Habitat, Ecology and Ekistics: Case Studies of Human-Environment Interactions in India [1st ed.] 9783030491147, 9783030491154

Citation preview

Advances in Asian Human-Environmental Research

Rukhsana Anwesha Haldar Asraful Alam Lakshminarayan Satpati  Editors

Habitat, Ecology and Ekistics Case Studies of Human-Environment Interactions in India

Advances in Asian Human-Environmental Research

Series Editors Prof. Marcus Nüsser, South Asia Institute, University of Heidelberg, Germany Editorial Board Prof. Eckart Ehlers, University of Bonn, Germany Prof. Harjit Singh, Jawaharlal Nehru University, New Delhi, India Prof. Hermann Kreutzmann, Freie Universität Berlin, Germany Prof. Kenneth Hewitt, Waterloo University, Canada Prof. Urs Wiesmann, University of Bern, Switzerland Prof. Sarah J. Halvorson, The University of Montana, USA Dr. Daanish Mustafa, King's College London, UK

Aims and Scope The series aims at fostering the discussion on the complex relationships between physical landscapes, natural resources, and their modification by human land use in various environments of Asia. It is widely acknowledged that human-environment interactions become increasingly important in area studies and development research, taking into account regional differences as well as bio-physical, socioeconomic and cultural particularities. The book series seeks to explore theoretic and conceptual reflection on dynamic human-environment systems applying advanced methodology and innovative research perspectives. The main themes of the series cover urban and rural landscapes in Asia. Examples include topics such as land and forest degradation, glaciers in Asia, mountain environments, dams in Asia, medical geography, vulnerability and mitigation strategies, natural hazards and risk management concepts, environmental change, impacts studies and consequences for local communities. The relevant themes of the series are mainly focused on geographical research perspectives of area studies, however there is scope for interdisciplinary contributions. More information about this series at http://www.springer.com/series/8560

Rukhsana  •  Anwesha Haldar Asraful Alam • Lakshminarayan Satpati Editors

Habitat, Ecology and Ekistics Case Studies of Human-Environment Interactions in India

Editors Rukhsana Department of Geography Aliah University Kolkata, West Bengal, India Asraful Alam Department of Geography University of Calcutta Kolkata, West Bengal, India

Anwesha Haldar Department of Geography East Calcutta Girls’ College West Bengal State University Kolkata, India Lakshminarayan Satpati Department of Geography UGC-HRDC, University of Calcutta Kolkata, West Bengal, India

ISSN 1879-7180     ISSN 1879-7199 (electronic) Advances in Asian Human-Environmental Research ISBN 978-3-030-49114-7    ISBN 978-3-030-49115-4 (eBook) https://doi.org/10.1007/978-3-030-49115-4 © Springer Nature Switzerland AG 2021 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

Foreword

The quality of human life in India especially in urban areas of India has appreciably improved during last few decades but the resultant stress on environment was also observed far above the permissible limit. The climate change, deteriorating air quality, pollution of both ground and surface water, unscientific disposal of waste and loss of biodiversity are issues of concern. The target to contain the trend of temperature increase within 1.5  °C from 1850 to 2100 seems impossible to achieve. The Central Pollution Control Board of India has identified 122 non-attainment cities where air quality deteriorates and becomes unfit for breathing especially during four winter months (November to February). Unfortunately, the list includes seven cities/towns of West Bengal. These are Kolkata, Haora, Haldia, Barrackpur, Durgapur, Raniganj and Asansol. The air quality management seems to be most important challenge as it affects all including the new born baby to senior most citizen of the country.Amongst the twelve identified pollutants PM 10 and PM 2.5 attract special attention. Several actions are being taken to improve the air quality. The delayed monsoon rainfall along with mid-monsoon breaks often affects the Kharif cultivation in the Indian Subcontinent. The sea level in the Bay of Bengal is reportedly rising and Sea Surface Temperature is increasing at a rate far above the global average. These meteorological events combined together gives rise to devastating geo-climatic hazards, thus affecting the ecosystem, especially in the Sundarban Delta. The slow subsidence of land along the coast, along with sea level rise results in encroachment of sea and erosion of the littoral tracts. It seems to be a paradox of nature that in spite of deposition of 100 billion tonnes of sediment load carried by the Ganga-Brahmaputra-Meghna system, no large scale building of land was recorded in the western part of the Delta, while the Meghna estuary in the east, has grown appreciably during the last two centuries. It may be noted that the average rate of land subsidence in coastal Bengal is 2.9mm/year and sea level rise being 3.6mm/year, the combined negative change in surface level may be 6.5mm/year approximately. The littoral tracts of Bengal stands at 2 to 4 m above mean sea level and the average bench mark of Kolkata is 6 m. So the engulfing of the living city and the major parts of south Bengal by 2070 within the sea is a myth. But the occasional high intensity, short duration rainfall and cyclonic storm surge may seasonally subv

vi

Foreword

merge the city at some pockets and devastate the life and livelihood of the people living along the coast. The pollution control in this country was taken into consideration with the introduction of Water Pollution Prevention Act (1974), Air Pollution Prevention Act (1981) and Environment Protection Act (1986). Earlier in absence of any regulatory measures our environment had already been impaired. Since mid 1960s India switched over organic to chemical agriculture. Though food grain production has increased from 80 million tons to 270 million tons, the indiscriminate application of chemical fertilizers, pesticides and over exploitation water resources have impaired our environment. Many rivers are longitudinally disconnected consequent to building of dams and barrages which do not allow minimum ecological flow in the channel. The aquatic habitats are thus fragmented. Further, towns, cities and grossly polluting industries discharging their liquid waste into the rivers resulting lowering of dissolved oxygen, increasing biochemical oxygen demand and thousands of coliform bacteria making the river polluted. The 118 towns/cities developed along the bank of the Ganga discharge wastewater more than 6000MLD making it unfit for bathing. In India, where population growth and urbanisation are happening at a rapid rate, waste management is considered as one of the topmost challenges. In 2011 the population of India was 1.21 billion and 31% of this live cities/towns. Only municipal solid waste in India was whopping 1,43,499 tonnes per day in 2014–2015. The segregation of waste at source and subsequent processing is far from reality. Therefore increased efforts are required for facing mammoth waste management problem. This collection of essays elaborates the challenges of environment management in this subcontinent. I wholeheartedly endorse this book and congratulate the contributors and the editors for their outstanding efforts. Kalyan Rudra Chairman, West Bengal Pollution Control Board Kolkata, West Bengal, India

Preface

This volume focuses on the importance and power of spatial thinking and planning by applying various statistical methods and geospatial technologies in solving both past and current problems pertaining to environmental degradation, climate change, habitat linkages, environmental pollutions, carbon foot print in urban area, land use pattern, agriculture, hazards and environmental management for sustainable development. It consists of a wide range of case studies from various regions of the developing countries, especially of India, which address to mainstreaming sustainable development paradigm into their socio-economy pursuits for improving environmental management in a befitting manner. Presently, our planet Earth is facing several environmental problems, from climate change and natural disaster to biodiversity loss, with visible effects of environmental degradation, economic slowdown and human distress. Given the fundamental geographic underpinning of environmental issues, geography and geoinformatics can be applied in assessing environmental problems and planning for sustainable development which is largely required by natural as well as social scientists, policy makers and communities at large. The contents of this book are divided into three main parts: habitat and environmental issues of human concern; ekistics and ecology of social environment; and hazards and environmental management for sustainable development. The book has 18 chapters contributed by authors of their respective fields of expertise, each focusing on a specific theme to cover diverse perspectives of geo-environmental knowledge. We hope the book will be well accepted by readers interested in contemporary environmental issues of the developing countries, particularly with regard to case studies on habitat, ecology and ekistics for environmental sustainability and human welfare. Out of the three parts, Part I consists of six chapters and scientifically portrays the habitat and environmental issues of human concern. The first chapter, entitled ‘An Overview of the Concept of Habitat, Ecology and Ekistics’, contributed by the editors, deals with the background, historical perspective, present situations,

vii

viii

Preface

i­mportance, methodology, indicators and determinants of human–environment interactions. The second chapter, ‘Contemporary Environmental Issues − The Indian Perspective’, contributed by P. K. Sikdar (Kolkata), is of enormous scholastic interest. This chapter depicts the various dimensions of environmental issues of contemporary India. Tapash Mandal, Jayanta Das, A.T.M. Sakiur Rahman and Piu Saha (India and Bangladesh) looked into the ‘Rainfall Insight in Bangladesh and India: Climate Change and Environmental Perspective’ (Chap. 3), which is an important issue in terms of global climate change. They have attempted to highlight detailed characteristics of rainfall in Bangladesh and India from 1951 to 2015. Rainfall features like Seasonality Index (SI) and Precipitation Concentration Index (PCI) were estimated to characterize spatial pattern of rainfall using Innovative Trend Analysis (ITA), Mann-Kendall (MK) and modified Mann-Kendall (mMK) test to detect the trend in the data and Sen’s slope (Q) estimator to calculate the magnitude of such changes. Chapter 4 entitled ‘Habitat Linkages for Asian Elephants in Central India’ by Abhijitha C.S., G Areendran, Pamposh, Krishna Raj, and Mehebub Sahana (Delhi) is another important contribution of this book. This study focuses on the fragmentation and habitat loss as well as encroachments by human beings, which are some of the few reasons behind the dreadful destruction of these habitats of Asian elephants in Jharkhand and West Bengal. Securing connectivity between habitat patches is the only way to prevent their degradation and isolation as these corridors maintain the movement of different wild species and biodiversity. Chapter 5 is entitled ‘Environmental Pollution and Municipal Solid Waste Management in India’, authored by Mithun Ray, Avaya Chandra Mohapatra, Suman Das, Asraful Alam and Biman Ghosh (Shilong and Kolkata). They discuss the issues related to municipal solid waste management in India and emphasize on identification and generalization of the shortcomings towards sustainable waste management for a cleaner and healthier urban environment. A comprehensive survey of literature has been carried out covering studies from every part of the country with special reference to class I and class II urban centres, which will be very helpful to urban environmentalists. The study by Rukhsana and Md Firojuddin Molla (Kolkata) on ‘Assessment of Carbon Footprint Across Urban Households in Kolkata’ (Chap. 6) is based on qualitative and quantitative analysis. This chapter focuses on accounting the amount of important greenhouses gases emission and carbon footprint at household level in the KMC area. Part II combines the description and analysis of five chapters (Chaps. 7, 8, 9, 10 and 11) relating to ekistics and ecology of social environment highlighting environment–human interactions. Teesta Dey (Kolkata) analysed the ‘Contested Urban Ecology: The Ekistics of Dilapidated Dwellings in Kolkata’. In this (Chap. 7), she

Preface

ix

attempted to discuss the spatio-temporal variations in the existence of dilapidated houses, to provide an overview of the actual structural conditions through Dilapidation Index, and to critically assess the perspectives of the residents of these buildings through behavioural analysis. She also tried to identify some possible reconstruction methods for developing a holistic inner city renewal and redevelopment plan. The work of Rajat Kumar Paul and Pradip Patra (Kolkata), ‘Spatio-temporal Transformations of Urban Built-up Areas in West Bengal’ (Chap. 8), addresses the transformation of urban built-up area and used freely available satellite images from the USGS and different types of remote sensing indices (RSI) for calculation of, namely, Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Enhanced Built-Up and Bareness Index (EBBI), and Built-up Index (BUI). It continued by focusing on the unplanned growths of built­up area. Chapter 9 is the study of ‘Population Pressure and Urban Sprawl in Kolkata Metropolitan Area’ and was carried out by Rukhsana and Md Hasnine (Kolkata) based on satellite data. This chapter provides an important method to evaluate the pace and magnitude of urban sprawl. In this chapter the study had been carried out in three time spans (1990–2000, 2000–2010 and 2010–2017). They applied ‘weight of evidence method’ to measure the transition probability of each zone. They also used Shannon’s entropy and Pearson’s Chi-square methods to evaluate the degree of sprawl and test of significance. Somnath Mukherjee and Uma Sankar Malik (Bankura and Santiniketan, respectively) authored ‘Social Ecology and Models of Santals and Kheria Sabars’ (Chap. 10). They emphasized on social ecology to correlate between humans and their environment and gave attention to social, institutional and cultural contexts of people–environment relations while drawing on a problem-based theoretical framework developed for the analysis of complex societal issues. Moumita Ghosh and Lakshminarayan Satpati’s (Kolkata) study (Chap. 11) of the cultural heritage of marginalized people attempted to highlight the very specific subject matter of Gajan-gaan (Gajan songs), which originated in the heart of common people, and this had been expressing their social, religious and political status, demonstrating the history of class struggle, social stratification and exploitation of marginal class by the upper class under contemporary political economic hegemony. They established that rich people seldom participated in Gajan; it’s only the backward sections of the society who participated in it. Part III of the book comprises seven chapters and exhibits the underlying facts or ideas relating to hazards and environmental management for sustainable development. The contribution of Anwesha Haldar, Samiparna Das, Riyanka Chatterjee and Lakshminarayan Satpati (Kolkata) in their study on ‘Adaptation Strategies for Erosion Induced Environmental Vulnerability and Displacement’ (Chap. 12) mainly

x

Preface

highlights the adaptation strategies, capacity and resilience of the people ravaged by the estuarine and riverine erosions and consequently displaced from livelihood and habitat, and investigates human coping mechanisms under adverse climatic and geo-hydrological systems. ‘Flood Frequency Analysis and its Management in the District of Bardhaman, West Bengal’ (Chap. 13) has been carried out by Subodh Chandra Pal, Biswajit Das, Sadhan Malik, Manisa Shit and Rabin Chakrabortty (Bardhaman) with focus on the application of statistics and GIS for the management of flood in Ketugram-I and II C.D. Blocks of Bardhaman District, West Bengal. They suggested useful measures like channel diversion, channel improvement and planting of vegetation. This management plan can minimize the magnitude of flood and eventually save the flood-­ affected people in the area. ‘Spatio-temporal Extent of Agricultural Drought in West Bengal’ (Chap. 14), authored by Mou Dey, Dipanwita Dutta and Abira Dutta Roy (Midnapore and Bankura, respectively), is another important contribution of this book. This study focused on the application of remote sensing and GIS for monitoring the spatiotemporal extent of agricultural drought over West Bengal, especially in its western districts. The authors considered, over the years, preparing indices for assessment of crop condition and used various techniques like VCI (Vegetation condition Index), NOAA, STAR, RAI (Rainfall Anomaly Index), and Yield Anomaly Index (YAI), which may be helpful for further research. Chapter 15 on ‘Drought Induced Human Mobility in Purulia District of West Bengal’, written by Shrinwantu Raha and Shasanka Kumar Gayen (Cooch Behar), is also an interesting and useful addition to this book. This chapter addresses four key areas: the first section deals with hazard hotspot zone identification based on Standard Precipitation Index (SPI), the second section deals with the livelihood turmoil due to drought-induced water stresses, the third section deals with the fluctuation of labour pattern and scenario of migration, and the final section deals with optimality and feasibility study with respect to demands of the migrants. The work of Sourav Saha, N. Deka and A. K. Bhagabati (Gauhati) on ‘Traditional Water Management and Agricultural Sustainability in a HimalayanFoothill Village of Assam’ (Chap. 16) is based on field work from a village in Baksa District, Assam. They studied the water management system followed traditionally by the people of the locality and examined the situation in the context of indigenous knowledge system and sustainable development of the area. N. C. Jana and Manas Pal (Bardhaman) contributed an interesting article (Chap. 17) on the topic ‘Management of Wastelands in the Chotanagpur Plateau Fringe – Lessons from a Village Level Study in the District of Birbhum, West Bengal’. In this chapter they selected four sample villages as representative of four different categories of wastelands and analysed information to reveal environmental control on concentration of different types of wastelands as well as their close association with each other and with the other forms of land use practices.

Preface

xi

The study by Anindya Basu (Diamond Harbour) on ‘Eyeing Sustainability: Electronic Waste Handling in Kolkata’ (Chap. 18) is an attempt to provide an idea about the e-waste composition, current scenario, effectiveness of the legal framework, nature of recycling, related environmental and occupational hazards and awareness level. This chapter is a micro level case-study conducted in the KMC area of West Bengal, India, which reflects the ground reality of the Global South. She has also stressed on formulating a roadmap for sustainable and effective e-waste management primarily through Extended Producer Responsibility (EPR) approach ensuring a healthy environment. Kolkata, West Bengal, India 30  December 2019  

Rukhsana Anwesha Haldar Asraful Alam Lakshminarayan Satpati

About The Book

Brief Description The present volume entitled ‘Habitat, Ecology and Ekistics Case Studies of Human-Environment Interactions in India’ is intended to incorporate much needed interdisciplinary approaches to include the neo-millennial agenda of environmental problems that mankind is facing for which human beings have played a significant adverse and irreversible role to redefine environmental issues in this era of the ‘Anthropocene’. This book encompasses theoretical as well as applied aspects using both thematic and regional case studies to highlight the dynamicity of human-­ environment relationships at various spatio-temporal scales. It will attract the attention of students, researchers, academic personnel, policymakers and other inquisitive readers interested in various aspects of human-environment relationships, particularly in the Indian context. The papers are organised into four sub-themes, each part including a set of articles dealing with a particular issue of human-environment linkages. The volume was prepared to include eighteen research articles, including the introductory note, under three major sections.

Key Features • The book presents an exhaustive discussion, that includes a more methodical and innovative approach, focused on overall environmental development that will usher sustainability both at the local and global perspective. • The focal themes elaborate the essential components of human interactions with nature, its impact on the surrounding natural and social environments, and ­management techniques. It aims to initiate new approaches and ideas on various issues of anthropogenic influences on environment in relation to sustainable development. This has been formulated on a comprehensive philosophical and

xiii

xiv

About The Book

theoretical basis and clearly linking the conceptual with the practical aspects of disciplinary studies. • The chapters include discussion on how maladjustments, disturbances and disasters have sometimes been inevitable by-products of the same human-­environment interactive systems that provide us with opportunity, inter-linkages and implications for strategies, policy and practice towards mutual coexistence. • The book includes more than 400 figures, coloured photographs, diagrams, and maps prepared using Geoinformatic tools to highlight major themes and to clarify the concepts introduced in each of the chapters. • The exhaustive references and review of literatures in each of the articles may be found useful for further research by the serious scholars. Kolkata, West Bengal, India 30 December 2019

Rukhsana Anwesha Haldar Asraful Alam Lakshminarayan Satpati

Contents

Part I Habitat and Environmental Issues of Human Concerns 1 Habitat, Ecology and Ekistics: An Overview����������������������������������������    3 Rukhsana, Anwesha Haldar, Asraful Alam, and Lakshminarayan Satpati 2 Contemporary Environmental Issues – The Indian Perspective ��������   11 Pradip K. Sikdar and Soumyajit Basu 3 Rainfall Insight in Bangladesh and India: Climate Change and Environmental Perspective ��������������������������������   53 Tapash Mandal, Jayanta Das, A. T. M. Sakiur Rahman, and Piu Saha 4 Habitat Linkages for Asian Elephants in Central Indian Landscape ����������������������������������������������������������������   75 C. S. Abhijitha, G. Areendran, Krishna Raj, Pamposh Bhat, and Mehebub Sahana 5 Environmental Pollution and Municipal Solid Waste Management in India����������������������������������������������������������   91 Mithun Ray, Avaya Chandra Mohapatra, Suman Das, Asraful Alam, and Biman Ghosh 6 Assessment of the Carbon Footprint Across Urban Households in Kolkata����������������������������������������������������������������  115 Rukhsana and Md Firojuddin Molla

xv

xvi

Contents

Part II Ekistics and Ecology of Social Environment 7 Dwelling in Decay: Analysing the Spatio-­Psychological Paradigm of Dilapidated Dwellings in Kolkata������������������������������������  135 Teesta Dey 8 Spatio-Temporal Transformation of Urban Built-Up Areas for Sustainable Environmental Management in Selected Cities of West Bengal������������������������������������������������������������  147 Rajat Kumar Paul and Pradip Patra 9 Population Pressure and Urban Sprawl in Kolkata Metropolitan Area����������������������������������������������������������������  163 Rukhsana and Md Hasnine 10 Socio-ecological Niche of Tribes of Purulia District, West Bengal, India ����������������������������������������������������������������������������������  179 Somnath Mukherjee and Uma Sankar Malik 11 Gajan: A Cultural Heritage of the Marginalized People in Kulpi CD Block, West Bengal������������������������������������������������  193 Moumita Ghosh and Lakshminarayan Satpati Part III Hazards and Environmental Management for Sustainable Development 12 Environmental Vulnerability and Displacement Due to Land Erosion: Selected Case Studies in West Bengal, India ������������������������������������������������������������������������������  207 Anwesha Haldar, Samiparna Das, Riyanka Chatterjee, and Lakshminarayan Satpati 13 Flood Frequency Analysis and Its Management in Selected Part of Bardhaman District, West Bengal��������������������������  225 Subodh Chandra Pal, Biswajit Das, Sadhan Malik, Manisa Shit, and Rabin Chakrabortty 14 Spatiotemporal Extent of Agricultural Drought Over Western Part of West Bengal ��������������������������������������������������������  247 Mou Dey, Dipanwita Dutta, and Abira Dutta Roy 15 Drought-Induced Human Mobility in Purulia District of West Bengal����������������������������������������������������������  263 Shrinwantu Raha and Shasanka Kumar Gayen 16 Traditional Water Management System and Agricultural Sustainability in a Himalayan Foothill Village of Assam, India��������������������������������������������������������������  279 Sourav Saha, N. Deka, and A. K. Bhagabati

Contents

xvii

17 Management of Wastelands in Chotanagpur Plateau Fringe: Lessons from Village-Level Experience in Birbhum District of West Bengal, India��������������������������������������������  293 N. C. Jana and Manas Pal 18 Eyeing Sustainability: Electronic Waste Handling in Kolkata ��������������������������������������������������������������������  309 Anindya Basu Index������������������������������������������������������������������������������������������������������������������  327

About the Editors

Rukhsana  is presently an Assistant Professor at Aliah University, Kolkata. She obtained a PhD degree in Geography from Aligarh Muslim University. Dr. Rukhsana has published more than 32 two research papers in reputed journals and three books at national and international levels. Dr. Rukhsana has presented a number of research papers and was conferred with the International Young Geographer Award in 2009. She has attended XXV FIG International Congress 2014,  Malaysia, and ICGGS-2018, Bangkok, Thailand. Her research interests include agriculture, urban population, environment and development in geography. She has successfully supervised three scholars leading to the award of Ph.D. degrees in Geography. Dr. Rukhsana has successfully completed one major research project sponsored by ICSSR, New Delhi. She has been the Head of the Department of Geography at Aliah University. Anwesha Haldar  is currently serving as Assistant Professor in Geography at East Calcutta Girls’ College, West Bengal State University, Kolkata, India and Guest faculty in the Department of Geography, University of Calcutta. Her research interest includes environmental issues of Kolkata and the Indian Sundarbans region. She has to her credit 15 scientific articles and reports published in various journals of national and international repute and has also edited a book entitled Climate and Society: A contemporary perspective. Dr. Haldar obtained her doctoral degree in Geography from the University of Calcutta. She has served as a full time project assistant under the West Bengal Pollution Control Board. Dr. Haldar is now the associate editor of Indian Journal of Landscape Systems and Ecological Studies. Asraful  Alam  is a Postdoctoral Fellow in the Department of Geography at the University of Calcutta, Kolkata. He obtained a Ph.D. degree from Aliah University, Kolkata, India. He has previously worked at Women’s College, Calcutta, India, as an Asst. Coordinator in the Department of Geography and at Aliah University, Department of Geography, as Research Assistant in a major project. Dr. Alam’s

xix

xx

About the Editors

research interests include population geography, climatology, agricultural geography, remote sensing and GIS, and development studies. He has 13 research papers/ articles and two book chapters and one book to his credit brought out by national and international publishers. He has attended more than 30 national and international seminars, conferences and workshops. Lakshminarayan  Satpati  is presently the Director, UGC-HRDC, and is a Professor at the Department of Geography, University of Calcutta, Kolkata. He has obtained a Ph.D. degree in Geography from the University of Calcutta, Kolkata. He has presented more than 60 research papers and chaired sessions in national and international platforms, of which the 32nd IGU Conference in Cologne, Germany (2012); 8th IAG Conference in Paris, France (2013); and PRSCO-2019 of RSAI, Bangkok, are notable. Besides, he has been invited to deliver lectures in various faculty development programmes of UGC, ICSSR, DST, etc. He has to his credit more than 40 publications in the form of research papers and articles published in reputed national and international journals and books. His research and teaching interests include climatology, geomorphology, hydrology, environment, population & development, quantitative geography and curriculum development in geography. He has edited a book on Climate and Society – a Contemporary Perspective published by the University of Calcutta in 2015. Fourteen scholars have already obtained their Ph.D. degrees under his supervision. He is also mentor to two Postdoctoral Fellows in the Department of Geography at the University of Calcutta. He has successfully completed two UGC-­sponsored research projects and organized three national workshops on Climate Science. Prof. Satpati is associated with a large number of academic and professional organizations of teachers, geographers and climate scientists of India. Currently he is the honorary Editor of UGC-recognized peer-reviewed biannual Indian Journal of Landscape Systems and Ecological Studies of Indian Institute of Landscape, Ecology and Ekistics, since 2017.

Contributors

C. S. Abhijitha  Guru Gobind Singh Indraprastha University, New Delhi, India Asraful  Alam  Department of Geography, University of Calcutta, Kolkata, West Bengal, India G. Areendran  IGCMC, WWF-India, New Delhi, India Anindya Basu  Department of Geography, Diamond Harbour Women’s University, Sarisha, West Bengal, India Soumyajit  Basu  Department of Environment Management, Indian Institute of Social Welfare and Business Management, Kolkata, India A. K. Bhagabati  Department of Geography, Gauhati University, Guwahati, Assam, India Rabin  Chakrabortty  Department of Geography, The University of Burdwan, Burdwan, West Bengal, India Riyanka  Chatterjee  Department of Geography, Maharani Kasiswari College, Kolkata, West Bengal, India Biswajit  Das  Department of Geography, The University of Burdwan, Burdwan, West Bengal, India Jayanta Das  Department of Geography, Rampurhat College, Rampurhat, Birbhum, West Bengal, India Samiparna Das  Department of Geography, University of Calcutta, Kolkata, West Bengal, India Suman Das  Department of Geography, Ramkrishna Mahavidyalaya, Kailashahar, Tripura, India N. Deka  Department of Geography, Gauhati University, Guwahati, Assam, India

xxi

xxii

Contributors

Mou  Dey  Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, West Bengal, India Teesta Dey  Department of Geography, Kidderpore College, Kolkata, West Bengal, India Dipanwita Dutta  Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, West Bengal, India Shasanka  Kumar  Gayen  Department of Geography, Coochbehar Panchanan Barma University, Cooch Behar, West Bengal, India Biman  Ghosh  Department of Geography, Visva-Bharati, Santiniketan, West Bengal, India Moumita Ghosh  Department of Geography, University of Calcutta, Kolkata, West Bengal, India Anwesha  Haldar  Department of Geography, East Calcutta Girls’ College, West Bengal State University, Kolkata, India Department of Geography, University of Calcutta, Kolkata, West Bengal, India Md Hasnine  Department of Geography, Aliah University, Kolkata, West Bengal, India N. C. Jana  Department of Geography, The University of Burdwan, Bardhaman, West Bengal, India Sadhan Malik  Department of Geography, The University of Burdwan, Burdwan, West Bengal, India Uma Sankar Malik  Department of Geography, Visva-Bharati, Santiniketan, West Bengal, India Tapash Mandal  Department of Geography and Applied Geography, University of North Bengal, Darjeeling, India Avaya  Chandra  Mohapatra  Department of Geography, North-Eastern Hill University, Shillong, Meghalaya, India Md Firojuddin Molla  Department of Geography, Aliah University, Kolkata, West Bengal, India Somnath  Mukherjee  Department of Geography, Bankura Christian College, Bankura, West Bengal, India Manas Pal  Department of Geography, B. B. College, Asansol, West Bengal, India Subodh  Chandra  Pal  Department of Geography, The University of Burdwan, Burdwan, West Bengal, India Pamposh Bhat  Guru Gobind Singh Indraprastha University, New Delhi, India

Contributors

xxiii

Pradip  Patra  Department of Geography, University of Calcutta, Kolkata, West Bengal, India Rajat  Kumar  Paul  Department of Geography, University of Calcutta, Kolkata, West Bengal, India Shrinwantu  Raha  Department of Geography, Coochbehar Panchanan Barma University, Cooch Behar, West Bengal, India A.  T.  M.  Sakiur  Rahman  Department of Earth and Environmental Science, Kumamoto University, Kumamoto, Japan Krishna Raj  IGCMC, WWF-India, New Delhi, India Mithun  Ray  Assistant Professor, Department of Geography, Malda College, Malda, West Bengal, India Abira  Dutta  Roy  Department of Geography, Bankura Zilla Saradamani Mahila Mahavidyapith, Bankura, West Bengal, India Rukhsana  Department of Geography, Aliah University, Kolkata, West Bengal, India Mehebub Sahana  IGCMC, WWF-India, New Delhi, India Piu  Saha  Department of Geography, Chhatrapati Sahu Ji Maharaj University, Kanpur, India Sourav  Saha  Department of Geography, Gauhati University, Guwahati, Assam, India Lakshminarayan  Satpati  Professor, Department of Geography and Director, UGC-HRDC, University of Calcutta, Kolkata, West Bengal, India Manisa Shit  Jamini Roy College, Bankura, West Bengal, India Pradip  K.  Sikdar  Department of Environment Management, Indian Institute of Social Welfare and Business Management, Kolkata, India

Part I

Habitat and Environmental Issues of Human Concerns

Chapter 1

Habitat, Ecology and Ekistics: An Overview Rukhsana, Anwesha Haldar, Asraful Alam, and Lakshminarayan Satpati

1.1  Introduction Anthropocene (Adetona and Layzell 2019), the present era of human-induced global change, has been witnessing augmented technological development to meet the ever-increasing demand for goods and services to satisfy human comfort, and thereby alteration of the earth’s natural setup has become irreversibly distressing. Keeping in view the importance of various issues such as global warming, drought, flood, population explosion, distorted urbanization, shortage of energy, water scarcity, soil degradation, unsustainable agriculture, etc., this edited volume entitled ‘Habitat, Ecology and Ekistics Case Studies of Environment-Human Interactions in India’ has been planned to include the contemporary burning issues before the policy makers and researchers to find effective solutions to tide over the crises. Human groups and societies establish and maintain viable relationships with their habitat through collective mechanisms that stem from their anthropos and generate a system of relations and networks rather than independent action. A habitat is a region or place or an environment where an organism makes its home. Habitat Rukhsana Department of Geography, Aliah University, Kolkata, West Bengal, India A. Haldar Department of Geography, East Calcutta Girls’ College, West Bengal State University, Kolkata, India Department of Geography, University of Calcutta, Kolkata, West Bengal, India e-mail: [email protected] A. Alam (*) Department of Geography, University of Calcutta, Kolkata, West Bengal, India L. Satpati Professor, Department of Geography, UGC-HRDC, University of Calcutta, Kolkata, West Bengal, India © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_1

3

4

Rukhsana et al.

destruction is the most important human impact on other species. It is the furthermost threat to ecosystem integrity and biodiversity (Malanson 2004). The history of mankind (Wallace 1870) shows that emergence and development of civilizations (David 1979) would not have been possible without harnessing the natural resources. But overexploitation of the resources resulted direct or indirect spoiling of the nature itself. In this changing and challenging technology-driven anthropocentric world, we need to rethink and reinvent man’s role in nature. Thus, the present volume intends to focus on the major themes to include: (a) habitat and environmental issues of human concerns, (b) ekistics and ecology of social environment and (c) hazards and environmental management for sustainable development.

1.2  Population and Resources Increasing use of resources by the growing population and augmentation of economic growth through industrialization has been putting pressure on the environment at global as well as local levels, which threatens balance in the earth’s natural systems (Rockström et al. 2009). It has been recorded that the average global surface temperature has increased by 0.85 °C during the period 1880 and 2012, and it is projected to rise up to 2.68–4.8 °C in 2100, with a consequent sea level rise up to 0.98 m (IPCC-WGI 2013). Environmental degradation challenges the very basis of peoples’ livelihoods, and poor people will be constrained to exploit natural resources for their survival aggravating environmental degradation (Bremner et al. 2011).Wild life reportedly has been declined more than 30% during the period 1970–2010 at global level, and annual economic loss attributable to forest degradation is estimated to be equivalent to USD 4.5 trillion (SCBD 2010). As industrialization, expanded development and embedded population growth continued to elevate demand for natural resources (Behrens et al. 2007) and their exploitation, there had been augmenting concern over the destructive impacts of man on global environment (Davenport and Davenport 2006), including social systems of humanity (Mesjasz 2009). Prehistoric records reveal the fact that there was co-evolution of man and nature, with deterministic role of the later on human habitats and livelihoods over millennia. Later history of human civilizations found increasing influence of man on nature through agriculture, industry, trade and commerce, urban ekistics, etc., towards all-possible human activity-oriented progress of mankind. It is found that humans have been impacting their environments for millennia, not all impacts have been harmful (Redman 1999). But in the beginning of the twenty-first century, the most critical problems faced by humanity comprise global pandemics, warming, water scarcity, energy demand, financial crush and terrorism. Today, in our extremely interrelated global systems, any environmental failure in one region may have huge consequences of terrorizing the entire global system (Schimel et al. 2007). Historical ecology (Crumley 1987, 1994; Balée 2006) provides platform for interdisciplinary dialogue to geographers, historians, archaeologists and anthropologists in investigating the results of human thought and action on landscapes. It is

1  Habitat, Ecology and Ekistics: An Overview

5

found that historically transformative human impacts are quite ubiquitous in all landscapes (Crumley 1998). Importance of human or environmental factors may differ across time-space framework, as the relationship is always found to be two ways, as nature and culture are basically indivisible. In addition, the changes in environment and societies are the result of conflicts between men and environmental actors (Crumley 1994, 1998). Thus, the societies’ relation with their environments has left legacies on their landscapes (Balée 2006; Crumley 1987, 1994; Crumley and Marquardt 1990). The challenges of global warming, population explosion, loss of agricultural land, food insecurity, expanding urban sprawl and various diseases faced by human beings are increasing at a very fast rate which will have disastrous effects on the future generations, unless properly taken care of right now. Various threats like drought, flooding, etc., generated by land cover change.

