Recent Technologies for Disaster Management and Risk Reduction: Sustainable Community Resilience & Responses (Earth and Environmental Sciences Library) 3030761150, 9783030761158

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Earth and Environmental Sciences Library

Praveen Kumar Rai Prafull Singh Varun Narayan Mishra Editors

Recent Technologies for Disaster Management and Risk Reduction Sustainable Community Resilience & Responses

Earth and Environmental Sciences Library

Earth and Environmental Sciences Library (EESL) is a multidisciplinary book series focusing on innovative approaches and solid reviews to strengthen the role of the Earth and Environmental Sciences communities, while also providing sound guidance for stakeholders, decision-makers, policymakers, international organizations, and NGOs. Topics of interest include oceanography, the marine environment, atmospheric sciences, hydrology and soil sciences, geophysics and geology, agriculture, environmental pollution, remote sensing, climate change, water resources, and natural resources management. In pursuit of these topics, the Earth Sciences and Environmental Sciences communities are invited to share their knowledge and expertise in the form of edited books, monographs, and conference proceedings.

More information about this series at http://www.springer.com/series/16637

Praveen Kumar Rai · Prafull Singh · Varun Narayan Mishra Editors

Recent Technologies for Disaster Management and Risk Reduction Sustainable Community Resilience & Responses

Editors Praveen Kumar Rai Department of Geography Khwaja Moinuddin Chishti Language University Lucknow, Uttar Pradesh, India

Prafull Singh Department of Geology School of Earth Biological and Environmental Science Central University of South Bihar Gaya, Bihar, India

Varun Narayan Mishra Centre for Climate Change and Water Research Suresh Gyan Vihar University Jaipur, Rajasthan, India

ISSN 2730-6674 ISSN 2730-6682 (electronic) Earth and Environmental Sciences Library ISBN 978-3-030-76115-8 ISBN 978-3-030-76116-5 (eBook) https://doi.org/10.1007/978-3-030-76116-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 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

Preface

Global experiences have indicated that the developing countries and poor communities are more vulnerable due to various types of disasters and hazards. Most of the deaths and property losses could be prevented as per the availability of timely and accurate information on the exposed populations and assets, environmental factors in disaster risk, and its patterns and behavior. This information is increasingly becoming available with the help of recent technologies such as meteorological and earth observation satellites, geographical information systems (GIS), communication satellites and satellite-based navigation system, coupled with modeling and analysis. The emergence of these recent technologies offers considerable potential to reduce the losses of life and property, when integrated into a disaster risk reduction approach and connected to national and community risk management systems. These recent technological developments mainly focus on emphasizing the investigation and identification of disasters through advanced computational techniques in conjunction with geoinformatics and earth observation data sets for better management, adaptation, and reduction of natural and man-made disasters. This book will be of great interest to those working in the domain of disaster management and risk reduction as it will provide valuable insights into enabling community resilience and responses. It addresses the interests of a wide spectrum of readers with a common interest in geospatial science, geology, water resource management, database management, planning and policy making, and resource management. This edited book consists of selected invited papers on disaster management and risk reduction based on experiences and closely examines the coordinated research activities involving all stakeholders, especially the communities at risk. In the chapter “Glacial Lake Outflow Hazard and Risk Probability in Sikkim,” an attempt is made to analyze the glacial lake outflow risk probability in Sikkim along with a spatio-temporal change investigation of the hazardous glacial lakes over a period of thirty years (1990–2017) and also comparing them with the previous decades till 1974. The inventory map was used for change detection of the glacial lake. It is found that there is tremendous increase in their size and volume increasing the vulnerability of the nearby villages and army camps of North Sikkim. In the chapter “Earthquake Hazards and Monitoring of Seismo-ionospheric Precursor,” brief about earthquake and its magnitude over different scales, earthquake v

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preparation processes and area affected, techniques to monitor it have been presented. Efforts have also been made to point out different techniques to find the seismoionospheric precursors. Application of statistical method in detail to find ionospheric precursor using GPS and VLF measurements has been discussed. This study may be beneficial in earthquake forecasting using ionospheric precursor technique with dense network of GPS receiver having very high resolution and precision. In the chapter “Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India,” a study has been carried out to prepare a seismic hazard zonation map using secondary data sources. A total of 14 earthquake location since 1985–2015 have been collected from the Geological Survey of India. In this study, the four most causative parameters have been taken such as land-use/land cover, slope, soil, and geology. The hazard zonation map has been done using frequency ratio model. The results of this study also reveal that the hazard zonation map can be useful for mitigate the hazard and is very helpful to planners and engineers. In the chapter “Rockfall Hazard Assessment Using RAMMS for the SE Facing Escarpment of Manikaran, Himachal Pradesh, India,” remote sensing and GIS platforms are used to map older scarp retreat in the main rock fall body of Manikaran Landslide for the past 40 years using rapid mass movements: rockfall module (RAMMS) for rockfall trajectory simulate ions of rock fall event of August, 2015, in Manikaran town. A rockfall hazard map of Manikaran town is prepared based on the results of RAAMS which demarcate unsafe, moderately safe and safe zones in terms of rockfall events from the SE facing escarpment. In the chapter “Surface Displacement Analysis of Road-Cut Slopes in the Vicinity of Koteshwar Area, Uttarakhand, India,” the slope surface displacements of two vulnerable and high priority slopes at 12.55 and 13.85 km from a reference point called Zero bridge along the Tehri-Koteshwar transportation route in the vicinity of Koteshwar area, Uttarakhand, India, are analyzed. The study is carried out using orthorectified LISS IV optical remote sensing data from the year 2012 to 2017 for estimating the surface displacements based on the image pixel shift with the help of Cosi-Corr tool. In the chapter “Seismic Vulnerability Assessment in the Built-Up Environment of Rispana River Catchment, Dehradun, Indian Himalayas,” a study is carried to assess the seismic vulnerability of the built-up environment of Rispana river of Dehradun, Uttarakhand, Indian Himalayas. Vulnerability was assessed by the detailed investigation and corrective measures rapid visual screening method. The spatial and non-spatial information has been collected by field survey and analyzed under remote sensing and GIS environment. In the chapter “Flood Mapping and Vulnerability Assessment Using Geospatial Techniques: A Case Study of Lower Periyar River Basin, Kerala,” because of a lowpressure framework close to the start of the month, the Indian territory of Kerala in August 2018 got an all-inclusive time of substantial precipitation, joined by storm wretchedness a few days after the fact. The purpose of the analysis is to identify the magnitude of the flood, the damage to the built environment, by mapping the vulnerable areas of the flood, based on different analytical techniques. This study

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shows that SAR and Landsat images can be utilized adequately to map, track, and evaluate the distribution of floodwater in flood-prone areas. In the chapter “RS-GIS Based Constructive Measures for Flood Prone Agricultural Land of Sabour Block of Bhagalpur District, Bihar,” a study is performed to assess the flood-prone areas of different panchayats in Sabour block with the aid of modern tools of RS & GIS. Landsat ETM+, IRS-LISS III, and Carto DEM have been employed to delineate the vulnerable zones in flood-prone areas of Sabour block and had visible impact on Kharif and Rabi crops. This study could emphatically conclude that judicious application of organic and inorganic fertilizer enables to maintain the soil health and soil quality, and adequate land-use planning offers to promote suitable aquaculture. In the chapter “Monitoring North Bihar Flood of 2020 Using Geospatial Technologies,” a research work is performed to estimate the impact of a flood using multitemporal Sentinel-1A (SAR) and Moderate-resolution Imaging Spectroradiometer Near Real-Time (MODIS NRT) flood data over North Bihar. It is reported that most of the districts of north Bihar received continuous heavy rainfall (340–400 mm/day). These results are important for policymakers to assess flood impacts. In the chapter “A Review of Tropical Cyclone Disaster Management Using Geospatial Technologies in India,” tropical cyclones (TCs), also known as typhoons or hurricanes, are among the most destructive weather phenomena which is commonly observed between 5° and 25° latitudes on both sides (N-S) of the equator. In this context, remote sensing can be a cost effective, accurate, and potential tool for mapping, analyzing, and mitigating the multiple impacts caused by TCs using high to moderate spatial and temporal resolution satellite imagery. It can be utilized in providing essential information for evacuation, relief, and the management during post disaster. In the chapter “Forest Fire Susceptibility Mapping for Uttarakhand State by Using Geospatial Techniques,” forest fire susceptibility map is generated for Uttarakhand state by using the MODIS TERRA and AQUA land cover type product (MCD12Q1) and SRTM digital elevation model (DEM) data sets. The input parameters like vegetation cover, moisture condition, slope, aspect, elevation, and human disturbance index are taken into consideration to generate the fire susceptibility map. The generated forest fire susceptibility map can be used for the prediction of the forest fire distribution. In the chapter “Investigation of Indian Summer Monsoon Rainfall Relationship with the Bay of Bengal Sea Surface Temperature and Currents,” an analytical effort has been made to find out the relationship between the Bay of Bengal (BoB) daily JJAS mean area averaged sea surface temperature (SST) anomaly and daily JJAS mean area averaged sea surface currents (SSC) to the Indian summer monsoon rainfall. The TRMM SST data set of 2001–2013 (i.e., 1586 days) at for the region 6°N to 22°N and 80°E to 94°E and sea surface current data set of Surface Currents from Diagnostic Model (SCUD) for the years from 2001 to 2008 was used in this analysis. In the chapter “Climate Change and Its Impact on the Outbreak of Vector-Borne Diseases,” earth’s climate is changing and this affects not only the temperature, rainfall, and weather patterns but is also expected to stimulate the emergence and spread of

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several infectious diseases. The review presented here examines the possible effects of changing climate on vector-borne diseases, the relation between various climatic variables and pathogen/vector as well as the mitigation measures against vector-borne disease risks with respect to Dengue, Chikungunya, and Zika, respectively. In the chapter “Human Health Hazards and Risks in the Agriculture Sector,” the health hazards of farmers or people who work in agriculture due to agricultural activities and to reduce or eliminate these hazards have been explained. Farmers can reduce health hazards by following certain safety measures, such as sometimes a tarp or a canopy can shade a work area. The test, called an audiogram, can show signs of loss of hearing. In the chapter “Impact of Climate Change on Crop Production and Its Consequences on Human Health,” time series analysis, machine learning-based predictive multivariate modeling approaches have been exploited to detect the climatic impact on crop production for rice and cotton crops of the highest productive districts of Maharashtra state. The ML-driven partial least squares regression (PLSR) technique is proved better over other investigated techniques. The results of this study illustrate that with the rise in temperature and rainfall during 2050, cotton production is projected to decline by 1 to 35%, whereas rice production looks to be increased by 0.4 to 20% by nullifying high temperature with excess rainfall except for one district. In the chapter “A Remote Sensing and GIS Approach Toward the Analysis of Patel Milmet Dam Burst, Kenya,” advanced geospatial techniques are used to map and assess the dam break phenomena in Kenya. Imagery acquired by Sentinel-1 and Sentinel-2 missions during pre- and post-dam burst event as well as daily precipitation data over the study area from Global Precipitation Measurement (GPM) mission is utilized. Elevation information was acquired from a 12.5 m spatial resolution digital elevation model (DEM), generated from ALOS PALSAR data. In the chapter “The Geography of Climate Change Adaptation in the Vietnam Northern Mountains: A Quantitative Analysis for Intentions of Indigenous Ethnic Minorities Using Structural Equation Modeling (SEM) and Protection Motivation Theory (PMT),” a combination of structural equation modeling (SEM) and protection motivation theory (PMT) are used to examine the community’s intention to climate change adaptation in a case study of the Van Chan (Yen Bai, Vietnam). Six constructs are developed based on PMT to conduct a questionnaire surveying 243 local peoples: risk perception, belief, subjective norm, adaptation assessment, production habits, and adaptation intention. In the chapter “Role of Space-Borne Remote Sensing Technology for Monitoring of Urban and Environmental Hazards,” the applications of space-borne remote sensing from optical, SAR, and thermal sensors have been discussed to monitor urban and environmental hazards. The various applications discussed are a part of real-time research conducted in areas of urban land subsidence monitoring and mapping, urban land-use/land cover mapping, urban heat island mapping, urban flood run-off estimation, and urban air pollution monitoring, The chapter covers different regions and urban centers spread across various regions of India. In the chapter “Urban and Environmental Hazards,” the ever-rising urbanization and economic aspirations of humans have led to the increased vulnerability of humans

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to future hazards. The prominent urban and environmental hazards that have emerged in the past few decades include pollution, floods, earthquakes, and the urban heat island effect. This review paper deals with these urban environmental hazards taking into account their causes, impacts, frequency of occurrence together with mitigation and management policies/practices worldwide and in the Indian context. In the chapter “Environmental Hazards Due to Grassland Ecosystem Degradation: Perspectives on Land Management in India,” the objective is to study grassland degradation restoration studies in India. This chapter highlights the significance of using remote sensing and GIS technique in grassland studies based on review of literature published in various platforms during 1981–2019. It also highlights the institutions and mechanisms for restoration of grasslands in India such as the Banni and Ronda grasslands. In the chapter “Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review,” the main goal of the chapter is to review of different types and structures erosion models as well as its some applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as: Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in USA and Modèle d’Evaluation Spatiale de l’ALéa Erosion des Sols (MESALES) model. These and more models are being discussed in present work alongside more experimental models and methods for assessing soil erosion risk such as artificial intelligence (AI), machine and deep learning, etc. In the chapter “A Comparative Study of Interpolation Methods for Mapping Soil Properties: A Case Study of Eastern Part of Madhya Pradesh, India,” a study is conducted to compare and analyze the inverse distance weighting (IDW), ordinary kriging (OK), and spline to determine the suitable interpolation technique for mapping soil properties. Relationships between the statistical properties of the data were analyzed using soil test of pH, electric conductivity, organic carbon, nitrogen, phosphorus, potassium, sulfur, and zinc, from 2150 different locations (0–15 cm). Lognormal kriging gave better result where coefficient of skewness larger than one. In the chapter “Detection of Coal Mine Fire Using Landsat-8 OLI/TIRS Satellite Data in Ramgarh and Hazaribagh Coalfields, India,” Landsat-8 Operational Land Imager (OLI) thermal infrared sensor (TIRS) satellite data of January 2019 is utilized to detect coal fire pockets in various coal mines in Ramgarh and Hazaribagh districts, Jharkhand, India. Delineated LST map based on band 10 indicates variation between 12.21 and 31.46 °C, whereas LST map prepared by band 11 shows between 8.13 and 23.50 °C in the study area. The study also provides a perspective that may help local planners, administrators, and responders to create or update the regional/district disaster management plans for cost-effective emergency planning around the coal mining region. In the chapter “Geospatial Application for Coastal Morphology Changes Along the Sand Mining Coast: A Case Study on Alappad, Kerala,” a study is conducted to examine the changes in shoreline position within the 16 km of the coastline between Alappad and Chavara using Landsat 1, Landsat 4, Landsat 7, and Sentinel 2A for the

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corresponding years 1973, 1988, 1997, 2001, 2012, and 2019. Among the 671 ha coastal area, around 85 hector land was digs out from the Alappad beach. This edited book entitled Recent Technologies for Disaster Management and Risk Reduction: Sustainable Community Resilience & Responses includes the chapters written by scholarly academicians, researchers, and experts. The primary focus of this book is to replenish the gap in the available literature on the subject by bringing jointly the concepts, theories, and experiences of the specialists and professionals in this field. Lucknow, India Gaya, India Jaipur, India March 2021

Praveen Kumar Rai Prafull Singh Varun Narayan Mishra

Acknowledgements

The completion of this edited book entitled Recent Technologies for Disaster Management and Risk Reduction: Sustainable Community Resilience & Responses could not have been possible without the grace of almighty God. The editors would like to express sincere gratitude to all the members of editorial advisory board for their endless support and valuable instructions at all stages of the preparation of this edited book. We also express our thankfulness to all the reviewers for their kind and timely support during the review process. We humbly extend our sincere thanks to all concerned person for their constant and moral support. The editors are eternally thankful to Springer Nature for giving the opportunity to publish with them. Praveen Kumar Rai Prafull Singh Varun Narayan Mishra

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Contents

Geophysical Disasters Glacial Lake Outflow Hazard and Risk Probability in Sikkim . . . . . . . . . Arunima Chanda and Brototi Biswas Earthquake Hazards and Monitoring of Seismo-ionospheric Precursor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanjay Kumar and A. K. Singh Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India . . . Brototi Biswas, Aneesah Rahaman, and Ashutosh Singh

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Rockfall Hazard Assessment Using RAMMS for the SE Facing Escarpment of Manikaran, Himachal Pradesh, India . . . . . . . . . . . . . . . . . Raj Kiran Dhiman and Mahesh Thakur

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Surface Displacement Analysis of Road-Cut Slopes in the Vicinity of Koteshwar Area, Uttarakhand, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Swati Sharma, Har Amrit Singh Sandhu, and Manoj K. Arora

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Seismic Vulnerability Assessment in the Built-Up Environment of Rispana River Catchment, Dehradun, Indian Himalayas . . . . . . . . . . . . Himani Bisht and D. C. Pandey

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Hydrological Disasters Flood Mapping and Vulnerability Assessment Using Geospatial Techniques: A Case Study of Lower Periyar River Basin, Kerala . . . . . . . 107 S. Suresh Kumar and K. Jayarajan RS-GIS Based Constructive Measures for Flood Prone Agricultural Land of Sabour Block of Bhagalpur District, Bihar . . . . . . . 121 Binod Kumar Vimal, Neeraj Bagoria, Rajkishore Kumar, Y. K. Singh, and Ragini Kumari

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Monitoring North Bihar Flood of 2020 Using Geospatial Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Jai Kumar and Soham Sahoo Climatological and Meteorological Disasters A Review of Tropical Cyclone Disaster Management Using Geospatial Technologies in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Ananya Sharma and Thota Sivasankar Forest Fire Susceptibility Mapping for Uttarakhand State by Using Geospatial Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Swati Singh and K. V. Suresh Babu Investigation of Indian Summer Monsoon Rainfall Relationship with the Bay of Bengal Sea Surface Temperature and Currents . . . . . . . . 189 Harshita Saxena and Vivek Kumar Pandey Biological Disasters Climate Change and Its Impact on the Outbreak of Vector-Borne Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Vanya Pandey, Manju Rawat Ranjan, and Ashutosh Tripathi Human Health Hazards and Risks in the Agriculture Sector . . . . . . . . . . . 229 Dimple, Jitendra Rajput, Indu, and Manoranjan Kumar Impact of Climate Change on Crop Production and Its Consequences on Human Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 Gopal Krishna, Mahfooz Alam, Rabi N. Sahoo, and Chandrashekhar Biradar A Remote Sensing and GIS Approach Toward the Analysis of Patel Milmet Dam Burst, Kenya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 M. N. S. Ramya, Thota Sivasankar, Swakangkha Ghosh, and Gundapuneni Venkata Rao The Geography of Climate Change Adaptation in the Vietnam Northern Mountains: A Quantitative Analysis for Intentions of Indigenous Ethnic Minorities Using Structural Equation Modeling (SEM) and Protection Motivation Theory (PMT) . . . . . . . . . . . . 275 An Thinh Nguyen, Ha Thi Thu Pham, Quoc Anh Trinh, Thuy Linh Do, Phuong Anh Dang, and Luc Hens Environmental Degradation and Disaster Role of Space-Borne Remote Sensing Technology for Monitoring of Urban and Environmental Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 Akshar Tripathi and Reet Kamal Tiwari

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Urban and Environmental Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Kriti Varma, Vaishali Srivastava, Anjali Singhal, and Pawan Kumar Jha Environmental Hazards Due to Grassland Ecosystem Degradation: Perspectives on Land Management in India . . . . . . . . . . . . . . . . . . . . . . . . . . 363 Kirti Avishek and Ankit Kumar Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Lachezar Filchev and Vasil Kolev A Comparative Study of Interpolation Methods for Mapping Soil Properties: A Case Study of Eastern Part of Madhya Pradesh, India . . . 431 Sateesh Karwariya, Pradip Dey, Narinder Singh Bhogal, Shruti Kanga, and Suraj Kumar Singh Detection of Coal Mine Fire Using Landsat-8 OLI/TIRS Satellite Data in Ramgarh and Hazaribagh Coalfields, India . . . . . . . . . . . . . . . . . . . 451 Akshay Kumar, Rahul Ratnam, and Akhouri Pramod Krishna Geospatial Application for Coastal Morphology Changes Along the Sand Mining Coast: A Case Study on Alappad, Kerala . . . . . . . . . . . . 465 K. K. Basheer Ahammed and Arvind Chandra Pandey

Geophysical Disasters

Glacial Lake Outflow Hazard and Risk Probability in Sikkim Arunima Chanda and Brototi Biswas

Abstract Glacial lakes are the main water source of Sikkim and its rivers, especially Teesta and Rangit without which economic activity in the state would have been next to impossible as agriculture and tourism are the main revenue sources. In this study, an attempt was made to analyse the glacial lake outflow risk probability in Sikkim along with a spatio-temporal change investigation of the hazardous glacial lakes over a period of thirty years (1990–2017) and also comparing them with the previous decades till 1974. The inventory map was used for change detection of the glacial lake. The hazardous lakes were determined using a site suitability model designed for the study area exclusively. The prediction of the hazard, which can be created by the hazardous glacial lakes, was done using the depth and volume determination of eight sample lakes with their probable water outflow. The susceptibility of villages was determined using network analysis of the flow rate of the glacial flood water. The lake area ranged from 0.005 to 200 Ha in the years 1974–2017 in Sikkim. A total of 282 glacial lakes (2017) were demarcated from the present work, and they are distributed throughout Sikkim mostly far from settlements and depending on the factors mentioned above—glacial lake connectivity, area, slope and distance from settlement and HEPs, growth of lakes, glacier connectivity—222 lakes were found to be potentially vulnerable. The hazardous lakes have increased from 138 out of 213 lakes in 1990 to 222 hazardous lakes out of 282 in 2017. Upon analysing the temporal changes and depth of the 8 sample lakes, it was found that there was tremendous increase in their size and volume increasing the vulnerability of the nearby villages and army camps of North Sikkim. Lachung and Thangu from North Sikkim are the most vulnerable villages, along with its nearby infrastructure (HEP), to GLOF hazard. An attempt has also been made to manage the risk of the impending disaster and to cope with its effects.

