almost separate parts. Figure 6a represents the image of crosspiece, obtained on August 3, 2006, from the Monitor-E spacecraft in the visible range. Its area at the time of shooting was more than 50 km2. Figure 6b shows clearly the zone of spread of the river suspended matters near the crosspiece site on June 21, 2009, recorded by the multispectral sensor established onboard ALOS satellite. The average speed of delta shift from origin to the formation of crosspiece was 112 m a year [22]. According to the participants of the complex hydrological-morphometric field survey carried out by the State Hydrological Institute and the North Caucasus Hydrometeorological Services in 2007–2008, the formed territory was impassable boggy swampy surface with a large number of shallow water bodies virtually inaccessible for ground-based surveys [22]. The

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

475

Fig. 6 Solid crosspiece after three decades later on filling the Krasnodar Reservoir: images from the Monitor-E satellite on August 3, 2006 (a) and from the ALOS satellite on June 21, 2009 (b) [21] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

Fig. 7 The current condition of the crosspiece: dried areas fixed by vegetation (a), willow thickets along the banks of one of the Belaya River channels seasonal or flood filling (b), the Tshchik Reservoir dam within the crosspiece (c). Photo from the site http://huntingandfishing.ru/k-70-letiyutshhikskogo-vodoxranilishha.html

dried-up areas of silt sediments were overgrown with dense shrubs, willows, bur reed, herb-horsetails, and tuber-bulrush communities (Fig. 7a). One of the Belaya River channels, filled with water only in flood or high-water periods, is shown in Fig. 7b. Figure 7c illustrates the current condition of the dam and part of the former water area of the Tshchik Reservoir, completely overgrown with trees and shrubs. Monitoring the further rise of inaccessible crosspiece is possible either on the basis of extremely rare and expensive aero-visual surveys or when using high-resolution remote sensing data. Changes of the Krasnodar Reservoir condition for 2013–2015 were traced in the work [23], in which the satellite Landsat 8 data from May 1, 2013, to March 20, 2015, were used. The main characteristics of its shooting equipment are shown in Table 3.

476

I. E. Kurbatova

Table 3 Main characteristics of Landsat 8 shooting equipment (https://ru.wikipedia.org/wiki/ Landsat-8) Spectral band Operational Land Imager (OLI) Channel 1 (coastal/aerosol, new deep blue) Channel 2 (blue) Channel 3 (green) Channel 4 (red) Channel 5 (near infrared, NIR) Channel 6 (short wavelength infrared, SWIR 2) Channel 7 (short wavelength infrared, SWIR 3) Channel 8 (panchromatic, PAN) Channel 9 (cirrus, SWIR) Thermal InfraRed Sensor (TIRS) Long wavelength infrared, TIR1 Long wavelength infrared, TIR2

Wave lengths (μm)

Resolution (for pixel) (m)

0.433–0.453 0.450–0.515 0.525–0.600 0.630–0.680 0.845–0.885 1.560–1.660 2.100–2.300 0.500–0.680 1.360–1.390

30 30 30 30 30 30 30 15 30

10.30–11.30 11.50–12.50

100 100

Fig. 8 Example of composite image of a satellite picture from Landsat 8 on August 21, 2013. They are marked with numbers: shallow area with high content of mineral suspension (1); area similar to the first, interspersed with drained areas (2); and drained territories covered by vegetation, the brightness characteristics of which are similar to the characteristics of shore areas (3) [23] (reused with permission from Maykop State Technological University)

Using satellite-derived data from the spacecraft of latest generation made it possible to obtain important information on temporal changing the crosspiece characteristics in the absence of ground-based observations. Data processing was carried out by the authors of the work [23] using UNESCO BILKO and ScanMagic program complexes (Fig. 8). Comparison of space images taken at different times and their subsequent processing allowed clarifying information on the development of siltation processes and change of morphometric characteristics, which can contribute to improve the quality of forecasting the reservoir condition.

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

477

Fig. 9 False color composites built from Landsat-8 (PAN) data on July 23, August 24, 2013, and March 20, 2015: images after contrasting (a), images after applying gradient operation (b) [23] (reused with permission from Maykop State Technological University)

For example, areas of the reservoir that became shallow in summer are highlighted in pink (Fig. 9).

3.2

Ecological Effects of Silting

Detailed tracing crosspiece formation dynamics was carried out using comparative analysis of topographic maps, results of decoding high-resolution space images, and

478

I. E. Kurbatova

Fig. 10 Silting the Krasnodar Reservoir: above-water and shallow parts of the Belaya River delta on the map of 1995 (a), the image from the Pleiades-1a satellite on May 6, 2012 (b) [21] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

data on the Krasnodar Reservoir level at the time of shooting from the Kuban Basin Water Department (BWD). On a topographic map with a scale of 1:200,000 (edition of 1995), there are clearly visible specific features of the reservoir formation processes, which are prerequisites of future problems: in accordance with the project of water body creation, the Kuban River flows not into the upper pool of the reservoir, but in its middle part, at the level of the Vasyurinskaya Cossack village, while the outlying delta of the Belaya River is already forming inside the dam of the Tshchik Reservoir (Fig. 10a). Thus, sediment deposits of both existence rivers occur in almost the same alignment of the Krasnodar Reservoir. Seventeen years later, space image from the Pleiades-1a satellite with resolution of 0.7 m, obtained on May 6, 2012, shows significant changes of hydrological and morphometric characteristics of the reservoir (Fig. 10b). Determining the extent of these changes by comparing satellite images and maps is legitimate, since very close positions of the levels are recorded on both the map and the image. It is known that the shoreline of reservoirs on topographic maps must correspond to their NRL (which in this case is 32.75 m a. s.l.). The water level in the reservoir at the time of shooting (according to the Kuban BWD) was 32.81 m, i.e., it exceeded NRL only for 6 cm and this value could be neglected. Over the years, the following irreversible changes were revealed. Outlying delta of the Belaya River with area of 9 km2 in 1995 turned into powerful crosspiece with total area of 73 km2. Its dried areas were covered with continuous vegetation. The waters of the Kuban, as the crosspiece expanded, were forced to cut silt deposits within the borders of its former channel, which existed prior to its flooding by the reservoir. By 2012, the river restored the section of the channel from the Vasyurinskaya Cossack village to the Starokorsunskaya Cossack village, river banks were also formed and fixed by woody vegetation, and at the exit from the crosspiece zone, the Kuban continued to form its outlying delta from the brought

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

479

Fig. 11 Image of crosspiece from the WorldView-2 satellite on March 14, 2011: connecting channel between the Kuban and Belaya (1); channel from Belaya into the Tshchik Reservoir (2); outlying Belaya delta (3); siltation of the Pshish estuary and restoration of its channel (4) [21] (adapted with permission from Current Problems in Remote Sensing of the Earth from Space)

sediments so that the delta moved toward the reservoir dam gradually. These processes are continuing at present. Now the Belaya River does not flow, as before, into the Kuban, but forms its channel in new direction almost parallel to the Kuban toward the dam while simultaneously increasing the crosspiece in the same direction. On the image from the WorldView-2 satellite with a resolution of 0.46 m obtained on March 14, 2011 (Fig. 11), it is clearly seen that in the place where the distance between the riverbeds is minimal, small channel was formed through which part of the Belaya liquid and solid runoff transports into the Kuban. The power of sediment supply can be characterized by change in color of the Kuban waters (area 1 in Fig. 11). The second channel connects the Belaya River with the Tshchik Reservoir. It is the only watercourse feeding the reservoir not only during floods but also in relatively low-water periods (area 2). The zone of spreading the river runoff, marked by suspensions, is clearly traced in the water area of the Tshchik Reservoir. The same suspension trains are observed in the channels of the outlying Belaya River delta (area 3). Condition of the former Pshish River estuary filled with sediments can also be considered as an indicator of the extent of the Krasnodar Reservoir siltation: the channel of this river, previously flooded, is now clearly visible in the picture, as well as its banks fixed by vegetation (area 4) (compare with Fig. 5a). According to the Kuban BWD data, water level in the reservoir on the shooting date was 31.78 m that is almost 1 m lower than the NRL. In this satellite image, sediment deposits in