1.3  Human and Environment There had been efforts to understand the relationship between population dynamics and environment (Petersen 1972; Cohen 1995), as long back Thomas Malthus studied population in relation to availability of resources to produce his famous in scientific document entitled ‘Essay on the Principle of Population’ (Malthus 1798). His famous hypothesis mainly advocates that population numbers tend to increase exponentially while food production increases linearly, thus resulting in natural ‘checks’, such as famine, to further extension in population. While George Perkins Marsh’s classic ‘Man and Nature’ (1864) focused human-induced soil depletion in the colonial Africa (Marsh 1864; Tiffen et al. 1994; Lindblade et al. 1998). In 1963, the U.S. National Academy of Sciences published the report about the consequences of global population growth (NSI 1963). Paul Ehrlich published ‘The Population Bomb (1968), which shown study of population growth, food production and environmental status; the ‘Club of Rome’ had published ‘Limits to Growth’ (Huskey 2006) which portrayed the first computer-based population-environment modelling at global scale (Meadows et al. 1972). Therefore, the efforts to understand the relationship between demographic and environmental changes are rather well-known. However, it was customary to project environmental change as a mere function of population growth. Researchers have found that population and consumption are the two major drivers of humanity’s ecological footprint at the global level (Dietz et al. 2007). In 1950, 5 years after the founding of the United Nations, world population was estimated to be numbered as c. 2.6 billion persons. It reached 5 billion in 1987 and 6 billion in 1999. In October 2011, the global population was estimated to include 7 billion persons (UNFPA 2017). A global movement ‘7 Billion Actions’ was launched to mark this milestone (UNDSA 2013), and as of the mid-2015 the world population reached 7.3 billion, meaning that the world has added approximately one billion people in the span of only 12 years. This dramatic growth has been possible largely because of increasing numbers of people surviving to reproductive age and has been accompanied by major changes in fertility rates, increasing urbanization and accelerating migration (Sławomir et al. 2015), and this trend

6

Rukhsana et al.

will have far-reaching implications for generations to come (World Population Prospects 2017). Environmental degradation, livelihood selection, settlement patterns and behaviour can all contribute to disaster risk, which will have definite influence on human development leading to further environmental degradation. The poor people are the most helpless segment of population to face the disasters because they have to settle on the most marginal and vulnerable lands to obtain their livelihoods and have the least access to avoidance, awareness and early warning systems. Aside, these people are the least resilient in improving their capacity against impending disasters because they lack strong social networks, useful material support and substitute livelihood choices. The incidents which cause damage, devastation, economic loss, land loss, ecological disturbance, human livelihood effect, human suffering, diseases and health problems, etc., are called disaster (World Health Organization 2007). It has been found that the number and magnitude of disasters have rapidly increased from 1950s, with the number of people affected also increased by approximately 235 million persons per  annum on the average, since 1990s (Boonmee et  al. 2017). The world is very likely to experience an augmenting frequency and intensity of different types of disasters to have overwhelming impacts. The Secretariat of the International Strategy for Disaster Reduction (UNISDR 2007) has recorded, during last 10 years, that 478,100 people were killed, more than 2.5 billion people badly affected, and about US$ 690 billion was the economic loss. Hydro-meteorological hazards accounted for 97% of the total people affected by disasters and 60% of the total economic losses. It has been also reported that in India, more than 150,000 lives were lost due to Indian Ocean Tsunami in 2004 (Wachtendorf et al. 2006). The greater tragedy is that many of the losses due to disasters could have been averted. It has been estimated that 376 natural disasters were reported, with the loss of economy accounted to be US$ 70.3 billion and death of 22,765 persons (Guha et  al. 2013). These increasing numbers of disasters warrant many researchers for attention to disaster management (DM) with the objective of assisting the persons recover from the effects of disasters (Akgün et al. 2015). The activity of DM includes four major approaches including preparation, mitigation, response and recovery (Coppola 2011). The most important perspective of DM comprises removal and disposal of debris from the affected sectors (Fetter and Rakes 2012). There is a need to emphasize the role of complete environmental management for minimizing the risk of disasters. Logging, both legal and illegal, contributed to the incidence of increasing run-off and combined with high intensity rainfall this increases the probability of flooding and landslides in the steeper slopes (Basu and Srinivas 2014). Land use and land cover (LULC) changes are deteriorating the natural buffers at global level, as environment-­ friendly LULC protect human societies from hazard-risks which reduce peoples’ capacity to recover from disaster.

1  Habitat, Ecology and Ekistics: An Overview

7

1.4  Environment Management Disasters, whether natural or man-made, is a major challenge to accomplishing global sustainability. Planning of disaster management has carried out widely around the world in various ways in developing and developed countries, but the functioning of such plans experiences challenges even in developed countries (United Nations, Economic and Social Commission for Asia and the Pacific (ESCAP 2017)). The corporate sector has also started to play a vital role in reducing the damage and impact after disaster as well as risk-reduction activities (UNDP 2013). Management of environment, food security and poverty alleviation are main factors contributing to the complexity of natural resource management. Proper resource management is very important for a sustainable livelihood. The rural poor depend on agriculture or are otherwise dependent on natural resources for their livelihood. Thus, the linkage between rural livelihood and natural resources is necessarily a close one (Chambers and Conway 1991). But the full potentials resulting from this linkage cannot be realized unless the poor have improved and more equitable access to those resources and the poor are better able to sustainably manage their resource base. Whether these challenges are longstanding or newly emerging, the livelihood and well-being of the rural poor depend fundamentally on the opportunities available to them; these opportunities are shaped in substantial part, by their access to natural resources. Those accesses and the capability of the poor to increase access and enhance their management of their resource base are, in turn, depend on numerous underlying political, social and macro-economic factors. These factors are also changing, as forces of globalization, migration, market integration, democratization and decentralization; among others, continue to alter the relationship between rural people, their resource base and their capacity to effect change. Making improved management decisions regarding resource use, formulating informed policy changes and public investments are all critical to expanding the choices available to poor people and for empowering them to improve their livelihoods.

References Adetona AB, Layzell DB (2019) Anthropogenic energy and carbon flows through Canada’s agri-food system: reframing climate change solutions. Anthropocene 27:100213. https://doi. org/10.1016/j.ancene.2019.100213 Akgün I, Gümüşbuğa F, Tansel B (2015) Risk based facility location by using fault tree analysis in disaster management. Omega 52:168–179. https://doi.org/10.1016/j.omega.2014.04.003 Balée W (2006) The research program of historical ecology. Annu Rev Anthropol 35(1):75–98. https://doi.org/10.1146/annurev.anthro.35.081705.123231 Basu B, Srinivas VV (2014) Flood frequency analysis using a novel mathematical approach International Journal of Engineering Research Innovative Research Publications 3:209–213 ISSN: 2319-6890

8

Rukhsana et al.

Behrens A et  al (2007) The material basis of the global economy. Worldwide patterns of natural resource extraction and their implications for sustainable resource use policies. Ecol Econ 64(2):444–453. https://doi.org/10.1016/j.ecolecon.2007.02.034 Boonmee C, Arimura M, Asada T (2017) Facility location optimization model for emergency humanitarian logistics. Int J Disaster Risk Reduct 24(June 2016):485–498. https://doi. org/10.1016/j.ijdrr.2017.01.017 Bremner J et al (2011) Population, poverty, environment, and climate dynamics in the developing world. Interdiscip Environ Rev 11(2/3):112. https://doi.org/10.1504/ier.2010.037902 Chambers R, Conway GR (1991) Sustainable rural livelihoods: practical concepts of the 21st century. IDS discussion paper 296. Institute of Development Studies, University of Sussex, Brighton Cohen JE (1995) How Many People Can The Earth Support? New York: Norton;. This is a highly readable, critical assessment of the widely varying estimates of Earth’s carrying capacity Coppola D (2011) Introduction to International Disaster Management. 2nd Edition, Massachusetts: Elsevier Crumley CCL (1987) Historical ecology. In: Regional dynamics: Burgundian landscapes in historical perspective, pp 1–21. Available at: https://books.google.com/books?hl=en&lr=&id=S_ LXEVuOi5EC&oi=fnd&pg=PA237&dq=carole+crumley+&ots=YMMl5xuEf5&sig=6T2140 6Bc5Tb_3himtcO1S34axM Crumley CL (1994) Historical Ecology: Cultural Knowledge and Changing Landscapes School of American Research Press, Santa Fe Crumley CL (1998) Foreword. In W. Balée, ed., Advances in Historical Ecology, ix-xiv. New York: Columbia University Press Crumley CL, Marquardt WH (1990) ‘Landscape: A unifying concept in regional analysis’ in K. M. S. Allen, S. W. Green, and E. B. W. Zubrow (eds.), Interpreting Space: GIS in archaeology, London: Taylor & Francis, 73–80 Davenport J, Davenport JL (2006) The impact of tourism and personal leisure transport on coastal environments: a review. Estuar Coast Shelf Sci 67(1–2):280–292. https://doi.org/10.1016/j. ecss.2005.11.026 Dietz T, Rosa EA, York R (2007) Driving the human ecological footprint. Front Ecol Environ 5(1):13–18. https://doi.org/10.1890/1540-9295(2007)5[13:DTHEF]2.0.CO;2 Fetter G, Rakes T (2012) Incorporating recycling into post-disaster debris disposal. Socio Econ Plan Sci 46(1):14–22. https://doi.org/10.1016/j.seps.2011.10.001 Guha-Sapir D, D’Aoust O, Vos F, Hoyois P (2013) The frequency and impact of natural disasters, in: The Economic Impact of Natural Disasters (Edited by D. Guha-Sapir and I. Santos); Oxford University Press: Oxford: pp.1–27 Huskey L (2006) Limits to growth: remote regions, remote institutions. Ann Reg Sci 40(1):147–155. https://doi.org/10.1007/s00168-005-0043-5 IPCC A Report S (2013) Global warming of 1.5°C-IPCC. Available at: https://report.ipcc.ch/sr15/ pdf/sr15_spm_final.pdf Lindblade KA, Carswell G, Tumuhairwe JK (1998) ‘Mitigating the relationship between population growth and land degradation: land use change and farm management in southwestern Uganda’, Ambio 27:565–71 Malthus, An Essay On The Principle Of Population (1798 1st edition) with A Summary View (1830), and Introduction by Professor Antony Flew. Penguin Classics. ISBN 0-14-043206-X. Marsh GP ([1864] 1965) Man and Nature; or, physical geography as modified by human action, Harvard University Press, Cambridge, MA, 1965) (first published Scribners, New York, and Sampson Low, London) Meadows DH, Meadows DK, Randers J, Behrens WW III (1972) The Limits to Growth, Universe Books, New York Mesjasz C (2009) Complexity and social systems. In: Proceedings of the 4th Polish symposium on econo- and sociophysics vol 117, issue 4, pp 706–715. Available at: http://en.cnki.com.cn/ Article_en/CJFDTOTAL-XTBZ200301004.htm

1  Habitat, Ecology and Ekistics: An Overview

9

Petersen W (1972) Readings in Population. New York: Macmillan. Redman CL (1999) Human Impact on Ancient Environments. The University of Arizona Press. Schimel J et al (2007) Is death really the worm at the core? Converging evidence that worldview threat increases death-thought accessibility. J Pers Soc Psychol 92(5):789–803. https://doi. org/10.1037/0022-3514.92.5.789 Sławomir K, Mirosław W, Jadwiga G (2015) The changing role of migration and natural increase in suburban population growth: The case of a non-capital post-socialist city (The Krakow Metropolitan Area, Poland). Moravian Geographical Reports 23(4):59–70 Tiffen M, Mortimore MJ, Gichugi F (1994) More People, Less Erosion: environmental recovery in Kenya, Wiley, Chichester UNDP (2013) Small businesses: impact of disasters and building resilience Analysing the vulnerability of micro, small, and medium, p 76 [UNISDR] United Nations Strategy for Disaster Reduction. (2007). Drought risk reduction framework and practices: Contributing to the implementation of the hyogo framework for action. United Nations Department of Economic and Social Affairs (2011) Seven billion and growing: the role of population policy in achieving sustainability. Technical paper United Nations Department of Economic and Social Affairs (2013) Population Division (UNDESA). World population prospects: The 2013 revision UNFPA (United Nations Population Fund) (2017) “State of World Population 2017: Worlds Apart.” New York: UNFPA United Nations, Economic and Social Commission for Asia and the Pacific (ESCAP) (2017) Achieving Sustainable Development Goals in East and North-East Asia. e-ISBN: 978- 92-1-060366-9 Wachtendorf T et al (2006) The social impacts and consequences of the December 2004 Indian Ocean tsunami: observations from India and Sri Lanka. Earthquake Spectra 22(Suppl 3):693–714. https://doi.org/10.1193/1.2202650 WHO (2007) Risk reduction and emergency preparedness: WHO six-year strategy for the health sector and community capacity development. WHO Document Production Services, Geneva World Population to Reach 7 Billion on 31 October | UNFPA – United Nations Population Fund (2017). Available at: http://www.unfpa.org/press/world-population-reach-7-billion-31-october

Chapter 2

Contemporary Environmental Issues – The Indian Perspective Pradip K. Sikdar and Soumyajit Basu

2.1  Introduction Environment plays a pivotal role in the life of every human being in terms of physical, mental and social well-being. Environment provides all the basic necessities of life, like water, food and air, and it also gives the resources required for economic growth. In other word, economy of developing countries is linked with the environmental conditions and opportunities it provides. The environment around us is degrading. El-Haggar (2007) defined environmental degradation as ‘the exhaustion of the world’s natural resources: land, air, water, soil, etc.’. The main causes of environmental degradation are environmental pollution, climate change, urbanization, rapid population growth, economic development and transportation, etc. Environmental degradation is a global issue and different international agencies like Earth System Governance Project (ESGP), Fridays for Future (FFF) also known as School Strike for Climate, Global Green Growth Institute (GGGI), Intergovernmental Panel on Climate Change (IPCC), International Union for Conservation of Nature (IUCN), United Nations Environment Programme (UNEP), European Environment Agency (EEA), Partnerships in Environmental Management for the Seas of East Asia (PEMSEA), etc., have come together along with organizations in different countries to resolve this serious issue. India is also facing serious problems due to environmental degradation caused by different factors. As a developing country with second highest population in the world, environmental degradation is seriously affecting the economic growth of the country along with deterioration of the health condition of its population. According to National Disaster Management Authority (NDMA), India is vulnerable to disaster like earthquake, flooding, drought, sea level rise because of its geographical P. K. Sikdar (*) · S. Basu Department of Environment Management, Indian Institute of Social Welfare and Business Management, Kolkata, India © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_2

11

12

P. K. Sikdar and S. Basu

location. According to European Parliamentary Research Service (EPRS), India was ranked 14th from the last in United Nations global climate risk index in 2017 and ranked second most-affected countries in terms of casualties related due to extreme weather. According to WHO (2018) 14 out of 15 most polluted cities in terms of PM2.5 concentration are from India. According to WHO (2013), environmental degradation in India accounts for approximately Rs. 3.75 trillion ($80 ­billion) per year which is equivalent to 5.7% of national GDP.

2.2  Major Environmental Concerns in India 2.2.1  Environmental Pollution: Air, Noise and Water Environmental pollution is considered as the leading concern in the world today. Environment pollution is defined as the introduction of impurities in the environment through natural or anthropogenic process/processes which affects the natural process and poses threat to human and other living organisms. The 2018 Environmental Performance Index (EPI) has ranked 180 countries on 24 performance indicators across ten issue categories covering environmental health (air quality, water and sanitation and heavy metals) and ecosystem vitality (biodiversity and habitat, forests, fisheries, climate and energy, air pollution, water resources and agriculture). These metrics indicate how close countries are to established environmental policy goals (https://epi.envirocenter.yale.edu; accessed on 14.12.2019). India ranked 177 out of 180 countries in terms of EPI in 2018 with a score of 9.3 out of 100 in environmental health which indicates very low economic growth and prosperity and 45.5 out of 100 in ecosystem vitality which indicates that the ecosystem is under strain from industrialization and urbanization. The major environmental concerns in India are explained below in terms of air, noise and water pollution. 2.2.1.1  Air and Noise Pollution Poor air quality due to excessive air pollution is the most prevalent environmental hazard which severely affects the public health. Poor air quality due to presence of airborne pollutant is responsible for ‘two-thirds of all life-years lost to environmentally related deaths and disabilities’ (http://www.healthdata.org/data-tools). India is committed to provide clean environment with pollution free air for its countryman through minimization of air pollution. Minimizing air pollution is one of the toughest challenges in India today. Ministry of Environment Forest and Climate Change (MoEF&CC) in its report (NCAP 2019) stated that the particulate matters (PM2.5 and PM10) are the major air pollutants responsible for air pollution in India. Other pollutants viz., SOx, NOx and ozone (O3) in air are found to be in compliance with the National Ambient Air Quality Standards (NAAQS). Data from World Health Organization (WHO 2018) reveal that 14 out of 15 most polluted cities of the globe

2  Contemporary Environmental Issues – The Indian Perspective

13

are in India in terms of PM2.5 concentration in the air. The average PM2.5 concentration in India was 72.54 μg/m3 (WHO 2017) and India ranked third in terms of worst air quality in the world (ranked 90 out of 92 countries assessed). Source apportionment study of Delhi NCR by TERI-ARAI (2018) indicates that the major sources of PM2.5 and PM10 concentration in Delhi are dust and construction, industry, vehicle and biomass for the year 2016 (Fig. 2.1). India’s total Greenhouse Gas (GHG) emission in 2014 was 3202 million metric tons of carbon dioxide equivalent (MtCO2e), which is equivalent to 6.55% of global GHG emissions (World Bank Data https://data.worldbank.org/indicator/EN.ATM. CO2E.PC). Out of the total GHG emission, 68.7% percent come from the energy

Fig. 2.1  Sources of PM2.5 and PM10 concentration in Delhi. (Source: NCAP 2019)

14

P. K. Sikdar and S. Basu

sector, followed by agriculture (19.6%), industrial processes (6%), land-use change and forestry (3.8%) and waste (1.9%) (USAID 2014). The contribution of particulate matter from vehicles and industries increases significantly in winter. The total number of registered vehicle in India increased from 81.5 million in 2005 to 230 million in 2016 (Fig. 2.2). The growth in vehicle quantities also increases the emission of carbon monoxide (CO) in air. Vehicles are also the major source of noise pollution. More than 6% people in India suffer from noise induced hearing loss (NIHL). Survey report of ‘Society to Aid the Hearing Impaired’ had shown that 76% of traffic policemen in the city of Hyderabad had suffered from NIHL as an effect of traffic noise. Another important source of noise pollution is the industrial noise. The occupational hearing loss can cause acoustic traumatic injury and noise-induced hearing loss (NIHL). A study on Occupational NIHL by Nandi and Dhatrak (2008) found that workers in construction sites, printing, saw mills and crushers are exposed to high noise level for more than 8 h and suffer from NIHL. 2.2.1.2  Water Pollution India ranks 120 out of 122 countries in Global Water Quality Index developed by UNEP. In ‘Composite Water Resources Management’ Report, Niti Aayog (2019) stated that 70% of India’s water resource is contaminated and more than 600 million people in India lives under water stressed condition. This clearly indicates that India is facing a major threat of water crisis along with water pollution. The water

Number of Vehicles in millions

250

200

150

100

50

0 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

Fig. 2.2  Total number of vehicles across India from 2005 to 2016. (Source: Statista Research Department 2019)

2  Contemporary Environmental Issues – The Indian Perspective

15

pollution in India can be described in terms of surface water pollution and groundwater pollution. Surface Water Pollution The annual average surface water availability in India is 690 BCM (Niti Aayog 2019). Nearly 92% of the total runoff in India is contributed by 12 major and 46 medium river basins. According to World Economic Forum, 70% of the total surface water resource in India is unfit for consumption. The main source of surface water pollution in India can be attributed to industrial effluent discharge, agricultural runoff and disposal of untreated sewage water into the rivers. The discharge of waste water into the river results in high metal concentration and high organic load in the surface water. The status of trace and toxic metal concentration in Indian rivers is given in Table 2.1 (CWC 2018). The major source of organic pollution in river water is the disposal of untreated sewage water. According to CPCB (2016), the amount of sewage water generation in urban areas of India is 61,948 million litre per day (MLD), whereas available treatment capacity is 23,277 MLD through sewage treatment plant (STP). In India, 522 STPs are operational out of 816. The state wise sewage generation in urban areas as well as available treatment capacity is given in Table 2.2. The rest 38,671 MLD of untreated sewage water is directly discharged into the land or rivers. This untreated sewage waters along with industrial waste water increases the total ­coliform and faecal coliform concentration in the surface water. The sector wise industrial waste water generation in India is given in Table 2.3. Highest concentration of faecal coliform (92 × 106 MPN/100 ml) and total coliform concentration (16 × 107 MPN/100 ml) was reported in River Yamuna by CPCB in 2016. These concentrations are 24,000 times higher than the CPCB prescribed standard.

Table 2.1  Status of trace and toxic metal concentration in Indian rivers Metal Arsenic (As) Cadmium (Cd) Chromium(Cr) Copper (Cu) Iron (Fe) Lead (Pb) Nickel (Ni) Zinc (Zn)

No. of sample tested 1734 2349 2400 2400 2400 2400 2023 2400

Source: CWC (2018)

No. of station exceed the limit 0 38 41 12 524 122 35 0

No. of stations 0 31 28 11 234 91 31 0

No. of rivers 0 25 21 10 137 69 25 0

16

P. K. Sikdar and S. Basu

Table 2.2  State-wise details of sewage generation in urban areas and treatment capacity available Sl. No. 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. 36.

State/union territory Andaman and Nicobar Islands Andhra Pradesh Arunachal Pradesh Assam Bihar Chandigarh Chhattisgarh Dadra and NagarHaveli Daman and Diu Goa Gujarat Haryana Himachal Pradesh Jammu and Kashmir Jharkhand Karnataka Kerala Lakshadweep Madhya Pradesh Maharashtra Manipur Meghalaya Mizoram Nagaland Delhi Odisha Pondicherry Punjab Rajasthan Sikkim Tamil Nadu Telangana Tripura Uttar Pradesh Uttarakhand West Bengal Total

Sewage generation, in urban areas (mld) 22

Installed treatment capacity (mld) –

2871 50 703 1879 164 951 26 29 145 4119 1413 110 547 1270 3777 2552 8 3214 8143 132 95 90 92 4155 1121 136 1664 2736 24 5599 1671 154 7124 495 4667 61,948

2470.27 – 0.21 124.55 3140.5 – – – 74.58 3062.92 852.7 114.72 264.74 117.24 1304.16 152.97 – 482.23 5160.36 – 1 10 – 2693.7 385.54 68.5 1245.45 865.92 31.88 1799.72 685.8 0.05 2646.84 152.9 416.9 23,277

Source: Government of India Ministry of Environment, Forest and Climate Change. Lok Sabha unstarred question NO.2541, updated on 28th May, 2018

2  Contemporary Environmental Issues – The Indian Perspective Table 2.3 Sector specific industrial wastewater generation

Total Type of industry units Chemical 27 Distillery 35 Food, dairy and Beverage 22 Pulp and paper 67 Sugar 67 Textile, bleaching and dyeing 63 Tannery 442 Others 41 Total 764

17 Wastewater generation (MLD) 97.8 37.0 6.5 201.4 96.0 11.4 22.1 28.6 501

Source: Government of India, Ministry of Water Resources, River Development & Ganga Rejuvenation. Lok Sabha unstarred question no. 1530, updated on 28th May, 2018

Groundwater Pollution India is largest groundwater user in the world and consumes nearly 230  km3 of groundwater annually. Nearly 90% of the total groundwater is used in agriculture and only 7% is used for drinking water supply. According to CGWB and State Groundwater Authorities, most of the aquifers in India are contaminated. Contaminants in groundwater are arsenic (10 States, 86 Districts), fluoride (20 States, 276 districts,), salinity (9 States, 4 Union Territories), iron (24 states, 1 UT, 297 districts), nitrate (21 states, 387 districts) and heavy metals such as Pb, Cd and Cr (15 states, 113 districts). The sources of contamination in groundwater are both geogenic and anthropogenic. Number of states and districts contaminated with salinity, arsenic, fluoride, iron, nitrate and heavy metals are given in Fig. 2.3. The anthropogenic sources of groundwater are waste water from toilets, excessive use of fertilizers and pesticides, and leachate from dumping ground. In high water table areas, waste water from toilets due to faulty toilet designs mixes with the groundwater and creates biological contamination of groundwater. Roy et al. (2019) have shown that faulty toilet construction in Sheohar district of Bihar resulted in biological contamination in groundwater. Another important anthropogenic source of groundwater contamination is the excessive use of fertilizers and pesticides in the agricultural field to meet the food demand of the country. Majumdar and Gupta (2000) have reported that excessive use of nitrogenous fertilizer in agricultural field is the source of nitrate pollution in groundwater in many parts of the country. Agarwal et al. (2015) have reported presence of organochlorine and organophosphate pesticides in groundwater in Delhi as a result of excessive use of pesticides in the agricultural field. McArthur et al. (2018) have reported presence of anthropogenic arsenic in groundwater of Kolkata city, West Bengal. These anthropogenic sources are polluting the available fresh water resource of the country and pose major threat for the future.

18

P. K. Sikdar and S. Basu GROUND WATER CONTAMINATION Chromium (above 0.05 mg/l) Cadmium (above 0.003 mg/l) Lead (above 0.01 mg/l) Iron (above 1 mg/l ) Arsenic (above 0.01 mg/l) Nitrate (above 45 mg/l) Fluoride ( above 1.5 mg/l) Salinity (EC above 3000 micro mhos/ cm) (EC : Electrical Conductivity) 0 Total no. of States

50

100

150

200

250

300

350

400

450

Total no. of Districts

Fig. 2.3  States wise details of affected districts with groundwater contamination Source: Ministry of Water Resources (2018)

2.2.2  Climate Change According to World Meteorological Organization (WMO), ‘climate’ is defined as the 30 year average weather of any given area and ‘climate change’ is defined as the changes in climate properties. It is a global phenomenon and probably the most discussed phrase in the world today. Shifting weather patterns that threaten food production and rising sea levels that increase the risk of catastrophic floods are direct impacts of climate change. The change in climate may arise due to natural processes such as change in solar radiation, volcanoes and internal variability in the climate system or due to human activities that change the composition of the atmosphere. UN Intergovernmental Panel on Climate Change (IPCC) in 2013 categorically concluded that climate change is real and human activities are the main cause. There are many evidences which suggest that Earth has experienced a number of climate change events in the past since its formation 4.5 billion years ago. The last ice-age event ended some 20,000 years ago and since then the Earth is in a warming phase. Although the natural processes are occurring, the rapid change in climate is due to various human activities since the first industrial revolution. Figure 2.4 shows the change in earth temperature during the last 2000 years and Fig. 2.5 shows the change in earth temperature in the last 140 years. The analysis of these two figures clearly indicates that there is a rapid change in earth temperature since 1880. IPCC have reported that the temperature of earth has increased approximately 1 °C (likely between 0.8 °C and 1.2 °C) in 2017, with respect to pre-industrialization temperature. This increase in temperature can be correlated with the augmented level of anthropogenic greenhouse gases in the atmosphere. According to WMO 2018, CO2 and CH4 concentrations in the atmosphere have increased 147% and

2  Contemporary Environmental Issues – The Indian Perspective

19

Fig. 2.4  Temperature variation in last 2000 years reconstructed for Northern Hemisphere. (Source: NOAA – https://www.ncdc.noaa.gov/global-warming/last-2000-years)

Temperature Anomaly (C)

1.5

1.0

0.5

0.0

-0.5 1880

1900

Source: climate.nasa.gov

1920

1940

1960

1980

2000

2020

YEAR

Fig. 2.5  Global rise in temperature since 1880. (Source: NASA 2020)

259%, respectively, with respect to their concentration level in atmosphere during pre-industrial time. UNDP described ‘climate change’ as the ‘biggest development challenge’ for the earth rather than just an environmental concern. India, one of the fastest growing economies in the world today is also vulnerable to climate change. According to World Economic Forum, India was the third highest contributor of CO2 emission in

20

P. K. Sikdar and S. Basu

2017 and contributed 2467 million tons of CO2 which was approximately 6.8% of the total global emission. In terms of per capita CO2 emission India ranked 159 out of 251 countries and accounted for 1.73 metric tons per capita emission in 2014 (World Bank last updated on 19.12.2019). As a growing economy, the per capita CO2 emission of India is low compared to other countries. India has promised to reduce 33–35% of CO2 emission for per unit of GDP with respect to its emission on 2005 by 2030 in Paris Conference, 2015. The analysis of per capita emission indicates that per capita CO2 emission shows an increasing trend (Fig. 2.6). The main source of CO2 emission in India in 2014 is energy sector that accounts for 68.7% of emission followed by agricultural sector that accounts for 19.6% of emission (USAID 2014). Long term changes have been observed in temperature and rainfall pattern in various states of India (Fig. 2.7). Sikkim has experienced the highest increase in annual mean temperature by 0.05 °C/year (Fig. 2.7a). Annual rainfall pattern has also changed as evident by increase and decrease in annual rainfall in various states (Fig. 2.7b). Impacts of climate change are global warming, heat flows, change in rainfall pattern, change in the seasonal pattern, sea level rise, etc. Climate change alters the rainfall pattern which seriously affects the crop production in India, along with drought in some places. According to World Bank report Jharkhand, Odisha and Chhattisgarh could experience drought due to climate change. Agricultural output is expected to fall significantly because of global warming by 2040s. Climate change may melt the Himalayan glaciers completely and will increase the water stress in India significantly.

2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

1978

1976

1974

1972

1970

1968

1966

1964

1962

0 1960

PER CAPITA CO2 EMISSION IN METRIC TONS

Per Capita Emission in India

YEAR

Fig. 2.6  Per Capita CO2 emission in India from 1960 to 2014. (Source: World Development Indicators, World Bank, https://data.worldbank.org/indicator/en.atm.co2e.pc?view=map)

2  Contemporary Environmental Issues – The Indian Perspective

21

Fig. 2.7  State wise annual increase or decrease of temperature and rainfall in India from 1951 to 2010. (a) State level annual mean temperature change. (b) State level annual rainfall trend (Source: Rathore et al. 2013)

2.2.3  Sea Level Rise Globally the sea level rises as a result climate change. The temperature of the earth is increasing due to climate change and this rising temperature is melting the glaciers and ice-caps on the mountains. This melt water flows into the sea and this extra amount of load causes sea level to rise. According IPCC 2013, global mean sea level rose by 0.19 m over the period of 1901 to 2010. According to Rebecca Lindsey from NOAA, the global mean sea level rose at an average of 0.14 inch per year within the period of 2006 to 2015. Between 1900 and 2016 the sea level rose by 16–21 cm (USGCRP 2017). Satellite radar measurements reveal a rise of 7.5 cm (3.0 in) from 1993 to 2017 (WCRP 2018), which is roughly 30 cm per century. This is due mostly to humancaused global warming, that is driving thermal expansion of seawater and melting of land-based ice sheets and glaciers (Mengel et al. 2016). Between 1993 and 2018 (Fig. 2.8), thermal expansion of the oceans resulted in 42% to sea level rise; 21% to the melting of temperate glaciers; 15% to the melting of Greenland; and 8% to melting of Antarctica. Climate scientists expect the rate to further hasten during the twenty-first century (Climate Change 2014). The rise in sea level will increase the flood intensity in coastal areas, saline water intrusion, submergence of coastal area, increase the frequency and intensity of storm surges, affect the aquatic ecology, etc. India has 7517 km coastline and approximately 260 million people live within 50  km of coastline. India experienced 283 cyclones in between 1877 and 2005 including 106 severe cyclones. The frequency and intensity of storm surges will continue to increase and it could lead to intrusion of saltwater, impact the soils

22

P. K. Sikdar and S. Basu

Fig. 2.8  Seal level rise between 1993 and November 2018

through inundation and degrade the groundwater quality near the coastal areas of India. Central Water Commission (CWC) has reported that sea level rose at an average of 1.06–1.75 mm per year for the last 40 years in India (http://cwc.gov.in/sites/ default/files/nwauser/climate-chnge11.pdf, accessed on 29.12.2019). CWC also reported that the sea level rise at the west coast of India is more pronounced compared to its east coast. According to Climate Central (2019), the annual average flood will affect 36 million people of India due to projected sea level rise by 2050 and flood vulnerability will be highest along the coast of West Bengal and Odisha. Recent studies have revealed that the estimated sea level rise along the India’s coast is 1.3  mm/year during the last 40–50  years. At Diamond Harbour in West Bengal, the rise is about 5.16 mm/year based on recordings over the period from 1948 to 2005. This is followed by Kandla port in Gujarat, Haldia in West Bengal and Port Blair in Andaman Island where the seal level rise has been recorded as 3.18  mm/year (1950–2005), 2.89  mm/year (1972–2005) and 2.20  mm/year (1916–1964), respectively (https://www.thehindu.com/news/national/bengal-portrecords-countrys-highest-sea-level-rise-in-50-years/article28364149.ece, accessed on 15.12.2019).

2.2.4  Waste Management and Circular Economy The huge population of India along with the consumption pattern is responsible for enormous generation of municipal solid waste (MSW) with 400 g per capita daily rate of generation (Bhat et  al. 2018). The various categories of MSW are

2  Contemporary Environmental Issues – The Indian Perspective

23

commercial waste, food waste, institutional waste, street waste, industrial waste, construction and demolition waste, and sanitation waste. MSW in India consists mainly of compostable items (51–53%), recyclable (17–18%) (MOEF 2010) and has C/N ratio approximately 32 with calorific value of about 1700–1800 Kcal/Kg (Bhat et al. 2018). Only 10–12% of the total compostable waste is used for composting. About 60–90% of MSW generated in cities and towns are disposed off on or into land in an unscientific manner causing environmental problems. The MSW management steps are shown in Fig. 2.9. The main reasons for poor waste management are unscientific treatment, improper collection of waste and ethical problems. Poor waste management leads to various hazards in the environment, which include environmental degradation, water pollution, soil pollution and air pollution. As second largest populated country in the world, India faces a critical problem in its development due to existence of a poor waste management system. Quantity of solid waste generated in Indian cities had increased from 6 million tons in 1947 to 48 million tons in 1997 with an annual growth rate of 4.25% (Bhat et al. 2018). According to MoUD (2016), MSW generation in the country was 1,00,000 Metric Tons or 0.1 million metric tonnes per day (MMT/d) during 2001–2002 (CPCB status Report, http://www.indiaenvironmentportal.org.in/files/file/MSW_Report.pdf, accessed on 22-12-2019). CPCB conducted study on 59 cities in India during 2004–2005 to assess MSW generation in the country (CPCB status Report). Based on this study along with Census data of 2008, the MSW generation in the country was estimated to be 0.573 MMT/d in the year 2008. According to CPCB, 1, 27,486 tons per day (TPD) municipal solid waste

Market/ Street wastes

Street sweeping/ Collection by Civic body

Waste to Unauthorised dumpsites

Waste to Transfer Station Household/ Commercial Waste Generators

Recovery by Household & Itinerant/ Buyers

Waste Storage in Community bins and collection by Civic body

Recovery by Rag Pickers from Dustbins

Waste for Land dumping by Civic body

Recycling Dealers

Rag-picking at Transfer station

Dump pickers

Fig. 2.9  Schematic flow chart of common MSW management process. (Source: Joseph 2002)

24

P. K. Sikdar and S. Basu

was generated in the India during 2011–2012. Only, 70% (89,334 TPD) of MSW was collected and 12.45% (15,881 TPD) was treated or processed. The MSW generation in India increased to 1,43,449 metric tonnes per day in 2015–2016 (CPCB 2015–2016) and will keep on increasing with increasing population. According to Balde et al. (2017), total e-waste generation in Asia was 18.2 million tons (Mt) in 2016 in which India contribute 2Mt of e-waste and ranked as third highest e-waste generator in Asia. According to CPCB (2019a, b), approximately 7.17 million metric tonne (MMt) of hazardous waste was generated in India from 56,350 hazardous waste generating industries during 2016–2017. Maximum hazardous waste was generated in Gujarat (39.20%) followed by Rajasthan (10.10%). India has 1733 authorized recyclers recycling hazardous wastes with capacity of 6.99 MMt. Out of 7.17 MMt of generated hazardous waste only 1 MMt of hazardous waste was recycled (CPCB 2019a, b). The hazardous waste generation of India is also increasing at a rapid rate. Generation of hazardous waste increased more than 16% compared to hazardous waste generated in India during 2009 (6.2 MMt per annum). India needs to manage its waste as a result of increasing waste generation along with growing population. A new concept is emerging in India which is ‘waste management and circular economy’. According to Ellen MacArthur Foundation, ‘a circular economy is based on the principles of designing out waste and pollution, keeping products and materials in use, and regenerating natural systems’. Waste management and circular economy encourages complete collection, transportation, recycle-recovery-reuse of waste to tackle the problem with increasing wastes. In India, the rules pertaining to waste management in the areas of municipal waste, electronic waste, construction and demolition waste, biomedical waste, hazardous waste and plastic waste have been revised in the year 2016 for implementation based on 5R (Reduce, Reuse, Recycle, Refuse and Recover) principle and circular economy concepts.

2.3  Deforestation and Loss of Bio-diversity 2.3.1  Deforestation According to WWF, ‘the importance of forests cannot be underestimated’. Our survival depends on forest, from the air we breathe to the wood we use. More than 2 billion people in the world depend on forests directly or indirectly. Forests provide home for more than 80% of terrestrial biodiversity and source of livelihood for 60 million indigenous people (WWF). Forests also help to reduce the effects of natural disasters, maintaining the soil quality and regulating the water circle. Due to unvalued contributions of forests, priority is given in conservation and protection of forests. Although more financing and investment are made to protect forest land, a major concern in today’s world is deforestation.

