Environmental Management and Sustainability in India: Case Studies from West Bengal 3031313984, 9783031313981

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Abhay Sankar Sahu Nilanjana Das Chatterjee   Editors

Environmental Management and Sustainability in India Case Studies from West Bengal

Environmental Management and Sustainability in India

Abhay Sankar Sahu • Nilanjana Das Chatterjee Editors

Environmental Management and Sustainability in India Case Studies from West Bengal

Editors Abhay Sankar Sahu Department of Geography University of Kalyani Kalyani, West Bengal, India

Nilanjana Das Chatterjee Department of Geography Vidyasagar University Midnapore, West Bengal, India

ISBN 978-3-031-31398-1 ISBN 978-3-031-31399-8 https://doi.org/10.1007/978-3-031-31399-8

(eBook)

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

Dedicated to our teacher Smt. Manjushri Basu

Madam Manjushri Basu, Retired Reader, Department of Geography, The University of Burdwan, Purba Barddhaman, West Bengal, India, was born on 25 August 1942, at 101/C Bullygunj Place, Kolkata, West Bengal, India. She completed her graduation in 1963 from Lady Brabourne College, Kolkata, with 1st class. She received her M.A. degree in Geography from Calcutta University, Kolkata, in 1965. In 1978, she received her

M.Phil. degree in Geography from Delhi School of Economics, New Delhi, India. She started her teaching career as lecturer in the Department of Geography, Women’s Christian College, Kalighat, Kolkata, in 1966. From 1972 to 1980, she was lecturer in the Department of Geography, Loreto College, Kolkata. Finally, she went to Barddhaman from Kolkata to join as lecturer in the Department of Geography, The University of Burdwan, aiming to serve the rural university, where she worked from 1980 to 2002. During her entire teaching and research career, she visited and presented her research papers in number of seminars and conferences in India and abroad. She has published many research articles in the National and International publications.

Foreword

It gives me immense pleasure to know that a book entitled Environmental Management and Sustainability in India – Case Studies from West Bengal edited by Dr. Abhoy Sankar Sahoo and Prof. Nilanjana Das Chatterjee will be published soon containing 29 chapters contributed by as many as 71 authors. These chapters have come under the purview of different parts involving environmental issues and human sustainability on one hand along with ecosystem restoration and sustainable development on the other. Such a bold academic endeavor which is the brain child of two young geographers deserves accolade from the teachers and research scholars who are actively engaged in the studies of various burning issues in environmental geography. Their decision to dedicate this edited volume to their octogenarian teacher and mentor Ms. Manjushree Basu seems to be wise one and will add another feather to their crown. It is believed that the persons who are ready to pay homage to the veterans and acknowledge their contributions are always honored by a suave society. This is part and parcel of Indian cultural tradition which is maintained through Gharana of a branch of knowledge and the baton is transferred through the generations. The term ‘sustainable development’ was first coined by Barbara Ward (Lady Jackson) in1970 and was recognized in Brundtland Report in 1987 prior to its acceptance on the floor of the Rio summit in 1992. Conference of Parties (CoP) in the Earth Summit started its journey in the Stockholm Conference way back in 1972 followed by the sequential conferences organized in Rio-de-Janeiro (1992), Johannesburg (2002) and again at Rio-de-Janeiro (2012) which is famous as Rio+20 Conference. Three most important thrust areas declared in the global forum involve achievements in economic, social and environmental issues with as many as 17 goals to fulfill in global, national and regional/local scale. This book has confined itself within the territory of West Bengal – a federal state of India picking up its multifarious environmental problems. Obviously loss of forest cover with its biodiversity in the different forest clad regions of West Bengal apart from sustainable livelihood of the tribal groups along with other people living adjacent to the Reserved Forests have come under focus. Anthropological issues, vii

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involved in fluvial connectivity in the lower Ganga Basin, are other areas of concern. Impact of channel shifting on the land cover and land use of the riparian areas, infrastructural development for paving the way of sustainable coastal tourism as well as characteristics of the sea waves as experienced in the sea beach are also in question. Human impact of tropical cyclones taking the shape of disaster, spatiotemporal variation of ground water table and implications of infrastructural development thereon, delineation of ground water potential zones, hazards induced by ground water irrigation have not escaped the notice of the authors. Scope of urban forestry, formation of urban heat island and valuation of urban heritage sites are other interesting areas of discussion. Constraints of urban transport during the recent pandemic phase along with other social issues in district level, women’s plight involved with population shifting are also under the scanner. Role of non-timber forest products in sustainable rural livelihood, degeneration of mangrove forest, integrated pest management, sustainable urban development of the smart cities, urban land use dynamics, municipal solid waste management, ecotourism and tourism development strategies are other areas of focus. In fact, there exists a conflict between brown and green technology so far sustainable development is concerned. I believe that this academic endeavor will add many precious elements to the bank of knowledge. A mix of qualitative and quantitative approaches with emphasis on digital cartography and analytical style of writing of the authors has added value to a specialized branch of Geography.

Professor (Retd.) of Geography University of Calcutta Kolkata, West Bengal, India Former ICSSR Fellow, Institute of Development Studies Kolkata Kolkata, West Bengal, India 22nd March, 2023

Ranjan Basu

Preface

In 2022, we celebrated the 50-years golden anniversary of the first world conference on human environment of the United Nations held in Stockholm in 1972. It is also the 30th anniversary of the first Earth summit concerning sustainable development held in Rio de Janeiro of the United Nations in 1992. In this background, our aim is to address geography of environmental issues and their sustainable management from local to global. Environmental issues are intricately related to spatial science. Today major environmental issues under physical and human environment are human population growth, climate change, urbanization, biodiversity loss, land degradation, economic inequality and war. Throughout the world, humanenvironment relationship study attains continuous significance since number of human population increases exponentially, non-renewable resources are being limited, and there are myriads of environmental problems as well. From the very beginning of our human civilization, the only one home is ‘blue planet – our earth’ continuously being grabbed for meeting unending human greed and economic activities. Today, throughout the world, as human population grows very rapidly, more specifically in some pocket areas over the tropics, due to over-exploitation and unscientific use as well as misuse of resources, haphazard and unscientific development of infrastructures, the earth faces massive environmental degradation. Geography being a spatial science always correlates ecological issues and human environment on varying spatial extension towards ecosystem restoration, social well-being and sustainable development. The debate concerning economic development and environmental protection is always alive from the last some decades of the twentieth century. To address different physical and human-related environmental issues from the 70th decade of the twentieth century, several environmental conventions, parties, treaties, policies, protocols, etc. have been worked out under the umbrella of the United Nations. Almost in these last 50 years, all over the world, there is a growing consciousness observed among people due to continuous different activities and environmental oriented programmes organized by the United Nations, governmental and non-governmental levels in different countries on different global to local environmental issues. Today, all the physical and human-related environmental issues specially overpopulation, global warming, sea level change, ix

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deforestation, soil erosion, land degradation, pollutions, plastic hazard, exploitation of natural resources, biodiversity loss, urbanisation, waste disposal, groundwater contamination, freshwater crisis, social and economic inequality, poverty, disease, crime and war, etc. attract more and more attention for urgent management and planning. As all the biotic and abiotic components of our environment like an environmental system or ecosystem always try to remain in a balanced state so that they function in a better equilibrium condition, but when the system has been disturbed due to uncontrolled human interventions beyond its limit of tolerance, it is then degraded as well. Balanced production and consumption of environmental resources create a stable equilibrium condition in our environment. But, over exploitation, unscientific and haphazard uses of those resources lead to fast deterioration, extinction and degradation. In the context of human environment, it is also true observing all socio-politico-economic-cultural elements in a system and those behave in balanced-unbalanced condition whenever changes occurred in the system. Since all components in a system, physical and human, are connected and depend upon each other, any kind of change or alteration in the system influences the whole. Humans and environment are strongly connected. There are approaches like environmental determinism, possibilism, neo-determinism, positivism, environmentalism, deep ecology and so on to understand the human-environment relationship. In physical environment, due to changing mountainous environment, river basin activities, unscientific use of water resources, deforestation, unscientific agricultural activities, unplanned land acquisition for urbanization and waste disposal, we observed issues like environmental degradation and depletion of natural resources. Haphazard urban growth at present time creates extreme pressure on land and water. In the name of economic development, huge pressure on non-renewable resources like coal and petroleum turns into manifold environmental problems. In human environment, significant environmental issues, e.g. poverty, migration, disease, crime, inequality, etc., are seeking urgent solution as well as sustainable management for human development throughout the world. In this condition, for sustaining our human environment along with physical environmental development for the present and also for future generation, we need proper restoration programme and planning from micro level to higher extension. In-between two conflicting mounds of economic development and environmental protection issues, we all are searching the way of living on this blue planet, in green environment, achieving what we actually need. Present sustainable environmental goals are to achieve a balanced standardized livelihood along with protecting our environment. To get global-level solution of environmental issues, it is important to promote local-level actions at first. The concept of sustainable development is continuously changing. It is observed number of human deaths always has largely controlled the scale of impact after every disaster like tsunami, earthquake, landslide, flood, disease, etc. It is true humans are the cause of human threats. Therefore, to achieve human sustainability as well as sustainable development, what do we need to manage properly? Is there any development that may be infrastructural or medicinal sustainable to our environment and also to us as well?

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Here, in this book, in the context of geography of environmental issues and their management and sustainability in India involving understanding the spatial signature of environmental issues, mapping, geographical information system (GIS), relationship between humans and environment, and sustainable management considering a spatio-temporal scale, we focused broadly on two major areas of interest for study from West Bengal. These are environmental issues and human sustainability, and ecosystem restoration and sustainable development. There are totally 29 papers on case studies from different parts of West Bengal, India. The introductory part contains the status, scope and challenges of the environmental sustainability considering some broad areas in general over the West Bengal. It meets present environmental challenges and their management for sustainable development in West Bengal. Environmental issues and human sustainability part manifested a relationship between humans and environment. This relationship is sometimes positive and sometimes negative as well. Environmental issues are related to our surrounding air, water and land. Over population is considered as the basic reason behind almost all types of environmental issues. Environmental issues have local to global extension. Here we considered some of the significant contemporary environmental issues as case studies from different sectors in West Bengal. There are mountainous environmental issues, fluvial environmental issues, coastal environmental issues, forest environmental issues, water-related environmental issues, agricultural environmental issues, urban environmental issues and social environmental issues. These issues are relating to physical and human environment ultimately retaining with human sustainability. Ecosystem restoration and sustainable development part is mainly to focus on those management strategies which meet several environmental issues of an area towards sustainable development. Simply, here is one solution for several issues. Towards sustaining our human environment, we need proper ecofriendly management strategies. Management needs micro-level to large-scale study on environmental issues and their spatial relations. Sustainable management strategies are also happened from micro level to wider extension. There are resource conservation and natural protection, alternative agricultural system, sustainable urban management, waste management and eco-tourism. The editors first of all acknowledge the authors for their valuable contributions. We would like to thank to all of our teachers who continuously teach and guided us. We would specially like to thank Ms. Manjushri Basu and Prof. Ranjan Basu for their special care in environmental study to us. Again, we would like to thank to Prof. Ranjan Basu for his kind words for this volume. Finally, we would like to thank the Springer team for publishing this volume.

Kalyani, West Bengal, India Midnapore, West Bengal, India

Abhay Sankar Sahu Nilanjana Das Chatterjee

Contents

Part I 1

Environmental Sustainability: Status, Scope and Challenges in West Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . Abhay Sankar Sahu and Nilanjana Das Chatterjee

Part II 2

3

4

5

6

Introduction 3

Environmental Issues and Human Sustainability

Forest Dependency and Rural Livelihood: Strategical Survival of People in Himalayan Foothills of Bengal Duars Region . . . . . . . Koyel Sam and Namita Chakma

21

Identification of Potential Anthropogenic Barriers to Fluvial Connectivity in the Lower Gangetic Basin of India . . . . . . . . . . . . Suvendu Roy

35

A Case Study of Channel Shifting and Its Impacts on Riverside Land Use and Land Cover Using RS and GIS in Teesta River in Jalpaiguri, West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . Samrat Podder, Prasanya Sarkar, and Shasanka Kumar Gayen Monitoring the Shifting Nature of River Singimari and its Impact on Riverside Land Use and Landcover in Dinhata-I and Sitai Blocks of Cooch Behar District, West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Koyel Roy, Pritam Saha, Sushanta Das, Madhumita Mandal, and Shasanka Kumar Gayen Societal Instabilities in the Wake of Shifting of River Course: A Study of Hotnagar Char of Bhagirathi River, West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohan Sarkar, Susmita Ghosh, Shah Nawaj Ahmed, Mallik Akram Hossain, and Aznarul Islam

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7

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Contents

Strategic Infrastructural Development to Promote Sustainable Coastal Tourism Through Geospatial Technology in Purba Medinipur District, West Bengal . . . . . . . . . Biraj Kanti Mondal, Aditi Acharya, Ming-An Lee, Sanjib Mahata, and Tanmoy Basu

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A Study on the Characteristics of Sea Waves at the Mandarmani Sea Beach of West Bengal . . . . . . . . . . . . . . . Shubhayan Roy Chowdhury and Arijit Majumder

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Determining Recent Trends of Forest Loss and Its Associated Drivers for Sustainable Management in the Dry Deciduous Forest of West Bengal, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dipankar Bera, Nilanjana Das Chatterjee, Sudip Bera, Akshay Rana, and Bipul Paul

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Impact of Land Inundation Caused by Cyclone ‘Amphan’ Across Bangladesh and India Using Spatial Damage Assessment Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Medha, Biswajit Mondal, Gour Dolui, S. M. Tafsirul Islam, and Murari Mohan Bera

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Developmental Project (Bandel Thermal Power Station) and Its Impact on Groundwater: An Empirical Study from an Indian Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Somnath Mandal, Subhasis Bhattacharya, and Suman Paul Spatio-Temporal Variation of Groundwater Table with Relation to Rainfall Distribution: A Study in Nadia District, West Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dipti Gope and Abhay Sankar Sahu Identification of Groundwater Potential Zones (GWPZ) Using Weighted Overlay Model: A Case Study on a Semi-Arid District of West Bengal, India . . . . . . . . . . . . . . . . Pijus Kanti Ghosh and Sahina Khatun Groundwater Irrigation and Consequent Hazards in East Barddhaman District, West Bengal, India . . . . . . . . . . . . . Mahamaya Laha Mukherjee Debates on Urban Environmental Issues and Trends of Urban Forestry in Kolkata Municipal Corporation: A Quantitative Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laxmi Narayan Saha

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Pandemic COVID-19, Reduced Usage of Public Transportation Systems and Urban Environmental Challenges: Few Evidences from India and West Bengal . . . . . . . . . . . . . . . . . Bhaswati Mondal Estimating the Variability of Ground-Level Annual PM2.5 and PM10 Using Land-Use Regression Model in Kolkata Municipal Corporation (KMC) . . . . . . . . . . . . . . . . . . . . . . . . . . . Kousik Das, Nilanjana Das Chatterjee, and Raj Kumar Bhattacharya Effects of Land Use and Land Cover on Surface Urban Heat Island (SUHI) in Durgapur–Asansol Industrial Region: A Linear Regression Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . Santu Guchhait, Nirmalya Das, Gour Dolui, Subhrangsu Das, and Tanmay Patra An Exercise on Valuation of Urban Heritage Site, A Comparative Study of Victoria Memorial Hall and Indian Museum, Kolkata . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tuhin Kanti Ray, Pallavi Sarkar, Bulti Das, and Eshita Boral Population Shifting and Its Consequences on Women’s Life: A Case Study Along the River Banks of Ganga-Bhagirathi in Jangipur Sub-division, West Bengal . . . . . . . . . . . . . . . . . . . . . Debika Ghosh and Abhay Sankar Sahu Social Issues and Sustainability of COVID-19: A District Level Spatio-Temporal Analysis in West Bengal . . . . . . . . . . . . . . . . . . . Tanmay Patra, Nirmalya Das, Santu Guchhait, Subhrangsu Das, Zarjij Alam, Munmun Nandy, and Koushik Mistri

Part III 22

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Ecosystem Restoration and Sustainable Development

Dependence on Forest Products to Sustain Rural Livelihood: An Experience from Bankura Forest, West Bengal . . . . . . . . . . . . Susmita Sengupta and Manika Saha

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Border Netting Technology with Integrated Pest Management (IPM) Strategies for Sustainable Chilli Leaf Curl Management . . . Abhijit Ghosal and N. C. Sahu

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Smart Cities and Sustainable Urban Development in India: A Case Study of West Bengal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paramita Roychowdhury

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Significance of Sustainable Transportation in Urban Mobility: A Special Study During COVID-19 Unlock Period in Kolkata . . . . Ajoy Sekhar Datta and Abhay Sankar Sahu

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Assessing the Impact of Urban Land-Use Dynamics on the Ecological Environment of East Kolkata: A Study for Sustainable Urban Development . . . . . . . . . . . . . . . . . Subrata Ghosh, Santanu Dinda, Nilanjana Das Chatterjee, and Shrabanti Dutta Residents’ Perception Towards Environmental Impact of Municipal Solid Waste Disposal and Suitability Analysis for Landfill Site Selection Using Geospatial Technique: A Case Study in Ranaghat Municipality, West Bengal . . . . . . . . . . Milan Ghosh and Abarna Mukhopadhyay Yoga Tourism as an Emerging Branch of Eco-tourism for the Restoration of Sustainable Human Environment . . . . . . . . Premangshu Chakrabarty and Subhajit Das Formulation of Geotourism Development Strategies for Potential Geoheritage Sites in Subarnarekha-Kangsabati Interfluve Zone Using Tourist Assessment Value and SWOT-AHP Hybrid Model . . . . . . . . . . . . . . . . . . . . . . . . . . . Manas Karmakar, Monali Banerjee, and Debasis Ghosh

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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About the Editors

Abhay Sankar Sahu is an Associate Professor and former HOD in the Department of Geography, University of Kalyani, Kalyani, West Bengal, India. He has also served as an Assistant Professor in the Department of Geography, Kashipur M.M. Mahavidyalaya, Purulia, West Bengal, India. He pursued his B.Sc. (Honours), M.Sc., and Ph.D. in Geography from The University of Burdwan, West Bengal, India. His Ph.D. thesis is on ‘Environmental Significance of Water-Front Embankments in South and Eastern Medinipur Coast’ and was awarded in the year of 2010. His research interest covers Environmental Geomorphology, Environmental Geography and Hazard Geography. He is continuously engaged in study and research works on river morphology, waterfront embankments, flood and waterlogging, riverbank erosion, coastal erosion, ecotourism, EIA and EMP, remote sensing and GIS, geographical bigdata and machine learning. He has nearly 20 years of experience in research and teaching, and has contributed more than 45 research papers published in different reputed national- and international-level journals and edited volumes. He has completed as a Principal Investigator of a project sponsored by the UGC, New Delhi, India, on ‘Toward an Exploration of an Embankment System in the Moyna Flood Basin’, and also completed as a Research Investigator of a project under the UGC-BSR start-up grant on ‘Environmental Consequences of Water-logging Problem in Purba Medinipur District, West Bengal: Evaluation, Mapping, and Management’. Currently, he is working as a Principal Investigator of a project under the RUSA Component-10 xvii

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About the Editors

(Entrepreneurship and Career Hub), University of Kalyani, W.B., India, on ‘Understanding the problems of homestay tourism in the Mousuni Island of Sundarbans and entrepreneurship development’. He has successfully completed a lot of courses, training programmes and workshops on geospatial technologies, climate change, data analysis and machine learning in geoinformatics organized by the reputed governmental institutions like NATMO, Kolkata; NRSC (ISRO), Hyderabad; ICFRE, Dehradun; IIRS, Dehradun; NRDMS-DST, New Delhi; DSTBT, Kolkata; etc. At present, he is guiding as a supervisor a number of scholars towards their doctoral degree, and already five research scholars achieved their Ph.D. award. He is a life member of more than ten different academic associations and institutions. Scopus Author ID: 56675455400 ORCID ID: https://orcid.org/0000-0003-3649-3647 Web of Science Researcher ID: O-8729-2015 Google Citation: https://scholar.google.com/citations? user=UAFZzSMAAAAJ&hl=en Research Gate: https://www.researchgate.net/profile/ Abhay-Sankar-Sahu Nilanjana Das Chatterjee is a Professor and HOD in the Department of Geography, Vidyasagar University, West Bengal, India. She received her Ph.D. from Burdwan University in 2009. She is an expert in the field of Bio-geography with special reference to humanelephant conflict in forest fringes and fragmented forest, environment, landscape ecology with special reference to urban ecology, tribal culture and women’s studies. She has nearly 25 years of experience in research and teaching and has authored more than 90 national and international research articles with high impact factor journals and received many prestigious awards. She is one of the 20 women scientists recognized by the Wildlife Institute of India, Dehradun. She is an enthusiastic IUCN Commission member of CEM. Seven research scholars achieved their Ph.D. award and 12 students completed their M.Phil. degree under her supervision. She has completed a major project funded by ICSSR, New Delhi. Now she has completed a research programme funded by ICSSR on ‘Crime against

About the Editors

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Women West Bengal’. She has authored the book Man– Elephant Conflict: A Case Study from Forests in West Bengal, India, published by Springer in 2016, and co-authored the book River Sand Mining Modelling and Sustainable Practice: The Kangsabati River, India, published by Springer Environmental Science and Engineering Series in 2021. Recently, she has published another book GIS Application for Crime Prediction: A Spatio-temporal Analysis for Crime Against Women in West Bengal, India. Being a devoted teacher, her passion is to maintain a good student-teacher relationship. Vidyasagar University Webpage http://vidyasagar.ac.in/Faculty/Profile?fac_u_id=FacGEO-79 Vidwan-ID: 61243 Web of Science Researcher ID: ABA-3477-2021 ORCID: https://orcid.org/0000-0001-9436-2173 Scopus Author ID: 57208756935 *GoogleCitation*: https://scholar.google.com/citations? hl=en&user=sjliAqcAAAAJ&view_op=list_ works&gmla=AJsN-F4aTRwtwZY5M8C-2 aNIxnLd50E2DZTgprcS5ABu3TVXWTpG_ rkQkEIAK4uE-VSWUkuCaD_-L-rHRE7E1_ npJhAVB0XjaPsRs1f0FqS4sBq7H1gXs0o&user= sjliAqcAAAAJ Researchgate: https://www.researchgate.net/profile/ Nilanjana-Das-Chatterjee-2

Part I

Introduction

Chapter 1

Environmental Sustainability: Status, Scope and Challenges in West Bengal Abhay Sankar Sahu

and Nilanjana Das Chatterjee

Abstract Achieving environmental sustainability has become a key challenge of the current century for all developing and underdeveloped countries, including India. The face of worldwide unprecedented population growth and resultant high demands for food and other resources, unplanned developmental activities and pollution have adverse impact on environmental health and quality. The present chapter aims to unfold the current status, identify future scope and review the challenges of environmental sustainability in West Bengal. To evaluate the current status of environmental sustainability, selected environmental aspects, viz., forest, air, water and land use/land cover, of West Bengal are taken into consideration. Remote sensing images and secondary data have been used for spatial mapping of these four environmental aspects in GIS environment. The study revealed that in West Bengal, only 16% of the total geographical area is under forest cover; air quality is tremendously poor; and 11 districts have per capita water availability less than 1000 m3. The whole scenario of West Bengal does not indicate a healthy and sustainable environment. Therefore, this chapter would be helpful for sustainable planning and uses of land, water, soil and other natural resources to attain environmental sustainability. Keywords Environmental sustainability · Sustainable development · Natural resource · West Bengal

1 Introduction The concept of environmental sustainability is basically rooted in the explanation of sustainable development introduced in the late twentieth century. Sustainable development brings a growing concern for the environment, climate change with social A. S. Sahu (✉) Department of Geography, University of Kalyani, Kalyani, West Bengal, India e-mail: [email protected] N. Das Chatterjee Department of Geography, Vidyasagar University, Midnapore, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_1

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A. S. Sahu and N. Das Chatterjee

issues viz., poverty, hunger, inequality, etc., into a common frame (Shajahan & Sharma, 2018). Sustainable development is a growing concept in the context of husbanding natural resources on the earth’s surface in a manner that one generation should pass the adequate resources to their next for their maximum potential development (Goodland, 1995; Vuuren et al., 2022). Barbara Ward (1914–1981) was probably the first who propounded the term sustainable development in 1970. In 1972, a group of scholars from Club of Rome gained serious attention after the publication of The Limits to Growth (1972), a book which predicts how depletion of natural resources may bring crisis to human welfare until the technological innovation match to provide supplementary options. In 1980, the International Union for Conservation of Nature (IUCN) used the term sustainable development for the first time, unfortunately without defining the term. In the same year, the IUCN set up the World Conservation Strategy (WCS), which defined the term – a development which is sustainable in terms of quality of human life and protection of natural resources (Giovannoni & Fabietti, 2013). In 1987, the first comprehensive definition of sustainable development was attempted in the Brundtland Report (Haland, 1999): ‘Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (Brundtland Report: WCED, 1987). In earlier period, there was an apprehension that sustainable development is a unified concept that belongs to environmentalism rather a bridge between environment and development. Actually, sustainable development is a holistic approach coherent to environmental issues like resource depletion, pollution, economic growth and social equalities (Baynes & Wiedmann, 2012; Bogers et al., 2022). Figure 1.1 presents the key elements of the sustainability approach, broadly environment, social and economic, and, thereafter, their overlapping areas. At present, it is very difficult to capitalise the concept of sustainable development for human sustainability when the concept is continuously changing with respect to total environmental changes, especially technology and socio-economic changes. Today, after the COVID-19 pandemic situation, one question arises – what is actual sustainability? It is observed that after every disaster, like tsunami, earthquake, landslide, flood, pandemic, etc., counting of human deaths measures the scale of impact of the issue. Though humans are the cause of human threats. Therefore, to achieve human sustainability as well as sustainable development, what do we need to do appropriately? Is any development that may be infrastructural, industrial or medicinal sustainable to our environment and to us as well? For the purpose of economic development today, there is huge pressure on non-renewable resources like coal and petroleum, which ultimately turns into manifold environmental problems. All physical and human environmental issues need urgent solutions as well as sustainable management for human development in general throughout the world. For sustaining our environment, we need proper adaptation and restoration programmes and planning from micro level to higher extension. In between the two conflicting mounds of economic development and environmental protection issues, we all are searching the eco-friendly sustainable way of living on this blue planet, in an all-round green environment, achieving happiness and calmness, which is what we actually need.

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Environmental Sustainability: Status, Scope and Challenges in West Bengal

5

Fig. 1.1 Key elements of the sustainability approach

Sustainable environmental goals are to achieve a balanced standardised livelihood along with protecting our environment. It is important to promote local level actions at first to get global level solution of the environmental issues. Now a days, all the physical- and human-related environmental issues, especially high population growth rate in some areas, along with worldwide overpopulation, global warming, sea level rising, deforestation, soil erosion, land degradation, different types of pollutions, plastic hazard, exploitation of natural resources, biodiversity loss, industrialisation, urbanisation, waste disposal, groundwater contamination, freshwater crisis, social and economic inequality, poverty, disease, crime and war, etc., attract more and more attention for urgent management and planning. Pachauri and Mehrotra (2020) studied from India’s perspective Sustainable Development Goals helped to socio-economic development and environmental conservation as well as development in different fields. West Bengal is a densely populated state of India with a population of 91,276,115 (Census of India, 2011), while the total geographical area is only 88,752 km2. Rapid population growth continuously lay immense pressure on the limited natural resources of the state. Overpopulation has triggered burning issues like haphazard urbanisation, deforestation, land degradation, shrinkage of wetlands, degeneration of river channel, and many others (Kwatra et al., 2016). Therefore, the scenario became worst for the environmental planners and policy makers for maintaining environmental sustainability as well as economic growth of the state. In this chapter, therefore, an attempt has been made to expose to what extent sustainability has been maintained in

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different environmental aspects in West Bengal. It highlights the current status, evaluates the future scope and reviews the challenges of environmental sustainability in some selected aspects of the environment, viz., forest, air, water and land use/land cover in West Bengal.

2 Forests Resources and Environmental Sustainability Forest is a precious natural resource on the earth’s surface which not only fulfils human needs by providing timber, food, medicine, etc. but also plays a vital role in the purification of air and water and acts as a buffer against climate change. Forests provide home to numerous animals and birds. Forests also have the ability to reduce occurrences of natural disasters such as flood, draught, landslide, desertification, and many other extreme events (Hahn & Knoke, 2010). In view of the above roles, Sustainable Development Goal 15 aims to ‘protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.’1 West Bengal is the second most densely populated state of India characterised with rapid population growth and haphazard urbanisation. As per Land Use Statistics, Ministry of Agriculture, Govt. of India, 2014–2015 report, only 16% of the total geographical area is under forest cover, which is less than the average national forest cover, that is, 21.71% (IFSR, 2019–2020). Although, the National Forest Policy (1988) suggested that a minimum 33% of the total geographical area should be under forest cover to maintain ecological balance. Figure 1.2 shows that more than half of the total geographical area is under agriculture, while only 16% of the total area is under forest cover. Total forest area of the state again classified into four categories, that is, very densely forest, moderate densely forest, open forest and scrub, as shown in Fig. 1.3. The diagram revealed that out of total geographical area, very densely forest is recorded by only 3% while moderate densely forest is recorded by only 5%. Open forest occupied maximum portion of the forest cover area, i.e., 57.50% of the total forest land and scrub has an area coverage of about 0.17% to total geographical area of the state. Present forest cover map of West Bengal for the year of 2021 has been prepared using Landsat-8 OLI satellite images in Google Earth Engine (Fig. 1.4). Figure 1.5 shows the total forest area of West Bengal from 1951 to 2015, in which it is observed that the total forest area has been decreased with time. The rate of deforestation was maximum between 1951 and 1971, then it remains almost same. The scenario is not the indication of sustainability, rather the manifestation of threat to environmental sustainability.

1

Available in https://sdgs.un.org/goals/goal15 (Accessed on 01-06-2023)

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Environmental Sustainability: Status, Scope and Challenges in West Bengal

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Forests wasteland 14% Not available for land culvaon Permanent pastures and other grazing lands

21%

0%

Land under misc. tree crops and groves

1%

Culturable

60% 0% 4%

0%

Fallow land other than current fallows Current fallows

Fig. 1.2 Major land use/land cover types of West Bengal. (Source: Land Use Statistics, Ministry of Agriculture, Govt. of India, 2014–2015)

3% 5% 11%

0%

Very Dense Forest Moderately Dense Forest Open Forest Scrub Non-forest Area

81%

Fig. 1.3 Area coverage of different forest types in West Bengal. (Source: Land Use Statistics, Ministry of Agriculture, Govt. of India, 2014–2015)

3 Air and Environmental Sustainability Atmosphere, especially fresh air, is a precious natural resource in terms of existence of all life forms on the earth (Borrego et al., 2006). Almost every life form requires oxygen to survive, and earth is the only place in the whole universe where all life forms can meet their needs. Air may be considered as sustainable if there is no pollution and no significant direct and indirect health impact on the organism. Ambient air quality has a direct and indirect impact on population health as well as all living things. Polluted air can harm the lungs, eyes and other sensitive organs of animals directly. Air pollution is also terrible for the natural environment through

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Fig. 1.4 Forest cover of West Bengal (2021). (Based on LANDSAT-OLI image (2021))

acid rain, nitrogen oxide deposition in estuaries, toxic substance deposition, etc. Even non-living things, especially human creations such as buildings, cars, bridges or any other metallic structures, can be affected by poor air quality (Graham & Guyer, 1999). Breathing fresh air not only can reduce critical diseases but also heal the environment; therefore, air quality is an important part of environmental sustainability. Sustainable Development Goal-3 (SDG-3.9) emphasizes on substantial reduction of health consequences from dangerous substances, and Goal-11.6 directly aims to reduce air pollution. SDG considered air pollution as a great threat to sustainability as it has adverse impact on human health, particularly in urban areas.

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Total Forest Area (in sq.km.)

12,300 12,250 12,200 12,150

Total Forests Area

12,100 12,050 12,000 11,950 11,900 11,850 1940

1950

1960

1970

1980 Year

1990

2000

2010

2020

Fig. 1.5 Year-wise forest cover area in West Bengal. (Source: IFSR, 2019–2020)

Air quality of any region depends on land use pattern, population density and anthropogenic activities; therefore, air quality differs in different parts of the state. The West Bengal Pollution Control Board (WBPCB) reported that the annual average concentrations of PM10 in the years 2017, 2018 and 2019 are 84.34, 90.29 and 100.17 ug/m3, respectively. It increases 20% within just 3 years, and all the districts have PM10 concentration greater than nationally permissible limit, except Kalimpong in the year 2019. Concentration of PM2.5 also increases from 50.80 to 55.16 ug/m3 between the years 2017 and 2019. Six districts – Nadia, Barddhaman (undivided), Haora, Kolkata, North 24 Parganas and South 24 Parganas – have recorded PM2.5 concentration beyond national limit. The approximate concentrations of PM10 and PM2.5 in Kolkata (KMC area) are 12480.4 MT/year and 4054.2 MT/year, respectively, followed by 10622.4 MT/year and 2785 MT/year, respectively, in Haora (HMC area). The average air quality index (AQI) of five major urban areas of the state has been shown in Fig. 1.6. It shows that the overall condition of air quality in Asansol is most pathetic, where air quality is below standard level throughout the year, except two months. Air quality is slightly better in Haldia compared to other urban area. The poor conditions of ambient air have direct impact on human health as West Bengal recorded the second highest percentage of death rate due to air pollution in the year of 2019 among the states in India.

4 Water Resource and Environmental Sustainability Water is very essential for life and living beings. Water sustainability means efficiency of providing safe, reliable and easily accessible water for drinking as well as domestic, agricultural and industrial purposes. However, water is considered

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A. S. Sahu and N. Das Chatterjee 350 300

Air quality Index

250 Siliguri

200

Durgapur 150 100

Asansol

Standard

Kolkata Haldia

50 0

Fig. 1.6 Air Quality Index of different urban areas in West Bengal. (Source: WBPCB, 2020–2021)

as ubiquitous in resource study, but in recent time, it is very challenging to achieve water sustainability. Sustainable utilisation of water resources is essential for maintaining food security and economic prosperity of a state like West Bengal (Bandyopadhyay et al., 2014). West Bengal occupied 7.5% water resource of India, which is becoming scarce due to the rising population, irrigation and developmental needs. Most parts of the state (excluding the northern hilly region) faced challenges of water scarcity during the lean period, whereas, the irregular rainfall distribution caused both flood and drought in many places of the state. The skewed rainfall pattern causes less infiltration and more runoff, and also the late monsoon cloudburst is responsible for inviting floods and consequent disasters (WBPCB, 2021). Although West Bengal receives abundant rainfall, the demand for freshwater is continuously rising with the increasing population and expansion of agriculture and industries. The southern part of the state lies mostly over the Lower Gangetic Delta (LGD), where the Ganga River alone contributes 525 bcm of water. North Bengal occupies about 25% of the geographical area and has a share of 38% of the state’s water resources. On the other hand, South Bengal has the remaining 62% of the water resources in share, occupying about 75% of the state’s geographical area. The western part of the South Bengal experiences slight to moderate draught condition, particularly in the pre-monsoon season. Per capita water availability is an effective tool to understand the scenario of water availability. Every individual needs at least 1700 m3 freshwater annually to fulfil all needs, and when water availability is below this level, it is considered as ‘water stressed’ condition and it may become ‘water scarcity’ when per capita water availability is less than 1000 m3. Figure 1.7 shows

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Environmental Sustainability: Status, Scope and Challenges in West Bengal

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Fig. 1.7 Water resource of West Bengal (2021). (Based on LANDSAT-OLI image, 2021)

the available water resources of West Bengal in the year of 2021 using Landsat-8 OLI satellite images in Google Earth Engine. Table 1.1 exfoliates presently Maldah, Murshidabad, Birbhum, Purba Barddhaman and Paschim Barddhaman jointly; Nadia, Hugli, Purba Medinipur, Haora, North 24 Parganas and Kolkata have per capita water availability less than 1000 m3. Per capita water availability in Uttar Dinajpur, Dakshin Dinajpur, Bankura, Purulia, Jhargram and Paschim Medinipur jointly, and South 24 Parganas is

12 Table 1.1 Declining per capita water availability in West Bengal (in m3)

A. S. Sahu and N. Das Chatterjee Districts Darjeeling Kalimpong Jalpaiguri Alipurduar Koch Bihar Uttar Dinajpur Dakshin Dinajpur Maldah Murshidabad Birbhum Purba Barddhaman Paschim Barddhaman Nadia Hugli Bankura Puruliya Jhargram Paschim Medinipur Purba Medinipur Haora North 24 Parganas South 24 Parganas Kolkata West Bengal

1951 18,355

1971 10,791

1991 6490

2011 4568

22,881

11,985

7590

14,064 5744 3758 2303 3244 2418

6653 2839 3279 2185 1344 1949 1353

4333 1657 2006 1336 833 1354 876

4824 6362 3337 1045 1472 883 556 988 687

2154 1732 3753 3851 3984

1105 968 2438 2809 2429

640 638 1765 2024 1855

477 504 1377 1537 1408

1003 3409

669 1799

98 4023

80 2388

1315 433 592 1906 57 1554

992 333 431 1335 56 1159

Source: Rudra (2015)

1000–2000 m3. Therefore, proper management and utilization is urgently necessary to maintain environmental sustainability in the context of water availability. In words of the National Water Policy (2012), ‘Planning, development and management of water resources need to be governed by common integrated perspective considering local, regional, state and national context, having an environmentally sound basis, keeping in view the human, social and economic needs’.

5 Land Use/Land Cover and Environmental Sustainability Land use is a broad phrase that refers to human endeavours to maintain or create a source of subsistence through the usage of land cover (Hassan & Nazem, 2016). Land use change mainly occurs due to socio-economic developmental activities by humans. Therefore, human is the driving force of land use pattern, where deforested lands and wet lands are converted into agricultural lands or urban lands. The change in land use pattern is very dynamic in nature and directed towards socio-economic progress (Verburg et al., 2019). Though land use change may boost the economic

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Environmental Sustainability: Status, Scope and Challenges in West Bengal

13

and socio-cultural development of a country, it may become a threat to the society as well as to the environment in the absence of environmental concern (Niemets et al., 2021). Land use change in an unscientific way, involving deforestation, wetland conversion and expansion of urban infrastructure, can interrupt the hydrological cycle, deplete groundwater resource and may lead to environmental hazards and disasters. Uttarakhand, Assam, Kerala and West Bengal are a few examples of Indian states that have witnessed severe disasters like flood, landslide, tropical cyclone, etc. due to land cover alteration. Environmental, economic, technological and institutional attentions are required for the drivers of land use changes everywhere since human intervention and anthropogenic activities can have unintended environmental implications, including depletion of natural resources (Prasad et al., 2020). Therefore, sustainable land use management has become a matter of practice. Sustainable land use simply is the fair distribution of land resource among nature and human competing while not only ensuring social development, economic growth and preservation of nature but also securing the needs of the future generations. The Earth Summit in Rio de Janeiro in 1992 significantly improved thinking on sustainability concerns and produced an action plan (Agenda 2l) for making development more environmentally, socially and economically sustainable. Chapter 10 of that doctrine specifically emphasized the necessity of an integrated approach to the planning and management of land resources. Therefore, sustainable land management (SLM) has become significant and is getting a lot of attention from researchers and decision-makers throughout the world (Syers et al., 1995). The number of scholars working on topics that are essential to comprehending land use and land cover change as a significant driver of environmental change at local, regional and global scale is gradually expanding (Braimoh & Osaki, 2010). The land use/land cover map of West Bengal has been prepared for the year of 2021 using Landsat-8 OLI satellite images in Google Earth Engine (Fig. 1.8). Figure 1.9 shows the percentage distribution of different land use and land cover classes, viz., waterbody, vegetation, agricultural land, wetlands, sand/barren land, built-up area and wasteland. Waterbody has occupied only 6% of the total geographical area. Only 17% of the total area of the state is under forest cover, which is below the country level, that is, 21.71% (IFSR, 2019–2020), and far below the level suggested by the National Forest Policy of India to maintain environmental sustainability. Agricultural land has an area coverage of about 53% to the total geographical area, which indicates human pressure on land resources and which may lead to land degradation. Only 3% area is under wetlands, and the percentage is continuously decreasing, which is causing flood and other environmental issues. Wetlands contain large biodiversities and help to form a healthy environment. However, the present scenario suggests that the home of numerous plants and animals is under human threat. The amount of sand/barren land and wasteland are 10% and 7%, respectively. These 17% of land indicates how human pressure is continuously degrading land resources and has no contribution towards sustainable environment. Only 4% of the area is under built-up area and is continuously increasing at a significant rate. About 64% of the total area (56801.28 km2) has been modified by humans through

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A. S. Sahu and N. Das Chatterjee

Fig. 1.8 Land use/land cover of West Bengal (2021). (Based on LANDSAT-OLI image, 2021)

cultivation, construction of infrastructure and other developmental activities, while only 36% (31950.72 km2) of the area has retained its natural state (Table 1.2). The study reveals that land use and land cover in West Bengal is significantly controlled by anthropogenic impacts driven by socio-economic parameters. Decrease in natural land cover, viz., forest, waterbody, wetlands, etc., indicates unsustainable management of land resources (Xue & Bakshi, 2022). The present

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Environmental Sustainability: Status, Scope and Challenges in West Bengal

7%

15

6%

4% Water body 17%

10%

Forest Agricultural lands

3%

Wetlands Sand/Baren Built-up area Wasteland

53%

Fig. 1.9 Percentage distribution of different land use/land cover classes (2021). (Based on LANDSAT-OLI image, 2021)

Table 1.2 Land cover type

Type Natural Human modified Total

Total area (km2) 31,950.72 56,801.28 88,752

% of area 36 64 100

Based on Fig. 1.9

study may be useful in triggering the sustainable use of land resources, protecting agricultural land from the encroachment of urbanization activities, ensuring that land is used appropriately and restricting activities that might otherwise deplete the vegetation on a given area.

6 Conclusion The selected areas of study, like forest, air, water and land use/land cover, show how environmental sustainability is continuously hampered due to unplanned and unscientific human actions. Vulnerability of all-round environmental conditions implies unhealthy human sustainability. Sustainable environment is essential for human sustainability and all-round socio-economic progress as well. Micro-level studies are therefore necessary in all environmental aspects for fruitful resource management and sustainable human civilization over the earth’s surface.

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References Bandyopadhyay, S., Kar, N. S., Das, S., & Sen, J. (2014). River systems and water resources of West Bengal: A review. Geological Society of India Special Publication, 3(2014), 63–84. Baynes, T. M., & Wiedmann, T. (2012). General approaches for assessing urban environmental sustainability. Current Opinion in Environmental Sustainability, 4(4), 458–464. Bogers, M., Biermann, F., Kalfagianni, A., Kim, R. E., Treep, J., & De Vos, M. G. (2022). The impact of the Sustainable Development Goals on a network of 276 international organizations. Global Environmental Change, 76, 102567. Borrego, C., Martins, H., Tchepel, O., Salmim, L., Monteiro, A., & Miranda, A. I. (2006). How urban structure can affect city sustainability from an air quality perspective. Environmental Modelling & Software, 21(4), 461–467. Braimoh, A. K., & Osaki, M. (2010). Land-use change and environmental sustainability. Sustainability Science, 5(1), 5–7. Census. (2011). Census of India 2011. Government of India. http://censusindia.gov.in/ DigitalLibrary/Archive_home.aspx Giovannoni, E., & Fabietti, G. (2013). What is sustainability? A review of the concept and its applications. In Integrated reporting (pp. 21–40). Springer. https://doi.org/10.1007/978-3-31902168-3_2 Goodland, R. (1995). The concept of environmental sustainability. Annual Review of Ecology and Systematics, 26, 1–24. Graham, B., & Guyer, C. (1999). Environmental sustainability, airport capacity and European air transport liberalization: Irreconcilable goals? Journal of Transport Geography, 7(3), 165–180. Hahn, W. A., & Knoke, T. (2010). Sustainable development and sustainable forestry: Analogies, differences, and the role of flexibility. European Journal of Forest Research, 129(5), 787–801. Håland, W. (1999). On needs – A central concept in the Brundtland report. In Towards sustainable development (pp. 48–69). Palgrave Macmillan. https://doi.org/10.1057/9780230378797_3 Hassan, M. M., & Nazem, M. N. I. (2016). Examination of land use/land cover changes, urban growth dynamics, and environmental sustainability in Chittagong city, Bangladesh. Environment, Development and Sustainability, 18(3), 697–716. IFSR [India State of Forest Report]. (2019–2020). Forest Survey of India. Ministry of Environment and Forests, Government of India. Kwatra, S., Kumar, A., Sharma, P., Sharma, S., & Singhal, S. (2016). Benchmarking sustainability using indicators: An Indian case study. Ecological Indicators, 61(2), 928–940. https://doi.org/ 10.1016/j.ecolind.2015.10.049 Ministry of Agriculture, GOI, Land Use Statistics. (2014–2015). https://eands.dacnet.nic.in/PDF/ Agricultural_Statistics_At_Glance-2015.pdf Niemets, K., Kravchenko, K., Kandyba, Y., Kobylin, P., & Morar, C. (2021). World cities in terms of the sustainable development concept. Geography and Sustainability, 2(4), 304–311. https:// doi.org/10.1016/j.geosus.2021.12.003 Pachauri, R. K., & Mehrotra, P. (2020). Vision 2020: Sustainability of India’s material resources. NITI Aayog. Government of India. http://164.100.161.239/reports/genrep/bkpap2020/13_ bg2020.pdf Prasad, S., Singh, A., Korres, N. E., Rathore, D., Sevda, S., & Pant, D. (2020). Sustainable utilization of crop residues for energy generation: A life cycle assessment (LCA) perspective. Bioresource Technology, 303, 122964. https://doi.org/10.1016/j.biortech.2020.122964 Rudra, K. (2015). PaschimbangerJalsampad/SankaterUtsasandhane (in Begali). SahiytaSamsad. Shajahan, P. K., & Sharma, P. (2018). Environmental justice: A call for action for social workers. International Social Work, 61(4), 476–480. Syers, J. K., Pushparajah, E., & Hamblin, A. (1995). Indicators and thresholds for the evaluation of sustainable land management. Canadian Journal of Soil Science, 75(4), 423–428. Verburg, P. H., Alexander, P., Evans, T., Magliocca, N. R., Malek, Z., Rounsevell, M. D., & van Vliet, J. (2019). Beyond land cover change: Towards a new generation of land use models.

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Current Opinion in Environmental Sustainability, 38, 77–85. https://doi.org/10.1016/j.cosust. 2019.05.002 Vuuren, D. P., Zimm, C., Busch, S., Kriegler, E., Leininger, J., Messner, D., et al. (2022). Defining a sustainable development target space for 2030 and 2050. One Earth, 5(2), 142–156. https:// doi.org/10.1016/j.oneear.2022.01.003 WBPCB [West Bengal Pollution Control Board]. (2020–2021). Annual report. Government of West Bengal. https://www.wbpcb.gov.in/files/Th-12-2022-12-52-08Annual%20Report% 202020-21.pdf WBPCB [West Bengal Pollution Control Board]. (2021). State of environment report-II West Bengal 2021. Department of Environment Government of West Bengal. https://www.wbpcb. gov.in/files/Fr-09-2021-09-10-01SoE Report VOL 02.pdf WCED [World Commission on Environment and Development]. (1987). Our common future. Oxford University Press. Xue, Y., & Bakshi, B. R. (2022). Metrics for a nature-positive world: A multiscale approach for absolute environmental sustainability assessment. Science of the Total Environment, 846, 157373. https://doi.org/10.1016/j.scitotenv.2022.157373

Part II

Environmental Issues and Human Sustainability

Chapter 2

Forest Dependency and Rural Livelihood: Strategical Survival of People in Himalayan Foothills of Bengal Duars Region Koyel Sam and Namita Chakma

Abstract Being located in the Eastern Himalayan foothill region, Bengal Duars (also named Dooars) is identified as a unique landscape enriched with forest resources and divergent indigenous culture. This region has gone through assorted changes in its natural and socio-economic milieu. With the changes in forest scape, ways of survival of villagers also modified since pre-colonial to after the Independence period. The objective of this study is not only to address all such struggles faced by the forest villagers for their survival in such a landscape but also to explore the influence of socio-economic variables on forest dependency by using the logistic regression model. The study has found that less than 20% of income is coming from non-forest-based activities and they are more involved in agriculture, agro-forestry, livestock rearing, etc. Besides that, the attack of wild animals and the erratic nature of climate deviates people to involve in non-farm sectors to earn their livelihood. The results indicate that age, household size, agricultural dependency, livestock population, and economic status play a significant role in forest dependency. Moreover, less dependency on forests may degrade the sense of protection and their right at the community level that can further cause deforestation. Keywords Eastern Himalaya · Bengal Duars · Forest dependency · Livelihoods · Logistic regression

K. Sam (✉) Department of Geography, Dr. B.N.D.S. Mahavidyalaya, Purba Bardhaman, West Bengal, India e-mail: [email protected] N. Chakma Department of Geography, The University of Burdwan, Bardhaman, West Bengal, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_2

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1 Introduction “The struggle for existence” of people is an individual’s struggle for resources that require living in a society. Landscape plays a vibrant role to provide a medium (like a car), where humans are the regulator (like a rider) to utilize resources (like fuel) according to their way to survive. Human ecology is an evolutionary study to make interrelation between people and their environment according to time and space. This domain considers adaptation as the continuous process of choosing and refining strategies to reproduce the way of life in a changing world (Knapp, 2007). A livelihood is a key in rural development thinking and practices. Livelihoods of people construct a bridge between people and their dependence on the environmental resources. Diversification of livelihood is a process to construct a diverse portfolio of economic and social support in humans’ struggle to improve their standards of living (Ellis, 1997). Sometimes, it may consider a coping strategy for reducing risk and associated vulnerability (Dercon, 2002; Start & Johnson, 2004). Several scholars have grouped the diversification of rural livelihood according to the sector (farm or non-farm), by function (wage-based or self) and location (on-farm or off-farm) (Loison & Loison, 2016; Saha & Bahal, 2012). The present study is an attempt to explore the dependency and diversification of livelihoods based on forest resources. The villagers living in the forested landscape of Bengal Duars are struggling for a long from pre-colonial to the present era to survive in this landscape. Their struggle is oriented by following the transformation of the natural landscape to a humaninduced landscape, started through the introduction of tea gardens in 1852–53 by the colonial planters. Since the life and livelihoods of forest villagers are molded according to British rules and regulations (Karlsson, 2013). Thereafter, the JFM (Joint Forest Management) was introduced in 1991 to empower the forest villagers. Hence, after nearly a decade only 18.75% of those committees were found to be benefited (Ghosh, 2000). Along with JFM, the panchayat system was introduced in 1999 but panchayats are not allowed to take part in any land-based activities because of the absence of land rights of the forest dwellers, as a result, they failed to uplift the livelihood status of the communities (Jha, 2010). This movement has got momentum after the notification of the Forest Rights Act (2006), the struggle began to continue against the biased implementation of this act. The formation of FRC (Forest Rights Committee) at the “multi village Gram Samsad level instead of single village Gram Sabha level” resulted in the manipulation to implement the overall process with the collaboration of corrupted panchayat members. Hence, to sustain in this landscape, they still tried hard to get their rights on land, livelihoods, and resources (Jha, 2010). The present study intends to explore the forested landscape of Bengal Duars specially, people’s struggle and survival which has not been systematically ventured by the earlier researchers working in this region. Therefore, the objective of the study itself can be considered the research gap in this context.