480

I. E. Kurbatova

Fig. 12 Formation of the Kuban River banks inside the Krasnodar Reservoir: images from QuickBird satellite on June 13, 2004 (a) and from WorldView-2 satellite on September 8, 2011 (b) [21] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

different stages of moistening, drying, and overgrowing with shrubbery or grassy vegetation are easily determined by the differences of color spectrum and image structure. The Kuban riverbed formation was also traced through the resulting comparative analysis of two high-resolution satellite images obtained with 7-year interval from QuickBird on June 13, 2004, and WorldView-2 on September 8, 2011. As of 2004 (Fig. 12a), the river banks fixed by woody vegetation extended beyond the crosspiece and were 10 km below the projected location of the Kuban River flowing into the reservoir. From 2004 to 2011, the border of the fixed river banks of the Kuban was further advanced by 9 km (to the Starokorsunskaya Cossack village) that practically separated the small coastal bay (Fig. 12b). Thus, over the last 17 years, since 1995, the outlying Kuban delta shifted by 19 km moving at average speed of just over 1 km a year. It should be noted that on September 8, 2011, the level of the reservoir was 27.10 m according to the Kuban BWD. It is 5.65 m below

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

481

the NRL and just 1.25 m above the dead storage level (DSL) equal to 25.85 m. Fixed in Fig. 12b such significant drop of the level made it possible to detect massifs of wet silt sediments with area more than 60 km2, occupying for 8 km the entire bed of the reservoir from the left to the right bank. The figure clearly shows the Kuban channel, along which, as a result of the accumulative activity of the river, in the near future there will be continued the formation of river banks and their fixing by vegetation upon reaching the stage of drainage. The division of the Krasnodar Reservoir into two independent water bodies and the existence of significant level difference between them were discovered and confirmed by ground-based measurements during the field seasons of 2007–2008 [24]. The level of the bottom and water surface of the Tshchik Reservoir is higher than the corresponding levels of the Krasnodar one, both during the filling the latter and during decreasing its volume of water. The preservation of water volume in the Tshchik Reservoir is ensured only through ground waters, discharges from reclamation systems, and occasional inflow of the Kuban and the Belaya waters during floods. This situation minimizes ability of the reservoir for self-clarification. Deprived of flowage and water inflow in large volumes, the reservoir still cannot recover from the effects of the catastrophic flood of 2002 during which wastes from cattle yards, fertilizers from open warehouses, sewage, etc. washed away from the territories of the settlements of the Khatukayskaya Valley entered the reservoir; after that the sanitary-epidemiological situation deteriorated sharply. The accumulated silt layer on the bottom of the reservoir is 3–4 m, and the depth is no more than 1.5 m in the autumn-winter period, which results in the freezing of the reservoir, death of fish, and reduction of its stocks. Shallow waters turn into swamp and overgrow with willow actively. As of 2013, overgrowing the water area from the crosspiece side was more than 800 ha [25]. The combination of the base topographic map with space images, on the date of obtaining which the water-level measurement data are known, made it possible to calculate decreasing water mirror area of the Krasnodar Reservoir and its parts as the result of intensive silting water area from 1973 to 2012 (Table 4). The following conclusions can be drawn which resulted from the joint analysis of cartographic materials and remote sensing data covering the entire period of the Krasnodar Reservoir existence.

Table 4 Changing the water mirror area of the Krasnodar Reservoir

Years 1973 1985 1993 2006 2009 2012 a

Water mirror area at NRLa, km2 All Western Eastern part (Tshchik reservoir part Reservoir) 400 320 80 400 320 77 382 302 74 382 270 67 382 264 60 382 258 51

Area of silting zone, km2 0 3 6 45 58 73

The value of NRL is 33.65 m a.s.l. before 1993 and 32.75 m a.s.l. after 1993

482

I. E. Kurbatova

The current negative situation in the Krasnodar Reservoir is due to the natural features of the region, first of all the surface river runoff, the errors in reservoir design, and the shortcomings in its operation. The main factors that determined the problems of its formation are, in our opinion, as follows: • The inclusion of the Tshchik Reservoir existed independently in 1941–1972 in its water area. • Preservation of the Tshchik Reservoir dam, detaining not only the flood waters of the Kuban and Belaya but also significant volumes of the solid runoff, which fell in the shallow part of the reservoir, intensively silting it when the river current rate decreased almost to zero. • The projected flowing the Kuban River not into the upper part of the reservoir but 15 km downstream near the Vasyurinskaya Cossack village, almost opposite the mouth of the Belaya River, which led to the total accumulation of sediment of both rivers in one section of the reservoir. • 90 cm decrease of the reservoir NRL in 1993 intensified the shallowing process and the growing the surface sections of the crosspiece, which served as prerequisite for separating the Tshchik Reservoir from the main water mirror, contributing to the creation of height difference and the exclusion of this water body from the Krasnodar Reservoir. The current decreasing the reservoir volume as a result of the entire crosspiece formation is fraught with serious environmental consequences if (or when) rising negative hydrological situations such as the passage of catastrophic high waters and floods, runoff volume of which may exceed the water holding capacity of the reservoir. During the catastrophic flood in the Kuban Region in summer of 2002, 2,050 m3 of water flowed into the Krasnodar Reservoir every second, and only 1,176 m3 was discharged [8]. Nevertheless, the reservoir volume was sufficient to prevent volley discharge of water into the lower reaches of the Kuban River and not to aggravate the situation. To date, according to some estimates [3], the useful volume of the reservoir has decreased by 0.39–0.53 km3, which casts doubt on the flood protection functions of the Krasnodar Reservoir in case of similar situation repeats. Regular satellite monitoring in periods of floods, filling, and discharge of the reservoir will allow tracing the development of siltation processes actively, determining the change of morphometric characteristics, and predicting further reservoir transformation. It should be noted that in recent years within the framework of the Federal Target Programs, the reconstruction of a number of the Krasnodar Reservoir objects was carried out in order to improve their technical condition [26]. Partial clearing of the Belaya River delta from sediments was implemented; the connecting channel was laid (cut was made along the bottom of the crosspiece) (Fig. 13a) to increase water running and transfer part of the Belaya River runoff to the Kuban riverbed. In 2019, near the Starokorsunskaya village, there were carried out dredging in shallow water (Fig. 13b) and clearing areas of the water protection zone from household waste and small shrubs.

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

483

Fig. 13 Implementation of measures to improve ecological condition of the Krasnodar Reservoir: (a) the connecting channel between the Belaya and Kuban river-beds (Google Earth archive, 2019, modified by I.E.Kurbatova); (b) clearing overgrown shallow water (photo from the site https://krd. ru/podrazdeleniya/administratsii-vnutrigorodskikh-okrugov/kvo/news/news_17032020_103707. html)

4 The Impact of the Belaya River Catchment on the Krasnodar Reservoir Pollution 4.1

Assessment of the Ecological Condition of the River Basin

As a rule, the ecological condition of water bodies depends not only on economic activity on their coasts but also on the nature and distribution of the anthropogenic load on their watershed territories. Therefore, one of the top-priority tasks to restore

484

I. E. Kurbatova

the ecological well-being of water bodies is to obtain qualitative and quantitative estimates of this load on different parts of the watersheds. The problems of Krasnodar Reservoir pollution are directly related to entering polluted waters of the Kuban River and side tributaries of the reservoir, the most significant of which is the Belaya River, as well as the coastal settlements and agricultural land. The quality of the incoming water depends on transit transportation of pollutants from the headwaters, discharge of poorly treated wastewater from industrial enterprises, diffuse inflow of fertilizers and organic matter from fields and cattle farms, etc. Assessment of the anthropogenic load on the territory of the Belaya River basin and the study of the impact of its catchment on the formation and pollution of the Krasnodar Reservoir constitute the part of the research described below.