A. Chanda (B) Department of Geography, Jamia Millia Islamia, New Delhi, India B. Biswas Department of Geography and RM, Mizoram University, Aizawl, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_1

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Keywords Glacial lakes · Climate change · Glacial hazard out flow (GLOF) · Risk analysis · NDSI · Temporal change · Vulnerability · Coping capacity

1 Introduction According to Shrestha (2010), Kumar and Prabhu (2012), in the last three decades, warming in the Himalayas has varied amid 0.15 and 0.60° C per decade which has led to glacial lake formation, and within the past 5 decades, the lakes have increased in number and size. There is a trans-boundary effect of these glacial lake floods resulting in destruction of physical and human environment. Thus, regular monitoring of glaciers and glacial lakes is required for suitable mitigation and adaptation measures, early warning systems in vulnerable areas near glacial lakes (Bajracharya et al. 2006; Kumar and Prabhu 2012). There is a rapid retreat in the Himalayan glaciers in the past few decades and to study this ICIMOD, UNEP and APN jointly carried out a project from 1993 to 2003 wherein they registered a total of 15,000 glaciers and 9000 glacial lakes in South Asia (ICIMOD 2007). The withdrawing glacier snout or terminus is responsible for numerous glacial lakes, among which most of them are like ticking time bombs which are and will lead to various disasters (Gilany et al. 2020). The lakes are mostly dammed by temporary or loose moraines which are mostly not capable of holding the increasing amount of water, after a point of time, in the lakes which are also filled with small to huge debris (Bhambri et al. 2013). During the last 10,000 years, there have been several glaciation and inter-glaciation periods where many lakes were formed and vanished (ICIMOD 2007). As research till now depicts that glaciers in Himalayas have reduced up to 1 km (minimum rate) since the Little Ice Age, providing space to retain water as moraine dammed lakes (LIGG 1988). The lakes which are connected to glaciers have sped up the retreating process with the help of thermal energy transmission and have also contributed to 15% area loss of these glaciers, but, there are also lakes which are disconnected from their feeding glaciers, stabilizing the lake increase. The most dangerous are the lakes forming from the debris covered glaciers and need full attention due to potential outburst situation (Nie et al. 2018). Glacial lakes, which are situated at the height of 3500 m and above, are denoted as lakes that appeared due to the retreat of glaciers. There are three types of glacial lakes—moraine dammed lakes, erosion lakes and ice dammed lakes. The shortened form GLOF is utilized for glacial floods caused by the seepage of naturally dammed lakes in the glacier, on or at the edge of glaciers. GLOF can be activated by—Collapse of moraine dams because of inner ice melts, avalanche, washout of fine materials through dam cracks, earthquakes, cloudburst/heavy rains, failure of moraine dams and lastly climate change. Earthquakes often destabilize the mountain glaciers and its moraine dammed lakes resulting in disequilibrium in the state of the lakes (Wang and Zhou 2017). As glacial lakes are the main water source of Sikkim and its rivers, especially Teesta and Rangit without which economic activity in the state would have been

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next to impossible as agriculture and tourism are the main revenue source (HDI Sikkim 2014). As long as water pools collected at the top of some alpine glaciers are held in place by ice dams, there is no danger, but when ice dams melt due to rising temperature the water flood will affect local communities, their activities and cities besides effecting water supply even. Although communities have thrived and survived on alpine glaciers for thousands of years, excessive melting would cause population displacement and need to find alternate water sources. The Himalayan glaciers are of a focussing point in social and scientific controversy. Widespread uncertainty is a major problem because speculation about their future has an irreplaceable effect on sources of water. Most Himalayan glaciers lose or gain mass with rates similar to glaciers elsewhere, excluding the emerging point of stableness or significant gains in Karakoram (Bolch et al. 2012). The vicinity investigation of the human settlements and its economic properties with that of the hazardous glacial lakes is very critical with to analyse and modelling of GLOFs hazard in Teesta Basin in the upper part. The modelling of hazardous glacial lakes can be made in hydrological tools of ArcGIS. The objectives of the study in lieu of the importance of the topic were determined as (i) to build glacial lake inventory map of Teesta Basin and (ii) to simulate the flood risk to the valley and to map and assess the potentially threatening lakes to the downstream settlement.

2 Materials and Methods 2.1 Site Description Sikkim (27°04 46 to 28°07 48 north latitude and 80°00 58 to 88°55 25 east longitude) is bounded by China in the north, West Bengal in the south, Chumbi Valley of Tibet and Bhutan in east and Nepal in west. It comprises of four districts (North, South, East and West Sikkim) and accounts for 0.22% (7096 km2 ) of total geographical area of India and according to the regional divisions defined by Census of India; it is one of the four micro-regions of north-eastern Himalayas. The state has tropical, temperate or alpine climate and weather is cold, wet and humid for most part of the year except from October to March when it is comparatively drier and snows. The temperature fluctuates from 4 to 35 ◦ C in the lower altitudes and 1–25 ◦ C in moderate and never above 15° C in very high altitudes. Gangtok receives maximum rainfall and Thangu minimum with 3494 mm and 82 mm, respectively, and moreover, an isohyetal investigation of information uncovers that there are two greatest precipitation territories in particular, the south-east quadrant (Mangan, Singhik, Dikchu, Gangtok, Rongli, and so on.) and the south-west uneven territory, and in the middle of the two is a low precipitation region around Namchi. 81.24% territory of the state (5765.10 km2 ) is under forest cover (Fig. 1) (GSI Sikkim 2012).

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Fig. 1 Glacial lake inventory

2.2 Data For glacial lake inventory mapping, data was taken from Sentinel 2 MSS data October 2017 with spatial resolution 10 m in visible spectrum and 30 m in SWIR band. For glacial lake temporal change, data was taken from Landsat 5 TM October 1990,

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

7

Landsat 7 SLC on October 2000 and Sentinel 2 MSS October 2017. Alos Palsar (12.5 m) DEM was used for calculating slope, mean depth, volume, elevation and drainage network for delineating hazardous glacial lakes; for infrastructure, ENVIS was used; growth in the lakes was calculated from glacial lake temporal change map, and glaciers were identified and mapped from Sentinel 2 October 2017. Field validation was done for landslides and glacial lake in various parts of Sikkim. Limitations of the data Like every research, this study also has its own limitation which can be enumerated as follows 1. 2. 3. 4.

As Sikkim is a border state, there were several restrictions related to toposheet and economic data. Primary survey could not be done extensively due to route restrictions to outsiders and difficult means of transportation. Inaccessibility and restrictions were the main reasons for non-ability to validate more than one glacial lake. Indian Army restriction in the border area.

2.3 Methodology The inventory map of lakes was constructed with the help of NDSI and NDWI performed on the image of 2017 Sentinel 2, and only those were taken whose area was more than 0.002 km2 and slope less than 10º. After marking glacial lakes which were in raster format, they were manually digitized to vector and mapped (Banerjee and Pratap 2013). The inventory map of hazardous lakes was constructed through the integration of different threshold layers, namely 1.

Minimum elevation

A minimum elevation of 3500 m was taken as the snowline ends at this elevation. DEM was used to fix this terrain. Existing literature suggests that average snow line in both northern and eastern Himalaya lies mostly above 3500 m from m.s.l. (Hewitt 2011). If, H ≤ 3500 m, pixel is considered as R (elevation) If, H ≥ 3500 m, pixel is not considered as R (elevation)

(1)

where H stands for the DN value similar to the height of the terrain. R (ELEVATION) stands for the region to be abstracted from the watershed on the basis of minimal elevation.

8

2.

A. Chanda and B. Biswas

Slope threshold

An outset of 0°–10° was used to differentiate betwixt hazardous and non-hazardous lakes. Slope of the subjacent lake also denotes its weakness for the lakes lying on gentler slopes have a tendency to exponentially grow, whereas those on steeper need to release water beyond its holding capability and as such cannot expand after a certain size. If, slope is ≤ 10◦ , lake is considered vulnerable If, slope is >10◦ , lake is not considered vulnerable 3.

(2)

Distance from connecting stream or outlet

DEM datasets for the location has been used to create a raster flow direction map which was used to compute distance of individual lakes from stream outlet. A threshold of 50 m was set to delineate the vulnerability of lakes. If lakes are connected to the stream outlet, they are possibly more dangerous than those away. Except for some remote and smaller lakes most fell in this category. 4.

Distance from settlement area

The vulnerability criteria for settlements were taken as less than 60 km from the glacial lakes and were marked as vector data points. The settlements found to be most vulnerable are—Lachen, Lachung, Chungthang, Thangu, Talam, Yathang, Lingthem, Manga, Latong, Dambung, Penshang. 5.

Distance from the hydroelectric power projects (HEP)

The vulnerability criteria for HEP were taken as less than 60 km from the glacial lakes and were marked as vector data points. 15 HEP in 2018, which are of immense economic importance to the state and surrounding states, are operating in Sikkim. A total of 3452 MW power is generated from these HEPs. The areas vulnerable for power disruption are the surrounding towns and villages of North Sikkim (within a radius of approximately 5–6 km) with its headquarters at Chungthang and Mangan (were 80% power used by North Sikkim and rest supplied to the other power plants). During the event of GLOF, debris and mud are brought down stream along with slurry rush causing permanent damage to the power plant, turbines and also increase the debris deposition in the reservoir. The more the outlet distances of the HEP from the outlet of the glacial lakes, lesser the vulnerability. The HEPs that were found to be most vulnerable are Lachen and Lingza HEP. The formula used for threshold is: HEP distance = Outlet distance − HEP to outlet distance

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

9

Where, outlet distance is the distance between lakes and its outlet,

6.

HEP to outlet distance is the distance between the project and its outlet, HEP distance is the distance between lake and project.

(3)

HEP distance is ≥ 50,000 meter, is not considered vulnerable HEP distance is ≤ 50,000 meter, is considered vulnerable.

(4)

Growth of lakes

Temporal change detection of the years 1990, 2000, 2017 was the tool used for detecting the areal change of the glacial lakes, and more than 50% growth is considered vulnerable. This is an important aspect for determining the lakes to be become dangerous for many lakes continuously release water through direct or indirect piping mechanism and thus do not grow, but generally the larger lakes which are capable of growth, but unable to release steadily are technically prone to glacial lakes outburst. The areas of the lakes varies from 2000 to 1,224,000 m2 , and 222 lakes were selected to be potentially vulnerable and the larger ones are located in the snout of the glacier through depression created during glacial retreat and many other lakes which are supra-glacial lakes increase in size by merging with other lakes. Growth of Area ≥ 50% Lake is considered vulnerable Growth of Area ≤ 50% Lake is not considered vulnerable Where, growth represents the cumulative % growth of the lake. 7.

(5)

Distance from the glacier

The lakes which are near the glacial body are vulnerable, because if any avalanches or slide falls into the lake, it can trigger breaching of the lake water and lakes, which are connected to the parent glacier, have 100% chance to increase in future because of unhindered supply of melt water, unlike isolated ones. A threshold distance of 500 m has been used. If, glacial distance ≤ 500 m Lake is considered vulnerable If, glacial distance ≥ 500 m Lake is not considered vulnerable

(6)

The thresholds were then integrated and vulnerability area marked, and the latter was used to clip the lakes of the area on glacial lake inventory map of 2017. Similarly, inventory maps of glacial lakes were made from the images of 1990 and 2000. The hazard lake area was overlaid and clipped for selecting the hazardous lakes of 1990 and 2000 for mapping the temporal change of the hazardous lakes. Lastly, the number of hazardous lakes was counted to calculate the rate of increase and growth of lake area.

10

A. Chanda and B. Biswas

Out of the total of 222 hazardous, 8 were selected on the basis of growth rate, tourism importance and age of lake. With the help of the temporal change maps of 1990, 2000, 2017, the selected lakes were extracted from the maps and were digitized manually and area calculated to analyse growth rate. The Alos palsar DEM was used to calculate the current depth, and thereafter, the volume was calculated for each of the 8 lakes. Toposheet of Sikkim (1974) was taken, and the lakes were digitized manually for the reference of the current scenario and the start of lake growth. The glaciers were then delineated manually with the help of NDSI, and their sizes were analysed for delineating the change in their areas for 1974, 1990, 2000 and 2017. A Euclidean distance was calculated from the hazardous glacial lakes and settlement points along with HEP points in order to delineate the vulnerable towns and HEPs within a radius of 60 km. followed by calculation of speed of water (on the basis of speed of clear water with viscosity 1), and distance between the most hazardous lakes (South Lhonak lake, Gurudongmar lake, Lake Shako Cho and Lake Kanchenjunga), with the vulnerable villages and HEPs. Time taken was 2 h from South Lhonak to Lachen village (as referenced from the theory of State Climate Change, Department of Sikkim). Thereafter, a network analysis of the streams was done to detect the shortest route of lake water to the villages and then the vulnerable villages were marked.

3 Results and Discussion 3.1 Glacial Lake Inventory While analysing glacial lake outburst flood, it is imperative to locate glaciers and hazardous and non-hazardous glacial lakes in the region. Ebbing glaciers lead to the development of glacial lakes by producing enough melt water to accumulate behind loose structures like moraines and ice. Bursting of these lakes by sudden breach of the loose dam causes flash flood in the downstream areas depending on the discharge and volume of the water. These outbursts are capable of instigating great destruction to human lives and property; therefore, monitoring the health of these lakes is necessary. For locating the glacial lakes, a specified height of 3500 m and a specific slope of less than or equal to 10° was taken. The total area of the lakes varied from 0.005 to 200 Ha in the years 1974–2017 in Sikkim. The glacial lakes are more or less located in the northern fringes of the state (Fig. 1), but the most dangerous and potent GLOFs are located in the north, north-eastern plateau region and north-western region. These lakes are connected to the first- and second-order streams feeding the main tributary channel of River Teesta, which can lead to rapid water flow towards the settlements located in the North District of Sikkim leading to its total or partial destruction. Mapping of glaciers is an essential piece of the glacial lake risk analysis, as its situation regarding lakes, their slope and surface territory assumes a noteworthy part

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

11

in assessing glacial lake danger and remote sensing is the main technique for mapping and observing of glaciers. It is to be noted that in the past four decades, Himalayan glaciers lost 10% of their volume (ICIMOD 2007) (Schickhoff et al. 2016), and an attempt has been made here to analyse the loss of Sikkim glaciers. The Teesta Basin in Sikkim comprises of 84 glaciers (Fig. 1) (Bahuguna et al. 2014). Zemu Glacier is the biggest from whose snout originates rivers Teesta and Rangit which are fed by monsoon rain as well. Unfortunately between 1975 and 1990, Zemu Glacier has been retreating by 20 m per year and it is to be noted that settlement Lachen is at a mere distance from it. In north-east too, there are high mountains and several glaciers abound in the region wherein the size of Gurudongmar Lake increased considerably with increasing tourism and biodiversity at stake. The dynamic behaviour of glacier ice (surface–subsurface movements) and the type of glacier defines the landforms. According to Basnett and Kulkarni (2011), the Rathong Glacier (in north-east Sikkim) is free of debris while Zemu Glacier is covered with it. They monitored 20 glaciers only, for the demarcation of the glacier boundary and terminus, as the presence of clouds, snow, shadow and debris cover obscured the remaining. The total area of 20 glaciers decreased from 1990 to 2004. 1990—202.13 ± 4.68 km2 . 1997—201.56 ± 4.71 km2 . 2004—199.76 ± 4.63 km2 . The total loss from 1990 to 2004 was 2.37 ± 0.113 km2 of which 0.57 ± 0.032 km2 between 1990 and 1997 and 1.80 ± 0.081 km2 between 1997 and 2004. 0.51% loss was observed for glaciers above 10 km2 and 6.67% (Table 1) for the smaller ones indicating that smaller glaciers responded more to climatic variations due to lower response time (Basnett and Kulkarni 2011). The glacier inventory map was made with the help of band rationing and Normalized Difference Snow Index (NDSI) accompanied with manual editing. Table 1 Changes in glaciers of Sikkim Name of glacier

Area in 2005 (km2 )

Kankyong

23.31

Talung Zemu

1976–1988 (change in m2 )

1988–2000 (change in m2 )

2000–2005 (change in m2 )

Total

Average

−78

−28

−124

−230

−7.67

25.51

0

−31

−102

−133

−4.43

90.94

−495

92

−19

−422

−14

Source Basnett and Kulkarni (2011)

12 Table 2 Number of hazardous glacial lakes in Sikkim

Table 3 Increase in number of glacial lakes in Sikkim

A. Chanda and B. Biswas Class

1990

2000

2017

Number of lakes

138

155

222

Percentage area of lakes

1161.39

1658.2125

3886.22

Years

Number of lakes

1972

177

1974

177

1990

213

2000

240

2002

247

2008

255

2012

270

2017

282

3.2 Hazardous Glacial Lakes The location of the hazardous lakes among all the glacial lakes is very important to assess the vulnerability of the local settlements where it is seen that although only North Sikkim is predominantly affected by these hazardous lakes in a destructive manner, but economic loss occurs to the state’s economy as a whole. A total of 274 glacial lakes were identified from the present study, and they are welldistributed throughout Sikkim mostly far from settlements and based on the criteria mentioned above—area, slope, glacial lake connectivity and distance from settlement and HEPs, growth of lakes, glacier connectivity—222 lakes were found to be potentially vulnerable (Tables 2 and 3).

3.3 Temporal Changes of the Vulnerable Lakes Glacial lake change detection has been done by monitoring glacial lakes over a period of time (Oct-1990, Oct-2000 and Oct-2017). Although summer months are better for detecting glacial lakes as the ice sheet over the lakes are completely melted and lake detecting ratios can be applied with near 100% accuracy, the month of October was taken for change detection as cloud cover was lower than the summer months (because of early onset of monsoon in the hills). Considerable changes were noted for in 1990, and there were far lesser lakes and area covered by lakes than in 2017 (Fig. 2). Figure 2 clearly shows that in 1990 there were fewer lakes in the north and north-eastern part of the state and none in the south of the vulnerable area near

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

Fig. 2 Change detection of hazardous glacial lakes

13

14 Table 4 Properties of selected hazardous glacial lakes

A. Chanda and B. Biswas Selected lakes

Depth (m)

Area (km2 )

Volume (m3 )

Gurudongmar Chho

40

1.135

4,54,00,000

Chho Lhamo

20

1.023

2,04,60,000

Lakes of Kanchenjunga A

55

1.017

5,59,35,000

Lakes of Kanchenjunga B

76

1.224

9,30,24,000

Lake Kanchenjunga

18

1.724

3,10,32,000

Lake 5

65

0.711

4,62,15,000

Lhonak lake

10

0.864

86,40,000

South Lhonak lake

60

1.268

7,60,80,000

the settlements of Lachen, Lachung and Thangu. Secondly, it is evident that Lakes of Kanchenjunga A and B were smaller in size and many other major lakes not yet formed. In all there were 138 hazardous lakes in 1990 covering an area of 1161.39 Ha. Year 2000 showed the increased areas or the areas where change is detected, and it is very clear that by 2000 there was tremendous increase both in lake size and number which is attributed to rising temperature, lower precipitation, melting glacier or retreating snout, increasing pollution leading to dust and black carbon deposition over glacier surface and increasing human interference. In all there were 155 hazardous lakes in 2000 covering an area of 1658.21 Ha. By 2017, further changes could be seen as north and north-east part of the vulnerable area which was filled up with numerous supra-glacial lakes and the areas near the settlements were too not spared. The conditions were conducive to the growth of lakes due to increasing pollution and climate change. In all there were 222 hazardous lakes in 2017 covering an area of 3886.22 Ha (Tables 4 and 5).

3.4 Change Detection of Selected Lakes Out of 222 vulnerable lakes, 8 were selected for change analysis (1974–2017) and assessment of risk associated with it. These are either important tourist destinations or age old prominent lake or have doubled or more in size (Figs. 3, 4, 5, 6, 7, 8, 9, 10 and 11). Lakes of Kanchenjunga (Fig. 3) have changed its area quite remarkably from 1974 to 2017. Lake A was 0.571 km2 in 1974 which increased to 1.017 km2 in 2017 (i.e. 79% increase). The depth of the lake is 55 m, giving an indication of 82 million cubic volume of water (2017). Lake B has changed 147% from 1974 (0 km2 ) to 2017 (1.227 km2 ). The depth of the lake is 76 m having maximum volume of water

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

15

Table 5 Temporal change in lake size Lakes

1974

1990

Change (1974–1990)

2000

Change (1990–2000)

Gurudongmar Chho

1.097

1.099

0.002

1.104

0.005

Chho Lhamo

0.931

1.015

0.084

1.016

0.001

Lakes of 0.571 Kanchenjunga A

0.741

0.17

0.795

0.054

Lakes of Kanchenjunga B

0

0.831

0.831

1.035

0.204

Lake Kanchenjunga

0.851

1.355

0.504

1.621

0.266

Lake 5

0

0.431

0.431

0.653

0.222

Lhonak lake

0.226

0.328

0.102

0.518

0.19

South Lhonak lake

0.249

0.56

0.311

0.67

0.11

Lakes

2017

Change (2000–2017)

Total change

Percentage increase in lakes

Gurudongmar Chho

1.135

0.031

0.038

3.46

Chho Lhamo

1.023

0.007

0.092

9.88

Lakes of 1.017 Kanchenjunga A

0.222

0.446

78.11

Lakes of Kanchenjunga B

1.224

0.189

1.224

147.29

Lake Kanchenjunga

1.724

0.103

0.873

102.59

Lake 5

0.711

0.058

0.711

164.97

Lhonak Lake

0.864

0.346

0.638

282.3

South Lhonak Lake

1.268

0.598

1.019

409.24

(114 million cubic metres) among all lakes. These lakes are in direct contact with Khangchung Glacier and are the feeder lakes of Gurudongmar Lake (Fig. 6). Lake A is extending northwards by creating smaller supra-glacial lakes and Lake B towards south-east, and it is predicted that in the coming years these small supra-glacial lakes will merge into master lake. South Lhonak Chho (Fig. 4) is close to South Lhonak Glacier and with an average depth of 60 m is by far the second most hazardous lake of Sikkim with a staggering volume of 90 million cubic metres. The percentage increase of lake is a shocking 409.24% for in 1974, and it was a mere 0.249 km2 which increased to 1.019 km2 in 2017. The loose moraine bed structure acts as a catalyst to the process of inter-moraine ice melting, which resulted to the increase in area.