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Forest Dependency and Rural Livelihood: Strategical Survival of People. . .

23

2 The Study Area The narrow strip of land, with a breadth of 20–30 miles and about 180 miles in length, assemblage with forest cover stretching between the river Sankosh in the east and the river Tista in the west, and Cooch Behar on the south, forms the northern boundary of West Bengal becomes known as Bengal Duars or Western Duars and the other part in Assam District is well known as Assam Duars or Eastern Duars (Gruning, 1911). Thus, river Sankosh acts like a divider of today’s Bengal and Assam Duars and it is well noted through the distribution of rivers. In 2014, the Jalpaiguri district was divided and the Alipurduar sub-division was evolved as the new 20th district of the State and named the Alipurduar district. The Kalimpong district was formed in 2017 after dividing the Darjiling district, earlier it was a sub-division of the Darjiling district. Presently, from the east of the river Tista to the river Sankosh sharing the boundary of Bhutan in the north, assemblage with a rich forest cover, district Kalimpong, Jalpaiguri, Alipurduar and a small portion of Koch Bihar is popularly known as the Bengal Duars (or Dooars) region of West Bengal (Fig. 2.1). The climate of the Bengal Duars region is interesting in nature. The region received the highest amount of rainfall in West Bengal, specifically during the monsoon season experiencing around 1800 mm rainfall. The mean annual rainfall varies from 2000 to 3500 mm. The temperature (maximum and minimum) can vary

Fig. 2.1 Spatial identity of Bengal Duars region from river Tista to Sankosh in the twenty-first century

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K. Sam and N. Chakma

for different meteorological stations. Generally, March to June are the hottest months all over the region and the maximum temperature ranges between 22 and 35 °C. Minimum temperature observed during December and January varies from 2 to 15 °C. This region is a hub of indigenous culture and traditions with Rabha, Mech, Toto, Rajbanshi and Drucpa communities. According to the census of India (2011), the status of sex ratio and literacy is good in Kalimpong and Alipurduar district but in Koch Bihar, it’s not so good around 35% people are illiterate and the concentration of ST population is more in Kalimpong and Alipurduar districts. The Bengal Duars region is also well recognised for its numerous and valuable forest resources. This area has experienced a long-term historical transformation from the pre-British to the British and Independence and after periods. This region is well acknowledged for the habitats of indigenous communities and their culture. Through the introduction of the taungaya system, forest villages were established (1905 onwards) inside the reserved forest and villagers started to work as a permanent labour force. The recent phase of transformation carried out beyond shifting cultivation through illegal felling, encroachment, mining and quarrying activities which further are enhancing flood vulnerability and decreases vegetation stabilized gravel bars. Riverside bolder mining and deforestation are causing the shifting of river courses from straight to meandering and braided patterns (Prokop & Sarkar, 2012). Frequently reported illegal timber extraction and poaching of animals in local media and press reflect relatively poor enforcement and protection level that raises a threat to the biodiversity hotspot. (Sam & Chakma, 2018a). Scientific reports are already stated that climate change is going to be a serious threat to the planet earth in the near future (IPCC, 2013; USGCRP, 2017). This region has experienced an increasing trend of anomalies in average temperature around 1.5–2% from 1990s onwards and extreme events become 5% higher since the 1970s onwards (Sam & Chakma, 2019). The sudden change of slope in the Himalayan foothills acts as a responsible factor of frequent flooding and damage to forested landscapes. Sam and Chakma in 2018b, address village-level vulnerability to climate change in this region. The study reveals that 61% of the area is highly vulnerable due to the change of climate, degradation of forest resources and socio-economic backwardness. By continuing this research, forest villages which are located in the highly vulnerable area have been chosen for in-depth analysis of human-environment interaction and its associated consequences.

3 Materials and Methods 3.1

Data Collection

In this study, after analysing the level of vulnerability (Sam & Chakma, 2018b) six forest villages namely, Dumchi Rava Basti, Uttar Khairbari Basti, Kodal Basti, Mendabari, 28th Mile Basti and New Land Basti were selected using purposive sampling method covering 196 households (n = 196). All the selected villages are

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found to be as highly vulnerable in the study area (Sam & Chakma, 2018b). A semistructured questionnaire was prepared for this. The questionnaire was framed by two main sections, Section 2.1 contained socio-economic details of households (Table 2.1). Respondents were asked further about the accessibility of local institutional facilities and their perception of surrounding forested landscape. Section 2.2 included responses related to forest dependency and livelihoods. In this section, seasonality and dependency on forest resources were noted by considering the first, second and third generations of family members (Table 2.2). It is hypothesized that young people are willing to get more opportunities outside the forest villages to get a job in comparison to age-old people. Consequently, their dependency on forest resources is relatively less. Moreover, the forest department does not encourage commercial income to sell forest products. The crop diversity is inversely proportional to forest dependency. Here, the economic status of households is identified based on the poverty level (APL or BPL).

3.2

Data Analysis

To get a general diversity of livelihoods, the share of household income from different sources was computed. Income from forest-based sources like the collection of fuelwood and forage was computed by multiplying the market price by quantity. However, income from non-forest-based sectors was calculated categorically. Data on the type of crops and their market prices were also categorically recorded (Table 2.2). The income of daily wage labour was computed by multiplying a number of days worked at a specific rate. To understand the income from livestock, data was collected by knowing the number of different types with the market prices. Other sources included local businesses and salaried jobs. In this study, binary logistic regression was used to analyse the forest dependency of people. Several researchers highlight the significance of logistic regression to deal with dependency (Lepetu et al., 2009; Tieguhong & Nkamgnia, 2012; Datta & Sarkar, 2012; Jain & Sajjad, 2015). Villagers are less dependent on forest resources (near 20%), only for fuelwood and forage, and on this basis, threshold cut-off level (20%) of dependency has been defined (Fig. 2.3b). The income of household lying equal and below 5% from forest-based sources is denoted as ‘0’and if household dependency is greater than 5%, the value is ‘1’ otherwise. The logistic regression model is useful to predict the probability of a dichotomous outcome (high and low dependency) based on explanatory variables. The model used to estimate forest dependency is: log

pi = β0 þ β1 x1i þ β2 x2i þ β3 x3i þ β4 x4i . . . . . . . . . . . . þ :βn xni 1 - pi

0.00 0.52 0.98 0.84 1.21

0.82

0.52 0.63 0.41

4.00

1.33

1.83 1.50 1.33

0.33

1.33

2.00 1.83

2.25 1.25

1.25

1.50

3.25 1.75 2.00

1.75

3.25

0.96 0.50

0.50

1.29

0.96 0.50 0.82

0.50

1.50

Uttar Khairbari Basti Mean SD 5.00 0.82 40.25 38.42 1.75 0.50

1.71 1.50

1.25

1.75

2.00 2.38 1.63

1.38

1.38

0.76 0.53

0.46

1.16

0.58 1.77 0.92

0.52

1.06

Kodal Basti Mean SD 4.13 1.25 38.50 30.95 2.00 0.00

1.79 0.87

1.20

1.64

3.15 2.60 2.74

1.20

1.31

0.76 0.53

0.40

1.14

1.28 1.54 1.87

0.41

0.94

Mendabari Mean SD 4.42 1.35 44.00 38.10 2.43 0.66

1.55 0.68

1.20

1.32

2.70 2.20 1.80

1.10

1.50

0.69 0.58

0.41

1.06

1.26 1.20 0.89

0.31

0.89

28th Mile Basti Mean SD 4.90 1.80 35.25 32.06 2.95 0.94

SD Standard Deviation, F ‘F’ Test Value Sig.: *Significant at 5% level ( p < 0.05), **Significant at 1% level ( p < 0.01), NS denotes no significant difference

Variables 1. Household size 2. Age of respondent 3. Caste/category (S.C = 1, S.T = 2, O.B.C = 3, general = 4) 4. Religion (Hindu = 1, Islam = 2, Buddhist = 3, Christian = 4) 5. Year of resident Before 1947 = 1, 1948–1990 = 2, After 1990 = 3 6. Number of male 7. Number of female 8. Education level of male: Primary = 1, secondary = 2, higher secondary = 3, graduation = 4 9. Education level of female: Primary = 1, secondary = 2, higher secondary = 3, graduation = 4 10. Economic status: BPL = 1, APL = 2 11. Livelihood options for male 12. Livelihood options for female

Dumchi Rava Basti Mean SD 4.33 1.51 31.00 27.75 2.00 0.00

Forest villages

Table 2.1 Demographic and socio-economic characteristics of sample forest villages (n = 196)

1.80 0.60

1.40

1.50

3.05 2.90 1.85

1.25

1.20

0.70 0.50

0.50

0.83

1.36 1.25 0.49

0.44

0.62

New Land Basti Mean SD 6.00 1.92 41.20 30.75 1.85 0.88

0.89 7.73

0.59

1.44

1.72 1.54 0.77

4.06

13.09

F 3.17 2.19 5.52

NS 0.000**

NS

NS

NS NS NS

0.002**

0.000**

Sig. 0.011* NS 0.000**

26 K. Sam and N. Chakma

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Table 2.2 Explanatory variables and their expected signs in the forest dependency model Variable Forest dependency Age Household size Education Crop diversity Livestock population Economic status

Explanation Dependent variable: Forest dependency 5% = 1 Age of the respondents in year Above 65 (1st gen)= 3, 25–65 (2nd gen) = 2, 15–25 (3rd gen) = 1 Number of family member

Expected sign Not assigned (+) (+)

Illiterate = 0, primary level = 1, secondary level = 2, higher secondary level = 3, graduate = 4 Number of crops practiced in a year Number of pigs, cows, goats etc.

(+/-)

BPL holder = 1, APL holder = 2

(+)

(-) (-)

where, p = probability of the outcome i = ith observation in the sample, β = intercept term, β0, β1, β2. . ...βn = coefficients associated with each explanatory variable x1, x2. . .. . .xn In this study, five explanatory socio-economic variables have been considered like age, education, crop diversity, livestock population, and economic status to establish their relationship (positive, negative, and both) with forest dependency (Table 2.2) The goodnessss of fit of those explanatory variables has been detected by Hosmer and Lemeshow test with chi-square. The perfectness of association between dependent and independent variables is judged by Cox and Snell R Square values. Households with more family members required more fuelwood for subsistence and positively associated with forest dependency and labour force (Gunatilake, 1998). Education intends to open diversified employment opportunities and educated people are less likely to encourage to collect forest products and subsistence agriculture (Adhikari et al., 2004; Hedge & Enters, 2000). The levels of education in sample villages are predominately restricted to primary education. However, landholding capacity is restricted to 3–5 ha, therefore, to find out agricultural dependency crop diversity has been chosen as an indicator. If a family is practised more crops throughout the year, it means more involvement in agriculture as a livelihood. The households are mostly livestock herders and an average number of livestock is 15. As pasture land, fodders and fuelwood are available freely, it encourages rearing more cows, goats, hens, etc. thus, it is positively associated with forest dependency. The economic status of people avails to access better opportunities and ways of living.

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4 Results 4.1

Profile of Households

The sample survey covers three generations of each family member with age ranges between 15 and 83 years. Villages have consisted of mixed populations accompanied by diversified categories and religious beliefs. The majority of the villagers settled from 1948 to 1990. Different generations have different perspectives to lead life and livelihood. People belonging to the first generation had not attempted school but in the third generation, they tend to avail higher education. The average family size consisted of five members and lived in mud and wooden houses. They are depended on the forest for fuelwood consumption (Fig. 2.2). They mostly engaged to practice cash crops, rearing of livestock and worked as daily wage labour. The majority of the households are BPL holders and underprivileged (Table 2.1). All villages are remotely placed without good transport and communication facilities. To get a regular mode of transport and better market facilities, villagers need to travel around 5–15 km or more than that. The situation became worsened during the monsoon period when most of the villages began to isolate from the nearer towns and have to survive like an island.

4.2

Forest Dependency

The analysis of forest dependency is shown in Fig. 2.3, where villagers are less dependent on forest-based sources (less than 20% share of income). Forest villagers in Mendabari and 28th mile Basti are comparatively less dependent on forest products than others that are attributed to more involvement in non-forest-based sources of income like agriculture, cash crops, livestock, etc. They are only dependent on forest resources because of fuelwood and cattle rearing. To assess the role of

Fig. 2.2 (a) Associate environment of a forest village; (b) Dependency on forest product for fuel purpose

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Fig. 2.3 (a) Villager’s source of income from different sectors (b) Share of dependency on forest and non-forest-based sectors. (Sam & Chakma, 2021a)

Table 2.3 Descriptive statistics and regression result of forest dependency model Variable Age Household size Education Crop diversity Livestock population Economic status Constant Cox & Snell R Square

Mean SD 1.39 1.38 31.13 14.73 5.21 8.73 3.03 3.31 1.40 0.49 1.03 1.96 15.2 12.16 0.670

B 0.246 0.569 0.204 -1.704 0.862 -2.865 -7.860

SE 0.115 0.282 0.194 0.815 0.435 1.282 5.553

Wald 4.569 4.058 1.111 4.377 3.928 4.995 2.003

df 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Sig. 0.034* 0.041* 0.292 0.037* 0.012* 0.023* 0.157

Exp(B) 1.279 1.767 1.227 0.182 2.368 0.057 0.000

SD Standard Deviation, SE Standard Error, df Degree of Freedom Sig.: *Significant at 0.05 level ( p < 0.05)

socio-economic determinants on the forest dependency, a logistic regression model has been used and the results are given in Table 2.3. The result of the Hosmer and Lemeshow test with chi-square value 4.70 implies that the regression model is good to fit to indicate a significant relationship between explanatory variables and forest dependency. The logit model predicted 67% (R2 = 0.670) of accuracy that indicates the proportion of dependent variables involved to predict explanatory variables. In this model, many explanatory variables have a significant effect on forest dependency. Age and household size are found to be positively correlated and significant at the 0.05 level. This is because an age-old group of family members specially belongs to the first and second generations, who are more actively involved in fodder and fuelwood collection. However, more family members are needed to collect a large quantity of fuelwood for cooking and other purposes. People who have more livelihood options as agriculture and cash crop, obviously have their economic status quite good. That’s why these variables are significant and negatively associated with forest dependency (Table 2.3) whereas, Gunatilake, 1998; Adam and Tayed 2014 were also found to have a negative impact of

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agricultural income on forest dependency. Villagers are also found to be involved in livestock rearing throughout the year. A large number of cattle are putting huge pressure on forest resources and grazing became a cause of conflict between villagers and the forest department. Jain and Sajjad in 2015 detected a similar relationship between cattle population and forest dependency in their study.

4.3

Livelihood Complex and Crisis

The State government has implemented MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act), a central government scheme, by providing its residents job cards and ensures the villagers to get jobs for 100 days a year under this scheme. But villagers seldom get work for 10–15 days/year on Rs. 200/day basis. As a result of dissatisfaction, villagers have to work as a daily labour in the surrounding villages and towns. Youths are migrated to Bhutan and other states like Kerala, Himachal Pradesh, and Gujarat in search of jobs. Most of the migrants are less educated (mostly educated up to class 4) and unskilled. According to a respondent: If plantation activity will take place regularly, then we don’t need to migrate to other places for searching jobs.

Villagers are mainly depended on rainwater for agriculture and crops (rice, jute, maize, and mustard) and vegetables are mostly cultivated during monsoon season (Fig. 2.4). As this region receives intensive rainfall during monsoon onwards, sometimes flood causes serious damage to the crops. The average livestock size of a household is around 5–10 and they are mainly rearing cows, pigs, and hens.

Fig. 2.4 Seasonal variability of livelihood activity practised by forest villagers. (Sam & Chakma, 2021a)

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Fig. 2.5 Generation-wise changing characteristics of livelihood. (Sam & Chakma, 2021a)

Villagers also sell their animals to generate income from a cow they earn Rs. 4000–10,000, from hen Rs. 150–200 and from pig Rs. 6000–8000 annually. During the survey, it was noticed that at Uttar Khairbari and Dumchi Rava Basti, people cultivated kochu (a root vegetable: ‘Colocasia esculenta’) in forest ground under the shade of trees. The other source of livelihood in this region is cash crop (mainly betel nut) farming by which they can earn an average of Rs. 10,000–12,000 per 100 trees in a year. When asked a villager about the reason behind the growing popularity of betel nut plantation, a respondent stated that: If I plant timber-based trees like Sal, Jarul, and Teak, when trees become matured I have to take permission from the forest department to sell the timber. But if I plant betel nut at once, I can earn money year after year by selling its fruits, without taking permission from the forest department.

The most prominent problem recently faced by the villagers is being attacked by wild animals. During the period of harvesting, wild animals are coming from the jungle (especially elephants). They eat paddy and also destroy growing crops and other household properties. In the recent past, human-animal conflict, lack of irrigation facilities and climatic variability reduce dependency on agriculture in the forest villages. Therefore, poor availability of local work awakens people to migrate and depend on non-farm sectors. Now, the young generation wishes to become more educated to get a better job as well as to increase livelihood diversity for their wellbeing. Some of the households started a business like small shops and also are doing government jobs (Fig. 2.5).

5 Discussion This region always came into focus to us because of human encroachment, illegal felling and poaching activities (Bhattacharyya & Padhy, 2013; Prokop & Sarkar, 2012; The Telegraph, 2006, 2018). In between 1991 and 2010 the rate of

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deforestation is more than afforestation, the rate of deforestation is around 26% and the maximum transformation of deforestation has noticed in the Alipurduar district. (Sam & Chakma, 2021b). The present study is not only highlighting the dependency of forest dwellers on forest resources by using quantitative methods but also their daily struggles and crisis of life and livelihood are elaborated through narratives. Previously, a perspective of rural livelihood and dependency on forest resources was analysed through quantitative methods (Datta & Sarkar, 2012; Adam & Tayeb, 2014; Bauri et al., 2015). Sometimes, it is difficult to quantify people’s perceptions, attitude, and emotions, and from that point, the importance of qualitative methods are realised. The understanding of qualitative discourse that happened within the landscape is vital to know the processes that are going on. In landscape ecology, the human realm is more dynamic than the natural realm. Here, the dependency on forest resources has been guided by certain parameters. But why their dependency has shifted from forest-based to non-forest-based can be expressed through the perceptional study. This study also reflects the fact that limited access and benefit from the forest create negative attitudes towards conservation further manufactured livelihood crisis. Therefore, any conservational programme with the involvement of forest villagers must consider improving the socio-economic upliftment of villagers.

6 Conclusion In concluding remarks, it may be stated that the villagers are facing a hurdle to survive in their lands. The use of forest resources by forest villagers is also getting restricted day by day, and the diversified option of livelihoods helps further in reducing the dependency on the forest. As a result, young generations are searching for other livelihood options and migrated to the nearby States and countries for their survival as well as to improve their economic status. However, those people who are willing to stay there, are facing tremendous struggles to protect their life and property from animal attacks and destructive impacts of the flood. Thus, it is very important to regenerate this forested landscape. The following pathways can be taken as: (a) Development of a strong local committee (as an Eco-development committee and Forest Protection committee), comprising members from each generation is required. Where every generation can share their problems and prospects, which will help to make community bonding as well. (b) As women are mostly engaged in collecting fuelwood from the forest they are more close to nature. Therefore, only women villagers should allow to enter into the forest to restrict illegal filling of a forest. (c) Skilled men can be involved as forest guards comprising three or four people, with periodical circulation in several forest ranges. (d) As old-age people have more knowledge about the tree species and the characteristics of such landscapes they can guide the next generation as well as forest

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department about the selection of tree species suitable for plantation in the forests. (e) Other than forestry, they also depend on agriculture and agro-forestry. As this region receive the heaviest rainfall during the monsoon period, to make climatesmart agriculture, rainwater harvesting is essential. The villager also has an advantage to store and use organic manure (because of livestock rearing) that can prosper with sustainable agricultural practices. (f) Due to the fragmentation of habitat of a wild animal, human-animal conflict increases. Therefore, habitat restoration and the use of boundary protection around the forest village are required. (g) Further, the promotion of vocational and tactical training to educate people about forest resources and their management can create more job opportunities and research prospects.

References Adam, Y. O., & Tayeb EL, A. M. (2014). Forest dependency and its effect on conservation in Sudan: A case of sarf-saaid reserved forest in gadarif state. Agriculture & Forestry, 60(3), 107–121. Adhikari, B., Falco, S. D., & Lovett, J. C. (2004). Household characteristics and forest dependency: Evidence from common property forest management in Nepal. Ecological Economics, 48, 245–257. https://doi.org/10.1016/j.ecolecon.2003.08.008 Bauri, T., Palit, D., & Mukherjee, A. (2015). Livelihood dependency of rural people utilizing non-timber forest product (NEFTS) in a moist deciduous forest zone, West Bengal, India. International Journal of Advanced Research, 3(4), 1030–1040. Bhattacharyya, M. K., & Padhy, P. K. (2013). Forest and wildlife scenarios of Northern West Bengal, India: A review. International Research Journal of Biological Sciences, 2(7), 70–79. Census of India. (2011). https://censusindia.gov.in. Accessed on 05th July 2015. Datta, S. K., & Sarkar, K. (2012). NTFPs and their commercialization issues from the perspective of rural livelihood and the state of Forest resources: A study of the Ranibundh Forest Range in West Bengal, India. Journal of Sustainable Forestry, 31(7), 640–660. https://doi.org/10.1080/ 10549811.2012.678097 Dercon, S. (2002). Income risk, coping strategies, and safety nets. The World Bank Research Observer, 17, 141–166. Ellis, F. (1997). Household strategies and rural livelihood diversification. Journal of Development Studies, 1–38. Ghosh, S. (2000). A study of JFM in North Bengal. Unpublished report. NESPON. Gruning, J. F. (1911). Jalpaiguri, Eastern Bengal and Assam District Gazetteers. WBDG, Govt. of West Bengal. Gunatilake, H. M. (1998). The role of rural development in protecting tropical rainforests: Evidence from Sri Lanka. Journal of Environmental Management, 53(3), 273–292. https://doi.org/10. 1006/jema.1998.0201 Hedge, R., & Enters, T. (2000). Forest products and household economy: A case study from Mudumalai Wildlife Sanctuary, southern India. Environmental Conservation, 27, 250–259. https://doi.org/10.1017/S037689290000028X IPCC. (2013) Climate change: The physical science basis. Contribution of Working Group I to the fifth assessment report of the intergovernmental panel on climate change (T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley, eds.) (1535 pp.). Cambridge University Press.

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Jain, P., & Sajjad, H. (2015). Household dependency on forest resources in the Sariska Tiger Reserve (STR), India: Implications for management. Journal of Sustainable Forestry. https:// doi.org/10.1080/10549811.2015.1099108 Jha, S. (2010). The struggle for democratizing forests: The forest rights movement in North Bengal, India. Social Movement Studies, 9(4), 469–474. Karlsson, B. G. (2013). Contested belonging; an indigenous people’s struggle for forest and identity in sub-Himalayan Bengal. Routledge. Knapp, G. (2007). Encyclopedia of environment and society, volume 3, sage publications (pp. 880–884). Editors. Lepetu, J., Alavalapati, J., & Nair, P. K. (2009). Forest dependency and its implication for protected areas management: A case study from Kasane Forest Reserve, Botswana. International Journal of Environmental Research, 3(4), 525–536. Loison, S. A., & Loison, S. A. (2016). Rural livelihood diversification in sub-Saharan Africa: A literature review. Journal of Development Studies, 51, 1125–1138. https://doi.org/10.1080/ 00220388.2015.1046445 Prokop, P., & Sarkar, S. (2012). Natural and human impact on land use change of the SikkimeseBhutanese Himalayan piedmont, India. Quaestiones Geographicae Bogucki WydawnictwoNaukowe, Pozna’n, 31(3), 63–75. https://doi.org/10.2478/v10117-012-0010-z, ISSN0137-477X Saha, B., & Bahal, R. (2012). Constraints impeding livelihood diversification of farmers in West Bengal. The Indian Research Journal of Extension Education, 12, 59–63. Sam, K, & Chakma, N. (2018a). Discourse on forested landscape of Bengal Duars, Eastern India. Discourse on Human Nature interaction in Eastern India, Rhito Prakashan. ISBN: 978-81938090-8-2. Sam, K., & Chakma, N. (2018b). Vulnerability profiles of forested landscape to climate change in Bengal Duars region, India. Environmental Earth Sciences, 77(459). https://doi.org/10.1007/ s12665-018-7649-2 Sam, K., & Chakma, N. (2019). An exposition into the changing climate of Bengal Duars through the analysis of more than 100 years’ trend and climatic oscillations. Journal of Earth System Science, 128, 67. https://doi.org/10.1007/s12040-019-1107-8 Sam, K., & Chakma, N. (2021a). Climate change in the forest of Bengal Duars response of life and livelihoods. Springer Briefs in Environmental Science. https://doi.org/10.1007/978-3-03073866-2 Sam, K., & Chakma, N. (2021b). Transformation of forested landscape in Bengal Duars: A geospatial approach. In P. K. Shit, H. R. Pourghasemi, P. Das, & G. S. Bhunia (Eds.), Spatial modeling in Forest resources management. Environmental science and engineering. Springer. https://doi.org/10.1007/978-3-030-56542-8_23 Start, D., & Johnson, C. (2004). Livelihood options? The political economy of access, opportunity and diversification. Overseas Development Institute. The Telegraph. (2006) Two courts to save forest. West Bengal, February 15. The Telegraph. (2018) More cameras for forest. West Bengal, February 16. Tieguhong, J. C., & Nkamgnia, E. (2012). Household dependence on forests around Lobeke National Park, Cameroon. International Forestry Review, 14(2), 196–212. https://doi.org/10. 1505/146554812800923426 USGCRP. (2017). Climate science special report: Fourth national climate assessment, volume I (D. J. Wuebbles, D. W. Fahey, K. A. Hibbard, D. J. Dokken, B. C. Stewart, & T. K. Maycock, eds.) (470 pp.). U.S. Global Change Research Program. https://doi.org/10.7930/J0J964J6

Chapter 3

Identification of Potential Anthropogenic Barriers to Fluvial Connectivity in the Lower Gangetic Basin of India Suvendu Roy

Abstract The intensifying need of human beings and the corresponding development of infrastructure are increasing the degree of interaction between river systems and anthropogenic constructions like dams, barrages, embankments, roads, railway lines, river crossings, and altering land cover. Therefore, it is essential to identify as well as quantify potential area and degree of interaction over the fluvial landscape, respectively. Freely available geospatial data have been used in the present study to identify such interaction across the Lower Gangetic Basin (LGB). The present study finds that the longitudinal connectivity of rivers of LGB has been disturbed by 96 major dams with their water holding capacity of ~1064 million cubic meters, 21 barrages, and 3548 road-stream crossings. The lateral connectivity is also affected by the alignment of ~28% length of the total transport network of LGB within the active and old floodplains only, which are significantly disconnected about 30% floodplain from its main channel. The proximity analysis shows about 40% area of LGB falls under the proximal distance of 1000 m only. Intensive agricultural practice and construction of built-up areas within the active floodplain are also major causes of fluvial disconnectivity across the LGB. Keywords Connectivity · Dam · Transportation Infrastructure · Lower Gangetic Basin · Floodplain

1 Introduction Connectivity is an inherent part of the geomorphological forms and processes and helps to integrate the patches of a landscape by continuous interchange of matter (water, sediments, nutrients, and organisms) and energy for functioning as a unit (Wohl et al., 2019). The patches of the landscape are varying with spatial and temporal scale of consideration, for example, it may concern about the degree of

S. Roy (✉) Department of Geography, Khalisani Mahavidyalaya, Chandannagar, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_3

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Fig. 3.1 Associations between types, dimensions, and components of geomorphic connectivity

connectivity between two subsequent pools or riffles within a channel to promote river biota (Bouska et al., 2010) or may concern about the level of coupling between a channel and a hillslope to understand the sediment and water input in a river system (Bracken et al., 2015; Hooke & Souza, 2021). Although the term connectivity in geomorphology explicitly deals with the capacity of material transfer among the geomorphological system components, it has been distinguished into different names based on its type, components, and dimensions (Ward, 1989; Turnbull et al., 2008; Wohl, 2017) (Fig. 3.1). The types of geomorphic connectivity are (a) landscape connectivity, which is defined by the physical linkages among the patches of landscape to transfer matter and energy; (b) hydrological connectivity, which refers to the water-mediated transfer of matter and energy among the components, and (c) sediment connectivity, which is particularly dealing with the fluxes of sediments from the source to sink within a fluvial system. Based on the components, such connectivity could be grouped into (a) structural connectivity i.e., landscape connectivity and (b) functional connectivity i.e., hydrological and sediment connectivity. Finally, geomorphic connectivity also works in four different dimensions like lateral, longitudinal, vertical, and temporal (Stanford & Ward, 1988; Ward, 1989; Junk et al., 1989). In riverscapes or river corridors, the lateral connectivity works through the exchange of sediment, nutrients, organisms, and water between the main channel and its adjacent floodplains (Wohl et al., 2019).Correspondingly, longitudinal connectivity helps to maintain the alteration of channel’s physical, chemical, and biological parameters from its upstream to downstream, as proposed by Vannote

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Fig. 3.2 Catchment level spatial variation in three-dimensional connectivity (lateral, longitudinal, and vertical); the width of the arrow indicates the magnitude of connectivity. (Source: Adopted from Boulton et al., 2017)

et al. (1980) in the River Continuum Concept (RCC). The vertical connectivity refers to the exchange of hydrological and biological components between channel surface and sub-surface (Hancock, 2002; Brierley et al., 2006), also applicable for surface and atmospheric exchange of water through evapotranspiration. Temporal dimension is also considered for changing the magnitude and pattern of other dimensions of connectivity. The degree of connectivity also varies from the headwater region of a catchment to the confluence zone and Boulton et al. (2017) have graphically and symbolically represented such variation within a drainage basin after classifying the catchment as per the scheme of Schumm (1977) i.e., zone of production, transfer zone, and zone of deposition (Fig. 3.2). However, at the same time, worldwide exponential growth of human population and associated anthropogenic activities have significantly induced the substantial changes in geomorphic connectivity by the catchment level alteration of natural land cover, river engineering and flow regulation, and transportation network. A study (Vercruysse & Grabowsk, 2021) on the effect of dam operation over the Himalayan rivers, Beas and Sutlej in particular, shows on centennial scale the channels became narrow and straight. The in-stream structures like bridges and culverts are significantly altering the channel geomorphology and create artificial knick-point immediately below the crossing structure (Galia et al., 2017; Roy & Sahu, 2018). Achillopoulou et al. (2020) have emphasized on the importance to study the interaction between transport infrastructures and different natural geo-hazards like floods with special attention on the role of digital technology in this regard. Maaß et al. (2021) have pointed that the presence of artificial embankment along the channel severely disturbed the lateral connectivity between the channel and floodplain. While the artificial structures induced the enhancement of connectivity could also be a problem for the river ecosystem. For example, the channelization of river might

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be significantly enhancing the longitudinal connectivity but it also severely affects the vertical and lateral connectivity, which directly affects the river biological and chemical parameters by damaging the hyporheic zone and floodplain retention capacity, respectively (Brooker, 1985; Franklin et al., 2009; Leibowitz et al., 2018).In such circumstances, it is essential to identify, map, and qualify the potential anthropogenic barriers for fluvial connectivity, when the concept of connectivity works as a holistic understanding for river basin management among the disciplines of river science (Wohl, 2017). Such a study is also essential for the Lower Gangetic Basin of India because the relationship between the Ganga River and its floodplain human civilization is very sensitive, interactive, and dynamic in terms of physical and cultural contexts. In addition, this is also the most populated alluvial region of the country and experiencing enormous development in all perspectives, which are directly or indirectly altering the forms and functions of this river system. With time dependency on the river is increasing, more and more settlements have grown close to the river bank. Therefore, the primary aim of this chapter is to identify as well as quantify the potential artificial barriers to the function of river connectivity of LGB.

2 Database and Methods 2.1

Study Area

The Ganga Basin (GB) is one of the largest river basins in the world as its name comes in the list of more than one million square kilometers of drainage area (~1,080,000 km2). It is also a transboundary river basin as flows through four major countries of Asia i.e., India, China, Nepal, and Bangladesh, whereas, about 80% of its basin area comes under India (~861,404 km2) only. Therefore, Ganga is the main river basin of India and lies over the eleven states of northern and eastern India for its run of about 2500 km. The basin area is the most populated region of the country with approx. Population density of ~760-person/km2 as per 2011 census of India, which has increased from ~520-person/km2 in 2001 and is also expected to certain rise in the upcoming census. In the present study, the lower section of the GB (here after Lower Gangetic Basin, LGB) has been selected for the identification of potential anthropogenic barriers on fluvial connectivity. The administrative boundaries of Bihar, Jharkhand, and West Bengal have been taken under consideration for the delineation of LGB (Fig. 3.3), which consist about 25% area of the entire basin and is also habitat for ~35% population of the GB. The socio-economic development of this region could also be assessed through the level of urbanization, which leaves profound impact on the river system. In particular, Bihar, Jharkhand, and West Bengal are characterized with the urban population of 11.75 million, 7.93 million, and 29.09 million with 143, 41, and 138 statutory towns, respectively (Census of India, 2011). The major left bank tributaries within the part of LGB are Gandak, Kosi, Fulahar, and Jalangi, while the major right bank tributaries are Sone, Falgu, Kiul,

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Fig. 3.3 Outline of the Lower Gangetic Basin (LGB) within the Ganga Basin of India

Mayurakshi, Ajay, Damodar, Dwarkeswar, and Kansabati. All of these rivers are delivering an enormous amount of water to the Ganga River, especially during the monsoon. For example, the mean annual flow of Ganga at Farakka, West Bengal, before entering Bangladesh is about 459 billion cubic meters. Most of these rivers are mainly monsoon-fed with an annual average rainfall amount of about 100–150 cm. Geologically, most of the LGB is developed by the Quaternary Sediments deposited through the fluvial, glacial, and deltaic/coastal actions. Whereas, the entire southwestern part is covered by Archaean–Proterozoic metamorphic rocks of the Chhotanagpur Gneissic Complex, patches of Gondwana Group etc. Major divisions on land use land cover map of the LGB shows ~59% of the area is used for agriculture followed by ~16% for the built-up area, ~15% vegetation cover, ~6% scrub lands, ~3.5% water bodies, and ~1% of bare ground.

2.2

Preparation of Vector Layers

Table 3.1 shows the major sources of raster and vector data used in the present study. In particular, the road network up to tertiary roads of Open Street Map, which is equivalent to rural road, has been considered here to extract the length of road within

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Table 3.1 Summary of the major used data with types and sources Sl. No 1

Used data River network

GIS data model Vector

2 3

Road and railway networks Floodplain (active and old)

Vector Vector

4 5

Flood prone area Location and details of major dams Location and length of major barrages Land use land cover map Major cities with population

Raster Vector

6 7 8

Vector Raster Vector

Source(s) Open Street Map Contributors/Google Earth/Bhukosh-GSI Open Street Map Contributors bhukosh.gsi.gov.in, Geological Society of India Dartmouth Flood Observatory (2020) National Register for Large Dams, CWC, Govt. of India Google Earth Image ESRI Land Cover 2020 (Sentine-2 10 m) Open Street Map Contributors and Census of India

the floodplain, point of intersection with rivers, and degree of proximity between stream and road lines. Active and old floodplains have been considered separately as per the purpose of the study, which is delineated by the Geological Society of India. The location of dams and their details of height and water holding capacity has been extracted from the National Register of Large Dams published by the Central Water Commission (CWC) of India. The location and length of the major barrages have been extracted manually from the Google Earth Image. Line and point density tools of ArcGIS Pro have been used to prepare the density maps of rivers, transport networks, and river crossings. ESRI (or Environmental Systems Research Institute) generated land use land cover map of 10 m resolution has been used for spatial analysis.

3 Results and Discussion 3.1

Construction of Dams and Barrages

Dams and barrages are constructed across the river channel to hold water coming from upstream for multiple purposes like irrigation, hydropower, flood management, utilization of water etc. However, such infrastructures significantly disturbed the longitudinal continuity of the channel and became a hydro-geomorphological and ecological barrier between upstream and downstream (Ward and Stanford, 1983; Yang et al., 2019). However, sometimes impoundment of water through damming is also enhancing the lateral connectivity in the upstream by increasing the water level and giving opportunity to inundate previously dry riparian zone and helps to grow new ecosystem (Leibowitz et al., 2018; Sun et al., 2021). As per the National Register of Large Dams (NRLD), Govt. of India, at present the country holds

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Fig. 3.4 (a) Distribution of major dams and barrages across the LGB including the proportional circles based on their height and length, respectively, with the maximum spatial extent of the floodaffected area; (b) Variation of gross water storage capacity (in million m3) of the respective dams of LGB

5334 completed and 411 ongoing projects of large dams (CWC, 2019). The International Commission on Large Dam (ICOLD, 2018) has defined a ‘large dam’ as “A dam with a height of 15 metres or greater from lowest foundation to crest or a dam between 5 metres and 15 metres impounding more than 3 million cubic metres”. In particular, a total of 96 large dams have been constructed within the LGB, of which the states of Jharkhand, Bihar, and West Bengal have 39, 26, and 31 dams, respectively. The major concentration of these dams is showing over the plateau region on the south-western part of LGB because of the existing favourable conditions as well as the regional need to construct a dam (Fig. 3.4a). However, no dam has been observed over the north and eastern part of LGB because of experiencing frequent floods predominantly over the plain regions as seen in the Fig. 3.4a. Whereas a number of barrages have been constructed in the plain region including the longest Farraka Barrage (~2300 m) across the Ganga River in Murshidabad district of West Bengal (Fig. 3.4b). Within the LGB a total of 21 major barrages (length >100 m) have been identified on different major rivers like Ganga, Son, Kosi, Damodar, Mayurakshi, Gandak, Falgu etc. with an average length of about 500 m. Apart from the large dams and barrages, the relatively minor rivers of this region are also significantly affected through longitudinal disconnectivity through the installation of numerous check dams to accumulate upstream water. The investigation of structural details shows among the 96 dams of LGB the Barnar Dam on Barnar River near Jamui, Bihar is the highest dam with 76.75 m of height (from the lowest foundation to the crest of the dam), followed by the North Koel (67.86 m), Konar (57.60 m), Badua (56.66 m), and Maithon (56.08 m) dams. The lowest and mean dam height in the LGB region is ~10 m and ~25 m, respectively. The Kangsabati Dam (WB) is the longest dam in this region with a length of 10.40 km

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Fig. 3.5 Relationship of water holding capacity with the variation of height and length of a dam across the LGB

followed by the Panchet (6.77 km), Tenughat (6.50 km), and Burhi (5.76 km), where the average length of all dams is 1.28 km. The dams of LGB cumulatively hold ~1064 million cubic meters (MCM) of water with an average capacity of ~1100 MCM. The maximum capacity of gross storage has been observed at Kangsabati Dam (~10,360 MCM) followed by Tenughat (~10,209 MCM), North Kole (~7020 MCM), Batane (~6787 MCM), and Massanjore (~6170 MCM). In Fig. 3.5, the relationship analysis shows the gross storage capacity of a dam that is more associated (positively) with the height of a dam (r = 0.67) than the length of a dam (r = 0.46). Some case studies on the selected dams of LGB have also highlighted significant changes in the hydro-geomorphological characteristics of downstream river systems of the respective dams (Table 3.2).

3.2

Alignment of Transportation Networks

The interaction between transportation networks (TN) and fluvial systems is very common as they often share a common landscape. Therefore, the alignment of TN sometimes creates problems for the geomorphic connectivity in different dimensions. In particular, the construction of TN along the river system could affect the lateral connectivity between the channel and floodplain by altering the exchange of water, sediments, nutrients, and biotic elements (Kondolf, 2006; Blanton & Marcus, 2009, 2014; Wohl, 2017; Roy & Sahu, 2017). Alternatively, to cross the river lines also need to construct different forms of river crossings like bridges, culverts, and low-water crossing across the channel, which are also profoundly affected the longitudinal continuity of the channel (Kondolf, 1997; Graf, 2006; Roy & Sahu, 2018). Studies around the major floodplains of the world show a significant portion of floodplains has been laterally disconnected by TN from their trunk channel. For example, ~44–69% floodplain of the Columbia River has been disconnected by TN across Washington State (Blanton & Marcus, 2014). The LGB also holds about 62,000 km2 floodplain, which is almost 29% area of the concerned region, of which active and old floodplains are categorized as about

FARAKKA BARRAGE (WB)

River: Ganga Year: 1975 Lat/long: 24°48′16.76″N/87°55′ 50.73″E

River: Mayurakshi River (Jharkhand) and Kushkarni River (WB) Year: 1955 and 1976 Lat/long: 24°06′25″N/87°18′31″ E and 23°56′46″N/87°31′30″E River: Atreyee Year: 2012 Lat/long: 25°32′23.28″N/88°45′ 35.39″E

MASSANJORE DAM (Jharkhand, India) and TILPARA RESERVOIR (WB)

Reducing seasonal discharge by 30.97%, 66.86%, and 64.01% during pre-monsoon, monsoon, and post-monsoon periods about 18.26% negative change in base flow, immediately after the dam construction On Padma: At Hardinge bridge station (Bangladesh), the average dry-season (Jan-may) discharge 2340 m3 s-1 of pre-Farakka (1934–1975) reduced to 1236 m3 s1 during post-Farakka (1975–1995). In particular, the maximum, average, and minimum discharges have

Decreasing monsoon and pre-monsoon water levels and about 34% (7.73–4.96 mg/l) reduction in suspended sediment load below the dam and reservoir

River: Dhepa River (in Bangladesh) of Punarbhaba River basin Year: 1992 Lat/long: 25°51′46″ N/88°39′ 52″ E

KOMARDANGA DAM (Bangladesh)

MOHANPUR DAM and RESERVOIR (Bangladesh)

Reducing the average water level of pre-monsoon and post-monsoon by 52.24% and 32.34%, respectively

Location

Name of the dam

Huge sediment load has been trapped from the upstream of the Ganga Basin and about 87 million cubic meters of water was impounded and the effect exhibit through changing course and severe bank erosion in Malda district (WB);

Reducing floodplain area by squeezing the river corridor and about 40% reduction in flood water extension over the basin; disconnection between active channel, floodplain, and wetlands. Water crisis for the wetland habitats Reducing the carrying capacity of upstream channels e.g., 26% for Kushkarni River, and declining the longitudinal bed slope and velocity Experience of river bank erosion

Post-dam/Reservoir/Barrage changes Hydrological changes Geomorphological changes

Table 3.2 Effect of selected dams on the hydro-geomorphological alteration of different rivers in LGB. (West Bengal)

Identification of Potential Anthropogenic Barriers to Fluvial. . . (continued)

Rudra (2014, 2016, 2018) and Rahaman and Rahaman (2018)

Pal (2016b)

Pal (2016a)

Talukdar and Pal (2017)

Source (s)

3 43

Location

River: Damodar Year: 1957, 1959, 1955 Lat/long: 23° 47′7″E/86° 48′43″ N; 23° 40′51″E/86° 44′50″ N; 23°28′35.95″N/87°18′5.16″E

River: Kangsabati Kumari Year: 1965 Lat/long: 86°47′20.14″E/°57′ 49.90″N

Name of the dam

MAITHON and PANCHET DAMS (JHARKHAND) AND DURGAPUR BARRAGE (WB)

KANGSABATI DAM (WB)

Table 3.2 (continued)

Significant reduction in peak flow and total annual discharge; non-monsoonal low flow increasing due to irrigational water supply; the frequency of low flood (2–10 years return period) reduce and large flood (>10 years) has been eliminated

been reduced around 22, 48, and 72%, respectively, in dry-season; about 13% increase in peakdischarge during the post-Farakka Period The salinity of the Padma in Bangladesh also increased from 380 μΩ/cm during the pre-diversion period in 1974 to about 29,500 μΩ/ cm in 1992 The monsoon discharge of 6081–10,676 m3 s-1 is reduced up to 2574–4470 m3 s-1 due to reservoir storage and diversion of flow through canals; forwarding the period of peak flood The dominance of aggradational landforms, braiding, avulsion, high width–depth ratio, breaching of the right bank, and valley widening up to 82 km from Durgapur barrage then phenomena of bank erosion, confined sinuosity, low width–depth ratio, and narrowness are more pronounced up to the confluence. Changes in river bed elevation by huge sedimentation and loss of habitat

On Bhagirathi-Hugli: Increasing the formation rate of cutoffs and oxbow lakes;

Post-dam/Reservoir/Barrage changes Hydrological changes Geomorphological changes

Mittal et al. (2014)

Ghosh and Guchhait (2014)

Source (s)

44 S. Roy

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Table 3.3 Distribution of different types of transportation networks over the LGB

Landscape Total LGB Active floodplain % of Total LGB Old floodplain % of Total LGB Total floodplain % of Total LGB No. of crossing

Total area (km2) 2,14,760

Length (km) of transportation networks within the LGB Railways Trunk Primary Secondary Tertiary running track road road road road 15,047 11,330 8265 11,495 26,541

Total 72,678

20,937

505

566

309

619

1505

3504

9.75

3.36

5.00

3.74

5.38

5.67

4.82

40,612

3775

3534

1587

3099

4804

16,799

18.91

25.09

31.19

19.20

26.96

18.10

23.00

61,549

4280

4100

1896

3718

6309

20,303

28.66

28.44

36.19

22.94

32.34

23.77

27.94

4477

929

807

487

717

1537

4477

10% and 19% area, respectively (Table). The extended part of such active and old floodplains around the trunk rivers of LGB is also facing the problem of disconnectivity due to the dense alignment of railways and roadways. The total length of TN over the LGB is almost 73,000 km, which is the assembled figure of total railway running tracks and major paved roads up to tertiary levels like National Highways, State Highways, and Districts Roads (Table 3.3). In particular, the total floodplain area of LGB contains almost 28% of total TN, whereas within the active and old floodplains about 5% and 23% of them have been constructed, respectively. Figure 3.6 shows the regional distribution of active and old floodplains around the river system of LGB, where the variation of color is showing the floodplain level differences in the total length of TN. The state-level variation also shows the floodplains of Bihar are holding more TN than West Bengal, whereas, no noticeable floodplain has been developed in Jharkhand. In particular, within the floodplain of Gandak River (Bihar), the maximum length of TN is located in comparison with other floodplains. In West Bengal, the floodplain around the Bhagirathi-Hooghly River also contains a higher percentage of TN than other rivers of the state. The density of the river network profoundly varies across the LGB with an average density of 80 m km-2and only ~25% area of the region experiencing drainage density more than 200 m km-2 (Fig. 3.7a). The major concentration of drainage density has been shown along the foothill region of northern LGB where numbers of streams are coming from the Nepal Himalayas. The middle-western part of LGB, mainly around the downstream region of Son River and the alluvial tract in between the Hazaribagh plateau and Ganga River, is also characterized by highdrainage density with numerous bifurcated channels originating from the southern highlands. In terms of transport network density, the region around Kolkata

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Fig. 3.6 Distribution of active and old floodplains around the river system of the LGB with spatial variation of TN length constructed within the units of floodplain

metropolitan is classified as the densest zone with more than 2 km km-2 of TN (Fig. 3.7b). The region around Patna, the capital city of Bihar, is also experiencing higher TN density. The points of road/railway–stream interaction, where different types of crossing structures have been constructed to cross the rivers, are closely associated with the variation of TN density and drainage density (Fig. 3.7c). A total of 4477 road-stream crossings (RSC) have been mapped over the LGB, of which the number of railway bridges is 929 and the number of roadway crossings is 3548. The spatial correlation between the distribution of RSC and major population centers (>5000 people) of LGB shows a positive correlation (r = 0.339, p < 0.001), which indicates that the degree of transport infrastructures development in terms of length of railway lines, roads, and bridges are increased with the expansion of population centers. The proximity analysis between the river and transport networks is able to highlight the degree of interaction between these two systems, where the close proximity indicates higher interference of TN in the river system and vice-versa. A higher degree of such interaction is present here as about 40% area of LGB comes under the proximal zone of 0–1000 m only (Fig. 3.7d and Table 3.4). As expected,

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Fig. 3.7 Distribution of drainage density (a), the density of transport network (b), road-stream crossing (RSC) density overlay with major population centers (c), and degree of proximity between transport and river networks across the LGB (d)

Table 3.4 Proximity classes showing the regional variation of distance between the river and transport networks across the LGB Zone of proximity (m) Width_in_Km_1990 c Width_in_Km_2010 = Width_in_Km_1990 d Wilcoxon signed ranks test e Based on negative ranks a

b

Remote Sensing and GIS for change detection studies of land cover/land use is highlighted. Cross tabulation and the ‘from-to ‘map provide valuable information about the spatial distribution and existence of land cover changes (Debnath et al., 2017) (Table 4.6). The ‘from-to’ map (Fig. 4.9a) shows that major land use change. The ‘from-to’ map (Fig. 4.9b) shows that the maximum agricultural land is occupied by settlement and sand. While on the other hand, the ‘from-to’ map (Fig. 4.9c) focuses the conversion of water body, which has decreased from 1990. ‘from-to’ change map (Fig. 4.9d) shows sand to other.