4.1.1

Natural Features of the Catchment Area

The Belaya River is the second largest tributary of the Kuban, its length is 266 km, and the basin area is 5,970 km2, which is almost 15 times larger than the area of the Krasnodar Reservoir. The source of the river lies on the Main Caucasian Ridge at altitude of 2,300 m (Fig. 14). The main sources of water supply in the basin are rain and snow precipitations (up to 2,700 mm of precipitation per year can fall in the headwaters of the basin) [18]. The river network in the basin is well developed. The density coefficient of the river network for the entire basin is 1.26 km/km2, and above Maykop City, it reaches 1.51 km/km2. The total length of all tributaries of the Belaya River is 7,527 km. According to hydrological observations, the annual river runoff volume is equal to 1.58 km3. The average annual water flow varies from 53 m3/s at the Grozny steading to 103 m3/s at the river mouth. The peculiarity of the water regime of the Belaya River is long spring-summer flood, characterized by general rise of the level due to snow melting in the mid-mountain and then in the high-mountain zones. The flood reaches its highest peak in late May-early June. Most of the catchment area is located in the zone of easily eroded rocks; therefore, the content of suspended solids in the Belaya River runoff is high, and it varies from 24 kg/s at the Grozny steading to 74 kg/s at the Severny steading (29 km from the mouth) [18].

4.1.2

Economic Development of the Catchment Area

The vast majority of pollutants from the catchment come into the river and is then transported by solid runoff particles to the receiving water body. Therefore, to determine the composition of the main pollutants brought with sediments of the Belaya River and accumulating in the reservoir water area, there was made analysis of the economic use of the Belaya catchment area, and the main centers of anthropogenic pollution were identified. These include cities and urban-type settlements in which almost all the industry of this territory, local centers of production specialization, as well as agricultural and cattle complexes are concentrated. It is clear that

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

485

Fig. 14 The Belaya River catchment: the image from the Landsat satellite, June 2004. Black line shows the area under study [12] (reused with permission from Current Problems in Remote Sensing of the Earth from Space)

settlements of all types are historically tied to water arteries and industry needs large volumes of water, at that significant part of landfills for solid household waste, agricultural lands and livestock complexes are located in delta-floodplain areas. Therefore it becomes obvious that most of the pollutants “float” along the river and its tributaries and ultimately accumulate in the formed delta and bottom sediments of the reservoir. Thus, the Krasnodar Reservoir is a powerful accumulator of heavy metals, oil products, synthetic surfactants, phenols, pesticides, and nitrogen compounds. The Belaya River basin is located on the territory of three administrative subjects: the Republic of Adygea (56% of the basin area), Apsheron (32%), and Belorechensky (12%) regions of the Krasnodar Territory. To identify the qualitative composition of the main chemical pollution of the surface waters of the Belaya River basin, one should consider the principal directions of economic development of these three regions.

486

I. E. Kurbatova

According to Rosstat (Federal State Statistic Service) (2020), 463 thousand people live in the Republic of Adygea, and approximately 47.3% are urban people, most of whom (140.5 thousand people) live in Maykop [27]. The key industries of the Republic are forestry; wood processing; wood-chemical, light, pulp-paper; gas producing (at local fields); food (including canning, wine, ethereal oil, tobacco fermentation, oil mill); and the production of building materials [28]. The power engineering of the Republic is mainly based on the water resources of the Belaya River where two hydroelectric power stations are built. The Maykop City (capital of Adygea) is the main center of coming pollutants into the Belaya River, since almost entire industry of the Republic is concentrated there. Since more than one million tons of waste is accumulated on the territory of Adygea annually, the collection, storage, and processing of industrial and household waste are the serious environmental problem for the Republic. Only 38% of the total amount of waste is recycled, and the rest is placed on this territory (Fig. 15) [29]. So, as far back as 2001, 62 landfills of solid household waste, 48 objects of temporary storage of livestock waste, and 165 objects of temporary storage of toxic waste at industrial sites and pesticide warehouses were registered in the Republic [30]. In most cases, these objects were located on the Belaya River catchment area and, as a rule, were located near the main watercourses or on flood plains. For example, the Maykop dump was placed on the first right-bank floodplain terrace with slopes of 0.001–0.005, and the dump of the Krasnogvardeiskoye village was formed at distance of 2.5 km from the Tshchik Reservoir. Both dumps were constant sources of surface and groundwater pollution. By 2011 on the Republic of Adygea territory, 20 authorized waste disposal sites with total area of 66 ha were organized; at the same time, 17 such unapproved sites were identified [31]. Also housing and communal enterprises are the main sources of pollution. So, as it was ascertained by the Kubanvodproekt Institute, in 2008 144 million m3 of wastewater was disembogued into the waters of the Kuban (mainly through the Belaya River and the Krasnodar Reservoir) from the territory of the Republic of Adygea. This volume included 23 million m3 of water containing pollutants at that 18.25 million m3 of them came from the sewage treatment plant of Municipal Unitary Enterprise (MUE) “Maykopvodokanal” which collected and purified municipal and industrial wastewaters of Maykop [32]. The total annual volume of polluted wastewaters discharged into the water bodies by a number of Adygean enterprises (MUE “Maykopvodokanal”, Maykop City; “Teploenergo” Company, Enem township; “Uslugu” Company, Adygeysk City; “Raduga” Company, Sovkhozny township; “Housing and communal services of the Teuchezhsky District” Company, Tlyustenkhabl township) amounted to 27.07 million m3, with the share of “Maykopvodokanal” of more than 80% from this volume in 2011 [32]. Discharge of such amount of contaminated water is largely due to inefficient operation of wastewater treatment plants, most of which are outdated both physically and morally. Chlorides, suspended solids, sulfates, calcium, organic matter, copper ions, total phosphorus, and oil products are typical pollutants coming from sewage. Great impact on the quality of the waters of the Belaya River and its tributaries is also exerted by

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

Garbage Landfills in the Territory of the Republic of Adygea

487

ban Ku

Ku ba n

Krasnodar

45°

r da sno oir Kra serv e R Bel aya

Belorechensk

he

kh a

Labinsk

Ps

Maykop

Apsheronsk

Tul'sky

h Ps

ek

Belay a

i ps zh rd Ku

ha

Dakh S a kh ra

Tuapse

y

Volume of Solid Garbage (m3) Tsitsa

> 1 500 000

Be

5 000 - 10 000

la y a

Kis h a

44°

Guzeripl

10 000 - 50 000

1 000 - 5 000

ea

39°

kS

< 100

ac

100 - 500

Caucasian State Wildlife Biosphere Reserve

Bl

500 - 1 000

40°

Fig. 15 Landfills of solid household waste of the Republic of Adygea [29] (adapted with permission from Kuban State Agrarian University, Krasnodar)

untreated storm runoff in all towns and settlements located directly on the river banks. On January 1, 2006, 94.7 thousand people (100.3 in 2019) lived in the Apsheron Region (Krasnodar Territory) of 2,443.2 km2 including 46,640 people lived on the territory of the Belaya River catchment area. There are 52 settlements in the Apsheron region consisting of 3 urban and 9 rural settlements (2019): Apsheronsk City with a population of 41.1 thousand people is the administrative center of the