16

A. Chanda and B. Biswas

Fig. 3 Increase in area of the lakes of Kanchenjunga

Lake Kanchenjunga (Fig. 5) is the largest lake in Sikkim connected closely to its glacier. The average depth of the lake is 18 m, which is shallow as compared to others, with a volume of 27 million cubic metres in 2017 (showing 103% increase from 1974). It is bounded in the north-west by three smaller lakes, which too are constantly increasing in number and size and are sure to merge with the main lake in two decades. Gurudongmar Chho (Fig. 6) of North Sikkim, which is the most important lake from the tourist point of view, has a depth of 40 m and volume 60 million cubic m (2017). The percentage increase of the lake is 3.46 which are very low as compared

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

17

Fig. 4 Increase in area of the lake South Lhonak

to others, as it is bounded by moraines on two sides and has indirect contact with Khangchung Glacier. Chho Lhamo (Fig. 7) is one of the oldest lakes of Sikkim with an area of 0.931 km2 in 1974 which increased to 1.023 km2 in 2017. It has a volume of 13 million cubic metres of water (2017) with a shallow depth of 20 m. The lake is surrounded by smaller lakes in the south of which one has already joined the main lake attributing to the increase in area and volume of Chho Lhamo. The lake has a very close contact with the outlet stream, but has indirect contact with its feeding glacier. Lhonak Lake (Fig. 8) is close counterpart of the dangerous south Lhonak Lake. Its area increased from 0.226 km2 in 1974 to 0.864 km2 in 2017, is fed by north Lhonak Glacier and has a volume of 15 million cubic metres (2017). The lake has shown a total of 282.3% increase. Lake 5 (Fig. 9) was non-existent in 1974, formed in mid-1980s and increased to 0.711 km2 in 2017 (165% increase). It has a volume of 97 million cubic metres and is surrounded by two minor lakes in the north and east and has full potential of merging with the main lake by eroding the moraine in between (Figs. 10 and 13).

18

A. Chanda and B. Biswas

Fig. 5 Increase in area of the lake Kanchenjunga

3.5 Causes for Water Rise in Glacial Lakes The rise in water level of the lakes leads to breaching of the moraine dams. There are several causes for the sudden or cumulative rise of water level (Fig. 11). Firstly, the rapid rise in solar radiation due to climate change leads to melting of glacier supplying the lake with added water (Fig. 13). Secondly, the blockage in seepage area leads to imbalance in the inflow and outflow of water. Thirdly, during glacial advance sedimentation and enhanced plastic ice flow leads to blocking of ice conduits (Khanal et. al 2015). Besides incessant precipitation also leads to rise in water level; landslide or avalanche blocking a section of the lake; blocking of outlet by tributary glacier; merging of smaller glacial lakes; dead ice and sediment load stopping infiltration or seepage of water from lake bottom; and lastly, pollution or dust over glacial ice causing further glacier melt (Gilany et al. 2020).

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

19

Fig. 6 Increase in area of the Gurudongmar Chho

3.6 Socio-Economic Impact of Rising Glacial Lakes The impact of a GLOF event downstream is wide-ranging in terms of damage to structures (such as roads, trekking trials and bridges), accumulation of debris in agricultural land as well as loss of human lives and need for rehabilitation of villagers. According to Lizong et al. (2003), in a study of GLOF occurrence in Nepal found that such events occurred once every three to ten years, with varying degrees of socio-economic impact. In this way, legitimate danger assessment studies must be done in conceivably vulnerable basins to assess the probable monetary loss and the most fitting strategy for alleviation activities. In the following paragraphs, the two most vulnerable villages, namely Lachen and Thangu of North Sikkim, have been studied (Fig. 14). Located adjacent to Gurudongmar Lake, Lachen is a heritage village as designated by Government of Sikkim in 2014 and there lies its problem too of increasing population and vehicular pollution, for soon after a number of homestays have come up to accommodate the tourists. In the last decade, the village has been faced with climate crisis as an outcome of reduced snowfall, unpredicted rainfall and increasing dry days leading to increase in forest fires. Moreover, Lachen is at high risk as it is in the path of streams flowing from the growing Lake Shako Cho and South Lhonak Chho, as identified on the basis

20

A. Chanda and B. Biswas

Fig. 7 Increase in area of the Chho Lhamo

of stream network analysis through shortest route of estimated speed of clear water flow (Fig. 14). Lake South Lhonak can burst any moment as the material bounding the lake is unconsolidated and loose. Thangu village south of lakes Shako Chho and Gurudongmar with 100 houses will be destroyed totally if either of the lake burst (Fig. 14). Thangu also lies in the flood route of Lake Kanchenjunga, which has increased by 103% which may lead to its outburst in the coming decades. Both from this study and a study by State Climate Change Cell of Sikkim, it was found that it will take 2–3 h for South Lhonak lake flood water to reach Lachen and 1.5 h for Shako Chho flood to reach Thangu (both at the speed of 28 km/h with clear water viscosity). These two villages and interspersed hamlets are in danger due to their nearness to river fed by and in direct contact with hazardous glacial lakes. The innumerable army camps, located at high altitudes near the lakes or in the path of the lake flood, too will be destroyed leading to loss of human life and property. Although the outburst of glacial lakes have not occurred in the recent past till date, from the analysis, it is clear that the state in the coming decades faces a potential threat from these floods causing serious damage to all sectors of the economy (agriculture, transportation, tourism, settlement, etc.).

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

21

Fig. 8 Increase in area of the lake Lhonak

Further, the villagers and army have no knowledge of the impending threat. Globally, it can be seen that the increase in glacial lakes post-World War II is an outcome of increasing glacial accessibility with increasing technology. There are few initiatives taken by the government for the people to increase their coping capacity and risk reduction which are: for decreasing the impact GLOF, Sikkim Government has started the way towards illuminating individuals about GLOFs and its perils as groups are the most vital partners in GLOF chance relief activities. The initial ones to confront GLOF affect, on vocation and foundation, are the groups living downstream of a chilly lake. It is much savvier to put resources into chance moderation and readiness at nearby organization and group level as regular risks will proceed in Sikkim. There are efforts to stop lake-overflow by the authorities by pumping out water of the extremely hazardous lakes. There are many projects which have been initiated for mapping the lakes and for early warning. For reducing glacial lake outflow flood vulnerability, some measures that can be taken are like regular mapping and monitoring of the lakes. There should be prompt and early warning during monsoon and melt season. Building protection wall around the breach point of the lakes and widening the streams. There should be proper information to the people about potential threat of the disaster. There should be limited

22

A. Chanda and B. Biswas

Fig. 9 Increase in area of the lake 5

Fig. 10 Volume of the lakes

South Lhonak Lake

Lhonak Lake

Lake 5

Lake Khanchenjunga

Lakes of Kanchenjunga

Lakes of Kanchenjunga

Chho Lhamu

200,000,000 100,000,000 0

Gurudongmaar Chho

Volume (in m3)

Volume of Lakes(2017)

Lakes

tourist permission to visit the glacier tourism spots. And for reducing vulnerability to disasters, there should be a strong DRR community and community-based disaster training. There should be a plan for insuring the property of the economically poor section of the risk area.

Mean Black Carbon ConcentraƟ on (in kg/m3) 12 /1 9/ /19 1 8 6/ /19 0 0 1/ 82 :00 3/ 19 0 : 12 1/1 84 00 /1 98 0:0 9/ /19 6 0 0 1 8 :0 6/ /19 7 0 0 1 8 :0 3/ /19 9 0 0 : 12 1/1 91 00 /1 99 0:0 3 / 9/ 19 0 0 1 9 :0 6/ /19 4 0 0 1 9 :0 3/ /19 6 0 0 : 12 1/2 98 00 /1 00 0:0 9/ /20 0 0 0 1 0 :0 6/ /20 1 0 0 1/ 03 :00 3/ 20 0 : 12 1/2 05 00 /1 00 0:0 9/ /20 7 0 0 1 0 :0 6/ /20 8 0 0 1 1 :0 3/ /20 0 0 0 : 12 1/2 12 00 /1 01 0:0 9/ /20 4 0 0 1/ 15 :00 20 0 17 :00 0: 00 /1 8/ /19 1 8 4/ /19 0 0 : 12 1/1 82 00 /1 98 0:0 8/ /19 4 0 0 1 8 :0 4/ /19 5 0 0 : 12 1/1 87 00 /1 98 0:0 9 / 8/ 19 0 0 1 9 :0 4/ /19 0 0 0 : 12 1/1 92 00 /1 99 0:0 4 / 8/ 19 0 0 1 9 :0 4/ /19 5 0 0 : 12 1/1 97 00 /1 99 0:0 8/ /20 9 0 0 1 0 :0 4/ /20 0 0 0 : 12 1/2 02 00 /1 00 0:0 8/ /20 4 0 0 1 0 :0 4/ /20 5 0 0 : 12 1/2 07 00 /1 00 0:0 8/ /20 9 0 0 1 1 :0 4/ /20 0 0 0 : 12 1/2 12 00 /1 01 0:0 8/ /20 4 0 0 1/ 15 :00 20 0 17 :00 0: 00

12

Mean Dust DeposiƟ on rate (in kg/m2/s)

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

3.00E-09 2.00E-09 1.00E-09 0.00E+00

Time

Fig. 12 Area averaged surface mass concentration of black carbon

Fig. 13 Increase in area of the selected lakes

23

Area Averaged Dry and Wet Mean Monthly Dust DeposiƟ on in Sikkim Himalyas (1980 - 2017) 6.00E-09 4.00E-09 2.00E-09 0.00E+00

Time

Fig. 11 Area averaged dry and wet dust deposition

Area Averaged Surface Mass Concentrat o i n of Black Carbon in Sikkim Himalayas (1980 - 2017)

24

Fig. 14 Vulnerable settlements

A. Chanda and B. Biswas

Glacial Lake Outflow Hazard and Risk Probability in Sikkim

25

4 Conclusion Glacial lakes are the main water source of Sikkim and its rivers, especially Teesta and Rangit without which economic activity in the state would have been next to impossible as agriculture and tourism are the main revenue source. The total area of the lakes varied from 0.005 to 200 Ha in the years 1974–2017 in Sikkim. The glacial lakes are more or less located in the northern fringes of the state but the most dangerous and potent GLOFs are located in the north, north-eastern plateau region and north-western region. These lakes are connected to the first- and secondorder streams feeding the main tributary channel of River Teesta, which can lead to rapid water flow towards the settlements located in the North District of Sikkim leading to its total or partial destruction. A total of 274 glacial lakes were identified from the present study, and they are well-distributed throughout Sikkim mostly far from settlements and based on the factors mentioned—222 lakes were found to be potentially vulnerable. The hazardous lakes have increased from 138 out of 213 lakes in 1990 to 222 hazardous lakes out of 282 lakes. Out of the 282 hazardous lakes, 8 lakes were selected, which showed tremendous increase in size and volume. Lachung and Thangu are the most vulnerable villages, along with its nearby infrastructure, towards the GLOF hazard. Although the outburst of glacial lakes have not occurred in the recent past till date, from the analysis, it is clear that the state in the coming decades faces a potential threat from these floods causing serious damage to all sectors of the economy (agriculture, transportation, tourism, settlement, etc.). Increasing temperature, dust deposition on glacier body and pollution gave way to rise in number and size of hazardous glacial lakes.

References Abbas Gilany SN, Iqbal J, Hussain E (2020) Geospatial analysis and simulation of glacial lake outburst flood hazard in Hunza and Shyok basins of upper indus basin. The Cryosphere Discuss [preprint]. https://doi.org/10.5194/tc-2019-292 Bahuguna IM, Rathore BP, Bramhbhatt R, Sharma CM, Dhar S, Ajai, Randhawa SS, Kumar K, Ganju RK, Shah D (2014) Are Himalayan glaciers retreating? Curr Sci 10(7):1008–1013 Banerjee BP (2013) Glof study using remote sensing and ground based measurement techniques. M.Tech dissertation. Indian Institute of Remote Sensing, Dehradun Basnett S, Kulkarni A (2011) The influence of debris cover and glacial lakes on the recession of glaciers in Sikkim Himalaya India. J Glaciol 59(218):1035–1046. https://doi.org/10.3189/201 3JoG12J184 Bajracharya RM, Sharma S, Dahal BM, Sitaula BK, Jeng A (2006) Assessment of soil quality using physiochemical and biological indicators in a mid-hill watershed of Nepal. In: Environmental and social impacts of agricultural intensification in Himalayan watersheds: proceedings of an international seminar held October 15–17, 2006. Kathmandu University, Kathmandu, Nepal, pp 105–114 Bolch T, Kulkarni A, Kääb A, Huggel C, Paul F, Cogley JG, Frey H, Kargel JS, Fujita K, Scheel M, Bajracharya S, Stoffel M (2012) The state and fate of Himalayan glaciers. Science 336(6079):310– 314. PMID: 22517852. https://doi.org/10.1126/science.1215828

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Bhambri R, Bolch T, Kawishwar P, Dobhal DP, Srivastava D, Pratap B (2013) Heterogeneity in glacier response in the upper Shyok valley, northeast Karakoram. Cryosphere 7:1385–1398. https://doi.org/10.5194/tc-7-1385-2013 Hewitt K (2011) Glacier change, concentration and elevation effects in the Karakoram Himalaya, upper indus basin. Mt Res Dev 31(3):188–200 ICIMOD (2007) Glacial lake and glacial lake outburst floods in Nepal. ICIMOD, Kathmandu Shrestha A, Karki S, Bhattarai M, Thapa S (2010) Gis based flood hazard mapping and vulnerability assessment of people due to climate change: a case study from Kankai watershed, East Nepal. Disaster Management, Kathmandu: National Adaptation Programme of Action (Napa), Ministry of Environment Kumar B, Murugesh Prabhu TS (2012) Impacts of climate change: glacial lake outburst floods (GLOFs). In: Arrawatia ML, Tambe S (eds) Climate change in Sikkim patterns, impacts and initiatives. Information and Public Relations Department, Government of Sikkim, Gangtok Khanal NR, Mool PK, Shrestha AB, Rasul G, Ghimire PK, Shrestha RB, Joshi SP (2015) A comprehensive approach and methods for glacial lake outburst flood risk assessment, with examples from Nepal and the transboundary area. Int J Water Resour Dev 31(2):219–237. https://doi.org/10.1080/ 07900627.2014.994116 LIGG/WECS/NEA (1988) Report on first expedition to glaciers and glacier lakes in the Pumqu (Arun) and Poique (Bhote-Sun Kosi) river basins, Xizang (Tibet), China, Sino-Nepalese investigation of glacier lake outburst floods in the Himalaya. Science Press, Beijing, China Lizong T, Rui X, Mool B (2003) Inventory of glaciers and glacial lakes and the identification of potential glacial lake outburst floods (Glofs) affected by global warming in the mountains of Himalayan region. Environmental, China: Cold and Arid Region Environmental and Engineering Research Institute Nie Y, Liu Q, Wang J (2018) An inventory of historical glacial lake outburst floods in the Himalayas based on remote sensing observations and geomorphological analysis. Geomorphology 308:91– 106. https://doi.org/10.1016/j.geomorph.2018.02.002 Schickhoff U, Singh RB, Mal S (2016) Climate change and dynamics of glaciers and vegetation in the Himalaya: an overview. In: Singh R, Schickhoff U, Mal S (eds) Climate change, glacier response, and vegetation dynamics in the Himalaya. Springer, Cham. https://doi.org/10.1007/ 978-3-319-28977-9_1 Wang S, Zhou L (2017) Glacial lake outburst flood disasters and integrated risk management in China. Int J Disaster Risk Sci 8:493–497. https://doi.org/10.1007/s13753-017-0152-7

Earthquake Hazards and Monitoring of Seismo-ionospheric Precursor Sanjay Kumar and A. K. Singh

Abstract Earthquake, a natural phenomenon, causes huge destruction to human life, economical loss, material properties, eco-system and environment. Strength of destruction depends upon the magnitude (or intensity) of the earthquake. Developments of earthquake prediction techniques both at the short- and long-time scales are useful to reduce losses. Understanding of seismo-ionospheric precursor technique is one of them and is emerging area and challenging task for scientific community to find realistic precursor associated with earthquake. In this chapter, brief about earthquake and its magnitude over different scales, earthquake preparation processes and area affected, techniques to monitor it have been presented. Efforts have also been made to point out different techniques to find the seismo-ionospheric precursors. Application of statistical method in detail to find ionospheric precursor using GPS and VLF measurements has been discussed. For earthquake forecasting point of view, ionospheric precursor signals are in short time scale and forecasting of earthquake in short time scale is challenging task to save property and life of human being. The future scope of this study may be useful in earthquake forecasting using ionospheric precursor technique with dense network of GPS receiver having very high resolution and precision. Keywords Earthquake · GPS · Ionosphere · Seismo-ionosphere precursor

1 Introduction Earthquake is one of the most important frightening and destructive phenomena of nature and has terrible after effects. In the course of history, earthquakes have killed many people and have destroyed many communities. Based on records of last 100 years, there has been an average of ~18 earthquake of large magnitude (magnitude 7.0–7.9) in each year (http://earthquake.usgs.gov). The aim of earthquake prediction S. Kumar · A. K. Singh (B) Department of Physics, Banaras Hindu University, Varanasi 221005, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_2

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is to give warning from high damaging earthquakes early on enough to allow appropriate response to the disaster. This warning allows people to minimize loss of life and property from earthquakes. The extent of loss due to the earthquake depends on magnitude (or intensity) of the earthquake which is described in following section. In addition to magnitude, the amount of damage that occurs due to earthquakes also depends on the following parameters (Yon et al. 2017). • The building designs, • The distance from the epicentre, • The type of surface material (rock or dirt) the buildings rest on. The goal of earthquake prediction is to give warning of potentially damaging earthquakes early enough to allow appropriate response to the disaster, enabling people to minimize loss of life and property. In predicting earthquake, peoples have tried to connect an impending earthquake with such varied phenomena as seismicity pattern (Wang et al. 2010), electromagnetic fields (seismo-electromagnetic) (Parrot 1994), ground movement (Hunt and Latter 1982), weather conditions and unusual clouds (Matsuda and Ikeya 2001), radon or hydrogen gas (Planinic et al. 2004) and space-based observations of energetic electron precipitation (Kudela et al. 1992). Many pseudo-scientific theories and predictions are made, which scientific practitioners find problematic. Thus, reliable, repeatable earthquake forecasting is a subject surrounded by controversy and scepticism. Substantial progress has been made on the development of earthquake methods for earthquake hazard analysis on a time scale of a few decades (Jason et al. 2003). However, the forecast of specific earthquake on time scales of a few years to a few days is highly complex problem. Nowadays, a special attention is devoted to an investigation of earthquakes precursors observed in the ionosphere as these precursor signals are in short time (Hayakawa and Hobara 2010). It is known that during periods preceding strong seismic events the ionosphere can be disturbed over the epicentre region as well as longer distances from it (Priyadarshi et al. 2011a, b). Numerous ground-based and satellite measurements have shown the ionospheric E- and F-layer density increasing or decreasing over epicentres before seismic events (Liu et al. 2000; Kumar and Singh 2017). The vertical electromagnetic F2-layer plasma E × B drift induced by a seismic electric field was proposed as the most probable physical mechanism of such anomalies formation (Namgaladze et al. 2007). Recent studies have shown that the precursory signals modify the ionization content of the ionosphere, which is reflected in the total electron content (TEC). Also, the phase and amplitude of VLF waves propagating through the ionosphere are changed (Pandey et al. 2018; Phanikumar et al. 2018; Ghosh et al. 2019). In this chapter, two techniques GPS and VLF wave measurements are discussed in detail to study seismo-ionospheric precursors associated with earthquakes.

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1.1 Intensity of Earthquake Magnitude and intensity are measure of different characteristics of earthquakes. Magnitude measures the energy released at the source of the earthquake. Magnitude of earthquake can be determined with the help of seismograph. Intensity tells about the strength of trembling produced by the earthquake at a give place. Intensity is determined by having the knowledge of effects of EQ on people, human structures and the natural environment.

1.2 The Richter Magnitude Scale This magnitude is used widely and is the first method. This is also known as the local magnitude (M) scale which can be assigned a number to quantify the amount of energy released from seismic activity (https://www.usgs.gov). It is logarithmic scale of base 10. The American seismologist Charles F. Richter set up a magnitude scale of earthquakes in 1935 as the logarithm to base 10 of the maximum seismic wave amplitude (in thousandths of a millimetre) which was recorded on a standard seismograph at a distance of 100 km from the earthquake epicentre of the EQ.

1.3 The Mercalli Scale The Mercalli scale is another way to measure the strength of an earthquake. This was invented by Giuseppe Mercalli in 1902. In order to estimate intensity of EQ, this scale uses the observations of the people who experienced the earthquake (https:// www.usgs.gov). The Mercalli scale is not scientific scale, whereas the Richter scale is scientific one. Some witnesses of the earthquake might overstate just how bad things were during the earthquake and you may not find two witnesses who agree on what happened; everybody will say something different. In such situation, the amount of damage caused by the earthquake may not be accurately recorded; i.e. how strong it was either.

1.4 Modified Mercalli Intensity Scale (MM-Scale) A single numerical value on the Richter magnitude scale is given for an earthquake. However, the intensity is variable and different over the area affected by the 15 earthquake. The area with high intensities is near the epicentre and with lower values

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further away. These are allocated a value which depends on the effects of the shaking. These effects are accounted according to the modified Mercalli intensity scale (https:// www.usgs.gov).

2 Techniques to Measure Earthquake The intensity of earthquakes is measured using seismographs, which monitor the seismic waves that travel through the earth after an earthquake strikes.

2.1 Seismograph or Seismometer A seismograph, or seismometer, is an instrument used to detect and record the signals from earthquakes. It consists of a mass attached to a fixed base. During an earthquake, the base moves, whereas mass do not move. The motion of the base with respect to the mass is commonly transformed into an electrical signal/voltage which is recorded on paper, magnetic tape, or another recording medium. This record is proportional to the motion of the seismometer mass relative to the earth. It can be mathematically converted to a record of the absolute motion of the ground (https://earthquake.usgs. gov/learn/glossary/?term=seismograph). A seismometer measures ground motions which are caused by earthquakes, volcanic eruptions and explosions. It is usually combined with a timing device as well as a recording device to form a seismograph (Andrew 2003). The output of such a device formerly recorded on paper (Figs. 1 and 2) or film. The recorded and processed digitally is known as a seismogram. The recorded data is used to locate and characterize earthquakes. A seismogram is a graph output generated from a seismograph. It is a record of the ground motion at a measuring station as a function of time which typically record motions of ground in three cartesian axes (x, y, and z). The Z-axis is perpendicular to the earth’s surface and the X- and Y-axes parallel to the surface. The energy resulted from an earthquake or from some other source, such as an explosion can be measured in a seismogram (https://en.wikipedia.org/wiki/Seismogram). Seismograms can record many things and record many little waves, called microseisms. The causes of tiny microseisms are: heavy traffic near the seismograph, waves hitting a beach, the wind, and any number of other ordinary things that cause some shaking of the seismograph (Figs. 1 and 2).