9 Conclusion The current study has demonstrated the value and practicality of remote sensing and GIS technologies, and gave an in-depth analysis of spatial and temporal changes in river channel processes and adaptation of LULC types in the studied area. Data integration and visualization were reasonably effective in the GIS system (Chakraborty et al., 2018; Kumar et al., 2016). This technology provides an analytical platform for data collection and analysis (Islam & Guchhait, 2017). Thus, the integrated GIS approach and remote sensing were used to locate the change of course in the flow. GIS provide a synoptic view of wide areas of the earth’s surface. In water management and change detection, RS and GIS are used. (Maurya & Yadav, 2016). The study concludes that the Teesta River has a constant change of channel with various time periods of varying design. The temporal essence of the migration of channels is not unidirectional. There is a sharp westward movement of the channel in

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Fig. 4.7 After Pal and Pani (2016), comparison map for investigation of using Transect subchannel change

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Fig. 4.8 Land use And Land cover Change Detection of Different Classes1990 (left Map) and 2010 (Right Map) Table 4.5 Land use and land cover change detection of different classes LULC classes Agricultural Land Dense Forest Sand Settlement Tree Garden Water Body Grand Total

1990(Area in sq. Km) 413.37

% of Total area 38.65%

2010(Area in sq. Km) 415.25

% of Total Area 38.82%

135.99 121.23 141.37 223.03 34.64 1069.63

12.71% 11.33% 13.22% 20.85% 3.24% 100%

124.30 145.72 193.92 157.81 32.64 1069.63

11.62% 13.62% 18.13% 14.75% 3.05% 100%

the foothill area (just below the Himalaya) whereas the channel is shifted eastward just above the barrage. The number of subchannels in the downstream area of the barrage has also increased with respect to the 1990 map. There is a significant difference in channel width in between 1990 and 2010. Based on the study, it can be concluded that channel shifts play a significant role in land use and land cover alternation, as shown by the transition map ‘from-to’ (Fig. 4.9a, b, c and d). The share of built-up area was eventually increased from 1990 to 2010. The agriculture class was also gradually increased and an increment of sand in the total share. The

2010

Agricultural land Dense Forest Sand Settlement Tea garden Waterbody Total (area in sq. km)

Agricultural land 236.66 5.31 42.40 91.86 29.05 8.10 413.37

1990 Dense Forest Sand 3.02 31.99 99.05 0.12 0.00 67.61 0.11 3.41 33.81 0.39 0.00 17.70 135.99 121.23

Table 4.6 Cross tabulation of land use and landcover classes between 1990 and 2010 Settlement 55.54 0.08 14.91 68.13 1.12 1.58 141.37

Tea garden 79.39 19.73 3.47 26.89 93.40 0.15 243.03

Water body 8.64 0.01 17.33 3.51 0.03 5.11 34.64

Total 415.25 124.30 145.72 193.92 157.81 32.64 1069.63

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Fig. 4.9 ‘From-to’ change map of the study area 1990 to 2010. (a) Major land-use change. (b) Agricultural land to other land use/land cover category (c) Water body to other land use/land cover category (d) Sand to other land use/land cover category.

land cover near and around the watershed’s major water bodies and streams has changed from other land uses to agriculture. From 1990 to 2010, the river Teesta’s channel changed from sinuous to braiding, resulting in a shift in floodplain land use and landcover.

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Kumar, M., Denis, D., & Gourav, P. (2016). Study of meandering of river ganga near Allahabad (India), using remote sensing and GIS techniques. Asian Journal of EnvironmentalScience., 11(1), 59–63. https://doi.org/10.15740/HAS/AJES/11.1/59-63 Maurya, S. P., & Yadav, A. K. (2016). Evaluation of course change detection of Ramganga river using remote sensing and GIS. India. Weather and Climate Extremes., 13, 68–72. https://doi. org/10.1016/j.wace.2016.08.001 Monserud, R. A., & Leemans, R. (1992). Comparing global vegetation maps with the kappa statistic. Ecological Modelling, 62(4), 275–293. https://doi.org/10.1016/0304-3800(92) 90003-W Nongkynrih, J. M. (2013). Shifting Courses and fluvial processes with special reference to Manas River System in the lower reaches, PhD Thesis. Department of Geography. Pal, R., & Pani, P. (2016). Recent Changes in Braided Planform of the Tista River in the Eastern Lobe of the Tista Megafan, India. Earth Science India, 9(2). https://doi.org/10.31870/ESI.09.2. 2016.6 Rudra, K. (2008). Banglar Nadikatha. Sahitya Samsad, Kolkata. Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611. https:// doi.org/10.4236/ijg.2017.84033 Singh, S. (2007). Geomorphology. Prayag Pustak Bhawan. Story, M., & Congalton, R. G. (1986). Accuracy assessment: A User’s perspective. Photogrammetric Engineering and Remote Sensing., 52(3), 397–399. Thakur, P. K., Laha, C., & Aggarwal, S. P. (2012). River bank erosion hazard study of river ganga, upstream of Farakka barrage using remote sensing and GIS. Natural Hazards, 61(3), 967–987. https://doi.org/10.1007/s11069-011-9944-z

Chapter 5

Monitoring the Shifting Nature of River Singimari and its Impact on Riverside Land Use and Landcover in Dinhata-I and Sitai Blocks of Cooch Behar District, West Bengal, India Koyel Roy, Pritam Saha, Sushanta Das, Madhumita Mandal, and Shasanka Kumar Gayen

Abstract The research was carried out to measure the shifting of the river and estimate-related effects on land use and land cover (LULC) using Geospatial approaches for the Singimari River between 1978 and 2021. The river Singimari is dynamic because of ongoing sedimentation, which alters the velocity and direction of the river’s flow and causes bank line movement due to ongoing bank erosion. The research has been based on quantifying channel shifting from 1978 to 2021 using 15 cross-sections. Maximum likelihood supervised classification was utilised to identify different LULC classes with greater accuracy. Between 1991 and 2001, the river migrated 1564 metres to the left at cross-section ‘L’. Between 1991 and 2001, 6.72 and 6.92 Km2 of erosion and accretion, respectively, took place. Between 1978 and 2021, positive change was prevalent for the classes of vegetation cover, fallow land and built-up area, while agricultural land and water bodies decreased as the river continuously deposited sediments in the form of various bars. Out of 188 villages of Dinhata-I and Sitai blocks of Cooch Behar district, 51 villages are exposed to channel shifting. The findings indicated that the dynamicity of the Singimari River and concomitant changes in the land use pattern brings new obstacles in front of the rural population of the study area. Keywords River shifting · Land Use and Land Cover · MNDWI · GIS & Remote Sensing · Supervised Classification

K. Roy (✉) · P. Saha · S. Das · S. K. Gayen Department of Geography, Cooch Behar Panchanan Barma University, Cooch Behar, WB, India M. Mandal Department of Geography, Bhairab Ganguly College, Belghoria, WB, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_5

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1 Introduction In India, rivers are inherent in people’s lives and livelihoods. Rivers are highly susceptible to environmental changes. At various spatial and temporal dimensions, the alluvial channel can adapt to the changes imposed by river water and sediment load, anthropogenic activities and active tectonic movements (Sinha & Ghosh, 2012). Any natural or artificial changes can bring a departure from dynamic equilibrium. This may lead to channel instability, including the alteration in channel form and pattern. River shifting is an essential topic of core Geomorphology. River shifting refers to the movement of river channels induced by riverbank erosion, high sediment discharge, lower gradient, and anthropogenic factors (Thakur et al., 2012; Yang et al., 1999). The river Singimari is braided, so the dynamicity is intimately linked to the instability of meander bends. The resultant channel alterations are crucial in the socio-economic development and environmental sectors. During the last few decades, researchers have been keen on studying channel dynamicity to assess the risk of bank dwellers (Arefin et al., 2021; Ophra et al., 2018; Debnath et al., 2017; Mukherjee et al., 2017; Das et al., 2007). The activity of river shifting captivates the interchange of land loss and gain simultaneously (Guchhait et al., 2016). The alteration directly influences LULC in terrestrial land. LULC changes caused by the channel shifting of the river may contribute to socio-economic disparities by lowering crop production, causing infrastructural damages, and posing threats to the livelihoods of poor residents (Wang et al., 2010). Therefore, change detection in every LULC class is essential for a better understanding of landscape dynamics through time and for the sustainable management of resources (Sahoo et al., 2017). Adapting GIS and Remote Sensing (RS) tools to identify LULC is a productive method for distinguishing areas within a region, such as vegetation, agricultural land, fallow land, built-up areas, and water bodies. Researchers worldwide have used various supervised classification approaches to detect LULC changes. The southern part of Cooch Behar district, consisting of two blocks of Dinhata sub-division, namely Dinhata-I and Sitai, is highly vulnerable to bank shifting from 1978 to 2021. The frequent shifting of the Singimari River imposes a paw mark on people’s livelihoods of adjacent mouzas. The agriculture-based society of this area hampers significantly because of the continuous erosion and accretion of the river Singimari. The research work recounts how the dynamicity of river Singimari leaves an imprint upon the LULC pattern of Dinhata-I and Sitai blocks in the Cooch Behar District. The followings are the primary objectives of the present work: • Delineation river bank lines using the Modified Normalized Difference Water Index (MNDWI) and measures shifting of the bank lines from a base year. • Dividing the river into reaches having equal lengths, calculating sinuosity and the amount of erosion and accretion of each determined period. • Preparation of LULC maps, accuracy assessment, and analysis of the vulnerability of villages based on the Historical Migration Zone (HMZ).

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2 Study Area The segment of the Singimari River that was selected for the present study forms the boundary between the Sitai and Dinhata-I blocks of the Cooch Behar district (Fig. 5.1). The study area is demarcated by 26° 00′ 01″N to 26° 13′ 46″N Latitude and 89° 15′ 24″E to 89° 31′ 16″E longitude having an area of 434.29 Km2. The study area is significant as it is situated near the Indo-Bangladesh border. The river Jaldhaka is known as Singmari in this area, while it was also known as Dharla before entering Bangladesh at the southern end of the area. The river’s length in the study area is 29 km, divided into 15 cross-sections.

3 Materials and Methods 3.1

Database Preparation

Diverse data sets were acquired from various sources to complete the study (Table 5.1). The researcher utilised satellite data to estimate land loss and create a LULC map. The Global Visualization Viewer site of the United States Geological Survey (USGS) provided the Landsat images of respected years. USGS Earth

Fig. 5.1 Location map of the study area. (Source: Prepared from Landsat images)

Sensor ID MSS

TM

TM

TM

OLI_TIRS

SRTM

Satellite Landsat 2

Landsat 5

Landsat 5

Landsat 5

Landsat 8

Digital elevation model

Dataset

138/042

138/042

138/042

138/042

Path/ row 148/042

30 m

30 m

30 m

30 m

30 m

Spatial resolution 80 m

Table 5.1 Satellite image data used for the study

11/02/2000

25/04/2021

13/03/2011

02/04/2001

06/03/1991

Date of acquisition 22/02/1978 Projection WGS1984-UTM zone 45 N WGS1984-UTM zone 45 N WGS1984-UTM zone 45 N WGS1984-UTM zone 45 N WGS1984-UTM zone 45 N WGS1984-UTM zone 45 N No

No

No

No

Cloud cover No

Source United states Geological Survey United states Geological Survey United states Geological Survey United states Geological Survey United states Geological Survey USGS

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Explorer website was used to collect the satellite images of the Landsat Multispectral Scanner System (MSS) for the year 1978, Landsat 5 Thematic Mapper (TM) for 1991, 2001, and 2011, and Landsat Operational Land Imager (OLI) for 2021. (Table 5.1). Additionally, several land-use characteristics were identified using a 2020 Google Earth image. The image resampling method has been applied to combine the satellite images of different resolutions. With the UTM projection system, the collected satellite images were georeferenced using WGS 1984 datum within the 45-degree north zone.

3.2

Extraction of River Boundary

Extracting a bank line converts a raster data structure into a vector layer from an image to separate the river’s left and right bank lines. Due to the ideal spectral reflectance of the plant’s leaf surface in varied wet and dry situations, green band spectral values have long been sensitive to turbid rivers and may also be used to discriminate between distinct broad vegetation types. (Jana, 2021). Water absorbs significant NIR radiation, making it possible to distinguish between water surfaces, wetlands, and flooded landscapes. Also, the range of the electromagnetic spectrum used to discriminate between vegetation and water is the same for the Shortwave Infrared (SWIR) of Landsat 5 and 8 and the Mid-infrared (MIR) of Landsat 7. The MNDWI value of clear water is higher than the NDWI value. To identify distinct water features in Landsat images, MNDWI has been applied (Figs. 5.2 and 5.3). Another rationale for selecting the MNDWI is that it efficiently removes built-up noise (Sun et al., 2012; Xu, 2006). The MNDWI has been expressed as follows (Eq. 5.1): MNDWI =

3.3

Green - SWIR Green þ SWIR

ð5:1Þ

Assessment of River Behaviour

Two methods can be employed to determine the shifting of active river channels: the transect method and the polygon method (Rapp & Abbe. 2003). For the current study, the transect method has been applied. The river channel shifting and migration rate have been calculated based on 15 cross-sections (Fig. 5.4). The sinuosity (P) index (Eq. 5.2) (Friend & Sinha, 1993) was applied between two transects after measuring the length of mid-channel and overall length of the channel.

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Fig. 5.2 MNDWI map for the year (a) 1978, (b) 1991, (c) 2001, (d) 2011, (e) 2021. (Source: Prepared from Landsat images)

P=

Lcmax LR

ð5:2Þ

Where, Lcmax denotes the midline’s length and LR is the total length between two reaches. The raster layers are generated in an Arc GIS environment from the Landsat images and then the layers are overlaid on each other to explore the changing nature of the Singimari River over Dinhata-I and Sitai blocks. The river courses of 1978 and 1991 were digitised from satellite images and superimposed one on top of the other. As an outcome, the river bank line was determined and the shifting was calculated. Knowing the direction of a channel’s flow is critical for analysing susceptibility zonation in connection to land loss (Mondal & Mandal, 2018). Then the areas of erosion, deposition, and unaffected areas were obtained by overlaying the bank lines of each year over another (Chakraborty & Saha, 2021). After digitising the mouza map relating to the study area, it was superimposed on the (HMZ) of the Singimari River. The HMZ is derived by summing the area of all active channels from 1978 to 2021 (Dey & Mandal, 2019; Mukherjee & Pal, 2018). The mouzas which fall in that HMZ are considered highly vulnerable villages, and the adjacent villages are categorised as moderate and low vulnerable zones according to the distances from HMZ. The remaining villages within a maximum distance from the HMZ are selected as non-vulnerable.

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Fig. 5.3 Chronological bank line positions of the Singimari River. Source: SRTM DEM

3.4

LULC Classification and Accuracy Assessment

A supervised classification approach using a maximum likelihood algorithm has been used to categorise the LULC classes over different times of the study area (Bhunia et al., 2016; Lillesand & Kiefer, 2000). Each LULC map was classified into five categories, vegetation, agricultural land, fallow land, built up and water bodies, (Table 5.2). The error matrix and kappa statistics have been used to assess the accuracy of the prepared LULC maps (Congalton & Green, 1999). The variables of the error matrix were used to derive two different measures: the producer’s accuracy and the user’s accuracy (Story & Congalton, 1986). Sample sites from google earth images have been selected for assessing the accuracy of LULC maps of

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Fig. 5.4 Cross profile across the Singimari River at different transects Table 5.2 LULC class characteristics LULC Class Vegetation Agricultural land Built up Water bodies Fallow land

Description Forest, mixed forest land, tobacco cultivation, land used for social forestation, Land used for cultivation of food crops Residential area, socio-economic infrastructure, mixed urban area Rivers, ponds, permanent open water Sand bars, unused arable lands

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Fig. 5.5 Workflow of the methodology. (Source: Prepared by authors from Landsat images)

1978, 1991, 2001, 2011 and 2021, and ground truth points have been collected for the year 2021. The calculation for obtaining the overall accuracy was done by using the following equation:

Overall accuracy =

Total number of corrected samples × 100: Totat number of samples

Kappa Coefficient (K ) has been calculated as follows (Rwanga and Ndambuki 2017): K = N Σr Xij –ðΣr Xiþ  X þ jÞ=N2–Σr ðX i þ X þ jÞ Where, N denotes the total number of pixels, r means the number of rows in the error matrix, Xij denotes the number of observations in row i and column j and Xi+ and X+j dictate marginal totals of row and column i and j respectively. The framework of the research methodology is given in Fig. 5.5.

4 Results and Discussion 4.1

Riverbank Shifting

Figure 5.6 and Tables 5.3, 5.4, 5.5, 5.6 and 5.7 exhibit the rate of river bank migration as well as the lateral direction of river bank migration for each bank along various reaches of the river from 1978 to 1991, 1991 to 2001, 2001 to 2011, and 2011 to 2021, respectively.

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Fig. 5.6 Chronological bank line shifting of the Singimari River. (Source: Prepared from Landsat images) Table 5.3 Shift of bank line from 1978 to 1991

Transect A B C D E F G H I J K L M N O

1978–1991 Distance (m) 666 354 475 617 347 851 188 75 125 219 541 213 220 695 245

Source: Calculated by authors from satellite images

Direction Left Left Left Left Right Left Left Left Left Right Right Right Left Left Right

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Table 5.4 Shift of bank line from 1991 to 2001

Transect A B C D E F G H I J K L M N O

1991–2001 Distance (m) 287 332 386 646 313 900 633 464 636 25 539 1564 472 492 380

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Direction Left Right Right Right Left Right Left Left Left Left Left Left Right Left Left

Source: Calculated by authors from satellite images Table 5.5 Shift of bank line from 2001 to 2011

Transect A B C D E F G H I J K L M N O

2001–2011 Distance (m) 1255 603 370 315 417 769 1033 557 364 354 234 574 672 717 775

Direction Right Right Right Right Left Right Right Right Left Left Left Right Right Right Left

Source: Calculated by authors from satellite images

Except for E, J, K, L, and O cross-sections, the reach shifted to the right throughout the first period (1978 to 1991). At this time, maximum and minimum shifts were observed on F and H cross-sections, respectively. During the second period (1991–2001), the highest shift was recorded in the L section, which was 1564 m. In this period, the river migrated in the left direction except for B, C, D, F, and M sections. Between 2001 and 2011, the river shifted towards the right, except

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Transect A B C D E F G H I J K L M N O

2011–2021 Distance (m) 554 276 1206 706 264 325 269 256 112 388 353 487 761 305 1020

Direction Right Right Left Left Left Left Right Left Left Right Right Right Left Left Right

Source: Calculated by authors from satellite images Table 5.7 Shift of bank line from 1978 to 2021

Transect A B C D E F G H I J K L M N O

1978–2021 Distance (m) 582 612 485 650 120 393 449 759 933 231 94 885 153 921 195

Direction Right Right Left Left Left Left Right Left Left Left Left Left Right Left Right

Source: Calculated by authors from satellite images

in five areas that shifted leftward. In the fourth timeframe (2011–2021), the river migrated to the right in seven cross-sections and to the left in eight. The maximum shifting was observed at the transects A, B, and D (582 m, 612 m, and 650 m) in the upper parts of the study area and at the transects L and N (885 m and 921 m) in the southern part, and H and I (759 m and 933 m) in the middle from 1978 to 2021.

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During this time, significant change to the right-hand side was observed at the transects A and B (582 m and 612 m) and left-hand shifting D, H, I, L, and N (650 m, 759 m, 933 m, 885 m, and 921 m). Maximum shifting occurred in Singimari, Daribash-I, Jaridharlandi, Bara Bangla, and Dakshin Singimari villages.

4.2

LULC Classification

LULC classification for the chosen 5 years (1978, 1991, 2001, 2011, and 2021) has been shown in Fig. 5.7 and Tables 5.8, 5.9, 5.10, 5.11 and 5.12. In 1978 vegetation, agricultural land, built up, water bodies and fallow land covered 22.15% (96.21 Km2), 54.06% (234.78 Km2), 3.01% (13.08 Km2), 2.22% (9.64 Km2) and 18.55% (80.58 Km2) of the study area respectively (Table 5.8). Table 5.11 shows that in 2021, 32.81% (142.51 Km2), 29.82% (129.53 Km2), 7.93% (34.43 Km2), 1.94% (7.56 Km2) and 27.69% (120.96 Km2) of the total area are covered with vegetation, agricultural land, built up, water bodies and fallow land respectively. The outcome shows a significant rise in vegetation cover, built-up area, and fallow land. Agricultural land and water bodies fluctuated from 1978 to 2021. In 2021, vegetation cover was at its peak, covering 141.51 Sq. Km. of the study area. Agricultural land and water bodies lost their 24.24% and 0.48% area from 1978 to

Fig. 5.7 LULC map for the year (a) 1978, (b) 1991, (c) 2001, (d) 2011, (e) 2021. (Source: Prepared by authors from Landsat images)

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Table 5.8 LULC change detection from 1978 to 1991

LULC class Vegetation Agricultural land Built up Water bodies Fallow land

1978 Area in Km2 96.21 234.78 13.08 9.64 80.58

Area in % 22.15 54.06 3.01 2.22 18.55

1991 Area in Km2 107.14 209.11 15.96 9.19 92.90

Area in % 24.67 48.15 3.68 2.12 21.39

1978–1991 Area in Km2 10.93 -25.67 2.88 -0.45 12.31

Area in % 2.52 -5.91 0.66 -0.10 2.84

Source: Calculated by authors from produced LULC maps Table 5.9 LULC change detection from 1991 to 2001

LULC class Vegetation Agricultural land Built up Water bodies Fallow land

1991 Area in Km2 107.14 209.11 15.96 9.19 92.90

Area in % 24.67 48.15

2001 Area in Km2 114.93 186.27

Area in % 26.46 42.89

3.68 2.12 21.39

19.72 8.71 104.66

4.54 2.01 24.10

1991–2001 Area in Km2 7.79 -22.84 3.76 -0.48 11.77

Area in % 1.79 -5.26 0.87 -0.11 2.71

Source: Calculated by authors from produced LULC maps Table 5.10 LULC change detection from 2001 to 2011

LULC class Vegetation Agricultural land Built up Water bodies Fallow land

2001 Area in Km2 114.93 186.27

Area in % 26.46 42.89

2011 Area in Km2 128.21 157.60

Area in % 29.52 36.29

19.72 8.71 104.66

4.54 2.01 24.10

26.48 8.17 113.83

6.10 1.88 26.21

2001–2011 Area in Km2 13.29 -28.67 6.76 -0.55 9.16

Area in % 3.06 -6.60 1.56 -0.13 2.11

Source: Calculated by authors from produced LULC maps

Table 5.11 LULC change detection from 2011 to 2021

LULC Class Vegetation Agricultural land Built up Water bodies Fallow land

2011 Area in Km2 128.21 157.60

Area in % 29.52 36.29

2021 Area in Km2 142.51 129.53

Area in % 32.81 29.82

26.48 8.17 113.83

6.10 1.88 26.21

34.43 7.56 120.26

7.93 1.74 27.69

Source: Calculated by authors from produced LULC maps

2011–2021 Area in Km2 14.30 -28.07 7.95 -0.61 6.44

Area in % 3.29 -6.46 1.83 -0.14 1.48

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Table 5.12 LULC change detection from 1978 to 2021

LULC Class Vegetation Agricultural land Built up Water bodies Fallow land

1978 Area in Km2 96.21 234.78 13.08 9.64 80.58

Area in % 22.15 54.06

2021 Area in Km2 142.51 129.53

Area in % 32.81 29.82

3.01 2.22 18.55

34.43 7.56 120.26

7.93 1.74 27.69

1978–2021 Area in Km2 46.30 -105.25

Area in % 10.66 -24.24

21.35 -2.08 39.68

4.92 -0.48 9.14

Source: Calculated by authors from produced LULC maps

2021, while vegetation, built up and water bodies expanded their area by 10.66%, 4.92% and 9.14%, respectively (Table 5.12).

4.3

Accuracy Assessment of LULC

Field data were utilised to test the accuracy of the LULC map of 2021, and Google Earth images have been used to validate the LULC maps of 1978, 1991, 2001, and 2011. The result of the accuracy assessment for the categorised map is shown in Table 5.13. The Kappa coefficient and overall accuracy were also calculated. The overall accuracy for the years 1978, 1991, 2001, 2011 and 2021 are 89.47%, 93.33%, 90.38%, 94.12% and 92%, respectively. The Kappa coefficient for the years 1978, 1991, 2001, 2011 and 2021 are 86.82%, 91.62%, 87.88%, 92.61% and 89.93%, respectively.

4.4

LULC Change Detection

A land-use categorised image was utilised to identify changes in order to quantify the effect of river shifting. Changing percentages of one LULC class over others were observed from 1978 to 1991, 1991 to 2001, 2001 to 2011, and 2011 to 2021. Figures 5.8, 5.9, and Table 5.14 show the selected area’s LULC change detection result. The maximum change found from Agricultural land to vegetation is 45.83% and Agricultural land to fallow land is 44.15% from 1978 to 2021. Other noticeable changes were found from vegetation to built-up 33.34%, Agricultural land to builtup 32.63%, Agricultural land to water bodies 33.49%, and Fallow land to water bodies 30.89%. An increasing percentage of fallow land indicates that many farmers opt out of farming activities day by day; they prefer to work as daily labour rather than as a farmer. Built-up areas are increasing because of the pressure of population

100 81.82 84.62 91.67 90.91 89.47 86.82

Source: Calculated by authors

Year LULC class Vegetation Agricultural land Built up Water bodies Fallow land Over all accuracy (%) Kappa-coefficient (%)

1978 User (%)

100 90 91.67 84.62 83.33

Producer (%) 100 88.89 90.91 100 85.72 93.33 91.62

1991 User (%) 100 88.89 100 90.91 85.72

Producer (%) 88.89 90 92.31 91.67 87.5 90.38 87.88

2001 User (%)

Table 5.13 Accuracy assessment results of different LULC classes of different years

100 81.82 92.31 91.67 87.5

Producer (%) 100 88.89 90.91 100 92.31 94.12 92.61

2011 User (%) 100 88.89 100 88.89 92.31

Producer (%)

100 90 91.67 100 84.62 92 89.93

2021 User (%)

88.89 81.82 100 87.5 100

Producer (%)

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Fig. 5.8 LULC change detection map from 1978 to 2021. (Source: Prepared by authors from LULC maps)

Fig. 5.9 Percentage-wise variation of major LULC conversions. (Source: Prepared by authors from Landsat images)

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Table 5.14 Major conversion of LULC classes from 1978 to 2021

Change Detection (1978–2021) Vegetation – Vegetation Agricultural land – Vegetation Built up – Vegetation Water bodies – Vegetation Fallow land – Vegetation Vegetation – Agricultural land Agricultural land – Agricultural land Built up – Agricultural land Water bodies – Agricultural land Fallow land – Agricultural land Vegetation – Built up Agricultural land – Built up Built up – Built up Water bodies – Built up Fallow land – Built up Vegetation – Water bodies Agricultural land – Water bodies Built up – Water bodies Water bodies – Water bodies Fallow land – Water bodies Vegetation – Fallow land Agricultural land – Fallow land Built up – Fallow land Water bodies – Fallow land Fallow land – Fallow land

% 22.84 45.83 2.22 0.65 28.46 23.48 44.81 2.72 0.48 28.51 33.34 32.63 8.71 3.68 21.64 9.11 33.49 4.48 22.03 30.89 16.55 44.15 2.88 4.41 32.02

Source: Calculated by authors

and the breakdown of the joint family into the nuclear family. Agricultural land changed to water bodies as a result of river bank erosion.

4.5

Erosion and Accretion

According to the nature of the river, it erodes one side and deposits the particles on the opposite bank. Due to loose bank particles in a braided river, lateral erosion is prominent; as a result, the river migrates for a long distance. The entire study area drained by the Singimari River has been classified into three classes based on contemporary technology: erosion, accretion, and unchanged area. The Singimari River’s accretion and erosion estimates from 1978 to 2021 are shown in Fig. 5.10 and Table 5.15, respectively. The highest erosion occurred from 1991 to 2001, mostly on the left bank; in contrast, minimal erosion occurred on both sides from 2011 to 2021. Maximum accretion occurred from 1991 to 2001 at the right bank.

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Fig. 5.10 Chronological variation in erosion and accretion of the Singimari River. (Source: Prepared by authors from Landsat images)

Table 5.15 Area change by erosion and accretion processes of the Singimari River (1978–2021) Year 1978–1991 1991–2001 2001–2011 2011–2021 1978–2021

Erosion (Km2) 5.59 6.72 6.47 5.08 5.85

Unchanged (Km2) 2.53 1.62 2.07 2.76 2.27

Accretion (Km2) 5.81 6.92 5.77 6.71 7.2

Source: Calculated by authors from satellite images

Minimum accretion occurred from 2001 to 2011 at the left bank. It is noticeable that from 1991 to 2001, both erosion and accretion are high. The maximum unchanged area was found between 2011 and 2021. In this region, humans extract excessive soil from the bank for their uses, accelerating river bank erosion. Nowadays, people in this area make their homes near the bank, which is also a significant cause of river bank erosion in this region. River bank erosion, mainly in Kajalikura, Natabari, Morebhanga, Kismat Adabari, and Baro Adabari villages, makes many people homeless as they lose their property and are forced to migrate elsewhere.

4.6

Channel Belt and Meander Belt

The zone where the channels are active is referred to as the channel belt. Thus, the delineation of the channel belt is based on identifying the areas where the rivers have

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Fig. 5.11 Meander belt from 1978 to 2021. (Source: Prepared by authors by overlaying meander belts on village map)

Table 5.16 Meander belt width

Transect A B C D E F G H I J K L M N O

Distance (m) 2048 1416 1556 1171 1055 1760 1456 1097 1350 815 1998 1673 1311 1584 1573

Source: Calculated by authors from satellite images

been in the previous years (Dhari et al., 2015). In the study area, the channel belt comprises both abandoned and active channels as the Singimari River flows in braided condition. The numerical value of the composite river of the meander belts’ active channel width (also known as the channel belt) from 1978 to 2021 is shown in Fig. 5.11 and Table 5.16. The area that the channel occupies in a given year is known as the Channel belt (Polygon). The term “meander belt” describes the

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Table 5.17 Sinuosity calculation result Year Sinuosity index

1978 1.12

1991 1.21

2001 1.23

2011 1.28

2021 1.32

1978–2021 0.2

Source: Calculated by authors from satellite images

geographical region that progressively moves downstream along the river’s main course. The five distinct years of the channel belt from 1978 to 2021 were combined to generate the meander belt for the proposed study. The largest meander belt width was discovered at reach number O (2048 m), while the smallest meander belt width was 815 m at reach number 9. The meander belt is 1457.53 m wide on average. Initially, the meander belt was wide then narrow, then again wider, and again narrow. Lateral migration is high in some reach because the meander belt is wide. Meander belt widths are higher near Kajalikura, Natabari, Andaran Singimari and Singimari villages. People in these regions are fed up with the frequent river bank shifting.

4.7

River Sinuosity

The result of the sinuosity index (Table 5.17) shows that the sinuosity value was lower in 1978 than in 2021. The maximum sinuosity value was 1.32 in 2021. However, in 1978, 1.12 was the lowest sinuosity value recorded. Sinuosity value gradually increased from 1978 to 2021. The sinuosity value changed by 0.20 from 1978 to 2021. When the sinuosity of a river is high, it means that a lot of sediment is being deposited on the riverbed and there is a lot of lateral movement. Because increasing resistance prohibits lower-level flow, resistance-dominated surfaces develop channels with more sinuosity than slope-dominated surfaces.

4.8

Channel Migration Vulnerability Zone

The estimation of the loss of terrestrial land from 1978 to 2021 at the village level (Fig. 5.12 and Table 5.18) showed that villages like Kajalikura, Natabari, Morebhanga, Kismat Adabari, Baro Adabari, Takiamari, Sagardighi, Nutanbash, Dakshin Singimari, Bara Bangla, Andaran Singimari, Singimari, Dhamailgach, Jaridharlanadi, Daribash-I, and Bharbandhi were experienced extensive land loss and are highly vulnerable villages. A significant number of people in these villages migrated during the flood. They lost their valuable land due to erosion. Moderately vulnerable villages are Atiabari gidra Adabari, Shilduar, Uttar Singimari, Deokhata, Gabua, Jatiagara, Dhekiajan, Kuthialerbash, Chhat Barobangla, Kaorai, Duksha, Panchadhaji, Raghunandan, and Bhoram Payasthi,

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Fig. 5.12 Channel migration vulnerability zone map

Table 5.18 Vulnerable villages Highly vulnerable villages

Moderately vulnerable villages

Low vulnerable villages

Kajalikura, Natabari, Morebhanga Kismat Adabari, Baro Adabari, Takiamari, Sagardighi, Nutanbash Dakshin Singimari, bara Bangla Andaran Singimari, Singimari Dhamailgach, Jaridharlanadi, Daribash-I, Bharbandh Atiabari gidra Adabari, Shilduar, Uttar Singimari, Deokhata, Gabua, Jatiagara, Dhekiajan, Kuthialerbash, Chhat Barobangla, Kaorai, Duksha, Panchadhaji, Raghunandan, Bhoram Payasthi Biljali Chatka, Petla Adabari, Dawabari, Garnata, Hokadaha Adabari, Chhat Singimari, Andaran Singimari, Tamaguri, Brahmattar Chhatra, Nakarjan, Khalisa Gosanimari, Jambari, Takimari, Jamadarerbash Chhoto Zilla, Chitmadnakura, Indra Narayan, Chandra Narayan, Dewanbash, Kharija Gitaldaha, Paramananda

Source: Identified by authors from satellite images

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etc. These villages are mostly affected during monsoon seasons when the discharge is high in this river. Low-vulnerable villages are Biljali Chatka, Petla Adabari, Dawabari, Garnata, Hokadaha Adabari, Chhat Singimari, Tamaguri, Brahmattar Chhatra, Khalisa Gosanimari, Chhoto Zilla, Dewanbash, Kharija Gitaldaha, Paramananda, etc. These villages are impacted during the highest discharge period of monsoon season.

5 Conclusion The study incorporates the RS and GIS methods to interpret the channel migrationinduced changes in LULC in the studied area, as it provides a long-term Spatiotemporal variation of satellite data. The Singimari River’s accretion and erosion constantly change the agricultural land and vegetation cover that make up the study area. The present study’s finding reveals that the transformation of LULC and river shifting are inherently correlated. The current study used long-term satellite images and cross-sectional analysis of riverbank shifting, which will be beneficial for demarcating the areas where the river is shifting continuously. Villages near the river can be located easily where people are experiencing loss of properties and lives due to erosion and depositional processes. Necessary actions should be taken immediately on the highly vulnerable villages to ensure sustainable development and management of livelihoods. Instead of concrete embankments, deep-rooted trees should be planted along the banklines to serve as sustainable embankments. The erosion of the river can be reduced using a soil erosion mat made with wood and coconut fibre. Mass awareness campaigns along with hazard preparedness at the administrative level should be arranged in two blocks to cope with the problems induced by river shifting. The results of this study will help policymakers and administrative officials implement sustainable measures in vulnerable villages.

References Arefin, R., Meshram, S. G., & Seker, D. Z. (2021). River channel migration and land-use/landcover change for Padma River at Bangladesh: A RS-and GIS-based approach. International Journal of Environmental Science and Technology, 18(10), 3109–3126. https://doi.org/10. 1007/s13762-020-03063-7 Bhunia, G. S., Shit, P. K., & Pal, D. K. (2016). Channel dynamics associated with land use/land cover changes in Ganges river, India, 1989–2010. Spatial Information Research, 24(4), 437–449. Chakraborty, K., & Saha, S. (2021). Assessment of bank erosion and its impact on land use and land cover dynamics of Mahananda River basin (Upper) in the Sub-Himalayan North Bengal, India. SN Applied Sciences, 4(1), 1–17. https://doi.org/10.1007/s42452-021-04904-x Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data principles and practices. Lewis Publishers.

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Das, J. D., Dutta, T., & Saraf, A. K. (2007). Remote sensing and GIS application in change detection of the Barak river channel, N.E. India. Journal of the Indian Society of Remote Sensing, 35(4), 301–312. https://doi.org/10.1007/BF02990786 Debnath, J., Pan, N. D., Ahmed, I., & Bhowmik, M. (2017). Channel migration and its impact on land use/land cover using RS and GIS: A study on Khowai River of Tripura, north-East India. The Egyptian Journal of Remote Sensing and Space Science, 20(2), 197–210. https://doi.org/10. 1016/j.ejrs.2017.01.009 Dey, S., & Mandal, S. (2019). Assessing channel migration dynamics and vulnerability (1977–2018) of the Torsa River in the Duars and Tal region of eastern Himalayan foothills, West Bengal, India. Spatial Information Research, 27(1), 75–86. https://doi.org/10.1007/ s41324-018-0213-z Dhari, S., Arya, D. S., & Murumkar, A. R. (2015). Application of remote sensing and GIS in sinuosity and river shifting analysis of the Ganges River in Uttarakhand plains. Applied Geomatics, 7(1), 13–21. https://doi.org/10.1007/s12518-014-0147-7 Friend, P. F., & Sinha, R. S. (1993). Braiding and meandering parameters. Geological Society Special Publications, 75, 105–111. https://doi.org/10.1144/GSL.SP.1993.075.01.05 Guchhait, S. K., Islam, A., Ghosh, A., Das, B. C., & Maji, N. K. (2016). Role of hydrological regime and floodplain sediments in channel instability of the Bhagirathi River, gangaBrahmaputra Delta, India. Physical Geography, 37(6), 476–510. https://doi.org/10.1080/ 02723646.2016.1230986 Jana, S. (2021). An automated approach in estimation and prediction of riverbank shifting for floodprone middle-lower course of the Subarnarekha river, India. International Journal of River Basin Management, 19(3), 359–377. https://doi.org/10.1080/15715124.2019.1695259 Lillesand, R. M., & Kiefer, R. W. (2000). Remote sensing and image interpretation (4th ed.). Wiley. Mondal, J., & Mandal, S. (2018). Monitoring changing course of the river ganga and land-use dynamicity in Manikchak Diara of Malda district, West Bengal, India, using geospatial tools. Spatial Information Research, 26(6), 691–704. https://doi.org/10.1007/s41324-018-0210-2 Mukherjee, K., & Pal, S. (2018). Channel migration zone mapping of the river ganga in the Diara surrounding region of eastern India. Environment, Development and Sustainability, 20(5), 2181–2203. https://doi.org/10.1007/s10668-017-9984-y Mukherjee, R., Bilas, R., Biswas, S. S., & Pal, R. (2017). Bank erosion and accretion dynamics explored by GIS techniques in lower Ramganga river, Western Uttar Pradesh, India. Spatial Information Research, 25(1), 23–38. https://doi.org/10.1007/s41324-016-0074-2 Ophra, S. J., Begum, S., Islam, R., et al. (2018). Assessment of bank erosion and channel shifting of Padma River in Bangladesh using RS and GIS techniques. Spatial Information Research, 26, 599–605. https://doi.org/10.1007/s41324-018-0202-2 Rapp, C. F., & Abbe, T. B. (2003). A framework for delineating channel migration zones. Washington State Department of Ecology Washington State Department of Transportation. http://www.ecy.wa.gov/bibli o/0306027.html Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8(04), 611. https:// doi.org/10.4236/ijg.2017.84033 Sahoo, S., Dhar, A., Kayet, N., & Kar, A. (2017). Detecting water stress scenario by land use/land cover changes in an agricultural command area. Spatial Information Research, 25(1), 11–21. https://doi.org/10.1007/s41324-016-0073-3 Sinha, R., & Ghosh, S. (2012). Understanding dynamics of large rivers aided by satellite remote sensing: A case study from lower ganga plains, India. Geocarto International, 27(3), 207–219. Story, M., & Congalton, R. G. (1986). Accuracy assessment: A user's perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397–399. Sun, F., Sun, W., Chen, J., & Gong, P. (2012). Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. International Journal of Remote Sensing, 33(21), 6854–6875. https://doi.org/10.1080/01431161.2012.692829

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Thakur, P. K., Laha, C., & Aggarwal, S. P. (2012). River bank erosion hazard study of river ganga, upstream of Farakka barrage using remote sensing and GIS. Natural Hazards, 61(3), 967–987. https://doi.org/10.1007/s11069-011-9944-z Wang, S. Y., Liu, J. S., & Ma, T. B. (2010). Dynamics and changes in spatial patterns of land use in Yellow River Basin, China. Land Use Policy, 27(2), 313–323. https://doi.org/10.1016/j. landusepol.2009.04.002 Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179 Yang, X., Damen, M. C. J., & Zuidam, R. A. (1999). Satellite remote sensing and GIS for the analysis of channel migration changes in the active Yellow River Delta, China. International Journal of Applied Earth Observation and Geoinformation, 1(2), 146–157. https://doi. org/10.1016/S0303-2434(99)85007-7

Chapter 6

Societal Instabilities in the Wake of Shifting of River Course: A Study of Hotnagar Char of Bhagirathi River, West Bengal, India Mohan Sarkar, Susmita Ghosh, Shah Nawaj Ahmed, Mallik Akram Hossain, and Aznarul Islam

Abstract Social instabilities in the context of river erosion-accretion sequence are commonly observed across the world, especially in the areas with higher population density and lower per capita land. The Hotnagar char is situated along the Bhagirathi River in the Murshidabad district, West Bengal portrays social turbulence related to the occupancy of Charland (river island). To this end, the present study aimed at comprehending the impact of the evolution of the char on socio-economic issues of the char dwellers during the period extending from1943–45to 2018 and to review the existing land distribution policies and management strategies for better life and livelihood. In carrying out the study, U.S. Military topographical sheet, LANDSAT TM, ETM+, and Sentinel 2.0 imageries were used to identify the evolution of Hotnagar char and the locational shift of the villages (ChakKatalia and Char Sujapur) on the char. It has been observed that the village location has been completely altered from being on the right side of the river to being on the left because of the westward movement of the river by 7.1 km from 1943–45 to 1987. The migration of the river created an oxbow lake and its extent shrunken from 1.42 km2 in 1987 to 0.85 km2 in 2018, and the inter-neck distance increased from 0.31 to 1.2 km during the same period owing to lag depositions. The household survey conducted in 2018 depicted that emerging lands due to ongoing depositions were the major causes of social turbulence including a few conflict-induced death cases in the study area. Moreover, the study reported social conflicts among the people, agrarian distress such as lower cropping intensity, and the problem of marketing the agricultural produce due to poor transportation system. Moreover,

M. Sarkar Interdisciplinary Programme in Climate Studies (IDPCS), Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra, India S. Ghosh · S. N. Ahmed · A. Islam (✉) Department of Geography, Aliah University, Kolkata, West Bengal, India M. A. Hossain Department of Geography and Environment, Faculty of Life and Earth Science, Jagannath University, Dhaka, Bangladesh © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_6

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due to the island-like positioning of the villages, especially during the monsoon months, the people lack proper transport for availing of administrative and medical services. The perception survey also indicated that finding solutions to these issues from the government and other stakeholders was deemed inadequate. The local people objected that the entitlement to the property, in many cases, has been granted based on political allegiances and public relations. Thus, in the end, the present investigation proposes a few directions for a viable economy and solidarity in community relations. Keywords Meander migration · Erosion-accretion · Charland occupancy · Geopolitical issues · Agrarian distress · Charland distribution

1 Introduction Meandering is an inherent characteristic of a natural channel or river but a particular stretch of a river may have a straight course (Bagnold, 1960; Leopold & Wolman, 1957; Charlton, 2007). A channel is considered meandering when a long course channel lacks a straight course (Knighton, 1998). The sinuosity index is the ratio between the observed and expected straight path and is used to identify a channel pattern, whether straight, meandering or braided (Williams, 1986; Charlton, 2007). Flowing over the alluvium substrate formation of bars, locally called chars in the Bengal Delta region, are common in meandering and braided channels, considering that a straight channel is transformed into a meandering course due to alternating erosion and deposition (Ashour et al., 2017; Lahiri-Dutt, 2014; Maiti, 2016). The process shaped the floodplain through different macro and micro landform formations. Bar, locally known as chars is one among the landforms. Depending on the location of the char, it is classified as a point bar, attached bar, concave bar, channel junction bar, sidebar, and mid-channel bar (Ashour et al., 2017; Hooke & Yorke, 2011). Water filaments and bars work interdependently, forming channel planform. Flow angle, flow direction, flow rate and lateral channel movement determine planform dynamics (Yong et al., 2018). Due to the formation of bars, a straight channel is transformed into a meandering course as the bars cause divergence in water flow that attacks the bank and erodes materials that are deposited in the downstream direction (Wang et al., 2019; Yan et al., 2021). Continuous sedimentation increases the bars’ height and continues until the bars get an altitude higher than the flood level (Sarker et al., 2003). The chars are available for cultivation when the chars attain a level above the peak discharge or high flood. Settlement development is a common phenomenon in the fertile floodplain of the Bengal delta. The process of land occupation follows a succession process, and the settlement is developed 12–13 years later (Rahman & Rahman, 2012). The main livelihood options available for the char communities are agriculture, livestock, fishing, labour, shopkeeping, clothing, boat and vehicle driving (Alam et al., 2018). However, the proximity of the chars to the rivers makes these chars prone to bank erosion and the people are highly vulnerable due to continuous exposure to

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floods (Echendu, 2020). The present-day climate change has also amplified the level of vulnerability of these char dwellers and they often lack basic amenities contributing to the increase(Mamun et al., 2020). Studies show that the people living in the chars of the Bengal Delta in India (Biswas & Anwaruzzaman, 2019; Howlader & Rahman, 2016; Islam et al., 2022; Islam & Guchhait, 2017; Majumdar et al., 2022; Mukherjee, 2011) and Bangladesh (Islam et al., 2014; Islam et al., 2016; Khan et al., 2014; Sarker et al., 2019) are highly vulnerable. Affray and contention among people are evident in land occupation (Lahiri-Dutt & Samanta, 2004; Sarker et al., 2019). Under the Bengal Alluvion and Dilluvion Regulation Act (1825), the lands are locally known as badajamis (Lahiri-Dutt, 2014). The act was enacted to maximise revenue and reduce the impediments in the court proceedings in the Bengal Province as the region is shaped by the Ganga-Brahmaputra and Meghna, the main rivers of the then Bengal (Davy, 2018). Later the Bengal Tenancy Act (1885), the East Bengal State Acquisition and Tenancy Act (1950), and lastly, the West Bengal: Land Reforms Act (1955) were implemented to resolve the land distribution issue in the deltaic part of West Bengal (Swamy, 2010). The possession of land can significantly reduce vulnerability because landholdings provide social stability and food security (Echendu, 2020). The people living on the chars become vulnerable as the chars are semipermanent and frequently inundated due to floods affecting their livelihood (Mollah & Ferdaush, 2015; Yang et al., 2021), and the problem intensifies due to river bank erosion during high discharge (Mukherjee, 2011). The effects are felt directly in the dwellers’ livelihoods, for example, economy, demography, agriculture, and gender (Khan et al., 2014; Mollah & Ferdaush, 2015; Sarker et al., 2003; Sarker et al., 2019). As a result, the people living in the chars have been identified as poor. Different measures have been taken to break the poverty cycle and improve the poor’s livelihood. In the neighbouring country Bangladesh, Char Livelihood Program (CLP) (Hossain, 2021), sandbar agriculture (Chowdhury, 2021), and the Char development and settlement project (Demaine, 2021) have been implemented and identified as suitable measures for livelihood improvement. Bhagirathi, the main river of West Bengal, having the highest population density in the world, is a primary concern for researchers. The classical research works on the region focus on geology (Bandyopadhyay, 2007; Jha & Bairagya, 2011; Sengupta, 1972), hydrology (Biswas & Pani, 2021; Guchhait et al., 2016), morphology (Islam & Guchhait, 2020; Laha, 2015; Pal & Pani, 2019; Rudra, 2014, 2018) morphological vulnerability (Pal et al., 2016), and recently various studies focused on the vulnerability assessment of the local communities and the effects of the river on the society(Islam & Ghosh, 2021). Some studies had considered both geomorphological and anthropological dimensions called anthropo-geomorphology (Das et al., 2020a, b). However, recent studies do not focus on emerging geopolitical issues due to channel migration and bank erosion and deposition. The study area Hotnagar char, located in the Berhampore block of Murshidabad district, has recorded social conflict regarding the occupation of newly emerged chars by diluvion and alluvion. From the above discussion, it can be understood that the major studies in the region have focused on the physio-socio scenario but lacks

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studies related to the emerging geopolitical issue due to river migration and associated effects on society. Hence, identifying the major socio-economical problems like social conflicts, agriculture, economic distress, and accessibility to basic facilities due to river migration will help to manage the problem. Besides these problems, the active floodplain region always has land distribution issues that need attention. Therefore, it is essential to address the present land distribution policies that will reduce social conflicts and geopolitical issues in this region. However, most studies do not include this dimension of the floodplain. Hence, keeping this in mind, the present study intends to address the following objectives systematically. 1. To trace out the trajectory of char dynamics and erosion-accretion sequence around Hotnagar char along the Bhagirathi River during1943–45 to 2018, 2. To assess the socio-economic dynamics and related issues in the wake of the evolution of char, 3. To review the existing land distribution policies in an attempt to propose alternative measures for char land distribution and related issues.