2  Contemporary Environmental Issues – The Indian Perspective

25

According to an article published on WHO website on August 31, 2013, World was losing about 14.5 million hectares of forests annually, whereas WWF reported in its website that the loss was 18.7 million acres of forests annually. The world’s forest area fell from 31.625% to 30.716% during the period 1990–2016 (World Bank data: https://data.worldbank.org/indicator/AG.LND.FRST.ZS?end=2016&sta rt=1990&view=chart, accessed on 27.12.2019). The main causes of deforestation are agriculture, commercial logging, mining, increase in population, urbanization and industrialization, construction of dam reservoirs, forest fires and overgrazing. Foster and Rosenzweig (2003) reported that forests area of India increased from 10 to 24% of the total geographical area between 1971 and 2003. According FSI (1999), the total forest land in India was 6,37,300 km2. According to FSI (2017), total forest cover in India is 7,08,273 km2 which is 21.54% of total area of India, but very dense forest is only 13.86% of the total forest area. It clearly indicates that forest cover in India has increased. Forest cover in India has increased by 6778 km2 in 2017 compared to total forest area in 2015 and this increase in forest cover is mainly contributed by three states, viz., Andhra Pradesh (2141 km2), Karnataka (1101 km2) and Kerala (1043 km2). This increase in forest cover is largely attributed to compensatory afforestation and new plantation under the Compensatory Afforestation Fund Act, 2016. According to FSI (1989), dense forest cover in India was 3,61,412 km2 in the first assessment which increased to 3,78,470  km2 in the second assessment which accounts for 40% of the total forest area in India. Dense forest cover in India significantly decreased to 98,158 km2 (FSI 2017) which accounts for 13.86% of the total forest in India. According to Ministry of Environment and Forests report, dense forests in India are thinning under human pressure. The report also states that very dense forest accounts for only 7.5% or 1.5% of the national area and 20% of reserved forests in India had more than 100,000  km2 area without trees. As per data of Compensatory Afforestation Fund Management and Planning Authority (CAMPA) (Scroll.in website), 14,000 km2 of forests were cleared between 1986 and 2016. For mining projects, defence projects and hydroelectric projects, 4947 km2, 1549 km2 and 1351 km2 of forests land were cleared, respectively.

2.3.2  Loss of Biodiversity One of the most serious consequences of deforestation is loss of biodiversity. Shah (2014) reported that ‘the current extinction rate is now approaching 1000 times the background rate and may climb to 10,000 times the background rate during the next century, if present trends continue’. According to UN (2019), since 1900, native species in most major land-based habitats has declined by at least 20%. The report also states that amphibian species (more than 40%), reef-forming corals (almost 33%) and marine mammals (more than one third) are threatened. Nine percent mammals of domesticated breed type which were used for food and agriculture had become extinct by 2016, and at least 1000 more breeds of mammals are threatened

26

P. K. Sikdar and S. Basu

(UN 2019). Loss of biodiversity is a threat to the ecosystem as a whole by disrupting the structure and proper functioning of the ecosystem. India is one of the world’s 12 mega-biodiversity centres. According to National Biodiversity Authority report 2017–2018, India has 91,200 animal and 45,500 plant species, comprising 6.5% of all known wildlife. The faunal diversity consists of 2500 fishes, 150 amphibians, 450 reptiles, 1200 birds, 850 mammals and 68,000 insects. The fish diversity in India represents 57% of the total families and 80% of the global fish. Although India is designated as a mega-biodiversity area, it also has two of the world’s most threatened ‘hot spots’: the Eastern Himalayan region and the Western Ghats. At least 10% of India’s recorded wild flora and possibly more of its wild fauna are threatened; many are on the brink of obliteration. Of the wild fauna, 80 species of mammals, 47 of birds, 15 of reptiles, 3 of amphibians and a large number of moths, butterflies and beetles are endangered. Twelve out of 19 species of primates are endangered. The cheetah (Acinonyx jubatus) and the pink-headed duck (Rhodonessa caryophyllacea) have become extinct. There must be many more that have been annihilated, unrecorded either because they were not that spectacular or because their existence remained unknown (http://southasia.oneworld.net/news/ paradise-lost-indias-biodiversity-towards-extinction#. S.  Balaji 2010; accessed on 27.12.2019). The reasons for biodiversity loss in India are (i) escalating human population, (ii) habitat destruction (deforestation) and fragmentation, (iii) overuse of natural resources, (iv) climate change, (v) introduction of alien species, (vi) environmental pollution, (vii) natural calamities, (viii) development and utilization of various forms of energy resources, (ix) changes in the land use and (x) direct exploitation of organisms.

2.4  Disaster: Natural and Man-Made 2.4.1  Natural India is one of the most vulnerable countries in the world in terms disasters both natural and manmade. According to the Disaster Management Act, 2005, disaster is defined as a ‘catastrophe, mishap, calamity or grave occurrence in any area, arising from natural or manmade causes, or by accident or negligence which results in substantial loss of life or human suffering or damage to, and destruction of, property, or damage to, or degradation of, environment, and is of such a nature or magnitude as to be beyond the coping capacity of the community of the affected area’. More than half of India’s landmass is vulnerable to more than one hazard of high-intensity. India witnessed 614 recorded disasters such as floods, earthquakes, extreme temperature, drought, landslides and storms during 1970–2015 which accounted for a loss $93 billion along with more than 198,000 deaths (Cred 2015).

2  Contemporary Environmental Issues – The Indian Perspective

27

Fifty-nine percent area of India is vulnerable to seismic damage, 68% area is susceptible to draught, 8% area is prone to flood and 76% of the total coastline of India is vulnerable to cyclones (NIDM and GIZ 2013). The natural hazard map of India is given in Fig. 2.10. Some major disasters in India between 1972 and 2014 are given in Table 2.4. In 2019, flood affected the states of Kerala, Gujarat, Karnataka, Maharashtra, Madhya Pradesh, Tamilnadu, Odisha, Goa, etc. India had been hit by cyclone ‘Fani’ in 2019 which accounts for a loss of Rs.120 billion and killed 89 people in Odisha only.

Fig. 2.10  Natural hazard maps of India. Source: Maps of India (https://www.mapsofindia.com/ maps/india/natural-hazard.htm, accessed on 2712.2019)

28

P. K. Sikdar and S. Basu

Table 2.4  Some major disasters in India S. No. Name of Event 1 Heat wave

State & Area Bihar

Fatalities >184

2 3

Odisha Pune, Maharashtra

89 21

Kerala Karnataka

101 61

North Bihar Kerala

130 >483

North India Lakshadweep

125 218

West Bengal and Jharkhand

58

North Bihar

514

Gujarat

224

4 5 6 7 8 9

10 11 12 13

14 15 16 17 18 19 20 21 22 23 24

Year May–June 2019 Cyclone Fani May 2019 Flood September 2019 Flood August Flood August 2019 Flood July 2019 Flood August 2018 Dust Storm May 2018 December Very Severe Cyclonic Storm 2017 Ockhi Flood August 2017 Flood August 2017 Flood June–July 2017 Cold wave January 2017

40 Himachal Pradesh, Jammu and Kashmir, Punjab, Haryana, Rajasthan and Uttar Pradesh. Heat wave April–May Rajasthan >160 2016 Earthquake January Manipur 11 2016 Flood July 2015 Gujarat 72 Cyclone June 2015 Gujarat 81 Floods October Jammu and Kashmir 300 2014 Cyclone September Andhra Pradesh and Odisha 22 (in Hyderabad) HudHud 2014 Odisha Floods October Odisha 21 2013 Andhra Floods October Andhra Pradesh 53 2013 Cyclone Phailin October Odisha and Andhra Pradesh 23 2013 Floods/ June 2013 Uttarakhand and Himachal 4094 Landslides Pradesh Cyclone May 2013 Tamil Nadu 08 Mahasen (continued)

2  Contemporary Environmental Issues – The Indian Perspective

29

Table 2.4 (continued) S. No. Name of Event 25 Cyclone Nilam 26 27

Uttarakhand Floods Assam Floods

28

Cyclone Thane

29

Sikkim Earthquake Odisha Floods

30

Year October 2012 Aug–Sep 2012 July–Aug 2012 December 2011 September 2011 September 2011 2011

State & Area Tamil Nadu

Fatalities 65

Uttarkashi, Rudraprayag and Bageshwar Assam

52 –

Tamil Nadu, Puducherry

47

Sikkim, West Bengal, Bihar

60

19 Districts of Odisha

45

North Eastern India with epicentre near Nepal Border and Sikkim Leh, Ladakh in J&K 252 Districts in 10 States Andhra Pradesh, Karnataka North Bihar

97 people died (75 in Sikkim)

1000 people died, 5,80,000 housed destroyed, Rs. 20.26 billion estimated damage

31

Sikkim Earthquake

32 33 34 35

Cloudburst Drought Krishna Floods Kosi Floods

2010 2009 2009 2008

36 37

Cyclone Nisha Maharashtra Floods

2008 July 2005

38

Kashmir

2005

39

Tsunami

2004

40

Gujarat Earthquake

2001

41

Orissa Super Cyclone Cyclone

1999

257 people died – 300 people died 527 deaths, 19,323 livestock perished, 2,23,000 houses damaged, 3.3 million persons affected Tamil Nadu 204 deaths Maharashtra State 1094 deaths 167 injured 54 missing Mostly Pakistan, Partially 1400 deaths in Kashmir Kashmir (86,000 deaths in total) 10,749 deaths Coastline of Tamil Nadu, 5640 persons missing Kerala, Andhra Pradesh, 2.79 million people Pondicherry and Andaman and Nicobar Islands of India affected 11,827 hectares of crops damaged 300,000 fisher folk lost their livelihood Rapar, Bhuj, Bhachau, Anjar, 13,805 deaths 6.3 million people Ahmedabad and Surat in affected Gujarat State Orissa Over 10,000 deaths

1996

Andhra Pradesh

42

(continued)

30

P. K. Sikdar and S. Basu

Table 2.4 (continued) S. No. Name of Event 43 Latur Earthquake 44 Cyclone

Year 1993 1990

State & Area Latur, Marathwada region of Maharashtra Andhra Pradesh

45

Drought

1987

15 States

46

Cyclone

1977

Andhra Pradesh

47

Drought

1972

Large part of the country

Fatalities 7928 people died 30,000 injured 967 people died, 435,000 acres of land affected 300 million people affected 10,000 deaths hundreds of thousands homeless 40,000 cattle deaths 200 million people affected

2.4.2  Man-Made India also suffers from number of man-made disasters which are results of human activity. Main causes of manmade disasters are grossly negligent acts, gross inaction and serious errors. The manmade disasters can be broadly classified into the following classes: (i) Fire outbreaks (ii) Collapse of buildings or infrastructure (iii) Industrial disasters viz., explosions, escape of noxious fumes and gases, mishaps in underground mines, etc., and (iv) Exposure to radio-active waste The most dangerous manmade disaster in India’s history was Bhopal Gas Tragedy which took the life of 3787 people and more than 500,000 people are exposed to methyl isocyanate gas and other chemicals. The other manmade disasters in India include fire disaster in AMRI Hospital at Kolkata in 2011, Lalita Park Building Collapse in West Delhi in 2010, etc. Decline of vulture population in India is a result of feeding carcass of animals administered by an anti-inflammatory drug called diclofenac (Oaks et  al. 2004). This drug, which is fatal to vultures, is administered to livestock to treat the symptoms of inflammation, fevers and/or pain associated with disease or wounds. Green et al. (2004) also demonstrated through a modelling study that diclofenac poisoning is the major cause, and possibly the only cause of rapid population declines of vulture in India. The intensity of natural disaster increases due to manmade activities and in some cases natural disaster can be termed as manmade disaster as the manmade activity is solely responsible for the disaster. In case of Maharashtra floods in 2005 where 5000 people died, the reasons for the disaster was heavy rainfall which was intensified and aggravated due to presence of poor drainage system in the city. Another similar example was drought in Maharashtra during 2013 which could be attributed to poor water management system.

2  Contemporary Environmental Issues – The Indian Perspective

31

2.4.3  Disaster Preparedness India’s national and state governments have taken major steps to deal with disaster risk by implementing legislation, plans and policies. The Disaster Management Act of 2005 provided a strong policy framework for dealing with natural hazards. Since then, many States and Union Territories have developed state disaster management plans (SDMPs) and district disaster management plans which have been very useful in dealing with disasters. Disaster Preparedness in India (2015–2016) described India as one of the most disaster prone countries in the world due to its geography, geo-climatic condition, high population and other socio-economic factors. According to GAR (2015) by UN Office for Disaster Risk Reduction, average economic loss in India due to disaster is $9.8 billion. Economic loss due to floods only accounts for $7 billion in India (GAR 2015). CAG audit report (2013) on disaster preparedness of India revealed that although India is one of the pioneering countries who have established three level disaster management institutional set up, there are critical gaps in the preparedness for various disasters, particularly for catastrophic disasters like earthquakes and floods. The gaps are lack of know-how for assessing risks at very local level, poor enforcement of standards and regulations, inadequate risk mitigation, more emphasis on response and relief after disaster rather than considering disaster management cycle holistically, inadequate integration of the socio-­economic vulnerability of women and very poorest people in vulnerability assessment, and inadequate baseline assessments and data to track future progress in disaster risk reduction.

2.5  L  and Degradation, Desertification and Soil Contamination Land is an essential resource to humankind for its survival. Land is degrading at an alarming rate which affects the productivity of fertile lands like cropland, rangelands. etc. United Nation Convention to Combat Desertification (UNCCD) defined land degradation as ‘reduction of biological and ecological productivity of land due to combination of processes, including human activities’. According to WHO, there are multiple forces polluting the land which include extreme weather condition due to climate change, human activities, etc. Land degradation negatively affects the food production, livelihoods, production of other ecosystem goods and services. According to UNCCD (2014), man is using more than 75% of the total land in the world excluding Greenland and Antarctica. The report also indicates that 25% of all globally available land is highly degraded. This land degradation results in 60% reduction of all ecosystem services and is also responsible for 20% of global carbon erosion. Approximately 5.2 million hectare of land is degrading annually during 2004 to 2014 (UNCCD 2014).

32

P. K. Sikdar and S. Basu

According to Desertification and Land Degradation Atlas of India (2016), 96.40 million hectares (mha) area or 29.32% of the total geographical area (TGA) in India is undergoing a process of land degradation during 2011–2013. Major land degradation was observed in the states of Rajasthan, Maharashtra, Gujarat, Jammu and Kashmir, Karnataka, Jharkhand, Odisha, Madhya Pradesh and Telangana in descending order, whereas rest of the states have individually less than 1% land degradation. Jharkhand, Rajasthan, Delhi, Gujarat and Goa have more than 50% of degraded land in India. The land degradation map of India is given in Fig. 2.11. The main reasons of land degradation in India are: 1 . Loss of forest land 2. Excessive cultivation leading to soil erosion and desertification 3. Increasing in agriculture, urban and industrial areas 4. Over-population leading to over-exploitation of land resources 5. Wetland drainage 6. Overgrazing 7. Unsuitable land use practices 8. Changing climate, prolonged droughts and increasing incidences of floods landslides and frost heaving According TERI’s ‘Study on Economics of Desertification, Land Degradation and Drought in India’, land degradation and land use accounted for 2.54% GDP loss which is equivalent to Rs. 3.17 lakh crore ($46.90 billion) in 2014–2015 (https:// www.teriin.org/infographics/economic-cost-land-degradation-india

Fig. 2.11  Land degradation map of India. (Source: ISRO 2016)

2  Contemporary Environmental Issues – The Indian Perspective

33

2.6  Loss of Wetland Wetlands are often considered as ‘Earth Kidneys’ as it absorbs wastes such as nitrogen, phosphorous, etc., from water (EPA, US 2014). The definition of wetlands according to Ramsar Convention of Wetlands in 1971 is ‘wetlands are areas of marsh, fen, peatland or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six metres’. Wetlands are the most important part of environment. The importances of the wetlands are: a. Wetland supports variety of species of microbes, plants, insects, amphibians, reptiles, birds, fish and mammals as part of wetland ecosystem b. Coastal wetlands protects the shoreline against the wave action and storm surges c. Wetland reduces the intensity of the flood d. Wetland absorbs the pollutants from the water and improves the water quality e. Wetland provides a wide variety of natural products which include fish, blueberries, cranberries, timber and wild rice f. Wetland supports a large part of fish production in the world To celebrate the importance of wetland, 2nd February is designated as the World’s Wetland Day. According to National Wetland Atlas 2011, prepared by the Space Application Centre, Ahmedabad there are 201,503 wetlands in India. The total wetland area based on Ramsar classification is about 7.6Mha and if open water, aquatic vegetation (submerged, floating and emergent) and surrounding hydric soils are also taken into account the total wetland area is estimated to be 15.26 MHa which covers 4.63% of total area of the country. Out of 15.26 Mha of wetland area, inland wetland covers 69.23%, coastal wetland covers 27.13% and other wetlands with area   20) concentration,

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental…

61

Table 3.1  PCI value wise rainfall regime Rainfall regime Low precipitation concentration Moderate precipitation concentration Irregular precipitation concentration Strongly irregular precipitation concentration Total

Values of PCI 20

Proportion – 64 154 353 571

Percentage – 11.21 26.97 61.82 100

Table 3.2  SI value wise rainfall regime Rainfall regime Rainfall extent throughout the year Rainfall extent throughout the year But with certain hazier season Relatively seasonal with a little drier season Seasonal rainfall Markedly seasonal with a lengthy dry season Most rainfall occurs in less than 3 months Extremely seasonal with all rainfall occurs in 1–2 months Total

Values of SI Proportion Percentage 1.20

20 91 188 237 31 571

3.50 15.94 32.92 41.51 5.43 100.00

distributed over whole study area except few pockets area of northern, north-­eastern, south-eastern and southern part. About one fourth (26.97%) of the stations showed irregular distribution (PCI = 16 − 20), followed by moderate (PCI = 11 − 15) precipitation concentration (11.21%). Orographic laden of monsoon by hills and mountains, terrain characteristics and latitudinal extensions are the key reasons for such regional variation in the distribution pattern of rainfall. Latitude is the chief geographical factor that effects annual precipitation change in Portugal, situated in the Mediterranean basin (Nunes and Lourenço 2015). On the other hand, Z-statistics of MK/mMK test of PCI showed both increasing and decreasing trend at 0.05 significance level (Fig. 3.2a). The significant decreasing trends (MK/mMK) were mostly found in the north-eastern, northern and south-­ western part of the study area (Fig.  3.2a), which indicates that the concentration pattern was changed from strongly irregular to moderate or irregular concentration. According to Karl and Trenberth (2003), the frequency of extreme daily precipitation can increase in regions with warm climates, which can lead to an increase in the precipitation concentration, even without any alteration in total rainfall. The results of RSI depicted that the average values varied from 0.29 at station Kupwara in Jammu and Kashmir to 2.26 at station Surguja in Chandigarh. These values were typically used to determine the pattern of seasonality in the study area. The whole study area was dominated by such seasonality where most rainfall occurs in less than 3 months (41.51%) (RSI = 1.00 − 1.19), concentrated in the central and western part of the study area except the state of Gujarat (Fig. 3.2b). About one third (32.92%) of the stations having markedly seasonal rainfall with a long dry season (RSI = 0.80 − 0.99) and seasonal rainfall (RSI = 0.60 − 0.79) was found over 15.94%

62

T. Mandal et al.

Fig. 3.2  Characteristics of rainfall: (a) precipitation concentration, (b) rainfall seasonality, (c) trends of precipitation concentration, (d) trends of rainfall seasonality

stations (Table 3.2) only, whereas Z statistics of RSI showed both increasing and decreasing trends at 95% confidence level throughout study area during 1952–2015 (Fig. 3.2d). However, significant decreasing trends were dominating over the study area, randomly found in the eastern, central and north-western part (Figs.  3.2d and 3.3).

Fig. 3.3  Rainfall trend using ITA method of pre-monsoon (a. North 24 Parganas, b. Bagalkot), monsoon (c. Ganjam Barishal, d. Barishal), post-monsoon (e. Idukki, f. Commilla) and winter (g. Kullu, h. Coimbatore)

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental… 63

64

T. Mandal et al.

3.4.2  Seasonal Rainfall Trend 3.4.2.1  Pre-monsoon Rainfall Trend The ITA slope of rainfall in pre-monsoon season is presented in Table  3.3 and Fig. 3.4a and the results obtained from MK/mMK and Sen’s slope are presented in Table 3.4, Fig. 3.4i and e. Although the obtained results showed few discrepancies between the methods, the results of all the methods showed that rainfall in most of the stations (total number stations = 571) had an increasing tendency in Pre-monsoon season (Fig. 3.4). ITA showed that increasing trends in rainfall in 74% stations and MK/mMK also showed similar results for 67% of stations. Furthermore, Z-statistics of MK/mMK test revealed that identified increasing trends were statistically significant at different confidence intervals (Table  3.4) for almost half (43.86%) of the stations, and only 27.65% of stations had significant decreasing trends. The spatial distribution of ITA slope is shown in Fig. 3.4a. The whole study area showed an increasing trend in rainfall except south-western and some pocket areas which are distributed discretely over the study area (Fig.  3.4a). This increasing trend may amplify the vulnerability of Landslides and flash flood in mountain region, riverbank erosion and waterlogging in Gangetic West Bengal, coastal Orissa and few pocket area of Bangladesh. The highest increasing rate (6.32 mm/year) was found in Teknaf station in Bangladesh and the highest decreasing rate (−8.83 mm/year) was found in Balaghat station under the state of Madhya Pradesh in India. Increasing sea surface temperature (SST) due to global warming is expected to be one of the reasons for such significant increasing trend of rainfall in these parts (Shahid and Khairulmaini 2009). A positive correlation was noticed by Salahuddin et al. (2006) between sea surface temperature (SST) and the rainfall of Bangladesh. However, Sen’s slope analysis revealed that amount of rainfall decreases on average at a rate of −1.46 mm/year over the study area in pre-monsoon. Similar to significant trend (Z-statistics) distribution, the higher magnitude of changes (>4  mm/year) were found in the northern, north-eastern and south-western parts of the area (Fig. 3.4e and i). 3.4.2.2  Monsoon Rainfall Trend The results of ITA slope of rainfall in the monsoon season showed the dominance of decreasing trends throughout the study area during 1951–2015 (Table  3.3). The obtained ITA and Z statistics test indicate almost similar results for decreasing (73%) and increasing (26%) tendency of rainfall in monsoon season (Table 3.3 and 3.4). The significant decreasing trends (MK/mMK) of rainfall were mostly found in the south-western, central and extreme eastern part of the study area (Fig. 3.4b), whereas it was distributed over whole study except south-eastern and few pocket region for ITA trend (Fig.  3.4b). The significant decreasing trend in the extreme eastern and western part of the study area is simplified as the influence of climate

Pre-monsoon Number of rainfall Trends in stations % of stations Rainfall Increasing 423 74.08 Decreasing 148 25.92 Total 571 100

Monsoon Number of rainfall stations 151 420 571

Post-monsoon Number of rainfall % of stations % of stations stations 26.44 190 37.27 73.56 381 66.73 100 571 100

Table 3.3  Number of rainfall stations with identified trends by ITA test in the study area Winter Number of rainfall stations % of stations 404 70.75 167 29.25 571 100

Annual Number of rainfall stations 187 384 571

% of stations 32.75 67.25 100

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental… 65

99% confidence level 95% confidence level 90% confidence level Insignificant Total Decreasing trends in rainfall 99% confidence level 95% confidence level 90% confidence level Insignificant Total

Increasing Trends in rainfall

10

14.88

10.44

56.14 67.08 % of stations

12.23

9.04

6.38

72.34 32.02

57

40

215 383 Number of rainfall stations 23

17

12

136 188

207 419

31

54

118 152 Number of rainfall stations 127

21

Monsoon Number of rainfall stations 3

Pre-monsoon % of Number of stations rainfall stations 71 18.54

49.4 73.38

7.4

12.89

30.31

77.63 26.62 % of stations

13.81

6.58

1.97

% of stations

330 403

26

26

156 168 Number of rainfall stations 21

4

6

81.89 70.58

6.45

6.45

5.21

92.85 29.42 % of stations

2.38

3.57

Post-monsoon % of Number of stations rainfall stations 2 1.19

221 227

4

–

288 344 Number of rainfall stations 2

23

18

Winter Number of rainfall stations 15

97.36 39.75

1.76

–

0.88

83.72 60.25 % of stations

6.68

5.23

4.36

% of stations

Table 3.4  Number of rainfall stations with identified trends by MK/mMK test at different confidence level in the study area

176 374

32

46

139 197 Number of rainfall stations 121

15

30

Annual Number of rainfall stations 13

47.06 65.50

8.56

12.30

32.35

70.56 34.50 % of stations

7.61

15.23

6.60

% of stations

66 T. Mandal et al.

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental…

67

Fig. 3.4  Distribution of slope and trend of seasonal rainfall (a) pre-monsoon, (b) monsoon, (c) post-monsoon, (d) winter

change due to Deforestation, Agricultural practices, Urbanization, Industrialization, emission of greenhouse gases, etc. In the meantime, formation of low pressure and depression over central India may be the cause of rainfall decreasing in the central and central-north part of the study area. Such significant decreasing trend of rainfall may affect the groundwater level, irrigation pattern and fodder crops in those areas. However, the highest decreasing trend (−43.63 mm/year) of rainfall obtained from ITA was observed in Sikkim station in India. While the average rate of decrease of rainfall obtained from Sen’ slope was −2.17  mm/year and the highest was also found in India at Imphal station (−37.70 mm/year) under Manipur State.

68

T. Mandal et al.

3.4.2.3  Post-monsoon Rainfall Trend The spatial distribution of the results of ITA, MK/mMK and Sen’s slope of rainfall of post-monsoon are presented in Fig. 3.4c, g and k. In this period, most of the stations (66.73%) showed negative trends (ITA) in rainfall (Table  3.3), distributed over whole study area except few pocket area (Fig. 3.4c) and approximately one fifth (19.11%) of the stations showed significant negative trend (Z statistics) at the confidence level of 99%, 95% and 90%, respectively, sparsely distributed over the study area with maximum concentrated in the northern part (Table  3.4 and Fig. 3.4k). Sen’s slope analysis revealed that average rainfall decreases at a rate of 0.08 mm/year over the area. The degree of decreasing trend for the major part of the area varied between −0.01 and −6.50 mm/year (Fig. 3.4h). However, the highest decreasing trend (−6.50 mm/year) of rainfall obtained from ITA was observed at Imphal in Manipur state of India. On the contrary, a little portion (7.15%) of the observation station showed significant positive trend (MK/mMK) during 1951–2015 (Table 3.4) with the highest significant positive trend (4.28 mm/year) for Teknaf in Bangladesh. 3.4.2.4  Winter Rainfall Trend The rainfall trend in winter season obtained from ITA revealed that more than half (70.75%) of the stations showed increasing trends, and rest of the stations (29.25%) showed decreasing trend throughout the study area during 1951–2015 (Table 3.3 and Fig. 3.4d). On the contrary, results of the Z statistics denoted that about one-­ sixth (16.28%) of the stations had a significant increasing tendency, and only 2.64% stations showed significant decreasing tendency at different confidence levels (Table 3.4 and Fig. 3.4l. The significant increasing trends were sparsely distributed in the study area, and the magnitude of slopes was comparatively lower (0.01 to 1.82 mm/year) during this season with an average of 0.16 mm/year (Sen’s slope) for the study area. The highest increasing (1.82 mm/year) trend was observed in India at Doda stations under the state of Jammu and Kashmir. While spatiotemporal variation of rainfall of ITA test result is shown in Fig.  3.4d which clearly showed an increasing trend in the whole study area except little portion of eastern, western and southern part, more or less similar to the results of Z statistics (Fig. 3.4l). The highest increasing rate (3.06 mm/year) (ITA) was found also in Doda located in the state of Jammu and Kashmir and the highest decreasing rate (−1.30 mm/year) was found in India at Bagalkot station under the state of Karnataka. The significant increasing trend of rainfall in this season is explained as the overactive of Western disturbance and retreat monsoon.

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental…

69

3.4.3  Annual Rainfall Trend The trend analysis of annual rainfall using ITA, MK/mMK and Sen’s slope test and their results are presented in Tables 3.3, 3.4 and Fig.  3.5. The ITA slope clearly revealed that the decreasing (67.25%) trends are dominating throughout the study area, mostly found in the eastern, central, western and south-eastern part except few pocket regions. It is also observed from the results of MK/mMK that two-third (65.50%) of 571 meteorological stations showed decreasing trends during the study period, out of these, 121 station (32.35%), 46 stations (12.30%) and 32 stations (8.56%) are statistically significant at 99 % , 95 % and 90%confidence level, respectively. The significant decreasing trends (Z statistics of MK/mMK) were mostly concentrated in the central, western, extreme east and extreme southern part of the study area and the magnitude of slope varied from −1.72 to −45.68 mm /year with an average of −6.27  mm/year (Sen’s slope). On the other hand, 34.50% stations showed an increasing trend (Z statistics) and of this 6.60%, 15.23% and 7.61% are statistically significant at 99%, 95% and 90% confidence level, respectively. Significant increasing trends are detected in the south-eastern part of India particularly in the coastal region of Bay of Bengal (Fig. 3.4c) and the trend varied from 2.26 mm/year to 27.86 mm/year (Sen′s Q =  − 1.20 to 27.86). But while focused individually, it was found that most of the stations in Bangladesh showed increasing trends (60%) in annual rainfall, among them 33.33% were statistically significant (α = 0.05). On the other hand, India was dominated by significant decreasing trend (66.66%) for annual rainfall. Significant increasing trend of annual rainfall in Bangladesh is explained as the location of the Mizo hills, Arakan Mountains, monsoon depression across the Bay of Bengal and early arrival of monsoon wind from Bay of Bengal in relation to the western, northern and eastern parts of the country (Ahmed and Kim 2003; Rahman et al. 2017).

Fig. 3.5  Distribution of slope and trend of annual rainfall (a) slope obtained from innovative trend analysis (b) Sen’s slope, (c) Z statistics

70

T. Mandal et al.

3.5  I mpact of Rainfall Trend on Trans-boundary Water Resources India and Bangladesh are adjacent neighbours, who share 54 rivers and 4096.7 km terrestrial borders (Rashid 1977). The environment and livelihood of these two countries are heavily reliant on the trans-boundary river; therefore, any one-sided and uneven management practice or judgment would being tragic damage to the whole province (Wirsing et al. 2013). The GBM (the Ganges, the Brahmaputra and the Meghna) basin constitutes the leading part of the river system in Bangladesh that serve a total area almost 1.72 million km2 (Ahmad et al. 2001). This basin receives more water than it needed for supporting life and economy of their watershed regions during the peak period (Bandyopadhyay and Perveen 2008) but it dramatically reduced during the lean period. By analysing the rainfall characteristics, it could be possible to discover if there any significant impact of increasing or decreasing trend on such excess and less flow in the GBM basin. Though, Mirza (2004) disagreed to consider precipitation changes as the most significant attributed for decreasing mean discharge in the Ganges. On the other hand, lower riparian countries are mostly suffered from environmental difficulties due to building dams and barrages for irrigation and other purposes by the upper riparian countries (Kliot et al. 1997). India has been blamed frequently for creating such obstruction in natural flows of the GBM basins, causing low inflow during dry season can result into serious environmental degradation (Nishat and Faisal 2000; Faisal 2002). However, most of the stations in Bangladesh showed increasing trends (60%) in annual rainfall during 1951–2015, among them 33.33% were statistically significant (α = 0.05), and India was dominated by significant decreasing trend (66.66%). Such increasing trend of rainfall in Bangladesh during dry season may bring solution to build respectable relation in these two countries.

3.6  Conclusions The present study explored the detail spatio-temporal characteristics of precipitation of Bangladesh and India. By analysing the concentration pattern seasonality and trend of precipitation, it could be assumed that the climate has been changed adequately within the study period (1951–2015) and the results clearly exhibit a drastic change of precipitation over whole study area except few pocket area. Such change in most of the stations makes the region most vulnerable and this practice continues at medium to high rate, which may cause variation in the sharing of water between two countries which affects the crop cycles, crop rotation as well as the total agricultural system. To get more precise evidence, it is rational to link up the micro-level variations of rainfall with climate change factors and to establish their relationship with the issue of climate change and agricultural system further studies are recommended. However, other climatic parameters especially temperature, air

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental…

71

pressure, humidity, wind pattern and direction, sunshine and the most important groundwater are not examined in this study. The information gathered from our study will help in future to estimate hydraulic procedures as well as to make sustainable water resource planning and management. In addition, the results which are assembled are also helpful for policy makers and scientist to focus on regional scale planning about post-Farakka Barrage water sharing, flood and drought situation between the country that will ultimately helpful for agricultural development. Acknowledgements The authors acknowledge India Water Portal and BARC website of Bangladesh for providing monthly rainfall data.