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Fig. 1 Diagram illustrating setup of seismograph (https://earthquake.usgs.gov/learn/glossary/? term=seismograph)

Fig. 2 A seismogram being recorded by a seismograph at Weston Observatory in Massachusetts (https://en.wikipedia.org/wiki/Seismogram)

2.2 Earthquake Preparation Process The changes in earth’s crust in the form of deformations, variations in seismic waves, emanation of gases including radon from the earth’s crust, air ion aerosol and changes in electrical conductivity were observed not only in the earthquake source region but it can also observed in extended area from the source region. This zone of circular area with a radius which can be calculated using the relation discussed elsewhere is

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known as the earthquake preparation zone (Dobrovolsky et al. 1979). ρ(in km) = 100.43 × M. where M being magnitude of earthquake.

3 Impending Earthquake Signals In the effort of predicting earthquake, peoples have tried to associate an impending earthquake with such varied phenomena as seismicity pattern (Wang et al. 2010), variation in electromagnetic fields which is known as seismo-electromagnetic effect or seismo-electromagnetism (Parrot 1994), movement of ground (Hunt and Latter 1982), changes in weather conditions and unusual cloud conditions (Matsuda and Ikeya 2001), emanation of hydrogen gas or radon (Planinic et al. 2004) and spacebased observations of energetic electron precipitation (Kudela et al. 1992). In addition, unusual animal behaviour is noticed prior to a significant earthquake. The unusual animal behaviour prior to a significant earthquake is seen from Greece in 373 BC. Some animals like rats, weasels, snakes, and centipedes supposedly left their homes for safety point of view several days before a destructive earthquake. Undependable evidence abounds of animals, fish, birds, reptiles and insects exhibiting odd behaviour from weeks to seconds before an earthquake. However, consistent and reliable behaviour prior to seismic events, and a mechanism explaining how it could work, still not clear. Most of researchers pursuing this ambiguity are from the Japan or China (https://www.usgs.gov/faqs/can-animals-predict-earthquakes?qtews_science_products=0#qt-news_science_products). The enormous increase of ground radioactivity (especially radon increase in the seismically active zone) may be the primary cause of all atmospheric and ionospheric anomalies observed prior to earthquakes. Emanation of radon prior to earthquakes produces air ionization in the atmosphere increasing the air conductivity which is able to modify the global electric circuit. This may induce the magnetospheric/ionospheric perturbation electric fields, thereby resulting in the precipitation of high energetic particles, especially in the lower D-region ionosphere. The precipitation of high energetic particles could probably alter the mesospheric ozone concentration (Pulinets and Ouzounov 2011; Phanikumar et al. 2018). Other possibility is modification in air ionization during the EQ preparation can simulate 14 N decay to produce 14 C. Enhancement in concentration of 14 C is useful to enhance the concentration of O3 and CO through chemical reactions. Recently, Phanikumar et al. (2018) analysed perturbation in atmospheric ozone and D-region of ionosphere for the Nepal earthquake of 25 April 2015. They have reported perturbations in the atmospheric ozone from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) 2–3 days before the main shock. They have further shown a strong linkage between anomalous variations VLF sub-ionospheric signal and mesospheric ozone prior to

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both 25 April 2015 (M w = 7.8) earthquake and its biggest aftershock on 12 May 2015 (M w = 7.3). Many pseudo-scientific theories and predictions are made, which scientific practitioners find problematic. Thus, reliable, repeatable earthquake forecasting is a subject surrounded by controversy and scepticism. Considerable progresses have been made in the growth of earthquake techniques for analysis of earthquake hazards on a time scale of a few decades (Jason et al. 2003). However, the forecast of specific earthquake on time scales of a few years to a few days is highly complex problem.

3.1 Seismo-ionospheric Precursor The effects produced in the ionosphere by seismic activity have attracted attention of scientific community for many years owing to the sharp need for the timely prediction of large earthquakes which is responsible massive destruction of property and human life. In this esteem, the study of the ionosphere state prior to the occurrence of large earthquakes is one of the most important tasks of modern geophysics and radio physics. Investigations of earthquakes precursors observed in the ionosphere have become important aspects nowadays, and special attention is devoted for this investigations. Before periods from strong seismic events the ionosphere can be disturbed over the epicentre region For this, numerous ground-based and satellite measurements have been used to show the ionospheric E- and F-layer density increasing or decreasing over epicentres before seismic events (Liu et al. 2000). The vertical electromagnetic F2-layer plasma E × B drift induced by a seismic electric field was proposed as the most probable physical mechanism of such anomalies formation (Namgaladze et al. 2007). Recent studies have shown that the precursory signals modify the ionization content of the ionosphere, which is reflected in the total electron content (TEC). In recent years, due to advent of GPS which can measure TEC in the ionosphere has become popular to study the seismo-ionosphere precursor (Priyadarshi et al. 2011a, b; Kumar and Singh 2017; Pandey et al. 2018). Very low-frequency (VLF; 3–30 kHz) signals from man-made are expected to propagate through the earth–ionosphere waveguide. This waveguide is formed by the lower D-layer of the ionosphere and the earth’s surface. The man-made signals transmitted by navigational transmitters have emerged as one of the important technique to probe the D region perturbations originating from seismic-induced effects (Hayakawa et al. 1996, 2010; Rozhnoi et al. 2007). This technique seems to be very promising for the prediction of short-term EQs. A very realistic result of ionospheric perturbations using shifts in terminator times from sub-ionospheric VLF/LF signal for the Kobe earthquake has been discussed by Hayakawa et al. (1996). Further, a number of evidences of VLF sub-ionospheric anomalies have also been reported and discussed elsewhere (Hayakawa 2007; Hayakawa and Hobara 2010). Recently, Ghosh et al. (2019) have considered two earthquakes, one on 11 March 2011 at 11:16:24 LT (M = 9) in Honshu, Japan, and another on 12 May 2015 at 12:35:19 LT (M = 7.3) in Kodari, Nepal, and analysed the VLF signal transmitted from JJI (22.2 kHz) in

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Japan (Lat. 32.05° N, Long. 131.51° E). They have shown strong shift in VLF-sunrise terminator times towards night-time few days prior to both the earthquakes. The VLF signals have also been used to study the perturbations in lower ionosphere (D and E layers) due to earthquakes (Pandey et al. 2018; Zhao et al. 2020). Considering two strong earthquakes in China (M > 7.0), recently Zhao et al. (2020) have shown that perturbations in VLF signal are quite consistent with the simulation results.

4 Results and Discussions 4.1 Monitoring of Seismo-ionospheric Precursor Using GPS In the present chapter, an attempt has been made to study some seismic-induced perturbation in total electron content (TEC) of ionosphere using data from Global Positioning System (GPS), which is able to show our exact position on the earth any time anywhere. GPS has three parts: space segment, user segment and control segment (Hofmann et al. 1992). The GPS is also being used for reliable estimation of water vapour, ionospheric total electron content (TEC) measurements and in studying the atmospheric and ionospheric characteristics (Hofmann et al. 1992). TEC estimated from GPS observation data normally known as slant total electron content (STEC). Using the mapping function STEC can be converted into vertical total electron content (VTEC) which is discussed elsewhere (Ramarao et al. 2006). The ionosphere is affected by so many parameters including solar and geomagnetic disturbances. Sometime the phenomenon in the lower atmosphere also plays a significant role in the ionospheric variations. Since ionosphere exhibits diurnal, monthly (or seasonal), day-to-day, and spatial variations (Ramarao et al. 2006), therefore, the knowledge of background condition of ionospheric is important to identify the seismic-induced anomaly from day-to-day changes. The data from the selected stations are subjected to statistical analysis to find out the presence of anomaly from day-to-day variations. The hourly mean value of VTEC for five days before and after the earthquake is subjected to statistical analysis (Liu et al. 2004), and anomalies variations in the data from the monthly average variation are selected for further analysis. An upper bound (UB) and lower bound (LB) are computed by using following equations (Liu et al. 2004). UB = TEC(MM) + 1.34σ

(1)

LB = TEC(MM) − 1.34σ

(2)

where TEC(MM) and σ are the monthly median and standard deviation value of TEC data, respectively. The data lying within the upper and lower bound are considered as normal values, and those lying outside the range are considered as perturbations.

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Before studying the seismo-ionospheric precursor, it is important to have knowledge about geomagnetic condition of period considered for the data analysis which can be done from variation of Dst-index (http://wdc.kugi.kyoto-u.ac.jp/dstdir/). The seismo-ionosphere precursor at EIA station Lhasa for the Earthquake of 03 January 2016 (M = 6.8) occurred at Tamenglong, Manipur, has been studied by Kumar and Singh (2017) and shown in Fig. 3a, b). Figure 3 shows GPS-TEC variation along with an UB and LB for a period of 11 days (±5 days from the EQ). UB and LB are also shown to filter our seismic-induced anomaly from day-today variation in TEC. Negative ionospheric perturbation in TEC is observed on 29 December (37%) and 30 December (9%) which is followed by an enhancement on 31 December 2015 (47% increase). This perturbation could not be attributed to the geomagnetic storm because its impacts on the ionosphere could only be felt after the commencement of the storm (i.e. 1200 UT, 31 December). Another enhancement in TEC (~70%) between 1400 and 1900 UT (i.e. after the storm commencement) has also been noticed from Fig. 3, which may be due to the impact of geomagnetic

Fig. 3 GPS-TEC variation along with an upper bound (UB) and lower bound (LB) computed for earthquakes of Tamenglong over GPS station Lhasa a during 5 days before the main shock of earthquake, b during 5 days after the main shock of earthquake (Kumar and Singh 2017)

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storm. Therefore, the enhancement observed between 0400 and 1000 UT is totally free from the effect of geomagnetic storm and may be attributed to the earthquake.

4.2 Seismo-ionospheric Precursor using VLF Generally, seismo-ionospheric effects in VLF signal can be identified by using two methods: (1) night-time average amplitude variation (trend analysis) and (2) the night-time fluctuation (NF). These methods are described in detail by Shvets et al. (2004). The level of fluctuation in the night-time VLF signal is obtained using the formula dA(t) = A(t) − and shown in the top panel of Fig. 4, where A(t) being the night-time amplitude of VLF at a given time t on a particular day and is the averaged amplitude at the same time estimate over ±15 days from the main shock (blue curve). Using this residue dA(t) (shown by black curve in the bottom panel of Fig. 4), we can estimate (1) trend (T ), which is the average of night-time dA(t) values for each day; (2) night-time fluctuation (NF), which is estimated by integrating [dA(t)]2 values over the respective night-time hours. At last we calculate

Fig. 4 Analysis of VLF amplitude data for the Indonesian earthquake of 11 April 2012 (Pandey et al. 2018). In top panel bar with red colour indicates the diurnal variation of signal intensity on a particular day (A(t)), and the variation with blue colour averaged over ±15 days of the day (). The residue term calculated using dA(t) = A(t) − is shown in bottom panel

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the normalized value of these physical quantities (trend, NF) in order to remove the long term (e.g. seasonal) effect. The normalized trend* is calculated using Trend* = [Trend − ]/σ, where ‘Trend’ is calculated for each particular day, and indicates the ‘Trend’ averaged over ±15 days from the EQ day and σ is the corresponding standard deviation. Using similar procedure NF* is also calculated. The bottom panel of Fig. 4 corresponds to the normalized average amplitude (or Trend*) at night (in a.u.). In this figure, a horizontal dashed line is drawn during the analysis period to indicate the mean (m) + 2σ . The temporal evolution of nighttime fluctuation is shown in lower panel, and of air dash-dot line indicates (m + 2σ ) criteria. Downward arrow represents the day of main shock of the EQ. The night-time period considered in this analysis was between 19:00 h and 03:00 h. During this time period, entire Varanasi-NWC path was in dark. Enhancements in NF were noticed during 8–9 April, which is at least 3 days prior to the EQ. This fluctuation exceeded the 2σ criterion and therefore indication of seismo-ionospheric effects. Declination in the trend is also noticed during 8–9 April though it did not exceed the 2σ mark on 9 April. The interaction of VLF signal interaction with seismo-ionosphere perturbation is discussed in detail by Molchanov et al. (2002). Resonant scattering of the VLF waves can be attributed to perturbations in ionospheric plasma density, which further causes significant drops of the VLF signal amplitude. In addition to ionosphere, atmospheric ozone is also very much sensitive to seismic activity. Recently, using VLF data from SABER Phanikumar et al. (2018) have reported pre-seismic, co-seismic and post-seismic perturbations for the earthquake of Nepal on 25 April 2015. They have further reported simultaneous anomaly in atmospheric ozone and D-region of the ionosphere prior to the Nepal EQ. The enormous increase of ground radioactivity (especially radon increase in the seismically active zone) may be the primary cause of all atmospheric and ionospheric anomalies observed prior to earthquakes. Further, variations in air conductivity leading to modifications in the global electric circuit may induce the magnetospheric/ionospheric perturbation electric fields, thereby resulting in the precipitation of high energy particles, especially in the lower D-region ionosphere. The precipitation of high energetic particles could probably alter the mesospheric ozone concentration (Pulinets and Ouzounov 2011). Other possibility is modification in air ionization during the EQ preparation which can simulate 14 N decay to produce 14 C. Enhancement in concentration of 14 C is useful to enhance the concentration of O3 and CO through chemical reactions. Pulinets and Boyarchuk (2004) have summarized the main features of ionospheric precursors arising from strong earthquakes. Various experimental observations and mathematical models used in previous studies within or far from the area of the earthquake preparation zone clearly indicate the evidence of lithosphere–atmosphere– ionosphere coupling (LAIC) (Karia and Pathak 2010; Kumar and Singh 2017). The LAIC can be either due to the seismo-genic electric field or the acoustic gravity wave generation inside the area of the earthquake preparatory zone. To explain LAIC, numerous possible sources have been recommended. These sources are: variation in the earth’s gravity, variation in the geomagnetic field, a quasi-stationary electric field, and penetration of seismo-electromagnetic emissions in the ionosphere, generation

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of internal gravity waves, radon gas emanation and turbulent diffusion. It is precious to note that none of the above sources alone fully explains the LAIC and associated ionospheric anomalies.

5 Summary Earthquake, a natural phenomenon, causes huge destruction to human life, economical loss, material properties, eco-system and environment. Earthquake preparation processes is responsible in lithosphere–atmosphere–ionosphere coupling attracted attention of research community to study the seismo-ionospheric precursor may be the basis for short-term prediction of EQ. The night-time fluctuation (NF) method of sub-ionospheric VLF signals is thought to be a more promising tool for precursors of an EQ event. GPS is a paradigm for navigation, and the ionosphere is the biggest hurdle in accurate determination of a particular location and can be used to study seismo-ionospheric precursor. Ionospheric perturbations in GPS-TEC data from ±5 day to few hours from the EQ has been reported for many earthquakes (Kumar et al. 2017; Priyadarshi et al. 2011a, b), since day-by-day network of GPS stations which is managed by IGS team is being strengthen and therefore can have potential application for in earthquake forecasting. In addition, atmospheric ozone is also found to be sensitive to the seismic activity which is attributed to the air ionization and hence modification in atmospheric conductivity prior to earthquakes which further affects the concentration of carbon monoxide and ozone in the atmosphere. Continuous measurements from dense network of GPS stations and simultaneous observation of atmospheric ozone may emerge as a significant tool to recognize the earthquake precursor signatures in the locality of earthquake-prone zone, and this gives idea of new dimension to the lithosphere–atmosphere–ionospheric coupling during the EQ preparation and eruption processes. Acknowledgements SK is thankful CSIR-New Delhi for providing financial assistance under scientist POOL scheme [13(9049-A)/2019-POOL] and SERB New Delhi under FASTRACK/YOUNG SCIENTIST scheme (SR/FTP/ES-164/2014). The work is partially supported by SERB, New Delhi, for CRG project (File No: CRG/2019/000573).

References Agnew DC (2003) History of seismology. In: International handbook of earthquake & engineering Seismology, Chap. 1, Part A, pp 3–11, ISBN 978-0-12-440652-0, LCCN 2002103787, p 269 (See also the USGS Seismometers, seismographs, seismograms webpage) Dobrovolsky IP, Zubkov SI, Myachkin VI (1979) Estimation of the size of earthquake preparation zones. Pure Appl Geophys 117:1025–1044

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Ghosh S, Chakraborty S, Sasmal S, Basak T, Chakrabarti SK, Samanta A (2019) Comparative study of the possible lower ionospheric anomalies in very low frequency (VLF) signal during Honshu, 2011 and Nepal, 2015 earthquakes. Geomat Nat Haz Risk 10(1):1596–1612 Hayakawa M, Molchanov OA, Ondoh T, Kawai E (1996) The precursory signature effect of the Kobe earthquake on VLF subionospheric signals. J Commun Res Lab Tokyo 43:169–180 Hayakawa M (2007) VLF/LF radio sounding of ionospheric perturbations associated with earthquakes. Sensors 7(7):1141–1158 Hayakawa M, Liu JY, Hattori K, Telesca L (2009) Electromagnetic phenomena associated with earthquakes and volcanoes. Phys Chem Earth 34(6/7):341–516 Hayakawa M, Hobara Y (2010) Current status of seismo-electromagnetics for short-term earthquake prediction. Geomat Nat Haz Risk 1(2):115–155 Hayakawa M, Kasahara Y, Nakamura T, Muto F, Horie T, Maekawa, S, Molchanov OA (2010) A statistical study on the correlation between lower ionospheric perturbations as seen by subionospheric VLF/LF propagation and earthquakes. J Geophys Res Space Phy 115(A9) Hunt TM, Latter JH (1982) New Zealand. J Volcanol Geoth Res 14:319–334 Hofmann-Wellenhof B, Lichtenegger H, Collins J (1992) Global positioning system, theory and practice, 4th edn. Springer, Berlin, Heidelberg, New York, p 389 Jason SJ, Pulinets S, Curiel ADS, Liddle D (2003) Phil Trans R Soc A 361:169–173 Karia SP, Pathak KN (2010) Change in refractivity of the atmosphere and large variation in TEC associated with some earthquakes, observed from GPS receiver. Adv Space Res. https://doi.org/ 10.1016/j.asr.2010.09.019 Kumar S, Singh AK (2017) Ionospheric precursors observed in TEC due to earthquake of Tamenglong on 03 January 2016. Curr Sci 113(4):795–801 Kudela K, Matisin J, Shuiskaya FK, Akentieva OS, Romantsova TV, Venkatesan D (1992) J Geophys Res 97:8681–8683 Liu JY, Chen YI, Pulinets SA, Tsai YB, Chuo YJ (2000) J Geophys Res 27:3113–3116 Liu JY, Chuo YJ, Shan SJ, Tsai YB, Chen YI, Pulinets SA, Yu SB (2004) Pre-earthquake ionospheric anomalies registered by continuous GPS TEC measurements. Ann Geophys 22:1585–1593 Matsuda T, Ikeya M (2001) Atmos Environ 35:3097–3102 Molchanov OA, Hayakawa M, Afonin VV, Akentieva OA, Mareev EA (2002) Possible influence of seismicity by gravity waves on ionospheric equatorial anomaly from data of IK-24 satellite 1. Search for idea of seismo-ionosphere coupling. In: Hayakawa M, Molchanov O, Terrapub (eds) Seismo-electromagnet lithosphere-atmosphere-ionosphere coupling, pp 275–285 Namgaladze AA, Shagimuratov II, Zakharenkova IE, Zolotov OV, Martynenko OV (2007) Possible mechanism of the TEC enhancements observed before earthquakes. XXIV IUGG Gen Assembly Perugia Italy 02:13 Pandey U, Singh AK, Kumar S, Singh AK (2018) Seismogenic ionospheric anomalies associated with the strong Indonesian earthquake occurred on 11 April 2012 (M = 8.5). Adv Space Res 61:1244–1253 Parrot M (1994) J Geophys Res 99:23339–23347 Phanikumar DV, Maurya AK, Kumar KN, Venkatesham K, Singh R, Sharma S, Naja M (2018) Anomalous variations of VLF sub-ionospheric signal and mesospheric ozone prior to 2015 Gorkha Nepal earthquake. Sci Rep 8(1):1–9 Planinic J, Radolic V, Vukovic B (2004) Radon as an earthquake precursor. Nucl Inst Methods Phys Res A 530:568–574 Priyadarshi S, Kumar S, Singh AK (2011a) Ionospheric perturbation in total electron content (TEC) associated with two recent major earthquakes (M > 5.0). Phys Scr 84:045901 Priyadarshi S, Kumar S, Singh AK (2011b) Ionospheric perturbations in total electron content (TEC) associated with some major earthquakes. J Geomantic Nat Hazards Risk 2(2):123–139 Pulinets SA, Boyarchuk KA (2004) Ionospheric precursors of earthquakes, Springer, Berlin, Heidelberg, New York Pulinets SA, Ouzounov D (2011) Lithosphere–atmosphere–ionosphere coupling (LAIC) model—an unified concept for earthquake precursors validation. J Asian Earth Sci 41(4–5):371–382

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Rama Rao PVS, Gopi Krishna S, Niranjan K, Prasad DSVVD (2006) Study of spatial and temporal characteristics of L-band scintillations over the Indian low-latitude region and their possible effects on GPS navigation. Ann Geophys 24:1567–1580 Rozhnoi A, Solovieva M, Molchanov O, Biagi P, Hayakawa M (2007) Observation evidences of atmospheric gravity waves induced by seismic activity from analysis of subionospheric LF signal spectra. Nat Hazards Earth Syst Sci 7(5):625–628 Shvets AV, Hayakawa M, Maekawa S (2004) Results of subionospheric radio LF monitoring prior to the Tokachi (m = 8, Hokkaido, 25 September 2003) EQ. Nat Hazards Earth Syst Sci 4:647–653 Wang HL, Chen HW, Zhu L (2010) Geophys J Int 183:1–19 Yon B, Sayan E, Onat O (2017) Earthquakes and structural damages, Earthquakes—Tectonics, Hazard and Risk Mitigation. https://doi.org/10.5772/65425 Zhao S, Shen Xu et al (2020) Investigation of precursors in VLF subionospheric signals related to strong earthquakes (M > 7) in western China and possible explanations. Remote Sens 12:3563

Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India Brototi Biswas, Aneesah Rahaman, and Ashutosh Singh

Abstract Earthquake are the common disastrous events in the study area, Gangtok Block, Sikkim; it shows a historical record in the study area. The heavy loadings of slopes by the residential as well as commercial buildings, construction work leads seismic activities, resulting in great loss of human lives and properties due to collapse building which is mainly constructed in local materials such as wood, bricks, and stone. Communication network is also disrupted due to these seismic activities. Therefore, it is necessary to prepare the seismic hazard or earthquake hazard zonation map of the study area to mitigate or manage the loss and damages. The objective of this present study is to prepare a seismic hazard zonation map. The present study has been carried out by using secondary sources of data. A total of 14 earthquake location since 1985–2015 have been collected from the Geological Survey of India. In this study, the four most causative parameters have been taken such as land-use/landcover, slope, soil and geology. The hazard zonation map has been done with the technique of frequency ratio model. Application of GIS techniques allowed to insert, extract, handle, and analyze the data for the zoning of seismic map. The end-product consists of information layers such as maps of earthquake intensity, the expected damage, and hazard involved, as well as numerical tables associated to maps. The seismic hazard zonation map was developed using ARC GIS 10.4 software and is structured in thematic vector and raster layers. Therefore, the results of this study also reveal that the final map of hazard zonation can be useful for mitigating the hazard and is very helpful to planners and engineers for determining the safe and suitable locations to continue the developmental works. The present study may focus on the changes of landscape which is a part of man-made activities. The changes of landuse/land-cover can bring the stress, and it is related to the magnitude of earthquake B. Biswas Department of Geography of RM, Mizoram University (A Central University), Aizawl, India A. Rahaman University of Madras, Chennai 600025, India A. Singh (B) Department of Geography, Mizoram University, Pachhunga University College Campus, Aizawl, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_3

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though it is a slow process but continuous too. So it can be recommended to give more emphasis on the landscape changes for further research. It is also recommended to focus on the awareness of local people, technical skill, application of engineering tools, and also developmental research work on the particular area. It is necessary to modify the developmental structural parameters which would be more genuine for the further seismic vulnerability mapping of the study area. Keywords Seismic · Earthquake · Hazard · Frequency ratio · GIS

1 Introduction According to Murty (2005), an earthquake is an unexpected and powerful energy that discharge and spread out through the waves of seismic which move by the way of body and across the surface of the earth. For instance, the Bhuj earthquake in 2001, released energy which is 400 times stronger than the Atom Bomb (1945) energy left on Hiroshima. Due to release of long time accumulation of strain, earthquake may happen which is the sudden earth’s movement. The plate tectonic force formed the shape of the earth since hundreds of millions of years. The plate moves gradually and passing to each other. Sometimes the interlocking plates become powerless to release the stored energy. When it is able to release that energy, the earthquake happens (Kaye et al. 2016). On the basis of the origin and impacts, earthquake may be single or combination of both. Each and every hazards distinguished by its geographical area, size, intensity magnitude, timing, and frequency (Gunthal et al. 1999). The zonation of earthquake hazard in an urban area, referred as seismic zonation mostly, which the first most step is to analyze the risk and mitigate the seismic hazard in the densely populated area (Slob et al. 2002). An earthquake comes without any warning where people are not prepared in an advance. As a result of this, large number of damages, deaths occurred to the human society. Various types of master plan can be taken to mitigate the earthquake disaster risk on the basis of suitable risk estimation (Duval & Silvia 2002). In the last few decades, various disastrous earthquake have beaten India, and the subsequent losses have brought out awareness toward the endangered seismic activity for the population whom resides in this zone of the world. For examples, earthquake of Bihar–Nepal (1988), Killari (1993), Chamoli (1999), and Gangtok (2006) comes under this zone. In India, the northeastern part is one of the most complex and tectonically extreme active provinces in the world due to the smashup of the two continental plates such as Indian plate with the landmasses of Tibetan plate in the north and in the eastern side the continuing process of subduction in between Indian plate and the ShanTenasserim block (Nandy 2001; Evans 1964; Verma et al. 1977; Khattri et al. 1992).

Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India

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In the study of seismicity of an area, pattern of earthquake distribution into various seismic zones is significant aspect and very helpful of that area. It determines the very high to very low seismic zones and as a result of that it helps to recognize the main fracture zone, thrust, lineament, and other geological features of the region. Generally, due to the tectonic origin, the earthquake occurs in the active seismic zone and it can be said that there is positive relationship between earthquake distribution and the regional tectonics (Talukdar & Barman 2012). The vulnerability of seismic activity is a great threat to the populated urban area, which is experiencing earthquake frequently. So it is essential to mitigate that dangerous hazard using effective tool (Khalida and Mahmoud 2016). Since last decades, the Geographic Information System (GIS) has been used broadly in all the stages of disaster identification, prediction, mitigation, response, and recovery (Roy et al. 2000; Balaji et al. 2002; Laefer et al. 2006; Rai et al. 2018). GIS has the ability to integrate data, analyze the spatial data, visual interpretation, and it can link with the other models (Rai et al. 2011; Henning 2011; Miles and Ho 1999). Urban susceptibility to natural calamities for example earthquake is a result of man-made activities. It explain the extent to which the system of socio-economy and physical resources in urban region either vulnerable or adaptable to the consequences of natural hazards and disaster (Rashed & Weeks 2003; Rai and Kumra 2011). To study, the estimation of loss and damages of earthquake hazard is a difficult process. The damage assessment study for a huge area may take few days to months to gather the fundamental data, and it needs an association of experts from different fields. In spite of many complexities, the study of loss estimation has clearly showed that it is an important functional tool for the improvement of emergency preparedness plan and for encouraging the mitigation of seismic risk (Agarwal 2004). Moreover remote sensing data such as satellite images can be used as a source of basic to complex information in a critical emergency situation during disaster. The study area is situated in Chungthang group rock which is lithologically prone to seismic activity. This area has a loose and unconsolidated soil. According to GSI, the study area falls under the seismic zone V which is highly prone to seismicity and causes earthquake. In this study, the statistical bivariate frequency ratio model has been applied on the GIS platform to prepare the seismic hazard map of the study area. As the study area fall under the seismic zone V which is moderate to high vulnerable zone according to Geological Survey of India (GSI), earthquake is very common hazardous phenomena in this region which is 4.5–5.5 magnitudes on the Richter scale. Geologically, the study area fully covered by faults/lineament which leads earthquake. Earthquake is the common disastrous events in the study area; it shows a historical record in the study area. The study area has a steep and rugged hilly topography with poor geological formations and is situated at an altitude of 1650 m above Mean Sea Level (MSL). Being a very attractive tourist spot, the Gangtok (capital town), the study area has been growing very fast, especially since last decade. Many multi-storied buildings have been constructed on the weak, fragile, and seismically active hill slopes to accommodate the influx of tourists. The heavy loadings of slopes by such buildings leads seismic activities, resulting in great loss of human lives and

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properties due to collapse building which is mainly constructed in local materials such as wood, bricks, and stone. Communication network is also disrupted due to it. Therefore, it is necessary to prepare the seismic hazard or earthquake hazard zonation map of the study area to mitigate or manage the loss and damages. The aim and objectives of the present study is to identify the root cause of seismic activity in this region and to demarcate the seismic-prone zone using frequency ratio model with the help of the ArcGIS software.

2 Materials and Methods 2.1 Study Area Geographically, the study area Gangtok sub-division of East Sikkim district is situated between the latitude of 27°20 20.1696 North and the longitude of 88°36 23.4216 East. The total geographical area is 563 km2 . According to 2011 census, a total of 70 villages are existing under this sub-division. In this study area, there are three Municipalities—Gangtok, Singtam, and Rangpo. The study area is existing under the lower hills. The exact altitude of this area is 1650 m above the Mean Sea Level (MS). The area is dissected by the number of drainages. The entire area has a terrain with ridges, valleys, and drainages. The major river is Rangit which is the tributary of the Tista River. The rivers are Rora Chu, Rani Khola, and Rongli Chu. As a part of the North Eastern Himalaya, geologically the state is covered by the younger rocks. Along the streams and rivers, the quaternary and alluvium type of rocks are found. Various types of geological structural and fractures, faults, joints, and folds have played an important role in the rock formation and development in the entire district. Lineaments are found in the direction of N–S, E–W, NE–SW, ENE–WSW, and NW–SE directions. Carbonate, pelitic, lime stone, sandstone, slate, and coal are the important rocks of this region (Fig. 1). In this present study, five causative factors have been used such as slope, soil, litholog,y and land-use/land-cover. Table 1 shows the data used of this study and the methodology of the study has been shown as a flow chart (Fig. 2). There are number of bivariate statistical method that can be used to prepare the susceptibility of seismic hazard mapping, among them frequency ratio (FR) model is one, and in the present study, FR model has been used (Pradhan and Lee 2009). The FR model can build the spatial relationship between the location of seismic activity and the factors (Lee 2005). GIS based frequency ratio model has been applied based on the causal relationship between the causative factors and the occurrence of seismic activities in this area. It has fixed value of 1. The value more than 1 indicates the higher correlation between the causative factors and the occurrence of seismic activities. On the other hand, value less than 1 shows the less correlation. After creating, all the thematic layers have been converted into raster and reclassification has also been done using ArcGIS 10.4 software.

Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India

45

Fig. 1 Location map of the study area

Table 1 Data used in the present study Data type

Source

Data derive

ASTER DEM (30 m resolution)

USGS earth explorer

Slope map

Sentinel 2A (10 m)

USGS earth explorer

Land-use/land-cover map

Geology (1:50,000 scale)

Mines, minerals, and geology department, Gangtok

Rock type map

Soil (1:50,000 scale)

NBSS and LUP, Kolkata

Soil type map

Seismic activity (1985–2015)

SSDMA, Gangtok

Seismic zone inventory map

3 Results and Discussion The result of this seismic frequency ratio has been shown in Table 6. The seismic frequency ratio suggested that the most important causative parameters with subclasses on earthquake epicenter. The land-use/land-cover classes are found to the major causative factors in earthquake hazards on the basis of frequency value. The maximum number of epicenter has been occurred on moderate (21°–30°) to high (31°–40°) among the all slope classes. Among the rock type classes, the high frequency ratio is observed in phyllite and quartzite rocks. In soil classes, the high frequency value has occurred in loamy skeletal entichapludols.

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Secondary Data

Satellite images

Geology

Soil

Rock type

Soil Type Map

ASTER DEM

Sentinel 2A

Slope Map

Seismic Hazard Zonation Map

Land-use/Landcover Map

Frequency Ratio approach through ArcGIS

Fig. 2 Flowchart of spatial interpretation

The earthquake inventory map has been prepared through three decades’ data from 1985 to 2015 which has been collected from the Sikkim State Disaster Management Authority (SSDMA). In this map, a total of 15 epicenter points have been taken to show the epicenter location in the study area (Fig. 3).

3.1 Relationship Between Slope and Seismic Activity Slope has been classified into five categories such as, 0°–8°, 9°–20°, 21°–30°, 31°– 40°, >40°. The maximum number of seismic activities has occurred in the slope category of 21°–40°, followed by 9°–20°, >40° and 0°–8°. Figure 3 shows the slope map of the study area. Table 2 shows the frequency ratio of each slope category. In this table, the slope category of 21°–40° shows the maximum number of landslides of 8out of total seismic activity 14 which is 57.14%. The frequency ratio of the slope has been calculated with the percentage of seismic activities occurrence divided by the percentage of pixel domain which is 2.69. Likewise, frequency ratio of all the other slope classes has been calculated.

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Fig. 3 Earthquake inventory map

Table 2 Frequency ratio of slope to seismic occurrences Slope class

Seismic occurrence

Seismic occurrence %

Pixels domain

% Pixel domain

Frequency ratio

0°–8°

1

7.14

29,108

4.43

1.61

9°–20°

3

21.42

80,124

12.19

1.76

21°–30°

4

28.57

201,556

30.68

0.93

31°–40°

5

33.33

313,612

47.74

0.0.69

>40°

2

14.29

32,479

4.94

2.89

3.2 Relationship Between Lithology and Seismic Activities The rock type has been classified into six classes such as Granite gneiss, Verligatedchartyphyllite, Glacial covered, Sillimanite granite gneiss, quartzite, and phyllite and quartzite. The maximum number of seismic activities has occurred in phyllite and quartzite 9 out of the total seismic activities 14, followed by Sillimanite granite gneiss and granite gneiss. Figure 4 shows the lithology map of the study area. Table 3 describes that the frequency ratio of each rock type classes. In this table, maximum number of seismic activities of 9 out of total 14 which is 64.29% has occurred in phyllite and quartzite. The frequency ratio of the rock class has

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Fig. 4 Slope map

Table 3 Frequency ratio of rock type and seismic activity Rock type

Seismic occurrence

Seismic occurrence (%)

Pixels domain

% Pixel domain

Frequency ratio

Granite gneiss

1

7.14

3590

7.58

0.94

Verligatedchartyphyllite

0

0

351

0.74

0

Glacial covered

0

0

1210

Sillimanite granite gneiss

5

33.33

20,354

Quartzite

0

0

1600

Phyllite and quartzite

9

64.29

20,255

2.55 42.98 3.37 42.77

0 0.76 0 1.5

been calculated with the percentage of seismic activities occurrence divided by the percentage of pixel domain which is 1.5. Likewise, frequency ratio of all the other classes has been calculated.

Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India

49

3.3 Relationship Between Land-Use/Land-Cover (LU/LC) and Seismic Activity The LU/LC has been classified into eight classes such as agriculture, deciduous forest, dense forest, road, barren land, settlement, waterbody, and snow cover. The maximum number of seismic activities has occurred in agricultural land 4 out of the total seismic points 14, followed by forest land (deciduous and ever green), road, snow cover, and water body. Figure 5 shows the land-use/land-cover map of the study area. Table 4 describes that the frequency ratio of each LU/LC classes. In this table, maximum number of seismic activities of 4 out of total 14 which is 28% has occurred in agricultural land. The frequency ratio of the LU/LC class has been calculated with the percentage of seismic activities occurrence divided by the percentage of pixel domain which is 1.33. Likewise, frequency ratio of all the other classes have been calculated.

Fig. 5 Lithology map

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B. Biswas et al.

Table 4 Frequency ratio of LU/LC to seismic occurrences LU_LC

Seismic occurrence

Seismic occurrence (%)

Pixels domain

% Pixel domain

Frequency ratio

Agriculture

4

28.57

142,975

21.53

1.33

Deciduous forest

3

20

62,314

9.39

2.13

Dense forest

2

14.29

279,083

42.03

0.339

Road

3

21.43

47,827

7.2

0.416

Barren land

0

0

14,026

2.11

0

Settlement

0

0

8439

1.27

0

Waterbody

1

7.14

3941

Snow cover

2

14.29

105,331

0.59 15.86

12.03 0.9

3.4 Relationship Between Soil and Seismic Activity The soil classification map has been collected from the ENVIS Sikkim portal. Based on the Alpena series, the soil has been classified. The type of soil has been classified into six classes such as coarse loamy humicde- dystrudepts is clay contained soil, loamy skeletal entic hapludolls is partially composed of leaf litter, course humic pachic dystrudepts soil is highly weathered type, coarse skeletal lithic is kind of very much rich in gravel coarse, coarse loamy typic hapludolls soil is fine loamy and super active, fine loamy fluventic eutrudepts soil is coarse and moist type and fine skeletal cumuli hapludolls is fine silty and fine loamy. The maximum number of seismic activities has occurred in coarse loamy humic destrudepts type soil 7 out of the total seismic points 14, followed by course humic pachic dystrudepts, loamy skeletal entichapludolls, coarse skeletal lithic, and coarse loamy typic hapludolls. Figure 6 shows the soil map of the study area. Table 5 describes that the frequency ratio of each soil types. In this table, the soil type coarse loamy humicdestrudept sshows the maximum number of seismic activities of 7 out of total 14 which is 50%. The frequency ratio of the soil class has been calculated with the percentage of seismic activities occurrence divided by the percentage of pixel domain which is 0.86. Likewise, frequency ratio of all the other soil type has been calculated. The SHZ map has been classified into five zones such as very high, high, moderate, low ,and very low hazard zone by using Jenks method of natural break in ArcGIS platform. The SHZ map shows that the maximum number of seismic activities has occurred in very high to and high hazard zone are 8 and 4 in moderate zone, 2 in low zone and 1 in very low zone. Out of the total area of 567.04 km2 , high and very high hazard zone covered 347.28 km2 area, moderate zone is covered 106.63 km2 , and low and very low zone covered 113.13 km2 area (Fig. 7). Therefore, the results of the present study reveal that the study area falls under very high hazards zone (Table 6 and Fig. 8).

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51

Fig. 6 LU/LC map Table 5 Frequency ratio of soil type and seismic activity Soil type

Seismic occurrence

Seismic occurrence (%)

Pixels domain

% Pixel domain

Frequency ratio

Coarse loamy humicdestrudepts

8

53.33

27,653

58.36

0.91

Loamy skeletal entichapludolls

2

14.29

3334

7.04

2.03

Course humicpachicdystrudepts

3

21.43

7542

15.92

1.35

Coarse skeletal lithic

1

7.14

2082

4.39

1.63

Coarse loamy typichapludolls

1

7.14

3519

7.43

0.13

Fine loamy fluventiceutrudepts

0

0

2166

4.57

0

Fine skeletal cumulichapludolls

0

0

1088

2.29

0

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B. Biswas et al.

Table 6 Number and percentage of seismic activities, area, and density in the seismic hazard zonation map (SHZM) Map Earthquake Seismic Colour Seismic Pixel Pixel Area Density Susceptibility occurrence occurrence Domain % (sq.km) SO/A Class (SO) Very high 5 33.33 12385 26.29 149.86 0.027 High 3 21.43 16564 35.16 197.42 0.01 Moderate 4 28.57 8812 18.71 106.63 0.04 Low 2 14.29 3650 7.75 44.17 0.05 Very low 1 7.14 5699 12.09 68.96 0.01

Fig. 7 Soil map

4 Conclusion Seismic activity leads earthquake hazard which is very dangerous natural hazards in the study area. The hilly terrain experiences seismic activity with earthquake in every year which causes number of loss and damages, even lives loss. Therefore, it can be said that the seismic hazard zonation (SHZ) mapping is one of the important which can reduce the loss and damages in the study area.

Seismic Hazard Zonation Mapping of Gangtok Block, Sikkim, India

53

Fig. 8 Seismic hazard zonation map

The seismic hazard zonation (SHZ) mapping based on the frequency ratio technique has given the acceptable results. The study also reveals that the land-use/landcover is more vulnerable factor for landslide on the basis of frequency ratio value flowed by slope, soil, and geology in the study area. Beside this hilly terrain with steep slope and the geological formation like lineament, folded structure also leads the major role on seismic activity. In this study the four most important causative factors such as slope, lithology, soil, and land-use/land-cover thematic layer has been prepared. The Geographic Information System (G.I.S) has been used to find out the relationship between causative factors and the seismic activities by using statistical method frequency ratio model in the study area. Weighted value of each factor has been obtained from frequency ratio (FR) technique, and summed up in weighted overlay analysis in spatial analyst tool to produce the final hazard map of the study area. The final map has classified the study area into five zones such as very high, high, moderate, low, and very low. The results of the study reveal that the SHZ map can be helpful to understand the risk of the seismic hazard or earthquake. Land-use planning can be managed on the basis of this hazard map. The final map can be used as base data for broad area planning and minimize the hazard not only in the study area but also in the hazardous area. In present days, the insurance industry uses that kind of hazard map to assess the risk of the hazard to set the insurance premium. From the above study, it has been said that the maximum earthquake incidence has been occurred in the urban area due to urbanization such as building construction, development of transport

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communication, dam construction, to increase the crop production, unsystematic agriculture is following in the study area. The present study may focus on the changes of landscape which is a part of manmade activities. The changes of land-use/land-cover can bring the stress, and it is related to the magnitude of earthquake though it is a slow process but continuous too. So it can be recommended to give more emphasis on the landscape changes for further research. It is also recommended to focus on the awareness of local people, technical skill, application of engineering tools, and also developmental research work on the particular area. It is necessary to modify the developmental structural parameters which would be more genuine for the further seismic vulnerability mapping of the study area.