2 Study Area The Hotnagar char, affected by complex locational factors owing to channel dynamics, comes under the community development (C.D.) block Berhampore of Murshidabad and is situated 5.6 km westward of Bhabta station (Fig. 6.1). The latitudinal and longitudinal extension of the village is from 23°59′09″ to ° 23 59′12.2″ N and from 88°12′28″ to 88°13′02″ E. According to the Census of India (2011), the Hotnagar char comes under the jurisdiction of the Char Sujapur and Chak Katalia mouzas (the smallest administrative unit for revenue collection) under C.D. block Berhampore. We found that the Census of India (2011) considered the older river position (1943–45), which located these villages on the right side of the river. However, these villages are now located on the left side of the river. The left bank location of the villages and Hotnagar char is confirmed from ground verification, local Gram Panchayat, Block Development Office of Beldanga-I & Berhampore. The settlement developed in these two villages are limited to Hotnagar char only and the rest areas of the villages are uninhabited. As a part of the Bhagirathi River flood plain, the village has recorded severe river bank migration. The main reason behind selecting the village is the unique locational change of the village due to river migration and its associated geopolitical and social conflicts. The Hotnagar char is located near the Bhagirathi River and the same sculptures the region’s physiography. The region’s altitude varies from 10 to 22 meters above the mean sea level (MSL). Topographically, the whole district has been divided into five sub-micro regions (i) Nabagram plain, (ii) Mayurakshi-Dwarka plain, (iii) Ganga-Bhagirathi basin, (iv) Jalangi-Bhagirathi interfluves and (v) Raninagar (Census of India, 2011). The eastern part of the district is formed of younger alluvium (carried by the river coming from the east) and the western part is composed of older alluvium and laterite coming from the western part by the western rivers (Fig. 6.2).

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Fig. 6.1 Location of the study area

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Fig. 6.2 Drainage system of Murshidabad district

The eastern part of the district is made of fine loamy soil drawn by the Ganga and Bhagirathi Rivers. In this district, the soil is very much fertile for agricultural practices. The eastern or left part of the Hooghly river is also known as Bagri. The soil of the Bagri region is made of a much finer texture. The Hotnagar falls under the Bagri region. On the other hand, the western portion of the Hooghly River is known as the Rarh. The soil of the Rarh region is lateritic. The district accommodates a population

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of about 7.1 million, among which 50.5% and 49.5% are male and female (Census of India, 2011). The district comes under a humid tropical region with an average rainfall of 1328 mm and temperature ranges from 11°c to 43°c in the winter and summer seasons. The roads and railways are considered infrastructure, and continuous development of connectivity infrastructure has reduced India’s vulnerability level). The Ganga River flows between Malda and Murshidabad, but the Farakka Barrage has divided these two districts. National Highway 12 (former NH 34) runs north and south of Murshidabad. It has divided the district into two equal parts and runs along the Bhagirathi River. The eastern railway of the Lalgola-Sealdah division runs parallel to the national highway (NH) 12. The railway started from Lalgola and is 227 km long to Sealdah. Another railway track started from Howrah to North Bengal and runs through Murshidabad along the right bank of the Hooghly-Bhagirathi River. The village is between the river and the oxbow lake (OBL) and is surrounded by water bodies from both sides. The local kaccha roads are the only way to connect to the surrounding regions. The altitude varies from 18–20 m in the area. The village comes under the Bhagirathi-Ganga basin and has been identified as very rich in nutrients and suitable for agriculture (Census of India, 2011).

3 Database and Methodology Both primary and secondary data have been used to carry out the present study. The primary data regarding the inhabitants’ livelihoods of Hotnagar char were collected through the structured questionnaire (pre-defined questions) survey. Since the Hotnagar char is the only settlement area extending into the two villages, Char Sujapur and ChakKatalia, samples have been selected from both these villages. As all the households are homogenous (char-dependent), a simple random sampling technique has been embraced to select the sample households. A sample size of 20% of the total households (determined by pilot survey) as per the census 2011 (latest census to date) in the villages has been considered for the present study. The number of total households was 209 and 135 in ChakKatalia and Char Sujapur in 2011 (Census of India, 2011), and hence the number of samples from both villages is 42 and 27, totalling 69. The evolution of Hotnagar char and channel dynamics is portrayed based on secondary data using geospatial techniques. The remote sensing data have been collected to analyse the channel movement. The river shapefile of 1943–45 is extracted by digitising toposheets downloaded from U.S. Military (https://www. usgs.gov/programs/national-geospatial-program). The map numbers of the toposheets are NF 45–3 and N.G. 45–15, series no U502, and the scale is 1: 250000. The LANDSAT (1987 and 2000) and Sentinel 2.0 imageries (2018) (Table 6.1) have been rectified and downloaded from Google Earth Engine (https://earthengine.google.com) in false colour composite form to show the changes in planform of the floodplain adjoined to the Hotnagarchar. Then, using

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Table 6.1 Description of multi-temporal satellite imageries Date of acquisition 24 Dec1987 26 Jan 2000 25 Jan 2018

Sensor LANDSAT TM LANDSAT ETM+ Sentinel 2.0

Path/Row/ Tiles no. 138 and 139/44 138 and 139/44 T45QXG

Resolution (m) 60 30 10

Source: https://earthexplorer.usgs.gov, https://earthengine.google.com

unsupervised classification, the water bodies were identified, and the ‘raster to polygon’ transformation tool was used to extract the shapefile of the river. The exact process has been repeated for imageries of1987, 2000 and 2018. Then, the shapefiles are superimposed to detect the change in the river position and evolution of the Hotnagarchar. The lithologic data are downloaded from Bhukosh (https:// bhukosh.gsi.gov.in) to prepare the soil map of Murshidabad. Digital elevation data (DEM) are downloaded from United Nations Geological Survey (https:// earthexplorer.usgs.gov) and used to generate a contour map of the village to understand the physiographic condition. Google Earth Pro is used to extract the floodplain region’s elevation profile to identify the village’s site and situation concerning the morphological features. The elevation profile is redesigned for better visualisation and understanding. The digitisation of the features develops the river network and transport network maps. The final maps are prepared in the GIS platform for better representation.

4 Results and Discussion 4.1 4.1.1

Evolution of Char and Erosion-Accretion Sequence Evolution of Hotnagar Char

The formation of bars differs from a meandering to a braided channel. Rivers develop alternating bars in a meandering course to maintain an equilibrium in energy distribution (Knighton, 1982; Leopold & Wolman, 1957). It has been seen that the areal distribution of mass in a meandering river is less than the areal distribution of water and is considered a single-thread channel. On the other hand, when the area of sand bars or mass in a channel is more than the water area, it is considered a multithread channel (Hooke & Yorke, 2011). The bars are classified into sidebars, mid-channel, concave, attached, and point bars (Hooke & Yorke, 2011). The helical flow attacks the concave bank and the eroded materials are deposited at the convex bank joined to the mainland, forming point bars (Ferguson et al., 2003). The height of the bars depends on the peak discharge height (Sarker et al., 2003).In a meandering channel, river shifting continues by erosion in the concave bank and deposition in the convex bank (Leopold et al., 1962; Williams, 1986). The study aims to evaluate the evolution of the Hotnagar char from Fig. 6.3a, b, c, d, e. and Fig. 6.4.

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Fig. 6.3 The location of the Hotnagar char amidst the changing river course of Bhagirathi in different periods. (a) 1943–45 from toposheet, and (b) 1987, (c) 2000, and (d) 2018 from satellite imageries, (e), superimposed location (1943–45 to 2018) to detect the nature of meander migration

it can be noticed that in 1943–45 the location of the Hotnagar char was on the western side of the river. The river flowed in a highly sinuous course with low bend tightness and extended upto the Bhabtastation. Due to the channel shortening, the meander detached from the river, and the main course of the channel shifted about 7 km westward (Fig. 6.3e).

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Fig. 6.4 Dynamism of neck cut-off (1987–2018)

The newly formed oxbow lake (OBL) is formed by the process of neck cut-off formation (Fig. 6.4). The newly developed alluvium plain surrounded by the meader and river is attached to the main river and is called attached char. The settlements of Chak Katalia and Char Sujapuris are located on this char as the char has an elevated altitude (18–22 m) than the surrounding region (14–18 m), giving comparative locational advantages during the flood from inundation and is identified as a dry point settlement. The river migration has changed the village’s situation because, in 1943–45, the village was on the right side of the river and is now on the left side. The shifting of the channel migration is evident from the river course of 1987. The formation of the Hotnagar char can be explained with the meander formation theory, which states that in a meandering course, the water filament hits the concave bank causing bank erosion on the left side of the river, and the materials are deposited at the convex bank by the under-flowing secondary water flow (Knighton, 1982; Maiti, 2016; Williams, 1986). After the cut-off formation, the eastern bank area has developed a ridge and swale topography, and the settlements are located on the ridges, which are also called chars

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Fig. 6.5 Cross-sectional view of the floodplain associated with Hotnagar char, Murshidabad

(Fig. 6.5). These are formed when the meander is separated off and becomes a slough or OBL, and gradual siltation encroaches the water area or fills it up. During the process of meander formation, the deposited bank materials emerge as a point bar that is relatively higher than the filled-up or encroached OBLs, and the combination of these two features is called ridge and swale topography (Morisawa, 1968). The settlements developed on the ridge have an 18–22 meters altitude. On the other hand, a minimum altitude is noticed near OBL (Fig. 6.5), which is why the settlement pattern is a dry point. Continuous deposition in the OBL has decreased the water area. The total area of the OBL was 1.42 km2in 1987, which was reduced to 0.88 and 0.85 km2in 2000 and 2018 (Fig. 6.5). The alluvium-filled areas are known as lag deposits, which are highly fertile and are occupied by the farmers for agricultural activities. The cut-off was filled up as time passed, and the char area increased. The char area in 2018 was measured as 2.93 km2, and in 1987 the area was approximately 1.53 km2. The filling up during flood, the sedimentation is initiated from the starting point of the OBL; as a result, the inter-neck distance increases.

4.1.2

Meander Migration and Erosion-Accretion Sequence

Meandering is expected in a sinuous course. In a sinuous course, the channel migrates through meander migration, bank erosion, and deposition. Different

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scholars have mentioned the inherent meandering course of the Bhagirathi River in their scientific pieces of literature (Guchhait et al., 2016; Pal & Pani, 2019; Rudra, 2014, 2018; Sengupta, 1972). The meander migration developed the Hotnagar char. The meander migration has been analysed based on almost 80 years of the reference period. In 1943–45, the course was highly sinuous, and the calculated sinuosity index (S.I.) was 3.28, characterised by high tightness and extended meander amplitude (Fig. 6.3a, b, c, d, e). The extended length increases the river gradient and creates pressure on the channel that decreases the channel width, which leads to a cut-off formation. The cut-off is formed when S.I. crosses its threshold and the water cannot carry the load (Knighton, 1998), which is usually found in areas with high bank erosion (Charlton, 2007; Leopold et al., 1962). The cut-off of the Hotnagar has reduced the length of the river to around 6.21 km. Due to this cut-off, the channel has carved out a new valley in the west, being shifted 7.1 km. This phase is characterised by high meander migration (Fig. 6.4). After this phase, the meander migration decreased. The measured meander migration during 2000–2018 is only 0.31 km. A significant decrease in meander migration is noticed after the cut-off formation because it decreases the channel gradient, meander amplitude, and bend tightness, resulting in less meander migration. The prominence of this phenomenon of meander migration in floodplain evolution has been asserted by different scholars (Bridge, 2004; Jana, 2019; Maiti, 2016). From Fig. 6.3e, it is apparent that the channel has developed new meanders where the loop adjacent to Hotnagar is migrating eastward, and the loop below Hotnagar is migrating westward. The char is located on the river’s convex bank; as a result, the bank gains sediments. Nevertheless, the loops, situated immediately below and above the char, are moving eastward, eroding materials from this bank (Fig. 6.3e). These loops are a concerning factor for the char because a high flood may erode the char making the people living on the char vulnerable. After a cut-off is successfully developed, sedimentation starts and every year’s flood fills up the OBL creating a swale landform (Morisawa, 1968). Swales adjacent to Hotnagar and opposite bank is identified in the floodplain (Fig. 6.5). The deposition in the current OBL is also evident in Fig. 6.4. The area of water in the OBL has decreased, measured as 1.42, 0.88, and 0.85 km2 in 1987, 2000, and 2018. The floods of the area cause lag deposition that adds new land to the char (Fig. 6.5). Proximity to the river increases the deposition at the OBL’s neck, so the inter-neck distance increases as the deposition continues. The inter-neck distances are measured as 0.31, 0.81 and 1.2 km in 1987, 2000 and 2018. In the OBL, a char is identified, and the increasing char’s perimeter was measured as 1.25, 1.41, and 2.10 km in 1987, 2000, and 2018. So, from the above discussion, it can be concluded that the OBL is shrinking in area. On the one hand, the river shifting erodes the agricultural land and takes under the river bed. On the other side, it constructs the land. The north-eastern portion of the village is experiencing massive river bank erosion due to meander migration in the upper loop, causing erosion of agricultural lands. The primary process identified as responsible for bank erosion can be explained as (i) erosion due to the primary flow

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Fig. 6.6 (a) Identification of river bank erosion process due to slab failure, (b) Tunnel formation by organisms leading to river bank erosion, (c) Agricultural practice in the locality of Hotnagar char and (d) fishing in the river. (Source: Field Survey, 2018)

of the river; (ii) alternating drying and wetting of the bank decreases the material cohesiveness and come under bank erosion; (iii) soil pipe formation where water penetrates through the soil pipes and wet the soil and when the water level falls it carries out the loose materials; (iv) bank undercutting and slab failure and (iv) the organisms living in the bank create holes that also promote bank erosion (Fig. 6.6a, b, c, d). Our study findings are supported by the works of Bag et al., (2019), Laha (2015), and Pal et al., (2016). River bank erosion is a natural phenomenon but some anthropogenic factors augment the erosion rate along with the natural factors. Area devoid of vegetation, variability in water discharge from Farakka Barrage (Guchhait et al., 2016; Islam & Guchhait, 2020), and illegal extraction of soil from the river bank are intensifying bank erosion. Vegetation has a determinant role in controlling soil erosion rate because from studies it has been found that areas with high vegetation density are less avulsion prone with more channel width than an area devoid of vegetation (Anderson et al., 2004; Hession et al., 2003; Smith, 1976). However, the bank material is mainly non-cohesive and younger (younger alluvium), which is prone to erosion. Therefore, the Hotnagar char may face an existential crisis owing to being in an avulsion zone.

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Evolution of Char, Society and Economy Peopling of Char and Associated Geopolitical Issues

A channel bar, locally known as char, usually takes several years to be stabilised because the first two to three years of erosion and deposition continue. Later, few people living outside the char start cultivating the chars until it is enriched with minerals and the char attains the average flood height (Rahman & Rahman, 2012). A study conducted by the Irrigation Support Project for Asia and North East (1993) concluded that almost 90 percent of chars are prone to high erosion and deposition in the first four years of formation and are used for cultivation but after 8 years, they can sustain livelihoods. Therefore, the suitability of cultivation is the primary determinant for developing settlement in a char, and this permanent occupation leads to social conflict. The process of settlement can be classified into two categories, i.e. (i) land reclamation, clearing, and cultivation; (ii) claim over newly emerged char by peasants (Zaman & Alam, 2021). The first process is apparent in the Sundarban region, and the second is found in the floodplain regions. Despite being located in a ‘Moribund’ delta, land erosion and deposition are very evident in the Murshidabad district. The availability of fertile soil and the population pressure in the non-riparian areas attract the population to settle. Classical literature (Mukerjee, 1938) mentioned the importance of Murshidabad in fluxing population, and the population was mainly fishermen. The process continues, but nowadays, most people participating in the land occupation are farmers. The process of occupying lands may result in four types of conflicts, i.e. (1) inter-individual conflict, (2) intra-individual conflict, (3) inter-group conflict and (4) intra-group conflict. The focused group discussion with people living in Hotnagar char, Sujapur, and Mahula villages portray the history of the conflict. During the initial time of char development, it was not suitable for cultivation. When the char developed to a considerable size for economic activities, the people of Hotnagar char and the Sujapur village started migrating to the char for cultivation. Conflicts occurred when the people of Sujapur and the opposite side of the river bank came to occupy the land. Hotnagar village experienced conflicts more than five times in occupying the char. A total of seven people died during conflicts in Hotnagar village before 2018. The inter-group collision also occurred in the char in two groups in 2000, and three people died in the conflict. The local administrative body deployed a few police officers and a small base camp. After the interventions from the administration, the problem seemed to be resolved, but collision is still evident regarding newly emerging lands. Local people think the favour was given to people with political connections and good public relations. Now that the collision has shifted at the intra-group level, the people are fighting among themselves for land ownership. The conflict and the outcomes create socio-economic stress in the population. People who lose their land in the conflict change their occupation from agricultural to non-agricultural activities. About 5% of the total household of Hotnagar migrated from the birth village to another place to avoid conflict. People directly engaged in

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agricultural activities after losing land during the conflict are compelled to change their occupation. The conflict and consequences demotivate the new generation to engage in agricultural activities instead, people prefer to migrate seeking jobs.

4.2.2

Agricultural Distress and Livelihood

River bank erosion, flooding, and extremities of seasonal weather changes make the lives and livelihood of char dwellers most vulnerable (Rahman and Rahman, 2019). In the char area, agriculture is considered the predominant livelihood option and directly or indirectly char dwellers are involved (World Bank, 2013). The cropping pattern of char area is different from mainland agriculture mainly due to the physicochemical composition of the soil (Kabir, 2006). Though in monsoon Asia, Kharif is the primary cropping season, which is identified with the onset of the south-westerly monsoon. However, for char areas, winter is favourable for cultivation due to the comparatively low temperature, high water retention capacity of the soil, and little risk of destroying the crop due to flood. Similarly, in the Hotnagar char, during the monsoon (July to September), cultivation is not possible as the Bhagirathi Hooghly River inundates the agricultural lands and the char becomes a river island. The elevation of Hotnagar char is about 18 to 22 m while the agricultural lands are situated comparatively at a lower elevation that surrounds the village (Fig. 6.7). The Hotnagar char dwellers are small-scale poor farmers, and they are the highest victims of these calamities. After the monsoon, until December or January, the farmers are unable to cultivate as the agricultural lands are under water and agriculture activities are only possible after the re-emergence of the lands. In the winter season (October to February), farmers produce vegetables, wheat, and mustard, while corn, jute, and rice production in the Zaid season (in between winter and monsoon) (Fig. 6.8). About 40% of the farmers become unemployed for about four months (June–September). Another difficulty of Hotnagar char is lacking synchronization between cultivation and marketing due to the worst commutation. Therefore, the farmers cannot sell their crops in the market at the right time, which reduces the market value of the product. Availability of capital is another acute problem for dwellers. If the capital is available, farmers may develop the quality of inputs like developing irrigational facilities, uses of fertiliser, and improving the quality of seeds which may increase the returns. Notwithstanding, the livelihood options in the char land are limited. Agriculture (44%), agricultural labour (27%), non-agricultural labours (15%), and others (14%) are the main occupation in the Hotnagar char. The field survey shows that 6% of cultivators are transformed into other activities due to agricultural distress in the last 20 years. Eventually, the less diversified economic options make the people economically more vulnerable.

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Fig. 6.7 Contour map of the village and the adjoining floodplain (contours are in metres)

4.2.3

Issues of Transport and Accessibility

The present location of Hotnagar char is between the Bhagirathi-Hoogly river and the cut-off near Bhabta station. The cut-off was the old course of the Bhagirathi River. The river flowed through the course during 1943–45, and the village was situated on the right side. As a direct consequence of river migration from east to west during 1943–45 to 1987, the village changed its location from being on the right bank to the east bank. Previously, the village Hotnagar and the administrative offices (Khargram) were both situated on the right side of the river but after the river’s westward shift, the village Hotnagar shifted to the left bank. At the same time, the administrative offices remained on the right side of the river. Therefore, regarding any administrative problem, the char dwellers are bound to cross the river. Due to the difficulties of crossing the river, many people from Hotnagar char have changed their workplaces. From the field survey, it has been observed that bicycles, motorbikes, and rickshaws are the only mode of communication during the lean period, whereas in the monsoon, the village becomes a river island, and boats are the only mode of communication. Every year during monsoon, char dwellers repair their boats, and sometimes, the government provides ferry facilities to the villagers

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Fig. 6.8 Cropping pattern of Hotnagar char in 2018

for transportation. Even if people are diseased or sick at night, they must wait till the morning to access the health facilities as the ferry is available up to the evening. Pregnant women suffer the most. The Hotnagar char is situated 5 km west of the Bhabta railway station (Fig. 6.9) and NH 12 (former NH 34). From the Bhabta station, the only way to reach the village is a kaccha road (unmetalled rural road). Therefore, a sound communication system is the pillar of economic development. However, the under-developing transport system failed to synchronise crop production and marketing. Besides the production of the crop, marketing of produced crops is also important and depends upon the transport network. Therefore, like the other parts of the Bhagirathi-Hooghly floodplain, this area is also dynamic and faces floods. Frequent high discharge flood threatens livelihood in this region, which is established by several studies (e.g. Das et al., 2020a). Floods accelerate bank erosion, and the younger alluvium adjacent to the banks promotes bank erosion during monsoon (Rudra, 2014, 2018). In a floodplain, bank erosion and deposition go hand-in-hand but conflicts emerge regarding land distribution which is evident in the Ganga-Brahmaputra-Meghna delta (Lahiri-Dutt, 2014). These conflicts restrain peaceful settlement and hinder livelihood development programmes.

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Fig. 6.9 Road connectivity near Hotnagar char

Unlike any other char, Hotnagar’s location is one of the reasons for less diversified livelihood options. Alam et al. (2018) mentioned ten livelihood options in a char. Nonetheless, agriculture is the only livelihood option that may increase livelihood vulnerabilities because studies have stated that less diversified economic activities increase communities’ vulnerability (Majumdar et al., 2022; Sarker et al., 2019). A region lacking critical infrastructure is highly vulnerable, and infrastructural development helps to build resilient societies (Vittal et al., 2020). Hotnagar lacks these basic facilities because the study found that during monsoon, the only available transportation facility is the ferry for a limited time. Lacking connectivity options deprive people of access to health facilities, education, and administration. Economic distress is caused by not getting fair prices for agricultural products. Thus, collectively the river bank erosion, social conflicts, and lack of critical infrastructure result in social instabilities in the char. Since Hotnagar has a unique locational shifting behaviour, site-specific management would be necessary. It is found that involving local people to reduce social instabilities through capacity building may improve adaptive capacity.

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Char Evolution, Land Disputes, and Management

India being an agricultural-driven country along with the continuously growing population and the fertile soil of the newly emerging chars, will bring more population to settle and practice agriculture in chars. Almost 70% of the total population relies on land-based activities, whether farming or non-farming, and possession of land is considered a social status and provides security to livelihoods (MLNR, 2013). Therefore, only providing rights to the people will not help make a resilient society in the unstable land, but it needs comprehensive social and economic assistance from the state (Lahiri-Dutt, 2014). In 1963, land distribution policy was recommended to minimise the gap and increase agricultural production but was implemented only in Kerala, West Bengal, and Jammu and Kashmir (Government of India, 1966). The land distribution policies aim to build a utopian society with equal distribution of wealth. However, it is tough to find in reality because during surveys identifying the landless people, measuring the availability of land per head is difficult. Since the land distribution comes under state affairs, most of the Khas (state-owned unused lands) are occupied by politicians or highly influencing people (Mookherjee, 2013).Char is considered a newly emerged land that faces issues regarding land distribution. Violence and social conflict are prevalent issues. So, first, it is essential to identify the permanent chars in the flood plain; the lands suitable for agricultural activities should be allocated for cultivation. Before developing a settlement, it is crucial to provide basic social facilities like schools, communication, health centre, and rehabilitation centre, which may reduce vulnerability and make people resilient against floods (Patrikakis et al., 2019). The succession process took place on the new emerging char at the first stage of its evolution. After succession, agriculture and habitation took place one by one. The research tries to discover the geopolitical issues regarding occupying the char land by the people who are settled near the river bank. Here the Hotnagar char is being affected by geopolitics and the river shifting. In the Bengal delta, the geopolitical problem of occupying land is prevalent, and Hotnagar char is no exception. Due to this kind of situation, the economic condition of a char never developed. Hence, the following measures can be considered to mitigate the problem. The 73rd amendment of the constitution in 1992 gave rights to Gram Sabha for local resource distribution under Panchayati Raj System. Therefore, the local Panchayat should identify the type of land. If old land resurfaces, then it should be allocated to the land owner, and if new lands form, then the land distribution should follow the amended land reform policy of 1955. That stated that the land distribution to the marginalised. So, in this case, the lower income group might be identified, and then the opportunity should be given to practice agriculture on the land with government aid. Because the lands are prone to flood and bank erosion, the cropfailure may make people more vulnerable. If the agriculture practices are profitable, land ownership or right should be provided.

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The agricultural distress of the char dwellers is severe, and the limited livelihood options make them economically more miserable. Levina and Tirpak (2006) investigated two types of adaptation strategies. Char dwellers are taken in Bangladesh, i.e. individual level adaptation based on the local inhabitants’ knowledge and planned adaptation with the help of NGOs and Govt. should be taken. The individual level adaptation includes farming of short-duration cropping varieties, mixed farming, and crop rotations, while planned adaptation may be taken in the form of rainwater harvesting technology and new cropping varieties. Apart from this, water-intensive irrigation facilities may help to cultivate during the summer. The migration to the adjacent areas during the unemployed season may help them to strengthen their economy and also help them to build their capital. The vegetables also failed to reach the market at the right time due to the poor transport system. Therefore, Govt. should look after the problem and help them to get crops and vegetables to market at the right time. The inhabitants must cross the river for administrative purposes; if Govt. can arrange a small centre for the char dwellers, then they can avoid the problem of accessibility to the administrative centres.

5 Conclusions The dynamism of the river is evident from the analysis, and it is proven that the channel planform is active. The images of 1943–45 and 1987 show that the channel has moved drastically 7 km westward. With increasing char area, the OBL is encroaching due to lag deposits. The village’s location has been found on an elevated surface and identified as a dry-point settlement. The activeness of the channel is identified by analysing the meander migration pattern that altered its course by moving 7 km westward, shortening its path due to high sinuosity. That results in the formation of a neck cut-off. The channel shortening changed the char’s location from the right to the left bank. The lag deposition during floods is responsible for continuous siltation, which is responsible for gaining land in the area. The process of ownership of the newly emerging chars causes social conflicts. The process of land occupation has resulted in violence in the village. The major collisions identified in the village are of four types, i.e., (1) inter-individual conflict, (2) intra-individual conflict, (3) inter-group conflict, and (4) intra-group conflict. The conflicts intensify when new lands resurface. In the past, the total death toll is seven. Besides, people have few options for livelihood, and the land distribution issues worsen the situation by making agriculture a source of conflict. Notwithstanding, the administration took an insignificant role except for putting up a police camp and deploying a few policemen. The accusation by the people of the administration is contemptible as the right to land is given to the people having higher public relations. The locational disadvantages from the purview of infrastructural development hinder access to essential social services and access to the market leading to the low-quality livelihood of the residents. The floods turn the village into an island disconnecting all the road connectivity of the village. So, during the monsoon,

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agriculture practice is avoided as floods inundate the agricultural land at relatively lower elevations. The distant location of the village affects the price of their product. During the monsoon, the ferry is the only way of connecting the village to the markets and administrative offices. During this time, those who suffer from diseases and pregnant women are the most affected. Therefore, the overall study portrays that the villagers’ livelihood is at stake. The river dynamism and the geopolitical issue are the major driving factors. Thus, to build a self-sustaining agricultural society, financial assistance from the government is essential. External input will reduce people’s vulnerability during floods. Moreover, financial support and infrastructure development by building primary health centres, schools, and roads will enhance community resilience in the fragile environment. Installing tube wells will increase access to safe and clean drinking water to help attain one of the sustainable development goals (SDG), i.e. SDG 6 (‘access to clean water’). On the other hand, to reduce land distribution issues, the administration should follow the prescribed policies for land distribution and take essential measures to improve the livelihoods of the char dwellers and make a resilient society.

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Pal, R., & Pani, P. (2019). Remote sensing and GIS-based analysis of evolving planform morphology of the middle-lower part of the Ganga River, India. Egyptian Journal of Remote Sensing and Space Science, 22(1), 1–10. https://doi.org/10.1016/j.ejrs.2018.01.007 Pal, R., Biswas, S. S., Pramanik, M. K., & Mondal, B. (2016). Bank vulnerability and avulsion modeling of the Bhagirathi-Hugli river between Ajay and Jalangi confluences in lower ganga plain, India. Modeling Earth Systems and Environment, 2(2), 1–10. https://doi.org/10.1007/ s40808-016-0125-7 Patrikakis, C. Z., Lee, M. R., & Laplante, P. A. (2019). Living on the edge. In I.T. professional (Vol. 21, pp. 16–18). https://doi.org/10.1109/MITP.2019.2910632 Rahman, M. A., & Rahman, M. (2012). Char formation process and livelihood characteristics of char dwellers of Alluvial River in Bangladesh key words. In ICSE6 Paris -august 27-31, 2012 (pp. 145–152). Rahman, M. S., & Rahman, M. A. (2019). Impacts of climate change on crop production in Bangladesh: A review. Journal of Agriculture and Crops, 5(1), 6–14. Rudra, K. (2014). Changing river courses in the western part of the ganga-Brahmaputra delta. Geomorphology, 227, 87–100. https://doi.org/10.1016/j.geomorph.2014.05.013 Rudra, K. (2018). Geography of the Physical Environment Rivers of the Ganga-BrahmaputraMeghna Delta A Fluvial Account of Bengal. http://www.springer.com/series/15117 Sarker, M. H., Koudstaal, R., & Alam, M. (2003). Rivers, chars and char dwellers of Bangladesh. International Journal of River Basin Management, 1(1), 61–80. https://doi.org/10.1080/ 15715124.2003.9635193 Sarker, M. N. I., Wu, M., Alam, G. M. M., & Shouse, R. C. (2019). Livelihood vulnerability of riverine-Island dwellers in the face of natural disasters in Bangladesh. Sustainability (Switzerland), 11(6), 1–23. https://doi.org/10.3390/su11061623 Sengupta, S. (1972). Geological frmework of the Bhagirathi-Hooghly basin. In The BhagirathiHooghly Basin(Proceddings of the Interdisciplinary Symposium) (pp. 3–8). Smith, D. G. (1976). Effect of vegetation on lateral migration of anastomosed channels of a glacier meltwater river. Bulletin of the Geological Society of America, 87(6), 857–860. https://doi.org/ 10.1130/0016-7606(1976)872.0.CO;2 Swamy, A. V. (2010). Anand swami in land and law in colonial India (pp. 1–21). Vittal, H., Karmakar, S., & Ghosh, S. (2020). A comprehensive India-wide social vulnerability analysis: highlighting its influence on hydro-climatic risk. Environmental Research Letters, 15(1), 014005. Wang, W., Zhou, K., Jing, H., Zuo, J., Li, P., & Li, Z. (2019). Effects of bridge piers on flood hazards: A case study on the Jialing River in China. Water (Switzerland), 11(6), 1–14. https:// doi.org/10.3390/w11061181 Williams, P. G. (1986). River meanders and channel size. Journal of Hydrology, 88, 147–164. World Bank, Rapid Social Response, & Global Facility for Disaster Risk Reduction and Recovery (GFDRR). (2013). Bangladesh’s chars livelihoods programme (CLP), case study. Retrieved from https://documents.worldbank.org/curated/en/248441468013823819/pdf/806240WP0P12 680Box037981 Yan, X., Zhang, J., Li, Y., & Sun, L. (2021). Channel migration of the meandering river fan: A case study of the Okavango delta. Water (Switzerland), 13(23), 3319. https://doi.org/10.3390/ w13233319 Yang, X., Guo, S., Deng, X., Wang, W., & Xu, D. (2021). Study on livelihood vulnerability and adaptation strategies of farmers in areas threatened by different disaster types under climate change. Agriculture (Switzerland), 11(11), 1–21. https://doi.org/10.3390/agriculture11111088 Yong, N. S., Mohamad, I. N., & Lee, W. K. (2018). Experimental study on river meander planform pattern. International Journal of Engineering and Technology(UAE), 7(3), 214–217. https://doi. org/10.14419/ijet.v7i3.11.15965 Zaman, M., & Alam, M. (2021). Background, approach, and issues. In M. Zaman & M. Alam (Eds.), Living on the edge. Springer Geography/Springer. https://doi.org/10.1007/978-3-03073592-0_1

Chapter 7

Strategic Infrastructural Development to Promote Sustainable Coastal Tourism Through Geospatial Technology in Purba Medinipur District, West Bengal Biraj Kanti Mondal and Tanmoy Basu

, Aditi Acharya, Ming-An Lee, Sanjib Mahata,

Abstract The current study is intended to assess the existing tourism infrastructure in the coastal areas of Purba Medinipur district of West Bengal, and to focus on its strategic development to promote sustainable coastal tourism and to enhance ecotourism potential through geospatial technology. The present study employed secondary datasets, Google Earth map, and primary surveyed data collected by using a stratified random sampling method using a structured questionnaire. Principle Component Analysis (PCA), Satisfaction Index (SI), and Strengths-Weaknesses-Opportunities-Challenges (SWOC) analysis of the internal and external factor’s estimated matrix of the tourist beaches have been employed to identify the tourists’ utilityfacility zones through the integration of GPS and GIS. The investigation of the existing infrastructure states that the utmost violation of CRZ norms was observed as about 58.54% of establishments of beach resorts and hotels located in the “No Development Zone” in the Mandermoni beach. Furthermore, the study showed that despite the lack of ecotourism facilities, the local stakeholder gets numerous economic benefits and this encouraging influence insists them towards sustainable ecotourism development. The analytical outcomes of the current endeavor find out the potentiality of tourism sites in the study area, which assist to formulate future road map, deliberated progress, constructive plans, and prospects leading toward sustainable tourism development. B. K. Mondal (✉) · S. Mahata Department of Geography, Netaji Subhas Open University, Kolkata, West Bengal, India e-mail: [email protected] A. Acharya Department of Geography, Adamas University, Kolkata, West Bengal, India M.-A. Lee Department of Environmental Biology Fisheries Science, National Taiwan Ocean University, Keelung, Taiwan T. Basu Department of Geography, Katwa College, Purba Barddhaman, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_7

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1 Introduction Globally, coastal tourism holds a very significant position and contributes largely to the economy and India is not an exception. As per the World Travel and Tourism Council (WTTC, 2017a, b), India’s tourism industry is ranked 7th. Many parts of India facilitate coastal tourism, among which the coastal belts of West Bengal have experienced a large volume of tourist influx all through the year and thus, it is immensely imperative for the development of the entire region. Currently, people are traveling to uncommon, unexplored destinations alongside popular tourist spots in coastal Bengal. This “Sweetest Part of India” launched a new circuit connecting “Udaypur-Digh-Sankarpur-Tajpur-Mandarmani-Frazergaunj-HenryIsland-Bakkhali” which has a strong impact on tourism in the coastal belts (Department of Tourism, Govt. of W.B.). The study coastal region of Bengal is immensely significant for various tourist spots and recently Mandarmani, Tajpur becoming popular and gaining affection from visitors. The pleasant climate of the sandy beaches attracts tourists and thus several potential spots have been emerged (Chakraborty, 2010; Mitra et al., 2013), which needs to be promoted by constructing infrastructural development. Digha, is the chief attraction of the coastal tracts of Bengal, which is gaining attention since a very long (Banerjee et al., 2002 Mitra et al., 2013; Mandal et al., 2013; Dandapath & Mondal, 2013). This foremost tourist spot yearly receives about 25 lakh domestic and one lakh foreign tourists (Department of Tourism, Govt. of W.B. [a,b,c, d]; Digha Sankarpur Development Authority, 2013). Mandarmoni, Tajpur, Shankarpur, Junput, Jaladha, Soula and Changasuli, Haripur, Junput, Bhogpur, Bankiput, etc. are the supplementary significant tourist spots of the region (Mandal et al., 2013; Dandapath & Mondal, 2013). This coastal region is the second-highest revenue-earning hub of West Bengal. Currently, Digha has witnessed mass tourism and unprecedented construction boom, and therefore its coastal tract has started facing many negative consequences of tourism. For the sake of development, multi-storied hotels have been constructed on the dunes, nearby the sea wall, especially in Digha, Mandermoni, and Sankarpur. Moreover, lack of proper planning for optimum utilization of coastal resources, neglected solid waste disposal, and illegal encroachment of prohibited coastal zone together with unsustainable livelihood practices have led to the depletion of resources. The complete destruction of the coastal dune complex has intensified the vulnerability of the low-lying, flat, narrow beaches to inundation from storm surges, and astronomic tides. The need of the hour is to address these situations efficiently and geospatial technology has enormous potentiality for the strategic development of coastal tourism. It has been reviewed that some significant works has been completed on tourism and ecotourism in India (Maharana et al., 2000; Chand et al., 2015; Das, 2011; Vinodan & Manalel, 2011; Kala, 2013; Kumari et al., 2010; Das & Chatterjee, 2015; Bhalla et al., 2016; Goodwin & Chaudhary, 2017; Hameed & Khalid, 2018; Karunakaran, 2018; Shamnas et al., 2018; Grover & Mahanta, 2018; Poyyamoli, 2018; Ranjith, 2020; Sachin & Niharranjan, 2020; Sati, 2020; Cabral & Dhar, 2020;); and specifically in West Bengal (Karmakar, 2011; Banerjee, 2014; Ahmed,

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2017; Bhaya & Chakrabarty, 2016; Chakraborty & Mandal, 2018; Ghosh & Ghosh, 2019; Chakrabarty & Mandal, 2019; Mahato & Jana, 2020; Mahato, 2021; Mahato & Jana, 2020; Ghosh et al., 2021); but a very few studies have considered the PCA, SWOC and factor analysis (Datta, 2020), potentiality and appliance of GIS (Bahaire & Elliot-White, 1999; Farsari & Prastacos, 2004; Hasse & Milne, 2005; Fung & Wong, 2007; Widaningrum, 2015; Samanta & Baitalik, 2015; Zarghi et al., 2019; Taye, 2019; Ambecha et al., 2020; Roque Guerrero et al., 2020). Thus, in this fresh attempt to record the scenario of tourism infrastructure and growth of alternative tourism and its prospect in the coastal tract of West Bengal instead of the existing form of tourism. The key objective of the current study is to examine the potentiality of ecotourism in coastal Bengal concentrating on its infrastructural facility, satisfaction level, and recognize the probable spots of tourism by analyzing facilities by SWOC and PCA study, and demonstrate its mapping. Henceforth, the study aims to promote ecotourism spots through SWOC, PCA, and factor analysis by cultivating geospatial techniques; thus the satisfaction level is completed to find out the necessity of infrastructural development.

2 Materials and Methods 2.1

Study Area

The present study covers the coastal belts of West Bengal mostly located in Ramnagar-I & II, Contai-I & Deshopran blocks in Purba Medinipur district. The area is positioned between 21°36′40″N to 21°53′37″N latitude and 87°28′57″E to 87°53′15″E longitude (Fig. 7.1). This coastal belt located in the west side of River Hugli is a contiguous section of deltaic Sundarbans (Chakraborty, 2010). Since the British period, Digha (New Digha, Old Digha) earned its reputation as one of the major tourist spots in West Bengal. Besides, Mandarmoni, Sankarpur, and Tajpur are the most popular and significant tourist spots of this coastal belt. The present study mainly focuses on the available infrastructure in the undeveloped coastal tourist beaches namely Jaladha, Soula and Changasuli, Junput, Bankiput, Haripur, Bhogpur, etc.

2.2

Methodological Framework

The present study employs suitable methodological steps with primary field survey. Secondary data have been collected from various government reports and documents, such as the Census of India (2011a, b), Development and Planning Department’s district human development report (2011) of Purba Medinipur district; The Economic Times (2017), UNCTAD (2011), UNWTO Annual Report (2015), and WTTC (2010, 2017a, b). The sequential steps illustrated in the figure (Fig. 7.2) present the structured framework of the study.

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Fig. 7.1 Setting of the location of study area focusing on existing transport and settlements

Phase I

Phase II

Phase III

Phase IV

Construction of the base concept

Literature review of published research

Methodology formulation and collection of necessary data

Analysis of the scenario of tourism infrastructure and scope of potential of tourism development

Study area selection

Selection of research aim and objectives

Data analysis using statistical and geospatial techniques

Result and discussion

Fig. 7.2 Methodological framework of the study

2.3 2.3.1

Scenario of Infrastructural Condition of the Tourist Spots GPS Survey

The infrastructure of the study beaches was collected and integrated through geospatial techniques (RS, GIS, & GPS) to depict the existing condition and future requirement to promote ecotourism development. Geotagging is employed to find the exact position of captured images and it helps to prepare a facility map.

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Geotagging is the process of adding geographic coordinates (X & Y) to digital photos. All infrastructural and others facilities have been collected by using this technology.

2.3.2

GPS and GIS Integration

In the study, RS data, GPS data positioning data, processing method, and data display techniques are integrated using the GIS platform using ArcGIS software (ArcGIS 10.3 of ESRI). The existing tourism infrastructure is obvious for evaluating the ecotourism development, and to establish its connection with the economic growth, which is incurred in the study. In the case of social infrastructure, education, health, commercial, etc. facilities are being included. Time period considered for the infrastructure study is 2016–2018 fields served data with the GPS points including geo-tagging photographs. Based on these, facility mapping (FM) was carried out to identify infrastructure to ensure the required infrastructure and related planning. It helps to identify the beach-wise facility, and the beach-wise utility facility zone has been demarcated using the buffer tool and thus beach-wise 100 m and 500 m catchment area facility has been identified.

2.3.3

Overlay Analysis

To prepare composite maps, overlay function has been created by using the GIS platform in the current study. It is used to identify the facility gap areas by detecting proximity relationships. It helps in delineating zones or areas of influence around a particular facility. After identifying 100 m and 500 m catchment areas of each facility, overlying all the facilitated areas layers, the areas of influence around a particular facility are prepared.

2.4

Principle Component Analysis of the Economic Impacts of Tourism Development

A structured questionnaire survey was conducted in the study area to depict residents’ perspective on the economic impact of tourism in Soula and Changasuli beaches, especially in Dakshin Purushottampur village. About 18 questions were assigned on economic impacts of tourism and reactions were captured depending on Likert scale (5-point scale, where 1 = strongly disagree and 5 = strongly agree). The sample range is selected using the formula below (Yamane, 1967, cited in Israel, 1992):

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Table 7.1 Sampling detail Location Dakshin Purushottampur

Household (population) 476 (2394)

Stratified random sampling 2394 × 150

Sample volume 150

Source: Census of India (2011a, b); Authors’ calculation

n = N= 1 þ N e2

ð7:1Þ

(Where; ‘n’ is the sample size; ‘N’ is the population size; ‘e’ is the level of precision) The study maintains 95% confidence level and ±5% precision level, 150 respondents in Dakshin Purushottampur village (2394) were surveyed in 2018 (January to April) by direct interviews using a stratified random sampling approach. Finally, 145 questionnaires were considered, and the remaining five questionnaires were eliminated due to incomplete answers. Furthermore, 18 items of economic impacts of tourism were selected for factor analysis and principal component analysis (PCA). The sampling detail is tabulated below (Table 7.1).

2.5

Satisfaction Index of the Tourist Spots

In order to provide an attractive and friendly environment for attracting tourists, it is imperative to provide various available facilities, evaluate them, and find out grounds for dissatisfaction including the areas requiring special attention. The satisfaction level of available facilities of the tourists at New Digha, Old Digha, Shankarpur, Tajpur, and Mondermoni, the factor-wise satisfaction index was calculated. About 105 number tourists were interviewed in the mentioned beaches and gathered information, and Satisfaction Index has been prepared to depict the status of various infrastructural facilities provided in the tourist spots. Another set of interview has been completed in the coastal villages considering accessible undeveloped beaches i.e. Bankiput (considering Bankipur, Tikhola, and Perijpur coastal village), Bhogpur (considering Gopalpur and Bhogpur coastal village), Junput (considering Dakshin Kadua and Biramput coastal village), Haripur (considering Shyam Jalpai, Mankarai Put, Sharad Pur, and Baguran Jalpai coastal village), Soula and Changasuli (considering Dakshin Purushottampur coastal village) and Jaladha (considering Jaldha, Chandapur, and Jamra Shyampur coastal village). The collected quantitative dataset has been analyzed by the Principal Component Analysis (PCA) for economic impact assessment and the opinion of the local people has been considered regarding the acceptance as an ecotourism site.

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SWOC Analysis

The SWOC analysis has been carried out by quantitative analysis to identify highpotential ecotourism spots of the study area. It is worthwhile to identify the areas requiring special attention as there is a general dissatisfaction regarding drinking water and parking facilities. The tourists visited in those areas are very much interested to explore some nearby alternative organized tourist spots and to explore this it is very necessary to identify the all available spots. After identification of the available undeveloped tourist area, it is very necessary to know out of those spots whose strengths are much greater than its weakness. For each beach spot, 100 inhabitants have been surveyed from March to May 2018 and January to April 2019 selecting the target villagers by the random sampling method. The secondary data for SWOC have been collected at different times. The obtained data by questionnaires (consisting of 33 questions) survey were analyzed using computer software to determine the factor analysis for the strategic enhancement of ecotourism in a sustainable way. Initially, the SWOC study was known as SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats), in which the ‘Threats’ were reframed as ‘Challenges’ and this research method is often used to natural resource and tourism management (Schmoldt et al., 2001; NOAA, 2011). In the study, internal factors are strengths and weaknesses, while the external factors are opportunities and threats (Harfst et al., 2010). The SWOC study was accomplished in six beaches to ensure the sustainable tourism development through employing the internal factor estimate matrix (IFEM) and external factor estimate matrix (IEEM). Each factor was given weights between zero and one (‘0’ = not important and ‘1’ = most important) in a uniform and similar manner. Further, each factor was scored between one and five (1 = poor; 2 = lower than average; 3 = median; 4 = above average; and 5 = good). Based on the calculated weight and score, the final weighted score has been assigned to each factor. The >2.5 weighted scores indicate strengths are higher than weaknesses and opportunities are more than strengths in IFEM and EFEM, respectively.

3 Results and Discussion 3.1

Tourist Infrastructure and Utility Facility Zone

Unplanted tourism is developed in this coastal area and currently there was a huge tourist influx (one lakh foreign and 25 lakh domestic) per year visited the place (Department of Tourism, Govt. of W.B. (n.d.-a–d). The tourism infrastructure includes the infrastructural facilities, network of tourism market, and distribution of tourism products (Development & Planning Department, 2011). It also includes tourism infrastructure of (a) Physical (Hotels & Resort, Transportation, Water, Electricity); (b) Cultural (Fairs and Festivals etc.); (c) Service (Banking facilities,

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Travel agencies, Health facilities, Sanitation facilities, etc.); and (d) Governance (Police Station, Govt. Offices). The fields survey observed the infrastructural condition in the study area and which is mapped using ArcGIS environment. After generating photos (geotagging photographs) to the point layer, this is an overlay to Google Earth Image 2018 and create along the beach wise (mainly New Digha, Old Digha, Shankarpur, Tajpur, and Mondarmoni) facility zone. Using the buffer tool along the beach, 100 m and 500 m catchment areas or 100 m and 500 m beach-wise facility zones have been identified. The present available infrastructure facilities are shown in the following figures of Old Digha (Fig. 7.3a), New Digha (Fig. 7.3b), Shankarpur (Fig. 7.3c), Tajpur (Fig. 7.3d), and Mondarmoni (Fig. 7.3e).