References Abdul Quadir D, Van Scheltinga CT, Ludwig F (2016) Recent changes in temperature and rainfall trends and variability over Bangladesh. The Gobeshona Conference for Research on Climate Change in Bangladesh, 1–17 Ahasan MN, Chowdhary MA, Quadir DA (2010) Variability and trends of summer monsoon rainfall over Bangladesh. J Hydrol Meteorol 7(1):1–17 Ahmad QK, Biswas AK, Rangachari R et al (2001) Ganges-Brahmaputra-Meghna region: a framework for sustainable development. University Press Ltd, Dhaka Ahmad I, Zhang F, Tayyab M et al (2018) Spatiotemporal analysis of precipitation variability in annual, seasonal and extreme values over upper Indus River basin. Atmos Res 213:346–360 Ahmed R, Kim IK (2003) Patterns of daily rainfall in Bangladesh during the summer monsoon season: case studies at three stations. Phys Geogr 24(4):295–318 Alam MT, Iskander SM (2013) Climate change impact: climate type, vegetation type, rainfall intensity over three decades in Bangladesh. IOSR J Environ Sci Toxicol Food Technol 4(6):56–59 Anderson RL (1954) The problem of autocorrelation in regression analysis. J Am Stat Assoc 49(265):113–129 Ay M, Kisi O (2015) Investigation of trend analysis of monthly total precipitation by an innovative method. Theor Appl Climatol 120(3–4):617–629 Bandyopadhyay J, Perveen S (2008) The interlinking of Indian rivers: questions on the scientific, economic and environmental dimensions of the proposal. Interlinking of Rivers in India: Issues and Concerns, 53 Bari SH, Rahman MTU, Hoque MA et al (2016) Analysis of seasonal and annual rainfall trends in the Northern region of Bangladesh. Atmos Res 176:148–158 Basher MA, Stiller-Reeve MA, Islam AS et al (2018) Assessing climatic trends of extreme rainfall indices over Northeast Bangladesh. Theor Appl Climatol 134(1–2):441–452 BBS (Bangladesh Bureau of Statistics) (2009) Statistical pocket book Bangladesh Caloiero T (2014) Analysis of daily rainfall concentration in New Zealand. Nat Hazards 72(2):389–404 Chatterjee S, Khan A, Akbari H et  al (2016) Monotonic trends in spatio-temporal distribution and concentration of monsoon precipitation (1901–2002), West Bengal, India. Atmos Res 182:54–75 Chu PS, Chen YR, Schroeder TA (2010) Changes in precipitation extremes in the Hawaiian Islands in a warming climate. J Clim 23(18):4881–4900 Cui L, Wang L, Lai Z et al (2017) Innovative trend analysis of annual and seasonal air temperature and rainfall in the Yangtze River Basin, China during 1960–2015. J Atmos Sol Terr Phys 164:48–59

72

T. Mandal et al.

Das J, Bhattacharya SK (2018) Trend analysis of long-term climatic parameters in Dinhata of Koch Bihar district. West Bengal Spat Info Res:1–10. https://doi.org/10.1007/s41324-018-0173-3 Das J, Mandal T, Saha P (2019) Spatio-temporal trend and change point detection of winter temperature of North Bengal. India Spat Inf Res. https://doi.org/10.1007/s41324-019-00241-9 Das J, Mandal T, Saha P et  al (2020) Variability and trends of rainfall using non-parametric approaches: a case study of semi-arid area. Mausam 71(1): 33-44. Faisal IM (2002) Managing common waters in the Ganges-Brahmaputra-Meghna region: looking ahead. SAIS Rev 22(2):309–327 Fathian F, Morid S, Kahya E (2015) Identification of trends in hydrological and climatic variables in Urmia Lake basin, Iran. Theor Appl Climatol 119(3–4):443–464 Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Wiley, New York Gocic M, Trajkovic S (2013) Analysis of precipitation and drought data in Serbia over the period 1980–2010. J Hydrol 494:32–42 Goswami BN, Venugopal V, Sengupta D et al (2006) Increasing trend of extreme rain events over India in a warming environment. Science 314(5804):1442–1445 Groisman PY, Karl TR, Easterling DR et al (1999) Changes in the probability of heavy precipitation: important indicators of climatic change. In: Karl TR, Nicholls N, Ghazi A (eds) Weather and climate extremes. Springer, Dordrecht Haktanir T, Citakoglu H (2014) Trend, independence, stationarity, and homogeneity tests on maximum rainfall series of standard durations recorded in Turkey. J Hydrol Eng 19(9):05014009 Hamed KH, Rao AR (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204(1–4):182–196 Hamilton JP, Whitelaw GS, Fenech A (2001) Mean annual temperature and total annual precipitation trends at Canadian biosphere reserves. Environ Monit Assess 67(1–2):239–275 Huang Y, Wang H, Xiao W et  al (2018) Spatial and temporal variability in the precipitation concentration in the upper reaches of the Hongshui River basin, southwestern China. Adv Meteorol. https://doi.org/10.1155/2018/4329757 IPCC (2007) Climate change. The physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge/New York IPCC (2014) Climate change 2014. Synthesis report. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva, Switzerland Jain SK, Kumar V, Saharia M (2013) Analysis of rainfall and temperature trends in Northeast India. Int J Climatol 33(4):968–978. https://doi.org/10.1002/joc.3483 Jaswal AK, Bhan SC, Karandikar AS et al (2015) Seasonal and annual rainfall trends in Himachal Pradesh during 1951–2005. Mausam 66(2):247–264 Kamruzzaman M, Rahman ATMS, Kabir ME et  al (2016) Spatio-temporal analysis of climatic variables in the western part of Bangladesh. Environ Dev Sustain 20:89–108 Karl TR, Trenberth KE (2003) Modern global climate change. Science 302(5651):1719–1723 Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin, San Francisco, CA Kisi O (2015) An innovative method for trend analysis of monthly pan evaporations. J Hydrol 527:1123–1129 Kliot N, Shmueli D, Shamir U (1997) Institutional frameworks for the management of transboundary water resources. Technion, Israel Institute of Technology, Haifa Li X, Jiang F, Li L et  al (2011) Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. Int J Climatol 31(11):1679–1693 Mann HB (1945) Nonparametric tests against trend. Econometrica. J Economet Soc:245–259 Markus M, Demissie M, Short MB et al (2013) Sensitivity analysis of annual nitrate loads and the corresponding trends in the lower Illinois River. J Hydrol Eng 19(3):533–543

3  Rainfall Insight in Bangladesh and India: Climate Change and Environmental…

73

Meshram SG, Singh SK, Meshram C et al (2018) Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010). Theor Appl Climatol 134(3–4):1231–1243 Michiels P, Gabriels D, Hartmann R (1992) Using the seasonal and temporal precipitation concentration index for characterizing the monthly rainfall distribution in Spain. Catena 19(1):43–58 MonirulQaderMirza M (2004) The Ganges water diversion: environmental effects and implications  – an introduction. In: MonirulQaderMirza M (ed) The Ganges water diversion: environmental effects and implications. Water science and technology library, vol 49. Springer, Dordrecht Mukhopadhyay S, Kulkarni S, Kulkarni P et al (2016) Rainfall statistics change in West Bengal (India) from period 1901–2000. In: Raju N (ed) Geostatistical and geospatial approaches for the characterization of natural resources in the environment. Springer, Cham Nalley D, Adamowski J, Khalil B et al (2013) Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967–2006 using the discrete wavelet transform. Atmos Res 132:375–398 Nishat A, Faisal IM (2000) An assessment of the institutional mechanisms for water negotiations in the Ganges-Brahmaputra-Meghna system. Int Negot 5(2):289–310 Nunes AN, Lourenço L (2015) Precipitation variability in Portugal from 1960 to 2011. J Geogr Sci 25(7):784–800 Oliver JE (1980) Monthly precipitation distribution: a comparative index. Prof Geogr 32(3):300–309 Onyutha C (2016) Identification of sub-trends from hydro-meteorological series. Stoch Env Res Risk A 30(1):189–205 Pearson ES, Hartley HO (1966) Biometrika tables for statisticians, vol 1, 3rd edn. Cambridge University Press, London Rahman A, Jiban MJH, Munna SA (2015) Regional variation of temperature and rainfall in Bangladesh: estimation of trend. Open J Stat 5(07):652 Rahman MA, Yunsheng L, Sultana N (2017) Analysis and prediction of rainfall trends over Bangladesh using Mann–Kendall, Spearman’s rho tests and ARIMA model. Meteorog Atmos Phys 129(4):409–424 Rahman ATMS, Jahan CS, Mazumder QH, et al (2017) Drought analysis and its implication in sustainable water resource management in Barind Area, Bangladesh. J Geol Soc India 89(1):47–56 Rahman ATMS, Ahmed MS, Adnan HM et  al (2018) Modeling the changes in water balance components of the highly irrigated western part of Bangladesh. Hydrol Earth Syst Sci 22(8):4213–4228 Ramos MC (2001) Rainfall distribution patterns and their change over time in a Mediterranean area. Theor Appl Climatol 69(3–4):163–170 Rashid H (1977) Geography of Bangladesh. The University Press Limited, Dhaka Sa’adi Z, Shahid S, Ismail T et  al (2019) Trends analysis of rainfall and rainfall extremes in Sarawak, Malaysia using modified Mann–Kendall test. Meteorog Atmos Phys 131(3):263–277 Salahuddin A, Isaac RH, Curtis S et al (2006) Teleconnections between the sea surface temperature in the bay of Bengal and monsoon rainfall in Bangladesh. Glob Planet Chang 53(3):188–197 Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389 Sen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046 Sethi R, Pandey BK, Krishan R et al (2015) Performance evaluation and hydrological trend detection of a reservoir under climate change condition. Model Earth Syst Environ 1(4):1–33. https://doi.org/10.1007/s40808-015-0035-0 Shah HL, Mishra V (2016) Uncertainty and bias in satellite-based precipitation estimates over indiansubcontinental basins: implications for real-time streamflow simulation and flood prediction. J Hydrometeorol 17(2):615–636 Shahid S (2010) Recent trends in the climate of Bangladesh. Clim Res 42(3):185–193 Shahid S, Khairulmaini OS (2009) Spatio-temporal variability of rainfall over Bangladesh during the time period 1969–2003. Asia-Pac J Atmos Sci 45(3):375–389

74

T. Mandal et al.

Tabari H, Nikbakht J, Some’e BS (2012) Investigation of groundwater level fluctuations in the north of Iran. Environ Earth Sci 66(1):231–243 Walsh RPD, Lawler DM (1981) Rainfall seasonality: description, spatial patterns and change through time. Weather 36(7):201–208 Wang Y, Sen OL, Wang B (2003) A highly resolved regional climate model (IPRC-RegCM) and its simulation of the 1998 severe precipitation event over China. Part I: model description and verification of simulation. J Clim 16(11):1721–1738 Wirsing RG, Jasparro C, Stoll DC (2013) International conflict over water resources in Himalayan Asia. Macmillan, London Wu H, Qian H (2017) Innovative trend analysis of annual and seasonal rainfall and extreme values in Shaanxi, China, since the 1950s. Int J Climatol 37(5):2582–2592 Yue S, Pilon P, Phinney B et al (2002) The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol Process 16(9):1807–1829 Yusuf AA, Francisco H (2009) Climate change vulnerability mapping for Southeast Asia. In: Economy and environment program for Southeast Asia (EEPSEA), Singapore, 10–15 Zhai P, Zhang X, Wan H et al (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18(7):1096–1108 Zhang IJ, Qian YF (2003) Annual distribution features of precipitation in China and their inter-­ annual variations. Acta Meteor Sin 17:146–163

Chapter 4

Habitat Linkages for Asian Elephants in Central Indian Landscape  C. S. Abhijitha, G. Areendran, Krishna Raj, Pamposh Bhat, and Mehebub Sahana

4.1  Introduction The Mahilong-Kalimati and Chandil-Matha are two inter-state corridors that are under the pressure of anthropogenic developments like hydro-electric projects, railway lines, state highways, human encroachments, etc. It usually requires large space for its shelter and survival. Elephas maximus (Asian elephant) is the largest terrestrial mammal in India that belongs to the family Elephantidae (Menon 2003). The IUCN Red List categorizes Asian elephants as ‘endangered’ (Choudhury et al. 2008). Asian elephants migrate over greater distances in search of food, water and safety and often use the same corridors regularly and if these corridors are blocked, then they tend to enter into human settlements in search of the crops that often leads to human-elephant conflict and poses a major threat to their survival (Areendran et al., 2020; Menon 2003). Asian elephants are severely affected by the loss of their natural habitat due to the increasing human population, habitat degradation and fragmentation (Pokhriyal et al., 2020; Karkala 2016). Poaching male elephants for their tusks is another major threat to the male elephants (Asian Elephant Secretariat 2019; Karkala 2016). Wildlife corridors provide for the linkage between two or more habitats with similar living conditions for the wild species as it maintains colonization, migration and breeding of fauna (Yadav et al., 2020; Johnsingh & Williams 1999). The corridors chosen by species are usually vegetation-based habitats that facilitate their movement, where there is less energy expenditure required and low risk of being predated. Corridors differ from one another in size, shape, length and composition (Johnsingh and Williams 1999). The wider the corridor, the C. S. Abhijitha · P. Bhat Guru Gobind Singh Indraprastha University, New Delhi, India G. Areendran · K. Raj · M. Sahana (*) IGCMC, WWF-India, New Delhi, India e-mail: [email protected]; [email protected]; [email protected] © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_4

75

76

C. S. Abhijitha et al.

more is its capacity of sustaining more diversity and less are the impacts on it from adjacent habitats and edge effects (Burkart et  al., 2016; Johnsingh and Williams 1999). The corridors of Asian elephants should be protected because they act as key elements and aid in the restoration of declining populations  in multiple habitats (Johnsingh and Williams 1999; Zhang & Wang, 2003). Corridors can be natural or man-made and can also be regional, sub-regional and local, based on their width (Johnsingh and Williams 1999). There are 28 Asian elephant corridors in south India, 25 corridors in central India, 23 corridors in north-eastern India, 1 corridor in northern West Bengal and 11 corridors in north-western India out of the total 101 corridors in India. 70% of these corridors are regularly used, 25 are occasionally used and 6% are rarely used (Right to passage: Elephant corridors of India, 2017). Conservation-based efforts should be undertaken in order to make all the corridors regularly preferred by elephants. West Bengal has the highest number of corridors followed by Tamil Nadu and Uttarakhand (Menon et al. 2017). Almost all the corridors in central India have agricultural land in them. According to the census carried out by Project Elephant in 2017, India had the greatest number of Asian elephants which was estimated to be approximately 27,312 (Right to passage: Elephant corridors of India, 2017). Habitat fragmentation and human encroachments and their insensitivity has led to the decline in their numbers. This has eventually forced the elephants to move out of their native habitat boundaries in search of food, shelter and space (Menon et al. 2017). All these reasons have undoubtfully led to Human-Elephant Conflict which is considered to be one of the most alarming issues in India. 400 human deaths, property loss up to millions and 1500 elephant deaths caused due to train hits, electrocutions, poisoning, poaching for tusks are few black marks that are added to the list (WTI article, undated). Unless the implementation of solutions that would effectively conserve elephants and at the same time give protection to human life and property, are coordinated, it is a waste of time just discussing just about species conservation. Restoration and protection of deteriorating corridors should be ensured because it ensures landscape connectivity between habitat areas across the landscape, habitat connectivity between habitat patches adapted to by the species, ecological connectivity related to ecosystems and evolutionary connectivity in terms of gene flow between populations in different habitat patches. Mapping of elephant corridors provides an effective way of monitoring their ecological status and whether the species movement is detrimental to human property and life.

4.2  Mahilong-Kalimati and Chandil-Matha Corridors Central India has an elephant corridor every 840 km2, and this piece of information itself states the importance of conservation of corridors in central India (Singh and Perinchery 2017). Human-elephant conflicts in West Bengal have been on a rise with an increase in the developmental projects (Katariya 2018). The elephants that find their way into human settlements tend to cause a lot of commotion and huge

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

77

damage to life and property (Katariya 2018). Forest officials and residents of the districts Midnapore, Bankura and Purulia are following a practice of throwing fire torches on the elephants that stray away from their paths known locally as ‘Hula Pati’, and this mechanism has been followed for more than a decade (Katariya 2018). It is understood that this is a problematic situation, but still it should be noted that the lives of these animals are also under risk as many of them suffer burns and injuries when fire torches are thrown at them (Katariya 2018). Supreme Court in November, 2018 recognized this inhumane practice prevalent in several parts of West Bengal and observed that violence is not the only way of tackling the entry of elephants into human settlements and agricultural land (Katariya 2018). The article says that the Supreme court restrained the west Bengal government from processing tenders issued by it for the supply of burnt mobile oil to stray away elephants (Katariya 2018). West Bengal has blocked Mahilong (Jharkhand)-Kalimati (West Bengal); Chandil (Jharkhand)-Matha (West Bengal); Jhunjhaka (Jharkhand)-Banduan (West Bengal); Dalapani (Jharkhand)-Kankrajhore (West Bengal) corridors by ‘Energized fence’, ‘Elephant Proof Trench’ since July 2016 (Basu 2017). The above-mentioned unethical mismanagement has caused limited resource availability to this long-­ ranging animal and also increased the depredation in the corridor-dependent villages. The Mahilong (Jharkhand)-Kalimati (West Bengal) corridor is a source of connectivity used by a  small herd of approximately more than 18 elephants that migrates till the forest of ‘Ajodhya Hilltop’ (Basu 2017). This corridor is an ecologically suitable habitat for the elephants throughout the year. The Chandil-­ Muri railwaytrack and hydro-electric plant are considered to be a big threats to the movement of elephants and make the forest fragmented and degraded (Basu 2017). This corridor maintains connectivity between the Mahilong and Bundu Ranges of Ranchi Forest Division and Kalimati Reserve Forest of Purulia Forest Division and is 18 km long and 0–0.5 km wide (Right to passage: Elephant corridors of India, 2017). There are several observed threats to this  corridor such as many densely populated villages are located in the vicinity of the corridor, agricultural lands are also present within these corridors, also a portion of the Chandil-Muri railway track bisects the corridor (Right to passage: Elephant corridors of India, 2017). An elephant was found dead after being hit by a train near Bhusudih village in 2008; also, a hydro-electric project setup in the Ayodhya Hills near Baghmundi hinders the movement of elephants from the Hensla Protected Forest to the Ayodhya hill ranges (Right to passage: Elephant corridors of India, 2017). The Chandil (Jharkhand)Matha (West Bengal) and Jhunjhaka (Jharkhand)-Banduan (West Bengal) corridor forest is occasionally used by elephants for their migration (Basu 2017). These corridors are also highly fragmented by human settlements, agricultural lands and railway tracks/roads and similarly threatened by timber poachers and forest fire (Basu 2017). The Chandil-Matha provides connectivity between the Chandil Range of Saraikela Forest Division and the Matha Range of Purulia Forest Division and is 16 km long and 1–2 km wide (Right to passage: Elephant corridors of India, 2017). The corridor includes 16 villages and agricultural lands of the villagers have disconnected the corridor forests between the Gundu and Ramnagar Protected Forests and

78

C. S. Abhijitha et al.

Matha Reserve Forest (Right to passage: Elephant corridors of India, 2017). The railway track between Chandil and Muri passes along the Ramnagar Protected Forests and has undoubtedly obstructed elephant movement between Ramnagar Protected Forest and Dalma Wildlife Sanctuary (Right to passage: Elephant corridors of India, 2017) (Figs. 4.1 and 4.2).

4.3  Hurdles to Corridor Conservation There are few hurdles to the conservation of elephant corridors in India such as the absence of no legal protection for the corridors under the Wildlife Protection Act or Environment Protection Act; also there is no effective and prompt land use policies in the lands that cover elephant corridors which eventually led to the fragmentation of habitats. Many corridors fall under private lands and this has caused human-elephant conflict. Lack of awareness among various stakeholders has caused tremendous loss and degradation of elephant corridors, especially in the areas where

Fig. 4.1  Map showing the location of the study area

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

79

Fig. 4.2  Map showing the Mahilong-Kalimati and Chandil-Matha corridors

sections of rails and roads cross through the corridors. This ignorance leads to sanction of more and more developmental projects and there is lack of funds for securing the corridors by land purchase or allocation of funds for relocation of people or community intervention (Rangarajan et  al. 2010). The presence of railways and roads is also not a small hurdle; it can eventually alter the entire dynamics of elephant habitats often leading to creation of edges and alienation of habitats, besides hindering the movement of elephants through their corridors. Moreover, various developmental activities on either side of the highways increases the anthropogenic pressure (Rangarajan et al. 2010). Mining is also another detrimental factor that acts like a big block across the routes used by elephants, creating impacts that are irreversible and cause environmental imbalance as it involves building of mining infrastructure (Rangarajan et  al. 2010). Forest clearance is the first step taken while undertaking any mining project, so it again leads to deforestation and fragmentation of forest patches as there are many other associated activities such as dumping of overburden, deposition of tailings, setting up of infrastructure for transport and surface facilities (Rangarajan et al. 2010). Carrying away substrate materials and creation of voids for mining also adds to the alteration of hydrology and topography of the land (Rangarajan et al. 2010). Open cast mining (surface mining technique for excavation of minerals) acted as a hinderance to the protection of elephant habitats in central India such as in in Singhbhum (Jharkhand), Keonjhar, Mayurbhanj, Dhenkanal, Angul and Phulbani (Orissa) and also in adjoining states of Chhattisgarh and West Bengal (Rangarajan et al. 2010). Inspite of many rules and regulations, the

80

C. S. Abhijitha et al.

setting up of mining projects, still leads to degradation of forest cover. At times EIA the fact that mining hampers the movement of animals, especially in sensitive areas (Rangarajan et al. 2010). Also, many gaps still exist in the available data area wise regarding the extent of degraded forest which hampers effective measures from being undertaken for mitigation. Lack of landuse planning also boosts the imbalance caused (Rangarajan et al. 2010). Electrocution is another reason that leads to a steep increase in the number of elephant deaths in India, as electric wires often hang down especially in forested areas (Rangarajan et al. 2010).

4.4  Database and Methodology Satellite images (Landsat 5 TM and Landsat 8 OLI) for preparation of land use land cover map of the study area were downloaded from USGS earth explorer. Software used includes Arc Map 10.1 and ERDAS IMAGINE for processing of the data and preparation of maps: Google Earth for digitization and MS-Excel for calculations and preparing graphs. The accuracy of the land use land cover map was determined through confusion matrix and Kappa coefficient (Dou et al., 2020) was determined as 81.0 and 83.0%, respectively. Normalized difference vegetation index (NDVI) was derived to differentiate the forested and non-forested areas. Finally, Land Fragmentation model (Land Use Education and Research (CLEAR, 2002)) was used for preparation of forest fragmentation maps (Sahana et al., 2015; 2016; 2018). The methodology used for the land use and land cover change analysis and extent of fragmentation is illustrated in Fig. 4.3.

4.5  L  and Use and Land Cover Changes and Extent of Fragmentation in the Landscape Land use and land cover classification for the entire landscape was carried out using geo-spatial techniques in order to identify the area covered by each land use and land cover class. The land use land cover map was prepared for the year of 1990, 2008 and 2018. From the, increase or decrease in the area covered by each class was calculated to understand the trend. The LULC change analysis showed that there was a decrease in the area covered by vegetation from 5309.9 km sq. in 1990 to 4744.85 km sq. in 2018 and also an increase in area covered by agriculture from 4996.84 km sq. in 1990 to 6671.21 km sq. in 2018. There was a drastic increase in the area covered by built-up from 59.14 km sq. in 1990 to 271.13 km sq. in 2018. Also, there was an increase in the area covered by water bodies from 119.55 km sq. in 1990 to 159.9 km sq. in 2018 and decrease in the area covered by riverbed from 157.86 km sq. in 1990 to 108.35 km sq. in 2018; and the area covered by open/barren land decreased from 5931.34 km sq. in 1990 to 4578.69 km sq. in 2018. The

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

81

Satellite imageries of years 1990, 2008, 2018 Layer stacking, mosaicking and creating subset

Supervised classification

Recoding

Accuracy assessment

NDVI (Normalized Difference Vegetation Index) Unsupervised classification

Classify into 2 classes: forest and non-forest Model executed in fragmentation tool

LULC

Preparation of maps

Fig. 4.3  Flowchart showing the methodology used for this study

maps below show the landuse and land cover changes in 1990, 2008 and 2018 (Figs. 4.4, 4.5 and 4.6 and Table 4.1). Asian elephant populations have been confined to smaller habitat patches due to increased fragmentation of their available habitats. Poaching for ivory has led to the depletion of the number of tuskers that has altered their sex ratio considerably (Easa 2017). Traditional movement paths of elephants have been lost due to various developmental activities and the resulting biotic pressure has degraded elephant habitats and increased human-elephant conflict, which has led to the loss of both human and elephant lives (Easa 2017). Habitat fragmentation and degradation are the alarming threats along with the changing climate that has led to species isolation and their extinction (Dennis et al. 2013). Habitat fragmentation eventually causes decrease in the surface area of the habitat patches that makes them suffer the adverse impacts of edge effect including noise from adjacent roads and surface runoff of pesticides from agricultural fields (Lemming 2016). Habitat fragmentation also leads to reduction of the landscape connectivity that leads to the loss of the species’ ability for dispersion, foraging and migration (Sahana and Sajjad, 2019; Lemming 2016).

82

Fig. 4.4  Land use and land cover map of the study area (1990)

Fig. 4.5  Land use and land cover map of the study area (2008)

C. S. Abhijitha et al.

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

83

Fig. 4.6  Land use and land cover Map of the study area (2018)

Table 4.1  Table showing the area calculations of LULC (1990, 2008 and 2018) S.NO. 1 2 3 4 5 6

LULC classes Vegetation Waterbody Riverbed Agriculture Open/barren land Built-up area

Area in km sq. 1990 5309.90 119.55 157.86 4996.84 5931.34 59.14

2008 4778.29 118.77 255.37 7384.15 4000.61 70.9

2018 4744.85 159.9 108.35 6671.21 4578.69 271.13

The fragmentation maps that were prepared shows six fragmentation classes that includes classes such as patch, edge, perforated, core I (2 km). The results revealed that fragmented forest patches increased in area from 16.72% in 1990 to 26.81% in 2018, the edge forests also showed an increase in area from 23.34% in 1990 to 28.12% in 2018; perforated forests showed a decrease in area from 28.69% in 1990 to 24.88% in 2018 and decrease in core I area (2 km) from 24.78% in 1990 to 14.39% in 2018. Also, it was observed that the

84

C. S. Abhijitha et al.

total core forest area including core I, II and III decreased from 2099.94 sq.km. in 1990 to 1346.96 sq.km. in 2018.  This  decrease in the core forest  area showed that they have been continuously changing to lower density classes such as perforated, edge and patch classes (Dutta et al. 2017). This is mainly because the formation of patches happened due to various anthropogenic factors, such as construction of roads, and  increase in built-up area. Due to the expansion of settlements and increase in agriculture as shown by the LULC, the shrinkage of forest takes place and becomes fragmented as well. Hence, it is evident that this would eventually make elephant populations isolated to small fragmented habitats. The maps below show the extent of fragmentation in 1990, 2008 and 2018 (Figs. 4.7, 4.8, 4.9 and 4.10 and Table 4.2).

Fig. 4.7  Map showing the forest fragmentation in 1990

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

Fig. 4.8  Map showing the forest fragmentation in 2008

Fig. 4.9  Map showing the forest fragmentation in 2018

85

86

C. S. Abhijitha et al.

Fig. 4.10  Map showing the conflict locations in the study area  and its relation to forest fragmentation

Table 4.2  Table showing the area calculations for forest fragmentation Year Fragmentation classes Patch Edge Perforated Core 1 Core 2 Core 3

1990 Area in km sq. 1124.65 1569.09 1928.73 358.82 74.93 1666.19

Area in % 16.72 23.34 28.69 5.33 1.11 24.78

 2008 Area in km sq. 1242.52 1116.68 1127.83 241.339 52.5158 1180.36

Area in % 25.04 22.50 22.73 4.86 1.059 23.79

 2018 Area in km sq. 1788.7 1875.51 1659.64 304.576 82.6223 959.762

Area in % 26.81 28.12 24.88 4.57 1.24 14.39

4.6  Human-Elephant Conflict Cases in the Corridor Region Few cases of human-elephant conflict have been reported in the study area (Table 4.3).

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

87

Table 4.3  Table showing the conflict locations derived from secondary data (Data source: Hindustan Times, 2017; India today 2016)  Sl. no 1

3

Name of newspaper HINDUSTAN TIMES HINDUSTAN TIMES INDIA TODAY

4

INDIA TODAY

5

TIMES OF INDIA

2

Date Nov 16,2017 Nov 16,2017 Nov 30, 2016 Sep 25, 2013 Apr 26, 2019

Location Bundu Birdih

Incident Trampled to death by wild pachyderm An individual was killed

Loharatola, Bundu Tamar

People surviving on trees due to elephant Individual was trampled

Ghatbera

Individual was picked by elephant

4.7  Conclusion Effective management of elephant corridors can be done to an extent by reducing human-elephant conflict, rather than implementing rules and regulations that cannot be fulfilled  as  human interference and  encroachment into elephant corridors and habitats  continues to increase. This requires adoption  of measures that would involve human beings or more specifically speaking, indigenous people who are mostly dependent on forests for their day-to-day livelihood. Joint Forest Management can be adopted as a measure in this case, that would ensue sharing of benefits on both the sides. Effective conservation-based strategies should be adopted that includes spreading of information regarding the importance of elephant corridors and the need to conserve their existing populations especially in central India, where forests getting fragmented at an alarming rate. The forest department also has a very important role to play by taking stringent action in cases where there is a need of providing compensation to the local people, as when their rights are denied once, these people will never support animal conservation. It leads them to retaliation, with people trying to target, harm and attack the elephants using various local mechanisms followed in the past decades. Electric fences also, at many times tend to be ineffective and causes loss of money involved in setting them up. So measures that are scientifically proven to be effective should be implemented. Not only are the elephant populations getting affected, but people living in the corridor forests are also suffering economic loss as they are entirely dependent on agriculture and NTFP from the forests. So from the human perspective, measures should be adopted in such a way that tribal people do not harm the elephants and at the same time, their lives are also secured. Many NGOs countrywide are putting lots of efforts to restore the declining animal numbers and are also sensitizing various stakeholders regarding the importance of securing the linkages between habitats. Besides all these measures that were taken in the past, many still in force at present, the declining species numbers are not fully restored. Still the sad fact prevails that many of the rules and regulations are implemented properly and hence these rules are taken for granted

88

C. S. Abhijitha et al.

always. There should be a change in this present situation as we all are responsible for the current status. So first of all, a change in our mentality should be made, an ‘ecocentric view’ rather than an ‘anthropocentric view’ should be adopted. Let us hope that a change for the best will positively happen.

References Areendran G, Sahana M, Raj K, Kumar R, Sivadas A, Kumar A, ..., Gupta VD (2020) A systematic review on high conservation value assessment (HCVs): Challenges and framework for future research on conservation strategy. Sci Total Environ 709:135425. Asian Elephant Secretariat (2019, April 26) Elephant picks up, runs off with man defecating in open. Retrieved from https://elephantcountry.org/news/elephant-picks-runs-man-defecating-open Basu S (2017, June) Conflict to coexistence: securing Jharkhand-West Bengal inter-state corridor, INDIA. Retrieved from https://elephantconservation.org/iefImages/2017/03/Samya-BasuIndia_Interim-Report-IEF-Project-2017-website.pdf Burkart S, Gugerli F, Senn J, Kuehn R, Bolliger J (2016) Evaluating the functionality of expert-­ assessed wildlife corridors with genetic data from roe deer. Basic Appl Ecol 17(1):52–60 CLEAR (2002) Forest Fragmentation in Connecticut: 1985–2006 Center for Land use Education and Research. http://www.clear.uconn.edu/projects/landscape/forestfrag. Accessed 05 May 2018 Choudhury A, Lahiri Choudhury DK, Desai A, Duckworth JW, Easa PS, Johnsingh AJT, Fernando P, Hedges S, Gunawardena M, Kurt F, Karanth U, Lister A, Menon V, Riddle H, Rübel A, Wikramanayake E, IUCN SSC Asian Elephant Specialist Group (2008) Elephas maximus. IUCN Red List Threat Spec 2008:e.T7140A12828813. https://doi.org/10.2305/IUCN. UK.2008.RLTS.T7140A12828813.en. Downloaded on 28 May 2019 Dennis RLH, Dapporto L, Dover JW, Shreeve TG (2013) Corridors and barriers in biodiversity conservation: a novel resource-based habitat perspective for butterflies. Biodivers Conserv. https://doi.org/10.1007/s10531-013-0540-2 Dutta S, Sahana M, Guchhait SK (2017) Assessing anthropogenic disturbance on forest health based on fragment grading in Durgapur Forest range, West Bengal, India. Spat Inf Res 25:501–512 Dou J, Yunu AP, Bui DT, Merghadi A, Sahana M, Zhu Z, ..., Pham BT (2020) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17(3):641–658. Easa PS (2017) Asian elephants in India: a review. In: Menon V, Tiwari SK, Ramkumar K, Kyarong S, Ganguly U, Sukumar R (eds) Right of passage: elephant corridors of India, 2nd edn. Wildlife Trust of India, New Delhi, pp 7–23 Hindustan Times (2017, November 16) Five people killed by elephants in 48 hours in Jharkhand. Retrieved from https://www.hindustantimes.com/india-news/five-people-killed-by-elephantsin-48-hours-in-jharkhand/story-J8L4gWClslEzxxyZkE9caO.html India Today (2016, November 30) Ranchi: Over 1,000 people have been killed by elephants in Jharkhand. Retrieved from https://www.indiatoday.in/india/story/ranchi-elephant-fearforced-families-trees-jharkhand-354809-2016-11-30 Johnsingh AJT, William AC (1999) Elephant corridors in India: lessons for other elephant range countries. Oryx 33(3):210–214. Karkala N (2016) Elephas maximus (on-line). https://animaldiversity.org/accounts/Elephas_ maximus/ Katariya M (2018, December 6) This photographer is trying to stop the ‘tradition’ of elephant abuse, One image at a time. Retrieved from https://www.scoopwhoop.com/wildlife-photographer-elephants-corridor-west-bengal/#.dhaxaq1il

4  Habitat Linkages for Asian Elephants in Central Indian Landscape 

89

Lemming LF (2016) Least-cost modelling: A potential tool for mapping ecological corridors in Danish municipalities? (Master’s thesis) Menon V (2003) A field guide to Indian mammals. India Menon V, Tiwari SK, Ramkumar K, Kyarong S, Ganguly U, Sukumar R (2017) In: right of passage: elephant corridors of India, Conservation reference series no. 3, 2nd edn. Wildlife Trust of India, New Delhi Pokhriyal P, Rehman S, Areendran G, Raj K, Pandey R, Kumar M, ..., Sajjad H (2020) Assessing forest cover vulnerability in Uttarakhand, India using analytical hierarchy process. Model Earth Sys Environ 1–11. Rangarajan M, Desai A, Sukumar R, Easa PS, Menon V, Vincent S, Ganguly S, Talukdar BK, Singh B, Mudappa D, Chowdhary S, Prasad AN (2010, August 31) Securing the future for elephants in India. Retrieved from http://www.moef.nic.in/downloads/public-information/ ETF_REPORT_FINAL.pdf Singh SS, Perinchery A (2017, November 11) Elephant corridors in India threatened, says study. Retrieved from https://www.thehindu.com/sci-tech/energy-and-environment/elephant-corridors-in-india-threatened-says-study/article20231670.ece Sahana M, Sajjad H, Ahme R (2015) Assessing spatio-temporal health of forest cover using forest canopy density model and forest fragmentation approach in Sundarban reserve forest, India. Model Earth Sys Environ 1(4):49. Sahana M, Ahmed R, Jain P, Sajjad H (2016) Driving force for forest fragmentation explored by land use change in Song watershed, India. Spat Inf Res 24(6):659–669. Sahana M, Hong H, Sajjad, Liu J, Zhu AX (2018) Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model. Sci Total Environ 627:1264–1275. Sahana M, Sajjad H (2019) Assessing influence of erosion and accretion on landscape diversity in Sundarban Biosphere Reserve, Lower Ganga Basin: a geospatial approach. In Quaternary Geomorphology in India (pp. 191–203). Springer, Cham. When art initiates conservation efforts in Jharkhand through school kids. Retrieved from https://www.wti.org.in/news/when-art-initiates-conservation-efforts-in-jharkand-throughschool-kids/ Yadav N, Areendran G, Sarma K, Raj K, Sahana M (2020) Susceptibility assessment of human– leopard conflict in Aravalli landscape of Haryana using geospatial techniques. Model Earth Sys Environ 1–15. Zhang L, Wang N (2003) An initial study on habitat conservation of Asian elephant (Elephas maximus), with a focus on human elephant conflict in Simao, China. Biol Conserv 112(3):453–459

Chapter 5

Environmental Pollution and Municipal Solid Waste Management in India Mithun Ray, Avaya Chandra Mohapatra, Suman Das, Asraful Alam, and Biman Ghosh

5.1  Introduction Solid wastes are wastes arise from human and animal activities that are normally solid or semi-solid in form and are discarded as worthless or unwanted. It includes non-liquid, non-soluble materials ranging from municipal garbage to industrial wastes that contain complex and sometimes hazardous substances. These are the unavoidable byproduct of human activities. In earlier days, the disposal of human waste did not pose significant difficulties since the population was small and the availability of land for assimilation of waste was large (Ray and Rahaman 2016). In addition, the amount of waste generated was also small and not diversified as the consumption was at a basic level. But with increasing and diversified wastes resulting from rapid economic growth and overpopulation, management of wastes has become one of the arduous jobs for many Urban Authorities in developed as well as in developing countries, particularly for the responsibility of supervising public health and sanitation (Siddiqui et al. 2013). There are potential risks to environment and health from improper handling of solid wastes. The most obvious environmental damage caused by municipal solid wastes is aesthetic, the ugliness of street litter M. Ray Assistant Professor, Department of Geography, Malda College, Malda, West Bengal, India A. C. Mohapatra Department of Geography, North-Eastern Hill University, Shillong, Meghalaya, India S. Das Department of Geography, Ramkrishna Mahavidyalaya, Kailashahar, Tripura, India A. Alam (*) Department of Geography, University of Calcutta, Kolkata, West Bengal, India B. Ghosh Department of Geography, Visva-Bharati, Santiniketan, West Bengal, India © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_5

91

92

M. Ray et al.

and degradation of the urban environment and beauty of the city (Lilliana Abarca Guerrero et al. 2013). More serious, however, and often unrecognized, is the transfer of pollution to water, groundwater. Air pollution can be caused by the inefficient burning of wastes, either in the open air or in plants that lack effective treatment facilities from the gaseous effluents (Nag and Vizayakumar 2005; Zurbrugg 2002). At present, India is the second largest country in the world with a population of 1.21 billion, accounting for nearly 18% of world’s population. During last decade (2001–2011), the population growth rate was 17.64% and more than 181 million people increased. Even though this was the sharpest decline in population growth rate registered post-Independence the absolute addition during 2001–2011 is almost as much as the population of Brazil, the fifth most populous country in the world. The proportion of population residing in urban areas has increased from 27.80% in 2001 to 31.80% in 2011 (Census of India 2011). The problem of municipal solid waste management (MSWM) has acquired alarming dimensions in India, especially over the last decade, before which waste management was hardly considered an issue of concern as the waste could be easily dumped in available open spaces (Ramachandra 2006). An unplanned rapid urban growth and expansion of slum-like conditions in small and mid-size towns and cities are bases of a growing public concern for sanitation and public health in the country (Hanrahan 2006). Moreover, with time, due to changing lifestyles of people coupled with developmental activities and industrialisation, the waste quantity and characteristics have changed, and as a result, managing solid wastes has become torturous. Although management of solid waste is one of the obligatory services provided by Urban Local Bodies (ULBs) in India (The Constitution Act 1992), this indispensable system has an enormous gap to fill and experienced several problems and challenges towards fulfilling its responsibilities in the country.