References Balaji D, Sankar R, Karthi S (2002) GIS Approach for Disaster Management through Awareness-An Overview. Paper presented at the proceedings of the 5th Annual International Conference-Map India, New Delhi, 6–8 Feb 2002 Duval TS, Silvia PJ (2002) Self-awareness, probability of improvement, and the self-serving bias. J Pers Soc Psychol 82(1):49–61 Evans P (1964) The tectonic framework of Assam. J Geol Soc India 5:80–96 Ganapathy GP (2011) First level seismic microzonation map of Chennai city—a GIS approach. Nat Hazards Earth Syst Sci 11:549–559 Goswami SB, Jyoti S, Bairagi GD (2011) Seismic hazard zonation mapping by using remote sensing and GIS techniques. Int J Adv Eng Sci Technol 9:227–236 He D, Renqi LU, Huang H, Wang X, Jiang H, Zhang W (2019) Tectonic and geological setting of the earthquake hazards in the Changning shale gas development zone, Sichuan Basin, SW China. Pet Explor Dev 46(5):1051–1064 Henning, BD (2011) Gridded cartograms as a method for visualising earthquake risk at the global scale. J Maps. https://doi.org/10.1080/17445647.2013.806229 Kaye M, Shedlock, Louis C (2016) Earthquakes. U.S. Geological Survey Khalida T, Mahmoud B (2016) Earthquake risk assessment of Blida (Algeria) using GIS. Energy Proc 139:645–650 Khattri KN (1987) Great earthquakes, seismicity gaps and potential for earthquake disaster along the Himalaya plate boundary. Tectonophysics 138:79–92 Laefer DF, Alison K, Pradhan A (2006) The need for baseline data characteristics for GIS-based disaster management systems. J Urban Plann Dev 132(3):115–119 Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491 Miles SB, Ho CL (1999) Applications and issues of GIS as tool for civil engineering modeling. J Comput Civ Eng, ASCE 13(3):144–161 Murty RVC (2005) Learning earthquake design and construction–1. What causes earthquakes? resonance–J Sci Edu 9(8):75–77 Nandy DR (2001) Geodynamics of Northeastern India and the adjoining region. ACB, Calcutta, India Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focusing on different training sites. Int J Phys Sci 3(11):1–15 Rai PK, Kumra VK (2011) Role of geoinformatics in urban planning. J Sci Res Faculty of Science, Banaras Hindu University 55:11–24. ISSN No. 0447-9483

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Rai PK, Mishra VN, Raju KNP (2018) Methodology and application of remote sensing & GIS in environmental mapping & monitoring. NGJI 64(1 & 2):266–276 Rai PK, Nathawat MS, Mishra A, Singh SB, Onagh M (2011) Role of GIS and GPS in VBD mapping: a case study. J GIS Trends. Academy Science Journals, North Americ 2(1):20–27. ISSN:2146 0892 Rashed T, Weeks J (2003) Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. Int J Geogr Inf Science, in press Roy PS, Westen CJ VVK, Lackhera RC, Chapari Ray PK (2000) Natural disasters and their mitigation-remote sensing and geographical information system Perspectives. Indian Institute of Remote Sensing Publication, Dehradun Talukdar P, Barman N (2012) Seismic Activity and Seismotectonic Correlation with Reference to Northeast India. IOSR J Appl Phy (IOSR-JAP) 2(2):24–29 Verma RK, Mukhapadhyay M, AhluwaJia MS (1976) Seismicity, gravity and tectonics of North-East India and Northern Burma. Bull Seismol Soc Am 66:1683–1694

Rockfall Hazard Assessment Using RAMMS for the SE Facing Escarpment of Manikaran, Himachal Pradesh, India Raj Kiran Dhiman and Mahesh Thakur

Abstract Several rockfall events occurred in the Manikaran town and surrounding area of Kullu district, Himachal Pradesh, India, due to heavy monsoon rains and snow melting on the mountain peaks. One major rockfall event occurred near the historical Gurudwara and Shiv Temple located in Manikaran town on August 2015, killing nearly more than 10 people and injuring 15 people sleeping in the Sarai of Gurudwara. Every year during monsoon season numerous rockfall events happen in Gargi Village which is situated uphill approximately 1.5 km from the base of Parvati River at Manikaran and many a times rock blocks topple up to Manikaran. The SE facing escarpment of Manikaran town is prone to landslide activity which needs to be monitored in order to prevent loss of life. In this study, we present geological investigation and geomorphological zonation mapping of the rockfall site which was done depicting forest cover, terrain material, identification of the detachment rock mass and certain possible invasion areas of future landslide blocks. Remote sensing and GIS platforms were used to map older scarp retreat in the main rockfall body of Manikaran Landslide for the past 40 years. We used RAMMS (Rapid mass movements: Rockfall Module) for rockfall trajectory simulations of rockfall event of August 2015 in Manikaran town. Based on field investigation of boulders (of rockfall) lying on the uphill slope, two boulder sizes are selected for simulation one for small volume of rocks (6.4 m3 ) and second for large volume of rocks (32 m3 ). We found that once rockfall starts it take ~8.2 s for 32 m3 rocks to reach the base at Manikaran town. The jump height of rock blocks during rockfall is very high >40 m which cannot be stopped by building any engineering design wall. The maximum total kinetic energy of >6000 kJ for 6.4 m3 and >200,000 kJ for 32 m3 has been observed in the numerical analysis with the maximum runout distance up to 1200 m. Due to pilgrim rush to Manikaran Gurudwara and Shiv Temple, the periodic rockfall events during monsoon season poses a grave danger to the life of people and infrastructure of Manikaran town. We prepared a rockfall hazard map of Manikaran town based on the results of RAAMS which demarcate unsafe, moderately safe and safe zones in terms of rockfall events from the SE facing escarpment. We propose an R. K. Dhiman · M. Thakur (B) Centre of Advanced Study in Geology, Sector 14, Panjab University, Chandigarh, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_4

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early warning landslide system to be installed in the Manikaran town for real-time monitoring and prediction of rockfall events. Keywords Rockfall assessment · RAMMS · 3D numerical modelling · Manikaran · NW Himalaya

1 Introduction Rockfall is a common natural hazard in mountainous terrains like Himalaya (Verma et al. 2019; Singh and Thakur 2019). Rockfall is defined as, when several rock blocks are detached from the surface of the slopes and fall downwards due to gravity (Cruden and Varnes 1996). Due to rapid movement of large boulders, they carry high energies and velocities which results in loss of life and damages to infrastructures (Hungr et al. 1999; Agliardi and Crosta 2003). Over the past many decades, computer programs are extensively used to do simulations for determining rockfall trajectories, velocity and runout distances (Turner and Duffy 2012). In general two types of approaches are adopted to simulate rockfall, first is lumped mass approach and second is rigid-body approach also some hybrid approach consisting of both lumped mass and rigid body are also there (Guzzetti et al. 2002). Detailed historical development of all rockfall simulation computer programs is given in Siddique et al. (2019). 2D modelling was the state-of-the-art method for modelling rockfall scenarios, e.g. rockfall (Spang and Sönser 1995) and ROFMOD (Mohr 2015); however, in the recent years, 3D modelling programs are emerging and used as an aiding tool in rockfall design, e.g. RockyFor (Dorren et al. 2004, 2006; Bourrier et al. 2009), RAMMS: Rockfall (Christen et al. 2007), RockFall Analyst (Lan et al. 2007), Trajec 3D (Basson 2012). Rockfall deposits spreads through many mountainous regions worldwide (Varnes 1978; Evans and Hungr 1993; Wieczorek 2002; Dorren 2003; Guzzetti et al. 2003). Rockfall deposits are studied to extract dimensions and shape of rocks which can be used as important data to complement rockfall modelling scenarios in order to assess future rockfall hazards (Agliardi and Crosta 2003; Guzzetti et al. 2003; Dorren et al. 2004; Porter and Orombelli 1981; Wieczorek et al. 2008; Stock et al. 2014; Borella et al. 2016). In this paper, we studied one complex rockfall site located in Manikaran town of Kullu district, Himachal Pradesh, India. Manikaran town is situated on the right bank of Parvati River (Fig. 1). Historically rockfall events were happening in this area, e.g. a rockfall event destroyed first geothermal plant of 5 MW (Installed by GSI) in 1980s. In August 2015, a disastrous tragedy happened in Manikaran Gurudwara Sahib, when a rock block came from the high peaks and crushed the langer hall of Gurudwara, killing more than 10 people and injuring more than 15 people (Fig. 3). In every monsoon rockfall activity is continuously happening at Manikaran rockfall site. Number of tourists visiting every year to Manikaran is in lakhs, and this number is increasing every year. Considering all these past events and tourists attraction to this place makes it a very risky zone which needs to be studied in detail.

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Fig. 1 Detailed geological map of Manikaran, Kullu, Himachal Pradesh, India (after Geological survey of India, 1991). Black box (solid rectangle) demarcates the boundary to Manikaran town. Inset image shows stereographic projection of various joint sets (J1–J5) present in the Manikaran Quartzite

In the present study, detailed investigation of the rockfall site was conducted with an engineering geological perspective. After that RAMMS (rapid mass movement simulation) bare-earth and forested numerical modelling scenarios were conducted to evaluate the influence of natural factors on rockfall distributions. Rock shapes and distribution of rockfall in the study area were identified in the field, and RAMMS analysis was performed using those real rocks. We use an integrated approach, which combines a consideration of geologic, geomorphic and anthropogenic influences on rockfall distributions with field-based rockfall datasets and numerical modelling. We addressed following questions in this study (1) demarcation of rockfall trajectories which are most probable to occur during an extreme event, (2) identification of most probable source zones that lead to disastrous event of August 2015 in Manikaran, (3) quantification of maximum velocity, jump height, kinetic energy and runout distance for the rock boulders during an extreme event, (4) development of most favourable solution to Manikaran rockfall site considering all geological and geomorphological conditions.

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1.1 Geology of Study Area Manikaran and Gargi are two villages located in Parvati Valley, Kullu, Himachal Pradesh, at an elevation of 1760 and 2340 m above mean sea level, respectively (Fig. 2). Continuous rockfall happens in the Gargi Village, and sometimes rockfall may reach upto Manikaran resulting in a disastrous event. The major rock types in the Manikaran area is a well jointed, white to greyish, thick sequence of quartzite, which has been named the Manikaran Quartzite, along with minor phyllites and slates (Bhargava and Srikantia 1998) (Fig. 1). These Manikaran quartzite intercalations with phyllite constitute the uppermost formation of the Rampur Group (Bhargava and Srikantia 1998). Kullu Formation of the Chail Group tectonically overlies the Manikaran quartzite (Sinha et al. 1997). The Kullu Formation, representing the overthrust block, comprises carbonaceous phyllite and limestone, garnetiferous schist and quartzite (Sinha et al. 1997) (Fig. 1). Manikaran is a famous hotspot for hot water spring and ancient Ram Mandir and Gurudwara Sahib, which makes it a major tourist attraction. Over the past years large number of tourists visits Manikaran to explore hotspring and ancient temples.

Fig. 2 Geomorphological map of rockfall site in Manikaran, Himachal (Imagery Source Esri (2020))

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The main reason to conduct this rockfall assessment study in Manikaran is to ensure safety of people and infrastructure.

1.2 Geomorphological Study of Rockfall Site at Manikaran Manikaran town is situated on the right bank of Parvati River (Figs. 1 and 2). Historically rockfall events are happening in this area since 1980s when a rockfall event destroyed first geothermal plant of 5 MW (installed by Geological Survey of India (GSI)). In August 2015, a disastrous tragedy happened in Manikaran Gurudwara sahib, when a rock block came from the high peaks and crushed the langer hall of Gurudwara, killing more than 10 people and injuring more than 15 people (Fig. 3). Source area of rockfall for August 2015 disaster is shown in Fig. 2. Number of tourists visiting every year to Manikaran is in lakhs, and this number is increasing every year. Considering all these past events and tourists attraction to this place makes it a very risky zone which needs to be studied in detail. Gargi Village is situated uphill approximately 1.5 km from the base of Parvati River (Fig. 1). A constant rockfall happens in Gargi Village especially during the monsoon season as shown in Fig. 4g, h. Due to rockfall along the Cho Nala (Nala means river, main drainage), community centre that was build uphill Manikaran town is destroyed (Fig. 4c, d). According to the villagers, 400 years back a big boulder slided along Cho Nala and is situated on loose debris flow material which can topple anytime due to earthquake activity (Fig. 4e, f). Majority of rockfall events occur along the Cho Nala through which the rocks enter the Gargi Village, and sometimes, if the velocity is high rock blocks fall up to Manikaran town (Fig. 4a, b). Parvati River is the major river which passes through Manikaran town, one major tributary of Parvati River is Brahmganga River and other small tributary is Cho Nala (Fig. 2). Both Brahmganga and Cho Nala are perennial tributaries. Also new drainage is developing parallel to Cho Nala as shown in Fig. 2. General topography of the rockfall site is rough and steep slopes (>60 degree) are present. Overall terrain of the area is described as hard, medium hard and extra hard on the basis of RAMMS: ROCKFALL manual. Upper and medium reaches of the mountain are classified as open forest, i.e. 20 m2 /ha (forest drag = 250 kg/s) as per RAMMS: ROCKFALL manual (Fig. 2). Principal crest line depicting the active rockfall source area is shown in Fig. 2. Primary ridge demarcates the safe and unsafe boundary of rockfall-prone area (Fig. 2). There is secondary ridge present in between the primary ridges (Fig. 2) which act as a barrier to divide rockfall coming from the upper reaches either towards Cho Nala resulting in no damage to Manikaran or towards Manikaran which leads to disastrous event like one in August 2015. For Gargi Village, continuous rockfall is happening from past 400 years (according to villagers) and whole village has become a cemetery for rockfall debris (Fig. 4e–h). Main rockfall body is shown in Fig. 2, upper reaches of this rockfall body is the main source for rockfall in the Gargi Village. Using Google Earth, temporal variation for the past 40 years of rockfall body was studied and finally older scarp (inactive) and active scarp which depicts

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Fig. 3 Picture taken after disastrous August 2015 rockfall at Gurudwara Sahib, Manikaran, Kullu, Himachal Pradesh, India. The boulder from uphill penetrated through six concrete walls finally falling into the Parvati River (Source www.sanskritimagazine.com)

past rockfall source zones and present rockfall source zones, respectively, are shown (Fig. 2). Rockfall debris lies in the Gargi Village and surrounding areas which was marked in the field and shown in Fig. 2. By studying rockfall debris, we calculated rock dimensions and shape (Fig. 5) which is very important and new development in RAMMS: ROCKFALL (Leine et al. 2013). Joint data was collected for the Manikaran quartzite and is represented on stereonet as shown in Fig. 1, location where joint data was collected is shown in Fig. 1.

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Fig. 4 a Big boulders are coming from the upper reaches of the mountain along the Cho Nala drainage falling into the Parvati River which is approx. 10 m from the Gurudwara in the Manikaran town. b Figure shows the size of boulder which is bigger than bus lying along the road .c, d Big Boulders coming along Cho Nala which damaged Community centre. e Boulder lying in Gargi Village from top view, villagers said it is 400 years old incident when this boulders came from mountain. f Same boulder as shown in (e), view from bottom. g, h Rockfall source zones in the Gargi Village and rockfall debris is shown

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Fig. 5 Measurement of various dimensions and shape of the real rockfall blocks at the rockfall site at a Gargi Village, b upslope of Manikaran

2 Materials and Methods Rockfall hazard assessment of the SE escarpment of the Manikaran area was estimated through RAMMS ROCKFALL module (v1.6) in order to investigate hazard. The RAMMS ROCKFALL module utilizes a hard-contact, rigid-body approach to model rockfall trajectories in three-dimensional terrain (Leine et al. 2013). Numerical modelling was applied to specific sites selected through rigorous field observations, geological and geomorphological field mapping. Through field investigations, the selection of the hypothetical release areas was possible, where rockfall phenomena are expected, considering the sites with the most critical failure conditions. The basic requirement to calculate rockfall trajectories is the specification of the position, orientation and initial potential or kinetic energy (fall heights or initial rotational or translational velocity) of the rock (Christen et al. 2012).

2.1 Working of RAMMS: ROCKFALL Module The first step was to collect high resolution digital elevation model (DEM) in order to run RAMMS simulations. For this study, we used ALOS PALSAR (12.5 m resolution) DEM data. RAMMS take input DEM in ASCII format, and projected coordinate system is used in RAMMS, we used UTM zone 43 for our analysis. Rockfall source zones are drawn as point features (.shapefile format) using Arc GIS software and imported in RAMMS environment. The definition and localization of a rockfall starting zone has a strong impact on the results of RAMMS simulations. RAMMS provide a rock builder menu, which was used to define the size, shape and density of rocks. There are already several realistic rock shapes included in the library of RAMMS. Dimensions of rocks that was calculated in field (Fig. 5) were taken as input to define the size of rocks. From the field description of rocks equant, real

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equant, flat, real flat and long types of rocks with variable volume, i.e. 6.4 and 32 m3 , were used for the simulations. RAMMS considers the drag/resistance to rockfall associated with terrain material and forest cover, for this, study area was explored and geomorphological map was developed as shown in Fig. 2. Terrain material and forest cover was classified as extra hard, medium hard, hard and open forest (20 m2 /ha), respectively, on the basis of RAMMS: ROCKFALL manual and imported in shapfile format to RAMMS software. After gathering all aformentioned input data, two rockfall scenarios were generated, i.e. first with 6.4 m3 and second with 32 m3 volume of rocks. Overall rocktype in the study area was quartzite, schist and phyllite; therfore, rock density was taken as 2700 kg/m3 . Once all input data is gathered, RAMMS generates friction parameters on the basis of input data and runs the simulation. RAMMS simulation can be seen in 2D and 3D plots. Once RAMMS simulations are generated different parameters of rockfall activity such as velocity, kinetic energy, range of rockfall, distance profiles, etc., can be generated and exported in different formats for use in other GIS softwares. RAMMS simulations path profiles can be exported in csv formats to be viewed in other graphic software’s.

3 Results and Discussions In RAMMS environment two simulation scenarios were generated one for small volume of rocks (6.4 m3 ) and second for large volume (32 m3 ) of rocks (Fig. 6).

3.1 Rockfall Scenario with 6.4 m3 Rock Volume Rockfall scenario for 6.4 m3 rock volume was done by considering source rock of real equant shape. It was defined that z-offset (height of source rocks above surface) is 10 m, also z-offset was varied between 0 and 10 m by considering 20 different values. Total seven number of source points were taken, and one type of rock, i.e. real equant, was simulated for rockfall analysis. Total five number of random orientation to rock were added before simulation. Finally total number of simulations per source rock were 100, and total number of simulations generated for 6.4 m3 rock volume were 700. Results of RAMMS simulation for 6.4 m3 rock volume are shown in Fig. 7. RAMMS simulation results for 6.4 m3 show that most rocks reach Parvati River. Mean and maximum kinetic energy attained by 6.4 m3 rocks is 987 kJ and 7263 kJ, respectively (Fig. 7a). Mean and maximum velocity of rocks is found to be 25 m/s and 80 m/s, respectively (Fig. 7b). Minimum and maximum jump height of 0.15 m and 90 m is obtained for 6.4 m3 rocks, respectively (Fig. 7c). Total number of rocks passing through a particular trajectory is shown in Fig. 7d, and maximum reach

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Fig. 6 3D view of SE escarpment of rockfall site at Manikaran, Himachal Pradesh. Red circle shows the location of source rocks for rockfall simulation, red polygons are showing steep slope with vertical, horizontal dimensions and small ridge which act as a barrier to divide rocks during rockfall, respectively. Black polygon shows the whole town of Manikaran

probability of rocks from source to destination point (Parvati River) is shown in Fig. 7e. Total number of rocks deposited after simulation is shown in Fig. 7f. As it is very clear from Fig. 7f that maximum rocks reach Parvati River, but some rocks are deposited along the way. Two-dimensional profile of 6.4 m3 rock is shown in Fig. 8, and this profile is one among the most probable trajectory of rockfalls for 6.4 m3 rocks. Variation of kinetic energy and jump height along a trajectory is also shown in Fig. 8, maximum kinetic energy and jump height is attained at ~700 m from the source zone (Fig. 8).

3.2 Rockfall Scenario with 32 m3 Rock Volume Rockfall scenario for 32 m3 rock volume was done by considering source rocks of equant, real equant, flat, long, real flat and real long shapes. It was defined that z-offset (height of source rocks above surface) is 10 m. Total seven number of source points were taken, and aforementioned seven types of rocks were simulated for rockfall

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Fig. 7 Rockfall scenario with 6.4 m3 volume of rocks a Kinetic rock energy for all simulations is shown (in kJ). b Velocity of rocks is represented in (m/s) for all simulations. c Jump height attained by all rocks during simulations is shown in (m). d Total number of rocks following trajectory during simulations is shown. e Maximum reach probability contours are shown for all simulated rocks, and higher value contours show the area which was followed by maximum rocks during simulations. f Total number of rocks deposited is shown, although maximum rocks reach the base, i.e. Parvati River, but some rocks could not reach the bottom and are deposited along the path

analysis. Total ten number of random orientation to rocks were added before simulation. Finally total number of simulations per source rock were 110, and total number of simulations generated for 32 m3 rock volume were 770. Results of RAMMS simulation for 32 m3 rock volume are shown in Fig. 10. RAMMS simulation results for 32 m3 show that most rocks reach Parvati River. Mean and maximum kinetic energy attained by 32 m3 rocks is 28151 kJ and

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Fig. 8 2D profile of rockfall trajectory for 6.4 m3 rock, a Kinetic rock energy during rockfall. b Jump height of rocks during rockfall

250,660 kJ, respectively (Fig. 10a). Mean and maximum velocity of rocks is found to be 31 m/s and 80 m/s, respectively (Fig. 10b). Minimum and maximum jump height of 0.26 m and 118 m, respectively is obtained for 32 m3 rocks (Fig. 10c). Total number of rocks passing through a particular trajectory is shown in Fig. 10d, and maximum reach probability of rocks from source to destination point (Parvati River) is shown in Fig. 10e. Total number of rocks deposited after simulation is shown in Fig. 10f. As it is clear from Fig. 10f that maximum rocks reach Parvati River, but some rocks are deposited along the way. 2D profile of 32 m3 rock is shown in Fig. 9, this profile is one among the most probable trajectory of rockfalls for 32 m3 rocks. Variation of kinetic energy and jump height along a trajectory is also shown in Fig. 9, Maximum kinetic energy and jump height is attained at ~700 m from the source zone. The results of the RAMMS: ROCKFALL analysis returns to be an excellent preliminary tool to assess the rockfall hazard of an area. Since, we are using 12.5 m resolution DEM which comes under optimal category for estimating rockfall trajectory in RAMMS environment (Bühler et al. 2011; Christen et al. 2012). However, better results of rockfall trajectories could be obtained if high resolution DEM (~1 m) is available (Bühler et al. 2011). RAMMS simulation results considering 6.4 and 32 m3 rocks (Figs. 7 and 10) have revealed a noticeable contrast for kinetic energy results. As it is clear from Figs. 7a and 10a that maximum kinetic energy of 7263 and 250,660 kJ is obtained for 6.4 and 32 m3 rocks, respectively. 32 m3 rocks deliver very high amount of kinetic energy which cannot be stopped by any engineering design. Jump height of more than 80 m (Figs. 7c and 10c) is observed in both 6.4 and 32 m3 rocks, this much jump height is extremely high, and building any barrier to stop these rocks is an impossible task especially under highly steep and rugged terrain. Figures 7e and 10e show that most probable path followed by 6.4 and 32 m3 rocks during RAMMS simulations is almost same which is very helpful is constraining the zone of rockfall. A very steep slope

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Fig. 9 2D profile of rockfall trajectory for 32 m3 rock, a Kinetic rock energy during rockfall. b Jump height of rocks during rockfall

(angle >85 degrees) was observed in the field (Fig. 6) which supports the rocks to freefall and increase in kinetic energy and velocity for rocks is observed at this point (Figs. 7 and 10). It was found that two most probable path were followed by rocks during RAMMS simulations, first along the left side of small ridge (Fig. 6), second along right side of the small ridge (Fig. 6). It is observed that rocks following first path shows branching into two paths along a small ridge as shown in Fig. 6. In general, it can be concluded that once rock starts to move from source zone and reaches approx. 700 m distance from source, it reaches a very steep point (Fig. 6), after this point rocks either move towards Manikaran valley causing damage to people and infrastructure or they shift towards the other side which meets Parvati River through Cho Nala (Fig. 6). In August 2015, rockfall happened from right side of secondary ridge (see Fig. 11b), i.e. following second path as mentioned before and rocks did not branch towards the Cho Nala after crossing steep point (Fig. 6) causing damage to Gurudwara Sahib in Manikaran and killed 10 people and injured more than 15 people (Fig. 11). Using RAMMS results and experience from field mapping, a rockfall hazard map of Manikaran area is developed as shown in Fig. 12. Rockfall hazard map zone out the Manikaran and surrounding area into three categories as unsafe (red circle), moderately safe (orange circle) and safe (green circle) areas (Fig. 12). Unsafe, moderately safe and safe areas were decided on the basis of past rockfall events and RAMMS simulations. RAMMS results of most probable trajectories (Figs. 7e and 10e) are used as a base for demarcating safe, moderately safe and unsafe areas. Rockfall hazard map (Fig. 12) can be used by District Disaster Management Authority (DDMA), Kullu, to develop an evacuation plan for Manikaran area and to mitigate any future rockfall hazard.