3.2

Zonation of Facilitated Area

Based on the beach-wise available facility within 500 m area (mainly New Digha, Old Digha, Shankarpur, Tajpur, and Mondarmoni) high, medium, low, and lack facilitated areas have been identified using overlay analysis. With respect to all available facilities in the beach to 500 m, catchment area has been divided into three zones. The high-facilitated area has been demarcated by all the available facilities within the 100 m area; while low-facilitated areas within 500 m and in-between high and low-facilitated areas 250 m catchment area have been demarcated mediumfacilitated zone. Each facility has been buffered with 100 m, 250 m, and 500 m and lack areas, where no facilities are available were identified. It was identified that except for Mondermoni beach no shelter house is available within 500 m. During a storm attack, it is very difficult. Only Digha has one State General hospital, another hospital is found, and another hospital is found almost 35 km. from Digha i.e. Contai Sub-divisional Hospital. So, during any health hazard and the natural hazard, it is very difficult to arrange proper treatment for the tourist. Except Old Digha, in all other tourist spots, only one public toilet (very unhygienic) has been available, i.e. not sufficient for the present tourist influx. It was observed that Old Digha consists of the highest concentration of hotels and resorts available within 500 m, ATM facility available within 500 m and 100 m, and Doctors’ private clinic available within 100 m of tourist destinations, whereas New Digha consists the highest concentration of pay-and-use toilet available within 500 m. The pay-and-use toilet available within 100 m is highly concentrated in both New Digha and Old Digha. This facility is also equally concentrated in Shankarpur, Tajpur, and Mandermoni. Mandermoni consists of the highest hotels and resorts facility available within 100 m. There is no ATM facility available in the tourist destinations of Shankarpur, Tajpur, and Mandermoni. Hospital and ambulance facilities within 500 m are available only at Old Digha (Figs. 7.4 and 7.5).

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Fig. 7.3 Existing infrastructural facility in Old Digha (a); New Digha (b); Shankarpur (c); Tajpur (d); Mondarmoni (e)

3.3

Scenario of Violation of Coastal Regulation Zone Norms

The violation of Coastal Regulation Zone (CRZ) norms is extensively observed, especially in Old Digha, New Digha. In Old Digha, the majority of the resorts and hotels are positioned in ‘No Development Zone’ (NDZ). This type of violation was

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140 120

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Hotel & Resort Facility (within 100m) 30

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Available Public Toilet Facility 4.5

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Fig. 7.4 (a) Existing hotel & resort infrastructure facility within 500 m & 100 m. (b) Existing toilet & ATM facility within 500 m & 100 m Tourism Infrastructural Facilities in Old Digha and New Digha 14 12

New Digha

Old Digha

Number

10 8 6 4 2 0

Fig. 7.5 Existing Tourist infrastructure facility within 500 m in Old & New Digha

also detected in the Sankarpur beach but it is apparently less in Tajpur. The extreme violation of CRZ regulation was noticed in the Mandermoni beach (above 80% resorts and hotels are located in NDZ). The study area has witnessed a massive influx of tourists, which appends to the increasing pollution, wastage, and more water requirement, which ultimately jounce enormous pressure on local habitats and

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Fig. 7.6 Coastal Regulation Zones (CRZ) in the study area

infrastructures. About 20.51% CRZ area is covered by the resorts and hotels, especially the Mandermoni beach (58.54% of NDZ) (Fig. 7.6). Presently, all these beaches are constantly expanding, which creates rapid and incessant beach erosion, a gradual retreat of the shoreline, and a sequel of jeopardizing people, habitation, and environment.

3.4 3.4.1

Economic Impacts of Tourism Development Using PCA Characteristics of Respondents

A total of 145 respondents surveyed including 66.90% males and 30.10% females with the age of 20 to >60 years, where the highest was 20–40 years old (49.66%), the second was 41–60 years old (42.07.1%), and only 8.28% were >60 (Table 7.2). Among the respondents Students (6.21%), Fisherman (33.79%), Fisherman & per Day worker (30.34%), Farmers (8.28%), Per Day workers (16.55%), and Others are engaged (4.83%) are the main profession. The academic qualification of the respondents was graduation (9.66%), high school (39.31%), secondary school (40.69%), and primary school (10.34%). Considering the monthly income of respondents, it is dived into four groups i.e. under 4000 Rs. (8.97%), Rs.4000 to 6000 (43.45%), Rs.6000 to 8000 (40%), and more than 8000 Rs. (7.59%).

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Table 7.2 Respondents’ socio-economic profiles Respondents’ profiles Variables Gender Male Female Age (years) 20–40 41–60 60 above Education Primary school or lower Secondary school High school Higher Education (B.A & B. Tech)

Na (145)

%b

97 48

66.90 33.10

72 61 12

49.66 42.07 8.28

15 59 57 14

10.34 40.69 39.31 9.66

Variables Occupation Student Fisherman Fisherman & per day worker Farmer Per day worker Other Income (monthly) Under 4000 Rs. Rs.4000 to 6000 Rs.6000 to 8000 More than 8000 Rs.

Nc(145)

%d

9 49 44

6.21 33.79 30.34

12 24 7

8.28 16.55 4.83

13 63 58 11

8.97 43.45 40.00 7.59

Source: a,bPrimary field survey; c,dAuthors calculation

3.4.2

Respondents’ Perception on Impact of Tourism by Factor Analysis

It reveals from the factor analysis that nearly all respondents are agreed to “tourism improves the local economy and earn greater income” (mean = 4.83). It “improves local economy” (mean = 4.17) and “people gain benefit from rent at a higher price” (mean = 3.33) (Table 7.3). Moreover, they also agree with “increases price of land” (mean = 3.25), and “increases cost of living” (mean = 3.08) and the lowest mean value is “jobs may pay low wages” (mean = 1.42). The collective mean value of positive economic impacts of tourism is 4.36. It signifies that tourism “improves local economy” (mean = 4.17) and “residents earn greater income” (mean = 4.83). On the other hand, the negative economic impacts of tourism are very low (mean = 2.45). The factor analysis of 18 items was carried out for the perception study of the economic impacts of tourism in Dakshin Purushottampur village. Principal component analysis showed the existence of four components with Eigenvalues exceeding 2, explaining 64.01%, 25.06%, 7.81%, and 3.12% of the variance, respectively (Table 7.4). The first factor of “economic benefits” signifies that tourism generates numerous economic benefits in the 1st and 2nd phases. The second factor “higher costs of living” signifies that tourism increases privileged costs of living for local inhabitants. The third factor “economic barrier” signifies that ‘increase in road maintenance and transportation systems costs’ has shown a negative trend in the 1st phase but the same factor shows a positive trend in the 2nd phase. The ending factor “supports local economy” signifies the same trends in the 1st phase that ‘local people income through rent’ and in the 2nd phase ‘increases in tax revenues’. The

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Table 7.3 Respondents’ perception on impacts to economy Respondents’ perception Positive impacts to economy Intension for greater income Improvement of local economy Open up new business opportunities Income from selling local products Bringing more investment in local areas Increase in tax revenues Local employment opportunity Improvement public utilities infrastructure Income through rent Negative impact to economy Increase in price of land Increase cost of living Competition for land with other economic uses Increase in price of goods and services Increase in road maintenance and transportation costs Requirement of specialized labor for tourism related services Profit goes to foreign investors Cost for additional infrastructure (water, power, etc.) Jobs may pay low wages

Mean

S.D

Rank

Variability

4.83 4.17 4.33 4.25 3.83 3.75 3.75 3.67 3.33

0.67000 0.84984 0.62361 0.73598 0.47140 0.54006 0.35355 0.42492 0.71686

1 2 3 4 5 6 7 8 9

13.87 20.38 14.40 17.32 12.31 14.40 9.43 11.58 21.53

3.25 3.08 3.00 3.00 2.58 2.33

0.20412 0.11785 0.35355 0.40825 0.31180 0.23570

1 2 3 4 5 6

6.28 3.83 11.79 13.61 12.09 10.12

1.92 1.50 1.42

0.65617 0.70711 0.58926

7 8 9

34.18 47.14 41.50

Scale: 5 = Strongly Agree, 4 = Agree, 3 = Neutral, 2 = Disagree, 1 = Strongly Disagree Source: Calculated by the authors

PCA of all these four components explained that the eigenvalues exceeding to variance between the factors, respectively (Fig. 7.7). The figures (Fig. 7.8b) reveal that most of the variables of components 1, 2, & 3 are positive, only variable 5 in component 2 and variable 2 have created negative impacts as economic barriers to the local economy. The increasing trend has been observed for the factor “profit goes to foreign investors” in the 2nd phase of planning and infrastructural development. The other factors, such as “increase in road maintenance” and “transportation systems costs” show a negative trend in the 1st phase and positive trend in the 2nd phase. In this context, impact factors and barriers to the development should be considered in the phases of planning. The 2nd phase promotes the local investors as well as includes the “threat” of the “profit goes to foreign investors”. Thus, the “cost for additional infrastructure (water, power, as such)” has been increasing in the 3rd phase. The road map should be prepared by prioritizing proper planning and maintenance of the other infrastructures to reduce the effect of economic barriers and strengthen the infrastructural base. The connection of the aforesaid factors with the improvement of confined market, neighboring employment opportunities, and opening up new opportunities should also be emphasized to uplift community participation and enhance the income benefits.

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Table 7.4 Output of principal component analysis

Major factor Economic benefits

Higher costs of living

Economic barrier

Supports local economy

Sub factor Intension for greater income Income from selling local products Bringing more investment in local areas Improvement of local economy Local employment opportunity Open up new business opportunities Increase in price of land Increase in price of goods and services Increase cost of living Competition for land with other economic uses Cost for additional infrastructure (water, power, etc.) Jobs may pay low wages Requirement of specialized labor for tourismrelated services Profit goes to foreign investors Increase of road maintenance and transportation costs Income through rent Improvement of public utilities infrastructure Increase in tax revenues

Source: Calculated by the authors

Rotated component matrix 2nd 1st phase phase (barrier (barrier 0.80) 0.90) 0.118 0.967

Total 4.081

% of variance 68.019

Cumulative % 68.019

0.949

-0.155

1.021

17.022

85.04

0.825

0.283

0.477

7.952

92.992

0.954

0.146

0.225

3.755

96.747

0.788

0.411

0.168

2.794

99.541

0.837

0.22

0.028

0.459

100

0.955 0.911

0.132 0.197

2.697 0.752

67.424 18.807

67.424 86.231

0.711 0.197

0.419 0.965

0.44 0.111

10.991 2.778

97.222 100

0.906

–

1.909

38.18

38.18

0.826 0.419

– –

1.372 0.951

27.43 19.019

65.61 84.629

–

0.858

0.504

10.077

94.706

-0.422

0.741

0.265

5.294

0.942 0.806

– 0.5

2.363 0.469

78.762 15.635

0.311

0.944

0.168

5.603

Initial eigenvalues

100

78.762 94.397 100

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Fig. 7.7 Component plot of factor analysis

Fig. 7.8 (a) Factor analysis of support to local economy. (b) Component plots of economic barriers

3.5

Satisfaction Index of Tourists Spots

The satisfaction level of tourists is considered here as an imperative tool for developmental assessment; thus indicator-wise average value is used to compute the satisfaction index and further the ranks were assigned (Table 7.5). The indicator-

Distribution of respondents’ satisfaction level Excellent (%) New Old Digha Digha Sl. Factor distribution of respondents 1 Parking and conveniences 3.81 14.29 2 Shopping and auxiliary facilities 1.90 8.57 3 Provision of water sports 0.00 0.00 4 Designed rest areas 9.52 23.81 5 Safety concerns 0.00 11.43 6 Bathing and other sanitary facility 0.00 33.33 near coasts 7 Drinking water facility 1.90 22.86 8 Congestion of site 7.62 10.48 9 Rescue center from natural disaster 0.00 1.90 10 Beautiful scenery 22.86 30.47 11 Availability of nightlife 0.00 2.86 12 Health 0.00 13.33 13 Food 15.24 21.90 Factor wise average of satisfaction (Ni) New Old Sl. Factor distribution of respondents Digha Digha 1 Parking and conveniences 4.35 6.45 2 Shopping and auxiliary facilities 4.28 4.55 3 Provision of water sports 1.64 1.00 4 Designed rest areas 4.63 5.87 5 Safety concerns 3.98 5.84 6 Bathing and other sanitary facility 3.57 6.61 Tajpur 1.90 0 0 0 0 0 0 0 0 39.05 0 0 0.95

Tajpur 2.44 2.30 1.00 3.06 2.78 2.35

Shankarpur 0 0 0 0 0 0 0 0 0 1.90 0 0 0.95

Shankarpur 2.46 3.10 2.00 2.36 3.58 3.02

Mondarmoni 2.92 3.20 4.24 3.24 4.21 3.40

0 0.95 0 17.14 8.57 0 8.57

Mondarmoni 0.95 0 4.76 0 1.90 0

Shankarpur 74.29 90.48 95.24 86.67 87.62 94.29

36.19 9.52 80.00 3.81 31.43 69.52 80.00 43.81 86.67 40.00 11.43 9.52 76.19 35.24 88.57 55.24 13.33 84.76 24.76 5.71 48.57 Factor wise satisfaction rank New Old Digha Digha Shankarpur 6 4 8 7 11 5 13 13 11 5 5 9 8 6 4 11 3 6

Unsatisfied (%) New Old Digha Digha 32.38 6.67 45.71 37.14 80.00 95.24 17.14 10.48 49.52 8.57 59.05 6.67

Table 7.5 Satisfactory status and rank of New Digha, Old Digha, Shakarpur, Tajpur, and Mondarmoni

Tajpur 10 12 13 5 6 11

76.19 80.00 73.33 2.86 71.43 83.81 39.05

Tajpur 82.86 82.86 95.24 92.38 81.90 80.95

Mondarmoni 13 10 4 9 5 8

66.67 58.10 60.00 15.24 45.71 71.43 25.71

Mondarmoni 52.38 64.76 54.29 70.48 57.14 71.43

140 B. K. Mondal et al.

Drinking water facility Congestion of site Rescue center from natural disaster Beautiful scenery Availability of nightlife Health Food

Source: Calculated by the authors

7 8 9 10 11 12 13

4.77 6.51 3.78 7.28 3.22 3.61 5.67

5.74 4.58 4.99 7.54 4.42 5.23 6.65

2.84 3.64 1.27 5.14 2.21 1.40 4.47

2.49 3.74 3.51 7.77 2.64 2.74 4.23

3.04 3.09 3.55 6.81 4.61 3.52 4.98

4 2 9 1 12 10 3

7 10 9 1 12 8 2

7 3 13 1 10 12 2

9 3 4 1 8 7 2

12 11 6 1 3 7 2

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wise distributions of each tourist spot (i.e. New Digha, Old Digha, Shankarpur, Tajpur, and Mondermoni) have been analyzed accordingly.

3.5.1

New Digha

The factor-wise level of satisfaction of New Digha (Table 7.5) signifies that the beautiful scenery is satisfactory but some tourists are not happy with the provision of water sports, safety concerns, and rescue center for natural disasters, availability of nightlife, and health facilities. New Digha is a very popular tourist spot; therefore, special emphasis should be given to the expectations of the tourist (Digha Sankarpur Development Authority, 2013). It is clear that tourist has ranked beautiful scenery and congestion of site as the first and second positions, which means there is scope to be improving serviceability.

3.5.2

Old Digha

According to a tourist interviewed at Old Digha, the condition of shopping and auxiliary facilities is excellent (8.57%), good (15.24%), satisfactory (34.29%), and unsatisfactory (37.14%). The other management factors like provision of water sports, availability of nightlife, rescue centers from natural disasters, etc. are not standard and satisfactory according to tourist requirements (Table 7.5). The beautiful scenery is the most favorable factor at Old Digha, followed by food; while the other management factors are least favorable for tourists. There is enough scope to improve the standards of drinking water facilities, safety concerns, shopping, auxiliary facilities, and water sports.

3.5.3

Shankarpur

Most of the tourists are only happy with the beautiful scenery at Shankarpur but the other management factors i.e. availability of infrastructure is not up to the mark (Table 7.5). It reveals that there is scope to develop the infrastructural facilities, management factors from a satisfactory level to an excellent level.

3.5.4

Tajpur

The major tourist attraction at Tajpur is the beautiful scenery but there is a scope to improve the status of shopping and auxiliary facilities, the provision of water sports, bathing, and other sanitary facilities near coasts, etc. (Table 7.5). The tourists ranked beautiful scenery and food as the first and second; while shopping, auxiliary facilities, and provision of water sports at the lowest position. It reveals that shopping and auxiliary facilities must be improved to promote ecotourism at Tajpur.

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Mondermoni

The Mondermoni beach is becoming popular day by day due to its beautiful scenery but nobody has registered their views as excellent for drinking water facility, shopping, auxiliary facilities, and other management facilities (Table 7.5). It indicates that total satisfaction of all the tourists is not achieved at that place and there is scope to develop further at an excellent level.

3.6

SWOC Analysis of Internal and External Factors of the Potential Undeveloped Sectors

After primary and secondary data collection, SWOC analysis was performed to assess the feasibility of ecotourism tourism (Table 7.6) by analyzing the results and determining the priorities.

3.6.1

Internal (IFEM) and External (EFEM) Factor Estimate Matrix of Jhalda Beach

Ten factors are identified for strengths and weights are between 0.0522 and 0.1111 and the score ranges from 1 to 4 for Jhalda beach (Table 7.7). Eleven factors of weaknesses are detected with the weight of 0.0457 to 0.0065 and the score ranging between 2 and 3. The final weighted score (2.4575) signifies that strengths are more strong than weaknesses. Furthermore, the weights between 0.0429 and 0.1210 and scores between 1 and 3 are observed for the eleven factors of opportunities (Table 7.7) and five threats are determined (weight of 0.0039 to 0.0351 and score 1). The final weighted score (1.7148) suggests that opportunities are more than threats.

Table 7.6 Coastal villages considering the accessible undeveloped beach Sl. No. 1 2 3 4 5 6

Local Beach Name Bankiput Bhogpur Junput Haripur Soula and Changasuli Jaladha

Coastal Village Name Bankipur, Tikhola and Perijpur Gopalpur and Bhogpur Dakshin Kadua and Biramput Shyam Rai Bar Jalpai, Mankarai Put, Sharad Pur and Baguran Jalpai Dakshin Purushottampur Jaldha, Chandapur and Jamra Shyampur

Source: Authors’ estimation based on primary field survey

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Table 7.7 Weighted Score for strengths and weaknesses [Internal factor estimate matrix; (IFEM) and External factor estimate matrix (EFEM)] for Jaldha, Soula & Changasuli, Haripur, Junput, Bhogpur, and Bankiput Beaches Internal factor estimate matrix; IFEM Strengths (S) Weighted Score Jhalda Soula & Changasuli S1 Existing transport 0.0980 0.1437 facility (mainly major pucca road to beach distance) S2 Scenic beauty 0.1307 0.3921 S3 Beach length 0.2745 0.4444 (maximum) S4 Beach to village 0.4183 0.3398 distance S5 Distance of cyclone 0.4444 0.0522 rescue center from natural disaster form beach 0.0719 0.1176 S6 Existing infrastructure facility (like electricity, toilet, drinking water) 0.2745 S7 Cultural perspective 0.0588 (any local cultural occasion) 0.1569 0.1960 S8 Tourist interest for ecotourism (visiting Digha, Shankarpur, Mondarmoni and Tajpur) S9 Nearest town / rail0.3399 0.3921 way station distance 0.0523 0.41830 S10 Road distance from nearest town Weaknesses (W) W1 People interest for 0.0588 0.0392 other occupations (like seasonal tourism/other) W2 Unwillingness for 0.0131 0.0522 tourism 0.0392 W3 Local people’s inter- 0.0261 est (mass/eco tourism) W4 Local people’s edu0.0784 0.0196 cational structure W5 Local people’s occu- 0.0655 0.0653 pational structure

Haripur

Junput

Bhogpur

Bankiput

0.0588

0.0588

0.0588

0.0588

0.1830 0.4444

0.1830 0.3333

0.0849 0.1307

0.1699 0.0653

0.1568

0.0653

0.3921

0.1437

0.3137

0.1699

0.1437

0.1568

0.1699

0.2352

0.1568

0.1830

0.0522

0.0522

0.0522

0.0522

0.2941

0.2156

0.2745

0.2941

0.0653

0.3921

0.3137

0.3137

0.0718

0.4183

0.3333

0.3333

0.0392

0.0130

0.0588

0.0784

0.0522

0.0392

0.0522

0.0588

0.0392

0.0065

0.02614

0.0392

0.0196

0.0457

0.0196

0.0196

0.0653

0.0784

0.0915

0.0915 (continued)

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Table 7.7 (continued) W6

W7

Local people’s satisfaction for their income Local people’s interest for skill up gradation and capacity building (tourism perspective)

0.0784

0.0784

0.0784

0.0326

0.0653

0.0653

0.0915

0.0915

0.0915

0.0261

0.1176

0.0784

3.1568

3.1568

2.366

2.3725

2.2026

0.4843

0.3632

0.3632

0.2421

0.3632

0.4531

0.2265

0.3398

0.2265

0.3398

0.4218 0.2929 0.1289

0.2109 0.2929 0.2695

0.3164 0.2695 0.1640

0.2109 0.2695 0.1640

0.3164 0.2695 0.1640

0.1757

0.2460

0.1953

0.2929

0.1953

0.1015

0.1484

0.1484

0.1484

0.1484

0.1796

0.0429

0.0429

0.0429

0.04296

0.1640 0.1484 0.1328

0.0507 0.1992 0.1757

0.0507 0.0664 0.1171

0.0507 0.0664 0.1171

0.0507 0.0664 0.1171

0.0039

0.0078

0.0039

0.0039

0.0039

0.0234

0.0234

0.0117

0.0117

0.0117

0.0273

0.0351

0.1054

0.0546

0.0546

0.0195

0.0195

0.0195

0.0390

0.0390

0.0351

0.0546

0.0273

0.0703

0.0703

2.7929

2.3671

2.2421

2.0117

2.2539

Total 2.4575 External factor estimate matrix; EFEM Opportunities (O) O1 Seasonal tourism 0.2421 (December to March) O2 Temporary accom0.05078 modation facility (tent) O3 Bio-toilet 0.2109 O4 Filter drinking water 0.3398 O5 Beach beautification 0.0585 (lighting and seating temporary) O6 Solid waste and 0.2460 sewage treatment plant O7 Open canteen and 0.0664 tent delivery for food O8 Water sports 0.0742 (boating, ski) O9 Sun bath facility 0.0429 O10 Sea food 0.0898 O11 Local handicraft 0.1953 workshop / documentary film on fisherman/ photography Challenges (C) C1 Medical and other 0.0039 facilities C2 Sanitation and 0.0117 cleanness C3 Police and adminis0.0273 trative help C4 Online weather fore- 0.0195 casting monitor C5 Security alert for 0.0351 each tent Total 1.7148 Source: Calculated by the authors

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Internal (IFEM) and External (EFEM) Factor Estimate Matrix of Soula & Changasuli Beach

In case of Soula & Changasuli beaches, the weights of strengths range from 0.0718 to 0.1045 and score ranges from 1 to 4; weight ranges of weaknesses are 0.0065 and 0.0457 with the score ranges between 2 and 3. The final weighted score (3.1568) signifies that strengths are more than weaknesses. Moreover, weights between 0.0429 and 0.1210 and scores between 2 and 4 for opportunities (Table 7.7); while the threats weight ranges from 0.0039 to 0.0351 and scores between 1 and 2. The final weighted score (2.7929) suggests that opportunities are more than threats.

3.6.3

Internal (IFEM) and External (EFEM) Factor Estimate Matrix of Haripur Beach

In case of Haripur beach, the weights of strengths range from 0.0522 to 0.1111 and score ranges from 1 to 4; weight ranges of weaknesses are 0.0065 and 0.0457 with the score ranges between 2 and 3. The final weighted score (2.2679) signifies that strengths are more than weaknesses. Moreover, weights between 0.0429 and 0.1210 and scores between 1 and 3 for opportunities (Table 7.7); while the threats weight ranges from 0.0039 to 0.0351 and scores between 1 and 2. The final weighted score (2.3671) suggests that opportunities are more than threats.

3.6.4

Internal (IFEM) and External (EFEM) Factor Estimate Matrix of Junput Beach

In case of Junput beach, the weights of strengths range from 0.0522 to 0.1111; weaknesses weight ranges from 0.0065 and 0.0457 with the score ranges between 1 and 2. The final weighted score (2.3660) signifies that strengths are more than weaknesses. Moreover, weights between 0.0429 and 0.1210 and scores between 1 and 3 for opportunities (Table 7.7); while the threats weight ranges from 0.0039 to 0.0351 and scores between 1 and 2. The final weighted score (2.2421) suggests that opportunities are more than threats.

3.6.5

Internal (IFEM) and External (EFEM) Factor Estimate Matrix of Bhogpur Beach

In case of Bhogpur beach, the weights of strengths range from 0.0522 and 0.1111; weaknesses weight ranges from 0.0065 and 0.0457. The final weighted score (2.3725) signifies that strengths are more than weaknesses. Moreover, weights between 0.0429 and 0.1210 and scores between 1 to 3 for opportunities (Table 7.7); while the threats weight ranges from 0.0039 to 0.0351 and scores

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between 1 and 2. The final weighted score (2.0117) suggests that opportunities are more than threats.

3.6.6

Internal (IFEM) and External (EFEM) Factor Estimate Matrix of Bankiput Beach

In case of Bankiput beach, the weights of strengths range from 0.0522 and 0.1111; weaknesses weight ranges from 0.0065 and 0.0457. The final weighted score (2.2026) signifies that strengths are more than weaknesses. Moreover, weights between 0.0429 and 0.1210 and scores between 1 and 3 for opportunities (Table 7.7); while the threats weight ranges from 0.0039 to 0.0351 and scores between 1 and 2. The final weighted score (2.2539) suggests that opportunities are more than threats. The study of internal and external factors clearly points toward a strong internal factor estimate than the external factor, which signifies high potentiality of alternate tourism development and sustainable ecotourism is the accurate alternative in these undeveloped beaches. The final weighted score of SWOC analysis (pair wise matching SO, WO, SC, and WC) of the six beaches has been plotted (Fig. 7.9) which signifies that Soula and Changasuli beaches have the highest potentiality for tourism as the strengths are very high than weaknesses and from other beaches. As per the analysis, the second well-established tourism destination is Haripur. The condition of other beaches are also sound as per the SWOC study, and the need of the hour is to increase the strengths and opportunities; diminish the weaknesses and threats for ecotourism development in a sustainable way.

Bankiput

Jhalda 3.5 3 2.5 2 1.5 1 0.5 0

Bhogpur

Soula and Changas uli

Strengths (S) & Weaknesses (W) Opportunities (O) & Challenges (C)

Haripur

Junput

Fig. 7.9 Representation of SWOC analysis of study beaches

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4 Conclusion The present study shows the existing tourism infrastructure in the coastal belts of Purba Medinipur district and to focus on its strategic development to promote sustainable coastal ecotourism using geospatial technology as a key tool. The economic affirmation of mass tourism has emerged some unavoidable consequences caused by the gigantic tourism. This high volume of tourism creates interesting revenue earning but brings associated problems. In this context, ecotourism is suggested to enhance as an alternative tourism approach in this region after its successful SWOC analysis and PCA analysis of several significant factors. It is identified that the Purba Medinipur coastal belt area has a popular tourist spot but only in New Digha and Old Digha have some facilities for the tourist but this is not sufficient. Tourists are not satisfied with the existing tourism structure and they are seeking to finding out some alternative tourism, which can be catering through promoting ecotourism development. The result of factor analysis signifies that most of the respondents strongly and positively advocates for the tourism development in the Dakshin Purushottampur village. The study also suggests that beaches like this need to have more attention to enhance the supportive and existing infrastructural development. Moreover, the positive and negative impact analyses indicate promoting ecotourism development in the study beaches by amplifying of the strengths and providing more opportunities. The involved community also receives benefits and experienced with an improvement in infrastructure and public facilities especially in the SWOC analysis villages. However, most of the inhabitants are coherent for the development of durable ecotourism in these newly developed beaches; and it could be ensured by addressing the negative impacts, developing infrastructural facilities with the employment of geospatial technology, which was established. Therefore, significant outcomes of the current effort affirm that rationality and utility of geospatial technology are encouraged to promote ecotourism development for future planning, preparing prospective road map, structuring resilient, and sustainable strategic development of ecotourism potentiality in the coastal beaches of the Purba Medinipur district. Acknowledgments The authors would like to thank the Indian Council of Social Science Research (ICSSR) for providing supportive research funding and Netaji Subhas Open University for the essential support. This effort is a tribute to the researchers and academicians who are working on the coastal areas of West Bengal from varied disciplines and dimensions for sustainable development and management. Declaration of Interest The authors declared that there is no conflict of interest. Funding This research work is supported by the research funding provided by the Indian Council of Social Science Research [ICSSR-MOST (Taiwan)/RP-1/2022-1C], New Delhi, India.

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Chapter 8

A Study on the Characteristics of Sea Waves at the Mandarmani Sea Beach of West Bengal Shubhayan Roy Chowdhury and Arijit Majumder

Abstract India has a very long coastline that extends for around 7516.6 km, with the Bay of Bengal in the east, the Indian Ocean in the south and the Arabian Sea in the west. The present study focuses on the characteristics of sea waves in the Mandarmani beach of West Bengal in May 2021 with the aim of quantifying the wave parameters. The data on sea wave parameters have been collected on field with the help of very simple survey instruments. As energy moves across the surface of the ocean, the particle moves in a circular orbit, returning to its original position. In order to comprehend the elliptical nature of the orbital motion in shallow water, the lengths of the semi-major and semi-minor axes have been calculated within a fluid medium. Results show that the length of the semi-minor axis declines with depth as the waves have less impact on sea bed but in shallow water, there is a negligible change in the length of the semi-major axis. Additionally, the shift in pressure causes a change in the length of semi-major and semi-minor axes. The emphasis of this study has been on determining the maximum pressure at which orbital motions are maintained. Near the sea bed, the mean pressure has been calculated to be 12111.49 N/m2 while a mean pressure of 3512.868 N/m2 has been calculated close to the sea surface. Wave power assessments have been widely carried out in recent years. The computation of wave energy and wave power in May 2021 along the Mandarmani beach is another important concern of this work. The renewable energy resources need to be explored to maintain and meet energy demand and reduce dependency on fossil fuels. This study also placed attention on the investigation of the correlation between wave energy and wave height, wave power, and wave velocity. Therefore, the overall analysis aims at sustainable development of the sea side destinations where the use of wave energy can open up alternate avenues of reducing the use of fossil fuels. Keywords Sea waves · Wave height · Wave velocity · Wave energy · Wave power · Hydrostatic pressure

S. R. Chowdhury · A. Majumder (✉) Department of Geography, Jadavpur University, Kolkata, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_8

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1 Introduction Sea waves are relied on a variety of factors and have enormous quantities of energy. For many research and applications, wind-generated sea waves serve as crucial inputs. Constructing coastal infrastructure might benefit from knowing the characteristics of sea waves, including wave period, wave height, and dynamic pressure (Battjes & Groenendijk, 2000; Silva et al., 2015). There are several theoretical frameworks that have been established to comprehend the mechanics of sea waves (Donelan & Hui, 1990; Drennan et al., 1992; Haas et al., 2008). To design offshore structures, it is necessary to address the characteristics and probable changes in both wind and waves (Bitner-Gregersen et al., 2016). Sea waves are consistently present on the ocean’s surface whenever a breeze blows across it. The water particles circulate in a motion as the wind-generated sea wave moves, passing the energy onward. As a result, a complex orbital motion of fluid particles across the whole water column coincides with the motions of the sea surface. The characteristics of the sea bed are significantly impacted by this orbital motion because it extends up to the sea bed. The nature of the water particle’s orbital motion is dependent on different water depths. The fieldwork was carried out in shallow water, and the orbital motion was found to be elliptical in nature. In order to comprehend the quantity of sediment movement along the sea bed, it is imperative to understand the nature of this orbital motion. Another crucial aspect of the movement of sediments is the wave orbital velocities (Wiberg & Sherwood, 2008). The circular orbital motion is formed under a dynamic medium. In addition, the nature of this dynamic medium has a great impact on these circular orbital motions. This orbital motion is formed under a particular amount of pressure which is thought to be hydrostatic pressure beneath the seawater. The hydrostatic pressure increases towards the sea bed, and thus the diameter of the orbits declines with depth due to an increase in hydrostatic pressure. For measurements of pressure under the progressive wave, pressure transducers have been long used since 1947 (Folsom, 1947; Seiwell, 1946). The pressure transfer function has been used from the linear wave theory to transform measured pressure data into surface wave information. However, many doubts have been raised about the use of the linear pressure transfer function (Biesel, 1983; Cavaleri, 1980; Wang et al., 1986). An empirical formula for the pressure transfer function has been developed by Kuo & Chiu in 1994 based on laboratory experiments and has derived the results by dimensional analysis and regression analysis. Additionally, the linear wave transfer function may also be used to determine surface wave height (Bishop & Donelan, 1987; Kuo & Chiu, 1994). The derivation of the pressure transfer function has been established within the framework of linear wave theory (Escher & Schlurmann, 2008). In this study, with the help of the pressure transfer function and wave height, the maximum pressure has been calculated, which helps to understand what amount of pressure exists to maintain the size of these circular orbital motions. Ocean wave energy has a large potential for containing the highest energy density as compared to other renewable energies (Clément et al., 2002). In countries like

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India, wind-wave can be one of the effective sources of renewable energy. As one of the marine renewable resources, the harvesting of wave energy can perform an important role in the use of alternative energy sources. Projects for using wave energy have been relatively slow due to the financial risks related to uncertainty in determining the wave energy resource over a variety of timescales (Neill & Hashemi, 2013). The 34-year EAR-I dataset has been used to analyze the temporal and spatial fluctuations of the wave power along India’s shelf seas (Sanil Kumar & Anoop, 2015). Wave energy was estimated in the eastern Bay of Bengal and Malacca Strait and it was found that July and August have the largest wave energy potential, whereas January has the lowest wave energy potential (Aboobacker, 2017). According to estimations, there is around 2 TW of wave energy in the world (Gunn & Stock-Williams, 2012). The global wave power is estimated to be 32 GW h/year (Reguero et al., 2015). In the case study of the Maritime Silk Road, the annual mean wave power density (WPD) in the Arabian Sea is 9–24 KW/m followed by the Bay of Bengal where the annual mean wave power density is 6–21 KW/m (Zheng, 2021). Some studies have been done on the accessibility of wave energy resources at a few locations in the world using parametric and numerical wave models. In recent decades, the wave energy potential assessment has been done using an advanced third-generation spectrum in the east coast of peninsular Malaysia (Mirzaei et al., 2014) whereas the East China sea and the south China sea were studied using the WAVEWATCH III model (Zheng et al., 2012). The Australian coasts (Hughes & Heap, 2010) and the Mediterranean were studied using the WAM model (Liberti et al., 2013), while the wave climate in Taiwan was investigated using the SWAN model (Ching-Piao et al., 2012). The comparison between measured wave data (AWAC/ADCP) and estimates of the third-generation spectral wave model (WAM) in Varkiza have also been analyzed (Foteinis et al., 2017). The present state extreme wave estimate from spectral wave models WAM and WAVEWATCH III has been improved (Benetazzo et al., 2021). Also, multiple wave energy analyses have recently been undertaken in various places by using different approaches such as the South China Sea (Chen et al., 2022; Li et al., 2022), Canary Islands in the North Atlantic (Avila et al., 2020), the Persian Gulf (Goharnejad et al., 2021), the Brazil’s coast (Sánchez et al., 2018), the southwestern Black Sea coast (Bingölbali et al., 2021), the Gulf of Mexico (Bento et al., 2021), the Greek (Kardakaris et al., 2021), the Japan (Sasmal et al., 2021), and the United States (Ahn et al., 2021). The Indian Institute of Technology Madras in Chennai has conducted studies on wave power assets and wave energy (Ravindran et al., 1991). This paper placed a strong focus on fieldwork-based assessments of the energy resources along the Mandarmani beach as a means of addressing the demand in the energy sector. The coastline is eroded by sea waves and it is anticipated that rising sea levels would accelerate this process and also impact the potential changes in sea waves (Hinkel et al., 2013). India has a long coastline of 5423 km along the mainland and it receives around 5.7 million waves per year (Chempalayil et al., 2012; Glejin et al., 2013). The change of significant wave height and global warming has resulted in an increase in the sea level which is increasing the risk in coastal regions (Kumar et al.,

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2018). Based on the Coastal Hazard Wheel (CHW) methodology and data of the primary survey, coastal flooding and saltwater intrusion are the most important factors along the coast of West Bengal (Paul & Das, 2021). The island of the Sundarbans and the coastal areas of the East Midnapur district are greatly experiencing the sea waves. Therefore, it is important to study the sea wave characteristics along the stretch of West Bengal, India. This work provides a simple method for collecting wave data that aid in understanding the characteristics of sea waves so that wave energy can be harvested for environmental sustainability. The field study was conducted at the Mandarmani beach of West Bengal in May, 2021with the following objectives: • To measure the sea wave parameters on field viz: wave height, wave velocity, wave period, and wave frequency. • To establish the relationship between the length of the axes of the orbital motion of the water particle and the hydrostatic pressure at corresponding points on wave orbit. • To study the relationship between wave energy, wave height, wave power, and wave velocity.

2 Study Area India is surrounded by a long shoreline enclosing the state from three sides, i.e. southeast, south, and southwest. The eastern coast of the Indian subcontinent experiences lots of dynamism in terms of coastal stability compared to the western part. The state of West Bengal has a substantially long coastline characterized by high floral and faunal biodiversity, diverse geomorphic features, and anthropogenic intrusions. The state’s coastline is 157.5 kilometers long (Be et al., 2013). The Mandarmani coastal stretch located in the East Medinipur district of West Bengal, India is the area under study (Fig. 8.1). The length of Mandarmani beach is 13.7 kilometers. Mandarmani is a seaside destination and is one of the largest and fastest developing tourist spots in West Bengal.

3 Materials and Methods 3.1

Wave Data Collection

The instruments used for data collection were a stopwatch, two measuring staff, a measuring tape, a GPS, and a notebook (Fig. 8.2).

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Fig. 8.1 Location of the study area

3.1.1

Wave Velocity

The wave velocity was measured at the field for the same waves approaching the shore. A staff was placed at a particular point and another staff was placed at a distance of 1 meter from the previous one towards the shore. The time interval

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Fig. 8.2 Measurements being taken for wave data

between the waves progressing from the first staff to the second staff was measured with the help of a stopwatch and thus the wave velocity of the waves was measured.

3.1.2

Wave Crest

The sea waves achieve the maximum amplitude just before it breaks and hence the crest of the sea waves was measured with the help of the staff at those points, where the still water depth was 0.85 meters.

3.1.3

Wave Frequency

A staff was placed at a particular position with 0.85 meters still water depth and the number of waves crossing the staff was measured within 1 min to determine the wave frequency.

3.2

Wave Parameters

Still water level is the level that the sea surface would assume in the absence of wind waves which is referred to as z. At a still water level, the value of z would be zero while it is negative below the still water level. A crest is a point on the wave where the maximum magnitude of upward displacement of water particles occurs within a cycle whereas trough is the maximum downward displacement of water particles in a cycle opposite to that of a crest. Wave amplitude is defined as the vertical distance between the crest and the still water level. Crest height is defined as the summation of the amplitude (A) and still water depth (d ). The wave height is calculated as twice the magnitude of wave amplitude (A) i.e., wave height (H ) = 2 × wave amplitude (A). The wave period (T) is the travel time between one crest to another crest while

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wave frequency ( f ) is the number of waves passing a fixed point in a given time. Wave velocity is equal to the distance traveled by the wave in unit time. Wave length (λ), is determined by dividing wave velocity by wave frequency. The wave number is a scalar quantity represented by k and the mathematical representation is as k = 1/λ where, k is the wavenumber and λ is the wavelength.

3.3

Continuity Equation ∂ ∂ ∂ ∂ρ ðρuÞ þ ðρvÞ þ ðρwÞ = 0 For steady flow =0 ∂x ∂y ∂z ∂t

ð8:1Þ

This Eq. 8.1 is based on the principle of conservation of mass. By the law of conservation of mass, there is no accumulation of mass i.e., mass is neither created nor destroyed in the fluid element. So, the net increase of mass per unit time in the fluid element must be equal to the rate of increase of mass of fluid in the element.

3.4

Velocity Potential Function

Velocity potential function is a scalar function of space and time. If ‘phi’ is the representation of velocity potential function, then the velocity function is given by the expression ϕ = f(x, y, z, t) for unsteady flow and ϕ = f(x, y, z) for steady flow. Mathematically, negative partial derivative of ϕ with respect to any respective axis gives the velocity component in that direction. -

∂ϕ ∂ϕ ∂ϕ = u, = v, =w ∂x ∂y ∂z

The value of ϕ decreases towards the flow direction, which indicates a negative value. From Eq. 8.1, 2

2

2

∂ ϕ ∂ ϕ ∂ ϕ þ þ =0 ∂x ∂y ∂z

ð8:2Þ

Equation 8.2 subjected to the boundary conditions gives for the velocity potential, ϕ=

ag cos hkðⅆ þ zÞ cosðkx - ωt Þ cos hkd ω

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So, velocity potential is a function of wave height (H ), wave period (T ), wave length (λ), and z which indicates the potential velocity varies from surface water level to sea bed. In order to determine the velocity potential function in a specific direction, it helps in determining the velocity in that particular direction. When waves are propagating in the open ocean, the potential velocity varies with depth. Therefore, both vertical velocity and horizontal velocity can be determined (2D structure). The horizontal water particle velocity u is given by u= -

∂ϕ ∂ϕ and the vertical water particle velocity v is given by v = ∂x ∂y

cos hk ðⅆþzÞ where, ϕ = ag ω cos hkd cosðkx - ωt Þ Integrating particle velocity with respect to time, the components are

uⅆt for horizontal displacement vⅆt for vertical displacement After integration with respect to time, the horizontal and vertical water particle displacement are δx =

H cos hk ðⅆ þ zÞ cosðkx - ωt Þ sin hkd 2

δy =

H sin hkðⅆ þ zÞ sinðkx - ωt Þ sin hkd 2

Let, D=

H cos hkðⅆ þ zÞ ðHorizontal semi - main axisÞ 2 sin hðkd Þ

B=

H sin hkðⅆ þ zÞ ðVertical semi - main axisÞ 2 sin hðkd Þ

when, kd tends to 0, which indicates very shallow water the length of the axes are D =

a z and B = a 1 þ Kd d

the particles move in elliptical orbit and it grows flatter towards the sea bed. At the bottom, the ellipse degenerates into a straight, horizontal line.

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161

Pressure Distribution Under Progressive Wave

Bernoulli’s equation is given by -

p ∂ϕ 1 2 þ u þ v2 þ w2 þ þ gz = 0 ρ ∂t 2

ð8:3Þ

In order to deal with linear waves, the higher order element from Eq. 8.3 is being avoided, and hence linearized Bernoulli’s equation is used -

∂ϕ p þ þ gz = 0 ∂t ρ

ð8:4Þ

Multiplying Eq. 8.4 by ρ the total pressure is given by Pressure ðPÞ = ρ where ϕ = ag ω Therefore,

cos hk ðⅆþzÞ cos hkd

∂ϕ þ ð- ρgzÞ ∂t

cosðkx - ωt Þ

Pressure ðPÞ = ρ Pressure ðPÞ =

∂ ag cos hk ðⅆ þ zÞ cosðkx - ωt Þ þ ð- ρgzÞ cos hkd ∂t ω ρgH cos hk ðⅆ þ zÞ cosðkx - ωt Þg þ ð- ρgzÞ cos hkd 2 Pressure ðPÞ = ρg k p η - z

where, kp =

cos hk ðⅆþzÞ cos hkd

η = a cosðkx - ωt Þ z=depth from still water level Pressure ðPmax Þ = ρgðkP ηmax - zÞ

ð8:5Þ

The maximum pressure has been given by Eq. 8.5 (Fig. 8.3) where, ρ = density of ocean water which is 1036 kg/m3 (Krishna et al., 1989), g = gravitational acceleration which is 9.8 m/s, kP = transfer function for pressure from linear wave theory, ηmax = maximum crest height, and z = below still water level.

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Fig. 8.3 Sketch schematic diagram for pressure distribution under the progressive wave

3.6

Wave Energy and Wave Power

From the velocity potential function (that can be obtained through linear wave theory), one can derive the kinetic energy and the potential energy. Kinetic energy and potential energy are two important factors in order to derive the total wave energy. The total wave energy is determined by the sum of these two energies and it is represented by Eq. 8.6 E=

ρgA2 2

E=

ρgH 2 8

ð8:6Þ

where, ρ is the density of water, g is the gravitational acceleration, and A is the amplitude of the wave. The wave power is obtained by multiplying the amount of . energy transported by sea waves and the group velocity C g = 2c 1 þ sin 2kⅆ hð2kd Þ

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Fig. 8.4 Flow chart of the methodology

Power = Ecg

ð8:7Þ

Thus, Eq. 8.7 allows to determine the wave power for a given wave period (T ) and wave height (H ). Figure 8.4 shows a flowchart summarizing the process presented in this study.

4 Results and Discussion 4.1

Results

After collecting the data from the field, detailed descriptive statistics of the wave parameters were computed, which are as follows (Table 8.1).

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Table 8.1 Wave parameters Wave parameters Wave height Wave amplitude Wave velocity

Maximum value 1m 0.5 m 4.97 m/s

Minimum value 0.3 m 0.15 m 3.71 m/s

Mean 0.69 m 0.346 m 4.23 m/s

Standard deviation 0.23 m 0.1 m 0.37 m/s

Source: Calculated by authors based on the data collected on field, May 2021 Table 8.2 Mean length of the semi-major and the semi-minor axes of the elliptical orbit at three different elevations z value (meters) 0 -0.346 -0.85

Mean length of semi-major axis (meters) 2.621 2.573 2.598

Standard deviation of semi-major axis (meters) 0.877 0.872 0.87

Mean length of semi-minor axis (meters) 0.346 0.189 0

Standard deviation of semi-minor axis (meters) 0.118 0.035 0

Source: Calculated by authors based on the data collected on field, May 2021

The mean crest height of sea waves has been calculated to be 1.419 meters and the standard deviation to be 0.1607 meters. The mean wave height (H ) in May 2021 was determined to be 0.69 m while the standard deviation was 0.23 meters. The maximum and minimum sea wave velocities have been found to be 4.97 m/s and 3.71 m/s, respectively. The mean value of sea-wave velocity was 4.23 m/s. The mean value of wave frequency ( f ) was observed to be 0.104 Hz. Shallow water waves have been used to examine the orbital motion of fluid particles. As the fluid particle moves, its orbital path is either circular or elliptical. Based on field data, the mean value of wavelength (λ) is 40.94 m has been calculated at the water depth (d ) 0.85 meters. Therefore, the wave has been classified as a shallow-water wave since the ratio of wavelength (λ) and water depth (d ) is less than one-twentieth. The semi-major axis of the elliptical orbit denotes the horizontal displacement while the semi-minor axis denotes the vertical displacement of the fluid particles. The length of semi-major axis and semi-minor axis can be used to determine the nature of the orbital path. In this study, the length of the semi-major axis and semi-minor axis of the orbital motion of the sea waves have been analyzed at three different elevations (z) within a fluid medium. The results of the axes for different values of z are tabulated in (Table 8.2). The pressure field under progressive waves is determined from the Unsteady Bernoulli’s equation where pressure within a fluid medium is equal to the dynamic component (ρgkp ηmax) and hydrostatic component (ρgz). The maximum hydrostatic pressure, which helps to maintain the size of the orbital motion, has been analyzed at three different elevations corresponding to the z values which have been used for determining the axes parameters within a fluid medium (Table 8.3).

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Table 8.3 Maximum hydrostatic pressure under progressive wave z value (meters) 0 -0.346 -0.85

Maximum hydrostatic pressure (Newton/meter2) 3512.868 7004.03 12111.49

Standard deviation of hydrostatic pressure (Newton/meter2) 1212.162 2389.877 13040.12

Source: Calculated by authors based on the data collected on field, May 2021

Fig. 8.5 Linear regression analysis between wave energy and wave height

The value of dynamic pressure is negligible near the sea surface because the effect of waves reduces from the sea surface to the sea bed, and hence the hydrostatic pressure is maximum near the sea bed. Wave height (H ) has an impact on how much energy is carried by the waves as they travel across the ocean. The wave energy and wave power have been examined in this study for May 2021. The mean wave energy was determined to be 671.6053 Nm based on the density of sea water and wave amplitude. Wave energy is dependent on wave height (H ), and more energy will be released by the sea wave with a greater wave height (H ). In this study, r-squared analysis has been done by using R-studio 4.1.2, and this analysis is used to understand the relationship between wave energy and wave height (H ) (Fig. 8.5). The linear regression equation is given by Eq. 8.8 Y = a þ bX þ ε

ð8:8Þ

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where, Y = dependent variable X = independent variable a = intercept b = slope ε = the residual (error) The relationship between wave energy and wave height (H ) has shown a positive correlation where the r2 value has been found 0.9709. So, the relationship between wave energy and wave height (H ) has accounted for 97.09% of the variation in the data and the p-value is less than 0.05 which entails that the model can be regarded as statistically significant. Sea wave energy and sea wave velocity have been used in this work to evaluate sea wave power. The mean sea wave power during the study was determined to be 2792.76 Nm/s. Sea wave energy and sea wave velocity are the two factors which have been used to perform multiple linear regression to derive the wave power. The multiple linear regression is given by Eq. 8.9 Y = a þ b 1 X 1 þ b 2 X 2 þ . . . þ bn X n þ ε

ð8:9Þ

where, Y = dependent variable X1, X2, . . ., Xn = independent variable a = intercept b1, b2, . . ., bn = slope coefficients for each X variable ε = the residual (error) These two factors have contributed significantly to the wave power with the rsquared value of 0.9835.