488

I. E. Kurbatova

region. The leading place in the economy of the region belongs to industrial production with predominance of woodworking. Machine-building and metalprocessing enterprises of the region produce oil field and woodworking equipment. Food-processing industry is developed too. Livestock dominates in agricultural production. Plant cultivation is represented by grain, potatoes, vegetables, and forage crops. The largest number of enterprises is concentrated in trade, public catering, and consumer services. In Apsheronsk there are enterprises producing agricultural machinery and equipment as well as logging and woodworking, and in the Chernigovskoe Township, there are two logging and woodworking enterprises. Waste products from the vital activity of people and domestic animals come to the water intake with municipal and household sewage. At the same time, according to expert estimates [33], the number of polluting suspended solids is approximately 65 g/day per 1 person. The total estimated volume of discharged municipal and domestic wastewater can reach 90 tons per month. On the territory of the Belorechensky Region (Krasnodar Territory) of 1,326.6 km2, there are 11 municipal settlements including 1 town (Belorechensk) which is the administrative center of the region. In 2019, 108.7 thousand people lived there; of these, 47.4% of people lived in the city. The population of the part of the Belorechensky region in the Belaya River basin boundaries is about 66 thousand people (2018). Economy of the region is represented mainly by industrial production (chemical and food industry, woodworking). Most of the industrial enterprises are located in Belorechensk. Large-scale production of mineral fertilizers and sulfuric acid are situated here. The region also produces canned vegetables, vegetable oil, confectionery, flour, and mixed fodders. The area under crop cultivation is quite small, about 40% is occupied by cereal, and by 25% are under fodder and industrial crops. Main sources and types of pollution including waste of industries located in the Belaya River catchment area are shown in Table 5. As follows from the above, the emergence of environmental problems in the Belaya River catchment area is primarily due to intensive economic activity on its territory. These problems include: • Degradation of land resources (wind and water erosion of soils, reducing agricultural lands, soil fertility decline, excessive application of fertilizers, chemical weed killers and pesticides, salinization, waterlogging, etc.) • Destruction of forest resources, resulting in drainage and siltation of small rivers, increasing the runoff turbidity, soil erosion and degradation of the surface layer, as well as rise of mudflow and landslide hazard • Unregulated and uncontrolled discharges of untreated industrial, municipal, household, agricultural sewage containing oil products, synthetic detergents, organic matter, etc. • Air emissions of carbon and sulfur oxides, etc. • Degradation of protected natural territories (destruction of existent landscapes, national parks, disappearance of rare animal and vegetation species)

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

489

Table 5 Main sources and types of the Belaya river catchment pollution Main pollution sources Industry Electric power industry and power engineering Chemical and oil-chemical industry

Mechanical engineering and metal working, instrument-making industry Timber industry, woodworking, furniture production Building materials industry Light industry Food processing industry Flour and cereals and mixed fodder industry Medical industry Printing industry Municipal economy Household wastewater

Agriculture Cattle breeding Arable farming

Main pollutant types Wastewater, ash and slag, chemical water treatment waste, chamotte Wastewater, acidic phosphogypsum, rubber waste (tires, inner tubes, spew of rubber products, latex waste), lignin, spent catalysts Waste of ferrous and nonferrous metals, wastewater, galvanic slimes Waste wood (sawdust, shavings) including formaldehyde-contained, wastewater Cement dust, wastewater, building material culling (reinforced concrete, brick) Sewage, mackle paper, textiles, spent catalysts and reagents Wastewater, defecate of sugar industry, waste containers and packaging Wastewater, expired products Packaging materials, spent reagents, specific waste of medical institution Paper waste product, sewage Suspended organic and inorganic substances, ammonium nitrogen, biochemical oxygen consumption, synthetic surfactants Organic substances with high bacterial contamination Herbicides, mineral fertilizers, organochlorine and organophosphorus pesticides

These and many other problems (not mentioned here) of the Belaya River catchment area led to the transformation of the Krasnodar Reservoir into the giant accumulator of a large number of various pollutants, the presence of which, in turn, provokes secondary contamination of adjacent areas. So, in 20 settlements located in the shore zone of flooding, there was observed sharp sanitary and epidemiological situation caused by bacteriological and chemical contamination of local water sources and agricultural products.

4.2

Ecological-Geographical Mapping the Belaya River Basin

For ecological mapping of the catchment of interest, it is necessary to use remote sensing data providing information coverage of the entire watershed or its large parts

490

I. E. Kurbatova

with high periodicity. The analysis of these materials, as well as thematic maps and background scientific information, allows at the first stage obtaining preliminary (qualitative) assessment of the anthropogenic impact on the surface waters of the catchment under study, which leads to decreasing deficit of network field observation data on the hydrological and hydro-chemical condition of the Belaya River waters. When solving the assigned problem, remote sensing data were used in clarifying the geographical-hydrological zoning of the territory, identifying the condition of the natural-territorial catchment complexes at the current moment, and analyzing the type of anthropogenic load on the river basin and its distribution over the area. On decoding schemes there were highlighted river valleys, forest borders, deforestation areas, large settlements, transport network, agricultural land, etc. The obtained information was supplemented by cartographical and scientific reference data. The overall assessment of the role of the Belaya River catchment in the contamination of the Krasnodar Reservoir in the absence of ground-based hydrological observations in the crosspiece area was carried out using geographical and social-economic indicators. Development of ecological-geographical map of the Belaya River basin is the logical conclusion of the study, the purpose of which is to assess the extent of impact of the river catchment with intensive economical using territory of the receiving water body. When compiling the ecological-geographical map of the Belaya River basin on a scale of 1:400,000 (Fig. 16), there were taken into account the natural landscape differentiation of the region and the results of the geographicalhydrological zoning of the Krasnodar Territory carried out in the paper [34]. Such zoning makes it possible to establish relationship of the watercourses and reservoirs of the region with the geographical landscape as a whole and with the geological and geo-morphological structure, soil, and vegetation cover. The map shows water bodies (permanent and drying rivers, reservoirs), the boundaries of geographicalhydrological regions and their characteristics, the transport network (indicating the average pollution along roads of different classes), industrial centers (with list of the main pollutants and the volume of products for each industry in prices of 2001), places of mining minerals, and settlements (with characteristics of the sewage treatment system condition). Besides the average annual sediment runoff (kg/s) along the entire Belaya River channel is shown and the Krasnodar reservoir area dried over the years of its existence is depicted. The compiled map can be used as the base for further environmental studies of this region, identifying areas of maximum and minimum anthropogenic pressure on water bodies and qualitative (by composition) assessment of pollutants accumulating in the Belaya River delta and, accordingly, in the Krasnodar Reservoir.