5.2  Methodology This review paper is mainly based on electronically available materials. A comprehensive literature search was carried out, focused on empirical studies describing problems and challenges to proper waste management in Indian urban areas. Literature database Web of Science (Version 5.22.3) and the commercial search engine like www.scholar.google.co.in were used to congregate peer-reviewed papers published between the years 1990 and 2017. Three search terms applied to collect literature. Using the first term ‘solid waste management in developing countries’ 3 papers and ‘municipal solid waste management in India’ 16 papers has been selected for this study. Subsequently, 103 papers were selected using the third term which had two parts, one is common, that is, ‘municipal solid waste management in’ and last part was changed according to city/town name like ‘municipal solid waste management in Kolkata’ or ‘municipal solid waste management in Mumbai’ and so on. In addition, hardcopy literature including14 research paper and 4 books on MSWM available in the Central Library of North-Eastern Hill University and 5 Ph. D theses available in Shodhganga @INFLIBNET had also been reviewed for

5  Environmental Pollution and Municipal Solid Waste Management in India

93

the purpose. 12 articles were later added in a snowball fashion based on citations in the papers reviewed. At the initial stage, we excluded literature primarily focused on chemical characteristics of municipal solid wastes as the main focus of the present study is to assess the problems and issues related to management of solid waste. Hence, after going through the literature we obtained 56 literatures with a specific focus on challenges and issues in the operation of waste management system in various towns and cities for final review. We focused on full papers, retaining those that investigated case studies through interviews, surveys, participant observation, focus groups and literature review and pointed out the key findings of those studies in a worksheet. In addition, 35 literatures including blogs, reports and newspaper articles were also incorporated using the commercial search engine www.google. co.in to highlight the latest developments, especially for successful SWM in various ULBs and economic potential of municipal waste in India. Finally, 91 literatures were reviewed critically. The literature selection process is illustrated in Fig. 5.1. This chapter reviewed the problems and issues associated with various stages of municipal solid waste management reported from different towns and cities from different regions of India. Moreover, recent initiatives and developments in MSWM sector have also been discussed with special reference to selected successful case studies regarding waste management and green marketing startups.

5.3  Municipal Solid Waste Management in India The management system of solid wastes generally involves activities associated with generation, storage, collection, transportation and processing and disposal processes. But in India, the system comprises mostly four activities, that is, waste generation, collection, transportation and disposal. Stage-wise evaluation of problems and issues associated with MSWM has been carried out as following:

5.3.1  Waste Generation and Composition in India As the quantity and composition of solid waste are determined by the level of economic development, dramatic change in generation of waste in India in terms of quantity and composition noticed due to population growth and rapid urbanization (Chattopadhyay et  al. 2009). At present, 62 million tonnes of waste is generated annually in India, out of which 5.6 million tonnes is plastic waste, 0.17 million tonnes is biomedical waste, hazardous waste generation is 7.90 million tonnes and 1.5 million tonnes is e-waste. It is estimated that waste generation will increase to about 165 million tonnes from 62 million tonnes in 2030 (MoEF 2016). With more than 1.8 million tonnes a year, the majority of it is generated from the unorganized sector; India is the fifth largest e-waste producing country in the world. The problem of handling of such large and ever-increasing quantity of solid waste has become a major issue for almost all the ULBs (Reddy 2011), although waste generation rate

87 literatures selected for full review on problems, issues and challenges regarding Municipal Solid Waste Management in India

31 literatures excluded because of their focus on quantity and characteristics of wastes

Fig. 5.1  Overview of literature adoption system

Review process

Library work

Finally, 167 literatures reviewed

23 reports, 21 blogs and 36 news articles selected to review the latest developments in SWM

Google.co.in

118 (83 soft copy + 14 hardcopy) literature (Developing/ South-East Asian Countries- 03, India in general- 16, ULB wise- 74) selected for initial review 04 books, PhD thesis 05 12 research papers later added in a snowball fashion based on citations

Search Terms: [Solid Waste Management in Developing Countries] or [Solid Waste Management in India] or [Solid Waste Management in … City]

Shodhganga @INFLIBNET Online Ph.D Thesis

Google Scholar

Available literature in NEHU Central Library

Web of Science

94 M. Ray et al.

5  Environmental Pollution and Municipal Solid Waste Management in India

95

in India is lower than the many low-income countries in other parts of the world and much lower compared to developed countries (Asnani 2006). Changing lifestyle, especially in the larger cities generate more of packaging material and per capita waste generation, is increasing by about 1.3% per year in India (Annepu 2012). Physical and chemical characteristics of solid wastes in Indian cities vary depending on population size and geographical location. Although the composition of urban waste is changing with increasing use of packaging material and plastics, yet, as compared to developed countries, Indian solid waste still comprises mostly of organic matter as well as inert material (Khan and Ahsan 2003) (Table 5.1).

5.3.2  Source Segregation and Primary Storage Segregation is the process of sorting and separate storage of various components of solid waste such as biodegradable, non-biodegradable, hazardous, construction and demolition wastes. Segregating of wastes at source ensures that waste is less contaminated and can be collected and transported for further processing. It also optimizes waste processing and treatment technologies. In India, it is hard to find an organized and scientifically planned system for segregation of municipal solid waste (MoUD 2005). The community bin collection is common for primary storage of wastes in urban areas but the bins for both decomposable and non-decomposable wastes are often the same ones. Generally, a container of 12–15 l (0.015 m3) capacity for a family of five members should be adequate for each dry and wet waste, if collection takes place daily (MoUD 2000). Availability of community bin is also not sufficient and distribution is often uneven between slum and non-slum areas (Das 2018). Lack of community bin increases the probability of throwing away the waste by the residents near or around their residences at different times of the day. This contributes to the environmental pollution, which in turn accelerates natural resources degradation impacting the quality of life of citizens (Kaushal et al. 2012). The unpleasant smell and repulsive sight of garbage dumped on the roadside, sometimes overflowing from drains is not at all rare in urban India. Only in very few cities, the waste generated from various sources such as residential, street sweepings, garden, parks, offices and shopping complexes are collected separately but often under a very unsafe and hazardous manner (Asnani 2006; MoUD 2000). But off course, ULBs like Tenali municipality of Andhra Pradesh have taken initiatives Table 5.1 Waste generation per capita in Indian cities (Kumar et al. 2009)

Population two million

Waste generation rate (kg/capita/day) 0.17–0.54 0.22–0.59 0.19–0.53 0.22–0.62

96

M. Ray et al.

to check littering on the streets by imposing fine and confirming timely collection of waste from the households while Panaji municipality maintains a unique way of source segregation of waste in eight clear streams by reorganizing the management strategies and introducing Public Private Partnership (PPP) model (Table 5.2).

5.3.3  Collection and Secondary Storage The functional element of solid waste collection system in Indian municipalities comprises gathering of wastes and their transportation, after collection, to the location where the collection vehicle is emptied. This location may be materials processing facility, a transfer station or a landfill disposal site. But sometimes problem takes place in the collection of solid waste from the different site or house to house because of transportation system or lack of workers. Solid wastes are collected in mixture form as the system of source separation is hardly practiced. Generally, collection and storage of waste execute at the doorstep but not available in every corner of urban areas. In India, most of the states are unable to provide waste collection services to all parts of cities (Rajput et al. 2009). In fact, the collection process is deficient in terms of manpower and availability of equipment and vehicles and on an average only about 75–80% of the municipal wastes get collected (MoEF 2016). Wastes are more often seen outside the bins. In many areas, roadside collection bins are not cleared regularly. Waste handling is done mainly manually and the existing system of loading/unloading of waste is labour intensive and also time-consuming (Nema 2004). ULBs generally fund their MSWM activities through a combination of government grants and internal revenues from property tax and non-tax revenues. Some ULBs have taken initiatives to ensure the financial viability of municipal solid waste management systems through revenue generation and encouraging Private Sector Participation (PSP) and PPP. It is found that the intensity of problems related to collection and storage are less in urban areas where private organizations and NGOs are active in managing MSW. But the success of PPPs depends on the three necessary conditions of competition, transparency and accountability. Management of PPPs is another critical issue, which will ultimately determine the success. Therefore, regular monitoring and reviewing of the performance of the private entity against predefined performance criteria by the ULBs are important for the success of PPP projects. Table 5.2  Availability and sufficiency of community bins in an Urban area Zone Slum dominant Market Middle-class residential Higher-class residential Administrative

Availability and sufficiency (relatively) Least availability and completely insufficient Available but not sufficient Not easily available and not much sufficient Easily available and nearly sufficient Available and sufficient

5  Environmental Pollution and Municipal Solid Waste Management in India

97

5.3.4  Transportation Transportation operation of solid waste involves several steps that are necessary for proper disposal. In India, the transportation system for solid waste management generally involves two stages: first is the transportation of the collected waste from a doorstep community bin or smaller collection vehicle to larger transport equipment. Secondly, the waste is transported usually over long distances to the disposal or recycling site. The waste is thrown on streets transported by hand-cart trolley to the nearby open dumps or to community bins, or directly by tractor trolley to the out-skirt of the city. Municipal workers then collect waste from collection points (open dumping spaces or bins) into various vehicles including tractors and ox-carts which is then hauled to disposal sites (Vij 2012). Transportation of waste is done mainly through the tractor-trailers or open trucks. These vehicles are often loaded manually. The frequency of collection of wastes and the number of transport vehicles are inadequate. Poor maintenance of transportation vehicles and machineries result in a large percentage of vehicles remaining off roads. Moreover, workshop facilities are inadequate for maintaining vehicles and other machines (Supreme Court of India 1999). In addition, use of open trucks and overloading of vehicles results in littering of waste during transportation and due to improper handling the segregated constituents get mixed up again mainly during open transportation. Moreover, lack of appropriate collection routes and often unavailability of collection vehicles cause problems in the waste management system in Indian ULBs (Ahmad et al. 2015; Panda et al. 2014).

5.3.5  Disposal of Waste Disposal of waste is a burning issue facing India today since about 90% of waste is currently disposed of by open dumping. The waste is disposed at a common disposal centre and often in low-lying areas without taking proper precautions or operational controls (Narayana 2009; MoUD 2005). Even in small municipalities, there are no notified dumpsites (Asnani 2006). As cities are growing in size with a rise in the population, the increasing amount of waste is becoming unmanageable. The methods adopted for the disposal of waste by ULBs are open dumps, landfills, sanitary landfills. Waste disposal in India simply involves rounding up the waste from different parts of the city and dumping everything in a landfill. Generally, once a landfill is completely occupied, a new landfill is identified in a different part of the city (Banerjee 2016). But India’s landfills are bursting at the seams and overflowing with items that should not be thrown in the trash (Hanrahan 2006). As a result, most of them are brimming and are way past their limit. At many landfill sites, open burning is done without segregation of wastes into biodegradable and non-degradable (Kansal 2002). This leads to the release of toxic gases that cause acute respiratory diseases and environmental degradation. The Energy Research Institute estimates

98

M. Ray et al.

that 1400 km2. of land would be required by 2047 for municipal waste (TERI 2014). As most of the urban areas in India are developed along a river or near, they use it as additional dumping ground. The state of the Yamuna River in Delhi is a testimony to this fact. The river practically doesn’t flow at all. Mumbai generates 6500 metric tonnes of garbage daily, including 2500 metric tonnes of silt and debris, besides 25 tonnes of biomedical waste. A significant amount of the waste (4500 metric tonnes per day) is dumped at the Deonar dumping ground, located in the eastern suburb of the city (Shanker 2010). According to Brihanmumbai Municipal Corporation, the Deonar landfill site will expire by the end of 2016. The landfills in Gorai and Chincholi Bunder have already been shut down due to over-use. The Mulund dumping ground has also been overused and the BMC is contemplating shutting this down as well (Laskar 2014). Improper SWM contributes to 6% of India’s methane emissions and is the third largest emitter of methane in India. This is much higher than the global average of 3% methane emissions from solid waste. It currently produces 16 million tons of CO2 equivalents per year and this number is expected to rise to 20 million tons of CO2 equivalents by 2020 (IEA 2009). In addition, high levels of nickel, zinc, arsenic, lead, chromium and other metals in the solid waste are also found at landfills in metro cities. Disposal of industrial and domestic waste without any treatment made water in 42 of 53 lakes unfit for drinking or bathing in Bangalore (Kiran 2018). An alternative to landfills which will solve the problem of leaching to some extent is sanitary landfill which is more hygienic and built in a methodical manner. These are lined with materials that are impermeable such as plastics and clay and are also built over impermeable soil (Sridhar 2016). Construction of sanitary landfills is very expensive and not problem free. The plastic liner often develops cracks as it reacts with various chemical solvents present in the waste. The rate of decomposition in sanitary landfills is also tremendously variable. Moreover, some biodegradable materials do not decompose in a landfill (Jain et al. 2015) (Table 5.3). A city-wise summarized explanation of major issues related to MSWM is presented in Table 5.3. A total of 32 ULBs have been selected with special consideration of rank achieved by towns/cities in Swachh Survekshan (2017) by Govt of India as solid waste management including door-to-door collection, processing and disposal, and open defecation free status carried 45% marks in the survey (MoUD 2017). On the other hand, 25 ULBs out of 32 are selected from the class-I category as about 60% of India’s solid waste generated in class-I cities which have a population of more than one lakh. It is found that in large urban areas availability of basic infrastructure and accessibility to modern technologies are evident but in small urban areas, there are some specific deficiencies in terms of basic infrastructure required for SWM. Although door-to-door collection and community bin systems are introduced in all reviewed ULBs, these systems suffer from insufficiency and irregularity. Source segregation is profoundly practiced in hilly urban areas like Shimla, Shillong, Gangtok and coastal urban areas like Coimbatore, Kochi, Port Blair, while in the major metro cities like Kolkata, Mumbai, Bangalore source segregation is introduced by limited to some localities. But in small urban areas like Cooch Behar, Dimapur, Kohima, there are no initiatives to promote source

5  Environmental Pollution and Municipal Solid Waste Management in India

99

Table 5.3  A city-wise summarized description of the major problems and challenges towards integrated solid waste management in India Class* and rank** Class I & ****

Segregation of waste*** No source segregation

Treatment and disposal Composting, open dumping, earth cover

Other issues Despite 70% of the SWM budget being allocated house-to-house collection is limited to 60% only; bin capacity provided is adequate but locations were found to be inappropriate; operational efficiency of the transport system is about 50%, with a fleet composed of about 30–35% old vehicles. The majority (80%) of these, particularly the hired vehicles, are more than 20 years old. Although it lies in fragile No scientific Darjeeling, West Class I No source ecology, no minimal segregation treatment, open Bengal (Limbu 2014) & regulatory framework for dumping **** SWM; rotting waste lying about in streets and Jhoras has created an unhealthy environment in the hilly region. Improper waste handling No treatment Cooch Behar, West Class No source and poor water drainage segregation, facility, open Bengal (Ray 2015) 2& are the most experienced dumping on the **** bank of Torsa river environmental problems; disposal or littering of waste on drains and roads results made this historically planning city unhygienic. Characterized by poor Composting, Class I About Bhopal, Madhya compaction, open infrastructure and low 10–15% Pradesh (Katiyar et al. & 2 dumping (60%) in service levels; in spite of waste is 2013) the existence of a strong low-lying areas segregated informal sector, the at source municipal corporation has made no effort to integrate this system into its formal system. Urban centre Kolkata, West Bengal (Das and Bhattacharyya 2013; Hazra and Goel 2009)

(continued)

100

M. Ray et al.

Table 5.3 (continued)

Urban centre Mysore, Karnataka (Siddiqui et al. 2013)

Class* and rank** Class I &5

Segregation of waste*** No source segregation

Mumbai, Maharashtra Class I Limited (Joshi et al. 2013) &8 source segregation

Vadodara, Gujarat (Mehta and Pandey 2014)

Class I No source & 10 segregation

Class I Source Coimbatore, Tamil segregation Nadu (Gautham 2018; & 16 Ganeshwaran and Shri 2015)

Treatment and disposal Composting (50%), uncontrolled dumping

Other issues Although door-to-door collection introduced, the common practice is dumping of MSW into RCC bins /masonry bins and on the roadsides (open collection points) in some wards. Composting, RDF/ Door-to-door collection of waste is limited to just WTE, LFG 15%; open burning of recovery, MSW on streets (2%) and biomethanation, at landfills (10%), along uncontrolled with landfill fires emit dumping 22,000 tons of pollutants into the lower atmosphere; the pollutants identified in due to uncontrolled burning of wastes are carbon monoxide (CO), carcinogenic hydrocarbons (HC), particulate matter (PM), nitrogen oxides (NOx) and sulphur dioxide (SO2). Composting, earth Open burning of waste including plastic waste in cover, open and around the dustbins by dumping the rag pickers leads to the deterioration air environment; the river, Vishwamitri is badly affected by open dumping and burning of waste almost over the river bank. Vermicomposting, Collection efficiency is about 91%; littering strictly earth cover, open prohibited; about 55.19% dumping households and 20.83% shops and establishments have started storing the waste at source. (continued)

5  Environmental Pollution and Municipal Solid Waste Management in India

101

Table 5.3 (continued)

Urban centre Gwalior, Madhya Pradesh (Singh 2016)

Class* and rank** Class I & 27

Segregation of waste*** No source segregation

Treatment and disposal Incineration, open dumping

Shimla, Himachal Pradesh (Bharti et al. 2014)

Class I Source & 47 segregation

Gangtok, Sikkim (Chhetri 2010)

Class III & 50

Bhubaneswar, Odisha (Panda et al. 2014)

Class I No source & 94 segregation

Uncontrolled dumping

Imphal, Manipur (Mahongnao 2017)

Class I No source & 122 segregation

Incineration, landfill, open dumping

Guwahati, Assam (Chakraborty et al. 2014; Gogoi 2013)

Class I Source & 134 segregation partially practised

Earth cover, uncontrolled dumping

Source segregation

Composting, sanitary landfill

Composting, open dumping

Other issues It was the most polluted city in India in 2016; lack of basic infrastructure; constraints on the availability of suitable land for disposal of waste. Successfully banned usage of plastic in the form of plastic carry bags; a sound municipal waste management system through the proper collection, transportation, treatment and disposal of solid waste in a planned and phased manner has been developed. Strict ban on the use of plastic; daily and regular collection; 3Rs strategy applied in collaboration with NGOs. The existing system is highly inefficient; little waste processing takes place for recyclables only through a chain of informal recyclers. The garbage cleaning is not satisfactory resulting in the emanation of obnoxious smell in the adjacent areas; segregation of waste is not practised and all the wastes are seen mixed up; the municipal solid waste is disposed by direct open dumping and not by the sanitary land-fill method. Door-to-door collection not as conceptualized due to the apathy of citizens; narrow roads are not favourable for positioning bins; 26 NGOs are working in collaboration for SWM. (continued)

102

M. Ray et al.

Table 5.3 (continued) Class* and rank** Urban centre Kanpur, Uttar Pradesh Class I & 175 (Zia and Devadas 2008)

Segregation of waste*** No source segregation

Treatment and disposal Composting, earth cover, open dumping

Puducherry, Puducherry (Pattnaik and Reddy 2010)

Class I Source & 189 segregation

Composting, open dumping

Kohima, Nagaland (Chatterjee 2009)

Class II & 208

Jodhpur, Rajasthan (Ambade et al. 2013; Choudhary et al. 2012)

Class I No source & 209 segregation

No source segregation

Bangalore, Karnataka Class I Source & 210 segregation (Bharadwaj 2018; is partially Lakshmikantha 2006) practised (10%)

Open dumping

Windrow and vermicomposting, unscientific open dumping

Composting, uncontrolled dumping

Other issues Door-to-door collection is not available; commercial waste is dumped at various collection points or just thrown away along the roadside; two workshops to repair transport vehicles are poor in overall infrastructure. PPP model adopted in recycling of non-metal waste and scrap; lack of co-operation from the public. The collection process is deficient in terms of manpower and vehicle availability; bin capacity provided is adequate but locations were found to be inappropriate, thus contributing to the inefficiency of the system; old dumping site on NH 39 affected by landslide and has been abandoned. Door-to-door collection available in selected wards only; transportation of MSW is carried out by the combination of municipal and private hired vehicles; most of the vehicles are of 13–17 years old. Large dustbins have been provided but people walk all the way up to it only to throw the trash just outside the bin; decentralized plant to process waste; 85% of the water bodies in and around the city are severely polluted due to direct disposal of waste. (continued)

5  Environmental Pollution and Municipal Solid Waste Management in India

103

Table 5.3 (continued)

Urban centre Itanagar, Arunachal Pradesh (Paron et al. 2013)

Class* and rank** Class III & 216

Segregation of waste*** No source segregation

Treatment and disposal Composting, landfill

Port Blair, Andaman and Nicobar Islands (Karelia 2017; Roy 2017)

Class II & 221

Source segregation

Windrow composting, effluent treatment plant

Srinagar, Jammu and Kashmir (Mohidin 2017; Wani and Ahmad 2014)

Class I No source & 241 segregation

Composting, open dumping

Other issues The problem of solid waste has become acute as the treatment plants are either not working or there are no landfills to safely dispose of the waste. Segregation of waste in four streams: Green bins for compostable waste, blue ones for non-­ compostable waste, yellow bins for disposing rubber items, while grey bins for the disposal of industrial waste; open burning of solid waste is strictly prohibited; the liquid waste produced from domestic units (kitchens, bathrooms and laboratories, including the waste from water closets) is treated in the ETP, which was set up in 2003; dry waste is transported to Chennai or Visakhapatnam for processing; large amount of plastic waste generated due to high influx of tourists is being used to aid the construction of road. Insufficient funding; waste is being collected manually; blockage of open drains; location of existing dumpsite is in the middle of settlements; high frequency of waste burning at the dump site. (continued)

104

M. Ray et al.

Table 5.3 (continued)

Urban centre Patna, Bihar (Sinha 2018; Bhanu and Kumar 2014)

Lucknow, Uttar Pradesh (Archana et al. 2014; Francis et al. 2013)

Class* and rank** Class I & 262

Segregation Treatment and of waste*** disposal No source Open dumping segregation

Class I No source & 269 segregation

Composting, earth cover, biomethanation, open dumping

Kochi, Kerala (Hridya Class I Source et al. 2016) & 271 segregation

Composting, biomethanation, open dumping

Shillong, Meghalaya Class I & 276 (WannLyngdoh and Nongbri 2017; Mipun et al. 2015)

Sanitary landfill, open dumping

Source segregation partially practised

Other issues There is no door-to-door collection system; space between two dustbins is about 1.5 km; about 75% of the households and 80% of shops and establishments throw the waste on the streets, which shows the lack of civic awareness. Inadequate infrastructure; waste disposed of in nearby low-lying areas; no facility of composting although 41% waste is organic; incineration is used only for hospital waste. Corporation has provided separate waste collection bins to each household, one for collecting biodegradable waste and the other for collecting non-biodegradable waste; the treatment plant is only for biodegradable waste and all the other waste, including plastic and sanitary is being dumped outside the plant. Collection efficiency is about 40%; the garbage that is to be disposed of in suitable dumping bins are not carried out and the garbage that was to be collected from the dumping bins and finally disposed to the landfill is not regular; only a small part of the refuses is converted into compost. (continued)

5  Environmental Pollution and Municipal Solid Waste Management in India

105

Table 5.3 (continued)

Urban centre Dimapur, Nagaland (Henllyphom 2013)

Class* and rank** Class II & 277

Segregation Treatment and of waste*** disposal No source Open dumping segregation

Agartala, Tripura (Ramteke 2015; Santra et al. 2001)

Class I Only 18% & 290 residents practice source segregation

Open dumping

Muzaffarpur, Bihar (Anand 2009)

Class I No source & 304 segregation

Open dumping, landfill

No source segregation

Open dumping

Class I No source & 351 segregation

Open dumping

Nainital, Uttarakhand Class (Kumar and Sah 2015; III & 330 Tewari et al. 2013)

Ghaziabad, Uttar Pradesh (Ali 2018; NCRPB 2009)

Other issues There are no public dustbins for disposal of solid wastes within the municipality; people throw household waste wherever they can, without caring for public health hazards. Present coverage of door-to-door collection is about 30–40%; only 26% people are satisfied with existing service; negligence with regard to river site dumping. Community bins are not placed properly at regular intervals; no fixed points for the collection of SW; people just throw the waste anywhere they like on the roadside; the disposal vehicles carry the SW un-covered, which go on littering the SW here and there on the roads. Complex geographical location and poor infrastructure; a large amount of waste produced by tourists. No work norm for the staff and vehicle movement is available; solid waste neither stored nor collected at source; tools and equipment are insufficient and improper. (continued)

106

M. Ray et al.

Table 5.3 (continued)

Urban centre Thiruvananthapuram, Kerala (Arya 2017; George 2000)

Class* and rank** Class I & 372

Segregation of waste*** No source segregation

Treatment and disposal Composting, earth cover, open dumping

Other issues Spends 30–50% of the total budget on solid waste management. But politicization of issues concerning SWM has had a negative effect in this city; shortage of staff, equipment and waste disposal vehicles, as well as their poor maintenance, lack of community participation, are among the many concerns that affect this process.

*Class I towns with more than 1, 00,000 population, Class II towns with 50,000–99,999 population, Class III towns with 20,000–49,999 population, Class IV towns with 10,000–19,999 population, Class V towns with 5000–9999 population and Class VI towns with less than 5000 population **Rank according to Swachh Survekshan (Govt. of India) 2017 ***Most of the ULBs, where source segregation is reported, adopted dual community bin system such as biodegradable and non-degradable ****West Bengal did not participate in this survey

s­ egregation; therefore, dustbin used for biodegradable as well as non-biodegradable is the same one. Vehicles used for collecting and transporting waste are often older than 10 years and a shortage of manpower are also found to be a general problem in each and every ULBs. The most striking finding is that except Shimla, Port Blair and Itanagar all other ULBs practice open dumping that indicates the inefficiency of ULBs and lack of initiatives for scientific treatment of solid waste. All class-I cities have more than one type of waste treatment and disposal facilities and composting is common in every metro cities. A clear negligence with regard to river site dumping is also found in ULBs like Cooch Behar, Agartala. In tourist towns like Nainital, Darjiling littering is a widespread issue due to lack of responsible tourism that leads to aesthetic as well as environmental pollution in and around.

5.4  W  ay Towards Integrated Solid Waste Management (ISWM) in India Sustainable waste management is an approach of using resources efficiently to cut down on the amount of waste produced and, where waste is generated, dealing with it in a way that actively contributes to the achievements of economic, social and environmental goals of sustainable development. Although management of MSW in India is not integrated at the national level, some urban areas have successfully

5  Environmental Pollution and Municipal Solid Waste Management in India

107

achieved the goal of integrated waste management in various sectors. The key issue of their success has been highlighted which can be followed by other ULBs. Panaji and Alappuzha municipalities made possible of 100% source segregation by distributing separate community bins and involving NGOs to make residents aware of the advantages of source segregation of waste. By organizing informal sector and confirming regular-timely collection of waste, Indore municipality has become a model for 100% door-to-door collection system. To stop littering on the roads and open spaces, initiatives taken by Tenali municipality are appreciable. By imposing fines for littering and penalty for using plastic, it has made its streets and drains litter free. This established the fact that application of rules and regulations at ground level can change the beauty of an urban area. Involvement of private organizations in composting has made it commercially successful in Mangalore municipality. Very often the thermal treatment of waste causes environmental pollution and to check this Solapur municipality uses an indigenous technology to generate energy from waste without any environmental degradation. Salur municipality becomes evidence that dumping ground can be a place of recreation if it is managed and beautified properly. Finally, Pune municipality has become a model for how PPP model can be applied and be succeeding from source to final disposal of waste.

5.5  E  conomic and Business Potential of Municipal Solid Waste Generated in India While SWM is challenging, it also has the capacity to improve societies’ quality of life and contribute to the economy as it has great resource value if managed properly. Therefore, waste management sector has great potential to contribute towards green economy by offering net carbon savings and a resource efficient contribution in the current situation which is largely reliant on the exploitation of non-renewable natural resources. The main goal of green economy is to achieve sustainable development by reducing environmental risks and degradation. The United Nations Conference on Sustainable Development held in June 2012 (Rio+20) ranked the green economy as one of the major international concerns and aims to motivate and assist governments and businesses in making a transition to a green economy in the waste sector [140]. India has great potentials of green economy in the waste sector because of its large quantum of waste generated in mainly urban areas. For example, at present, the plastic recycling industry in India employs over 1.6 million people and has more than 7500 recycling units (Mahanty 2018). It is now accepted that waste can be turned into a raw material or into resource if managed properly by recycling and reusing. Recycling of waste materials not only save the over exploitation of natural resources but also minimize the emission of carbon dioxide into the atmosphere. Water crisis is becoming acute day by day due to over-exploitation of groundwater; therefore, reuse of wastewater after recycling is a welcomed step and in this regard, VA Tech Wabag company made it possible in Chennai. In every year

108

M. Ray et al.

a huge amount of e-waste including precious metals dumped in India without any treatment but most these can be reused by recycling which can generate revenue and contribute to the national economy.

5.6  L  egal Framework for Waste Management System in India The management of solid wastes is one of the basic responsibilities provided by Urban Local Bodies to keep urban environment hygienic in India. A number of laws and rules have been enacted and implemented to preserve the quality of environment and for proper management of solid waste generated in India such as The Waste Act (Prevention and Control of Pollution)-1974, The Water (Prevention and Control of Pollution) Cess Act-1977, Hazardous Wastes (Management and Handling) Rules-1989, The Biomedical Waste (Management and Handling) Rules-1998, Municipal Solid Wastes (Management and Handling) Rules-2000, The Batteries (Management and Handling) Rules-2001, Plastic Waste Rules- 2011, E-waste Rules-2011. In 2016, the, Govt. of India revised Solid Waste (Management and Handling) Rules after 16  years replacing the Municipal Solid Wastes (Management and Handling) Rules 2000. Beyond municipal area, the present rules are now applicable in urban agglomerations, census towns, notified industrial townships, areas under the control of Indian Railways, airports, airbase, port and harbour, defence establishments, special economic zones, State and Central government organizations, places of pilgrims, religious and historical importance also and, hence, the word ‘municipal’ has been removed (SWM Rules 2016).

5.7  Conclusion and Recommendations It is clear from the above discussion that management of municipal solid waste has become a serious issue due to enhanced economic activities and rapid urbanization. But still, it is one of the most neglected areas in the country although waste management activities generate potential environmental benefits if managed effectively. Collection, segregation, transportation and disposal of solid waste are often unscientific, without adequate plans of treatment and disposal. Unscientific dumping of wastes on the outskirts of towns and cities have created overflowing of landfills, which have environmental impacts in the form of pollution to soil, groundwater and air, and also contribute to global warming. The issue of scarcity of land for waste disposal in and around urban areas led to overflowing dumpsites and waste treatment facilities receiving more waste than what they were designed for. Further, the situation has been worsening due to poor technical skill, lack of political willpower and civic awareness. The difficulties in providing the desired level of public services

5  Environmental Pollution and Municipal Solid Waste Management in India

109

in the urban centres are often attributed to the poor financial status of the managing ULBs. Funds for SWM in India are typically assigned as part of the annual municipal general budget. Moreover, the rules regarding SWM have not pushed for decentralized management of waste but have encouraged centralized treatment such as waste-to-energy, the present state of which is not good in the country. Also, the informal sector has been considerably neglected in the new rules. Apart from the Jawaharlal Nehru National Urban Renewal Mission (JNNURM), there has been no national level effort required to address the problem. Even though JNNURM was phenomenal in stimulating the industry and local governments, it is not enough to address this gigantic problem. This is because JNNURM is not a long-term financing programme. In the regional context, especially in the northern states, there is lack of skills and awareness of the need to adopt proper MSWM services, resulting in the inadequate allocation of financial and human resources by the public authorities and a general public apathy, and therefore, the existing scenario of MSWM has become quite disordered. Finally, lack of proper database regarding quality and quantity of waste at micro- as well as macro-level also creates difficulty to undertake proper management policy. Although waste management is under negligence, in general, the good thing is that in different parts of India, ULBs have started to recycle and process SWM to reach zero waste city tag. Increased attention has been given in recent years to handle this problem in a safe and hygienic manner. In this regard, government’s initiative of the smart city is highly appreciable as sustainable waste management is a core element for smart urban planning by retrofitting new technologies into existing waste management structures. Following recommendations which are based on successful case studies from different Indian urban areas could solve or minimize the problems associated with SWM: • The serious problem of garbage management is not about finding the right technology for waste disposal. The problem is how to integrate the technology with a system of household level segregation so that waste does not end up in landfills, but is processed and reused (Narain 2016; Rana et al. 2015). It is clear that there will be no value from waste, as energy or material, if it is not segregated. But this is where waste management system stops short. Therefore, segregation at source should be at the heart of municipalities’ solid waste management system. For this segregation of waste should be made mandatory at ground level for all the residents with the levy of user charges. At the same time there may be a tax rebate for those who have onsite waste disposal facilities. • Community participation in waste management activities is critical for ensuring a well-functioning collection system. Involvement of the community in the primary collection system, specifically in determining the timings for waste collection, is important for the effective planning and implementation of the primary waste collection system. Community initiatives need to be inclusive (Agarwal et al. 2015; Chakrabarti et al. 2009). Active engagement of men, women, youth and children should be given equal importance. Separate group discussions, involvement of community leaders, community associations, SHGs and local

110

M. Ray et al.

members who represent the interest of the community at large (especially with a focus on bringing in the voices of women) must be adopted. • There is a great need to move away from the disposal-centric approach and toward the recovery-centric approach of waste management (Agarwal et  al. 2013; Mor et al. 2006). As improper solid waste management contributes to various environmental pollution and public health challenges, it is imperative to move towards a green economy in the solid waste sector by prioritizing waste avoidance, minimization and promoting the ‘Four R’s’ (Refuse, Reuse, Recycle and Recover). As estimated by the Central Pollution Control Board, 15,342 tonnes of plastic waste are produced every day, of which about 60% is recycled. While the recycling rate of plastic waste is quite high as global average is 14%; over 6100 tonnes end up in the landfill polluting groundwater resources. On average, the recovery and recycling rate of waste paper in India is presently below 30%, whereas it is 73% in western Europe and global 57% (Mahanty 2018). • PPP model can apply like many urban areas in India like Indore (Madhya Pradesh), Tenali Municipality (Andhra Pradesh), Pune (Gujarat); Public-Private Partnerships for Service Delivery (PPPSD) is one of the proven approaches to resource management planning. The main objective of the programme is to promote sustainable, self-supporting partnerships between businesses and local governments to support the formation and operation of new enterprise-municipal co-operation in solid waste management and recycling systems. The main goal of the programme is to stimulate improved co-operation between public, private and citizen stakeholders that: contributes to sustainable improvement of recycling and solid waste management; minimizes negative effects of waste in poor communities; and improves the lives and livelihoods of people and enterprises in cities and rural communities (Vasanta and Priyasauni 2013). • There is need for a massive awareness campaign in association with communities, NGOs, students and other stakeholders that needs to be planned to push for better management of wastes. Information to the community on different types of special wastes including domestic hazardous waste and their related impacts on human life and environment should be provided. So that they become interested to use sustainable materials such as jute or cloth bags, energy-efficient lighting and electronic appliances, and multi-use consumables which as an effective strategy to minimize waste generation.