4.2

Discussion

In this study, the wave height, wave velocity, wave period, and wave frequency have been measured on field by using the instruments viz: staff, stopwatch, measuring tape, and GPS. Based on field data, wavelength of the sea waves, the length of the semi-major axis and semi-minor axis of the orbital path, hydrostatic pressure under the progressive wave, wave energy, and wave power have been calculated for this study. In shallow water, the orbital motion of fluid particles in the entire water column moves in an elliptical pattern. The results indicate that in shallow water, the maximum axes length has been found at the sea surface and the minimum axes length of the elliptical orbit has been found at the sea bed. There is a negligible change in the mean length of the semi-major axis of the elliptical orbit but there is a

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significant change in the mean length of the semi-minor axis of the orbital path from the sea surface towards the sea bed. At the sea bed, the mean length of the minor axis of the elliptical orbit tends to be zero. Using linearized Bernoulli’s equation, the hydrostatic pressure under the progressive wave has been calculated. The results indicate that the pressure increases vertically downwards from the sea surface to the sea bed. The minimum hydrostatic pressure of 3512.868 N/m2 has been found at the sea surface and the maximum hydrostatic pressure of 12111.49 N/m2 has been found at the sea bed. Due to the increase in pressure, the axes length of the elliptical orbit decrease as it descends from the sea surface towards the sea bed. Hence, the axes length of the elliptical orbit and hydrostatic pressure are inversely proportional to each other. The sea waves generate a lot of power as they carry a lot of energy. It has been calculated that the mean wave energy is 376.1748 Nm, producing a mean wave power of 2792.76 Nm/s. In this study, the relationship between wave energy, wave height, wave power, and wave velocity has been demonstrated. An indispensable role as part of one of the core subsectors of the tourism industry has been played by hotels and resorts and are essential for most tourist destinations. Mandarmani is one of the fast-developing tourist destinations in West Bengal, India. Hotels and resorts are two subsets of the tourism industry which have a significant impact on the economy. Energy is one of the most important resources for this business. The energy use of hotel buildings varied within a range of 28.55 KWh/m2/ year and 357.08 KWh/m2/year, with a mean of 99.43 KWh/m2/year (Bose & Bardhan, 2021), and the daily energy usage per guest was a mean of 5.92 KWh/ guest/day. Research about energy consumption by this industry has been a core subject due to its vast impact on the environment and this study provides a way to estimate the sea wave energy and power.

5 Conclusion Measurements of the sea wave parameters on field using simple methods with regular survey instruments have been a major challenge and essence of this study. It emphasized on examining the relationship between the length of the axes of the orbital motion of the water particles and the hydrostatic pressure at corresponding points on orbit. This information enables to understand the micro-level properties of the sea wave. In addition, this study also has highlighted the variability of the wave energy and wave power along the Mandarmani beach of West Bengal during May of 2021. The linear regression analysis between wave energy and wave height and the multiple linear regression analysis between wave power, wave energy, and wave velocity helped in establishing the relationship between the sea wave parameters. The renewable energy resources need to be explored to maintain and meet energy demand and reduce dependency on fossil fuels. The study shows the extent to which the wave energy can be used in Mandarmani, West Bengal which has developed as a popular tourist destination so as to reduce the use of fossil fuel. Thus, it can be

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concluded that if it is feasible to utilize the energy produced by the sea waves in the coastal areas then it would certainly enhance the region’s economic, social, and environmental sustainability.

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Chapter 9

Determining Recent Trends of Forest Loss and Its Associated Drivers for Sustainable Management in the Dry Deciduous Forest of West Bengal, India Dipankar Bera and Bipul Paul

, Nilanjana Das Chatterjee

, Sudip Bera, Akshay Rana,

Abstract Forest loss is an issue for livelihoods in rural areas of the world. The patterns and trends of forest loss and its associated drivers are relevant for sustainable management. This study focused on forest loss and its associated drivers, integrated with satellite data and perceptions of people in five selected zones in the dry deciduous forest of West Bengal. Simple linear regression was conducted to analyse trends of forest loss. A household survey with Participatory Rural Appraisal (PRA) approach was used to examine the perceptions of people on forest loss and its associated drivers. The result showed that forest loss is increasing, and varied over the spatial-temporal context. Between 2001 and 2018, forest cover decreased by 1.3% and the five selected zones contributed 79% of total forest loss. Overall 84% of households perceived a decline in forest cover. Agricultural land extension, timber, fuel collection, and wood marketing were common key drivers and increasing threats in the intermediate areas of the zones. Satellite image data only reveal that spatial-temporal forest loss but local people’s experience could perceive related drivers of forest loss, and threat zones of forest loss in the future. Therefore, both integrated methods will be useful for forest management. Keywords Forest cover loss · Associated driving factors · Google Earth Engine · Simple linear regression · Participatory Rural Appraisal · Dry deciduous forests

D. Bera (✉) · N. Das Chatterjee · S. Bera Department of Geography, Vidyasagar University, Midnapore, West Bengal, India e-mail: [email protected] A. Rana · B. Paul Department of Women Studies, Vidyasagar University, Midnapore, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_9

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1 Introduction Forest loss is one of the major environmental challenges, and it contributes to climate change (Watson et al., 2018). Forest cover reduction or increasing deforestation have negative effects on the environment as well as on the human being, people who are directly dependent on forest resources (Ellison et al., 2017). Therefore, measurement of the patterns and trends of deforestation, and its associated drivers at the local level are important for management. Forest loss is the primary issue in tropical regions (Myers, 1994) because of various anthropogenic activities, such as rotational farming, agricultural land extension, harvesting of fuel and industrial wood, pasture land extension, draining, and burning of peatland (Houghton, 2012). But drivers’ magnitude varies at local to global scale (Curtis et al., 2018). Therefore, the use of a local perspective is essential for understanding the drivers on a local scale (Shriar, 2014). Until recent studies, most of the studies based on forest cover change (Yang & Lo, 2002; Hayes & Cohen, 2007; Daniels et al., 2008; Lung & Schaab, 2010; Kirui et al., 2013; Kumar et al., 2014; Potapov et al., 2014; Keenan et al., 2015; Romijn et al., 2015) and its associated driving factors (Chowdhury, 2006; Gibbs et al., 2010; Houghton, 2012; Imai et al., 2018). Limited studies are based on the experiences of local people (Rudel et al., 2009; Twongyirwe et al., 2017). Although, the global dataset is freely available for forest loss analysis (Hansen et al., 2013), but there is a lack of integrated assessment between satellite data and perceptions of people to understand forest loss. People’s knowledge could provide the historical status of forests (Alfonso et al., 2016), which is useful for identifying the driving factors (Fisher & Hirsch, 2008; Fisher et al., 2013; Yang et al., 2015). Various software has been used to estimate forest loss, but very little research has been done on the Google Earth Engine (GEE) platform. Therefore, for this study, forest loss was quantified using GEE, a cloud-based API. We used GEE because there is a growing trend to use GEE for various environmental purposes (Tsai et al., 2018) as the GEE platform provides a faster and more efficient environment for satellite image analysis (Gorelick et al., 2017; Kumar & Mutanga, 2018; Praveen et al., 2019). Within this context, there is no evidence of studies on the integration of satellite data and perceptions of people for measuring the forest cover loss and its related drivers in dry deciduous forests of West Bengal. Although dry deciduous forests historically played a large role in livelihoods, natural forests are declining in recent times (Bera et al., 2022). The limited availability of data has created further challenges for sustainable management. After all, in this study, we focused on a few questions such as (1) how is forest cover declining over spatial-temporal context? (6) what are the patterns and trends of forest loss? (2) what are the major forest loss drivers? (3) do the associated driving factors vary over space? (4) what are the perceptions of people regarding forest loss? and (5) do the local people’s perceptions vary over space? Therefore, in this study, we analysed forest loss and its related drivers with an emphasis on remote sensing data and perceptions of people in dry deciduous forest regions of West Bengal.

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2 Data Sources and Methods 2.1

Study Area

The location of study area is in the south-western part of the Indian state of West Bengal. This area is between 22° 10′ 15″ north to 24° 09′ 54″ north and 85° 49′ 35″ east to 88° 14′ 07″ east (Fig. 9.1). The forest consists of various species of trees, but according to West Bengal Forest Department, the main forest type is tropical dry deciduous (WBFD, 2018). Tropical savanna with dry winter is the main feature of the climate of this region (Beck et al., 2018). The main occupations of the people in this area are agriculture, stone mining, sericulture, tourism activity, pasture, leaf plate making, timber marketing, and some medium to small-scale industrial activity. People of this region are dependent on natural resources, mainly on agricultural and forest resources. But the growing population is putting enormous pressure on the forest ecosystems of the area.

2.2 2.2.1

Collection of Data and Analysis Forest Loss Mapping and Trend of Forest Loss

The global forest cover change dataset (Hansen et al., 2013) was used for this study, and analysed in GEE to measure forest loss from 2001 to 2018. In this dataset, trees greater than 5 m in height are defined as vegetation, and vegetation coverage is

Fig. 9.1 Location map of the study region

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Fig. 9.2 Selected villages and forest loss zones: (a) Five selected dominating forest loss zones between 2001 and 2018; (b) Surveyed villages and delineated forest loss zones; (c) Separately demarcated five forest loss zones

represented as a percentage between 0 and 100, and stand-replacement disturbance of tree cover or a change from forest to other non-forest is defined as forest loss. We considered it as forest if land covering >0.5 hectares with canopy cover >10%. We demarcated five different forest loss zones from the forest loss map of global forest cover change dataset over the last 18 years (Fig. 9.2c). Annual forest loss has also been measured in five different zones from 2001 to 2018. The trend of forest loss was estimated using R-studio. The most common and widely used simple linear regression was used for trend analysis in the whole study area and five selected zones.

2.2.2

Study Site Selection and Defining Zone Boundaries

To analyse the broad pattern of forest loss and its associated drivers, we selected the deciduous forest regions of West Bengal because man-made activities have increased threats to the forest ecosystem and surrounding livelihoods (Bera et al., 2022). For the field survey, we selected five lost forest zones on the basis of the dominance of forest loss during 2001–2018 (Fig. 9.2a). Firstly, we estimated the forest loss of all villages in each zone during 2001–2018, and 10 most affected villages (forest loss) were selected from each zone (Fig. 9.2b). Then, 10 households from each village were selected for the household survey. Forest-dependent households were selected in consultation with villagers and local government respondents to obtain accurate information on forest loss and its drivers. Villages and households were selected all around the zones to cover the maximum part of the zones. The

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outward part villages of the zones were joined by a line. Then 2 km buffers were taken around the joining line of the villages to delineate the zone boundary. Thus, we demarcated five forest loss zones for study (Fig. 9.2c).

2.2.3

Household Surveys

A household survey of 500 households in 50 villages across five regions was conducted to collect data on households’ perceptions regarding forest loss and its drivers over the past 15–20 years. We collected perceptions on forest loss for validating the global forest loss data, and on drivers to identify the driving factors of forest cover loss in this region. Therefore, we asked two questions: (1) Have you seen changes in your surrounding forests during 2000–2018 (past 15–20 years)? (2) What are the driving factors of forest cover changes or loss in your surrounding? The perceptions related to changes in forest cover were collected as “unchanged”, “decreased” or “increased” over time. The statistical analysis of household data was run using R-studio software (version 3.5.1). Chi-square (χ2) test of association (independence) was performed to express (at 5% significance level) the similarity of perceptions about forest cover across the zones, and to understand the uniformity of perceptions across the villages, chi-square (χ2) goodness of fit was used.

2.2.4

Participatory Rural Appraisal (PRA)

PRA is a methodological approach that enables rural people to share knowledge about life and incorporate the thoughts of rural people in development programs (Castelloe & Gamble, 2005). PRA is a useful method for local communities to understand the perception of land use and cropping pattern (Chambers, 1994). Now, PRA techniques have been the widely accepted method to understand grass root problems and relations at the village level (Ellis, 2000; Narayanasamy, 2009). We applied this approach to collect information from local people about forest cover loss and its associated drivers. In this study, PRA was conducted in five zones. In every zone, 30–35 participants (6–7 participants from each selected village of the household survey) included elderly (knowledgeable about forest). A total of 20–23 male and 10–12 female participants (4–5 male and 2–3 female from each village) are included who have knowledge about forest and forest resources. This discussion mostly focused on what percentage of participants agreed with the particular driving factors in each zone. The statistical analysis of PRA data was run using R-studio. Chi-square (χ2) test of association (independence) was performed to reveal the significant (at 5% significance level) similar driving factor across the zones.

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3 Result 3.1

Spatial-Temporal Forest Cover Loss

The spatial loss of forest from 2001 to 2018 in the study area and five zones are shown in Fig. 9.3. Total forest cover loss, its intensity and relative contributions in five selected zones are shown in Table 9.1. Total forest loss and its intensity in the study area from 2001 to 2018 were 121.33 sq. km (1.3% of total forest cover) and

Fig. 9.3 Spatial forest loss during 2001–2018 across the whole study area and five selected zones

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Table 9.1 Forest loss, its intensity and relative contributions to forest loss in the whole study area and selected five zones between 2001 and 2018

Forest loss (km2) Total area (km2) Intensity (forest loss/ total area) Relative contributions (%)

Zone 1 6.680 356.425 0.019

Zone 2 37.670 1714.540 0.022

Zone 3 34.710 712.046 0.049

Zone 4 12.630 461.613 0.027

Zone 5 4.790 577.624 0.008

5.506

31.048

28.608

10.410

3.948

Whole study area 121.330 32047.500 0.004 100.000

0.40%, respectively. In the selected five zones, forest cover loss was 6.68 sq. km in Zone 1, 37.67 sq. km in Zone 2, 34.71 sq. km in Zone 3, 12.63 sq. km in Zone 4 and 4.79 sq. km in Zone 5. The contributions of the selected five zones were 96 sq. km or 79% of total forest cover loss, covering only 12% of the study area. The intensity of loss was relatively higher in the selected five zones compared to the whole study area. In the five selected zones, the intensity of loss was higher in Zone 3, followed by Zones 4, 2, 1and 5. The temporal loss of forest cover from 2001 to 2018 in the study area and five zones are shown in Fig. 9.4. Temporal loss varied annually in the whole study area and five selected zones, but the average forest cover loss was increasing from 2001 to 2018. However, the annual average loss in the whole study area was 6.74 sq. km from 2001 to 2018. But during 2001–2011 and 2012–2018, the average annual forest loss was 2.32 sq. km and 13.69 sq. km, respectively. In the selected five zones, average annual forest loss followed the trends of the whole study area. In the whole study region, the annual average forest loss was 0.074% (loss of total forest cover) between 2001 and 2018. In the selected five zones, annual forest loss with respect to total forest cover was higher in Zone 4, followed by Zones 3, 2, 5 and 1. Forest loss versus time with other parameters is shown in Fig. 9.5. From the graphical representation, we found that the forest loss is increasing over time. Trends were good fit at a 0.01% significance level for the whole study area and five selected zones. Also, trends of forest loss were higher in Zone 2, and then in Zones 3, 4, 1 and 5.

3.2

Perceptions of People on Forest Loss and Its Associated Drivers

From the household survey, it was found that approximately 84%, 4%, and 12% of households perceived forest cover as decreasing, increasing, and unchanged over time. The Perceptions of households about forest cover change in five selected zones are shown in Fig. 9.6. Among the five zones, in Zone 3 largest number of households (90%) perceived that forest cover is decreasing over time compared to 88% of

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Fig. 9.4 Temporal forest cover loss between 2001 and 2018: (a) Whole study area; (b) Zone 1; (c) Zone 2; (d) Zone 3; (e) Zone 4; (f) Zone 5

households in Zone 4, 86% of households in Zone 2 and 78% of households in both Zone 1 and Zone 5. We observed significant similarities in perceptions about forest cover change across five selected zones (X2 test of independence: 10.321, p: < 0.05). This means people’s perceptions were similar across the zones. We also found uniformity of perceptions (X2 goodness of fit test: 38.587, p: < 0.05) across the villages regarding forest loss, but significant differences across the villages were observed for forest cover increase (X2 goodness of fit test: 184.589, p: < 0.05), and unchanged (X2 goodness of fit test: 368.608, p: < 0.05). This means forests are decreasing in the study region which is similar to global forest loss data. From the household survey, we also found that associated driving factors in five zones were agricultural land expansion, timber and fuel collection (for households), wood marketing (by local people and community), stone mining, road expansion, wood extraction (for industrialization and urbanization), overgrazing and settlement expansion. PRA analysis revealed that participants (%) agree with the particular driving factor to forest cover loss in the selected five zones (Fig. 9.7). 100% of participants agreed with agricultural land expansion and timber and fuel collection by

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Fig. 9.5 Trends of forest cover loss in whole study area and five selected zones. Light green colour area indicates the confidence interval (95%). R correlation coefficient, P probability value of linear regression (ANOVA test)

households leading to forest cover loss. Relative contributions of each driving factor to forest cover loss in selected five zones are shown in Table 9.2. The result of the chi-square (X2) test of independence showed a significant difference in people’s perceptions of all drivers across the five selected zones (X2 test of independence: 78.427, p: < 0.05). But significant similarities were observed for agriculture land expansion, timber and fuel (for households) and wood marketing (by local people and community) across the five zones (X2 test of independence: 12.842, p: < 0.05). Therefore, these three factors were the common key driving factor of forest loss in the five zones as well as in the whole study area.

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Fig. 9.6 Proportions of households (%) perceived forest cover changes. Average was calculated based on five zones

Fig. 9.7 Participants (%) agreed with particular driving factors. Average was calculated based on five zones

It was also found that the main driving factors in the surrounding areas of households were timber and fuel collection by households, settlement expansion, overgrazing, timber and non-timber product marketing by local people and community. Near the major road, the main driving factors were road expansion and wood extraction for industrialization and urbanization. Agricultural land expansion, timber and fuel collection by households, stone mining, and wood marketing by local

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Table 9.2 Relative contribution of forest disturbance drivers (%) Driving factor Agricultural land expansion Timber and fuel (for households) Wood marketing (by local people and community) Stone mining Road expansion Wood extraction (for industrialization and urbanization) Overgrazing Settlement expansion

Zone 1 23 23 18

Zone 2 21 21 14

Zone 3 20 20 16

Zone 4 20 20 17

Zone 5 19 19 13

Average 20 20 15

6 12 5

17 9 6

17 14 6

13 9 7

11 15 9

13 12 7

6 8

6 5

4 4

8 7

7 8

6 6

people and community, and overgrazing were dominated in between major roads and households.

4 Discussions 4.1

Forest Loss Varies in Spatial-Temporal Context

Based on the result of satellite data, we found that forest loss varied in spatialtemporal context. But the loss of forest was mainly concentrated in the selected five zones. But forest loss also varied among the zones as well as in the whole study area. Variation in forest loss in spatial-temporal circumstances is mainly due to variation in anthropogenic activities and their intensity (Sulieman, 2018). The forest report from Food and Agriculture Organization (FAO, 2018) and West Bengal Forest Department (WBFD, 2018) showed that the loss of forest cover has decreased in this region. But our study results revealed that cover loss is increasing from 2001 to 2018 in this region. People across the zones perceived continuous forest cover loss. Generally, human perceptions verify remote sensing data on forest loss. Few studies reported that people’s perceptions contradict the result of the satellite data (Pfund et al., 2011), but perceptions can provide detailed explanations about drivers of loss. We found that people’s perceptions were similar to satellite data analysis. This result was similar to Fisher et al. (2013), which focused on the analysis of forest loss in Indonesia. There was a significant similarity of perceptions of people across the zones about forest loss, and local people also perceived a uniform decline in forest cover across the zones. This result proved forest loss was highly concentrated around the villages and neighbouring areas across the zones. The diversity of anthropogenic activities and their intensity affect forest loss, so community-based local management can provide opportunities to increase forest cover, improve forest conditions, and deliver economic benefits (Robinson et al.,

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2014). However, Joint Forest Management (JFM) policy helps to manage forest cover and distribute economic assistance to rural people (Pattnaik & Dutta, 1997). Therefore, strong implementation of JFM can protect forests by helping people economically (Guha et al., 2000).

4.2

Diverse Pattern of Driving Factor Over Space

Like forest cover loss, associated driving factors also varied over space. Driving factors differ due to differences in perceptions of local people, and variations of anthropogenic activities and their intensities over space (Hosonuma et al., 2012). Several studies have reported forest cover loss in different social-ecological circumstances in the variation of associated driving factors (Geist & Lambin, 2002; Hosonuma et al., 2012). Agricultural land expansion, timber and fuel collection and wood marketing were common drivers across the zones, and played an important role in the forest loss. Agricultural land expansion, timber and fuel collection were the most effective driving factors in the study area. However, this result is fully consistent with studies of forest loss drivers in developing countries (Sloan & Sayer, 2015). Driving factors also varied within selected zones along with increased distance from households. Among the three common key drivers, timber and fuel collection, and wood marketing were the main driving factors in household surrounding areas. But all three common key driving factors were found in intermediate areas (areas between households and roads), but common driving factors were not found near the roads. As result, these are increasing threats to the forest ecosystems in intermediate areas. We also found expansion of agricultural land increased more exploitation of the forests in the intermediate areas.

5 Implications of the Study for Sustainable Management Dry deciduous forest plays a huge role in rural people’s livelihoods, but man-made activities have created a great threat to forest ecosystems in these areas (Bera et al., 2022). Therefore, an urgent balance is needed between deforestation and livelihoods through appropriate management strategies. For taking decisions about the management of forests by reducing deforestation basic two things are important to know (1) how does forest cover loss vary in a spatial-temporal context? and (2) what are the related driving factors of forest loss? This study focused on the spatial-temporal forest cover loss in dry deciduous forests with an emphasis on five selected zones. It also depicts the forest loss and its related drivers using remote sensing data and people’s perceptions. The most relevant part of this study is the integration of remote sensing data and people's perceptions. Remote sensing data only reveal that spatialtemporal forest loss, but people’s experience could perceive related drivers and

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threatening zones of loss in the future, and also fill the gap between remote sensingbased data and ground truth. Therefore, both integrated analyses can be useful in making decisions for sustainable management.

6 Conclusion Our study mainly focused on the forest loss and its related drivers by using remote sensing data (global forest loss data) and people’s perceptions from 2001 to 2018. Previously, many researchers have used satellite data for forest loss analysis, but very limited studies have explored forest loss by using the GEE platform and validating the data using people’s perceptions in local areas. GEE is a very recent advanced technology to monitor forest cover and track changes over time and space, which is quick and accessible for everyone. It is also found that different remote sensing data and methods can provide different results but analysis using satellite data and validating satellite data using perceptions of people can be useful for management purposes. From the research findings, we conclude that forest loss has varied in spatialtemporal circumstances and is increasing over time. Forest cover loss is also concentrated in particular areas of this region. People also realized that the continuous loss of forests in their surrounding areas and its associated driving factors were agriculture land expansion, timber and fuel (for households), wood marketing (by local people and community), stone mining, road expansion, wood extraction (for industrial and urbanization), overgrazing and settlement expansion. Associated driving factors also varied over space, but agricultural land expansion, timber and fuel collection and wood marketing were common key drivers over the space. Agricultural land expansion and timber and fuel collection were the most effective drivers and intermediate zones are in threatening conditions to forest cover loss. Acknowledgements The authors are grateful to the Council of Scientific and Industrial Research (CSIR), Government of India for funding as a Senior Research Fellowship (SRF) (Sanctioned file no. is 09/599/(0083)/2019-EMR-I).

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Chapter 10

Impact of Land Inundation Caused by Cyclone ‘Amphan’ Across Bangladesh and India Using Spatial Damage Assessment Framework Medha, Biswajit Mondal, Gour Dolui and Murari Mohan Bera

, S. M. Tafsirul Islam,

Abstract The tropical cyclone affects millions of people living in the coastal regions. The changing climate has led to an increased intensity and frequency of cyclones, therefore, increasing the damage caused to people, the environment, and property. The Bay of Bengal is most prone to tropical cyclones, which affects Bangladesh and the eastern coastal region of India due to geographical proximity. Hence, a comprehensive understanding of the inundation damage and intensity caused becomes essential to focus the relief efforts on the affected districts. This study identified the shock zone and assessed the inundation-associated damage caused by the recent cyclone Amphan in the area of Bangladesh and West Bengal in India. The shock zonations have been identified based on the pathway of cyclones with their wind speed, elevation, wind impact potentiality, and agricultural population area. The identification of the affected area was done using integrated Landsat and Synthetic-Aperture Radar (SAR) data, and economic damage cost was assessed using the Asian Development Bank’s (ADB) Unit price approach. The total people affected due to inundation are 2.4 million in India and 1.4 million in Bangladesh and the damage totalled up to 5.4million USD. The results of this study can be used by concerned authorities to identify the shock zones and be used for rapid assessment of the damages. Keywords Tropical Cyclone · Landsat and SAR data · Damage Assessment · Shock Zonation Medha · B. Mondal Jawaharlal Nehru University, New Delhi, India G. Dolui (✉) Panskura Banamali College (Autonomous), Panskura, West Bengal, India S. M. Tafsirul Islam Sustainable, Healthy and Learning Cities and Neighbourhoods (SHLC), Khulna, Bangladesh M. M. Bera Vidyasagar University, Midnapore, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_10

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1 Introduction Tropical cyclones are storms that cause extensive damage to property, disruption of transport and communication networks, loss of human and animal lives, and environmental degradation (Dube et al., 2009; Krapivin et al., 2012; Needham et al., 2015; Sahoo & Bhaskaran, 2018, Bakkensen & Mendelsohn, 2019). Around the world, around 90 tropical cyclones are formed per year, which causes catastrophic disasters (Murakami et al., 2013). Globally, tropical cyclones have caused deaths of about 1.9 million people over the past two centuries (Shultz et al., 2005; Hoque et al., 2018). The approximate damage estimated was 26 billion USD each year (Mendelsohn et al., 2012a, b; Hoque et al., 2019). Many studies have predicted increased number and intensity of the tropical cyclones over the years (Mendelsohn et al., 2012a, b; Ranson et al., 2014; Varotsos et al., 2015; Alam and DomineyHowes, 2015; Walsh et al., 2016; Moon et al., 2019). This increases the risk of impact on coastal communities, animals, environment, and properties (Varotsos & Efstathiou, 2013; Hoque et al., 2019). According to UNISDR’s recent report ‘Economic loss, poverty, and disasters 1998–2017’ climate-related disaster made over 4.4 billion people homeless, displaced, and injured worldwide. In India and Bangladesh, approximately 5.5% of the population was directly exposed to disasters in this period. India faced an absolute economic loss of 79.9 billion USD during 1998–2017. The World Bank estimates suggest disaster causing over 16 billion USD in total damage in Bangladesh during 1980–2008.1 Floods and inundation are one of the costliest types of disasters, which alone share around 26% of the global crop, livestock, forestry, and fisheries loss during 2006–2016 (UNISDR, 2017). The Bay of Bengal (BOB) is frequently affected by tropical cyclones and resulting flood and inundation. The geographical proximity of Bangladesh and the eastern coast of India to BOB make the regions highly prone to cyclonic disasters (Islam & Peterson, 2009; Paul et al., 2010; Ahmed et al., 2016; Islam et al., 2016). In the last 100 years, around 17% of tropical cyclones have made landfall on coastal areas of the Bay of Bengal (Hoque et al., 2019; Biswas, 2020). High-intensity tropical cyclones re-occur frequently causing extensive damage to the coastal region of both countries (Alamand & Collins, 2010; Mallick et al., 2017). Further, these coastal regions are highly vulnerable due to large population density, high poverty rates, and presence of temporary infrastructure. According to Paul and Dutt (2010), more than one million people were killed by cyclonic disasters since 1877 in coastal Bangladesh. Further, sea-level rise due to global warming will intensify the impacts of tropical cyclones on people’s lives and livelihood across the coastal districts of both India and Bangladesh (Karim & Mimura, 2008; Sarwar, 2013; Abedin et al., 2019). On 20th May 2020, the tropical cyclone ‘Amphan’ hit the coast of India and Bangladesh, accompanied by severe storm surges and rainfall (wind speeds up to 195 kmph or 121 mph). The cyclone caused causalities, killing around 88 people and 1

Bangladesh: Disaster Risk Reduction as Development, UNDP.

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leaving thousands homeless in India and Bangladesh (Aljazeera, 2020; ABC Report, 2020; TET Report, 2020; Pinto, 2020). The cyclone struck at a time when the region had already been ailing with the impact of the COVID-19 pandemic. In such a situation, the relief and recovery measures get further complicated. Therefore, finding the risk zones and estimating the damage is essential to provide an idea about the loss of property, agricultural and livestock, and various primary livelihoods. Some news reports and government organizations published estimated damage for a particular area (Sud & Rajaram, 2020; Hindustan Times, 2020; Chaturvedi, 2020; Pundir et al., 2020) or specific aspects. Detailed reports on risks and overall damages in the entire cyclonic-affected coastal and adjacent districts were not available. The risk mapping is a fundamental technique to derive spatial information. Risk mapping assesses the impacts of any hazard or disaster that make people, properties, and environments vulnerable (Pradhan & Lee, 2010; Mohammady et al., 2012; Zare et al., 2013; Rashid, 2013; Pradhan et al., 2014; Youssef et al., 2015; Aghdam et al., 2016). Remote sensing and geospatial techniques have been used effectively for mapping risk-prone areas (Poompavai & Ramalingam, 2013; World Meteorological Organization Communications and Public Affairs Office Final, 2011). MODIS, Sentinel, Landsat data, and Census information are frequently used to understand flood, landslide, earthquake, cyclone impact on land-use land cover and socio-economic situations (Agnihotri et al., 2019; Haraguchi et al., 2019; Jeyaseelan, 2003; Tay et al., 2020; Aksha et al., 2020). A review of existing literature shows the spatial analysis techniques are commonly used for mapping risk (Kunte et al., 2014; Mori & Takemi, 2016; Hoque et al., 2017; Hoque et al., 2018; Karim & Mimura, 2008; Kumar et al., 2011; Dasgupta et al., 2014; Roy & Blaschke, 2013) with the use of multi-criteria based approach being used the most (Poompavai & Ramalingam, 2013; Gao et al., 2014; Tien Bui et al., 2016; Quader et al., 2017; Chou et al., 2020). A spatial risk assessment model has proven to be useful in minimizing the loss of life and the socio-economic impact (Mahapatra et al., 2017; Masuya et al., 2015; Cortés-Ramos et al., 2020), while current GEOSOM-based shock assessment model can assist in mitigation and current impact assessment for a specific event. Studies related to tropical cyclone risk mapping are done widely but studies on spatial damage and loss estimation and mapping due to cyclones are very inadequate. An understanding of the socio-economic damages caused by tropical cyclones (Ahmed et al., 2016; Joyce et al., 2009) is important to undertake the proper recovery measures (Ravikiran, 2020). Moreover, spatial loss assessment is crucial for the allocation of resources for agricultural activities, regeneration of jobs, and other socio-economic activities by the funding agencies. There are two popular typologies, direct and indirect estimation. Direct cost considers the immediate cost of any disaster, whereas indirect estimation focuses on the disaster-associated consequences. Rather than distinguishing direct and indirect loss, several studies focus on the assets and output loss approach (Hallegatte, 2015). Multi-sectorial inputoutput model (Haque & Jahan, 2015) and unit cost-based model (Roy et al., 2009; Government of Odisha, 2013) are commonly used to estimate the output loss. Significant advancements have been made in the damage assessment framework

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Fig. 10.1 Location map of the study area

(Dolman et al., 2018). The availability of real-time satellite data and global socioeconomic datasets has significantly improved the damage and loss estimation accuracy. There are lots of other robust econometric models to estimate the economic loss. However, to conduct such analysis updated sectoral economic information is needed, which is not available. Therefore, this study used a simple and reliable UN geospatial framework to estimate disaster loss. In context, our recent study provides deep and elaborate details of damage estimation of the entire flood-inundated areas after the cyclone (Fig. 10.1). This study develops a spatial framework that includes cyclone shock zones and damage and output loss intensity. UN-SPIDER recommended damage estimation practice2 and unit cost methods were combined to estimate output loss for the entire flood-inundated areas caused by cyclone Amphan. This study seeks to analyse the situation of the areas majorly affected by recent inundation and flooding caused by the Amphan cyclone. Firstly, the study assesses the categories of Amphan shock zones to identify potentially exposed areas rather than following the traditional risk zonation approach. Secondly, developing a spatial damage assessment framework to account for the economic cost of inundation and flooding on the crop, livestock, and 2

For more details, step-by-step discussion available on http://www.un-spider.org/advisory-support/ recommended-practices/flood-mapping-and-damage-assessment-using-s2-data/in-detail

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housing units. In this study, the maps produced by risk assessment would be very helpful to identify the spread and intensity of disaster to create the most effective disaster mitigation plan in this area. This understanding of the socio-economic damages caused by tropical cyclones is important for reducing the losses by adopting proper recovery measures.

2 Database This study uses socio-economic, disaster, and climate-related data, administrative GIS layers, and satellite data to estimate the cyclone severity and damage intensity. Socio-economic data were used to estimate human exposure to disaster and the household crisis. Climate data depict the last 48 h. update of the cyclone event i.e., its track, intensity, and area of influence. The historical disaster records were used to assess the current loss and to compute area-wise disaster damage intensity. Further, GIS layers and remote sensing data served the local to regional level damage and loss information of crop, forest, property, and human life. The database and its preliminary preparation process are illustrated in Table 10.1.

3 Methods The study was carried out in four major steps. First, shock zones were defined using the cyclone characteristics and socio-demographic situation shock zones. Second, using remote sensing and GIS tools LULC and flood-affected areas were demarcated. Third, the impacts of inundation were estimated using LULC, inundation area, population, and poverty situation. Finally, cyclone shock zones and their associated cost are estimated to understand the association between cyclone intensity and damage. The detailed workflow of these steps is illustrated in Fig. 10.2.

3.1

LULC and Flood Mapping

Medium resolution optical (30 m × 30 m) Landsat 8 data along with 10 m resolution Sentinel-1 C-band GRD data dated 4th May 2020 was used. A combination of SAR and optical data is used to reduce the chances of misclassification. The present study follows the Anderson (1976) LULC classification scheme to prepare the LULC data in the Google Earth Engine (GEE). A Random Forest (RF) algorithm is used for the large area because of its high accuracy (Hassan & Southworth, 2017). Six broad spectral classes were used, i.e., Built-up, Open land, Cropland, Vegetation, Sand, and Water bodies using 100 training pixels for each class. The overall accuracy of the LULC data was computed to be 91% and the Kappa coefficient was 89%.

Census of India 2011; DACNET; Agmarknet; NABARD; PMAY-G; Poverty Grid, Livemint

Humanitarian Data Exchange (HDX); Datameet; FAO agricultural statistics; European commission; WorldPop

District total population 2011, crop yield; unit price of crop and livestock 2019–20; unit price for property 2018–19; poverty rate

Previous cyclone path; global exposure (low-income group) 2015; Bangladesh district administrative boundary from HDX and Indian district boundary 2011 from Datameet (open platform); FAO Livestock population 2010; GHSL 2014; Population grid 2020

India socio-economic

GIS layers

Source Bangladesh Bureau of Statistics; Yearbook of Agricultural Statistics 2019; Department of Agricultural Marketing; Food for Nation, Ministry of Agriculture; HIES 2016

Variables District total population 2018, Crop Yield 2018–19; Unit price of crop and livestock unit 2019–2020; Unit price for property 2018; Poverty rate 2016

Data Bangladesh socioeconomic

Table 10.1 Details of data used to estimate cyclone shock zones and damage intensity Details of data preparation Official population data are used to validate the WorldPop population grid data Crop yield, livestock product/unit, and unit price data used to estimate the output value/unit crop and livestock Standard property reconstruction price/ unit data used to estimate the total cost of reconstruction Official population data are used to validate the WorldPop population grid data Crop yield, total livestock, and unit price data used to estimate the output value/unit crop and livestock Standard property reconstruction price/ unit data used to estimate the total cost of reconstruction History of previous cyclone track data is valuable to understand the regional risk from the cyclone events Spatial estimated population dataset is useful to estimate the current population under threats The reliability of the population spatial dataset is checked using 2011 as a reference year Total low-income rural population data is extracted from the global human exposure dataset 2015 The 2010 man-animal proportion is used to compute the livestock population in 2020

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Flood inundation extent map on 1st May 2020 and 22nd May 2020; LULC based standing cropland, built-up, flood inundation 4th May 2020; Elevation

Cyclone Amphan track

Category wise damage and loss from previous cyclones

Sentinel-1C-band, and Landsat 8, SRTM DEM data

Climate

Disaster

Government reports on Feni, Alia and Phalin, Sidr; Bangladesh Disaster Report 2009–2014; Reliable online media

IMD and BMD

LANCE; USGS

Built-up grid data is used to validate the household unit for the year 2014 WorldPop population grid data is used to estimation the inundation impact on human life SAR, Landsat 8 data, and random forest classification scheme are used to prepare the LULC map for the year May 2020 in the Google earth engine DEM elevation data are used to validate the inundated area Details track of Amphan 2020 is used to demarcate the risk zones based on wind speed: High risk [wind speed: Above 120kmph], medium risk [wind speed: 90-120kmph], low risk zone [Below 90kmph]) Reviews on previous cyclones, its intensity, damage, and loss amount are collected; current media gross damage information is used to validate our estimated information

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Fig. 10.2 Workflow of the spatial damage assessment framework

Inundation change analysis performed in the GEE Sentinel-1C-band GRD imagery with VV, VH polarization, and ‘DESCENDING’ pass direction for the dates 4th May 2020 and 22nd May 2020 was used for pre and post-inundation situation. In the GEE, all the data are pre-processed (i.e., noise removal, radiometric correction, and terrain correction, and finally, backscatter scattering to decibel conversion). The intensity of change per pixel was estimated by dividing after flood mosaic with before flood mosaic. A binary flood layer was prepared using a threshold of 1.25, where values above 1.25 were assigned a score of 1 and all other pixels are assigned a score of 0.3

3.2

Damage and Loss Estimation

Details on average damage and loss pattern of cyclone events were compiled from different reports. Based on the data availability, this study estimated damage for the housing, crop, and livestock sectors including human life. Damage and loss quantity vary significantly between rural and urban areas. The damage assessment for the infrastructure and forestry sectors was not attempted due to inconsistencies in the media report and the absence of reliable spatial data.

3 http://www.un-spider.org/advisory-support/recommended-practices/recommended-practice-goo gle-earth-engine-flood-mapping/in-detail

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Table 10.2 Grid-level sector-wise loss estimation Sectors Crop loss estimation

Livestock loss estimation

Housing loss estimation

Population

India Bangladesh Crop Cost = Innundation Affected Cropped Area × ∑ (Yieldij × Unitpriceij) Where (i) represents districts and (j) represents crop types Livestock Cost = (livestock densityij × Innundated Ccrop and Builtup Areaia) × Unit price oflivestocki Where (i) represents grids within a specific district and (j) represents livestock types; (a) represents grids within the (ith) district Housing Cost =

Innundated Builtup Areai Standard Reconstruction Area

×

Unit Price of Partial ReconstructionNational Where (i) represents grids within a specific district Low-income rural population around the flooded ( 0.05

area and sand deposition (Fig. 18.3). Interestingly, having a great coverage of agricultural land in the entire study area, still the result shows an insignificant correlation to LST (Table 18.1). Agriculture land can be helpful in reducing the surface temperature as it covered by green plants and sufficient soil moisture.

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Fig. 18.5 Geological map of the study area

Therefore, the areas dominated by agricultural land have no chance to increase the surface temperature above average, whereas the SUHI map (Fig. 18.4) shows some zone of concentration of SUHI formation, mainly in urban centres, where the presence of agricultural land is very minimum, which means the zones of SUHI formation are primarily determined positively by the factors like barren land, coal mine, residential and industrial complex and influenced negatively by the presence of water bodies and natural vegetation. That is why, having the capability to reduce temperature, still agricultural land has no significant impact on the formation of SUHI in urban areas. One of the unique land-use characteristics of the Asansol– Durgapur industrial region is the extensive coal mine area and barren lands with laterite exposure, which the increase temperature more of the surroundings. Geologically, the western part of the study area near Asansol urban areas is mostly dominated by hard granitic gneiss, sandstone and conglomerate mixed pebbly surface that is very favouring to increase surface temperature (Fig. 18.5). The urban residential area and industrial pollution also increased the temperature of the study area. Our previous study (cite) also found that most of the heat islands are identified near the coal mining area and barren lands followed by urban residence and industrial area. The lowest temperature is estimated near water bodies and dense vegetation area. To build the statistical relationship between land surface temperature and land-use characteristics, the correlation test for each land class is accomplished. The degree and direction of the relationship of each land class with LST are

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clearly depicted in Table 18.1 with significance level and R square value. Correlation with all seven-land classes is significant except agricultural area. Vegetation and water bodies have a negative correlation with LST meaning; thereby, the existence of both land classes lowers the surface temperature. Barren land, residential area, coal mining area, industrial area and sand deposition have a positive correlation, which increases the heat island effects. A low correlation coefficient value indicates that the temperature is also influenced by other factors like soil type and properties, elevation from sea level, slope of the land, aspect of the area and of course the longterm effect of climate change.

4.2

Effect of Land Class on LST with Linear Regression

Eight types of land-use and land cover classes are converted to eight dummy variables for regression analysis. To study the effect of land-use and land cover class on LST, the linear regression model is considered where LST has been taken as dependent variables and dummy variables are as independent. For the regression model, the correlation value should be significant; otherwise, the dependent variable will wrongly be estimated. From the analysis, it is found that the correlation value of agriculture is insignificant (r = -0.003, p = 0.439) (Table 18.1). So, agriculture (D2) is not considered for any regression model. The first seven models (Model 1 to Model 7) have been executed separately for each dummy variable and estimated regression coefficient (slope of the regression line) against each independent variable (Table 18.2). Model 1 is executed with vegetation (D1) only, and the resulting regression coefficient explains that LST will decrease at the rate of -0.464 °C. Model 2 (D3: Water bodies) explains water bodies will decrease surface temperature by -3.79 °C. Model 3 to Model 7 explain LST will increase by 1.56 °C (D4: barren land), 1.72 °C (D5: coal mine), 0.93 °C (D6: residential area), 0.54 °C (D7: industrial area) and 2.09 °C (D8: sand deposition). Model 8 has been executed with vegetation and water bodies, and the coefficient value explains the decrease of temperature at a high rate compared to the single variable model executed separately. All the variables have a positive correlation with LST included in Model 9, which results in the high rate of temperature increase (predicted LST is 39.73 °C) among all the other models. High temperature (the result of Model 9) can be reduced when vegetation (D1) and water bodies (D3) are included in Model 10.

4.3

Approach to Mitigate SUHI with Regression Model

Urban heat island means the occurrence of high temperature, which is directly related to the characteristics of land-use and land cover pattern. Immediate reduction of temperature is not possible. It will take a long time and only after the execution of

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Table 18.2 Regression models and predicted LST Models Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Equation Ye = β0 + β1D1

Intercept 31.97

SE 0.057

Regression coefficient -0.464

Predicted LST 31.50

Ye = β0 + β1D3

31.88

0.041

-3.79

28.09

Ye = β0 + β1D4

31.63

0.043

1.557

33.12

Ye = β0 + β1D5

31.74

0.043

1.722

33.46

Ye = β0 + β1D6

31.65

0.045

0.927

32.58

Ye = β0 + β1D7

31.74

0.043

0.536

32.28

Ye = β0 + β1D8

31.74

0.042

2.09

33.83

Ye = β0 + β1D1 + β2D3

32.21

0.054

-0.709 (D1), -4.114 (D3)

27.39

Ye = β0 + β1D4 + β2D5 + β3D6 + β4D7 + β5D8

31.4

0.046

39.73

Ye = β0 + β1D1 + β2D3 + β3D4 + β4D5 + β5D6 + β6D7 + β7D8

31.63

0.073

1.789 (D4), 2.056 (D5) 1.178 (D6), 0.878 (D7), 2.429 (D8) -0.134(D1), -3.538 (D3), 1.554 (D4), 1.821 (D5), 0.943 (D6), 0.643 (D7), 2.194 (D8)

34.99

Note: D1 = Vegetation, D3 = Water bodies, D4 = Barren lands, D5 = Coal mine area, D6 = Residential area, D7 = Industrial area, D8 = Sand deposition

proper mitigation measures. To propose some measures, it needs to identify the actual causes of high urban heat island formation and build the cause–effect relationship between high temperature (dependent variable) and inducing factors (independent variables). By the correlation analysis, it is found that water and vegetation negatively correlated with land surface temperature, whereas urban residence, industrial area, bare land with laterite exposure and sand deposition have a positive relationship with LST. All ten executed regression models depicted the differential effect of each land-use class on surface temperature with single and multiple regressor effects. The result shows vegetation and water bodies reduce the land surface temperature (LST) with a high rate. It also explains that barren land and sand deposition have a positive effect on LST, which increases the temperature at an alarming rate. With this cause–effect relationship, if barren land and sand deposition land may be changed by vegetation and water pool, it will reduce the heat island effect. It can be modelled through a regression approach, which might be the future scope of the study.

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5 Conclusions Through this study, an attempt has taken to understand the effect of different landuse types on land surface temperature. To map the surface urban heat island of the study area, the LST has been extracted from the thermal band of the Landsat image and also classified the different land-use types from spectral bands. Linear regression and correlation analysis have been adopted to identify the relationship between landuse types and land surface temperate as the function of independent variables. Those independent variables are basically categorical in nature, and thus, dummy variables were generated to execute the regression model. The correlation values predict the negative relationship of water bodies and vegetation with surface temperature and, in contrast, the positive relationship with residential areas, industrial areas, coal mines, barren lands and sand deposition. Model 2 clearly indicates that the LST will be reduced by -3.79 °C if the water bodies and vegetation cover can be increased. A regression of positively associated five variables (e.g. barren lands, coal mine areas, residential areas, industrial areas and sand deposition) depicts the highest predicted LST (39.73 °C) compared to all other probable regressions. Therefore, this study reveals that, if the land uses, especially barren lands and sand deposition could be managed to modify the effect of SUHI will be reduced to some. This study provides an idea of management procedures in industrial areas like Durgapur–Asansol, where a proper plan of alteration of land-use patterns can solve a critical issue like an urban heat island.

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Chapter 19

An Exercise on Valuation of Urban Heritage Site, A Comparative Study of Victoria Memorial Hall and Indian Museum, Kolkata Tuhin Kanti Ray, Pallavi Sarkar, Bulti Das, and Eshita Boral

Abstract The qualities of a city that have been passed down through the generations constitute its urban heritage. Compared to more pressing concerns, the preservation of urban cultural artefacts like historic structures and monuments is often given short shrift. However, efficient conservation of heritage materials not only aids in reviving local economies but also gives residents a sense of pride in their city. The current article investigates the numerous characteristics of historical value conservation and management via valuation. Valuation is the process of assigning values to resources and the environmental consequences and it helps to measure the values of non-market commodities and services by quantifying the losses caused for human responses. The two heritage sites of Kolkata that are the focus of this research are the Victoria Memorial Hall and the Indian Museum. Both the Contingent Valuation Method (CVM) and the Travel Cost Method (TCM) have been implemented, with the former serving as the major data source. A man’s Willingness to Pay (WTP) for cultural landmarks is affected by his socioeconomic status. About 44% of the WTP variance in the Indian Museum is due to differences in income, while 56% of the variance in the Victoria Memorial Hall’s WTP variance is due to differences in income. Keywords Travel Cost Method (TCM) · Valuation of urban heritage · Total Economic Value (TEV) · Willingness To Pay (WTP) · Contingent Valuation Method (CVM)

T. K. Ray Department of Geography, Vidyasagar College, Kolkata, West Bengal, India P. Sarkar Geography, Delhi Public School, Vijayawada, Andhra Pradesh, India B. Das · E. Boral (✉) Department of Geography and Disaster Management, Tripura University, Suryamaninagar, Tripura, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_19

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1 Introduction The tourism industry relies heavily on heritage and culture. A society’s cultural heritage consists of the tangible and intangible objects and features it has received from previous generations, preserved in the present, and passed on to the next. Both material and immaterial artefacts are equally important for the cultural legacy (Nilson & Thorell, 2014; Dalmas et al., 2015). According to UNESCO in 1972, cultural heritage can be interpreted as monuments, groups of buildings, or locations of great universal worth from the perspective of history, art, or science. Cultural heritage ensures the sustainable development, which means cultural heritage can be passed on to future generation, and at the same time, cultural heritage have an economic value (Merciu et al., 2021). The preservation of cultural artefacts is often viewed as a public good because of the value placed on them from the perspective of socio-economic and environmental factors. Their open availability and inexhaustibility are two of their defining characteristics. Delivering the ideal quantity of a public benefit can be challenging. To do so, governments should devise measures to evaluate the cost of delivering the public benefit and then compare that number to the value placed on the good by individual members of the community. A public good is anything that cannot be sold on the open market. Therefore, people want to pay for them, and they are there for their enjoyment. These public goods are provided free of charge (Poor et al., 2004). These factors make it hard to put a dollar amount on the worth of public cultural products. When a product or service has a market price, it’s easy to calculate the impact of a change in supply and demand. However, when a public item is provided for free, its monetary worth is harder to quantify. Thus, both the use value and the non-use worth of legacy must be considered in any valuation exercise (Chiam et al., 2011; Navrud et al., 2002). These items also have values apart from their practical utility. People’s desire to give their time or money to a cause they view as culturally significant is one measure of these ideals. Numerous examples of the valuation process being applied to cultural objects can be found in scholarly writing. CVM has for the most part been used on environmental goods (Hansen, 1997). There are some examples of utilization of CVM for cultural goods (Throsby, 2003; Santagata & Signorello, 2000; Noonan, 2003), and most of these studies have used CVM on very broadly defined goods. CVM has been widely accepted by academics and policy makers for the valuation of resources, cultural heritage, environmental goods, and services (Whittington, 2002; Wang et al., 2006; Han et al., 2011; Maddison & Mourato, 2001). While the earliest cultural CVM research dates back to the 1980s, the vast bulk of studies in this field has only appeared in the previous decade. Improvements in valuation methods, rising demand for cultural public goods, and an increasing awareness of the tradeoffs implicit in public provision of cultural goods all contribute to the current uptick in interest in applying financial valuation techniques to the cultural arena. The history of the literature on cultural valuation provides valuable context for thinking

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about the worth of various cultural artefacts. It also highlights culturally specific methodological concerns and how these have been solved in practice. In this regard, a selected number of significant studies on the monetary value of cultural heritage can be cited. Marilena Pollicino and David Maddison used the WTP exercise to evaluate the value of cleaning Lincoln Cathedral in 2001. This study asks how much it would be worth to shorten the fictitious cleaning cycle for Lincoln Cathedral from 40 years to 10 years in order to protect it from soiling due to air pollution. Estimating mean WTP values is a substantial new contribution of this paper. Additionally, it provides an approximation of the WTP‘s geographic boundary in terms of its distance from the Cathedral. In 1994, K. G. Willis conducted another fascinating investigation of the medieval Durham Cathedral in Durham, England. This paper presented the results of a survey that was developed specifically to assess the Cathedral’s worth in terms of its various functions. G. D. Garrod conducted research on the intangible benefits of restoring Grainger Town’s historic buildings in Newcastle in 1996. In the survey, participants were asked if they would be willing to contribute financially to the repair of historic structures. In addition, they inquired as to how respondents would distribute additional funds among Grainger Town’s many establishments. In 1998, the World Bank undertook a valuation analysis of the Fes Medina, a city in Morocco that is a World Heritage site. Its purpose was to quantify the returns on investment in preserving this historic landmark. In this backdrop, the present research focuses on the monetary worth of cultural relics with significant historical and social relevance in the context of a long colonial legacy. Both the Victoria Memorial Hall and the Indian Museum are recognised by UNESCO as part of the organization’s list of global heritage monuments due to the legacy of colonial rule they represent.