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

Fig. 16 Ecological-geographical map of the Belaya River basin

491

492

I. E. Kurbatova

5 Conclusion Problems of pollution and siltation are more or less relevant for all artificial water bodies and primarily for reservoirs. Since the reservoir with its catchment makes up common geo-system space, the ecological condition of the reservoir in many respects depends on the natural features of the catchment and economic activity in its area. Therefore, the main idea of any programs for enhancement of this condition should be based on the concept of the organic unity of the reservoir and its catchment, and all measures to improve the living conditions of the reservoir should begin in the catchment. Regular observation of the various “catchment-watercoursereservoir” system components can be advisable to realize in the frame of the integrated geo-ecological monitoring in which the cartographical-aerospace information block is the important structural part. Inventory of the system components and monitoring their condition were carried out in the study according to the results of the analysis of cartographic materials obtained at different times and remote sensing data. This made it possible to trace the 40-year history of the Krasnodar Reservoir formation, to highlight the main stages of its development, and to assess the extent of the changes taken place. Assumptions are made on the causes which resulted in cardinal transformation and pollution of the reservoir. In our opinion, these reasons are due to project decisions adopted when creating the reservoir, the natural features of the Belaya River catchment located in the zone of easily eroded rocks, and the consequences of the economic development of its territory. Using space images of high and ultra-high resolution gave opportunity to study the structural features of the crosspiece and its overgrowing, to monitor changes of the zones of suspension propagation depending on the season, runoff volume, and the reservoir discharge magnitude. Resulted from comparing images obtained at different times, the rise of the Kuban River delta over the past 20 years was recorded, and the average extension speed was estimated. Free access to the daily hydrological data of the Kuban BWD allowed identifying the correspondence of satellite images to the reservoir-level marks at the date of shooting, which made it possible to carry out correct comparison of shoreline images in different years (and so to avoid erroneous estimates). It should be noted that the intensive changes of morphometric characteristics of the reservoir and its parts require to organize regular observations using spectrozonal satellite images (at least two times during each time interval of the greatest filling and discharge of the reservoir). The cartographical approach has shown to be effective when assessing anthropogenic load on the Belaya River catchment and when determining the spatial location of pollution sources relative to the main river and its tributaries. The deficit of ground-based observations was partially compensated by information from scientific reference sources on the presence of specific pollutants in the wastewater of various industries. The results of this study can be used to organize and carry out geo-ecological monitoring and to make forecast of the Krasnodar Reservoir development. Space

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

493

monitoring, as a constituent part of geo-ecological monitoring, becomes an important information link in the management of reservoirs and their basins, the main purpose of which is the conservation of water resources and the introduction of natural-preserving technologies. Acknowledgments The study was carried out within the framework of the State Program No 0147-2014-0005. Author thanks Prof. M.K. Bedanokov from Maykop State Technological University, Editor-inChief of Proceedings of the II International and Practical Conference “Applied aspects of geology, geophysics and geo-ecology using modern information technologies,” Maykop, 2015, for permission to reproduce two figures from the paper of V.I. Sychev and P.A. Rublev published there in my paper. Author thanks Deputy Editor-in-Chief O.Yu. Lavrova and Executive Secretary of Journal Current Problems in Remote Sensing of the Earth from Space T.Yu. Bocharova for permission to reproduce my author’s figures published in the Journal in preceding years in my paper. Author thanks Executive Secretary of poly-thematic online electronic scientific journal of the Kuban State Agrarian University Prof. E.V. Lutsenko for permission to reproduce one figure from the published there paper of S.A. Vladimirov and A.O. Karchevsky in my paper.

References 1. Lur’e PM (2002) Water resources and water balance of the Caucasus. Gidrometeoizdat, Saint Petersburg, p 506. (in Russian) 2. Voropaev GV, Avakyan AB (1986) Reservoirs and their impact on the environment. Nauka, Moscow, p 368. (in Russian) 3. Berkovich KM (2012) Riverbed processes in rivers influenced by reservoirs. Faculty of Geography, Moscow State University, Moscow, p 163. (in Russian) 4. Andreeva L. Update Shapsugskoye reservoir. Free Kuban. http://gazetavk.ru/?d¼2014-11-01& r¼4&s¼15587 5. Khapaev A (2020) Reconstruction of the Shapsug reservoir in Adygea is planned to be completed by 2026. TASS. https://tass.ru/v-strane/7767411?utm_source¼yxnews&utm_ medium¼desktop&utm_referrer¼https%3A%2F%2Fyandex.ru%2Fnews 6. Malik LK (2010) Emergencies at water facilities: causes and consequences. In: Koronkevich NI, Barabanova EA, Zaitseva IS (eds) Extreme hydrological situation. Media-Press, Moscow, pp 57–88. (in Russian) 7. Melnikova TN, Brusenskaya YV (2010) Hydro-meteorological water resources study the Republic of Adygea. Success Modern Sci 9:104–105. (in Russian) 8. Vorobiev YL, Akimov VA, Sokolov YI (2003) Catastrophic flooding in the early XXI century: lessons and conclusions. Dax Press, Moscow, p 352. (in Russian) 9. White GF (1986) Geography, resources and environment, volume 1: selected writings of Gilbert F. White, vol 1. University of Chicago Press, Chicago, p 486 10. White GF (1986) Geography, resources and environment, volume 2: selected writings of Gilbert F. White, vol 2. University of Chicago Press, Chicago, p 392 11. Kurbatova IE (2013) Geo-ecological monitoring Krasnodar Reservoir: systematic approach to organizing and information provision. Applied aspects of geology, geophysics and geo-ecology, using modern information technologies. In: Proceedings of the II international scientific and practical conference, Magarin O.G, Maykop. pp 126–136 (in Russian) 12. Kurbatova IE (2010) Utilization of space monitoring data for assessing ecology condition of large river catchments. Curr Prob Remote Sens Earth Space 7(2):157–166. (In Russian)

494

I. E. Kurbatova

13. Vereshchaka TV, Kurbatova IE (2002) Assessing the environmental value of water bodies on topographical maps. Izvestiya vysshikh uchebnykh zavedeniy. Geodesy Aerophotosurvey 6:101–117. (In Russian) 14. World Orbital Constellation of Remote Sensing Satellites (2009) Geomatics 3, 100–103 (in Russian) 15. Dvorkin BA (2009) The grouping of remote sensing satellites rapid eye: unique opportunities to address the monitoring problems. Geomatics 3:17–21. (in Russian) 16. Kurbatova IE (2012) Space monitoring negative situations in coastal zones of large reservoirs. Curr Prob Remote Sens Earth Space 9(2):52–59. (In Russian) 17. Khrustalev YP, Kolesnikov YS (1986) Natural conditions and natural resources. Rostov State University, Rostov-on-Don, p 368. (in Russian) 18. Lur’e PM, Panov VD, Tkachenko YY (2005) The Kuban River: hydrography and regime of runoff. Gidrometeoizdat, Saint Petersburg, p 498. (in Russian) 19. Borisov VI (1978) Rivers of Kuban. Krasnodar Book Publishers, Krasnodar, p 80. (in Russian) 20. Kuban (River) https://ru.wikipedia.org/wiki/%D0%9A%D1%83%D0%B1%D0%B0%D0% BD%D1%8C_(%D1%80%D0%B5%D0%BA%D0%B0) 21. Kurbatova IE (2014) Monitoring the transformation of Krasnodar reservoir utilizing high resolution satellite data. Curr Prob Remote Sens Earth Space 11(3):42–53. (in Russian) 22. Environmental Impact Assessment (2011) Project on the topic “Development of standards of permissible impact on the Kuban river basin”. Sovintervod, Moscow, 72. http://av.disus.ru/ programma/1448599-1-ocenka-vozdeystviya-okruzhayuschuyu-sredu-teme-razrabotkaproekta-normativov-dopustimogo-vozdeystviya-basseynu-reki-kuban-generalniy-dir.php 23. Sychev VI, Rublev PA (2015) Current condition and dynamics of the coastal zones and water bodies from satellite data of high resolution (for example, the Krasnodar reservoir) Applied aspects of geology, geophysics and geo-ecology, using modern information technologies. In: Proceedings of the II international scientific and practical conference, Maykop State Technology University, 2015, May 11–14, Kucherenko V.O., Maykop, pp 207–213. (in Russian) 24. Laguta AA, Pogorelov AV (2019) Change of morphometric characteristics of the Krasnodar water reservoir for the period of its operation (1973–2018). Intercarto. InterGIC. Maps and GIS in research environment of climate and environment changes, vol 25(2), pp 5–15 25. Alifirenko B, Bkahtoyarov A (2012) Tschikskoe Reservoir yesterday, today, tomorrow. In: Newspaper of the Krasnogvardeisky Administrative District of the Republic of Adygea “Friendship” of November 27, 2012. http://amokr.ru/files/amokr/uni_documents/187.pdf. (in Russian) 26. Salov GV (2018) Special aspects of Krasnodar reservoir operation with reduced flood-control storage level. In: Water resources of Russia. Current condition and management. Proceedings of the all-Russian scientific and practical conference, Sochi, 2018. October 08–14, vol 1. Novocherkassk. LIK. pp 190–197. (in Russian) 27. Atlas of the Republic of Adygea (2005) Publishing house “Lev Tolstoy”, Maykop. 79 p. (in Russian) 28. Bedanokov MK, Chich SK, Chetyz DY, Trepet SA (2020) Physical-geographical conditions of the republic of Adygea. In: Bedanokov M, Lebedev S, Kostianoy A (eds) The republic of Adygea environment. Springer, Cham 29. Vladimirov SA, Karchevsky AO (2005) Ecological and landscape monitoring of municipal solid waste landfills in the republic of Adygea. Poly-Thematic Online Sci J Kuban State Agrarian Univ 13(5):0130505020. (in Russian) 30. State report “Russian Federation on the state and protection of the environment in 2001” (2002) Russian Ministry of Natural Resources, NIA-Nature, Moscow, 451 p (in Russian) 31. State report “Russian Federation on the state and protection of the environment in 2011” (2012) Russian Ministry of Natural Resources, NIA-Nature, Moscow, 351 p (in Russian) 32. Environmental impact assessment on the topic “Development of the project Schemes of complex use and protection of water bodies of the Kuban River basin” (2010) 010184043914-2008-K. Krasnodar: Engineering Research Institute “Kubanvodproekt”. 196 p (in Russian)