References Agarwal R, Chaudhary M, Singh J (2015) Waste management initiatives in India for human well being. Eur Sci J 11(10):105–127 Agarwal A, Pandey R, Agrawal ML (2013) Water quality surrounding to MSW dumps of Raipur city-a case study. Recent Res Sci Technol 5(5):07–11 Ahmad L, Bhat FA, Sultan S (2015) Municipal solid waste Management in Srinagar City, Kashmir, India. Int J Innov Res Sci Eng Technol 4(7):6355–6361 Ali S (2018) Ghaziabad fails a fourth time to make smart city list, Hindustan Times, 19 January

5  Environmental Pollution and Municipal Solid Waste Management in India

111

Ambade BR, Sharma S, Dass A, Sharma Y (2013) Municipal solid waste management in Jodhpur city, Rajasthan (India)–current status. Int J Environ Waste Manag 11(4):430–440 Anand A (2009) Planning for solid waste management of Muzaffarpur City Bihar, M.Tech dissertation, Indian Institute of Technology Roorkee. Available online at: http://hdl.handle. net/123456789/7983. Accessed 10 July 2017 Annepu RK (2012) Sustainable solid waste management in India. Columbia University, New York Archana AD, Yunus M, Dutta V (2014) Assessment of the status of municipal solid waste management (MSWM) in Lucknow – capital city of Uttar Pradesh, India. IOSR J Environ Sci Toxicol Food Technol 8(5):41–49 Arya U (2017) Your garbage, your responsibility, says Thiruvananthapurm, The Times of India, 12 August Asnani PU (2006) Solid waste management (New Delhi, India: infrastructure report). Available online at: http://www.iitk.ac.in/3inetwork/html/reports/IIR2006/Solid_Waste.pdf. Accessed 12 Sept 2016 Banerjee P (2016) Gone to waste: how India is drowning in garbage, Hindustan Times, 9 February Bhanu AK, Kumar A (2014) Strategy for municipal solid waste management: - a case study of Patna. Int J Chem Environ Biol Sci 2(4):223–227 Bharadwaj A (2018) Garbage burning on the rise, The Hindu, 5 January Bharti O, Singh A, Singh DP, Sood V (2014) Effective municipal solid waste management practices: a case study of Shimla, Himachal Pradesh, India. Waste Manag Res Util:173–182 Chakrabarti S, Majumder A, Chakrabarti S (2009) Public-community participation in household waste management in India: an operational approach. Habitat Int 33(1):125–130 Chakraborty A, Goswami A, Deb A, Das D, Mahanta J (2014) Management of Waste Generated in Guwahati City and the incorporation of Geocells at the landfill site. J Civil Eng Environ Technol 1(4):5–7 Chatterjee R (2009) Municipal solid waste management in Kohima city, Nagaland, India. Waste Manag 29(11):2909–2910 Chattopadhyay S, Dutta A, Ray S (2009) Municipal solid waste management in Kolkata, India–a review. Waste Manag 29(4):1449–1458 Chhetri B (2010) Solid waste management: status and some improvement initiatives in Gangtok. J Hist Cult Rev 1(2):94–98 Choudhary A, Ojha D, Chowdhary ML (2012) Biomedical waste Management in Jodhpur City: a case study. Nat Environ Pollut Technol 11(4):741–744 Das DK (2018) Solid waste management biggest challenge before cities, The Times of India, 4 January Das S, Bhattacharyya BK (2013) Municipal solid waste characteristics and Management in Kolkata, India. Int J Emerg Technol Advan Eng 3(2):147–152 Francis RP, Singh LP, Prakash EV (2013) Solid waste management and characteristics in Lucknow, Uttar Pradesh, India. Int J Sci Eng Res 4(11):1645–1648 Ganeshwaran P, Shri SD (2015) A solid waste management in Coimbatore city. ARPN J Eng Appl Sci 10(14):6177–6183 Gautham K (2018) Coimbatore in top 10 amongst best performing smart cities, The Times of India, 20 January George AP (2000) Solid waste Management in Thiruvananthapuram City: a case study. Available online at: http://dspace.sctimst.ac.in/jspui/bitstream/123456789/2205/1/603.pdf. Accessed 29 July 2017 Gogoi L (2013) Municipal solid waste disposal: a case study in Guwahati city to mitigate the man-­ made disaster. IOSR J Human Soc Sci 9(3):55–60 Hanrahan D (2006) Improving management of MSW in India – overview & challenges. World Bank, New Delhi Hazra T, Goel S (2009) Solid waste management in Kolkata, India: practices and challenges. Waste Manag 29(1):470–478 Henllyphom S (2013) Dimapur, the garbage capital of Nagaland, Eastern Mirror, 2 August

112

M. Ray et al.

Hridya KK, Lukose P, Rajesh R (2016) Solid waste Management in Cochin, India: practices, challenges and solutions. J Bas Appl Eng Res 3(9):746–751 International Energy Agency (2009) Turning a liability into an asset: the importance of policy in fostering landfill gas use worldwide. International Energy Agency, Paris Jain S, Jain S, Wolf IT, Lee J, Tong YW (2015) A comprehensive review on operating parameters and different pretreatment methodologies for anaerobic digestion of municipal solid waste. Renew Sust Energ Rev 52:142–154 Joshi MP, Patil SB, Mourya MK (2013) Solid waste management on dumping ground in Mumbai region – a study. Int J Comp Appl:18–22 Kansal A (2002) Solid waste management strategies for India. Ind J Environ Prot 22(4):444–448 Karelia G (2017) Port Blair ends open defecation, gives a push to Andaman & Nicobar’s open defecation free target of 2018, Banega Swachh India, 5 October Katiyar RB, Suresh S, Sharma AK (2013) Solid waste management in Bhopal (India): present and future challenges. Ultra Chem 9(2):197–214 Kaushal RK, Varghese GK, Chabukdhara M (2012) Municipal solid waste management in India-­ current state and future challenges: a review. Int J Eng Sci Technol 4(4):1473–1489 Khan IH, Ahsan N (2003) Textbook of solid waste management. CBS Publisher and Distributors, New Delhi Kiran F (2018) In Bengaluru, water in 42 of 53 lakes unfit for drinking or bathing, Scroll in, 22 January Kumar H, Sah BL (2015) Current scenario of solid waste Management in Nainital Town. Int J Manag Soc Sci Res 4(9):15–21 Kumar S, Bhattacharyya JK, Vaidya AN, Chakrabarti T, Devotta S, Akolkar AB (2009) Assessment of the status of municipal solid waste management in metro cities, state capitals, class I cities, and class II towns in India: an insight. Waste Manag 29(2):883–895 Lakshmikantha H (2006) Report on waste dump sites around Bangalore. Waste Manag 26(6):640–650 Laskar S (2014) For clean India to work, country needs to solve its waste disposal problem, Scroll in, 4 November Lilliana Abarca Guerrero, Ger Maas, William Hogland, (2013) Solid waste management challenges for cities in developing countries. Waste Manag 33(1):220–232 Limbu D (2014) Solid waste Management in the Town of Darjeeling: environmental concern. Int J Human Soc Sci Inven 3(6):26–34 Mahanty JC (2018) Swachh Bharat through recycling of waste, Clean India, 23 January Mahongnao M (2017) Urban solid waste Management in Imphal, Manipur. J Ind Pollut Control 33(1):696–701 Mehta KP, Pandey H (2014) Status of solid waste in the City of Vadodara. Int J Darshan Inst Eng Res Emer Technol 3(1):24–28 Ministry of Environment, Forest and Climate Change (2016) Solid waste management rules, 2016. Government of India, New Delhi Ministry of Housing & Urban Affairs (2017) Swachh Survekshan – 2017: sanitation rankings of cities/towns state/UT-wise. Government of India, New Delhi Mipun BS, Hazarika R, Mondal M, Mukhopadhyay S (2015) Solid waste Management in Greater Shillong Planning Area (GSPA) using spatial multi-criteria decision analysis for site suitability assessment. Int Arch Photogramm Remote Sens Spat Inf Sci 40(7):657–662 Mohidin R (2017) Solid waste management in Srinagar still on paper, The Tribune, 17 July Mor S, Ravindra K, Visscher AD, Dahiya RP, Chandra A (2006) Municipal solid waste characterization and its assessment for potential methane generation: a case study. J Sci Total Environ 371(1):1–10 MOUD (2000) Manual on municipal solid waste management. Ministry of Urban Development, Government of India, New Delhi MOUD Report (2005) Management of solid waste in Indian cities. Ministry of Urban Development, Government of India, New Delhi

5  Environmental Pollution and Municipal Solid Waste Management in India

113

Nag A, Vizayakumar K (2005) Environmental education and solid waste management. New Age International, New Delhi Narain S (2016) Solving India’s garbage problem. Available online at: http://www.cseindia. org/content/solving%C2%ADindia%E2%80%99s%C2%ADgarbage%C2%ADproblem. Accessed 9 Mar 2016 Narayana T (2009) Municipal solid waste management in India: from waste disposal to recovery of resources? Waste Manag 29(3):1163–1166 NCR Planning Board (2009) Solid waste management master plan of Ghaziabad. Asian Development Bank and Wilbersmith Associates, Ghaziabad Nema AK (2004) Collection and transport of municipal solid waste. In: training program on solid waste management. Springer, Delhi Panda P, Harichandan A, Patra HS, Parashar K (2014) Present practice of Municipality Solid Waste Management in Bhubaneswar, Odisha, India. International Journal of Energy, Sustainability and Environmental Engineering 1(1):28–34 Paron O, Kumar S, Bharti SKA (2013) Home composting: a sustainable approach for MSW Management in Itanagar Capital Complex. Int J Innov Res Sc Eng Technol 3(4):214–213 Pattnaik S, Reddy MV (2010) Assessment of municipal solid waste management in Puducherry (Pondicherry), India. Resour Conserv Recycl 54(8):512–520 Rajput R, Prasad G, Chopra AK (2009) Scenario of Solid Waste Management in present Indian Context. Caspian J Environ Sci 7(1):45–53 Ramachandra TV (2006) Management of municipal solid waste. The Energy and Resources Institute, New Delhi Ramteke M (2015) Report on solid waste management in Agartala City. Agartala Municipal Council, Agartala Rana R, Ganguly R, Gupta AK (2015) An assessment of solid waste management system in Chandigarh City, India. Electron J Geotech Eng 20:1547–1572 Ray M (2015) Municipal solid waste management: a case study of Koch Bihar municipality, West Bengal, India. In: Karmakar M (ed) Major environmental issues: vulnerability and impacts. NSPS Publisher, Siliguri, pp 26–33 Ray M, Rahaman M (2016) An overview of legal framework for waste management system in India with special allusion to SWM rules, 2016. Int J Interdis Multidis Stud 4(1):13–19 Reddy J (2011) Municipal solid waste management: processing energy, recovery, global examples. BS Publications, Hyderabad Registrar General of India (2011) Census of India 2011: provisional population totals-India data sheet. Indian Census Bureau, New Delhi Roy SK (2017) Prohibition on open burning of solid waste by PBMC, Andaman Sheekha, 8 February Santra A, Debbarma D, Mitra S (2001) Perception of the urban dwellers on the solid waste Management in Agartala City, India. IOSR J Human Soc Sci 5(3):76–82 Shanker H (2010) Waste disposal in India, The Views Paper, 8 August Siddiqui J, Pandey G, Akhtar S (2013) A case study of solid waste management in Mysore city. Int J Appl Innov Eng Manag 2(11):290–294 Singh K (2016) Gwalior is the most polluted Indian city, not Delhi: WHO report, The Indian Express, 27 September Sinha N (2018) Better than last survey but clean sweep can wait, The Telegraph, 21 January Solid Waste Management Rules (2016) Online available at: http://www.indiaenvironmentportal. org.in/content/427824/solid-waste-management-rules-2016. Accessed 8 July 2016 Sridhar V (2016) Sanitary solutions & waste disposal, Clean India, 18 April Supreme Court Committee Report (1999) Report of the committee on solid waste Management in Class I cities in India. Supreme Court of India, New Delhi Tewari G, Bhatt D, Junne S, Mundhe R, Parale S, Baig MMV (2013) Municipal solid waste Management in Nainital, Uttarakhand: a case study. Quest J UGC-ASC Nainital 7(3):288–295

114

M. Ray et al.

The Constitution (Seventy-Fourth Amendment) Act (1992) Available online at: http://indiacode. nic.in/coiweb/amend/amend74.htm. Accessed 12 Sept 2016 The Energy Research Institute (2014) Waste to resources: a waste management handbook. TERI Press, New Delhi Vasanta K, Priyasauni (2013) Sustainable municipal solid waste management strategies: need of Ppp model for Agra. Asia Pacific J Market Manag Rev 2(5):118–132 Vij D (2012) Urbanization and solid waste management in India: present practices and future challenges. Procedia Soc Behav Sci 37:437–447 Wani MA, Ahmad S (2014) Challenges, issues of solid waste Management in Himalayas: a case study of Srinagar City. Afr J Bas Appl Sci 5(1):25–29 WannLyngdoh S, Nongbri G (2017) Meghalaya: increasing waste generation in tourist spots of Shillong, an emerging threat!, The North-East Today, 29 November Zia H, Devadas V (2008) Urban solid waste management in Kanpur: opportunities and perspectives. Habitat Int 32(1):58–73 Zurbrugg C (2002) Urban solid waste management in low-income countries of Asia: how to cope with the garbage crisis. Scientific Committee on Problems of the Environment, Durban

Chapter 6

Assessment of the Carbon Footprint Across Urban Households in Kolkata Rukhsana and Md Firojuddin Molla

6.1  Introduction Cites are central to climate change mitigation. They account for 76% of CO2 emissions in terms of total global final energy utilization and 43% of CO2 emissions in terms of global primary energy consumption (Ahmad et al. 2015). According to the International Energy Agency (IEA), energy consumption in Asia is expected to increase by 1.7% annually from 2015 to 2040 (IEA 2017). In the recent era, it has been observed that CO2 emissions are controlled by various determinants and that CO2 emissions by households are positively related to energy consumption (Akpan and Akpan 2012; Meanbua et  al. 2019; DEDE 1995, 2000). Because of rapid increases in CO2 emissions, global warming and climate change have become global environmental threats, attracting the attention of scientists and researchers (Saboori and Sulaiman 2013). According to an Intergovernmental Panel on Climate Change (IPCC) report, the average global temperature could increase by between 1.1 and 6.4 °C, and the sea level could rise by between 16.5 and 53.8 cm by 2100 (IPCC 2007). In its Fourth Assessment Report, the IPCC strongly recommended limiting the average global temperature increase to below 2 °C, relative to the average temperature in the preindustrial era. It has been reported that the temperature has already risen by 0.74 °C; thus, climate scientists are focusing their attention on urgent action to control global warming (IPCC 2007; Kerr 2007). The increase in global temperature is causing mountain snow and ice to melt, leading to a sea level rise of 3 mm per year (Kerr 2006; Rignot and Kanagaratnam 2006; IPCC 2007). It has been projected that almost 1–2 billon people will be under water stress, the productivity of crops will suffer, and wildlife and biodiversity will be threatened by environmental change (Kerr 2007). As noted by Stern and Jacobs (2006), the world is running short of time and options, from a socioeconomic perspective, and there is a high risk of Rukhsana (*) · Md. F. Molla Department of Geography, Aliah University, Kolkata, West Bengal, India © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_6

115

116

Rukhsana and Md. F. Molla

environmental disaster due to global warming and climate change. Immediate and robust actions are needed to stabilize emissions in a justified manner. Growing understanding of the science and consequences of the global warming phenomena, and consequent concerns about the need to prevent disastrous climate change, have led to a substantive action in the form of ratification of the Kyoto Protocol (UNFCCC 1997), which recommended important reductions in emissions of greenhouse gases (GHGs)—namely, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), perfluorocarbons, and hydrofluorocarbons. The gases covered by the Kyoto Protocol are referred to collectively as “Kyoto gases” (WRI/WBCSD 2004, 2005, 2006). The World Bank (2007) has reported that among all GHG emissions, CO2 is the principal gas that leads to global warming and climate change, and about 80% of it comes from the energy sector (Akpan and Akpan 2012). The industrialization era transformed agricultural societies into nonagricultural societies, creating modernized societies and leading to urbanization. The urban population of the world grew rapidly from 751 million in 1950 to 4.2 billion in 2018, by which time it was estimated that more than half the world’s population (55%) now lived in urban areas, accounting for up to 70% of production, consumption, energy consumption, and CO2 emissions. Three developing countries—India, China, and Nigeria—are together expected to account for 35% of the global projected urban population growth between 2018 and 2050 (United Nations Department of Economic and Social Affairs, Population Division 2018). The carbon footprint is a process of measuring and determining the amount of various greenhouse gases that impact on the global environment due to anthropogenic activities (Lenzen and Dey 2000; Wiedmann and Minx 2008; Carbon Trust 2007a, b). The CO2 equivalent (CO2e) is a unit used for comparing the radiative forcing of a GHG with that of CO2; i.e., it states how many units of emitted CO2 one unit of that GHG equates to, in terms of its global warming impact, when emitted into the atmosphere (ISO 2006a, b; Carbon Trust 2007a, b).

6.2  Methods 6.2.1  Study Area The selected study area was the Kolkata Municipal Corporation (KMC), a metropolitan city in West Bengal state in eastern India. It is a highly urbanized city with a large population. KMC extends from 22°30′28″N to 22°37′20″N (latitude) and from 88°17′50″E to 88°23′45″E (longitude) in the southern part of the Kolkata Metropolitan Area (KMA). KMC has 141 wards, divided into 15 boroughs, and covers an area of 187.33 km2 (Census of India 2011). accounts for 11.07% of the total KMA area and 35.15% of the total KMA population.

6  Assessment of the Carbon Footprint Across Urban Households in Kolkata

117

6.2.2  Objectives This study aimed to quantify the amounts of CO2e emissions caused by various activities directly or indirectly linked with energy consumption and utilization by households, in order to ascertain the carbon footprint pattern and to examine household CO2e emissions relative to household economic status.

6.2.3  Data and Methodology This study was based on both primary and secondary sources of data. Secondary data were collected from published and unpublished sources such as the Census of India, KMC Office, National Atlas and Thematic Mapping Organization (NATMO), and National Sample Survey Office (NSSO). For the primary data collection, six sample wards (out of a total of 141 KMC wards) were selected for the field investigation. A total of 432 sample households were surveyed from the six selected wards (ward numbers 4, 15, 53, 100, 127, and 133), at a 1% sampling rate. The sample respondents were stratified into income groups: low income (greater than INR 10,000), middle (INR 10,000–25,000), high (INR 25,000–50,000) and very high (higher than INR 50,000). The data were analyzed using regression, a correlation matrix, CO2 emission equations, and per capita carbon footprint calculation.

6.2.4  C  alculation of the Primary Carbon Footprint at the Household Level The household primary carbon footprints in the study area were calculated on the basis of various sources in the literature and using reported calculation methods produced by various organizations (country specific and default) (DEFRA 2012, 2017). The IPCC guidelines for calculation of emission factors were used. The total CO2e gas emissions from all individual sources are expressed in the equation below, and emission of CO2 gases from electricity use, as well as consumption of different fuels at the household level in KMC, were included in the equation (Global Footprint Network 2007; Pandey et al. 2011; Ramachandra and Shwetmala 2012; Srivastav 2013; Ramachandra et al. 2014). CO2 equivalent emissions from electricity utilization in the household sector: where:

E Electricity = Electricity energy Con . ( KWh ) × CO 2 E.F ( Kg.CO 2 e / KWh )



118

Rukhsana and Md. F. Molla

EElectricity = emission of CO2e gases from electricity consumption at the household level; Electricity Energycon (kWh) = amount of electricity consumed at the household level; EF  =  emission factor for electricity as per default and country-specific emission factors. CO2 equivalent emissions from individual fuel utilization at the household level: E Fuel energy = Consumption of Fuel( Dif .Types )× CO 2 Emission FactorFuel Dif .





where: EFuel Energy = emissions from use of fuel of different types; Consumption of Fuel(Dif Types) = quantity of different fuels used at the household level; Emission FactorFuel Dif = emission factor for different types of fuel as per default and country-specific emission factors. CO2 emissions from individual transport at the household level: The transport sector is one of the dominant sectors of anthropogenic sources of carbon emissions at the household level in the study area (Mitra and Sharma 2002); thus, carbon emissions depend on the type of vehicle, technology, and distance of transportation (Ramachandra and Shwetmala 2012). Two main ways are used for emissions calculation: either fuel consumption or distance traveled. At the national level, the fuel consumption approach has mainly been used (IPCC 1996, 2006; Sikdar and Singh 2009; Indian Network for Climate Change Assessment 2010; Ramachandra et al. 2014). For this study, the distance traveled in a year and the different emission factors for different types of vehicle were used, and CO2 emissions were calculated on the basis of the available data at the household level in the selected sample wards of KMC (Ramachandra et al. 2014; Srivastav 2013): Emission PV = ∑ ( vehiclei × Dt ) × EFPv ,t , Km





where: EmissionPV = emissions from all different vehicle types (personal cars and motorcycles); Vehiclei = number of different types of vehicle; Dt = distance traveled in a year per different type (t) of vehicle; EFPV,t,Km = emission factor for vehicles (PV) per type (t) per kilometer (Km) traveled. Primary carbon footprint at the household level: Emission

where:

CO2e = carbon footprint;

Emission

CO 2e = ∑ Category QF×EF



6  Assessment of the Carbon Footprint Across Urban Households in Kolkata

119

QF = emission generated from use of individual sources of fuel; EF = emission factor for different types of fuel. Per capita carbon footprint at the household level:



( PCF ) =

∑ Category QF × EF N

where: PCF = per capita carbon footprint; QF = emission generated from use of individual sources of fuel; EF = emission factor for fuel; N = number of occupants. The reported emission factors for use of electricity, different fuels, and different vehicles in India are listed in Table 6.1.

Table 6.1  Emission factors for use of electricity, different fuels, and different vehicles in India

Fuel Electricity Wood and wooden scrap Coal Kerosene Liquefied petroleum gas Petrol car Diesel car Motorcycle

CO2 emission factor 1.4226 kg CO2e/kWh 0.1 kg CO2e/kg 3.26 kg CO2e/kg 2.53 kg CO2e/L 1.7244 kg CO2e/L 0.24234 kg CO2e/km 0.22428 kg CO2e/km 0.14238 kg CO2e/km

Sources: BSI (2008), Nieves et al. (2019), Shailesh (2011), DEFRA (2012), IPCC (2006) kgCO2e kilograms of CO2 equivalent Households were classified into four income groups, which were defined on the basis of observed sample income values: 1. A low-income household has a monthly income no greater than INR 10,000. 2. A middle-income household has a monthly income of INR 10,000–25,000. 3. A high-income household has a monthly income of INR 25,000–50,000. 4. A very high-income household has a monthly income higher than INR 50,000.

120

Rukhsana and Md. F. Molla

6.3  Results and Discussion Table 6.2 shows the degree of distribution of the per capita carbon footprint according to different development indices, in different income groups, and in different regions, countries, and major Indian cities. It is clear that individuals in high-income countries have the biggest footprint, while the footprint of those in developing countries is substantially smaller. In recent times, carbon footprints have been used as an important indicator of event management (London 2012 Sustainability Plan 2007). Individuals in high-income OECD (Organisation for Co-operation and Development) member countries have the largest carbon footprint (13.2 tCO2e), whereas those in the lowest-income countries have the smallest (0.2 tCO2e). Similarly, individuals in countries with a high Human Development Index (HDI) score release the most CO2e emissions into the environment (10.1 tCO2e), while those in countries with medium and low HDI scores release only 2.5 tCO2e and 0.3 tCO2e, respectively. According to comparative carbon footprint analysis, the global annual per capita carbon footprint is 4.0 tCO2e. The USA has the largest per capita carbon footprint (20.0 tCO2e). Elsewhere, the respective values are 10.76 tCO2e in Japan, 9.0 tCO2e in the UK, and 3.2 tCO2e in China. In comparison, the respective values are only 1.33 tCO2e in India, 1.3 tCO2e in the region of South Asia, and only 1.1 tCO2e in the city of Kolkata. Thus, the per capita carbon footprint in Kolkata is lower than anywhere else in the world because the total energy consumption is much less than in other developed nations. Worldwide, it has been observed that high per capita energy consumption is recorded in developed and more industrialized counties, while lower levels of consumption are recorded in developing countries. Countries with higher per capita energy consumption have higher levels of atmospheric emissions. It can also be noted from Table  6.2 that whereas the global per capita carbon footprint is 4.0 tCO2e, the respective values in major Indian cities vary from only 1.1 tCO2e in Kolkata to 1.2 tCO2e in Ahmedabad, 1.3 tCO2e in Bangalore, Chennai, Hyderabad, and Mumbai, and 1.5 tCO2e in Delhi. It is clear that the per capita emission level is lower in Kolkata than in other major cities of India; thus, in terms of energy consumption, it can be stated that Kolkata consumes less energy per capita that other cities in India.

6.3.1  Household Carbon Footprints in Kolkata On the basis of the survey data, the following results were obtained. The average carbon footprints are reported in Tables 6.3–6.6. The data in all tables are based on the results of the household survey, which was used to quantify CO2e emissions, and calculation of the per capita carbon footprint at the household level in the KMC study area used for this analysis.

6  Assessment of the Carbon Footprint Across Urban Households in Kolkata

121

Table 6.2  Annual per capita carbon footprints according to different development measures, in different income groups, and in different regions, countries, and major Indian cities Global average Measures of development  Developing country status  Developing countries  Least developed countries  OECD membership status  OECD member countries  High-income OECD member countries  HDI scores  High-HDI countries  Medium-HDI countries  Low-HDI countries Income groups  High income  Middle income  Low income Regions  Arab states  East Asia and the Pacific  Latin America  South Asia  Sub-Saharan Africa  Europe and the Commonwealth of Independent States Countries  China  India  Japan  UK  USA Major Indian cities  Ahmedabad  Bangalore  Chennai  Delhi  Hyderabad  Kolkata  Mumbai

Per capita carbon footprint (tCO2e) 4.0

2.4 0.2 11.5 13.2 10.1 2.5 0.3 13.3 4.0 0.9 4.5 3.5 2.6 1.3 1.0 7.9 3.2 1.33 10.76 9.0 20.0 1.2 1.3 1.3 1.5 1.3 1.1 1.3

Sources: UNDP (2007), World Bank (2010) and Pandey et al. (2011) HDI Human Development Index, OECD Organisation for Economic Co-operation and Development, tCO2e tonnes of CO2 equivalent

122

Rukhsana and Md. F. Molla

Table 6.3 shows the distribution of different sources of CO2 emissions in the KMC study area. Of all CO2 emission sources, use of electricity was the leading source because most households are highly dependent on electricity to fulfill their basic lighting and other energy needs, and the source of the lowest CO2 emissions is utilization of firewood in the household sector. The highest CO2 emissions from use of electricity (526,800 kg CO2e) were recorded in ward number 127, and the lowest were recorded in ward number 53 (188,773 kg CO2e). Table 6.4 shows the distribution of primary CO2 emissions from various sources among households in different income groups in KMC. It was found that emissions of CO2 from electricity consumption were highest in very high-income households (693,825  tCO2e), while the lowest values were seen in low-income households (27,041 tCO2e). Low- and middle-income households had higher emissions from consumption of wood (186 and 210  tCO2e, respectively) and coal (1565 and 6455  tCO2e, respectively), whereas high- and very high-income households had high emissions from consumption of liquefied petroleum gas (LPG) and from use of petrol- and diesel-powered vehicles, leading to increased CO2 emissions in the city environment. Table 6.5 shows spatial differences in CO2e emissions at the household level in KMC.  Of the total sample, ward number  127 had the largest household carbon footprint (7.9  tCO2e) and the largest per capita carbon footprint (2.0  tCO2e), whereas ward number 4 had the smallest household and per capita carbon footprints (5.2 and 1.23  tCO2e, respectively). With regard to the other wards, the household and per capita carbon footprints were 5.6 and 1.30 tCO2e, respectively, in ward number 15; 5.7 and 1.35 tCO2e, respectively, in ward number 100; 6.4 and 1.44 tCO2e, respectively, in ward number 53; and 6.9 and 1.41 tCO2e, respectively, in ward number 133. On a per capita basis, the carbon footprint in an Indian city is just 30% of the global average and 6% of that in the USA (Table 6.2). With regard to households (Table  6.6), a low-income household in Kolkata has a mean household carbon footprint of 1.65 tCO2e and a per capita carbon footprint of 0.62 tCO2e. Even in very high-income Kolkata households, the average household and per capita carbon footprints (11.50 and 2.30 tCO2e, respectively) are only 57.5% and 11.5%, respectively, of the per capita carbon footprint in the USA (20.0  tCO2e). Meanwhile, because low-income households in Kolkata consume minimal energy, they generate only minor CO2e emissions and their per capita carbon footprint is only 0.62 tCO2e, which is just over one quarter of that in very high-income Kolkata households. Figure 6.1 illustrates the relationship between family size and the per capita carbon footprint at the household level. Per capita CO2e emissions are negatively correlated with the household family size, because per capita CO2e emissions decrease with an increase in population size. The value of the correlation is (−0.4596), whereas Figs. 6.2, 6.3, and 6.4 show positive correlations. The per capita carbon footprint is highly positively correlated with the per capita energy consumption at

X1 86 54 54 73 112 53

X2 367 231 239 309 443 259

X3a 295,929 196,233 188,773 252,415 526,800 200,109

X4a 36 0 0 210 138 48 0 0    0 782 3130 1760

X5a

X6a 2216 2398 8926 13,783 4554 4402

X7a 46,631 28,817 25,673 31,175 59,205 27,769

X8a 71,015 30,355 96,839 51,135 153,319 99,487

X9a 34,197 40,262 23,066 62,567 138,460 32,057

X10a 0 3230 0 5383 0 0

Source: field survey, 2018 X1 number of households, X2 sample population, X3 CO2e emissions from electricity, X4 CO2e emissions from wood, X5 CO2e emissions from coal, X6 CO2e emissions from kerosene, X7 CO2e emissions from liquefied petroleum gas, X8 CO2e emissions by motorcycles, X9 CO2e emissions by petrol cars, X10 CO2e emissions by diesel cars a Measured in kilograms of CO2 equivalent

Ward number 4 15 53 100 127 133

Table 6.3  Annual CO2 equivalent (CO2e) emissions in different wards in Kolkata Municipal Corporation

6  Assessment of the Carbon Footprint Across Urban Households in Kolkata 123

X1 24 138 158 112

X2 64 560 670 554

X3a 27,041 347,996 591,398 693,825

X4a 186 210 36 0

X5a 1565 6455 0 0

X6a 5829 22,497 5829 2125

X7a 4977 58,943 87,236 68,112

X9a 0 2825 32,777 295,006

X8a 0 63,388 228,373 210,389

X10a 0 0 2019 6594

Source: field survey, 2018 X1 number of households, X2 sample population, X3 CO2e emissions from electricity, X4 CO2e emissions from wood, X5 CO2e emissions from coal, X6 CO2e emissions from kerosene, X7 CO2e emissions from liquefied petroleum gas, X8 CO2e emissions by motorcycles, X9 CO2e emissions by petrol cars, X10 CO2e emissions by diesel cars a Measured in kilograms of CO2 equivalent

Income group INR 50,000

Table 6.4  Annual CO2 equivalent (CO2e) emissions in different income groups in Kolkata Municipal Corporation

124 Rukhsana and Md. F. Molla

6  Assessment of the Carbon Footprint Across Urban Households in Kolkata

125

Table 6.5  Annual carbon footprints in different wards in Kolkata Municipal Corporation Ward number 4 15 53 100 127 133

Number of households 86 54 54 73 112 53

Number of occupants 367 231 239 309 443 259

Household carbon footprint (tCO2e) 5.2 5.6 6.4 5.7 7.9 6.9

Per capita carbon footprint (tCO2e) 1.23 1.30 1.44 1.35 2.00 1.41

Source: field survey, 2018 tCO2e tonnes of carbon dioxide equivalent

Table 6.6  Annual carbon footprints in different income groups in Kolkata Municipal Corporation Number of Ward carbon footprint Income group occupants (kg CO2e) INR 50,000 547 1,276,051

Household carbon footprint (tCO2e) 1.65 3.61 6.00 11.50

Per capita carbon footprint (tCO2e) 0.62 0.90 1.41 2.30

Source: field survey, 2018 kg CO2e kilograms of CO2 equivalent, tCO2e tonnes of CO2 equivalent

Relatonship Between Family Size And Per Capita Carbon Footprint (tCO2 e) 2.50 per capita carbon foot print (tcCO2 e)

Fig. 6.1 Relationship between family size and per capita carbon footprint (tCO2 e)

2.00 1.50 1.00 y = -0.4127x + 3.2418 R² = 0.2112 r = -0.4596

0.50 0.00 3.50

4.00

4.50 Family Size

5.00

126

Rukhsana and Md. F. Molla Relation Between Percapita Energy Consumption and Per Capita Carbon Footprint (tCO2 e)

Fig. 6.2 Relationship between per capita energy consumption and per capita carbon footprint (tCO2 e) per capita carbon foot print (tcCO2 e)

2.50 2.00 1.50 1.00

y = 0.0079x + 0.1321 R² = 0.9923 r = 0.9961

0.50 0.00 100

150

250

200

Per capita Energy Consumption (Koe)

Relationship Between Literacy Rate and Per Capita Carbon Footprint (tco2e) per capita carbon footprint (tCO2 e)

Fig. 6.3 Relationship between litracy rate and per capita carbon footprint (tCO2 e)

2.50 2.00 1.50 1.00

y = 0.0621x - 4.3941 R² = 0.185 r =0.4301

0.50 0.00 90.00

95.00

100.00

Literacy Rate

the household level in the sample area. The coefficient of correlation is very strongly positive (r = 0.9961), and the per capita CO2e emissions are also positively correlated with household income (r = 0.8737).

6  Assessment of the Carbon Footprint Across Urban Households in Kolkata

Relatonship Between Per Capita Income and Per Capita Carbon Footprint (tCO2e) per capita carbon footprint (tCO2 e)

Fig. 6.4 Relationship between per capita income and per capita carbon footprint (tCO2 e)

127

2.50 2.00 1.50 y = 0.0001x + 0.0611 R² = 0.7634 r =0. 8737

1.00 0.50 0.00 7000

8000 9000 10000 11000 12000 13000 Per capita income (Rs. / month)

Table 6.7 displays a correlation matrix of a number of variables tested with a student’s t test. The per capita income is not the only determining factor of CO2e emissions at the household level, which depend on the household per capita energy consumption, household energy expenditure, per capita electricity consumption, literacy level, household air conditioner ownership, car ownership, washing machine usage, etc., with a 95–99% level of significance. On the other hand, CO2e emissions is highly negative with solar energy utilization.

6.4  Conclusion From this study, we conclude that development occurs in a progressive manner and per capita income households move up the income ladder. When households move from lower income levels to higher ones, their total CO2 emissions consequently change, and higher-income households adopt a more energy-intensive standard of living than before. In such cases, the total emissions increase as a result of an increase in the household income, if the population remains constant. Our study clearly suggests that with increased incomes and improved quality of life, the per capita energy consumption and resultant emissions in the household sector in Kolkata also increase, which is a matter of grave concern as far as energy security and climate sustainability are concerned. This issue in general is of particular importance in a city such as Kolkata, where the number of middle-income

0.84*

−0.46

0.69

0.71

0.68

0.70

0.68

0.80

0.56

0.95**

1

Car ownership

−0.40

0.80

0.64

0.70

0.73

0.80

0.74

0.56

1

Air conditioner ownership

0.21

0.27

0.33

0.26

0.34

0.25

0.64

1

−0.61

0.76

0.93**

0.86*

0.87*

0.71

1

Use of Use of water washing heater machine

Source: field survey, 2018 CO2e CO2 equivalent, koe kilograms of oil equivalent, tCO2e tonnes of CO2 equivalent *P  6482 sq.m.

Ghoramara

Fig. 12.7  Comparative perception of loss

From the above discussions, it can be inferred that psychologically, higher the environmental challenges more will be the tendency of the inhabitants to look for safer options; and if these secured locations are at proximity, more will be the displacement (Lee 1966). The displaced population groups, in the present case, faced acute loss of land – both agricultural and residential – at their original habitation, and hence most felt it was advantageous to shift to the resettlement colonies appropriated by the government. But, this has brought in far less success than expected in terms of socio-economic and security measures of the concerned population.