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Fig. 10 Rockfall scenario with 32m3 volume of rocks a Kinetic rock energy for all simulations is shown (in kJ). b Velocity of rocks is represented in (m/s) for all simulations. c Jump height attained by all rocks during simulations is shown in (m). d Total number of rocks during trajectory simulations is shown. e Maximum reach probability contours are shown for all simulated rocks, and higher value contours show the area which was followed by maximum rocks during simulations. f Total number of rocks deposited is shown, although maximum rocks reach the base, i.e. Parvati River, but some rocks could not reach the bottom and are deposited along the path

4 Conclusions The main findings of this study are pointed as: 1.

Maximum kinetic energy and maximum jump height is attained at a distance of ~700 m from the rockfall source by both 6.4 and 32 m3 (Figs. 8 and 9).

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Fig. 11 a Source area for the rockfall event that happened on 15 August 2015. Also critically stable boulder (red colour circle) is shown. b Large view of the secondary ridge (as shown in Fig. 2) is shown, left side of ridge shows Gargi Village along with large boulder that lies in Gargi Village for 400 years. c Figure shows critically stable boulders (yellow arrows) lying on right side of secondary ridge

2.

3.

4.

5.

6.

When rock volume is 32 m3 , maximum kinetic energy and jump height continues to increase towards the Manikaran town as compare to 6.4 m3 boulders (Fig. 9). Similar size (32 m3 ) boulder had struck the Manikaran Gurudwara as shown in Fig. 11. After reaching distance of 700 m, rocks either move towards Manikaran valley causing damage to people and infrastructure or they shift towards the other side which meets Parvati River through Cho Nala (Fig. 6). Once rockfall starts, it take approx. 8.2 s for 32 m3 rocks to reach the base, i.e. Parvati river. Jump height of rocks is very high, and maximum jump height of > 80 m is also observed which cannot be stopped by building any engineering design (Fig. 10c). RAMMS results of most probable trajectories (Figs. 7e and 10e) are used for developing a rockfall hazard map of Manikaran area by demarcating unsafe, moderately safe and safe areas (Fig. 12). Development of an evacuation map and committee in collaboration with DDMA, Kullu, is proposed. The main work of evacuation committee members will be to help and evacuate people during future rockfall activity.

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Fig. 12 Rockfall hazard map of Manikaran and surrounding area, red circle indicate unsafe zone, orange circle indicates moderately safe zone, and green colour indicates safe zone

7.

As a remedial measure, we suggested through this study, development of an early warning system (EWS). EWS will predict any future rockfall event before few days of the actual event depending upon the sensors used in EWS.

Acknowledgements We are thankful to WSL Institute for Snow and Avalanche Research SLF, Davos, Birmensdorf, Switzerland, for providing the RAMMS: ROCKFALL module under the licence for academic research. We are thankful to the Chairperson, Department of Geology, Panjab University, Chandigarh, for logistic support for the field work. The study is a part of early warning system project for Manikaran supported by Ministry of Science and Technology, DST project by Govt. of India, project no. NGP/LS/MaheshThakur/TPN-34319/2019(C). We thank Mr. Abhishek Kralia, Mr. Gurwinder Singh Abhaypal and Ms. Samriddhi Sharma for their assistance during the field work.

References Agliardi F, Crosta GB (2003) High resolution three-dimensional numerical modeling of rockfalls. Int J Rock Mech Min Sci 40:455–471. https://doi.org/10.1016/S1365-1609(03)00021-2

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Basson FRP (2012) Rigid body dynamics for rock fall trajectory simulation. American Rock Mechanics Association. In: Geomechanics symposium held in Chicago, USA, pp 12–267 Bhargava ON, Srikantia SV (1998) Geology of Himachal Pradesh. Geological Society of India, Bangalore, India, pp 51–52 Bühler Y, Christen M, Kowalski J, Bartelt P (2011) Sensitivity of snow avalanche simulations to digital elevation model quality and resolution. Ann Glaciol 52(58):72–80 Borella J, Quigley M, Vick L (2016) Anthropocene rockfalls travel farther than prehistoric predecessors. Sci Adv 2:e1600969. https://doi.org/10.1126/sciadv.1600969 Bourrier F, Dorren L, Nicot F, Berger F, Darve F (2009) Towards objective rockfall trajectory simulation using a stochastic impact model. Geomorphology 110:68–79 Christen M, Bartelt P, Gruber U (2007) RAMMS—a modelling system for snow avalanches, debris flows and rockfalls based on IDL. PFG Photogrammetrie–Fernerkundung–Geoinformation 4: 289–292 Christen M, Bühler Y, Bartelt P, Leine R, Glover J, Schweizer A, McArdell B, Gerber W, Deubelbeiss Y, Feistl T, Volkwein A (2012) Numerical simulation tool “RAMMS” for gravitational natural hazards. In: Proceedings of the 12th interpraevent congress. Grenoble, France, pp 77–86 Cruden DM, Varnes DJ (1996) Chapter 3-landslide types and processes. In: Landslides: investigation and mitigation. Transportation Research Board, pp 36–75 Dorren LKA (2003) A review of rockfall mechanics and modelling approaches. Prog Phys Geogr 27:69–87 Dorren LKA, Maier B, Putters US, Seijmonsbergen AC (2004) Combining field and modelling techniques to assess rockfall dynamics on a protection forest hillslope in the European Alps. Geomorphology 57:151–167 Dorren LKA, Maier B, Putters US (2006) Real-size experiments and 3-D simulation of rockfall on forested and non-forested slopes. Nat Hazards Earth Syst Sci 6:145–153 Evans SG, Hungr O (1993) The assessment of rockfall hazard at the base of talus slopes. Can Geotech J 30:620–636 Geological Survey of India 1991. Geothermal Atlas of India (Special Publication No. 19) Guzzetti F, Crosta G, Detti R, Agliardi F (2002) STONE: a computer program for the three dimensional simulation of rock-falls. Comput Geosci 28:1079–1093 Guzzetti F, Reichenbach P, Wieczorek GF (2003) Rockfall hazard and risk assessment in the Yosemite Valley, California, USA. Nat Hazards Earth Syst Sci 3:491–503 Hungr O, Evans S, Hazzard J (1999) Magnitude and frequency of rockfalls and rock slides along the main transportation corridors of south-western British Columbia. Can Geotech J 36:224–238 Lan H, Martin D, Lim C (2007) RockFall analyst: a GIS extension for three-dimensional and spatially distributed rockfall hazard modeling. Comput Geosci 33:262–279 Leine RI, Schweizer A, Christen M, Glover J, Bartelt P, Gerber W (2013) Simulation of rockfall trajectories with consideration of rock shape. Multibody SysDyn 32(2):241–271 Mohr H (2015) Geologischer Bericht Sturzmodellierung Mit ROFMOD4.2, Büro für Technische Geologie AG, Schweiz Porter SC, Orombelli G (1981) Alpine rockfall hazards: Recognition and dating of rockfall deposits in the western Italian Alps lead to an understanding of the potential hazards of giant rockfalls in mountainous regions. Am Sci 69:67–75 Siddique T, Pradhan S, Vishal V (2019). Rockfall: a specific case of landslide. In: Landslides: theory, practice and modelling, advances in natural and technological hazards research, vol 50. https://doi.org/10.1007/978-3-319-77377-3_4 Singh J, Thakur M (2019) Landslide stability assessment along Panchkula–Morni road, Nahan salient, NW Himalaya, India. J Earth Syst Sci 128(6): 0–15. https://doi.org/10.1007/s12040-0191181-y Sinha KA, Misra DK, Paul SK (1997) Geology and tectonic features of Kulu and Spiti-Lahaul sector of NW Himalaya. Hmalayan Geol 18:1–16 Spang R, Sönser T (1995) Optimized rockfall protection by ‘rockfall’. In: Proceedings of the 8th international conference on rock mechanics, A.A. Balkema, Tokyo, Rotterdam, pp 1233–1242

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Stock GM, Luco N, Collins BD, Harp EL, Reichenbach P, Frankel KL (2014) Quantitative rockfall hazard and risk assessment for Yosemite Valley, Yosemite National Park, California, US geological survey scientific investigations report 2014–5129, US geological survey, Reston, VA, p 52. https://doi.org/10.3133/sir20145129 Turner AK, Duffy JD (2012) Modeling and prediction of rockfall. In: Turner AK, Schuster RL (eds) Rockfall: characterization and control. Transportation Research Board, National Research Council, Washington, DC, pp 334–406 User manual, RAMMS: Rockfall module, WSL Institute for Snow and Avalanche Research SLF, Davos, Birmensdorf, Switzerland. Available online: https://ramms.slf.ch/ramms/index.php?opt ion=com_content&view=article&id=66&itmid=93. Accessed 20 Dec 2019 Varnes DJ (1978) Slope movement types and processes. In: Schuster RL, Krizek RL (eds) Landslides: analysis and control, special report 176, Transportation Research Board, National Research Council, Washington, pp 11–33 Verma AK, Sardana S, Sharma P, Dinpuia L, Singh TN (2019) Investigation of rockfall-prone road cut slope near Lengpui Airport, Mizoram, India. Int J Rock Mech Geotech Eng 11:146–158 Wieczorek GF (2002) Catastrophic rockfalls and rockslides in the Sierra Nevada, USA. Geol Soc Am Rev Eng Geol 15:1–26 Wieczorek GF, Stock GM, Reichenbach P, Snyder JB, Borchers JW, Godt JW (2008) Investigation and hazard assessment of the 2003 and 2007 Staircase Falls rock falls, Yosemite National Park, California, USA. Nat Hazards Earth Syst Sci 8:421–432. https://doi.org/10.5194/nhess-8-4212008

Surface Displacement Analysis of Road-Cut Slopes in the Vicinity of Koteshwar Area, Uttarakhand, India Swati Sharma, Har Amrit Singh Sandhu, and Manoj K. Arora

Abstract It is essential to assess the gradual slope deformations which can be used to interpret the temporal changes in any mountainous terrain provided the magnitude and orientation of the downward displacements are known. In this study, the slope surface displacements of two vulnerable and high priority slopes at 12.55 km and 13.85 km from a reference point called Zero bridge along the Tehri-Koteshwar transportation route in the vicinity of Koteshwar area, Uttarakhand, India, were analysed. The study was carried out using orthorectified linear imaging self-scanning (LISS IV) optical remote sensing data from the year 2012–2017 for estimating the surface displacements based on the pixel shift with the help of COSI-CORR (co-registration of optically sensed images and correlation) tool in ENVI (Exelis visual information solutions) platform. The slopes considered in this study were mapped on the field and were demarcated on the high-resolution optical imageries, i.e. LISS IVmx (5.8 m resolution). Initially, the various optical images (slave images) ranging from the year 2012–2017 were co-registered using a master image (geo-referenced). These co-registered imageries were run for correlation using five sets of image pair (i.e. the year 2012–2013, 2013–2014, 2014–2015, 2015–2016 and 2016–2017) which reflected the pixel shifts in the images with respect to the each other. The pixel shifts in all the image pairs were modelled using ERDAS imagine software to calculate the slope surface displacements, and a similar model was run for all the image pairs. This resulted in various values of slope surface displacements over the time period of five years. The resultant displacement rasters were then analysed for displacement vector fields which indicated the direction (bearing) of material movements. The average pixel shift trends for various image pairs have indicated that the slope surface displacements increased between the year 2012–2015 whereas, after 2015, the correlation indicated a decrease in the surface displacements at the study sites. This study has highlighted the use of optical remote sensing data in analysing the gradual slope movements and their orientation. Such a study can be utilized S. Sharma (B) · H. A. Singh Sandhu Department of Civil Engineering, Punjab Engineering College, Chandigarh, India M. K. Arora BML Munjal University, Gurgaon, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_5

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to estimate slope deformation annually. This may help researchers/stakeholders to understand the planning/managing mitigation measures on the required locations of vulnerable slopes. Keywords COSI-CORR · LISS IV data · Slope surface displacements

1 Introduction Monitoring the surface displacements is essential to determine the amount of slope movement and its orientation to quantify the future hazard on the surrounding elements. This aspect can involve various methods such as field (in-situ) determination, remote sensing-based monitoring techniques and ground-based remote monitoring (Rai et al. 2018). All these monitoring systems are dependent on a cloudless line of sight. Slope movements in any hilly areas can be a complex phenomenon to determine based on the field observations of many disconnected surface displacement locations with fewer periodical variations (Guzzetti et al. 2012; Peppa et al. 2017) as it costs higher plus the human errors are inevitable. Therefore, remote sensingbased data which can be acquired as cyclic pairs (temporal period) over years with high resolutions for even smaller spatial tiles have proved much efficient for monitoring surface displacements (Leprince et al. 2007; Ayoub et al. 2009). Use of the optical remote sensing data in monitoring the slow surface displacements where the changes in an outcropare evident through the removal of regolith cover has been used efficiently (Martha et al. 2012; Stumpf et al. 2014, 2017). The surface velocities of landslides/glacier can also be estimated through synthetic aperture radar (SAR) interferometry (Zhao et al. 2012). The SAR interferometry analogizes two datasets of a different time but of similar location which makes it efficient in reflecting the periodical variations over a slope surface. But the SAR data shows limitations (image distortion, shadowing effect especially in the mountainous terrains) with landslides/glaciers moving at speed of less than 1 m/year. The method of optical image correlation using observation of pixel/sub-pixel annual shift has proved as a reliable method to compare the object displacement over a period (Leprince et al. 2008; Tiwari et al. 2014) where the underlying errors can be eliminated through precise ortho-rectification, co-registration, de-striping, etc.). There are various methods (algorithms) that have been developed to correlate optical imagery for tracking the movements annually (Scambos et al. 1992; Kaab et al. 2002; Kaufmann and Ladstadter 2003; Leprince et al. 2008; Stumpf et al. 2014; Lacroix et al. 2015), and some of the algorithms can be used under the open-source softwares such as IMCORR and COSI-CORR which concentrate on the ultimate objective of determining surface displacements using multi-temporal image pairs by rectifying the errors first (Ayoub et al. 2009). Availability of a large data archive (multi-temporal data) these days helps in the use of such algorithms provided the results are filtered precisely. This study uses the application of ENVI integrated algorithm, i.e. COSI-CORR with a sub-pixel

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accuracy potential (Ayoub et al. 2017) which provides many filters to maximize the error removal (noise in the resultant data) under one platform (Leprince et al. 2007; Ayoub et al. 2009). COSI-CORR has also been used to detect the spatio-temporal landslide motion (Peppa et al. 2016, 2017; Ding et al. 2016; Lacroix et al. 2019) using SAR, ASTER and LANDSAT datasets. In this study, we have used the LISS IV-mx optical images (5.8 m resolution) from the year 2012 to 2017 to detect surface displacements for two prominent road-cut slopes [chainage 12.55 km (slope A) and chainage 13.85 km (Slope B)] (Fig. 2) near the Koteshwar Dam (reservoir rim area), Uttarakhand, India. The data rectification, processing and analysis have been carried out using ERDAS IMAGINE, COSICORR and ARC GIS softwares.

2 Study Area and Data Used The Koteshwar reservoir rim area is located in the vicinity of the Bhagirathi River (a tributary of Ganga) running from north (Tehri Dam’s reservoir) to the south (Koteshwar Dam) (Fig. 1). The stretch of interest, i.e. the slopes on the right bank of the Koteshwar reservoir rim area, falls under the Lesser Himalaya of the State of Uttarakhand, India. The construction of the Koteshwar Dam (down slope) and the sequential development of a reservoir (20 km long) have altered the natural slope morphology at several locations owing to the development of settlements and transportation routes. Five pairs of orthorectified optical image datasets (LISS IV-mx) were used for correlation as given in Table 1 to interpret the changes in the road-cut slopes along the reservoir rim region.

3 Methodology In this study, the slope displacements for two slopes “A” and “B” near the Koteshwar Dam area (Fig. 2) were estimated between the years 2012 and 2017 using orthorectified LISS IV-mx data which was firstly imported in ARCGIS for enhancement. The raw satellite imageries were also compared with one another using the multi-layer viewer capability of ERDAS IMAGINE software to view any large data shifts among the multi-temporal image pairs, so that the correlation quality is not compromised. The optical imageries were correlated to detect sub-pixel shift over the years using the method devised by Leprince et al. (2007). For the correlation process, COSI-CORR software was used based on the open-access user manual by Ayoub et al. (2009) (http://www.tectonics.caltech.edu/). The method uses moving window iterations based on the step size for frequency correlation as per the user’s demand. In this study, a 32 × 32 pixel window with a step size of two was used to correlate the optical images from various years’ dataset with respect to each other. This correlation

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Fig. 1 Study area location map

process resulted into pixel-wise horizontal displacement among the image pairs (pre and post) in form of three display bands, i.e. E–W (east–west), N–S (north–south) and SNR (signal-to-noise ratio), at 10 m resolution as step size of two was chosen. The SNR display band reflects the pixels with noise ratio >0.9 or 50 < 100 (4–8flr) >100 < (9–12flr)

5.9. Window support type: Punched Glass & Metal framing Ribbon Point supported

5.10. Type and organization of the resisting system: R.C. wall

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Tie-beam/ties at all level

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Solid infill + frame

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Good interlocking of walls

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No tie-beams, bad interlocking

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5.11. Quality of Resisting System: 5.12. Floor systems: Staggered floor Well conn Badly conn

5.13. Elevation shape: Porch/open gallery area Soft story, ground level

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5.14. Roof System: Tie-beam at roof level. Steel tie at roof level Roof perimeter

References Agrawal SK, Chourasia A (2007) Methodology for seismic vulnerability assessment of building stock in mega cities. In: A workshop on microzonation interline Publishing, Bangalore, pp 182– 190 ATC-21 (1988) Rapid visual screening of buildings for potential seismic hazards: a handbook. Applied Technology Council, Redwood city, CA, USA ATC-21-1 (1988) Rapid visual screening of buildings for potential seismic hazards: supporting documentation. Applied Technology Council, Redwood City, CA, USA Census of India (2011) District census handbook Dehradun, village and town directory. Directorate of Census Operations, Uttarakhand, Census of India, Dehradun Haldar P, Singh Y, Lang DH, Paul DK (2010) IVARA—a tool for seismic vulnerability and risk assessment of Indian housing. In Kumar A, Sharma M (eds) Symposium On Earthquake Engineering, vol 14, pp 1405–1415 Joshi GC (2019) Seismic vulnerability of lifeline buildings in Himalayan Province of Uttarakhand in India. Int J Dis Risk Reduction 37:101168 Rai PK, Kumra VK (2011) Role of geoinformatics in urban planning. J Sci Res 55:11–24 Rai PK, Mishra VN, Raju KNP (2018) Methodology and application of remote sensing & GIS in environmental mapping & monitoring. NGJI 64(1 & 2):266–276 Roy D (2007) Urban seismic risk assessment in Dehradun City using remote sensing and geoinformation techniques Soumendu C, Khan A (2014) Urban geomorphology of Dehradun, India. LAP Lambert Academic, Dehradun Sur U, Sokhi SB (2005) Vulnerability assessment of building and population related to earthquake hazard in Deharadun City using remote sensing and geoinformation techniques. HUSAD, IIRS Sur U, Singh P, Rai PK (2021) Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India is accepted in environment, development and sustainability. Springer Tilling JK (1996) The dynamic earth: the story of plate tectonics. USGS, Denver Udhoji SG (ed) (2000) Jabalpur Earthquake, 22 May, 1997: a geoscientific study. Geological Survey of India

Hydrological Disasters

Flood Mapping and Vulnerability Assessment Using Geospatial Techniques: A Case Study of Lower Periyar River Basin, Kerala S. Suresh Kumar and K. Jayarajan

Abstract The flood can strike anywhere without warning. Flooding is a very common environmental hazard. Due to this natural disaster, infrastructure damages and human life losses occur every year. In August 2018, because of a low-pressure framework close to the start of the month, the Indian territory of Kerala got an allinclusive time of substantial precipitation, joined by storm wretchedness a few days after the fact. About 400 people were killed by the ensuing floods and a million more were displaced. Here, delineation of the spatial extent of flooding is of great importance for the dynamic monitoring of flood evolution and corresponding emergency strategies. An interest for satellite-based immersion planning in close to constant has been shown by late flood occasions. Simulating and forecasting the magnitude of floods is critical for risk mitigation. Flood mapping is a process used during the flood for damage assessment and risk evaluation and to assist rescues. The purpose of the analysis is to identify the magnitude of the flood, the damage to the built environment, by mapping the vulnerable areas of the flood, based on different analytical techniques. To classify the affected areas based on variations between the two images, the applied approach is to examine and compare two images (one before and one after the disaster). This study shows that SAR data and Landsat images alongside GIS can be utilized adequately to map, track, and evaluate the distribution of floodwater in flood-prone areas of the lower reaches of the Periyar river basin. Keywords Flood extent · Flood mapping · Risk assessment · GIS · Synthetic aperture radar (SAR)

S. S. Kumar (B) HSST (Higher Grade), Department of Education, Govt. HSS Vayala, Anchal, Kollam, Kerala, India K. Jayarajan Department of Geography, Govt. College, Chittur, Kerala, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_7