Geo-ecological Monitoring Main Water Bodies of the Republic of Adygea Using. . .

495

33. Nezhihovsky RA (1990) Hydrological and environmental bases of water management. Gidrometeoizdat, Leningrad, p 228. (in Russian) 34. Nagalevsky YY, Korovin VV (1989) Geographic and hydrological zoning of the Krasnodar territory and environmental measures. Problems of research and use of natural resources NorthWestern Caucasus. Geographical Society of the USSR, Leningrad, pp 35–40. (in Russian)

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in 2015–2017 Vitaly I. Sychev

Contents 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Water Reservoirs in the Republic of Adygea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Remote Sensing of Water Bodies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Krasnodar Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Kryukovskoye Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Varnavinskoye Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Shapsugkoye Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Oktyabrskoye (Takhtamukayskoye) Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Shendzhiyskoye and Cheytukskoye Reservoirs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Maykop Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8 Kuzhorskoye Reservoir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

498 498 501 502 512 513 515 518 518 519 523 523 524

Abstract Climate and weather changes in recent years, along with other factors, have significantly affected the water regime of the water management complex located in the Kuban River basin in the North Caucasus. Such changes were especially significant in 2015–2016 in influencing the state of the water balance of reservoirs, their area, water volume, and other characteristics. The main components of the complex are Fedorovsky and Belorechensky retaining waterworks, Tikhovsky water-separating waterworks, and four large reservoirs: Krasnodar, Shapsugskoye, Kryukovskoye, and Varnavinskoye, designed to supply irrigation (primarily rice) and fish-reclamation systems, flood control, and prevention of catastrophic floods. Keywords Krasnodar reservoir, Kryukovskoye reservoir, Kuzhorskoye reservoir, Maykop reservoir, Oktyabrskoye (Takhtamukayskoye) reservoir, Remote sensing, V. I. Sychev (*) Russian State Hydrometeorological University, Sankt-Peterburg, Russian Federation e-mail: [email protected] Murat K. Bedanokov, Sergey A. Lebedev, and Andrey G. Kostianoy (eds.), The Republic of Adygea Environment, Hdb Env Chem (2020) 106: 497–524, DOI 10.1007/698_2020_594, © Springer Nature Switzerland AG 2020, Published online: 29 August 2020

497

498

V. I. Sychev

Republic of Adygea, Shapsugkoye reservoir, Shendzhiyskoye and Cheytukskoye reservoirs, Varnavinskoye reservoir

1 Introduction Water reservoirs, lakes, and ponds of the Krasnodar Territory and the Republic of Adygea have accumulated water reserves of about 2.5 billion m3. The main water basin of the Krasnodar Territory and the Republic of Adygea is the Kuban River. The total usable volume regulated by 148 reservoirs is 2.7 km3, a significant proportion of which (2.2 km3) falls on the Krasnodar Reservoir, located in the middle reaches of the river. The main purpose of the reservoir is to provide irrigation for more than 200 thousand ha of agricultural land, to protect about 600 thousand ha of farmland from the lower reaches of the Kuban from floods, and to provide fish spawning and transport releases to the estuaries of the Kuban and Protoka. Kryukovskoye (0.1 km3), Varnavinskoye (0.02 km3), and Shapsugskoye (0.13 km3) reservoirs that regulate the flow of the Trans-Kuban rivers are used to irrigate land and protect agricultural land from floods. The purpose of other smaller reservoirs (Fig. 1) is irrigation and fish farming [1]. Adygea is located in the central part of the Northwest Caucasus, in the basins of the Kuban, Laba, and Belaya rivers. The territory of the republic in the north and northeast is limited by the Kuban River and its tributary Laba River, in the south by the Main Caucasian Range, and in the southeast and southwest the border which runs through a broken line, reflecting the features of the settlement of Circassians in the river basins of Chekhrak, Fars, Belaya, Pshish, Psekups, Afips, and Kurdzhips. The length of the republic’s borders is more than 900 km; one third is water: in the Kuban, Krasnodar Reservoir, Labe, and Belaya rivers. In addition to Krasnodar and Shapsugkoye, other reservoirs of Adygea – Takhtamukayskoye, Shendzhiyskoye, Maykopsky, Cheytukskoye, and Kuzhorskoye – have a small area.

2 Water Reservoirs in the Republic of Adygea The Krasnodar Reservoir is located in the middle reaches of the Kuban River at 248 km from the mouth, directly above the city of Krasnodar, and controls more than 95% of the total flow of the basin. The reservoir bowl is located on the territory of two constituent entities of the Russian Federation: the Republic of Adygea (87% of the area) and the Krasnodar Territory (13% of the area) and extends on the floodplain lands of the Kuban River from the village of Voronezh to the City of Krasnodar. The Krasnodar Reservoir – the largest artificial reservoir in the North Caucasus – was built in 1973 and put into design operation mode in 1975. As of 2017, the reservoir

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in. . .

499

Fig. 1 A map of main rivers and water reservoirs in the Republic of Adygea

has been in operation for 42 years. The mirror area of the reservoir is 397.8 km2, the average depth is 5 m, and the maximum is up to 18 m at the dam. Other characteristics are given in Table 1. The Krasnodar Reservoir controls 96% of the annual flow of the Kuban River and is intended for cutting peak floods in order to eliminate flood threats in the territory with a total area of 600 thousand hectares with a population of about 300 thousand people; provision of municipal, agricultural, and industrial water supply; water supply for rice irrigation systems; providing water releases at the mouth of the Kuban River and its right arm of the channel for spawning migrations of sturgeons, fishes, etc.; supply of fresh water to fish farms with an area of about 150 thousand hectares in the Azov estuaries; and improving the conditions of navigation on the Kuban River and the channel on the Protoka for over 400 km [2].