2 Objective of the Research 1. The study’s overarching goal is to propose an assessment structure of the Total Economic Value (TEV) of cultural heritage conservation and protection using combined stated preference and revealed preference data, on the assumption that people are not fully aware of the benefits obtaining from the use of the two selected heritage sites for recreation. 2. Evaluating the heritage site’s financial worth by calculating how much people are willing to pay to use the site’s associated recreational facilities (CVM). 3. The other purpose of the research is to examine the visitors’ expressed preference via the lens of the trip cost methods of valuation (TCM). This study aims to do two things: (a) identify the factors that influence people’s choices and (b) offer planning rules that prioritize visitors’ impressions when conducting valuation exercises.

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3 Methods and Methodology Following the formulation of research problem, the study regions for the case studies were chosen. Literature review gathered information on the historical assessment techniques, the recreational value of urban heritage assets, the site’s management, and related topics. For the present study, both the primary and secondary data have been used. The historical background of the development of the two selected case study regions as well as relevant information about the site’s management, development, and design were researched using secondary sources. However, the study relies heavily on primary data collected from government officials, local workers, and tourists. Face-to-face interviews were done to help with the questionnaires because it is important to explain the needed socio-economic and recreational portfolio of the visitors. This includes their thoughts on preserving cultural heritage and their willingness to pay (WTP) for preserving heritage, as well as the cost of travel for respondents to use a recreation facility. For this research work, a sample size of 100 visitors from each of the two heritage sites, viz. Victoria Memorial Hall and Indian Museum were considered through stratified random sampling. All the respondents considered were Indians and/ or residents of India, with different educational and income levels. A structured schedule with four sub-sections including socio-economic profile of the respondents, their travel behaviour, perception on cultural heritage and the willingness to pay, compiled into 24 questions was prepared. The survey was undertaken during morning (8: 30 am – 11:00 am) and evening (3:00 pm – 5:00 pm) on holidays i.e. Sundays and other holidays for a period between October – December, 2019. After gathering all of the data for the field survey, some basic statistical techniques such as descriptive statistics and simple bivariate regression analysis have been run to see what factors affected WTP. And, at last, analyses and data were presented in an ordered manner to make the conclusion.

3.1

Details of the Valuation Methods Used in the Study

Through the social and cultural values, heritage buildings have an important effect on the people’s well-being and quality of life. At the same time, heritage buildings have an economic value. In identifying the economic value of heritage properties, a distinction is made between the use and non-use values (Merciu et al., 2020). Considering the use value, the resource is valuable as it provides goods and services that are used by the society members. On the other hand, a certain resource may be considered valuable in its own rights, independent of any uses, which is called non-use value (Kurowski et al., 2007). The use value incorporates both the direct use value and the indirect use value. Values that are “direct use” are based on things that may be used immediately, such as food, clothing, or entertainment. Services provided by the good are one source of its indirect use value. Direct use worth for

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Table 19.1 Cultural heritage value and applicable assessment method

Categories of valuea Use Extractive, consumptive

Recreational

Aesthetic value

Nonuse

Existence and bequest

Components of valueb Scientific or research, historic

Social, economic, aesthetic Aesthetic

Aesthetic, historic, scientific or research, social or economic

Indicators Archaeological treasures, historical exhibits, structures(tangible resources) Transportation cost, opportunity cost, access fee Transportation cost, opportunity cost, access fee

Willingness to pay avoid damages to cultural resources

Applicable pricing methodology Market pricing methods

Travel cost

Travel cost, hedonic Pricing, contingent valuation Contingent valuation

Advantages of methodology Use market price

Based on generalized travel cost to destination Market price of rent and wage, and generalized travel cost to destination Able to capture the non-marketed attributes of the goods

Source: Parumog et al. (2003) a Based on Pagiola’s definition (1996) b Based on Tabororoff’s definition (1994)

cultural heritage objects consists of things like monuments, exhibits, landscape, building, etc., that already exist at the site and are used in the valuation process. The recreational and instructional benefits that can be gained from these locations are included in the indirect use value that they provide. While current markets are used to materialize the value of direct use, surrogate markets—often appraised with the help of the Travel Cost Methodology—are typically employed to arrive at estimates of the value of indirect use (TCM). The value of holding onto the right to utilized a product at some point in the future is known as the “option value” (Dixon and Pagiola, 1998; Morey, 2002). These cultural items have value beyond their practical application because of the satisfaction they bring to their owners even when they are not using them (Table 19.1).

3.2

Travel Cost Method (TCM)

One way to handle the challenge of estimating the value an individual place on a specific attraction independent of its nature and irrespective of whether an entrance fee is charged is by allocating the travel expenses from the visitor’s start point to the location (Bedate et al., 2004). The travel cost method (TCM) is extensively used to

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quantify amenity values of outdoor places such as parks or lakes. It is the earliest of the non-market evaluation methodologies (Alberini & Longo 2006). It was proposed by the eminent economist Harold Hotelling but it was fully launched by Clawson and Knetsch in 1966 (Centeno & Prieto, 2000). TCM is a revealed preference method which tries to relate the costs of recreational activities and the characteristics of the resource. It is based on the demand theory and assumes that the demand for a site is inversely related to travel costs (Torres-Ortega et al., 2018). The primary idea of the travel cost technique is that the time and travel cost charges that users incur to visit a site indicate the “price” of the access to the site. These people’s willingness to pay visit the place can be evaluated depending on the number of trips that they make at varied travel prices. This is akin to gauging people’s willingness to pay for a marketed good depending on the number required at different pricing. However, there are limitations to this approach. To begin, it needs a huge trove of primary and secondary sources. Second, there are several statistical difficulties and phases in the appraisal process. The third disadvantage is that TCM relies on travel expenses, making it less useful for urban conveniences that are within walking distance.

3.3

Contingent Valuation Method (CVM)

The Contingent Valuation Method of cultural heritage has mainly focused on the economic valuation of specific historic monuments and archaeological sites (Bertacchini & Sultan, 2020, Salazar & Marques, 2005; Dutta et al., 2007). The Contingent Valuation Method (CVM) is a survey-based technique (Kopsidas & Batzias, 2011). It was first used by Davis (1963) for the valuation of recreational camping and hunting in the Maine Woods, USA (Han et al., 2011). Contingent valuation method (CVM) is used to estimate economic values. It can be used to estimate both use and non-use values, and it is the most widely used method for estimating non-use values. This method involves directly asking an individual, in a survey, how much they would be willing to pay for specific environmental services (Chiam et al., 2011; Mason, 2005; Mitchell & Carson, 2013). A plethora of guidelines for the proper implementation of CVM has been developed as a result of the method’s widespread use in the field for the past several decades. The evaluation context, or how the willingness to pay issue is framed, can have a significant impact on the results of a contingent valuation exercise. Such a disposition can be heightened while dealing with historical artefacts.

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Justification for the Application of Environmental Valuation Method in the Context of Cultural Heritage

Is an environmental tool suitable for the use in the cultural sphere is a question that might arise here. Actually, many cultural and environmental products share crucial parallels. While recreational or “use” values are a common descriptor for environmental amenities, many also include non-use values. Existence value and inheritance value are two examples. Each of these esoteric qualities is also highly significant in the realm of popular culture. There is a tried-and-true valuation system for gauging these kinds of intangible benefits; applying it to cultural products and services only makes sense.

4 Study Area The Victoria Memorial Hall and the Indian Museum, both located in the central part of Kolkata and separated by about 1.5 km distance, were chosen as the study’s cultural heritage sites. In honour of the British monarch and Empress of India, Victoria, there stands the Victoria Memorial Hall. The architectural landmark houses both a museum and a tourism hotspot. Visitors will find that the Memorial Hall is situated in a scenic environment with plenty of greenery and water features to enjoy. The Victoria Memorial Hall is considered to be one of the best examples of a unique paradigm of the visual arts where four diverse fields of arts—architecture, sculpture, painting and gardening together (Victoria Memorial Hall, Kolkata).Whereas the Indian Museum, the country’s largest and oldest museum, houses priceless artefacts from India’s prehistoric to Islamic past, including Mughal paintings, antiques, armour, and mummies. Located in Chowringhee, it was established in 1814 (Indian museum,) by Danish botanist Dr. Nathaniel Wallich but did not officially open until 1875. Art, Archaeology, Anthropology, Geology, Zoology, and Economic Botany are just a few of the 35 galleries it houses, all of which showcase cultural and scientific objects. Because of its broad scope and variety of disciplines served, it has been designated as an Institute of National Importance under India’s Constitution. Similar to the Victoria Memorial Hall, this entity operates independently but is funded by the Indian government’s Ministry of Culture.

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5 Results and Discussion 5.1

Demographic and Socio-Economic Profile of the Visitors

The demography of an area primarily points out the standard of that place in respect to be a recreational zone. There are various aspects of demography that control the recreational perception and reaction towards the conservation of the heritage sites. Almost 36% of the visitors are female. In the Victoria Memorial Hall at least 60% are from the age group of 26–35 years and 36–45 years. This means maximum number of respondents is in the working age group. Among cent percent of the respondents, 36% people completed their graduation and 16% completed postgraduation. The income group of Rs. 10,000–15,000 has the highest frequency in the visitors of the Victoria Memorial. This group shares 22% of the total samples. But, where there are only 20% people come under the income group of Rs. 5000–Rs. 10,000. The figure is same for the income group of Rs. 15,000–25,000. Only 5% fall under the income group of more than Rs. 25,000. In Victoria, the occupational pattern is maintained mostly by the servicemen (56%). Only 4% visitors are businessmen. There are 18% and 4% housewives and retired persons respectively who also come here. In Indian Museum, about 28% of the visitors are female. Here, 18% of the respondents are under the age group of 15–25 years; where as 22% of the respondents of the Victoria Memorial Hall are under this age group. In Indian Museum, most of the people (62%) are from the age group of 26–35 years and 36–45 years i.e. in the working age group. Among cent percent of the respondents, 54% people completed their graduation and 10% completed post-graduation. In this site, 11% of the respondents and in Victoria 14% of the respondents completed their H.S. level. The income group of Rs. 10,000–15,000 has the highest frequency in the visitors of the Indian Museum. This group shares 24% of the total samples. In Indian Museum, the occupational pattern is maintained mostly by the service men (54%). Only 8% people are businessmen. There are 14% and 18% students and housewives respectively, who also come here.

5.2

Recreational Profile of the Visitors

In this study, visitors’ perception on recreation has been assessed. The visitors of Victoria and Indian Museum depicted individual preferences and opinion for the maintenance of the heritage site and the area. Also, their types of recreation and choice of the area vary for different reasons as given below. In Indian Museum, almost 34% of the visitors chose to visit to the heritage site for their recreation while the rest happened to pay a visit to the site alongside watching a movie or for shopping, as both these areas lie near to Rabindra Sadan, the cultural and entertainment centre of the metropolis, where cultural programmes, dance drama

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and exhibitions are aired on a daily basis, as well as to Esplanade area and New Market, the shopping hub of the city. Interesting observation is that near about 42% of the respondents at Victoria visited it since it was near to their movie theatre while few others were attracted by the ride in a horse drawn chariot to visit this spot. Though these two sites are very significant from their cultural and historical perspective but as deciphered from the survey that facilities like sitting arrangements, the presence of park, lake, lawn, and security of the area are factors that attract the visitors to these sites more rather than its aesthetic value. The choice of place as a recreational site in case of Indian Museum was much more for its archaeological and historical importance, about 62% of the responses revealed this fact, though presence of sitting arrangements (about 40%) played another role in attracting people to this site after a hectic shopping experience. Contrarily for the Victoria Memorial Hall it is astonishing that majority of the visitors do not pay a visit to the museum, which is of great archaeological and historical importance, rather they (76%) are more attracted to the surrounding beauty and ambience, the presence of park, lawn, and lake. Adding value to an aesthetic site is equally important and likewise almost all the respondents at Indian Museum agreed with the entry fee and it is almost same for Victoria Memorial. Additionally, in case of the later, many of the respondents were in favour of initiating some payment system for the morning walkers.

5.3

Economic Feasibility

In the case of the Victoria Memorial Hall, the sources of income are (i) Sales, (ii) Grants/Subsidies (these include Plan and Non-Plan), (iii) Fees/Subscription, (iv) Income from investments (these include Income on Investment from earmarked/endow, Funds transferred to Funds), (v) Income from Royalty, Publications etc., and (vi) Interest from fixed deposit. Expenditure includes (i) Establishment Expenses, (ii) Other Administrative Expenses, (iii) Expenditure on Grants etc. (plan), and (iv) Depreciation. Two major sources of income are Grants or Subsidies of central government and income generated from Fees or Subscription. In the last 3 years, the income generated from these two sources was near about Rs. 40,000,000 and Rs. 20,000,000, respectively. These two sources share more than 95% of the total income of Victoria Memorial Hall. Among the other income sources, the income from royalty, publication and income from interest have a major contribution. Expenditure for establishment expenses and expenditure on grants had the maximum percentage share in total expenditure. Financial statements of the last 3 years reveal that the expenditure for each of these two sectors always ranges from Rs. 20,000,000 to Rs. 25,000,000. Other sectors of expenditure like administrative expenses and depreciation amount to near about Rs. 7,000,000 and Rs. 5000,000–Rs. 9,000,000, respectively. Thus, the total income stands at over Rs. 57,000,000–Rs. 66,000,000 per year and the total expenditure reaches at Rs. 56,000,000–Rs. 64,700,000. Therefore, the total profit was around Rs. 1,000,000–Rs. 1,500,000.

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In the case of Indian Museum, the sources of income and expenditures are as same as Victoria Memorial Hall. But the total amount of income generated from different sources and the sector wise amount of expenditures are not same; hence, it is not required to repeat the same list containing the sources of income and expenditures. But it will be better to give a brief description about the amounts of income generated from different sources and amount of expenditures for different purposes. As per the financial statement concerned it can be found that just like Victoria Memorial Hall, plan and non- plan grants and subsidies are the major sources of income which contribute more than 90% of the total income and in previous 3 years it ranges from near about Rs. 150,000,000 to Rs. 210,000,000. Income from royalty and publications is the another major source of income for Indian Museum which generate more than Rs. 200,000 per year. In case of expenditure it can be said that expenditure on grants (plan and non-plan) and establishment expenses had the maximum percentage of share in total expenditure and the figures range from Rs. 110,000,000 to Rs. 170,000,000 and Rs. 39,900,000 to Rs. 40,950,000, respectively. Therefore, the total expenditure was about Rs. 160,000,000 to Rs. 210,000,000.

5.4

Travel Cost Analysis

The conceptual idea of the TCM is that information on travel costs and reduction of visitation rates with distance from a site of interest can be used to estimate its recreational use value (Tourkolias et al., 2015). The examination of the frequency of visit of the respondents is required for this purpose. Almost 84% of the respondents of Indian Museum visits the site rarely. Rests of them come here once in a month. In the Victoria Memorial hall, rare visitors amounted to 66%. 16% and 8% of the respondents visited monthly and quarterly respectively. Only 6% of the respondents at Victoria Memorial Hall paid daily visit, majorly the morning walkers, whereas daily visitors are absent in Indian Museum. Distance is another interesting matter of discussion in this analysis. Primary survey reveals that in both of the places most of the respondents come from a long distance which is more than 10 kms. There are few people who come here from outside of West Bengal and abroad. In Victoria Memorial, however the daily visitors hailed from less than 5 kms distance. In this study, another matter of observation is the mode of transport used by the visitors to reach the destination. The percentage of Metro rail user is 50% of the total respondents found in Indian Museum. 34% of the respondents come by bus, 10% used private car. Among 50% of the Metro rail user, 22% use local train also indicating that the heritage site draws people from the suburbs of Kolkata as well. Very few of them (6%) use two wheelers while another 6% of the respondents come here by foot. In case of Victoria Memorial Hall, 84% used public transport modes, i.e. 56% used bus and 28% used Metro rail. Among them, about 12% also used local train before availing bus or metro rail to reach this heritage destination. About 8% visited the site by foot while private car users

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Table 19.2 Estimated total travel cost per visit Total travel cost (In Rs.) 20

Victoria Memorial Hall (percentage of respondents) 22 38 40

Indian Museum (percentage of respondents) 30 46 24

Source: Computed from primary survey Table 19.3 Estimated contingent valuation Range of W.T.P. (in Rs.) 0–10 10–20 20–50 50–100 >100

Victoria Memorial Hall (in percentage of respondents) 22 54 18 6 0

Indian Museum (in percentage of respondents) 20 60 10 8 2

Source: Computed from primary survey

accounted to 8%. Only 4% of the respondents used two wheelers as mode of transport. In this context, the most important thing which should be discussed is the total travel cost per visit. From the (Table 19.2), it has been observed that 30% of the respondents of Indian Museum pay less than Rs. 10 as their travel cost. Forty six percent of the respondents pay Rs. 10–20 and rest of the respondents pay more than Rs. 20 as travel cost.

5.5

Contingent Valuation

It is the stated valuation of these sites. This shows how much the person is willing to pay for the site directly. It depends on the profile of the person like his or her education level, age, income and frequency of visit. In both of the sites, the maximum number of respondents was willing to contribute in the conservation of these sites. In the Victoria Memorial Hall, cent percent of the visitors were willing to pay its conservation but in case of Indian Museum around 4% were uninterested to contribute. Table 19.3 shows that about 60% of the respondents of Indian Museum opined that they are willing to pay Rs. 10–20 for management of the site as their contribution. 10% of the respondents were willing to pay Rs. 20–50 while 16% agreed to pay less than Rs. 5 for the conservation of this site. Only 8% of the respondents were willingly contributing Rs. 50–100 for the upkeep of the heritage site. The study of WTP reveals some interesting facts, which are noticeable. Both genders were not equally interested in contribution. Male respondents were more

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Table 19.4 Willingness To Pay (WTP) for heritage conservation Criteria of WTP Conservation and management of the heritage building Campaigning for the awareness about heritage buildings Development and maintenance of the open space/ water bodies/ green spaces within the boundary of the heritage site Arrangement of recreational and informative activities like light and sound show Preservation of the historic and pre-historic articles and objects of the site Development and maintenance of morning and evening walk zones at the open spaces of the heritage site Creating a pollution free site for the heritage buildings

Indian Museum Mean Median 52.04 52

Mode 52

SD 5.57

Victoria Memorial Mean Median Mode 33.44 34 31

SD 4.37

1.56

1

1

1.89

2.56

2

1

1.77

13.14

13

11

4.11

21.19

21

25

3.42

4.98

5

2

2.54

7.73

8

9

2.33

20.5

19

5.19

10.85

10.5

9

2.69

2.22

2

2

2.16

17.08

18

18

3.66

5.45

5

5

2.79

7.15

7

9

2.88

20.61

Source: Computed from primary survey

willing to pay than the female. Another interesting thing about the education level is also notable that graduates & post-graduates are more interested in paying for conservation of both the sites, be aware of the importance of conservation of heritage site for the aesthetic value. In this regard, when income level is considered, it was found that respondents belonging to the income group of Rs. 10,000–15,000 were most willing to pay. Frequency of visit is also related with the willingness to pay and by studying the visitors’ opinion it can be said that willingness to pay is most displayed among the very rare visitors in both sites. This is indicative that people are willing to pay for the conservation only sometimes and not for every visit. In the present exercise, the mean, median and modal values of the WTP are very important to note which are given in this context (Table 19.4). From the above table, it is observed that in Indian museum, about 52% people are willing to pay for conversion and management of the heritage site whereas in Victoria Memorial Hall that is only 33.44% people. 20.61% people willing to pay for the preservation of historic and pre historic articles and object of the site of Indian museum but the percentage is quite less in Victoria Memorial Hall, only about 10.85%, which may be because people visiting this site is more keen on enjoying

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Table 19.5 Determinants of Willingness to Pay for heritage conservation Independent variables Gender Age Education Income Occupation Frequency of visit

Coefficients Indian Museum -0.0414424 0.2583 0.1788854 0.4368 0.0927 0.1287746

Victoria Memorial Hall -0.061534 0.101090904 0.219413726 0.56743 0.09899482 0.1567921

Source: Computed from primary survey (N = 100 for each site), Significant at 0.05 Significance level (two-tailed)

the open space rather than the museum. Likewise, a very high percentage of people are willing to pay for the development and maintenance of the open space, water bodies, green spaces within the boundary of heritage site of Victoria Memorial, along with the WTP for conservation of morning and evening walk zones. In case of Indian Museum the absence of such spaces does not draw much contribution. However, few respondents considered that the open spaces and walk zones may be developed in Indian Museum as well. Contributions for creating pollution-free site in both the heritage buildings were less as people were not very sure as to how to influence the behavioural characteristics of tourists who litter these sites, though there happened to be few environment friendly tourists who were willing to invest in making the heritage sites pollution free. Table 19.5 suggests how the socio-economic conditions influence a man’s willingness to pay for heritage sites. In Indian Museum, almost 44% variability in WTP is determined by income and in Victoria Memorial Hall 56% of distribution in WTP is determined by income. Therefore, the income level is the most influencing variable. In Indian Museum, the next most influencing variable is the age group i.e. 25% while in case of Victoria Memorial education determines 22% of WTP.

5.6

Management Profile and Planning Strategies

For both of the sites, majority of the respondents agreed with the fact that protection of both of these sites is necessary. Awareness of the present generation is crucially important for the conservation of heritage sites. About 86% of the respondents of Indian Museum opined that the present generation should be aware of this important heritage site, similarly in Victoria, more than 90% of the respondents gave same opinion. Responsibility of the management is an important point to ponder and when respondents were asked about the management responsibility, they gave their opinion. In Indian Museum, most of the visitors opined that the management of the place is the duty of Central Government and same is also applicable for the Victoria Memorial Hall.

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Few plans have been made for the administration and refurbishment of both sites. Victoria Memorial was initially revived in 1992 by the Calcutta Tercentenary Trust. Some restoration work was done by the Archaeological Survey of India after they signed a memorandum of understanding with the Trust in 2003 and made use of the Trust’s observations. The National Institute of Design (NID) originally presented a renovated redesign plan in 2007; however, they ultimately decided not to go about with the project. For the next round of repairs and upgrades, an additional Rs. 145 crore in funding and the assistance of a panel of experts from diverse sectors have been allocated. With an agreement struck in 2011, National Building Construction Corporation will renovate the monument from the inside out. The heritage site’s governing board and the Ministry of Culture have been working on a comprehensive plan for the site’s rehabilitation for the past year. The plan’s initial focus will be on the building itself, with subsequent work on the garden. The concept calls for a complete overhaul of the gallery layout to create a more satisfying experience for guests. The expansion of the laboratories and the library, as well as the updating of the stockroom and inventory system, are also part of this plan. The museums are going to be rearranged in a more logical way. The proposals from the NID have been worked into the present plan. Contrarily, the massive Indian Museum building has never been properly renovated since it opened. The building deteriorated and cracked as a result of the construction of the Park Street flyover. Midway through 2009, the museum was forced to abandon its ambitious proposal to build a mansard roof and a canopy over the courtyard, and it refunded the Rs. 20 crores in central money that had been made available in 2008–2009. The National Buildings Construction Corporation NBCC and the museum authority have signed a memorandum of understanding. The National Structure Construction Corporation was given the contract to make cosmetic upgrades to the building without altering its structural integrity. The NBCC has outsourced this work to the consulting firm Chapman Taylor. LED lighting, custom displays, and glass showcases are all part of the idea. Modern amenities including a cafe, gift shop, and restrooms will be added to the museum as part of the expansion project.

6 Conclusion This research applies the Travel Cost Method and Contingent Valuation Method to appraising cultural heritage sites. Combining revealed and stated choice improves the cultural heritage site’s valuation. The research demonstrates that both Travel expense and Contingent Valuation analyses can be easily organised within the context of a city. The methods can also quantify utilization and non-use in financial terms. Victoria Memorial Hall is a relatively maintained heritage site with the facilities around a lake having numerous functions including recreation, beauty, and playing field for children etc. Morning walkers and joggers are only two examples of the

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many users. These people come here to workout at morning from 4 a.m. to 8 a.m., the remaining day is inhabited by diverse groups of individuals like couples and students. In evenings and weekends, it is largely used for family outing, although Indian Museum is somehow mismanaged. There are numerous precious items and detailed building blueprints. By analysing the degree to which WTP depends on a variety of independent variables, it has been found that those with greater education and disposable income have a clearer appreciation for the significance of cultural heritage. In the research analysis, the significance of urban cultural heritage assets as recreational services and their worth for preserving city culture and history were explored. The study aimed to examine tourists’ visit objectives and perceptions about heritage site conservation. Due to the small sample size, no general generalizations can be drawn about urban historic units. But there are several important points. First, protecting urban heritage units fulfils social functions and psychological needs of residents, making them a significant resource for the cultural sustainability of a legacy city like Kolkata. Valuing and assessing intangible services and benefits is vital to justifying urban sustainability efforts. Public involvement, citizen participation, and a qualitative assessment of their needs and interests are urged to better identify common values, which may then be utilised as reference standards by local planners to establish more sustainable city policies.

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Chapter 20

Population Shifting and Its Consequences on Women’s Life: A Case Study Along the River Banks of Ganga-Bhagirathi in Jangipur Sub-division, West Bengal Debika Ghosh and Abhay Sankar Sahu

Abstract This study is on the effect of population shifting as a result of river bank failure on women’s life. To meet the goal of this study, 19 erosion-prone areas at the bank’s side of river Ganga-Bhagirathi has been taken. Statistical models have been used in this study to show the effects of population shifting on women’s life. The outcomes of this study show that women in the erosion-prone areas have been severely suffering from the river Ganga-Bhagirathi bank failure problem at the Jangipur sub-division of the Murshidabad district under the state of West Bengal. Field survey reveals that women have been displaced many times as a result of the river Ganga-Bhagirathi bank failure. Their entire life is highly affected by it. A considerable percentage of female child labour has been observed in the study units (16–85%). The correlation between the percentage of female child labour and displacement of people as a result of river bank failure is positive (r ¼ 0.752) and significant at 0.01 level of confidence. The displacement of people as a result of bank failure has negative consequences on womens’ monthly income, education and also in their marriage. Keywords River bank erosion · Number of displacement of people · Women’s life · Education · Economic condition · Marriage

1 Introduction Avulsion denotes changing direction of river course, which is a natural process (Singh, 2012). River bank failure has also been introduced by changing the course of a river or simply by shifting of river bank lines (Knighton, 1998; Charlton, 2008;

D. Ghosh (*) Post-Graduate Department of Geography, Krishnagar Government College, Nadia, West Bengal, India A. S. Sahu Department of Geography, University of Kalyani, Kalyani, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_20

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Ghosh & Sahu, 2019a, b). Erosion of river banks creates various physical as well as social problems (Guite & Bora, 2016). Majumder et al. (2022) studied on Ganga bank erosion problem and its related vulnerability to the livelihood of local people at the Manikchak block and they showed this area along the river bank is characterized by frequent flooding and heavy bank erosion. This erosion introduced high to moderate vulnerability for the local people. Khatun et al. (2022) focused on shifting of river Bhagirathi from Rajmahal hill to Farakka barrage from the year 1980–2020 and they showed major land use land cover change due to bank erosion along the river bank area. Ghosh et al. (2022) studied on the channel migration of river Bhagirathi in the Barddhaman District, West Bengal and showed high magnitude of river shifting and its related vulnerability along the river bank. There are lots of adverse consequences of river bank erosion on human livelihood. Briggs et al. (2008) focused on the consequences of bank failure of river Penobscot of USA. According to Flood Preparedness and Management Plan (FPMP) (2014), about 102 km embankments of river Ganga/Padma from Farakka to Jalangi are in vulnerable conditions because of Ganga river bank erosion. And, at the same time, many villages are in highly vulnerable situations due to river bank erosion. It brings displacement of people, loss of agricultural land, loss of production, loss of human structures, loss of properties etc. (Uddin & Basak, 2012, FPMP, 2014, Amarnath et al., 2016, Ghosh & Sahu, 2018). River bank erosion has a very bad effect on the education of the erosion victims also (Ghosh and Sahu, 2019, Conteh, 2015). People who are living at river bank’s side and continuously facing the problem of river bank erosion, among them women are mostly affected. Women are vulnerable or virtuous in relation to the environment (Jonsson, 2011). Women are more vulnerable to climate-related problems than men in terms of occupation in Nigeria (Onwutuebe, 2019). Natural disaster does not affect all groups of people equally and coping with the adverse effect of natural disaster depends on economic status, capability, and opportunities got by people (Neumayer & Plumper, 2007). Natural disaster more decreases life expectancy of women than men (Neumayer & Plumper, 2007). Bradley and Martin et al. (2011) showed adverse impact of natural disaster like earthquake on women in Nepal. Bhadra (2017) considered women the most vulnerable groups in disaster and conflict situations. In this research, an attempt has been made to identify the consequences of number of displacement of people due to river bank erosion on women’s life. To satisfy the aims of this research, a total of 19 study units along the banks of river Ganga-Bhagirathi in Jangipur sub-division of Murshidabad district have been selected. The study units with their jurisdiction list (JL) numbers are- Farakka barrage township, Kuli (058), Arjunpur Census Town (CT), Paranpara CT, Dhulian municipality, Nimtita (108), Aurangabad CT, Chameghoan (70), Icchlampur CT, Arazi Gotha (074), Chak Saiyadpur (087), Jangipur municipality, Diar Ramnagar (144), Char Dafarpur (091), Giria CT, Mithipur CT, Sekendara (014), KismatGadi (042), and Kamarpara (183) (Fig. 20.1). This research has its own significance. It shows the real consequences of displacement of people caused by river bank erosion on women who are living in

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Fig. 20.1 (a) Location of the study area, (b) location of study units. (Based on National Atlas and Thematic Mapping Organization (NATMO) and Census of India, 2011a, b)

river bank erosion-prone areas, especially, in developing countries like India. The taken methodologies may be useful to explore the effect of natural hazards on human beings of other affected areas in the world. This study may help the policy makers to take necessary strategies to combat against burning problems of river bank erosion.

2 Study Area The Jangipur sub-division (Fig. 20.1a, b) of Murshidabad district is located between 24 130 1400 north to 24 520 1500 north latitude and 87 480 0000 east to 88 150 3900 east longitude. The total area of the Jangipur sub-division is about 1097.82 sq. km. (District Census hand book, Murshidabad, 2011b). The Jangipur sub-division covers 20.65% area of the total Murshidabad district. According to the report of the district Census hand book, Murshidabad (2011a), the river Bhagirathi has parted the Murshidabad district into two broad geographical regions. These two regions have almost equal areas. One is the Rarh area and another is the Bagri area. As per the report of the district Census hand book, Murshidabad (2011), the western tract or the Rarh area is situated to the west of the river Bhagirathi. And, it is a continuation of the Sub-Vindhuyan region composed of lateritic clay. It is also characterized by nodular ghuting and slightly undulating by nature. Generally, the colour of soil of

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this area is greyish and reddish. And, the soil is enriched with lime and iron-oxide (District Census hand book, Murshidabad, 2011a). As per the report of the district Census hand book, Murshidabad (2011a), the eastern tract or the Bagri area is on the eastern portion of the river Bhagirathi. This area is generally composed of Gangetic alluvial deposits. Bagri is situated between the Ganga, Bhagirathi, and Jalangi rivers. Bagri is formed after the formation of the Rarh area (District Census hand book, Murshidabad, 2011). This area is generally low in nature. And, it is suffering from annual inundation problems. According to the report of the district statistical hand book, Murshidabad (2010–2011), the district has very hot summer and high humidity throughout the year. Rainfall is occurred due to south-west monsoon from June to September. The Bagri region is very fertile due to the accumulation of fresh silt almost every year (District Census hand book, Murshidabad, 2011). As per the report of the Census of India (2011a), the total population of the district is about 7,103,807, out of which 5,703,115 people live in rural areas and 1,400,692 people live in urban areas. The Out of total population of the district, the total male population is 3,627,564 and 3,476,243 is female population. The Murshidabad district comprises 7.78% of the total population of the state of West Bengal.

3 Data Base and Methodology 3.1

Data Source

Primary and secondary data have been used in this research. Primary data have been collected directly from the field by using questionnaires through interviews with the people. Secondary data have been collected from the official website of the census of India (https://censusindia.gov.in/census.website/). Offices like National Atlas and Thematic Mapping Organization (NATMO), District Land and land Reform office, Murshidabad, West Bengal have been visited to collect data for the preparation of maps of the study area.

3.2

Sample Survey

First of all, a literature survey has been done to cure the problem of the study area related to river bank erosion. Field visits have been conducted from the year 2015 to 2019, 2022, to collect responses regarding the impact of river bank erosion on the life of the people who are living along the river banks. To satisfy the needs of this research, a purposive sample technique has been used. Only women who are living at the river banks of the selected study units have been taken into consideration. From every study units, 40–45 households have been selected based on the total number of households present there.

20

3.3

Population Shifting and Its Consequences on Women’s Life: A Case. . .

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Database Preparation and Analysis

The study is about to identify the impact of displacement of people due to river bank erosion on women’s life who are living along the river banks. To conduct this study, parameters like the number of displacement of people due to river bank erosion (more than 40 years), average female monthly income in rupees, percentage of female mean years of schooling, marriage age in average, percentage of female marriage within local area, percentage of female child labour have been taken. Here, the number of displacement of people due to river bank erosion has been taken because it is the main consequence of river bank erosion. When people are displaced from their own land as a result of river bank erosion, they have lost almost everything in terms of land, properties etc. This effect of displacement can be felt generation after generation. Coping with the loss caused by natural hazards depends on the economic condition of the people. But the condition of the people in the selected study units is not economically good. Here, mean years of schooling is used instead of literacy rate because if a person can read, understand, and write any one language which is recognized by the state has considered a literate person. So, here literacy rate cannot be able to express the real scenario of a particular area about its educational status itself. In place of literacy rate, the mean year of schooling has been selected as a parameter. UNDP (2010) used the mean year of schooling instead of the literacy rate to measure educational development. Multiple linear regression models have been used in this research to show the effect of number of displacement of people due to river bank erosion on women’s life. During the field survey, it has been observed that the monthly average income of the women is very low, very low mean years of schooling of women is observed, early age of marriage of women is observed, and their marriages basically happened within the local area. Here, it is needful to see whether the number of displacement of people due to river bank erosion has any impact on these observations or not. To identify the impact of the number of displacements of people due to river bank erosion on monthly income of women, multiple linear regression analysis has been performed with the help of SPSS software. In the present study, the dependent variable is the monthly average income in rupees of females and the independent variables are female child labour in percentage, number of displacement of people due to river bank erosion, female marriage age in average, marriage inside the area in percentage of female, and female mean year of schooling. The model fitted for multiple linear regression analysis is as follows: Y ¼ f ðX1 , X2 , X3 , X4 , X5 , X6 Þ

ð20:1Þ

(Based on Gaur et al., 2009; Ofuoku, 2011; Ghosh & Sahu, 2019a) [Where, Y ¼ Monthly average income in rupees of females, X1 ¼ number of displacement of people due to river bank erosion, X2 ¼ female child labour in percentage, X3 ¼ Marriage age in average, X4 ¼ Marriage inside the area in percentage, and X6 ¼ Female mean year of schooling, f ¼ Factor of].

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To find out the impact of number of displacement of people due to river bank erosion on women’s education, multiple linear regression analysis has been accomplished with the help of SPSS software. There is one dependent and various independent variables in multiple linear regression analysis. In the present study, the dependent variable is the female mean year of schooling and the independent variables are: female child labour in percentage, number of displacement of people due to river bank erosion, female marriage age on average, marriage of females inside the area in percentage, and monthly average income in rupees of females. The model fitted for multiple linear regression analysis is as follows: Y ¼ f ðX1 , X2 , X3 , X4 , X5 , X6 Þ

ð20:2Þ

(Based on Gaur et al., 2009; Ofuoku, 2011; Ghosh & Sahu, 2019a) [Where, Y ¼ Female mean year of schooling, X1 ¼ female child labour in percentage, X2 ¼ number of displacement of people due to river bank erosion, X3 ¼ marriage age in average, X4 ¼ marriage of female inside the area in percentage, and X6 ¼ monthly average income in rupees of female, f ¼ factor of]. Again, for the identification of the effect of displacement of people due to river bank erosion on women’s marriage, multiple linear regression analysis has been performed using SPSS software. Here, the dependent variable is the marriage age of females on average and the independent variables are: female child labour in percentage, number of displacement of people due to river bank erosion, female mean year of schooling, female marriage age in average, marriage inside the area in the percentage of female and monthly average income in rupees of female. The model fitted for multiple linear regression analysis is as follows: Y ¼ f ðX1 , X2 , X3 , X4 , X5 , X6 Þ

ð20:3Þ

(Based on Gaur et al., 2009; Ofuoku, 2011; Ghosh & Sahu, 2019a) [Where, Y ¼ marriage age of females in average, X1 ¼ female child labour in percentage, X2 ¼ number of displacement of people due to river bank erosion, X3 ¼ female mean year of schooling, X4 ¼ marriage inside the area in percentage, and X6 ¼ monthly average income in rupees of female, f ¼ factor of] Pearson correlation analysis has been done to retrieve the relationship among the selected variables using SPSS software.

4 Results and Discussion The detailed field study reveals that the main consequence of river Ganga-Bhagirathi bank failure is the displacement of people from river bank lines. Specially, it can be observed that people of Dhulian, Kuli, Arjunpur, and Paranpara are displaced many times from their land as an impact of river bank failure. The highest numbers of displacement of people are observed in Dhulian and Kuli i.e. six times and five times

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4500 Average monthly income in Rs.

4000 3500 3000 2500 2000 1500 1000 500 0

Fig. 20.2 Monthly average income of women in rupees. (Source: Field survey 2015–2019) Table 20.1 Summary of the first model Model 1

R .819a

R-square 0.670

Adjusted R-square 0.543

Std. error of the estimate 520.230

a

Predictors: (constant), percentage of female child labour, mean years of schooling, marriage age in average, displacement of people as a result of bank failure, marriage inside area in percentage

respectively till now. Based on direct interaction with the people of study units, the displacement of people as a result of river bank failure introduces various kinds of problems in human livelihood. The population shifting as a result of bank failure has a makeable effect on the income of erosion victims. In the selected study units, the monthly average income of women is very low. In most of the cases, it is only from 900 to 3500 in rupees per month (Fig. 20.2). Most of the women are engaged in bidi (a special kind of cigar) binding activities. Nearly about 85–95% women are presently engaged in this profession. The fitted first model summary shows that 67% variable is simplified by the independent variable (Table 20.1). The adjusted R-square value and R-square value fitted first model (Table 20.1) are 0.543 and 0.670, respectively. The first fitted model is also significant as the p value is less than 0.05 (Table 20.2). The independent variable with the highest beta value has the highest explanatory power to simplify the dependent variable (Ghosh & Sahu, 2019a). Here, from Table 20.3, it can be said that the displacement of people as a result of bank failure has the highest beta value (0.749) and after it, mean years of

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Table 20.2 ANOVA of the first model Model 1 Regression Residual Total

Sum of squares 7143338.230 3518341.876 10661680.105

Degree of freedom 5 13 18

Mean square 1428667.646 270641.683

F-test value 5.279

Significance .007a

a

Predictors: (Constant), Percentage of female child labour, Mean years of schooling, marriage age of female in average, displacement of people as a result of bank failure, marriage inside area in percentage

Table 20.3 Coefficients of the first model

Model 1 (Constant) Displacement of people Marriage age of female in average Mean years of schooling Marriage inside area in percentage Percentage of female child labour

Un-standardized coefficients Std. B Error 3884.430 2266.202 320.909 139.622

Standardized coefficients

0.749

t- test value 1.714 2.298

Beta

Significance 0.110 0.039

249.381

121.845

0.435

2.047

0.061

489.369

181.191

0.641

2.701

0.018

7.587

10.915

0.230

0.695

0.499

7.337

9.533

0.201

0.770

0.455

Table 20.4 Summary of the second model Model 1

R .844a

R Square 0.712

Adjusted R square 0.601

Std. error of the estimate 0.637340666767365

a

Predictors: (constant), displacement of people, monthly income of female in average, marriage age of female in average, percentage of female child labour, marriage inside area in percentage

schooling has the second highest beta value (0.641). Therefore, the effect of displacement of people as a result of bank failure can’t be ignored in case of low monthly income of women. Again, the second fitted model summary shows that 71% variance of the dependent variable can be explained by the independent variables (Table 20.4). The adjusted R-square and R-square values of the second fitted model are 0.601 and 0.712, respectively (Table 20.4). Table 20.5 shows the model is significant because the p-value is less than 0.05. The coefficient table of second fitted model (Table 20.6) shows the variable with the highest beta value. Here, monthly average income in rupees has the highest beta value i.e. 0.560 and the variable with the second highest

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Table 20.5 ANOVA for the second model Model 1 Regression Residual Total

Sum of squares 13.039 5.281 18.320

Degree of freedom 5 13 18

Mean square 2.608 0.406

F-test value 6.420

Significance .003a

a

Predictors: (Constant), displacement of people, monthly income of female in average, marriage age of female in average, Percentage of female child labour, marriage inside area in percentage

Table 20.6 Coefficients of the second model

Model 1 (constant) Percentage of marriage with in area Percentage of female child labour Average age of marriage Average monthly income Population displacement

Un-standardized coefficients Std. Error B 4.518 2.807 0.014 0.013

Standardized coefficients

0.325

t- test value 1.610 1.076

Beta

Significance 0.131 0.302

0.018

0.011

0.379

1.673

0.118

0.117 0.001 0.299

0.169 0.000 0.185

0.156 0.560 0.533

0.695 2.701 1.618

0.499 0.018 0.130

beta value is the number of displacement of people as a result of river bank erosion. The independent variables with the highest beta values have the highest explanatory power to simplify the dependent variable i.e. mean years of schooling. In these selected study units, the mean years of schooling are very low in almost cases. Specially, in Kuli, Dhulian, Arjunpur, Paranpara and for happening of this low mean year of schooling, the effect of low monthly average income and number of displacement of people as a result of river bank erosion cannot be ignored. Here, the correlation between mean years of schooling of females and number of displacement of people negatively correlated (r ¼ 0.517) and their correlation is significant at 95% level of confidence (Table 20.7). The variable mean years of schooling of female is positivity (r ¼ 0.673) and significantly (0.01 level) correlated with the variable monthly income of females. Again, this effect of displacement of people as a result of river bank failure has clearly been seen in the women’s marriage of the study area. A very low average marriage age is found at the selected study area in most of the causes the average marriage age is 15 to 16 years, specially, low average age of marriage is found at Kuli, Dhulian, Arjunpur, and Paranpara. The third fitted model summary exhibits that 57.5% variance of the dependent variable can be simplified by independent variables and the R-square value of the third fitted model is 0.575 (Table 20.8). The ANOVA table of the third fitted model shows the P-value i.e. 0.03.

0.500* 0.466* 0.636**

0.550* 0.673** 0.125 0.465*

0.594**

0.517* 0.752**

0.806**

1

0.550*

1

0.178

* Significant at the 0.05 level ** Significant at the 0.01 level

Displacement of people as a result of bank failure Monthly income of female in average Marriage age of female in average Mean years of schooling Percentage of female child labour Percentage of marriage of female inside area

Marriage age of female in average 0.594**

Monthly income of female in average 0.178

Displacement of people as a result of bank failure 1

Table 20.7 Correlations of taken parameters

0.680**

1 0.221

0.500*

0.673**

Mean years of schooling 0.517*

0.621**

0.221 1

0.466*

0.125

Percentage of female child labour 0.752**

1

0.680** 0.621**

0.636**

0.465*

Percentage of marriage of female inside area 0.806**

420 D. Ghosh and A. S. Sahu

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Table 20.8 Summary of the third model Model .758a

R 0.575

R-Square 0.411

Adjusted R-Square 1.029823549973281

Std. error of the estimate .758a

a

Predictors: (constant), percentage of female child labour, monthly income of female in average, mean years of schooling, marriage inside area in percentage, displacement of people as a result of bank failure Table 20.9 ANOVA of the third model Model 1 Regression Residual Total

Sum of squares 18.634 13.787 32.421

Degree of freedom 5 13 18

Mean square 3.727 1.061

F-test value 3.514

Significance .031a

a

Predictors: (constant), percentage of female child labour, monthly income of female in average, mean years of schooling, marriage inside area in percentage, displacement of people as a result of bank failure

Table 20.10 Coefficients of the third model

Model 1 (Constant) Displacement of people Monthly income of female in average Mean years of schooling Marriage inside area in percentage Percentage of female child labour

Un-standardized coefficients Std. B error 16.784 1.733 0.413 0.307 0.001 0.000 0.306 0.006 0.002

Standardized coefficients

0.553 0.560

t- test value 9.683 1.346 2.047

Significance 0.000 0.201 0.061

0.440 0.022

0.230 0.110

0.695 0.288

0.499 0.778

0.019

0.038

0.124

0.903

Beta

Therefore, the third fitted model is significant (Table 20.9). Table 20.10 exhibits the independent variable with the highest beta value. Here, monthly average income and the number of displacement of people have the beta values of 0.560 and 0.553, respectively. Therefore, these variables have the highest explanatory power to simplify the dependent variable i.e. marriage age on average. So, the effect of population shifting as result of bank failure can be prominently observed on low marriage age of women in the study area. The correlation between monthly average income and marriage age of females is positive (r ¼ 0.550). The correlation is also significant at 95 level of confidence. The correlation between the displacement of people and marriage age is negative (r ¼ 0.594) and significant at 0.01 level of

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confidence (Table 20.7). And again, in the taken study units, marriages of women generally happen inside the local area. Based on field visits and interviews with local people, the reason for marriages inside the local areas is—people who are not living in river bank erosion-prone areas do not want to get married to the erosion victims. The correlation between the displacement of people and marriages inside the area is negative and significant at 0.01 level of confidence (Table 20.7). The selected study area is continuously suffering from action river bank erosion from the past decades. Ghosh and Sahu (2019a, b) showed that Jangipur sub-division has been constantly suffering from the problem of active river bank erosion. Rudra (1996, 2006) focused on the continuous problem of Ganga river bank erosion in Murshidabad district. FPMP (2014) reported about the problem of river bank failure in Jangipur sub-division. Therefore, there is always a threat of further bank failure and population shifting from the river bank areas. Female child labour is also observed in these study units. The relationship between female child labour and number of displacement of people is positive (r ¼ 0.752) and their relationship is significant at 99% of confidence (Table 20.7). It can be observed that social environment or simply social livelihood has been adversely influenced by population shifting as a result of river bank failure.

4.1

Limitation of the Research

This research has its limitation. Basically, the study area is situated very nearer to the India-Bangladesh border area. Therefore, all the places are not easily accessible. To do any academic work in this area, permission from the border security force is a must. Much information (topographical sheets, maps, etc.) of the study area are totally restricted to the academicians due to the international border areas. Conduction of door-to-door survey is another risky work which has been conducted with full of difficulties. The India-Bangladesh border area is characterised by antisocial activities also.

5 Conclusion It can be concluded based on the results of this study that the displacement of people, as a result of bank erosion really brings a very problematic condition for the women of river bank erosion-prone area. The effect of river bank erosion is felt in the long term. It basically brings a chain effect on women’s life, women’s education, women’s monthly income, their marriages actually their entire life gets adversely affected by this displacement as a result of river bank failure. From this study, it is very clear to say every important phase of women’s life is being highly affected by

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Ganga- Bhagirathi river bank erosion. Therefore, the pre-conceived idea of this research is fitted well. So, the concerned authority needs to take promote action in this connection to manage the social environment of the study area. Whether the erosion victims are getting the proper fruitfulness of already existing government schemes need to be verified first. Acknowledgements We are very thankful to local people of the study area for their valuable response. We are very grateful to Sri Samiron Das for his worthy cooperation during this study.