12.4.2  Socio-Eeconomic Stresses in the Case Study Areas The physical and psychological conditions of living in both of the study areas were quite differed from those residing in adjacent permanent residential clusters. With the changing course of the Singhimari River, people were rarely eager to build up a concrete structure and spent minimal on dwelling unit in Gitaldaha. In Jibantala, people simply relied on the government provisions of free housing schemes as they felt they had no role in losing their land to the sea. The major socio-economic issues of habitability and adaptations of the two villages have been compared and correlated to understand the various problems of the climatically displaced population of West Bengal. The reclaimed zone of Jibantala colony area was initially divided into three sectors, and each family was allocated 2000 m2 of land with a one-room house and a pond. The plots were later extended to the adjacent areas (Fig. 12.8), and the number of people increased as gradually the households expanded, and relatives of the family started to occupy the vacant lands available in the vicinity or by clearing forests or reclaiming swamps. Due to lack of space, people in both the locations have clustered and cramped dwelling with a majority of the hutments accommodating more than eight persons. Considering sex ratio, Ghoramara has more female population than Gitaldaha due to the temporary or permanent out migration of male working population in search of job opportunities. Thus, the changes in quality of life mostly impact the women population in both the regions. Even though the number of rooms per household in Sagar Block is more than that of Dinhata, their sizes

12  Environmental Vulnerability and Displacement Due to Land Erosion: Selected…

219

Fig. 12.8  Jibantala Rehabilitation Colony Area (Sagar C.D. Block) Source: Modified from Kar and Bandhyopadhyay (2014) Google Earth Pro 7.3.2.5776 (2.10.2019), 21.698890 N 88.140261E Maxar Technologies www.earth.google.com [April 26, 2012]

are relatively small. Primary data reveal there are only 0.25 person/room in villages in Gitaldaha, whereas 0.5 person/room in Jibantala colony. The usage of space in both the study areas is extremely insufficient for proper living quality. In both of the study areas, access to basic domestic facilities is low due to lack of space. In the Rehabilitation Colonies of Jibantala, about 69.5% of households have IHHL (Individual Household Latrine) facility. On the other hand, villages of Gitaldaha have only 20.5% IHHL coverage. Toilets are an expensive option in these areas as the construction would cost INR 1300 and a personal water connection was beyond imagination. Members from more than 30 households join the daily queue for hours to collect water from the public tube wells often situated at a distance between 5 and 15 min walk. 54% respondents from Jibantala and 27% in Gitaldaha resettlement area have drinking water facilities within 0.5 km. Tube wells are the only source of fresh water, and in Gitaldaha it is mostly wells; this too remains waterlogged for a number of days at a stretch during the active monsoon months from July to September. The chores are majorly carried out by women and children who have adjusted to their fate of hardships. Awareness regarding hygiene and sanitation is very low, and lack of government initiatives is a stark reality. However, water quality is slightly better in the rehabilitation colonies as not only are these public taps new, but also free from possible contaminations and salinity. Both the resettlement areas are still not hazard proof, with Gitaldaha being more v­ ulnerable to flooding every year. In these circumstances, even though maintaining the houses to protect

220

A. Haldar et al.

their belongings is a necessity, the inhabitants are mostly reluctant to spend much on them. 80% of the respondents from Gitaldaha and 59% in Jibantala prefer maintaining their houses only once in every 3 years, respectively. Currently 44% of the households in Ghoramara are prone to damage from environmental hazards, and relocation or rebuilding is necessary every year. The situation is almost same for the houses on the lower grounds of Gitaldaha towards the Singimari floodplain. Another major problem affecting the re-settlers is outbreak of water- and insect-borne diseases, snake bites and skin diseases. On the average, about 80% of the respondents have complained of these problems – at least once after relocation – mostly occurring during the monsoons. In Gitaldaha the nearest healthcare centre is located at about 5  kms away, while hospitals are situated more than 10  km away without proper accessibility, and therefore most of the patients are left at the mercy of nature and are treated, rather ill-treated, by local quacks. Healthcare is more accessible in Jibantala than they had previously in Ghoramara which has brought in some psychological security. In the face of a calamity or emergency in almost both the cases, aid and alms are often too late to arrive due to their remoteness and inaccessibility even in the resettled locations. Apart from habitation and health, quality of life also depends on the level of awareness and livelihood opportunities. Most of the households surveyed received education only in their local primary schools, with only a few students, clearing their matriculation examination. 65% in Dakshin Kharija village and around 81% in Ghoramara, Bakimnagar and Kamalpur villages are officially literates as per census in 2011. Family income is the main hindrance for dropouts from the displaced families. Another deterrent is the unavailability of better educational facilities, as most of the school buildings too are affected by erosion and shifted sites several times. 86% of respondents from Jibantala and 93% from Gitaldaha have responded to the absence of education facilities near their place of residence. Scope for adult education too is extremely poor. A few vocational training workshops were organized by the local governments earlier, but very few had been able to join in these programmes. Most of the young girls (48%) are married off before the age of 15 in Gitaldaha. Confronted with acute poverty, most young boys are school dropouts early in their lives and have to work as day labourers in other states or even other country. The re-settlers have all lost their prior (traditional) occupations which was majorly fishing and agriculture. Loss of cultivable lands, lack of irrigation and sea water ingression rendering the soil infertile are the major causes inferred from focused group discussions. The diversity in occupation too is far less in these remote rural areas. Hence, presently of the total working population, 59% in Jibantala and 62% in Gitaldaha are engaged in daily wage labour or apprenticeship to small-time businesses where there is a capacity of earning anything between INR 150 and 800 per day according to the job type. About 65% of the families in Jibantala and 77% in Gitaldaha claim to earn less than INR 5000 a month. The low income in turn affects food, health, housing, education and other livelihood options and possibilities. Increasing family sizes too have affected their per capita availability of resources, lowering the quality of life. No social security benefits such as old-age pension, medical support or free education was ever offered to these people who had

12  Environmental Vulnerability and Displacement Due to Land Erosion: Selected…

221

not only physically lost their home, family and relatives but are psychologically feeling lost in the new resettlement colonies. For the people of Ghoramara as well as the original settlement clusters of the displaced dwellers of Gitaldaha, both reminiscence the plight to be less emotionally strained in their previous conditions than now.

12.5  Conclusion As perceived in both the study areas, climate-induced hazards are the main driving forces in changing the courses of the rivers like Jaldhaka in the north and the Hugli in the south in the state of West Bengal. The villages of Gitaldaha and Ghoramara equally had been experiencing high vulnerability from high intensity floods and river bank/coastal erosion grossly affecting their life, habitation and livelihood. Therefore, they are forced to shift to the higher grounds and rehabilitation camps elsewhere. The areas being remote and rural have few connections to the local administrations and hence are still deprived of a better condition of living. As discussed, after the relocation of population, the main problems identified were the lack of proper housing, fresh drinking water, unavailability of healthcare and educational facilities, lack of steady income and job opportunities and unavailability of cultivable land to carry on their traditional livelihood practices. As per surveys, it has been observed that the resettlement locations have not been always favourable to the homeless population. The proposed plots were suited mostly for temporary occupancy initially but with time permanent habitation continued. Human encroachments beyond the resettlement camps have clogged the Chemaguri creek in Sagar C.  D. Block and cleared off forested tracts along the Singimari River along Gitaldaha-II Gram Panchayat area – both increasing the susceptibility to an impending flooding scenarios. As the stability of the island of Ghoramara is questionable due to continued bank erosion, it is to be predicted that people from the islands will continue to look for safer habitations, preferably in and beyond the Sagar Island. Similarly the char and bank dwellers along the Singimari River will continue to move inwards with the lateral shifting of the channel. At this juncture, provisions must be made for their relocation which has already become a crisis. Most regions have poorly prepared laws, policies and strategies to deal with the displaced people moving from areas of increasing climate risk into areas that may already be heavily populated; thus long-term planning and incorporation of action proposals should be a part of future development goals in the state. As adopted by various nations, developmental frameworks either need to consider the phases, direction and adaptation strategies of the displaced population due to environmental factors or generate strong resilience and capacity building techniques to sustain their life and livelihood at the origin. The second option being almost impossible in most of the cases and the major elements agreed upon by international agencies in general have included: (i) viable adaptation to help ­communities stay in place with local resilience, climate-smart infrastructure, etc.

222

A. Haldar et al.

and (ii) identifying the risk zones and enabling mobility as well as provision for rehabilitation for people who are in real need. A threefold plan has been proposed by the International Organization for Migrants (IOM) which starts off with as follows: (A) when the limits of local adaptation and viability of ecosystems cross, governments should facilitate safe, orderly, and dignified migration (or planned relocation) towards areas of lower risk and higher opportunity by providing skills training, information and legal support; (B) after migration ensuring that sending and receiving areas, and their people, are well connected and adequately prepared; and (C) policy makers should develop and implement migration preparedness plans for the immediate and longer-term population growth from migration, and these should include viable livelihood opportunities, skills training, critical infrastructure and services, registration systems for migrants (to access services and labour markets) and the inclusion of migrants in planning and decision-making (World Bank 2018). Thus, to provide stability and dignity to the displaced population who consistently feel ‘left out’, the concept of planned relocation is a necessity. Acknowledgments  The authors are greatly thankful to the scholars and faculty members of the Department of Geography, Cooch Behar Panchanan Barma University, and students of the Department of Geography, University of Calcutta (Specializing in Climatology of Humid Tropics, batch 2016–2018). Mr. Nabendu Sekhar Kar, Dr. Arindam Sarkar, Mr. Sanjoy Ahir, Mr. Utpal Burman, and Mr. Anup Sen are duely acknowledged for their valuable comments. The authors are also grateful to the respondents of the study areas for their consent and time to discuss their problems in such details.

References Adamo S (2009) Environmentally induced population displacements. IHDP Update 1:2009 AGCCHM (Advisory Group on Climate Change and Human Mobility) (2014) Human mobility in the context of climate change: recommendations from the advisory group on climate change and human mobility. COP 20 Lima, Peru Bandyopadhyay S, Mukherjee D, Bag S, Pal DK, Das RK, Rudra K (2004) 20th Century evolution of banks and islands of the Hugli Estuary, West Bengal, India: evidences from maps, images and GPS survey. In: Singh S, Sharma HS, De S (eds) Geomorphology and environment. ACB Publication, Kolkata, pp 236–263 Banerjee S, Basu Ray Chaudhury A, Guha A (2017) The 2015 India-Bangladesh land boundary agreement: Identifying constraints and exploring possibilities in Cooch. Observer Research Foundation. https://www.orfonline.org/research/the-2015-india-bangladesh-land-boundaryagreement-identifying-constraints-and-exploring-possibilities-in-cooch-behar/. Accessed on 10.11.19 Campbell JR (2010) An overview of natural hazard planning in the Pacific Island Region. Aust J Disaster Trauma Stud 2010–2011. ISSN: 1174-4707. http://www.massey.ac.nz/~trauma/ issues/2002-2/mcdowell.htm. Accessed on 25.09.2019 CPRD (Center for Participatory Research and Development) (2015) Promoting rights of the climate change migrants in National and International Policy framework. World-Protestant Development Service, Germany Dey S (2015) Floods in Cooch Behar: nature, analysis and mitigation. IJELLH Int J Engl Lang Lit Hum III(V). ISSN 2321–7065

12  Environmental Vulnerability and Displacement Due to Land Erosion: Selected…

223

EJF (Environmental Justice Foundation) (2017) Beyond borders: our changing climate – its role in conflict and displacement. EJF, London. https://ejfoundation.org//resources/downloads/ BeyondBorders-2.pdf El-Hinnawi E (1985) Environmental refugees. UNEP, Nairobi EM-DAT (2015) Human cost of natural disasters: a global perspective. Available from http:// emdat.be/human_cost_natdis European Union (2019) The concept of ‘climate refugee’: towards a possible definition. http:// www.europarl.europa.eu/RegData/etudes/BRIE/2018/621893/EPRS_BRI(2018)621893_ EN.pdf. Accessed on 17.09.19 Froehlich W, Soja R, Sarkar S (2000) Generation of blood and sediment load. in Rain. flood and landslide in Darjeeling Himalaya. INSA 115–123 GoI (2011a). https://www.census2011.co.in/data/village/308561-daksin-kharija-gitaldaha-westbengal.html GoI (2011b) Census of India 2011: Provisional Population Totals, South 24 Parganas. Registrar General and Census Commissioner of India. Ministry of Home Affairs, New Delhi GoI (Government of India) (2001) Census of India. Population totals. Ministry of Home Affairs, Government of India, New Delhi Government of India, GoI (2016) Brief industrial profile of Cooch Behar District West Bengal Micro, Small & Medium Enterprises, MSME-Development Institute, Kolkata. Available from http://dcmsme.gov.in/dips/2016-17/COOCHBEHAR.pdf. Accessed on 29.06.2019 Hazra S (2010) Temporal change detection (2001–2008) of Sundarbans. https://ejfoundation. org/what-we-do/climate/protecting-climate-refgees?gclid=Cj0KCQjwoqDtBRDARIsAL4pv iAskC19A9HTGBKSxJd-RS1pKHo4EY2VtNYIBpXtEc2JEunvuqki08EaAjFgEALw_wcB. Accessed on 17.09.19 Hugo G (2010) Climate change-induced mobility and the existing migration regime in Asia and the Pacific. In: McAdam J (ed) Climate change and displacement, multidisciplinary perspectives. Hart Publishing, Oxford/Portland, pp 12–15 IDMC (2016) Global report on internal displacement. May 2016. pp  20–21. Available from http://www.internaldisplacement. org/assets/publications/2016/2016-global-report-internaldisplacement-­­IDMC.pdf IDMC (2017) Internal displacement by country database. IDMC, Geneva. http://www.internaldisplacement.org/database IDMC (Internal Displacement Monitoring Centre) (2011) Displacement due to natural hazard-­ induced disasters: Global estimates for 2009 and 2010. Internal Displacement Monitoring Centre/Norwegian Refugee Council, Geneva IOM (International Organization for Migration) (2014) Migration, environment and climate change: evidence for policy (MECLEP). International Organization for Migration, Geneva Jana A, Sheena S, Biswas A (2012) Morphological change study of Ghoramara Island, Eastern India using multi temporal satellite data. Res J Recent Sci 1(10): 72–81. ISSN 2277–2502 Kar NS, Bandyopadhyay, S. (2014). Rehabilitating the Environmental Refugees of Sundarban: A Study Of Jibantala Colony, Sagar Island, West Bengal. Seminar proceedings of the horizon and beyond – contemporary research in geography. ed. Sil et al. Bhairab Ganguly College and Himalayan Books, Kolkata, pp 101–105 Lee ES (1966) A theory of migration. Demography 3(1): 47–57. Springer. Population Association of America. https://doi.org/10.2307/2060063. Available from https://www.jstor.org/stable/2060063. Accessed on June, 2019 Lewis B (2015) A human rights-based approach to disaster displacement in the Asia-Pacific. Asian J Int Law. https://doi.org/10.1017/S2044251315000168 Nishimura L (2015) Climate change migrants’: impediments to a protection framework and the need to incorporate migration into climate change adaptation strategies. Int J Refug Law 27(1):107–134. https://doi.org/10.1093/ijrl/eev002 OHCHR (Office of the High Commissioner for Human Rights) (2018) The Slow onset effects of climate change and human rights protection for cross-border migrants. Annual report of

224

A. Haldar et al.

the United Nations High Commissioner for Human Rights and reports of the Office of the High Commissioner and the Secretary-General Human Rights Council, Thirty-seventh session. https://www.ohchr.org/Documents/Issues/ClimateChange/SlowOnset/A_HRC_37_CRP_4.pdf Peth SA (2014) What is translocality?A refined understanding of place and space in a globalized world, Blog: Connecting the spots-Notes on migration and environment from a geographical perspective, Nov 2014 Ponserre S, Ginnetti J (2019) Disaster displacement: a global review, 2008–2018 (ed. Lennard J). IDMC, Geneva Rabbi H, Saifullah ASM, Sheikh Md S, Sarker Md MH, Bhowmick AC (2013) Recent study on river bank erosion and its impacts on land displaced people in Sirajgonj riverine area of Bangladesh. Int J Appl Environ Sci 2(2):36–43 Roy S, Sarkar S (2011) Flood hazards in Jalpaiguri district and its management. Chapter 7 Flood Hazard Assessment Department of Geography and Applied Geography, University of North Bengal, pp 176–197. Available from http://hdl.handle.net/10603/149931 Rudra K Haldar A (2017) Sundarbaner Bhristir Taratammo: Tathya Bhitiik Bishleshon (in Bengali). Sudhu Sundarban Charcha vol 5, 4th issue, pp 9–15 Sarkar S (2004) Effects of the 1993 extreme flood on the channel morphology of Jainti river, India. In: Bandopadhyay (ed) Landform processes and environment management. ACB Publication, Kolkata Stapleton SO, Nadin R, Watson C, Kellett J (2017). Climate change, Migration and Displacement The n=Need for a risk-informed and coherent approach. Overseas Development Institute. United Nations Development Programme UNESCAP (The United Nations Economic and Social Commission for Asia and the Pacific) (2012) Reducing vulnerability and exposure to disasters: Asia Pacific Disaster Report 2012. The United Nations Office for Disaster Risk Reduction (UNISDR). Online. UNESCAP http:// www.unescap.org/idd/pubs/Asia-Pacific-Disaster-Report-2012.pdf UNFCCC (United Nations Framework Convention on Climate Change) (2016) Introduction to the convention. Available from https://unfccc.int/process-and-meetings/the-convention/whatis-the-united-nations-framework-convention-on-climate-change. Accessed on June 2019 UNHCR (United Nations High Commissioner for Refugees) (2010). https://www.unhcr. org/3b66c2aa10. Accessed on 17.09.19 Warner K, Afifi T (2014) Where the rain falls: evidence from 8 countries on how vulnerable households use migration to manage risk of rainfall variability and food insecurity. Clim Dev 6(1):1–17 Wilkinson E, Kirbyshire A, Mayhew L, Batra P, Milan A (2016) Climate-induced migration and displacement: closing the policy gap. Overseas Development Institute, London World Bank (2018) Groundswell: preparing for internal climate migration (2018). https://openknowledge.worldbank.org/bitstream/handle/10986/29461/GroundswellPN2.pdf?sequence=7 &isAllowed=y Yonetani M (2011) Displacement due to natural hazard-induced disasters.global estimates for 2009 and 2010. Internal Displacement Monitoring Centre, Geneva Zetter RW (2007) More labels, fewer refugees: remaking the refugee label in an era of globalization. J Refug Stud 20(2):172–192

Chapter 13

Flood Frequency Analysis and Its Management in Selected Part of Bardhaman District, West Bengal Subodh Chandra Pal, Biswajit Das, Sadhan Malik, Manisa Shit, and Rabin Chakrabortty

13.1  Introduction Floods are undoubtedly one of the most dangerous natural hazards in the world, especially in the monsoon dominated humid tropical region (Sanyal and Lu 2004). It causes heavy damages to different sectors like agriculture, housing, industry, infrastructure, etc. (Ali 2007; Hsu et al. 2011; Mirza 2011). It is difficult to predict the occurrence and magnitude of floods. Unusual rainfall pattern due to climatic variability and global warming is causing disaster like floods throughout the world (Parry et al. 2007; Nwe and Tokuzo 2010). In the present condition, a lot of occurrences are going on like population explosion, deforestation, environmental degradation, urbanization, global warming, human encroachment, land use and land cover change, etc. which have accelerated the intensity and frequency of flood more and their concentration on hazard-prone areas make the impact of natural disasters even worse (Alexander 1993). There are so many flood controlling measures which have already been taken from very early, but now people do feel that the human interference in the river valleys become important one reason behind flood. In this region, the lower part of the Damodar River, flood is one of the major problems from the very beginning. This river is classically known as Sorrow of Bengal which had affected the entire lower Damodar Basin (Bagchi 1977; Bhattacharyya 2011; Ghosh 2011). Construction of the Damodar Valley Corporation (DVC), a multipurpose project, has modified the high peak flood (above 12,744 m3s−1) and the duration of the flood (Ghosh 2013). Along with this massive diversion of river water into canals gradually resulted in the decreasing of natural flow, de-linking of its

S. C. Pal (*) · B. Das · S. Malik · R. Chakrabortty Department of Geography, The University of Burdwan, Burdwan, West Bengal, India M. Shit Jamini Roy College, Bankura, West Bengal, India © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_13

225

226

S. C. Pal et al.

distributaries and turning them into dried bed locally known as Kana Nadi, e.g. Ghia, Behula, Kantool and Kana Damodar (Bagchi 1977; Bhattacharyya 2011; Ghosh 2011). According to Lahiri-Dutt (2012), the sufferings of the people living in the lower part of this river valley never really diminished. Therefore, the present flood protection measures have failed to protect people from the devastating effects of floods; there is an urgent need for new solutions for the flood management programs (Mukhopadhyay 2010). Thus, more resource and skilled manpower are essential to handle the situation (Ali 2007). There are so many types of flood, but riverine flood is the most common and dominant one in this region. It has become an annual phenomenon in the eastern parts of India, especially in West Bengal, where 55.43% area is flood prone (Mukhopadhyay 2010; Roy 2012). Floods in the deltaic region have the capacity to destroy the environmental setting of the region (Jha and Bairagya 2011). Among the global trends of natural disasters, Asia is becoming increasingly vulnerable (Shaw and Krishnamurthy 2009). According to Central Water Commission, Government of India, on an annual average, 7.21 million hectares of land is inundated, and nearly 32 million people are affected by floods (Kale 2003; Bandyopadhyay et al. 2016). Formulation of an effective flood management strategy in developing countries like India is very essential, because flood happens very frequently over large parts of the country (Sanyal and Lu 2004; Nath et al. 2008; Pandey et al. 2010). Remote sensing and GIS has become an important tool to prepare the maps for the inundated areas and to assess the damages moreover to suggest the most suitable development plan to reduce the intensity of flood (Anselmo et al. 1996; Islam et al. 2001; Jain and Sinha 2003; Jain et al. 2005; Bera et al. 2012; Masood and Takeuchi 2012). Updated floodplain maps play an important role to avoid several social and economic losses during the floods (Sanyal and Lu 2003). Identification of flood-­ prone properties for timely warning is essential to reduce the damages (Schumann 2011). Government organizations can take remedial actions before the disaster if proper information is available to them (Godschalk 1991; Ali 2007). Escalante-Sanboval and Raynal-Villasenor (1998) used the trivariate gumbel distribution for estimating the frequency of flood. In this purpose, the logistic model has been taken into consideration for trivariate analysis. The parameters of this distribution have been derived numerically for the complexity of likelihood functions, which has been in the form of estimators. They concluded that the trivariate distribution has been established as a reliable tool for flood frequency analysis. Rahman et al. (2013) used the site-specific flood frequency, which is based on the probability distribution of long period stream flow data. In this research, the choice of appropriate probability distribution in terms of the nature of the data sets is more vital for finding out the actual scenario. Apart from this, region-specific difference has been found in terms of the appropriate probability distribution. Bezak et al. (2014) used annual maximum (AM) and peaks over threshold (POT) to compare better accuracy for estimating the frequencies of the flood. They have identified that the POT method is more realistic than the AM method. In this case, the Poisson distribution is more realistic than the Binomial distribution, considering the annual threshold limit. Arnaud et al. (2016) used the stochastic simulation which is based on hourly rainfall

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

227

records. They considered more than 1000 rainfall gauging stations for generating the observed datasets. This type of scenario is highly correlated between observed rainfall data and traditional rainfall and runoff model. Ahmadi et  al. (2017) estimated the flood frequency using power law methods with different types of probability distribution. The duration of flood series in different temporal period has been worked out, and then the result has been compared with annual maxima. Finally, they concluded that the annual maximum is not suitable in terms of the frequency analysis and the monthly partial flood duration is more realistic which is derived from the power law function. Farooq et al. (2018) estimated the flood frequency and its magnitudes of Swat River using different techniques which is based on different return period, such as 5, 10, 25, 50 and 100, and intimates that the simulation based on different return period is very much useful in regarding the flood frequency. Flood report from the Govt. of West Bengal (Irrigation and Waterways Directorate, Government of West Bengal, 2015) shows that lower part of the east-­ flowing river is experiencing frequent flood and inundation like situation. In this study, Ketugram-I and II C.D. blocks of Bardhaman district has been taken for the study. These two blocks are located near the flood plain of Ajay River basin. Most of the study in this region was done for identifying the spatial variation of the flood, impact of LULC on flood susceptibility, flood inventory, causes of flood and so on. In this study, the village and Mouza level flood affected and non-affected areas are identified. The area is dominated by subsistence-based agricultural system and very fertile in nature, so structural measures cannot be appropriate in this region for the monetary problem as well as ecological disturbances. Here the non-structural measures like vegetative measures are suggested in various spatial units with keeping in the view of the location of settlement and local environment. The main objectives of the paper is to find out the flood frequency and its magnitudes using statistics and GIS to estimate the Mouza level (small administrative unit) flood-prone areas and to determine the possible way for escaping from this type of disasters by using the sustainable measures those can be obtained from the knowledge of the local stakeholders.

13.2  Study Area This study area is located in the north-eastern part of the Bardhaman district which is under Katwa subdivision, bounded by 23°37′29″N to 23°50′20″N latitudes and 87°54′15″E to 88°14′05″E longitudes. This study area (Fig. 13.1) covers an area of 354.01 square kilometres within which Ketugram-I block covers 193.98 square kilometres and Ketugram-II block covers 160.03 square kilometres. This study area is surrounded by three districts, namely, Birbhum, Murshidabad and Nadia districts. The present study area belongs to the flood-prone area of Bardhaman district which is mainly affected by two rivers Ajay and Bhagirathi. Ajay is one of the important right bank tributary of River Bhagirathi, which is very much destructive during the

228

S. C. Pal et al.

Fig. 13.1  Location of the study area

monsoon season particularly in the lower part (Kadam and Sen 2012; Bandyopadhyay et al. 2016). In the present study area, newer alluvium depositions are found near the bank of Ajay and Bhagirathi Rivers (Peterson 1910) (Table 13.1). Older alluvium deposition, laterite, reddish soil with laterite and limonite concretion, quartz debris, gravel, laterite debris with pieces of gritty, ferruginous sandstone and shale are occasionally found in the northern and the southern part of the Ketugram-I C.D. block (Figure 13.2a).

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

229

Table 13.1  Stratigraphic succession (geological formation) and their characteristics Sl. No. Formation 1 Hugli formation 2

Chinsurah formation

3

Sijua formation

Characteristics Hugli formation comprises recent sediment of present day, coarse to medium grained light yellow to light grey sand mixed with grey to burnish grey fines (silt and clay) are abundant throughout the region Chinsurah formation occupies the southern part, mostly low lying area of Burdwan. The width of this area is about 2 km. Mica, silty clay, ferruginous concentration are mostly deposited Sijua formation occupies the entire northern undulating terrain of the Damodar and other river systems. The horizon is represented by hard, sticky, brownish, yellow grey cliche. The size of cliche is 2 mm–6 cm. Quartz, feldspar and other eroded materials of the Vindhyan formation are found in the sandy horizon

Fig. 13.2  Geo-environmental set up of the study area  – (a) Geology; (b) Slope; (c) Drainage system; (d) Soil texture; (e) Communication network

230

S. C. Pal et al.

The study area is situated near the confluence zone of Ajay and Bhagirathi River which belongs to the flood plain region. This study area is very flat and low elevated zone. The figure (Figure 13.2b) in the following is showing the variation of slope within this region. Ketugram-I and Ketugram-II C.D. block has very good drainage network system (Figure 13.2c). The Ajay River is situated in the southern part of this study area in between Mongalkote block and Ketugram-I block, and the Bhagirathi River is located in the north-eastern part of this study area which is again located within Nadia district and Ketugram-II block of Bardhaman district. Both the river Ajay and Bhagirathi confluence near the south-eastern corner of the Ketugram-II block and north-eastern corner of Katwa-I block. As this study area belongs to very low gentle slope, therefore deposition of sand, silt and clay going on and on day to day that is why this region is one of the flood prone areas within the Bardhaman district. The study area belongs to ‘Aw’ type of climate which is tropical savanna type of climate. The climatic condition of this region is hot and humid. The summer condition of this region continues between May and June, and monsoon season is between July, August and September. Almost 85% of the annual rainfall occurred during the monsoon season. Winter period is very short from the end of November to mid-February. The centre portion of this region is alluvial plain region mainly composed with sub-recent alluvial soils which are generally very deep, poorly drained, fine cracking soils occurring on level to nearly level low lying alluvial plains with clayey and loamy surface which are associated with very deep, poorly drained or imperfectly drained, fine soils. Northern and southern part near the bank of river Ajay are composed with fine loamy soils which are generally very deep, moderately well drained, fine loamy soils occurring on very gently sloping flood plain with loamy surface, moderate erosion and moderate flooding which are associated with very deep, well-­ drained, sandy soils. Eastern part of the study area is composed with coarse loamy soils which are generally very deep, moderately well-drained, coarse loamy soils occurring on level to nearly level meander plain with loamy surface and moderate flooding which are associated with very deep, imperfectly drained, fine loamy soils (Fig. 13.2d).The communication system like rail and roads are well connected in all direction of the present study area. Here Ketugram-I and II block are connected with the rail line. State highway, metalled, unmetalled roads are also available for making communication system much better (Fig. 13.2e). Ketugram-I and II block have been suffering from flood since very early. In the satellite image, yellowish to red type of tone are available through which it can be said that the sand splay often takes place in that region. During the British period in the nineteenth century, quite a few numbers of major floods were taken place in 1867, 1877, 1885 and 1896. The unusual high flood occurred in 1913 and 1914 which causes serious loss of life and property. The major recorded flood years are 1956, 1959, 1970, 1971, 1973, 1978, 1984, 1987, 1994, 1995, 1999, 2000, 2006, 2007, 2009 and 2015 (Irrigation and Waterways Directorate, Government of West Bengal; Mukhopadhyay 2010).

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

231

13.3  Materials and Methods 13.3.1  Data Used Various types of datasets have been used in this study. Primary data related to the problems of the people during flood, availability of government shelters and aid, etc. has been collected from the local people during the field interview. Secondary data related to the flood height and affected area has been collected from the Irrigation and Waterways Directorate, Government of West Bengal. Survey of India (SOI) topographical sheets (73  M/13, 73  M/14, 79A/1 and 79A/2) on 1:50,000 scales have been used as a base map for the preparation of this study. Contours available on SOI topographical maps have been used for the preparation of slope map. SRTM data, geological map (1:253,440 scale) published by Geological Survey of India, was also used for the present study. Soil map published by National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) is also used for this study. Except these, rainfall data, LISS-IV satellite data (Table  13.2) are also used for this work.

13.3.2  Methodology Primary focus of this study is to use the remote sensing and geographical information system for flood management. Numerous books, reports and research papers have been followed for basic understanding. Primary and secondary data has been used in this paper. Gumbel’s extreme value distribution technique (Gumbel 1941) has been used for the flood frequency analysis. Flood frequency analysis has been done to understand the frequency of floods, and following this method we have predicted the gauge height of flood events for 100 years of return periods. A systematic flowchart of methodology is given below (Fig. 13.3). According to Gumbel, the probability of occurrence (P) of a flood (X) is given by −y

P = 1 − e−e



Return period (T) of a specific magnitude of flood is given by −y

T = 1 / 1 − e−e



y is the reduced variate, and it is given by y = a(X − Xf), Table 13.2  Details of the satellite data used in this study Satellite IRS

Sensor LISS-IV

Path/row 107/055

Bands 2,3,4

Date of acquisition 20/01/2014

Spatial resolution 5.8mts.

232

S. C. Pal et al.

Collection of information, data and maps

Topographical maps, Geological map, Soil map, rainfall data etc.

Satellite image

Hydrological data

(LISS-IV)

(e.g. gauge height)

Information collected from the field visit

Flood Frequency Analysis Rectification, creation of raster and vector layer

Preparation of maps and diagrams

Flood Inventory Map

Identification of vulnerable areas, possible evacuation routes and management strategies

Fig. 13.3  Methodological flowchart

where a = δn/Sx

Xf = X − (Yn ∗ Sx / δ n )



δn  and Yn are given in the Gumbel’s extreme value distribution table, Sx is the 2 standard deviation of the data sets  Sx = √ ( X − X ) / ( n − 1)  .   Probable peak values for the higher return period also can be calculated by using the Gumble’s extreme value distribution method. The formula for the calculation of probable values is

{



}

X t = X + kt ∗ Sx

where kt = (Yt − Yn ) / δ n

Yt = − In {In ( T / T − 1)}



Xt is the probable peak value of the corresponding return period.

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

233

13.4  Results and Discussion 13.4.1  Temporal Variation of Flood Water discharge of the Ajay and Bhagirathi River is at its peak in the June–October period due to huge rainfall in the monsoon season. It has been observed that the maximum flood level varies each and every year since 1956. In the year of 1978, 1995, 1999 and 2000, the flood level exceeds the utmost level of flood for both the gauge station Nutanhat and Gheropara.

13.4.2  Flood Frequency Analysis Flood frequency analysis is a type of risk analysis where we use past observed data related to flood (e.g. gauge level) to predict the future flood events along with their return period and probabilities. Various kinds of methods can be applied to find out the best suitable method for analysing the flood frequency of a particular site (Deraman et al. 2017; Abdullah et al. 2018). Extreme value distributions are one of the standardized methods of flood frequency analysis, and Gumbel’s extreme value distribution is widely used for this type of analysis (Chow 1951; Chow et al. 1988; Mukherjee 2013). Flood frequency analysis has been done for Gheropara and Nutanhat stations, which are situated over the Ajay River. Gheropara and Nutanhat are situated 55 km and 30 km in the upstream area from ketugram. Highest return period of the observed dataset is 28.72 years, and the corresponding gauge height is 42.97 m (Fig. 13.4). Gauge level of 42.97 m was observed in the year of 2000 when a major flood happened over the entire south Bengal (Fig. 13.6).