500

V. I. Sychev

Table 1 Main characteristics of the Krasnodar Reservoir Minimum pool level or dead storage level (DSL) Level, Volume, m million m3 25.85 192

Normal рool level, NPU Level, m, abs. Volume, million m3 (VNPU) (НNPU) 32.75 1,798 (33,65 in initial (2,149 in initial project) project)

Maximum pool level or highest reservoir level (HRL) Level, Volume, m million m3 35.23 2,794

Over the years of operation of the Krasnodar Reservoir, as a result of channel processes, there has been a constant decrease in the water level in the Kuban River; therefore, there is a danger of violation of the regime in the downstream. The Krasnodar Reservoir, being an integral part of the Kuban water management complex, provides regulation of river flow for its optimal use in the national economy. During the operation of the reservoir, significant changes occurred in the bowl of the former Tshchikskoye Reservoir – it turned out to be practically isolated from the western part of the Krasnodar Reservoir and in the estuary of the Belaya River formed a forested watershed, blocking the river from the reservoir. The siltation of the bowl of the former Tshchikskoye Reservoir is much more active than in the rest of the area; the thickness of silt deposits increased up to 2.0–2.5 m here. The filling of the reservoir begins approximately from mid-November and lasts until May to June, after which it is discharged and the water level in the reservoir decreases. Minimum levels are observed in September to January. The discharge flow to the downstream is determined by the requests of water consumers, the availability of water in the reservoir, the flow of water along the rivers flowing into the reservoir, lateral inflow below the reservoir, and the channel capacity below the reservoir. The Krasnodar Reservoir changed the hydrological regime of the lower Kuban by regulating both liquid and solid runoff, which accelerated the process of deep erosion. A noticeable effect of the reservoir on deep erosion and a decrease in bottom marks in the river are observed over a 50 km stretch (from the dam to Afipsip aul). The Varnavinskoye and Kryukovskoye reservoirs, located in the left-bank floodplain of the Kuban River, are mainly used for irrigation and cutting of high flood peaks. Together with the Krasnodar Reservoir and the Kuban and Protoka Rivers deboning system, they are part of a single water management complex for flood protection in the Lower Kuban. The normal pool level (NPU) of the Varnavinskoye Reservoir is 9.5 m (abs., Baltic height system BHS-77). The total volume of the reservoir at the NPU is 160 million m3, the useful volume is 40 million m3, the water mirror area at the NPU is 45.2 km2, the length of the reservoir is 11 km, the width is 4.23 km, and the prevailing depth is 1.5–2 m. The catchment area is 984 km2. The average long-term runoff is 366 million m3. The Adagum and Abin rivers flow into the Varnavinskoye Reservoir, and the Kuafo, Shibs, and Shibik rivers are their tributaries. By 2011, the

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in. . .

501

siltation capacity of the reservoir bed reached 1 m in the confluence zone of the Abin and Adagum rivers. The Varnavinskoye Reservoir, if necessary, receives the waste waters of the Kryukovskoye Reservoir, which does not have an autonomous discharge. For the functioning of the reservoir in the required mode, it is necessary to carry out work to remove sediment. Kryukovskoye Reservoir was built in 1967–1972 on the site of flooded swamps, impassable shrubs, the so-called waste land, located on the former Kryukovsky estuary, in the Seversky District of the Krasnodar Territory, 17 km north of the village of Severskaya. It is used for irrigation and cutting of high flood peaks, as well as for water supply to the Kryukovsky irrigation system and fisheries, included in the single water management for complex flood protection of the Lower Kuban, and formed by water-damming dams on the site of the Kryukovsky estuary and the surrounding land. The total length of the dam surrounding the reservoir is 23.37 km. The reservoir was created to regulate the floods of several rivers – the Ile, Peschanka, Bugai, Akhtyr, Eybza, Bugundyr, and Sukhoi Khabl, which is a continuation of the Nagorny Canal with an inflow volume of 18.5 thousand m3. The main volume of water discharged from the reservoir is carried out along the Kryukovsky discharge channel with a length of 21.5 km and a bottom width of 6–30 m in the amount of 418 thousand m3. The NPU is 14.4 m. The total reservoir volume at the NPU is 111 million m3, the useful one is 100.4 million m3, the maximum is 203 million m3, the water mirror area at the NPU is 40.2 km2, and the length of the reservoir is about 8 km; width, 5.7 km; average depth, 3.2 m; and maximum, 3.9 m. The reservoir consists of several objects: spillway, water intake, fish protection, forest shelter, waste, and feeding structure. The reservoir includes pumping stations and several dams – western, northern, and southeastern. The water in the reservoir belongs to the class of moderately polluted. In water in large quantities, there is a snag. In addition to those listed in the Krasnodar Territory, the Neberdzhaev Reservoir, the reservoir of the Belorechensk Hydroelectric Power Station, and the Ganzhinsk Reservoir are operated, but a description of their condition remains outside this article.

3 Remote Sensing of Water Bodies The main problem in ensuring the safety of hydraulic structures of the Krasnodar Reservoir is the need for their reconstruction in order to comply with current regulatory documents, including with the aim of raising the capital class to the first. Currently, the hydraulic structures of the Krasnodar Reservoir have a normal level of safety. Kryukovskoye and Varnavinskoye reservoirs are in good condition but have a reduced level of safety [2]. The most significant and informative ways of providing data with continuous coverage of vast areas of reservoirs and river catchments in the face of a shortage of network field observations of the state of their waters include the use of satellite imagery, which are designed to identify actual areas of water bodies and the nature of

502

V. I. Sychev

the spread of river sediment over the reservoir, depending from the regime and volume of solid runoff. Such information, combined with the results of observations for different dates of field surveys, allows us to estimate the extent and speed of siltation of the reservoir. The information obtained is important for solving various water management problems, in particular, obtaining a prognostic assessment of the capacity of a reservoir during its intensive siltation. Analysis of satellite images of the Krasnodar, Kryukovskoye, Varnavinskoye, and Shapsugkoye reservoirs allowed us to show their condition at a NPU (32.75 m abs.), as well as with a significant decrease in the level in emergency situations at water bodies. For example, at the beginning of 2015, the water level in the Krasnodar Reservoir was 28.69 m, and the volume (V) was 648 million m3. In accordance with the Rules for the Use of Water Resources, the Krasnodar Reservoir at the beginning of the year usually operates in the filling mode. With heavy rainfall on 12 January 2015, there was a rise in water level and river spills in the Abinsky and western regions of the Krasnodar Territory, resulting in partial flooding of some territories. There were no casualties or destruction. In Fig. 2 shows Landsat-8 OLI data from 15 January to 30 October 2015.