References Amarnath, G., Alahacoon, N., Gismalla, Y., Mohammed, Y., Sharma, B. R., & Smakhtin, V. (2016). Increasing early warning lead time through improved transboundary flood forecasting in the Gash River basin, horn of Africa. In T. E. Admas & T. C. Pagano (Eds.), Flood forecasting: A global perspective (1st ed., pp. 183–200). Academic Press: Elsevier. https://doi. org/10.1016/B978-0-12-801884-2.00008-6 Bhadra, S. (2017). Women in disasters and conflicts in India: Interventions in view of the millennium development goals. Intentional Journal of Disaster Risk Science, 8, 196–207. https://doi.org/10.1007/s13753-017-0124-y Bradley, T., & Martin, Z. (2011). Gender and disaster: The impact of natural disasters on violence against women in Nepal. Journal of Asian and African Studies, 1–18. https://doi.org/10.1177/ 00219096211062474 Briggs, N. A., Freeman, R., Larochelle, S., Theriault, H., Lilieholm, R. J., & Cronan, C. S. (2008). Modelling river bank stability and potential risk to development in the Penobscot river estuary of Maine, USA. Environmental Problems in Coastal Regions, 99(7), 111–118. https://doi.org/ 10.2495/CENV080101 Census of India. (2011a). District census hand book: Village and town dictionarypart-XII- A (20). Murshidabad, West Bengal. Census of India. (2011b). District census hand book: Village and town dictionarypart-XII- B (20). Murshidabad, West Bengal. Charlton, R. (2008). Fundamentals of fluvial geomorphology. Routledge. Conteh, I. K. (2015). The impact of flood hazard on primary school education in Zambia. Doctoral thesis, Maastricht University, Maastricht, Netherlands. Retrieved from https://cris. maastrichtuniversity.nl/portal/files/1088798/guid-33dcfb62-ec63-4e29-a623-057ee6344a9dASSET1.0 Flood Preparedness and Management Plan (FPMP), Murshidabad. (2014). Government of West Bengal, West Bengal, India. Gaur, A. S., & Gaur, S. S. (2009). Statistical methods for practise and research: A guide to data analysis by using SPSS. Sage. https://doi.org/10.4135/9788132108306 Ghosh, D., & Sahu, A. S. (2018). Problem of river bank failure and the condition of the erosion victims: A case study in Dhulian, West Bengal, India. Regional Science Inquiry, 10(2), 205–214. Ghosh, D., & Sahu, A. S. (2019a). The impact of population displacement due to river bank erosion on the education of erosion victims: A study in jangipur sub-division of Murshidabad district, West Bengal. India. Bulletin of Geography. Socio-economic Series, 46(46), 103–118. https:// doi.org/10.2478/bog-2019-0037 Ghosh, D., & Sahu, A. S. (2019b). Bank line migration and its impact on land use and land cover change: A case study in Jangipur subdivision of Murshidabad District, West Bengal. Journal of Indian Society of Remote Sensing, 47, 1969–1988. https://doi.org/10.1007/s12524-019-01043-0

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Ghosh, D., Banerjee, M., Pal, S., & Mandal, M. (2022). Spatio-temporal variation of channel migration and vulnerability assessment: A case study of Bhagirathi River within Barddhaman District, West Bengal, India. In P. K. Shit, B. Bera, A. Islam, S. Ghosh, & G. S. Bhunia (Eds.), Drainage Basin dynamics. Geography of the physical environment. Springer. https://doi.org/10. 1007/978-3-030-79634-1_14 Guite, L. T. S., & Bora, A. (2016). Impact of river bank erosion on landcover in lower Subansiri River flood plain. International Journal of Scientific and Research Publications, 6(5), 480–486. Jonsson, S. A. (2011). Virtue and vulnerability: Discourses on women, gender and climate change. Global Environmental Change, 21(2), 744–751. https://doi.org/10.1016/j.gloenvcha.2011. 01.005 Khatun, M., Rahaman, S. M., Garai, S., Das, P., & Tiwari, S. (2022). Assessing River Bank erosion in the Ganges using remote sensing and GIS. In P. K. Shit, H. R. Pourghasemi, G. S. Bhunia, P. Das, & A. Narsimha (Eds.), Geospatial technology for environmental Hazards. Advances in geographic information science. Springer. https://doi.org/10.1007/978-3-030-75197-5_22 Knighton, D. (1998). Fluvial forms and processes: A new perspectives. Routledge. Majumdar, S., Das, A., & Mandal, S. (2022). River bank erosion and livelihood vulnerability of the local population at Manikchak block in West Bengal, India. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-021-02046-z Neumayer, E., & Plumper, T. (2007). The gendered nature of natural disasters: The impact of catastrophic events on the gender gap in life expectancy, 1981-2002. Annals of the American association of geographers, 97(3). https://doi.org/10.1111/j.1467-8306.2007.00563.x Ofuoku, A. U. (2011). Rural farmers’ perception of climate change in central agricultural zone of delta state, Nigeria. Indonesian Journal of Agricultural Science, 12(2), 63–69. Onwutuebe, C. J. (2019). Patriarchy and women vulnerability to adverse climate change in Nigeria. Climate Change-Original Research, 9(1). https://doi.org/10.1177/2158244019825914 Rudra, K. (1996). Problems of river bank erosion along ganga in Murshidabad district of West Bengal, India. Journal of Geography and Environment, 1, 25–32. Rudra, K. (2006). Shifting of the Ganga and land erosion in West Bengal / A socio-ecological viewpoint. Indian Institute of Management Calcutta. Singh, S. (2012). Geomorphology. Prayag Pustak Bhawan. Uddin, A. F. M., & Basak, J. K. (2012). Effects of River Bank Erosion on Livelihood, Unnayan Onneshan, Bangladesh. Retrieved from http://www.unnayan.org/reports/climate/Effect_of_ Riverbank_Erosion_onLivelihood.pdf United Nations Development Programme (UNDP). (2010). Human Development Report 2010.The real wealth of nations: Pathways to human development, UNDP. Retrieved from http://hdr. undp.org/en/content/human-development-report-2010

Chapter 21

Social Issues and Sustainability of COVID-19: A District Level Spatio-Temporal Analysis in West Bengal Tanmay Patra , Nirmalya Das , Santu Guchhait Zarjij Alam, Munmun Nandy, and Koushik Mistri

, Subhrangsu Das

,

Abstract Epidemic is widespread occurrence of an infectious disease in a community at a particular time. So, geographically epidemic has two-dimension spatial expansion and temporal dynamism. An attempt has been made to highlight the nature of spatial spreading and temporal changes in COVID cases. To achieve this objective, some COVID-related socio-demographic variables have been selected on the basis of the nature of this disease. The socio-economic life of people has been disrupted by its detrimental effects. Social stability has been lost. In this regard, an exertion has been extended to uphold some sustainable measures to cope up with the social issues. Secondary data sources are used to collect COVID-related data. To determine the causal relationship between COVID cases with socio-demographic variables, correlation matrix has been constructed. It indicates that population density and urbanization rate have very high positive relation with COVIDconfirmed cases and fatality rate. Contrary to this, negative relation has been identified with agricultural labourer and cultivator. Temporal changes of different COVID-related cases have also been illustrated here through moving average method. Keywords Epidemic · COVID-19 · Socio-demographic · Urbanization · Case fatality rate

T. Patra (✉) · N. Das · S. Guchhait · Z. Alam · K. Mistri Department of Geography, Panskura Banamali College, Purba Medinipur, India S. Das Department of Geography, Utkal University, Bhubaneswar, India M. Nandy Department of Geography, Hiralal Mazumdar Memorial College for women, Kolkata, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_21

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1 Introduction Whole world now confronts with an invisible enemy, COVID-19 (Huang et al., 2020). Human life is in threat for this infectious disease caused by recently discovered corona virus. Human health as well as socio-economic and cultural aspects of human life have been disrupted by its deadly nature of transmission (Nicola et al., 2020). In one word, the existence of human civilization becomes a question mark. Along with the world, the Indian state West Bengal also struggles stringently with this virus. The Wuhan, capital city of Hubei provinces, China, is the source of this disease where the first pneumonia case of unknown cause was found in November 2019 (Huang et al., 2020; Kim & Castro, 2020; Kumar, 2020; WHO, 2020). Later it is become known that this pneumonia to be caused by a new ‘severe acute respiratory syndrome coronavirus’ (SARS-CoV-2) (Acharya & Porwal, 2020; Liu et al., 2020; Martin et al., 2020). Consequently, The World Health Organization (WHO) declared the outbreak a pandemic on 11 March 2020 (World Health Organization, 2020). The first case of COVID-19 in West Bengal was confirmed on 17 March 2020 in Kolkata (Government of West Bengal, 2020). After the first case detection of COVID, continuous increase in the number of confirmed cases has been notified with rather disparate situation. After the identification of first case of COVID, one after another district of West Bengal being affected by this most infectious disease. Due to very high population density of Kolkata, the state capital city of West Bengal is mostly affected district in this state. Among the 23 districts of this state, the Jhargram confirmed very low number of active cases because of its low population density and random population distribution as well as of its terrain condition that creates some barrier on free and easy movement of people. From geographical perspectives, an attempt has been made here through this chapter to highlight the important factors that are responsible in spreading this disease. This chapter also envisages the spatial pattern as well as temporal changes of COVID cases in the districts of West Bengal. Socio-economic and demographic variables are mostly responsible for this disease as it is mostly contagious. An attempt also has been made to correlate some socio-environmental factors and issues with COVID active cases and case fatality rate and to propose some management strategies. Most countries have implemented lockdown measures to prevent the spread of the COVID-19. However, lockdown has grievously invaded the economy because the pandemic has decimated employments and put millions of livelihoods under threat (Wei et al., 2021). The majority of informal workers lack social protection and are unable to access quality healthcare, making them mostly vulnerable. During lockdown periods, many are unable to fulfil food and basic needs for themselves and their families due to very low or no income. Consequently, the nutritional status of millions of women and men is at risk, particularly marginalized groups in low-income countries like small-scale farmers and indigenous peoples (WHO, 2020). Homeless peoples and migrant labours are highly susceptible to this virus (UN, 2020). The lockdown has increased cases of domestic violence and women have felt very unsafe. Spillover effects of COVID-19 may hinder to achieve the

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Sustainable Development Goals (SDGs) adopted by the United Nations (UN) such as SDG-5 (Gender Equality), SDG-9 (Infrastructure & Innovation), SDG-10 (Reducing Inequalities) and SDG-11 (sustainable cities) (Blanco et al., 2022; Shulla et al., 2021). The achievement of SDG-10 (reduced inequalities) has been adversely affected by the emerging intra-country and inter-countries inequality because the most vulnerable groups of society (women, low-wage labours, informal workers and small and medium-sized enterprises) have to cope with the most damaging impacts of COVID-19 (Berchin & Guerra, 2020). Thus, COVID-19 pandemic led to increased inequality, discrimination and exclusion in every corner of the society.

2 Study Area West Bengal as a state of India located in the eastern region of its along with Bay of Bengal. It is the fourth most populous state of India with more than 91 million inhabitants, covering an area of 88,752 sq. Km. It is located between 21°20′43″ to 27°32′62″ North latitude and 85°50′42″ to 89° 52′55″ East longitudes. It is bordered by Bangladesh in the east and Indian states like Odisha, Jharkhand and Bihar are in the west. Nepal, Bhutan and Indian state Sikkim located in the north of its. Bay of Bengal is in the south. Physically it is the most diversified state in India. Himalayan hilly region covers the north and north western part. Rarh region is in the west and plain and fertile land in the east. Ganges delta and coastal Sundarbans located in the south. It is also enriched by its socio-cultural heritage and diversity. Near about 30% of the total population live in urban areas in West Bengal. But there are district-wise variations in the rate of urbanization. It is second most densely populated state with 1028 population per sq. km (2011 Census of India). Presently this state consists of twenty-three districts with capital city Kolkata, one of the metropolitan cities in India (Fig. 21.1).

3 Database Conducting a survey to collect the primary data is mostly impossible due to this unexpected COVID situation. So, this study has been done by secondary data sources. Population-related data has been collected from Primary Census abstract of West Bengal 2011. The health-related COVID data has been extracted from West Bengal Health portal. District statistical handbooks of the district of West Bengal are also used here as sources of secondary data. District-wise COVID-related data has been used from 4 May 2020, to 25 Sep 2020, for spatio-temporal analysis of different aspects of COVID cases.

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Fig. 21.1 Location of Study area

4 Methodology Spreading is an important aspect of infectious disease like COVID-19. How much it is fatal depends on its spreading character of this disease. Spreading has two facets, spatial and temporal. Spatial spreading indicates the spatial extension or spatial diffusion of this disease. Near about 215 countries in the world are affected by this corona virus (World Health Organization, 2020). Such kind of global diffusion indicates the devastating nature of this virus. This spatial propagation of COVID19 is controlled by some socio-demographic factors (Sannigrahi et al., 2020) such as urbanization rate, population density, Literacy rate, occupation structure. These

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socio-demographic variables have been identified on the basis of the contiguous nature of the virus. This correlation matrix has been done to find out the relationship between the total COVID cases and death with socio- demographic factors. The 7 days moving average methods in Excel has been applied here to know about the transmission dynamics (Chong et al., 2020) with the help of temporal changes in total COVID-confirmed cases, death, discharged rate and total active cases. In epidemiology, the case fatality rate used as a measure the severity of disease (Tulchinsky, 2014). This rate is not constant. The severity of disease varies between population and time with the interplay of causative agents of this disease. So, the case fatality rate of every district has been calculated to know about the deadly nature of this virus. CFR =

Td × 100 Tc

ð21:1Þ

where CFR = Case Fatality Rate; Td = Total death due to COVID; Tc = Total confirmed COVID case. As there is wide variation in number of COVID cases so standardize score or Z score has been calculated to make a comparison of monthly spatial spreading pattern of COVID cases among the districts of the West Bengal. Standardize score is very common way to make comparisons between very high as well as low score. Standardize Score ðZ scoreÞ =

Χ-μ δ

ð21:2Þ

where Χ = district-wise total COVID cases, μ = mean value of COVID cases, δ = Standard deviation of COVID cases. Arc GIS 10.1 software programme has been used here to depict the spatial pattern of COVID confirmed cases and COVID fatality rate (i).

5 Result of the Study This study reveals that the socio-demographic factor plays a crucial role in shaping the Spatio-temporal pattern of COVID-19-positive cases and deaths in west Bengal. These factors have great impact on spatial propagation of this disease. A gradual increasing trend of COVID cases from the month of May–September 2020 has been identified through monthly Z score plotting. The correlation matrix of socio-demographic factors with the COVID cases and case fatality rate shows the similar r value.

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6 Discussion 6.1

Spatio-temporal Pattern of COVID-Confirmed Cases

District-wise spatio-temporal changes in COVID-confirmed cases are illustrated (Fig. 21.2) to know about its spatio-temporal pattern. For this analysis, the data of COVID cases from May 2020 to September 2020 have been selected. The standardized score (Table 21.1) of COVID cases reveals some interesting facts that in the month of May only one district Kolkata shows very high confirmed COVID cases. The district of Howrah and North 24 Parganas confirmed above to the mean Z score vale and rests of the 23 districts of West Bengal indicate very near to mean value of the distribution. So, in the initial phase of the transmission, there were no such variations in spatial distribution of COVID-confirmed cases. But with the progress of time, spatial spreading nature of this disease has been changed. Month-wise Z score distribution (Table 21.1) of districts shows that from the very beginning of the infectious disease, Kolkata remains in the highest transmissive position. After the month of July, North 24 Parganas also join in this group where Z score (>2) is more than the mean value. Because of nearness and high connectivity with the capital city, Kolkata, the district like Haora, Hugli and South 24 Parganas, shows high Z score value (0–2). Malda and Darjeeling and, after the month of August, the district of Purba Medinipur also fall in this group where Standardized score (-0.2 to 0) is very close to the mean. Western side districts like Bankura, Purulia, Jhargram and Birbhum and along with other districts like Uttar Dinajpur, Kalimpong and Alipurduar are far from the mean 20000 y = 24.217e1.2082x Mean of Covid cases

15000 10000 5000 0 –5000 Mean S.D.

May 62.83 166.37

June 333.65 612.02

July August 996.57 3769.83 1705.71 6295.04

September 7857.48 10818.19

Fig. 21.2 District-wise distribution of COVID Cases (May to September) of West Bengal

2 0 to 2

Alipurduar, Bankura, Birbhum, Dakshin Dinajpur, Darjeiling, Hugli, Jalpaiguri, Jhargram, Kalimpong, Koch Bihar, Maldah, Murshidabad, Nadia, Paschim Barddhaman, Paschim Medinipur, Purba Barddhaman, Purba Medinipur, Puruliya, south 24 Pargana, Uttar Dinajpur

May Kolkata Haora, north 24 Pargana

Bankura, Birbhum, Koch Bihar, Maldah, Murshidabad, Nadia, Paschim Medinipur, Purba Barddhaman, Purba Medinipur, Uttar Dinajpur Alipurduar, Dakshin Dinajpur, Darjeiling, Jalpaiguri, Jhargram, Kalimpong, Paschim Barddhaman, Puruliya

June Kolkata Haora, north 24 Pargana, Hugli South 24 Pargana

Alipurduar, Bankura, Birbhum, Dakshin Dinajpur, Jhargram, Kalimpong, Koch Bihar, Murshidabad, Purba Barddhaman, Paschim Barddhaman, Puruliya

Darjeiling, Jalpaiguri, Purba Medinipur, Paschim Medinipur, Nadia, Uttar Dinajpur

July Kolkata Haora, north 24 Pargana, Hugli, south 24 Pargana Maldha

Table 21.1 Month wise distribution of districts under different Z-score range

Alipurduar, Bankura, Birbhum, Jhargram, Kalimpong, Koch Bihar, Murshidabad, Purba Barddhaman, Puruliya, Uttar Dinajpur

Jalpaiguri, Dakshin Dinajpur, Purba Medinipur, Paschim Medinipur, Nadia, Paschim Barddhaman

August Kolkata, north 24 Pargana Haora, Hugli, south 24 Pargana Maldha, Darjeiling

Dakshin Dinajpur, Darjeiling, Jalpaiguri, Koch Bihar, Maldah, Murshida bad, Nadia, Paschim Barddhaman, Paschim Medinipur, Alipurduar, Bankura, Birbhum, Jhargram, Kalimpong, Purba Barddhaman, Puruliya, Uttar Dinajpur

September Kolkata, north 24 Pargana Haora, Hugli, south 24 Pargana Purba Medinipur

21 Social Issues and Sustainability of COVID-19. . . 431

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Fig. 21.3 Shifting trend of mean score of COVID

Z score value. Remoteness from Kolkata and their physiographic conditions as well as socio-demographical factors are responsible for their continuous very low Z score (-0.4). The monthly plot of mean and standard deviation of COVID cases indicate that there is exponential growth rate. Rapid growth has been seen after the month of July 2020 and it continues to September 2020. Not only mean value the standard deviation also increases with the progress of time (Fig. 21.3).

7 Spatial Pattern of Case Fatality Rate COVID case fatality rate is an important aspect to determine the devastating nature of this virus. The spatial distribution pattern of fatality rate primarily reveals that where there confirmed case is high, the case of fatality rate is also very high. From this perspective, Kolkata and Howrah recorded very high fatality rate. Similar way North and South 24 Parganas, Hooghly and Darjeeling indicate high fatality rate. Moderate fatality rate has been seen in Paschim Medinipur, Purba Medinipur, Nadia

21

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433

Fig. 21.4 COVID case fatality rate of West Bengal

and Murshidabad. Low fatality rate has been seen in Bankura, Purba and Paschim Bardhaman, Uttar Dinajpur, Malda, Kalimpong, Jalpaiguri and Alipurduar district. Very low fatality rate has been seen in the district of Jhargram, Purulia, Birbhum, Dakshin Dinajpur and Koch Bihar (Fig. 21.4, Table 21.2).

Hens (2020)

Degree of urbanization Density of population Literacy rate Cultivators Agricultural labourers Household industry workers Other workers Total COVID cases -0.02

-0.08

0.40 0.75

0.05

0.72 0.85

0.61 0.62

1.00 -0.19 -0.14

0.40 -0.52 -0.47

0.63 -0.61 -0.51

Literacy rate

1.00

Density of population

0.80

Degree of urbanization 1.00

0.36 0.06 -0.29

-0.06 -0.34

1.00

Agricultural labourers

0.29

1.00 0.95

Cultivators

Table 21.2 Correlation Matrix of socio-demographic variables with total COVID cases

0.29 0.01

1.00

Household industry workers

1.00 0.83

Other workers

1.00

Total COVID cases

434 T. Patra et al.

21

Social Issues and Sustainability of COVID-19. . .

435

8 Socio-environmental Factors Related to the COVID-19 Socio-environmental factors are important in analysing the spatio-temporal spreading trend and nature of the COVID-19 diseases. In the social domain, important parameters are the degree of urbanization, density of population, literacy rate and working class such as cultivators, agricultural labourer, household industry worker and other workers (Hens, 2020). A correlation matrix has been introduced to identify the relationship and the causality of COVID cases and fatality rate with the social factors. The correlation matrix indicates that there is highly positive correlation with the degree of urbanization and COVID cases (r = + 0.85) and case fatality rate (r = + 0.88). In this regard, Kolkata, the metropolitan city in West Bengal, completely urbanized along with Howrah and North and South 24 Parganas also achieved high degree of urbanization. Due to the unplanned and uncontrolled urban growth, workers from different part of the country migrated to the urban centre and create pressure on slum area. These unhygienic living conditions increase the susceptibility of most infectious disease (COVID-19). Coming to the population density, some districts show very high population density, which increases the risk of getting infections from an asymptomatic and/or infected individual. It became impossible to maintain the social distancing or physical isolation (Kaur et al., 2021) in these densely populated areas. So, densely populated districts like Kolkata, Howrah, Purba and Paschim Bardhaman, North and South 24 Parganas are very much vulnerable in case of COVID cases and fatality rate. Education is the essential resource to defeat the disease. It creates the awareness and consciousness which prevent getting infected from this disease (Zhong et al., 2020). Employment is the next important socio-economic factor after education. Type of employment indicates the expose to risk factors of COVID-19. In this respect, negative correlation has been found in-between cultivators and agricultural labourers with total COVID cases and fatality rate. Household industry worker shows that a feeble relation is spreading the diseases. Low physical mobility as well as isolation and social distancing all these preventing measures are well maintained through these economic activities. In census of India, other workers indicate the industrial workers service sectors workers; all these workers are very much vulnerable for their more physical exposure (Table 21.3 and 21.4).

9 Temporal Changes in COVID Casualties Temporal dynamism is an important aspect in epidemiological study of any disease. From the first detection of this disease in West Bengal, the rate of infective cases is continuously uprising. In the month of May, COVID-confirmed cases are gradually

-0.02

0.40 -0.52 -0.46

-0.07 0.40 0.67

0.61 -0.62 -0.48

0.09

0.73 0.88

0.60 0.65

1.00 -0.19 -0.14

1.00

Literacy rate

0.80

Density of population

Sources: Computed by authors from Table 21.1

Degree of urbanization Density of population Literacy rate Cultivators Agricultural labourers Household industry workers Other workers Case fatality rate

Degree of urbanization 1.00

0.38 0.09 -0.36

-0.10 -0.48

1.00

Agricultural labourers

0.25

1.00 0.90

Cultivators

Table 21.3 Correlation matrix of socio-demographic variables with case fatality rate (CFR)

0.32 0.29

1.00

Household industry workers

1.00 0.71

Other workers

1.00

Case fatality rate

436 T. Patra et al.

Jhargram

Alipurduar

Kalimpong

Koch Bihar

Darjeeling

Jalpaiguri

Uttar Dinajpur

Dakshin Dinajpur

Malda

Murshidabad

Nadia

Birbhum

Purulia

Bankura

Paschim Medinipur

Purba Medinipur

Paschim Bardhaman

Howrah

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

District

Purba Bardhaman

1

SL No

48,50,029

54,28,002

50,95,875

47,76,909

3,596,674

29,30,115

35,02,404

51,67,600

71,03,807

39,88,845

939

945

938

966

957

957

956

947

958

944

956

939

16,76,276

953

30,07,134

970

942

959

949

977

922

Sex ratio

23,70,863

15,95,181

28,19,086

251,642

1,501,983

1,136,548

2,289,561

Total population

63.60

39.87

11.65

12.03

8.36

12.75

12.80

27.81

19.78

13.80

14.13

13.80

27.00

38.99

10.25

9.08

20.62

5.43

62.37

2011

Degree of urbanization

3306

1099

1081

631

523

468

771

1316

1334

1069

755

958

622

586

832

239

480

370

890

Density of population

83.31

76.21

87.02

78.00

70.30

64.48

70.68

74.97

66.59

61.73

72.82

59.07

73.25

79.56

74.78

82.05

72.29

71.94

76.20

Literacy rate (%) Cultivators

80,575

342,166

345,215

572,268

309,723

268,800

227,254

308,742

381,076

255,182

193,276

257,377

214,932

76,178

364,797

25,488

85,641

115,398

25,623

181,662

973,182

297,774

124,558

118,816

142,602

1,105,201 702,304

61,386

87,560

65,062

2E+05

5E+05

2E+05

32,290

37,978

30,090

16,579

40,593

2141

12,088

50,844

10,434

Household industry workers

647,374

492,205

611,510

556,134

842,294

545,759

279,932

442,328

349,672

66,041

391,875

19,657

145,826

287,284

65,093

Agricultural labourers

Category of workers

1,259,834

1,471,345

743,985

689,088

447,737

401,077

427,777

808,593

900,530

540,219

197,489

337,943

918,216

524,928

330,712

48,392

325,637

122,944

290,478

Other workers

17,391

6696

10,095

9132

5105

3145

3539

6729

5488

6491

5117

3369

5716

7474

5327

876

4327

672

4647

Total cases

15,428

5747

8926

7832

4306

2533

3012

5843

4764

5936

4703

2983

5049

6613

4599

708

3653

472

3926

Total discharged

COVID-19 cases

Table 21.4 District-wise distribution of socio-demographic variable and COVID-19 cases (May to September 2020) West Bengal

529

56

122

112

49

17

27

79

61

55

32

35

55

104

33

7

44

4

47

Total death

3.04

0.84

1.21

1.23

0.96

0.54

0.76

1.17

1.11

0.85

0.63

1.04

0.96

1.39

0.62

0.80

1.02

0.60

1.01

Case fatality rate

(continued)

1434

893

1047

1188

750

595

500

807

663

500

382

351

612

757

695

161

630

196

674

Total active cases

North 24 Parganas

South 24 Parganas

Kolkata

20

21

22

23

44,96,694

81,61,961

1 0,009,781

55,19,145

Total population

908

956

955

961

Sex ratio

100

25.61

57.59

38.62

2011

Degree of urbanization

24,306

819

2445

1753

Density of population

86.31

77.51

84.06

81.80

Literacy rate (%)

Sources: 1. Socio-Demographic data from primary census abstract, West Bengal, 2011 COVID-19 cases data from West Bengal health portal and computed by authors

District

Hooghly

SL No

Table 21.4 (continued)

583,380

806,562 12,388

16,039

599,039

355,350

288,058

259,680

Cultivators

Agricultural labourers

Category of workers

68,438

240,976

155,762

111,828

Household industry workers

1,698,875

1,561,606

2,528,765

1,197,982

Other workers

53,148

16,251

48,183

12,075

Total cases

46,689

14,271

42,431

10,533

Total discharged

COVID-19 cases

1639

297

1050

208

Total death

4820

1683

4702

1334

Total active cases

3.08

1.83

2.18

1.72

Case fatality rate

21

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439

Fig. 21.5 Month-wise moving average of COVID cases of West Bengal (May to September, 2020). (Source: Prepared from Health Bulletin, Govt. of West Bengal)

increasing day after day. In this time, discharge or recovery rate was lower than the COVID active cases because of inadequacy in health infrastructure and proper knowledge about this virus. COVID case fatality rate was also high in this time. But in June discharge rate becomes high due to proper heath facilities and consciousness about this virus. After June, though the uprising trend of COVIDconfirmed cases prevails, the discharge rate is gradually increasing. To illustrate the temporal changes, month-wise trend of COVID cases has been shown (Fig. 21.5).

440

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Management

This study has revealed that there are district-wise variations in spatial pattern of COVID-confirmed cases. As this disease is the most contiguous, some socio-demographic variables play most determinant role to spread this disease in community. Demographic variables like population density and urbanization rate are key controlling factors to diffuse this infectious disease. Like demography, some socioeconomic variables such as agricultural labourers, cultivators, industrial labourers and other workers are also mentioned worthy as distancing among the people must be maintained to prevent this disease. The capital city of West Bengal, Kolkata, is the most affected district in West Bengal because of its high population density (22,000 people/sq. Km.) (2011 census of India) and national and international connectivity. On the other hand, the district of Purulia for its physiographic hindrances and low population density contains very minimum number of infection rate and case fatality rate. Along with spatial variation, there are temporal changes also present in this study. Infectivity and fatality indicate the deadly nature of this corona virus. Infection rate was high and recovery rate was low in initial phase. But the advancement of time, tremendous research activity and the commencement of health-related facilities make fatality rate low as well as increase discharging rate. Obviously, it is a health hazard. Some drastic changes have been identified in socio-economic life of people, which create the hindrances to spend a joyful life. So, some desirable management policy should be adopted by Govt. to revitalize the socio-economic life. In this respect, a holistic management strategy has been proposed in this work.

21

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442

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Conclusion

COVID is the most infectious disease in the world. The Indian states like West Bengal also fight against this disease. Through this chapter, an attempt has been made to depict the district-wise spatio-temporal pattern of COVID cases. This study reveals that Kolkata and its surrounding districts are the mostly effected by this corona virus. This district level analysis as well as the mapping of COVID cases will help the government to take necessary steps. It will also help to reduce human interaction in public sphere of containment zones as the socio-demographic factors like population density and rate of urbanization are key determinant factors in spreading this disease. Beside these variable health facilities, social distancing, lock down, all these factors also have some impacts on the pattern of this disease. Sometime remoteness, communication barrier and physiographic condition all these factors indirectly control the transmission rate of the disease. To stop the spreading of this disease, consciousness among people is very important. This study helps to understand the spreading nature and pathways of this disease in West Bengal.

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Part III

Ecosystem Restoration and Sustainable Development

Chapter 22

Dependence on Forest Products to Sustain Rural Livelihood: An Experience from Bankura Forest, West Bengal Susmita Sengupta and Manika Saha

Abstract What determines the household’s dependency on forest products? How far does the dependence pattern differ between core and fringe areas of the forest? The present study opts to establish the linkages between forest resources and the livelihood of forest dwellers to address the above research questions. It measures the spatial dynamics of forest dependence within the core as compared with forest fringe areas of Bankura district that situates in the “Rarh” region of West Bengal. The study adopts “Triangulation” method to measure the strength of the relationship between demographic and socio-economic variables on forest dependence by collecting relevant quantitative and the qualitative data simultaneously from the field, having nearly equal priority. The findings reveal that the proximity of forests aggravates the likelihood of households exhibiting greater reliance on forest resources than the fringe part. Moreover, the study explored educational attainment of household heads, social participation, ownership of land and livestock, and opportunity for farm and off-farm income to reduce forest dependency significantly. In contrast, large family size, forest-based occupation, and relying on dry wood as a primary fuel type enhance the dependency of the forest dwellers on forest products significantly. Keywords Forest resource dependency · Subsistence needs · Diversification · Organic linkage · Multiple regression analysis · Sustainable livelihood

1 Introduction We are the people of the jungle; we live here, we shall die here in this jungle also. The jungle is our God. It provides food, fodder, and shelter to us. (2021) – Putul Das, Female, 57 years, Basudebpur village, Bankura.

S. Sengupta (✉) Rabindra Mahavidyalaya, Champadanga, Hooghly, West Bengal, India M. Saha Asansol Girls’ College, Asansol, Paschim Barddhaman, West Bengal, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 A. S. Sahu, N. Das Chatterjee (eds.), Environmental Management and Sustainability in India, https://doi.org/10.1007/978-3-031-31399-8_22

447

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The comments from the forest villagers uncover the fact that rural livelihoods are still highly dependent on natural resources, especially in developing countries. Cheng et al. (2019) estimated that 20% of the global population depends on forest products to meet their daily livelihood needs. Forests provide nearly 22% of the income of rural households in developing countries, both through earning cash and by meeting up subsistence needs (Vedeld et al., 2007). In this case, India is no exception. The ecological resources like dry leaves, timber, firewood, food, medicine, and others play vital roles in sustaining rural livelihood (Fikir et al., 2016; Angelsen & Kaimowitz, 1999) of forest villagers of India. The forest-dependent people in the core part of the forest utilize forest products for their subsistence and earn significant income by selling forest products (Mamo et al., 2007). A statistical report of the Ministry of Environment and Forest (2006) measures forestry and logging accounting for 1.1% of India’s Gross Domestic Product (GDP) in 2001 from 1.73 lakhs forest-covered villages in India. Another report by World Bank (2006) and MoEF (2009) assesses that 350–400 million rural people of India rely mainly on the forest to maintain their livelihoods. According to the report, forest dwellers, mostly tribals, constitute the poorest and most vulnerable groups in society (Ray & Mukherjee, 2021). Half of India’s 89 million tribal people tend to have close cultural and economic linkages with the forest. The fact is that this “linkage” along with “dependency” is related to “distance” and “remoteness,” “poverty,” and “socioeconomic stagnancy” of the forest people, which undoubtedly attracts social scientists to explore the dynamics of forest dependence of rural livelihood and several hidden factors behind it. Generally, the habitats of different communities within the forest core and the forest frontier are not homogeneous. A rich body of literature emphasizes the contribution of forest products to rural livelihood (Cavendish, 2000; Fisher, 2004; Mamo et al., 2007; Vedeld et al., 2007; Godoy et al., 2002; Prado Córdova et al., 2013; Uberhuaga et al., 2012; Kalaba & Dougill, 2013) across varying socioeconomic groups geographically (Bwalya, 2013). In Asia, forest income contributes a significant amount of nearly 10–20% of the average income of households (Ahmed, 2016). The difference in the households reliant on the forest is something essential (Prado Córdova et al., 2013), which includes gathering of edible fruits, flowers, tubers, leaves, and roots for food and medicinal purposes, dry wood for cooking, construction of houses, and fencing, fodder for livestock, raw materials for handicrafts and cottage industries, and variety of nontimber forest products (Bharathkumar et al., 2011; Bhatia & Yousuf, 2013). The existing research works on India enlightened either on the valuation of various forest products extracted from India’s forests (Chopra, 2006) or on earning cash from Non-timber Forest Product (NTFP) activities (Mahapatra et al., 2005) or on forest dependence on household income (Narain et al., 2008). Several research works (Nayak et al., 2012; Saha & Sundriyal, 2012) worked out on the multiple usages, such as socio-economic, environmental, cultural, recreational, consumptive, and spiritual, with varying interests of rural people (Sarmah, 2011; Islam et al., 2013). Gharai and Chakrabarti (2009) have given thrust on the crucial role of forests in forest people’s cultural and socio-political systems, whose life cycle generally

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centers around the forest and forestry. Several studies on rural livelihoods (Tiwari & Joshi, 2015, Sharma & Vetaas, 2015; Singh & Meentemeyer, 2015) highlighted the subsistence needs directly or indirectly relying on forests. Another research work by Wagner and Cobbinah (1993) revealed that forest provides vital economic and ecological functions, supporting commercial trade and employment opportunities. Blay et al. (2007) have shown that forest offers a suitable environment for cocoa farming practices. A few studies have addressed livelihood insecurity associated with rural forest-based communities, which is significantly high in the Indian scenario (Das, 2012; Wood, 2003); most of the people below the poverty line live in those pockets where massive poverty accomplishes among the forest-dependent people (Mehta & Shah, 2003). Very little research focused on the predictors of forest dependence and the role of distance from the forest core in aggravating or diminishing the pattern of dependence on its periphery. In this context, research questions emerge what determines the household’s dependency on forest products? and to what extent does the household depend on forests resources? How far does the dependence pattern differ between core and fringe areas of the forest? The study opts to establish the strong linkages between forest resources and the livelihood of forest dwellers, to measure the pattern of forest dependence within the core as compared with the forest fringe areas, and to examine the value addition of NTFPs in terms of domestic as well as commercial importance. The study further measures the strength of the relationship of several demographic and socio-economic variables on forest dependence and, lastly, recommends some area-specific appropriate livelihood strategies across NTFP sectors for the forest villagers of the study area. The remainder of the chapter proceeds with the section “study area” carrying a brief description of forest-reliant rural livelihoods of the forest region of Eastern India, followed by the methodology section. Then it deals with the results and discussion, and lastly, some area-specific recommendations conclude the chapter.

2 Study Area The forest serves as the essential source of income in both forest core and periphery. An annual report on forest management published by the Government of West Bengal (2009) states that nearly 0.30 million rural households daily collect NTFPs and approximately 60–70% of their total incomes come from forests (Ray & Bhattacharya, 2011) in the state of West Bengal. Nearly 30–60% of rural households stand on forests for their daily livelihoods (Ray, 2018). Local forest users of the western part of the state carry on mainly Sal forests (Shorea robusta). Mahapatra et al. (2005) estimate the total turnover of the sal plate industry to be 70–80 million in the eastern part of India, covering West Bengal. Against this context, the present study has taken the district of Bankura for microlevel analysis. Bankura, the fourth biggest district of West Bengal, is situated in the western part of the state, physiographically referred to as “Rarh” (Fig. 22.1) of West Bengal. It

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Fig. 22.1 Location map

occupies an area of 6882 km2 (Census, 2001) and has a population density of 523 persons/km2. This district forms an intermediate tract lying between the riceproducing alluvial plains of Bengal to the east and the Chota Nagpur plateau to the west. The topography gradually rises in the undulating plain from the district’s middle. It surrounds Paschim Medinipur and Hugli district in the east, Purulia district in the west, and Barddhaman district in the north and east. The overall economy of

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the Bankura district is predominantly agrarian by following the traditional agricultural techniques. Unconducive topography, dry climate with continental character, rough topography with a low water retention capacity of the soil, on the one hand, and minimal size of landholdings, poor irrigation coverage, little agro-based knowledge of most of the farmers along with lack of mechanization in agriculture offer limited scope for agriculture. The district lies at a low rung of human development and its rank has been eleventh (Human Development Report of West Bengal, 2008). The relative position of the district in terms of the three human development indices indicates that concerning health (0.67) and educational attainments (0.62), the district is closer to the state average, whereas in income (0.26), it lags far behind. Bankura is only second (0.52) from the bottom, before Purulia in the income sector. The district occupies an enviable forest reserve of 21.5% of its geographical area (District Human Development Report, Bankura, 2007). The total forest coverage of the district is 1285.5 km2, of which open forest occupies 667.98km2, moderate dense forest occupies 395.27 km2, and very dense forest covers 222.33 km2. A large segment of its population is forest-dependent to sustain livelihood. The inhabitants of the core forest and forest fringe villages mainly belong to scheduled social groups, either tribals or people of lower social affiliation. Forests and trees customarily play a critical role in their livelihood. They depend entirely or partly on forest resources to meet their subsistence needs, thus having an organic link with the forest (ibid, 2007). The report on Forest Resources of Bankura District of West Bengal (1985) estimates a sizeable portion volume of 50–70 m3 forest per hectare at Jaypur forest range, and the Bishnupur forest range possesses the maximum volume recorded as 30 m3. In this south-eastern part of the district, many tribal people live in the tropical dry forest of Jaypur and Bishnupur blocks and follow a primitive livelihood. Forest plays a significant role in enhancing livelihood requirements for the rural community in the Bankura forest region (Sengupta & Saha, 2013).

3 Methods 3.1

Approaches and Techniques

The study has followed a mixed-method approach (Fig. 22.2), often referred to as the “Triangulation” method, by collecting quantitative and qualitative data having nearly equal priority (Bryman, 2016; Creswell, 2003) to explore the contribution of forest products to augment the livelihood of forest-dependent people of the Bankura forest region. The mixed methods research applied in the study represents a good blending (Neupane, 2019) of collected qualitative and quantitative data to complete the study. The phasing of data collection followed a simultaneous fashion in the field and then merged to get an integrated picture as a whole. In the initial stage, describing the preliminary statistics (mean, standard deviations, and skewness), and after that predicting the strengths of socio-economic parameters on forest dependence by regression analysis, the study opts to explore the contribution of

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Fig. 22.2 Methodology adopted for the research. (Diagram modeled after Cresswell & Plano Clark, 2011)

forest resources to sustain the livelihood of the local people living within the forest and the outsiders. Simultaneously, depicting narratives and extracting “mattering” keywords and taking photographs from the field have explained the “why” and “how” of the livelihood pattern of the forest villagers of the study region.

3.2

Data Collection and Selecting Target Group

The study follows an Inductive research method in which generalizations have been made by gathering data through a review of relevant literature, direct observation in the field, and interviews with local forest villagers and key participants of the study. The secondary database includes Census (2011), District Statistical Handbook of Bankura (2014), several Governmental reports. The study diagnosed the respondents into two groups: villagers within the forest and outsiders at the forest fringe areas, to obtain the dynamics of usage of forest products by the villagers living within the core of the forests and also at the fringe. Based on the communication and transport facility, that is, a connection of the villages to the central district road and nearest pucca road (distance in kilometers), availability of miscellaneous services, that is, the distance of weekly Haat and regular market (Mandis), accessibility of the nearest Secondary School and Primary Health Centre, the study identified seven villages located within the core of the forest and another seven villages located at the forest fringe areas (Appendix Table 1). In

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addition, several demographic and socio-economic data, such as household density, people with scheduled social affiliation, literacy status, and occupational structure, were also extracted from the last published Census (2011) to choose the sampled villages. A required sample size of (N = 385 from 14 villages, at a 95% confidence level) villagers has been determined following Krejcie and Morgan’s (1970): S = χ 2 NP1 - P ÷ d2 N–1 þ χ 2 Pð1–PÞ, where S = required sample size, χ 2 = the table value of Chi-Square for 1 degree of freedom at the desired confidence level (3.841), N = the population size, P = the population proportion (assumed to be 0.50 since this would provide the maximum sample size), and d = the degree of accuracy expressed as a proportion (0.05). The number of sampled households in every village within the forest was proportionate to the number of households.

3.3

Instruments and Measures

The questionnaire aimed to solicit information on the contribution of forest resources to the livelihoods of local people and to unfold their nature of dependencies on the forest. The sampled households within the forest were chosen at random, keeping in mind the forest-dependent livelihood pattern. A semi-structured interview was administered to the respondents addressing the critical aspects of forest usage, NTFP harvesting types (e.g., collection of different types of forest products and the amount of collection, procedures, and frequency of collection, seasonality of availing forest products, fluctuation of price of products in different hands from forest-villagers to market via middlemen, trading pathways), accessibility within the forest (distance of forest from households, transportation of forest products, and access of forest in different seasons), and socio-economic parameters of households (demography, educational attainment, occupational structure, and labor force, livelihood and dependence on forest products). The locals and the outsiders of the forest fringe informed the network system existing for selling the forest product in the market. By this snowball sampling procedure, all the target groups were interviewed regarding the access to forest and NTFP harvesting procedures. Subsequently, an open-ended interview was conducted with the forest Range Officer of Bankura Forest Division, village leaders (Panchayat Pradhan), and members of the Forest Protection Committee (FPC) to validate their role in forest protection and measures taken for the management of forest and its products and also to cross-check the model of forest usage by the local villagers as well as the outsiders, especially in the context of present increased market demands for forest products. The open-ended questions were designed to elicit information of a range and depth that is not attainable with a standard framed questionnaire (Freudenberger, 1994; Chambers, 1997). Therefore, focus-group interviews were also arranged involving the

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sub-chiefs (community heads) and the residents to discuss general issues. The forest products were valued according to the local market prices and justified with the prices informed by the local people.

4 Data Analysis and Results 4.1

Livelihood of Forest-Dependent People

Forest plays a significant role in augmenting livelihood requirements for forestdependent people of the Bankura forest region. These people’s economy is primarily based on forest resources (Fig. 22.3). Nearly 85% of people earn directly or indirectly from the forest, followed by 7.5% from livestock, 6% from off-farm jobs, and 1.5% from agriculture. Consumption for households’ needs along with petty trading of stitched sal leaves, locally called “Siapata” (93%), dry firewood (79%), timber (9%), mushroom (8%), livestock (21%), vegetables (8.5%), and herbal medicines (0.5%) (Fig. 22.4) are integral parts of their forest-based livelihood (Appiah, 2003). It is pertinent to note that a single household follows more than one way to meet their daily basic domestic needs. The majority of households living within the forest core do not follow off-farm jobs due to the household’s landless condition or perceiving “place attached” to the forest by the elderly people of the concerned households. However, the younger generations are more prone to adopt off-farm casual jobs at nearby Joypur, Bishnupur, or Bankura Sadar. The focus

Fig. 22.3 Income sources of rural livelihood in Bankura forest region (2021). (Based on Primary Survey and author’s calculation)

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Fig. 22.4 Sources of income from forest products within the forest (2021). (Based on Primary Survey and author’s calculation)

group among the present young generation, elderly people, and forest officers is discussed: It is not possible to afford today's lifestyle depending only on forest products. Though most families mainly depend on the forest from the past, our elders do not think about their existence without the forest, but nowadays, we get not so much from the jungle, so we try to go outside the forest to earn more money from some daily paid casual job. Sometimes, we migrate to other districts temporarily. (2021) JudhisthirHansda, Male, 51 years, Basudebpur, Bankura.

Besides collection from the forest, selling livestock like hen, kitten, goat, and cow, 7.5% of households within the forest earn some additional income, whereas cultivating two times a year rice in the rainy season and potato and Boro rice in the post-monsoon season, a few households (1.5%) sustain livelihood simultaneously by collecting forest resources. In addition, the tribal people of forest core greatly rely on indigenous knowledge and herbal drugs for curing the ailment, as they are readily available, safe, and cost-effective (Sengupta & Saha, 2013). In an open-ended interview, the Range Officer informed: Due to poor accessibility and connectivity of the health center from the forest core, which is at least 15–20 km away, cultural exclusivity of the tribal people from the modern healthcare system, and also due to unaffordability of modern drugs, they depend on herbal drugs, there are no other options left for them. (2021) – Subir Sen, Male, 42 years, Jaypur, Bankura

On the other, the forest meets the commercial needs of the fringe area people through the collection of forest products like Kurchi plant, Kuchi stick, mushroom,

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dry date leaves, which play significant roles in providing the opportunity to supplement household income. The Development and Planning Department, Government of West Bengal (2007) has estimated that each year about 55% of household income in the district of Bankura comes from NTFPs harvesting. “It has been estimated that only in Jaypur and Bishnupur forest range, nearly 70–80% economic livelihood is largely based on forest products, particularly by collecting sal leaves and dry firewood” – commented the Forest Bit officer of Jaypur forest. Based on the availability of the forest products every year, their monetary value, seasonality, commercial importance, and multiple usability, the significant forest products have been tabulated (Table 22.1), applying the participatory appraisal technique.

4.2

Role of Dry Sal Leaves (Sal Pata) in Forest Livelihood and Its Marketing Mechanism

“If we sale stitched sal leaves, then we get food, we can meet our daily needs; otherwise, we have to starve.” This comment by an old lady of Basudebpur village unfolds the crude reality of the livelihood of the forest-dependent people in the Bankura forest region and the role of siapata in sustaining their livelihood. The field study observes that every house within the forest weave sal leaves. The womenfolk generally collects sal leaves; sometimes, girls also participate in siapata collection from the forest. The women collectors informed that one sunny day is required to make the sal leaves dry. With the help of a dry neem stick, they weave sal leaves to form round-shaped siapata. Five to six siapata are mechanically stitched for making one plate, whereas two are required to make a bowl. Each can stitch 150 plates maximum in a single day. Nearly all female members of a household are used to weave sal leaves. The stages from sal leaf to siapata to making plates involve some value addition; though the collection of sal leaves and the primary shaping of plates are mainly done with the forest core by the forest women, the maximum valueaddition takes place in the process of making sal plates in the market. Within the forest, the villagers get Rs. 26 by selling 100 hand-stitched siapata to the middlemen, whereas the shopkeepers make the finished product “salplate” by their machine and sell 100 ready sal plates to the people at Rs. 75 in the market. Unfortunately, the traders and commercial entrepreneurs dominate in this last stage of maximum value addition (District Human Development Report of Bankura, 2007). The collectors and primary producers living within the forest, thus, get deprived of their dues, and it continues day after day. One old tribal lady of Basudebpur said: The jungle provides raw Sal leaves only. It is a never-ending source of sal leaves. We collect it all year-round. In addition, we get almost nothing from the jungle through which we can earn some money. So, we have to depend on sal leaves to nourish our livelihood. We collect it, make it dry, stitch it and sell it to the people coming from the market every week. Most of the physical labor is given by us, but we do not get what we should receive. The traders of the market get the most benefits. Thus, the living standard of the forest-dependent people like us remains the same. (Fig. 22.5)

Kuchi stick (Kuchi Kathi) Date palm leaves

Mahwa flower (Kachla)

6

8

Within the forest

Within the forest

Available within the forest

Within the forest by fringe area villagers only

Within the forest

Within the forest

10 10 >10 >10 >10