Peak Gauge Height and Return Period at Gheropara

Gauge Level (metre)

45 44 43 42 41 40 39

Observed Probable

38 37

0

20

40

60 80 Return Period (Years)

100

120

Fig. 13.4  Return period of floods with the corresponding gauge level over Ajay River at Gheropara

234

S. C. Pal et al.

Danger level has crossed in 13 years out of 30 years for Gheropara (Tables 13.3 and 13.4). Flood water has crossed the danger level mark in 18 years out of 30 years for Nutanhat. Highest return period of the dataset is 30.77 years, and the corresponding gauge level is 23.21 metre (Table 13.5 and Figs. 13.5, 13.6). Nutanhat gauge station is situated in the downstream part of the river, naturally the frequency of flood is more at this station compare to Gheropara (Fig.  13.7). Probable peak gauge height has been calculated for the upcoming years with the corresponding return period which shows that return period of extreme danger level is less than 5 years for both the sites. So the frequency of extreme flood will be more Table 13.3  Return period calculation by Gumble’s extreme value distribution method for Gheropara station

Year 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Peak gauge level in metre (X) 40.64 38.5 39.6 39.23 39.2 38.9 39.26 40.33 42.3 39.76 39.97 39.26 42.25 42.97 38.59 38.86 38.79 39.97 38.33 41.2 41 38.34 40.15 37.55 38.97 37.93 39.1 38 40 38.1

X-X̄ 1.07 −1.07 0.03 −0.34 −0.37 −0.67 −0.31 0.76 2.73 0.19 0.40 −0.31 2.68 3.40 −0.98 −0.71 −0.78 0.40 −1.24 1.63 1.43 −1.23 0.58 −2.02 −0.60 −1.64 −0.47 −1.57 0.43 −1.47

(X-X̄)2 1.1484694 1.1413361 0.0010028 0.1144694 0.1356694 0.4466694 0.0950694 0.5801361 7.4620028 0.0367361 0.1613361 0.0950694 7.1913361 11.571336 0.9571361 0.5017361 0.6058028 0.1613361 1.5334694 2.6623361 2.0496694 1.5088028 0.3383361 4.0736694 0.3580028 2.6841361 0.2193361 2.4596694 0.1863361 2.1560028

Reduced variate (y) 1.42 −0.35 0.56 0.26 0.23 −0.02 0.28 1.16 2.79 0.69 0.87 0.28 2.74 3.34 −0.27 −0.05 −0.11 0.87 −0.49 1.88 1.71 −0.48 1.01 −1.13 0.04 −0.82 0.15 −0.76 0.89 −0.68

Return period in years (T) 4.66 1.32 2.3 1.86 1.82 1.56 1.89 3.72 16.79 2.54 2.92 1.89 15.99 28.72 1.37 1.54 1.49 2.92 1.24 7.07 6.04 1.25 3.28 1.05 1.62 1.12 1.73 1.13 2.97 1.16

Probability of occurrence [P] (%) 21.4592 75.7576 43.4783 53.7634 54.9451 64.1026 52.9101 26.8817 5.9559 39.3701 34.2466 52.9101 6.2539 3.4819 72.9927 64.9351 67.1141 34.2466 80.6452 14.1443 16.5563 80.0000 30.4878 95.2381 61.7284 89.2857 57.8035 88.4956 33.6700 86.2069

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

235

Table 13.4  Calculation of probable peak gauge height for corresponding return period T 2 5 10 20 30 50 100

Yt 0.37 1.5 2.25 2.97 3.38 3.9 4.6

Ӯn 0.5362 0.5362 0.5362 0.5362 0.5362 0.5362 0.5362

∂n 1.1124 1.1124 1.1124 1.1124 1.1124 1.1124 1.1124

kt −0.1494067 0.866415 1.5406329 2.1878821 2.5564545 3.0239123 3.6531823

Sx 1.35 1.35 1.35 1.35 1.35 1.35 1.35

X̄ 39.57 39.57 39.57 39.57 39.57 39.57 39.57

Xt 39.366634 40.737994 41.648188 42.521974 43.019547 43.650615 44.500129

Table 13.5  Return period calculation by Gumble’s extreme value distribution method for Nutanhat station

Year 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Peak gauge level in metre (X) 20.74 18.6 19.7 19.33 19.3 19 19.36 20.43 22.3 19.86 20.07 19.36 22.89 23.21 18.69 18.96 18.89 20.07 18.43 20.49 21.37 18.44 20.25 17.65 19.07 18.03 19.2 18.1 20.1 18.2

X-X̄ 1.07 −1.07 0.03 −0.34 −0.37 −0.67 −0.31 0.76 2.63 0.19 0.40 −0.31 3.22 3.54 −0.98 −0.71 −0.78 0.40 −1.24 0.82 1.70 −1.23 0.58 −2.02 −0.60 −1.64 −0.47 −1.57 0.43 −1.47

(X-X̄)2 1.1456134 1.1441868 0.0009201 0.1153734 0.1366534 0.4484534 0.0958934 0.5781068 6.9186534 0.0362268 0.1602668 0.0958934 10.370547 12.53396 0.9597468 0.5036268 0.6078801 0.1602668 1.5367734 0.6729468 2.8911334 1.5120801 0.3367868 4.0790534 0.3596001 2.6885068 0.2205868 2.4638534 0.1851868 2.1599201

Reduced variate (y) 1.41 −0.32 0.57 0.27 0.24 0.00 0.29 1.16 2.67 0.70 0.87 0.29 3.15 3.41 −0.25 −0.03 −0.09 0.87 −0.46 1.21 1.92 −0.45 1.01 −1.09 0.06 −0.79 0.16 −0.73 0.89 −0.65

Return period in years (T) 4.62 1.34 2.32 1.87 1.84 1.58 1.9 3.72 14.95 2.55 2.92 1.9 23.84 30.77 1.38 1.55 1.5 2.92 1.26 3.88 7.33 1.26 3.28 1.05 1.64 1.12 1.74 1.14 2.97 1.17

Probability of occurrence [P] (%) 21.64502 74.62687 43.10345 53.47594 54.34783 63.29114 52.63158 26.88172 6.688963 39.21569 34.24658 52.63158 4.194631 3.249919 72.46377 64.51613 66.66667 34.24658 79.36508 25.7732 13.64256 79.36508 30.4878 95.2381 60.97561 89.28571 57.47126 87.7193 33.67003 85.47009

236

S. C. Pal et al.

Peak Gauge Height and Return Period at Nutanhat 27

Gauge Height (metre)

25 23 21 19 Observed Probable

17 15

0

20

40

60 80 Return Period (Years)

100

120

Fig. 13.5  Return period of floods with the corresponding gauge level over Ajay River at Nutanhat

Gauge Level (m)

Highest Flood Level of Major Flood Years 44 43 42 41 40 39 38 37

Station: Gheropara

Year Flood Level

Danger Level

Extreme Danger Level

Gauge Level (m)

Fig. 13.6  Flood level at Gheropara gauge station

24 23 22 21 20 19 18 17 16 15

Highest Flood Level of Major Flood Years Station: Nutanhat

Year Flood Level

Danger Level

Fig. 13.7  Flood level at Nutanhat gauge station

Extreme Danger Level

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

237

Table 13.6  Calculation of probable peak gauge height for corresponding return period T 2 5 10 20 30 50 100

Yt 0.37 1.5 2.25 2.97 3.38 3.9 4.6

Ӯn 0.5362 0.5362 0.5362 0.5362 0.5362 0.5362 0.5362

∂n 1.1124 1.1124 1.1124 1.1124 1.1124 1.1124 1.1124

kt −0.1494067 0.866415 1.5406329 2.1878821 2.5564545 3.0239123 3.6531823

Sx 1.38 1.38 1.38 1.38 1.38 1.38 1.38

X̄ 19.67 19.67 19.67 19.67 19.67 19.67 19.67

Xt 19.46382 20.86565 21.79607 22.68928 23.19791 23.843 24.71139

in the near future (Tables 13.4 and Table 13.6). It has been noticed during the field visit that the flood hazard of this region has become a regular phenomenon. According to the senior-aged and literate persons, this region is witnessed with severe major flood hazard in every 3- or 4-year interval. The return period calculation following statistical method is also showing the same thing. Apart from this, it was also found from the literature survey that during the British period, quite a large numbers of major floods were taken place in 1867, 1877, 1885 and 1896, respectively. The remarkable high flood occurred in 1913 and 1914 which causes serious threats to the human life and property. The major recorded flood years of this region are 1956, 1959, 1970, 1971, 1973, 1978, 1984, 1987, 1994, 1995, 1999, 2000, 2006, 2007, 2009 and 2015 (Irrigation and Waterways Directorate, Government of West Bengal; Mukhopadhyay 2010).

13.4.3  Causes of Flood There are so many physical and human-induced factors responsible for the flood. It has been observed that a huge amount of rainfall occurred within very short period of time creates the flood, like 80% of rainfall occurred within 3–4 monsoon months of the year. Another one factor responsible for flood is that sudden decrease of river width towards the lower catchment area. Huge discharge during the peak monsoon season often exceeds the carrying capacity of the river and water flows over the floodplain. As Ketugram-I and II blocks are located near the confluence point of Ajay and Bhagirathi rivers, flood occurs naturally when river fails to carry the extra water due to heavy rainfall. Human encroachment on both sides of the river bank is another important reason behind flood. Other responsible physical factors are the long, narrow shape of the river basin, very gentle gradient of the river towards the mouth of the basin area ultimately favours the situation of riverbed siltation and reduction of carrying capacity. Bankfull cross-sectional area of flow of the river decreases towards its downstream direction. We have measured the bankfull cross-­ sectional area of Ajay River from Noapara to its mouth near Katwa using 12.5 metre spatial resolution digital elevation model collected from Alaska Satellite Facility. The bankfull channel capacity near Noapara station is about 1114.051 sq. metre,

238

S. C. Pal et al.

and it becomes 972.93 sq. metre near Charkhi, and at the junction point cross-­ sectional area comes down to 780.57sq metre; this may be due to the extreme anthropogenic impact like human encroachment along the river bed, embankment on both side of the river and so on.

13.4.4  Vulnerable Areas Ketugram-I and II blocks are one of the worst flood-affected blocks of Bardhaman district. Out of 15 Gram Panchayats (G.P), seven are severely affected by floods (Figs.  13.8 and 13.9). Southern part of the area is the worst affected by flood (Fig.  13.10). List of the villages under these Gram Panchayats are given below (Table 13.7). A buffer zone of 2 km radius from the river has been drawn to identify severe affected blocks (Fig. 13.11).

Fig. 13.8  Flood affected areas of Ketugram-I and II blocks, 2015

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

239

Fig. 13.9  Field photograph showing flood-affected areas of (a) Talari, (b) Khatundi, (c) Serandi and (d) Ankhona villages of Ketugram-I and II blocks

Fig. 13.10  Flood inventory map of Ketugram-I and II blocks

240

S. C. Pal et al.

Table 13.7  Flood affected areas of Ketugram-I and II blocks, 2015 Sl. No. 1

Gram Panchayat Ankhona

2

Billeswar

3 4

Gangatikuri Maugram

5

Nabagram

6 7

Palita Pandugram

8

Sitahati

Village name Ankhona, Murutia, Haldi, Mahula, Ichhapur, Kusutia, Majhina, Chechuri, Mauri, Chakta Billeswar Rasui, Khenaibanda, Charkhi, Maliha, Komdanga, kauri, Chitahati, Kopa, Taipur, Gurpara, Teora Gangatikuri, urundi, Birahimpur, Anantapur, Jhamatpur, Balutia, Baharan Kamalabari, Raghupur, Char Sujapur, Sujapur, Bishnupur, Kalyanpur, Narayanpur, Natangram, Maugram, char Hareypur Begunkola, Kankurhati, Nabagram, Talari, Senpara, Purulia, Ambalgram, Siblum, Gomai, Chak Kharulia Narenga, Palita, Bankui, Bira, Serandi, Alyapur Chak, Ehiapur, Dadhia Chakdaha, Bakalsa, Ganful, Bhandargaria, Noapara, Jamalpur, Jamalpur Chak, Kulun, Kulai, Hatpara, Kanchra, Mitratikuri, Khatundi, Pandugram, Ghatkuri Enayetpur, Uddharanpur, Naihati, Sitahati, Duttabati, Siruli, Paschim Sujapur, Naliapur, Keoguri, Sankhai

Fig. 13.11  Buffer zone of Ajay and Bhagirathi rivers

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

241

13.4.5  Flood Mitigation Measures 13.4.5.1  Diversion of Channel Diversion of channel is another significant aspect by which we can take measures to reduce the magnitude of floods in this area. It is mainly constructed to divert waters from the main channel to control the flood. It can be controlled by diverting excess water to purpose-built canals or floodways, which in turn deflect the water to temporary holding ponds or any other bodies of water where there is a minor risk or impact to flooding. Here the study area is located at the junction between Ajay and Bhagirathi rivers. Farakka Barrage can control the flow condition of Bhagirathi River, and diversion of flow can be done for the Bhagirathi River through Farakka Barrage during the flood season (Fig. 13.12), which will reduce the flow. In case of Ajay River, we have not found any suitable area for diversion of flow due to absence of palaeochannels as this region is agricultural dominated and highly populated about 1000–1500 person/km2 (Census of India 2011); it is difficult to divert the river through this region. So flow diversion of Ajay River will cause inundation or flood-­ like situation in other areas which will not be a sustainable solution for region. 13.4.5.2  Channel Improvement The main cause of flood in this region is gradually narrow river channel. So, channel improvement will be the most important one step to mitigate the flood. We have found that the Ajay River has decreases about 333.47 sq. metre of its bankfull cross-­ sectional area from Noapara to its mouth. So, it is strongly recommended that if we have to mitigate the flood condition in this region, we have to improve the channel capacity. It can be done by deepening, widening and cleaning out of vegetation and debris from the river bed; these changes in the river channel will help to increase the water discharge capacity of the river. Channel improvement is supplemented by natural river bank stabilization by constructing ripraps, planting deep root trees on both sides of the river. Here the zone of no human encroachment has been demarcated based on the fluvial signature of the river (Fig. 13.13); this will give the space for natural oscillation of river as well as to improve the bankfull carrying capacity during peak flow. It is also recommended that if this method is being followed for rest of the rivers in this region, it will reduce the flood magnitude to a greater extent. 13.4.5.3  Planting of Vegetation Planting of vegetation can help to reduce rainfall runoff in flood-prone areas because when plants grow in an area, the roots of plants dig deep into the soil and create space between soil particles. When it rains in highlands, water that flows downhill gets drained into the space created by the root system of plants. Due to this, chance

242

S. C. Pal et al.

Fig. 13.12  Possible ways of outletting the excess amount of water from the river Ganga through Farakka Barrage

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

243

of flooding is greatly reduced. In case of rocky terrain surface where no such plants are available, we often see reverse condition. Planting of vegetation along both sides of the river bank can also be considered as non-engineering measures for protecting river bank erosion as well as flood-like situation within the region. Vegetation normally holds the water in soil in their root zone and prevents scouring erosion and sloughing of river banks. We have demarcated buffer zone towards both side of the river bank up to 1 km after considering the surrounding environment. Apart from the restricted zone of human intervention, rest of the buffer region may be considered for planting of vegetation (Fig. 13.13). Indigenous plants like mango (Mangifera indica), guava (Acca sellowiana), jamun/blackberry (Syzygium cumini), sisul (Dalbergia sissoo), kul (Ziziphus jujube) and neem (Azadirachta indica) may be considered as sui` vegetation keeping in the view of economy, erosion and inundation within the region for minimizing the magnitude of the flood-like situation.

13.5  Conclusion Flood is a natural event in Ketugram-I and II blocks during the monsoon season. Frequent flooding affects the livelihood pattern of the local people. Most of the areas of these blocks are highly flood affected. River water crosses danger level very frequently during the monsoon. Thousands of people, their property, crops and infrastructure all of these are vulnerable to flood. Flood frequency analysis shows that there is a possibility of extreme flood in the future. Proper monitoring and management of flood are essential to reduce the potential damages. As flood is a natural event, we cannot eradicate it completely, but we can minimize the negative impact to a certain amount with better management. In this case study what we have observed during the field visit is that the people are very happy to reside in that region whatever may be the situations. Therefore, ‘living with flood’ may be the better solution to them. Application of GIS is very important for the management of this type of events. Inundation map helps us to identify the vulnerable areas, which is important for preparedness and management of the situation. It is also favourable to take certain measures like planting of vegetation along both sides of the channel, channel improvement by giving sufficient space to the river for its natural activity along with dredging of channel to minimize the magnitude of flood for the long-­ term benefit of the society. Past experiences show that embankment is for the immediate solution not for the sake of long term. Timely management is still an issue in this part of the world. So there is an urgent need for the formulation and ­implementation of effective flood management policies to reduce the damages. Compliance with Ethical Standards On behalf of all authors, the corresponding author states that there is no conflict of interest.

244

S. C. Pal et al.

Fig. 13.13  Mitigating the flood through minimizing human encroachment and planting of vegetation (a). Mitigation measures may be taken into consideration following built-up area (b)

13  Flood Frequency Analysis and Its Management in Selected Part of Bardham…

245

References Abdullah J, Muhammad NS, Julien PY, Ariffin J, Shafie A (2018) Flood flow simulations and return period calculation for the Kota Tinggi watershed, Malaysia. J Flood Risk Manag 11:S766–S782 Ahmadi F, Radmaneh F, Parham GA, Mirabbasi R (2017) Comparison of the performance of power law and probability distributions in the frequency analysis of flood in Dez Basin, Iran. Nat Hazards 87(3):1313–1331 Alexander DE (1993) Natural disasters. Springer Ali AMS (2007) September 2004 flood event in southwestern Bangladesh: a study of its nature, causes, and human perception and adjustments to a new hazard. Nat Hazards 40(1):89–111 Anselmo V, Galeati G, Palmieri S, Rossi U, Todini E (1996) Flood risk assessment using an integrated hydrological and hydraulic modelling approach: a case study. J Hydrol 175(1–4):533–554 Arnaud P, Cantet P, Aubert Y (2016) Relevance of an at-site flood frequency analysis method for extreme events based on stochastic simulation of hourly rainfall. Hydrol Sci J 61(1):36–49 Bagchi K (1977) The Damodar Valley development and its impact on the region. In: Noble AG, Dutt AK (eds) Indian urbanization and planning: vehicle of modernization. Tata McGraw-Hill Publishing, New Delhi, pp 232–241 Bandyopadhyay S, Ghosh PK, Jana NC, Sinha S (2016) Probability of flooding and vulnerability assessment in the Ajay River, eastern India: implications for mitigation. Environ Earth Sci 75(7):578 Bera K, Pal M, Bandyopadhyay J (2012) Application of RS and GIS in flood management: A case study of Mongalkote blocks, Burdwan, West Bengal, India. Int J Sci Res Publ 2(11):1–9 Bezak N, Brilly M, Šraj M (2014) Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis. Hydrol Sci J 59(5):959–977 Bhattacharyya K (2011) The lower Damodar River, India: understanding the human role in changing fluvial environment. Springer, Cham Chow VT (1951) A general formula for hydrologic frequency analysis. EOS Trans Am Geophys Union 32(2):231–237 Chow VT, Maidment DR, Mays LW (1988) Applied Hydrology. McGraw-Hill, Singapore Deraman WHAW, Mutalib NJA, Mukhtar NZ (2017) Determination of return period for flood frequency analysis using normal and related distributions. J Phys Conf Ser 890(1):012162 Escalante-Sanboval CA, Raynal-Villasenor JA (1998) Multivariate estimation of floods: the trivariate Gumbel distribution. J Stat Comput Simul 61(4):313–340 Farooq M, Shafique M, Khattak MS (2018) Flood frequency analysis of river swat using log Pearson type 3, generalized extreme value, Normal, and Gumbel max distribution methods. Arab J Geosci 11(9):216 Ghosh S (2011) Hydrological changes and their impact on fluvial environment of the lower damodar basin over a period of fifty years of damming the mighty Damodar River in eastern India. Procedia Soc Behav Sci 19:511–519 Ghosh S (2013) Estimation of flash flood magnitude and flood risk in the lower segment of Damodar River basin, India. Int J Geol Earth Environ Sci 3(2):97–114 Godschalk DR (1991) Disaster mitigation and hazard management. In: Emergency management: principles and practice for local government, pp 131–160 Government of West Bengal (2015) Annual flood report for the year 2015. Irrigation & Waterways Directorate, Government of West Bengal Gumbel EJ (1941) The return period of flood flows. Ann Math Stat 12(2):163–190 Hsu WK, Huang PC, Chang CC, Chen CW, Hung DM, Chiang WL (2011) An integrated flood risk assessment model for property insurance industry in Taiwan. Nat Hazards 58(3):1295–1309 Islam MM, Sado KIMITERU, Owe M, Brubaker K, Ritchie J, Rango A (2001) Flood damage and management modelling using satellite remote sensing data with GIS: case study of Bangladesh. IAHS Publ:455–457

246

S. C. Pal et al.

Jain V, Sinha R (2003) Geomorphological manifestations of the flood hazard: a remote sensing based approach. Geocarto Int 18(4):51–60 Jain SK, Singh RD, Jain MK, Lohani AK (2005) Delineation of flood-prone areas using remote sensing techniques. Water Resour Manag 19(4):333–347 Jha VC, Bairagya H (2011) Environmental impact of flood and their sustainable management in deltaic region of West Bengal, India. Caminhos de Geografia 12(39) Kadam P, Sen D (2012) Flood inundation simulation in Ajoy River using MIKE-FLOOD. ISH J Hydraul Eng 18(2):129–141 Kale VS (2003) The spatio-temporal aspects of monsoon floods in India: implications for flood hazard management. In: Disaster management. University Press, Hyderabad, pp 22–47 Lahiri-Dutt K (2012) Large dams and changes in an agrarian society: gendering the impacts of Damodar Valley Corporation in eastern India Masood M, Takeuchi K (2012) Assessment of flood hazard, vulnerability and risk of mid-eastern Dhaka using DEM and 1D hydrodynamic model. Nat Hazards 61(2):757–770 Mirza MMQ (2011) Climate change, flooding in South Asia and implications. Reg Environ Chang 11(1):95–107 Mukherjee MK (2013) Flood frequency analysis of river Subarnarekha, India, using Gumbel’s extreme value distribution. Int J Comput Eng Res 3(7) Mukhopadhyay S (2010) A geo-environmental assessment of flood dynamics in lower Ajoy River inducing sand splay problem in eastern India. Ethiop J Environ Stud Manag 3(2) Nath SK, Roy D, Thingbaijam KKS (2008) Disaster mitigation and management for West Bengal, India – an appraisal. Curr Sci:858–864 Nwe TT, Tokuzo H (2010) Sediment transport due to flooding from levee breach: a numerical application of the 2004 Niigata flood. Int J River Basin Manag 8(1):3–14 Pandey AC, Singh SK, Nathawat MS (2010) Waterlogging and flood hazards vulnerability and risk assessment in Indo Gangetic plain. Nat Hazards 55(2):273–289 Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (2007) Climate change 2007: impacts, adaptation and vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Peterson JCK (1910) Bengal district gazetteers: Burdwan. Bengal Secretariat Book Depot, Calcutta Rahman AS, Rahman A, Zaman MA, Haddad K, Ahsan A, Imteaz M (2013) A study on selection of probability distributions for at-site flood frequency analysis in Australia. Nat Hazards 69(3):1803–1813 Registrar General of India. Census of India (2011) Instruction manual for houselisting and housing census. Registrar General of India, New Delhi. http://censusindia.gov.in/2011-manuals/ Index_hl.html Roy S (2012) Spatial variation of floods in the lower Ajay River basin, West Bengal: a geo-­ hydrological analysis. Int J Remote Sens GIS 1(2):132–143 Sanyal J, Lu XX (2003) Application of GIS in flood hazard mapping: a case study of Gangetic West Bengal, India. In: Proceedings of map Asia conference, GISdevelopment.net Sanyal J, Lu XX (2004) Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat Hazards 33(2):283–301 Schumann AH (ed) (2011) Flood risk assessment and management: how to specify hydrological loads, their consequences and uncertainties. Springer Shaw R, Krishnamurthy RR (2009) Disaster management: global challenges and local solutions. Universities Press, Hyderabad, pp 1–18

Chapter 14

Spatiotemporal Extent of Agricultural Drought Over Western Part of West Bengal Mou Dey, Dipanwita Dutta, and Abira Dutta Roy

14.1  Introduction Drought is a devastating, inveterate, and pervasive natural hazard with complicated socioeconomic, environmental, and ecological impacts (AMS 1997). In the agricultural sector, drought affects soil moisture availability that leads to crop failures and pasture decline, posing risks on food security (Lu et al. 2017). Agricultural drought is a situation when rainfall and soil moisture is not sufficient for crop growth (Baik et al. 2019; Zhang et al. 2019; Parsons et al. 2019). More than 500 million people live in these drought-prone areas globally (Sheffield et  al. 2004; Rathore 2004; Wilhite 2000). Indian economy too is periodically affected by drought as well, because 60% of it is sustained by the agricultural sector (Jha et al. 2019; Sharma and Goyal 2018). According to Chatterjee et al. 2016 West Bengal too, a heavily dependent agrarian state faces major weather anomalies which includes delayed monsoons and prolonged breaks in monsoon resulting in drought-like situations. But Bandyopadhyay et al. 2014 correctly pointed out that there is absence of meteorological drought and hydrological drought in West Bengal. But according to WBPCB 2009 based on infiltration-runoff ration, volume of groundwater storage, the western hard rock plateau can be designated as drought prone, that too agricultural drought. Crop failure occurrences in this area have been reported in several government reports (NADAMS 1989). Even Ghosh 2018 through various statistical analyses of climate data have M. Dey · D. Dutta Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, West Bengal, India A. Dutta Roy (*) Department of Geography, Bankura Zilla Saradamani Mahila Mahavidyapith, Bankura, West Bengal, India e-mail: [email protected] © Springer Nature Switzerland AG 2021 Rukhsana et al. (eds.), Habitat, Ecology and Ekistics, Advances in Asian Human-Environmental Research, https://doi.org/10.1007/978-3-030-49115-4_14

247

248

M. Dey et al.

identified the northern Rarh region and upper moribund delta region to be highly sensitive to droughts. She has emphasized that the western-degraded plateau and its adjacent regions which encompass the districts of Purulia, Bankura, Purba, and Paschim Medinipur are the most vulnerable. According to Dash et al. (2019) and Cornish et al. (2015) the western part of West Bengal as one of the worst drought-­ prone areas of India. Datta and Das 2018 have identified in their research work significant decline in monsoon rain in sub-Himalayan West Bengal and have forecasted its adverse impact on western parts of West Bengal. Hence an urgent need for monitoring agricultural drought based on remotely sensed images is required, especially to reduce crop failures. Different literatures show that accurate prediction of drought and estimation of losses were not possible due to the lack of meteorological information, costly data, cumbersome, and lengthy data processing techniques, whereas remote sensing has proved to be a better alternative (Brown et al. 2008; Gu et al. 2007; Palmer 1968; Dutta et al. 2015; Kogan 1995; Rhee et al. 2010). Various band ratio techniques have been applied on the different remotely sensed images in order to compute drought indicators. Among them, the normalized difference vegetation index (NDVI) developed by Rouse et al. (1974) has been widely used for drought monitoring (Peters et al. 2002). Kogan (1995) developed the vegetation condition index (VCI) by linearly scaling NDVI values from 0 to 1 for each pixel to separate weather-related components from ecosystem components. In addition to VCI, thermal band-based temperature condition index (TCI) was developed to provide additional information on land surface temperature to distinguish vegetation stress caused by drought events from other factors (Kogan 1995). VCI and rainfall anomaly index (RAI) have demonstrated its usefulness in establishing a relation between agricultural drought and meteorological drought (Patel and Yadav 2015). Literatures (Quiring and Ganseh 2010; Dutta et al. 2011, 2015) have proved the efficiency of VCI, RAI, and YAI and thus has been adopted for this study. The present study endeavors to identify the spatiotemporal variation of agricultural drought using various remote sensing and GIS techniques and uses different indices to monitor the agricultural drought at regional scale on the western districts of West Bengal.

14.2  Study Area The study area is situated in the western part of West Bengal. The location extent spreads between 21.94° to 22.60° North latitude and 85.75° to 87.78° East longitude (Fig. 14.1). This area covers the largest part of the state and consists of 4 districts, 10,000 villages, and 4 major towns (Census 2011). The study area is a part of Chota Nagpur Plateau. There are many monadnocks, scattered especially in Bankura and Purulia districts. The southern part of the region is flat, occupied by East and West Medinipur districts near Bay of Bengal. Slope of this area is toward East as can be seen in Figure 14.2a. Greater parts of study area are covered by laterite and alluvium

14  Spatiotemporal Extent of Agricultural Drought Over Western Part of West Bengal

249

Fig. 14.1  Study area

Fig. 14.2 (a) Slope map (b) soil map (c) LULC of the study area

soil as shown in Figure 14.2b obtained from NBSSLUP (National Bureau of Soil Survey and Land use Planning). Figure 14.2c shows the land use land cover conditions of the region derived from MODIS MCD12Q1, which is predominantly agrarian in nature. The area is covered mostly by residual soil formed as a result of weathering of bed rocks. The soil types are shown in Table 14.1. To the west, the Chota Nagpur Plateau gradually slopes down creating an undulating area with infertile laterite rocks and soil. The western part has poor, ferruginous soil which is not suitable for agriculture (Dunn and Dey 1942). Table  14.1

250 Table 14.1 Soil study area

M. Dey et al. type

of

Map symbol W069 W096 W104 W036 W038 W041 W044 W047 W064 W065 W067 W068 W073 W078 W089 W091 W092 W093 W094 W095 W096 W097 W098 W099 W100 W101 W102 W105 W106 W108 W110 W111 W112 W113

Description Fine loamy, Aeric ochraqualfs Loamy, Lithic Haplustalfs Fine, Typic Haplustalfs Fine, Vertic ochraqualfs Very fine, Vertic Haplustalfs Fine, Vertic Haplustalfs Fine, very deep Very fine, Aeric Haplustalfs Coarse loamy Fine loamy Coarse loamy, Typic Haplustalfs Fine loamy, Ultic Haplustalfs Aquic ustipsamments Fine, Vertic Typic Haplustalfs Fine, moderate salinity Fine loamy, moderately well drained Very shallow, gravelly loamy Shallow loamy Deep fine loamy Shallow course loamy Gravelly loamy Imperfectly drained, fine Fine loamy, very deep Shallow, gravelly loamy Very deep, fine loamy Imperfectly drained, fine loamy Well drained, fine loamy Shallow, well drained, gravelly loamy Very deep, well drained, fine loamy Very shallow, well drained, gravelly loamy Shallow, moderately well drained, coarse Deep, moderately well drained, fine loamy Very deep, moderately well drained, fine Shallow, imperfectly drained, coarse loamy

Source: NBSSLUP

shows the different soils classified as per NBSSLUP. Although several rivers such as Darakeshwar, Kangsabati, Rupnarayan, and Subarnarekha flow across these districts, 50% of the water flows as runoff into the sea due to the undulation of the topography (Haldar and Saha 2015). From  the climatological normals of IMD

14  Spatiotemporal Extent of Agricultural Drought Over Western Part of West Bengal

251

(India Meteorological department, 1981–2010), it is evident that the climatic condition of the study area is hot and dry in summers, pleasant and cool in winters, and experiences rainy monsoons. The maximum temperature ranges from 28  °C to 40  °C, and minimum temperature ranges between 12  °C and 9  °C.  The average annual rainfall varies between 1100 and 1500 mm. There are two cropping seasons in the study area, Kharif and Rabi (Panigrahy et al. 2005). Kharif crops are dependent on monsoonal rainfall for water and are sown in June–July and harvested in the months of September and October. Rabi crops are cultivated in winter. They are sown in October–November and harvested in the months of March–April. Drought is a common phenomenon here (Haldar and Saha 2015).

14.3  Materials and Methods For this study, three major indices were used, namely, VCI, RAI, and YAI, from which the intensity and extent of the drought was characterized. NOAA-STAR (National Oceanic and Atmospheric Administration Center for Satellite Application and Research, www.star.nesdis.noaa.gov) derived weekly VCI data was used which was freely downloadable for study area. It is a vegetation health (VH) product. The satellite-based global VH system was designed to monitor, diagnose, and predict long- and short-term land environmental condition and climate-dependent socioeconomic activities. The data are globally available with 4 km resolution and are a 7-day composite data. The system contains vegetation health indices and products like VCI, TCI, etc. Kogan (1995) developed this VCI using the range of NDVI which is a good indicator for assessing the severity of agricultural drought. It is defined as shown in Eq. (14.1):

VCI = ( NDVI − NDVI min ) / NDVI max − NDVI min ) ∗ 100



(14.1)

Where NDVI is the actual value of NDVI and NDVIMax and NDVIMin are smoothed weekly NDVI absolute maximum and minimum values, respectively. Primarily maximum growth of Kharif crops in this region starts in the fourth week of July and stays till the entire August. So, this week was chosen for study. The weekly data were downloaded and analyzed for the period of 20  years (1998–2017) for July to August. When study area is very large, then this type of data provides much more accuracy (Dutta et al. 2015). The VCI of the fourth week of July and fourth week of August for 20 years was layer stacked separately in ENVI 4.7 software. VCI value ranged from 1 to 100 in the merged data. The values ranging within 50–30% indicated moderate drought condition, and less than 30% indicated severe drought condition (Kogan 1995). In the file of VCI images, the value of certain pixels showed −9999; these were on the oceans. So, in order to overcome this situation at first, a mask with min value 0 and max value of 100 was applied, and then these images were opened in Arc. GIS 10.1. With the help of the

252

M. Dey et al.

administrative boundary layer of the study area, the image was given the desired shape. The VCI images were classified in three classes: severe, moderate, and normal drought condition in Arc GIS 10.1 software on the basis of VCI values. VCI values of each districts in different years were derived from VCI image through means of spatial average statistics or zonal statistics, the values obtained for each district for each year were then tabulated in MS excel, and VCI trend were drawn as a line graph. There are two rainfall stations in entire study area, and the rainfall data is not regular as well as costly, so CHIRPS data (Climate Hazard Group Infrared Precipitation with station) was chosen for computing RAI.  CHIRPS is a product derived through collaboration among scientists at the US Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. It is reliable, up to date, and more complete datasets which have already been used for a number of early warning studies such as seasonal drought monitoring (Rojas 2018). CHIRPS data is a 30+ year quasi-global rainfall dataset. Spanning 50°S-50°N and at all longitudes, this data is available from 1981 to near present. CHIRPS incorporates 0.05° resolution satellite imagery with in situ station data to create gridded rainfall time series for seasonal drought monitoring. As of February 12, 2015, version 2.0 of CHIRPS is complete and available for estimating rainfall variation in space and time (Funk et al. 2015). CHIRPS’s rainfall maps are useful, especially for places where surface data is sparse (Funk et  al. 2015). However, estimates derived from satellite data provide areal average that suffers from biases due to complex terrain and often underestimate the intensity of extreme precipitation events (Funk et al. 2015). In line with the methodology followed by Kundu et al. (2016), this study also uses CHIRPS rainfall data product for Bankura, Purulia, Purba, and Paschim Medinipur district which were downloaded from ftp://ftp.chg.ucsb.edu/pub/org/ chg/product/chirps-2.0/global daily/tifp05 for over 20 years (1998–2017) for July and August, to enable assessment of the spatiotemporal dynamics of meteorological drought and its relation with agricultural drought, that is RAI and VCI respectively. This data was then processed through ENVI Classic 4.7 software. At first the monthly dataset was generated by layer stacking the daily downloaded datasets. This stacked layer was masked using the ROI (region of interest) masking layer to obtain the shape of the study area. Average of monthly rainfall was successively derived using formula of computing average or mean in band math option [Formula: (day1 + day2…. +day31)/31, where month is of 31 days]. The monthly dataset of July and August was again averaged for 20 years. Then the mean and standard deviation of the 20 years average were computed. After post-processing of rainfall datasets, RAI values were derived from the images through Arc GIS10.1 using the formula given in Eq. (14.2). Zonal statistics in Arctools were used to generate district level RAI data for each year. The derived values were tabulated in excel, and line graph was drawn to represent the RAI trend for the four districts from 1998 to 2017. On the basis of Rooy 1965 the rank of precipitation values were calculated to be positive and negative anomalies, and accordingly RAI values were interpreted. Equation 14.2 represents computation of deviation in rainfall from long-term average (Dutta et al. 2013, 2015; Padhee et al. 2017).

14  Spatiotemporal Extent of Agricultural Drought Over Western Part of West Bengal



RAI =

R−µ σ

253

(14.2)

Where RAI = Rainfall anomaly index, R = Rainfall, μ = Long-term average rainfall, σ = Standard deviation Yield anomaly index (YAI) was computed from the yearly crop production data available from District Statistical Handbooks. The annual production of principal crops such as rice, wheat, barley, maize, tur, gram, millets, and urad that grow in different districts of Bankura, Purulia, and East and West Medinipur were considered. The average production and the standard deviations were computed and compared with the VCI as well as RAI (Dutta et al. 2013). Yield anomaly index is a very useful technique for monitoring of drought of long-term period, and it is also useful for showing the average yield production and the deviations from the normal (Dutta et al. 2011). By this process one can represent the yield production of a large or small area graphically.



YAI =

Y −µ σ

(14.3)

Where YAI = Yield anomaly index, Y= Yield production, μ = Long-term average yield, σ = Standard deviation The flowchart in Fig. 14.3 gives an overview of the methodology which illustrates the objective of the study.

14.4  Result and Discussion 14.4.1  VCI and Drought Monitoring In the present study, this vegetation index is used to elucidate the vegetation condition and drought year. Figure 14.4a and b represents the vegetation condition for 2 months (July & August) during Kharif season for the years 1998–2017. From the map of VCI, it can be identified that the year 1999, 2000, 2001, 2008 and 2017 had experienced severe drought. In the drought year 2008, Paschim/West and Purba/ East Medinipur districts were highly affected. Works by Bera and Bandyopadhyay (2017) and Sharma and Goyal (2018) also are supportive evidences of drought occurrences during these years. According to Kogan 1995 if the VCI value is below 35%, then it will be drought year. So the years were subsequently categorized as agricultural drought year and normal years. The VCI values for July and August were found to be very low in drought years as compared to that of the normal year. The values in 2000–2001,

254

M. Dey et al.

Fig. 14.3  Methodological flow chart

2008 and 2011 were mostly below 40 in large parts of the area. Hence it has been considered as drought year. On the other hand, 2002, 2012 and 2013 had values above 60 and are considered as normal years. The district- wise monthly VCI during the study period has been graphically represented in Figure 14.5, which shows that Purulia, Bankura, and Paschim Medinipur are the worst-affected districts during the drought years for July and August. The Purba Medinipur district has experienced relatively better conditions as the VCI values have not been lesser than that of 40.

14.4.2  Drought Monitoring Through RAI In order to monitor the agricultural drought and identify a parity with the results obtained from VCI, the RAI was calculated. Figure  14.6 represents the rainfall departure condition from normal rainfall for 2 months (July and August) that is the peak crop-growing months of the Kharif season for the year 1998 to 2017. It was found that severe meteorological drought condition prevailed during Kharif season of the year 2000–2001, 2008, and 2010–2011 over Purulia, Bankura, and Paschim Medinipur districts and over Purba Medinipur district. The spatial variability and intensity of drought were evident from the RAI maps shown in Fig. 14.6. Low RAI

14  Spatiotemporal Extent of Agricultural Drought Over Western Part of West Bengal

255

Fig. 14.4 (a) Vegetation condition index (VCI) on fourth week of July (b) on fourth week of August during 1998–2017

values signify that the years had low rainfall, indicating negative departure from mean rainfall which means it is a drought year (Rooy 1965; Barring and Hulme 1991; Otun and Adewumi 2009; Ganapuram et al. 2014). So accordingly the RAI value of July during the years 2000-2001 were low, and simultaneously the VCI values of Purulia, Bankura, Purba Medinipur, and Paschim Medinipur districts were also less than 40 for the same month. Comparably RAI values of these four districts had near −1 values in the same months for the same years. It was thus indicative that vegetation condition was very poor due to lack of rainfall. Even in August, 2010–2011 rainfall anomaly was very low, and for this condition VCI value was below 40 for whole study area. 2005, 2006, 2007, 2013, and 2014 were normal year having positive RAI values. Figure 14.7 shows the RAI graphs for each district. After analyzing VCI and RAI trend for July and August, it was established that there has been a relation between agricultural drought and meteorological drought because in the years where RAI values were   0 determines that the component and transformed matrix α ∗ x is at the positive direction of axis simultaneously and α