3.1

Krasnodar Reservoir

After heavy rainfall, the influx of water into the Krasnodar Reservoir, part of which was covered with ice, increased from 209, 394, 546, to 800 m3/s from January 12 to 15, discharge remained within 101–102 m3/s, and the water level rose slightly from 29.63 to 30.04 m abs. (NPU ¼ 32.75 m abs.). The volume of water in the reservoir varied from 855, 893, to 953 million m3. Emergencies at water bodies were not observed. In Fig. 2 (a) according to Landsat-8 OLI data dated January 15, in the eastern part of the image on land there is snow cover; the Krasnodar Reservoir is partially covered with ice. By the end of March, the water level in the reservoir was 31.98 m, and the volume was 1,512 million m3. The intermediate values from January 15 and March 20 (Table 2) correspond to the Landsat-8 OLI satellite data shown in Fig. 2a, b. Table 2 shows the characteristics of the Krasnodar Reservoir in 2015, corresponding to satellite images in Fig. 2. Note that in the high-resolution image dated March 25 (inflow of 156 m3/s, discharge of 110 m3/s, water level is 31.91 m abs., and the volume of water in the reservoir is 1,489 million m3) at a level 74 cm lower than the NPU, the gulf near the village of Starokorsunskaya is still connected by a narrow strait with the Krasnodar Reservoir (see Fig. 3). The color of the water of the Belaya River and the canals differs from the water of the Kuban River, the color of which at a distance of about 1 km returns to the original. In the southern part of the image, the color of Belaya mineral suspension characterizes the growing influx of water into the reservoir. In early April, the water level in the Krasnodar Reservoir was 31.99 m (V ¼ 1,517 million m3), snow melting in the mountains began, and the average inflow increased.

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in. . .

503

Fig. 2 The dynamics of the coasts of the Krasnodar, Shapsugkoye, and Kryukovskoye reservoirs according to OLI Landsat 8 in 2015: (a) 15 January 2015, (b) 20 March 2015, (c) 23 May 2015, (d) 27 August 2015, (e) 30 October 2015

504

V. I. Sychev

Fig. 2 (continued) Table 2 Water management situation in the zone of the Krasnodar Reservoir in 2015 (NPU ¼ 32.75 m, abs.) Krasnodar Reservoir Water inflow, m3/s Reset, m3/s Water level, m, abs. Water volume, million m3

15.01 800 103 30.04 953

20.03 105 102 31.85 1,468

23.05 506 617 32.12 1,563

27.08 44 425 27.77 476

28.09 61 80 25.97 206

30.10 426 123 26.99 347

Subsequently, the average discharge was lower than the inflow, which led to an increase in the reservoir capacity. Due to heavy rainfall on the rivers of the Kuban River basin, in the southeastern part of the Krasnodar Territory, floods occurred in May, which were accompanied by an increase in the average inflow into the reservoir. This contributed to raising the level in the upper pool of the reservoir above the level of NPU. Table 2 shows the level (32.12 m) and the volume of water in the reservoir (1,563 million m3) on 23 May 2015, and the corresponding satellite image Landsat-8 OLI is shown in Fig. 2c). Due to the lack of precipitation in the

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in. . .

505

Fig. 3 The northern part of the Krasnodar Reservoir on 25 March 2015: a channel (1) between Belaya River (2) and Kuban River (3), a bay near the village of Starokorsunskaya (4), remote deltas of the Kuban (5) and Belaya (6) rivers. (Image2016 © DigitalGlobe)

summer on the rivers of the Kuban River basin, the average discharge was higher than the inflow, which led to a decrease in the volume of water in the reservoir. By the end of September, according to the data of the Kuban Basin Water Administration (BWA), one of the lowest water levels in the reservoir in recent years was 25.93 m (V ¼ 199 million m3), and on September 28, a level close to the minimum was 25.97 m (Table 2), which is 6.90 m lower than the oil treatment facilities and only 0.12 m higher than the dead volume level (DSL) of 25.85 m. In Fig. 4 shows a Landsat-8 OLI photograph illustrating the advancement of the boundary of the fixed channel ramparts of the Kuban River, which at a level close to the NPU forms a small coastal bay (region 4 in Fig. 3). However, as the level near the village decreases, the Starokorsunsky Bay completely separates from the reservoir, and the area of the isolated basin decreases. The Kuban remote delta continues to move westward. Only the Pshish River, despite the siltation of the estuary, restored the channel and, like the Kuban, carries water to the reservoir. The remote delta of the Belaya River, at values close to the NPU, which usually dumped water into the reservoir (Fig. 2), no longer reaches it. Short-period changes in the level and volume of water in the Krasnodar Reservoir were analyzed for the period 2014–2017. Comparison of satellite images of the coastal zone of the Krasnodar Reservoir (plot 4 in Fig. 3) with photographs of the

506

V. I. Sychev

Fig. 4 Dynamics of the coastal zone of the Krasnodar Reservoir according to OLI Landsat 8 28 September 2015

Fig. 5 The coastal region of the Krasnodar Reservoir in the area of the village of Starokorsunskaya on 7 July 2016, at the level of 33.06 m. (Photo by V.I. Sychev)

area of the village of Starokorsunskaya (Fig. 5) shows its dynamics in 2014–2016 (Fig. 6). By the end of September 2014, according to the Kuban BWA, the water level in the reservoir was 27.24 m (V ¼ 395 million m3), decreasing from 32.9 m in early July, which is 5.51 m lower than the NPU. In Fig. 6a, an exposed wide coastal bank is clearly visible, which in many places is covered with vegetation and flowering algae. In spring, the reservoir level became much higher: 25 March 2015 was

Dynamics of Water Bodies of the North Caucasus by Remote Sensing Data in. . .

507

(a)

(b)

(c)

(d)

Fig. 6 The coastal zone of the Krasnodar Reservoir in the vicinity of the village of Starokorsunskaya: (a) 23 September 2014, (b) 25 March 2015, (c) 8 April 2015, (d) 1 October 2016 (Image2016 DigitalGlobe)

508

V. I. Sychev

Table 3 Characteristics of the level, inflow, and discharge of water in the Krasnodar reservoir, corresponding to the data in Fig. 6 Date 25/03/2015 8/04/2016 1/10/2016

Level (upper pool), m, abs. 31.91 32.91 29.03

Inflow, m3/s 155 195 394

Discharge (lower pool), m3/s 110 283 253

31.91 m (Table 2) and 8 April 2016 – 32.91 m. Moreover, in Fig. 6b, c, there is a noticeable difference in the coastal area at different levels of the reservoir. Prior to flooding, the modern Kuban floodplain towered 0.5–2 m above the water edge. Several steps of various heights, corresponding to seasonal fluctuations in the water content of the river, stood out on its surface. The floodplain terrace of the right bank of the Krasnodar Reservoir was separated from the floodplain by a steep ledge with a height of 5–15 m. Currently, the most intensive reformation process takes place along the right steep and deep bank. In Fig. 5 a steep coast is clearly visible at a level of 33.06 m on 7 July 2016 (Table 3). In vast areas of the right bank of the Krasnodar Reservoir, the flooded channel of the Kuban comes very close to the shore, and the collapsing material is quickly washed and drifted down. The transfer of collapsed material is facilitated by a deep and intense drawdown. This prevents the formation of an abrasive coastal shallow with a width that would be sufficient to absorb the energy of high waves. From the side of the village Starokorsunskaya, there is a free passage to the water. In winter, the water level drops, and there forms an extensive sandbank (Fig. 6a, d). In some places, the remains of the banks washed up by the water look like fantastic ruins. Locals sometimes find ancient vessels washed out of a powerful cultural layer located from Starokorsunskaya and further along the former channel toward Ust-Labinsk town. Layers of clay earth fall off the steep banks into the water every spring, exposing traces of the culture of the past millennia, and carry them to the bottom. After heavy rains in early June 2016, the discharge of water from the Krasnodar Reservoir was increased; its level was stabilized, but to stop its growth, the discharge of water was increased from 1,100 to 1,200 m3/s (Table 4). Such an increase in discharge did not lead to flooding in the lower reaches of the Kuban River, which may be possible only if the discharge volume is more than 1,250 m3/s. For estimates of changes in the basin area in June to August 2016, Fig. 7 shows the data from Landsat-7 ETM + and Landsat-8 OLI sensors from 18 and 26 June 2016 at a time when the level marks of 32.93 and 33.08 m slightly (