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1 Introduction Floods represent the most generous natural disaster that may occur at different levels, having an impact on environment, ecology, agriculture, and infrastructure (Dadhich et al. 2019). Rainfall extremes cause flooding. Increased surface run-off and rainfall are greater than the outgoing discharge potential which cause the level of water to increase, leading to area submergence, landslide, debris flow, water-related health disaster, etc (Ray et al. 2019). By damaging ecosystems, disrupting lives, harming infrastructure, etc., floods impact the environment and community. Remote sensing and, in particular, synthetic aperture radar (SAR) sensors are ideal for data gaining under conditions of dense precipitation and provide quick assessment and longterm flood zone monitoring. Due to specular reflection, the synthetic aperture radar (SAR) system is sensitive to water and capable of acquiring images both day and night, giving it a characteristic specification. Flood mapping gives an idea about the change in the existing scenario and possible future area likely to be affected by floods. Remote sensing and, in particular, SAR sensors are ideal for flood cloud conditions and quick assessment and long-term flood area monitoring (Megha et al. 2019). Due to specular reflection, SAR sensors are sensitive to moisture and are capable of acquiring day and night imagery. In rapid disaster response preparation and management, the rapid generation of flood extent maps from SAR data provides access to useful data and in the management of disaster situations, monitoring of impacted areas by flooding and damage to agriculture and infrastructure evaluation is an important activity. The definition of vulnerability reflects the multidimensionality of calamities by concentrating on the totality of experiences in a particular social situation, which is a circumstance that creates a catastrophe in conjunction with environmental forces. In knowing the true degree of risk, exposure to natural hazards is an integral factor. Vulnerability factors can be separated into three main areas physical vulnerability, social vulnerability, and economic vulnerability (Rakib et al. 2018). In the 1990s, in the sense of disasters, the term ‘vulnerability’ was first used, but vulnerability quantification is a complex one, there are four dimensions of vulnerability evaluation, i.e., physical, economic, social, and environmental (Ahmed and Kranthi 2018). Among them, in this article, only the physical dimension of vulnerability is discussed. For future references and scheduling, mapping the flood is very critical. In Kerala, there was a big flood in 1924, almost 100 years ago. We cannot predict future events and thus a flood map becomes essential for future references. Kerala encountered a strongly elevated level of precipitation from June 1, 2018, to Aug 19, 2018, bringing about unusual flooding in 13 districts of Kerala (Sankar 2018). The rainfall resulting from this was 42% above the expected value (G.O.I. Centre water commission 2018). During the monsoon months of June, July, and August, June faced 15% more than average rainfall in Kerala, July 18% more and 164% more from August 1 to August 19. Intensive rain began on the 14th of August and ended on the 19th of August, resulting in a flood that affected 13 of the 14 districts. 5.4 million people were

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affected, 1.4 million people left their home and displaced, and saddening is 433 people lost their lives during the destructive flood (Joy et al. 2019). This study aims to demonstrate the adequacy of Sentinel-1 SAR to estimate the extent of flooding and built-up areas at risk due to flooding based on data availability. In this respect, data on the rainfall rate was used to know the severity and length of the study area’s rainfall rate. Based on this, Sentinel-1 SAR data acquired on August 21, 2018, pertaining heavy rainfall period were used to estimate the coverage of the flooding around Lower reaches of Periyar River basin. This was followed by extraction of the built-up land which was affected by inundation during the flood using Open Street Map (OSM). This exercise is an operational method for providing critical inputs for evaluating and mitigating flood risk. This study aims to examine the vulnerability of floods along the Lower Periyar River basin to provide more options for flood risk management and control.

2 Materials and Methods 2.1 Site Description The study area extend from 9° 49 35 N to 10° 17 07 N latitude and 76° 08 34 E to 77° 33 32 E longitude (Fig. 1). The longest river (244 km) in Kerala is Periyar with a drainage area of 5398 km2 (Chattopadhyay 2007; Chattopadhyay and Sureshkumar 2013) of which 114 square kilometers are located in Tamil Nadu. Periyar, being an eighth order stream, originates at an elevation of 1560 m from

Fig. 1 Lower reaches of Periyar River basin

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the high hills of the Western Ghats toward the southeast corner of the basin and initially follows a northward course. Major reservoirs like Periyar Lake, Idukki, Anairangal, Idamalayar, Mattupetty, and Sethuparvathipuram are within the basin area. Periyar River debouches in the Lakshadweep sea through Vembanad lake in the West. Mathirapuzha and Idamalayar are two among several important tributaries of the river. Drainage pattern is primarily dendritic to trellis and parallel in some places. The study area occupies 1282.74 km2 . There are two different forms of topography in the river basin, the mountainous upper region, and the flat coastal regions. The study area is very much vulnerable to flooding because of the comparatively lowlying areas. The vegetation of this area is a combination of coconut, rubber, tapioca, paddy, marshy lands, and grass.

2.2 Data and Methods The extensive list of study area data, needed for informed interpretations and decision making, is required for mapping of flood and vulnerability assessment.

2.3 Datasets SAR Data: We use a wide collection of SAR images acquired by land observation with progressive scans acquisition mode C-band Sentinel-1A and B satellites and revisit the period six days before and during the flood event to map and compare the flood extent. Using a collection of SAR images acquired before the flood event, a flood-free reference amplitude image is also produced (Cian et al. 2018; Sherpa et al. 2020). Apart from its all-weather ability, one of the key advantages of using SAR imagery is its ability to discriminate against water from other categories. Radio detection and ranging (RADAR) synthetic aperture is a strong active remote sensing technology used for many applications, especially in flood monitoring. RADAR is an active device that illuminates the Earth’s surface and, thus, without sunlight, images can be obtained by day during any lighting conditions or at night. These images are also not impacted by cloud cover, fog, or smoke as these covers can be penetrated by the RADAR signals (Carreño Conde and Matta 2019). Water characteristics function as a surface mirror, their responses are low (low coefficient of backscatter in SAR image) and thus appears as a dark area. Owing to the surface roughness, the landmass, for its part, gives a large amount of radar energy, and this produces high contrast between surfaces: soil and water. The satellite images were acquired sequentially by satellite Sentinel 1A, before and after the flood took place. Sentinel 1 SAR (VV) Polarization data, Vertically Transmitted Vertically Receive was the satellite dataset used in the analysis. Therefore, Sentinel-1 SAR data acquired on August 21, 2018, of this event are selected based on the availability.

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Landsat: Landsat-8 OLI imagery acquired on February 3 2017, data were downloaded from the open-source repository of USGS’s EarthExplorer in GeoTIFF format. During the pre-flooded time, Landsat-8 OLI data from the study area were analyzed using geospatial techniques, particularly for calculating the normalized difference water index (NDWI) using the spatial analyst tool. Open Street Map (OSM) data: OSM has provided the cheapest source of geographic information from a web service. They have been imported in QGIS through the QuickOSM plugin (Pasi et al. 2015; Suthakaran et al. 2018).

2.4 Method The workflow of the current investigation has four components: (1) delineating extent of non-flooded surface in and around the major flood zone; (2) extracting the landuse category from the OSM data; (3) extracting the buildings and transport network from OSM web service; (4) importing the above three layers into a vector GIS environment and performing spatial analysis to obtain relevant results. A.

Flood Extent Mapping.

Synthetic aperture radar (SAR) data has played an important role for decades, enabling flood extent maps to be extracted during disasters. One of the main barriers to using optical remote sensing in flood control is the predominance of cloud cover during the flooding season (Mohanty et al. 2019). The approach suggested is based on variations in the physical relationship between standing water and rough ground. In both polarizations, the returned signal intensity to the side-looking antenna is negligible as long as the water surface is still, due to the specular reflection over standing water. The rough land surface, in contrast, has a huge amount of signal return to the radar (Jo et al. 2018). Delineation of the non-flooded area is particularly important because these areas can serve as a temporary shelter for the nearby settlements. Such data is important for the identification of settlements that are extremely vulnerable to flooding. Settlements having no immediate access to dry regions would be considered highly vulnerable to flooding (Sanyal and Lu 2004). The method is based on the statistical analysis of SAR images: one containing only images before the flood, i.e., reference images, and another one containing reference images and images of the event. The images were preprocessed and analyzed using Arc GIS software, ASF Custom Toolbox developed by Alaska Satellite Facility. It is accompanied by the generation of the backscatter coefficient histogram, and it

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has been used to set a value that most accurately represents the threshold between non-water and water characteristics. Finally, the resulting binary raster data has been transformed for analysis into a vector file. For damage estimation, these vector datasets are superimposed on the built-up region (Armenakis et al. 2017).

2.5 Processing Sentinel-1 Image In this study, the Sentinel-1A data acquired in August 21, 2018, during the flood event was used to develop flood inundation map. ASF tools that used to perform geoprocessing tasks useful for working with synthetic aperture radar (SAR) data. The analyzing procedures are including unzip compressed files, scale conversion method, reclassify RTC, and calculate log difference. Unzip compressed files Unzip Files Tool assists in file management when downloading.zip files from Alaskan Satellite Facility. This tool offers a quick and easy way to extract multiple zip files to the desired destination folder. It extracts the contents of the downloaded zip files to the desired destination folder and then deletes the original zip files from the download folder. Scale conversion Scale conversion tool converts SAR imagery from one scale into another. The scales most commonly used with SAR data are power, amplitude, and dB. The output is a new raster dataset, with the pixel values in the designated output scale. Reclassify RTC A reclassified raster based on a threshold value is generated by the reclassify RTC tool. It is designed to use for identifying water, which has very low radiometric returns when the surface of the water is calm. Water is often best delineated using dB values, which offers better differentiation between very dark pixels and the rest of the image. VV polarization often offers the best value delineation, but is sensitive to the wind; on windy days, the radar backscatter is higher, and water may be difficult to differentiate from other surfaces. VH is less susceptible to changes in surface roughness, so over various wind conditions, the values would be more stable. Consider thresholding all available polarizations and combining the outputs, or selecting the polarization that gives the best results for a specific application. This tool produces a raster that contains only those pixels below a threshold value specified by the user and is intended to identify water pixels. (Fig. 2).

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Fig. 2 Reclassified raster showing water pixels

Calculate Log difference Log difference tool calculates the log difference between the two rasters. It was designed to work with a 2-point time series of radiometric terrain corrected (RTC) SAR products in amplitude scale, but can be used to look for differences between any two rasters.

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The four tools mentioned above can be effectively utilized to identify floodaffected areas. Flood-affected areas were identified using the VV Polarization in the SAR image which offers the best result. The black pixels in the processed SAR image represent water and, the gray and white color indicate rough land surfaces. B.

Delineate open water features

Landsat-8 OLI has been used to delineate and refine the characteristics of open water. By implementing the following proven geospatial procedures, open water features of the study area were extracted. The measurement of the NDWI (Normalized Difference Water Index) was used: (Green-NIR)/ (Green + NIR), where band 3 and band 5 respectively corresponds to Green and NIR (Near Infra-Red). For this, NIR and green channels of Landsat-8 OLI were used to delineate and enhance open water features. NDWI results computed from Landsat-8 OLI (February 03, 2017) detected the region of the water bodies. C.

Extract built-up areas and transport network

Detailed data collection of the built-up area is essential for conducting a vulnerability assessment. For this purpose, various types of information have been collected using the web services of OSM. The information needed for this study was collected using the Quick OSM plugin of QGIS software. OSM data provides information about residential buildings, public facilities (houses, police stations, schools and universities), commercial buildings; cultural heritage sites (museums, theaters, historic buildings). Various kinds of transport network data have also been collected using OSM.

3 Results and Discussion The susceptibility to flooding is not limited to existing structures but also includes the basin’s numerous landuse patterns. Particularly, vulnerable buildings such as hospitals, schools, university, houses, parking, industrial, college, as well as transportation networks are taken into account with a corresponding vulnerability for potential damages. The land transportation networks include highway service, highway secondary, highway residential, highway construction, highway tertiary, highway primary, highway unclassified, road, footway, and trunk. OSM web service helps to acquire various spatial information such as residential buildings, public facilities

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Fig. 3 Flooded areas in radar image

(schools, hospitals, police stations, and universities), commercial activities (industrial and parking), and transportation networks. An overlying flood inundation map was used to carry out the damage analysis over the land cover map, and the flooded area was estimated for each class category. It has been observed that the majority of the area affected was settlement with mixed tree crops and farmland. Flood-affected areas were extracted from the radar image (Fig. 3). An area of 28 sq km of the study area was submerged underwater. Frequent floods are affecting these areas the most. Analyzing the landuse in the Lower Periyar area, 2842.40 ha area was found to be flooded. Of these, the settlement with mixed tree crop category

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Table 1 Flood-inundated landuse categories Landuse

Area (ha)

Settlement with mixed tree crops

2429.70

Aquaculture Commercial Farmland

2.16 0.03 297.26

Forest

0.25

Grass

98.73

Greenfield

3.11

Industrial

5.98

Landfill

1.81

Military

0.21

Quarry

0.89

Railway

0.80

Recreation ground

0.02

Residential

0.27

Village green

1.18

Total

2842.40

had the highest flood threat. It is 2429 ha area of settlement with mixed tree crops submerged underwater (Table 1). 297 ha of farmland was flooded. Elsewhere, the grass land 98 ha area and the industrial area 5.98 ha area were inundated (Fig. 4). These flood-prone areas need to be effectively maintained. Of course, such flood threats will continue in the future. The highway unclassified road network was the most flooded when it came to analyzing flood-affected transportation systems. It was submerged in several places for a distance of about 12 km. The highest level of flooding was on the highway residential road. It flooded 151 places at a distance of about 10 km (Table 2). Through such analyses, it is possible to understand the extent of the flood threat and intensity in the region. When submerged buildings and land transport networks were identified, 21 large and small buildings were completely and partially submerged, and 32 km of transport networks were completely submerged. Considering the transportation network alone, 357 places were flooded in 2018 (Fig. 5). OSM data was used for such findings.

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Fig. 4 Landuse categories submerged under water

In the study area, eleven houses, three school buildings, two colleges, and a hospital were flooded in 2018 (Table 3). Such buildings will be flooded again in the future. This type of study shows that it is possible to identify which buildings are most at risk of flooding. Analysis of the landuse, transport network, and buildings in the lower course region of the Periyar River has revealed the intensity of flooding in the area. It is a fact that mitigation measures can be taken only if such flood-prone areas are identified.

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Table 2 Transportation network submerged under water Land transportation network (OSM) Railway Highway trunk Highway service

Flooded (Length in m) 1560.39 54.63 2380.9

Total number of segments flooded 11 1 10

Highway secondary

634.69

15

Highway residential

10,590.95

151

409.03

1

Highway tertiary

2379.81

36

Highway primary

289.63

7

12,628.29

107

666.77

11

41.55

1

600.51

6

32,237.15

357

Highway construction

Highway unclassified Road Footway Trunk Total

Frequent floods in Kerala will put low-lying areas in crisis. Proper management and planning can help reduce the severity of floods. Identifying flood-affected areas and finding out which areas will adversely affect buildings and transportation networks will help mitigate future flood damage. Creating flood maps can help with a variety of planning and safety activities.

4 Conclusion The frequent flood inundated the study area regularly. Many houses, schools, industrial, and colleges of Periyar lower reaches are under flood affected. Many infrastructures of this study area are also destroyed by the flood. Riverine floods in the state are not a recent phenomenon. The low-lying areas of the Periyar River, which is close to the coast, are depositional area. These areas are most prone to floods. In the low-lying areas, the height of the levee is low in some places, causing flooding in flood plains and back swamp areas. The increase in construction areas and reclamation of wetlands increased the intensity of flooding in these areas. As the slope of the hilly region gradually increases, the intensity of river flow increases and as a result deposits in low-lying regions increase. Deposit accumulation has also contributed to a rise in the severity of floods. Flooding can occur regularly, but people in the area should be accustomed to the flood, and adequate measures should be taken by the government to reduce the vulnerability of the flood.

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Fig. 5 Transporatation network submerged under water Table 3 Buildings and amenities submerged under water

Buildings and amenities

No. of building flooded

House

11

Industrial

2

Hospital

1

University

1

School

3

College

2

Parking Total

1 21

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References Ahmed CF, Kranthi N (2018) Flood vulnerability assessment using geospatial techniques: Chennai, India. Indian J Sci Technol 11(6):215–223 Armenakis C, Du E, Natesan S, Persad R, Zhang YF (2017) Flood risk assessment in urban areas based on spatial analytics and social factors. Geosciences 7(4):123 Carreño Conde F, De Mata MM (2019) Flood monitoring based on the study of Sentinel-1 SAR images: The Ebro River case study. Water 11(12):2454. https://doi.org/10.3390/w11122454 Chattopadhyay M (2007) Morphometric analysis of the Periyar river, Kerala, India. The geographer vol 54 Chattopadhyay S, Sureshkumar S (2013) Glimpses of Kerala through maps. Centre for Earth Science Studies Cian F, MattiaMarconcini PietroCeccato, Giupponi C (2018) Flood depth estimation by means of high-resolution SAR images and lidar data. Nat Hazards Earth Syst Sci 18:3063–3084 Dadhich G, Miyazaki H, Babel M (2019) Application of Sentinel-1 synthetic aperture radar imagery for floods damage assessment: a case study of Nakhon SI Thammarat, Thailand. Int Arch Photogrammetry Rem Sens Spat Inf Sci XLII-2/W13, 2019 ISPRS Geospatial Week 2019, 10–14 June 2019 G.O.I. Centre water commission (2018) Kerala floods 2018. Government of India Central Water Commission Hydrological Studies Organisation Hydrology (S) Directorate Jo MJ, Osmanoglu B, Zhang B, Wdowinski S (2018) Flood extent mapping using dual-polarimetric Sentinel-1 synthetic aperture radar imagery. Int Arch Photogramm Rem Sens Spat Inf Sci 42(3). In: ISPRS TC III Mid-term Symposium “Developments, technologies and applications in remote sensing”, 7–10 May, Beijing, China Joy J, Kanga S, Singh SK (2019) Kerala flood 2018: flood mapping by participatory GIS approach, MeloorPanchayat. Int J Emerg Technol 10(1): 197–205 Megha V, Joshi V, Kakde N, Jaybhaye A, Dhoble D (2019) Flood Mapping and Analysis using Sentinel Application Platform (SNAP)—a case study of Kerala. Int J Res Eng Sci Manage 2(5) Mohanty PC, Panditrao S, Mahendra RS, Kumar HS, Bharadwaj SP, Nayak RK, Ramarao EP (2019) Geospatial Assessment of Flood Hazard Along the Tamil Nadu Coast. J Indian Soc Rem Sens 47(10):1657–1669 Pasi R, Consonni C, Napolitano M (2015) Open community data & official public data in flood risk management: a comparison based on InaSAFE. In: Geomatics workbooks n° 12—“FOSS4G Europe Como 2015” Rakib MR, Islam MN, Islam MN (2018) Flood vulnerability mapping to Riverine floods: a study on the Old Brahmaputra River. Curr Res Geosci 7(2):47–58 Ray K, Pandey P, Pandey C, Dimri AP, Kishore K (2019) On the recent floods in India. Curr Sci 117(2):204–218 Sankar G (2018) Monsoon Fury in Kerala—a geo-environmental appraisal. J Geol Soc India 92(4):383–388 Sanyal J, Lu XX (2004) Remote sensing and GIS-based flood vulnerability assessment of human settlements: a case study of Gangetic West Bengal, India. Hydrol Process 19:3699–3716 Sherpa SF, Shirzaei M, Ojha C, Werth S, Hostache R (2020) Probabilistic mapping of August 2018 flood of Kerala, India, using space-borne synthetic aperture radar. IEEE J Sel Top Appl Earth Observ Remote Sens 13:896–913 Suthakaran S, Withanage A, Gunawardhane M, Gunatilake J (2018) Flood risk assessment based on OpenStreetMap application: a case study in Manmunai North Divisional Secretariat of Batticaloa, Sri Lanka. In: FOSS4G Asia 2018 conference. Department of Town and Country Planning, University of MoratuwaMorawa. Sri Lanka 02–05 Dec 2018

RS-GIS Based Constructive Measures for Flood Prone Agricultural Land of Sabour Block of Bhagalpur District, Bihar Binod Kumar Vimal, Neeraj Bagoria, Rajkishore Kumar, Y. K. Singh, and Ragini Kumari Abstract Flood seems like a pragmatic natural disaster when water stagnant has been prolonged up to 3 to 4 months. Severe water-logged situation poses to negative impact on agricultural production and productivity. In this context, the present study was investigated to assess the flood-prone areas of different panchayats in Sabour block with the aid of modern tools of RS&GIS. Landsat ETM+ , IRS-LISS III and Carto DEM have been employed to delineate the vulnerable zones in flood-prone areas of Sabour block, and had a visible impact on Kharif and Rabi crops. NDVI and spectral enhancement techniques had also been advocated to characterize and trace out tress less ecology in Tal land (heavy textured) and Diara land (light-textured soils). The results revealed that, out of 100%, only 32% of the agricultural lands (Chandheri, Baijalpur, Khankitta and Parghari panchayat) were highly affected due to frequent subsequent flood. From physio-chemical properties of soils, soil pH value was ranged from 6.25 to 7.89 which justify the slightly acidic to neutral in reaction. While the EC value content is found to be less than 1 dSm−1 and enable to justify safe for agricultural operation. From fertility point of view, low oxidisable organic carbon and available nitrogen have been observed in flood-prone areas. Whereas, low to medium and medium to high available phosphate and available potassium content were observed in the studied area. The geostatical point of view, there was more coefficient of variation of observed (>20%) in all soil nutrient’s except soil pH, i.e., all soil nutrients geographically well distributed in study areas. From ongoing discussion, we could emphatically conclude that judicious application of organic and inorganic fertilizer enables to maintain the soil health and soil quality, and adequate land use planning offers to promote suitable aquaculture in spite of fallow land situation after recession of flood. Keywords Agricultural land · Flood · Mitigation · RS-GIS · Tal and Diara Land

B. K. Vimal (B) · N. Bagoria · R. Kumar (B) · Y. K. Singh · R. Kumari Department of Soil Science and Agricultural Chemistry, Bihar Agricultural University, Sabour 813210, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_8

121

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1 Introduction An approximate of 140 million people are affected per year because of flooding (WHO 2003). Bihar has also been suffered through flood, and its 70% of geographical area is devasted by flood, and plains of North Bihar have also been witnessing over the subsequent years. During monsoon, Assam, West Bengal, Bihar and Orissa are among the high flood-prone affected states have been reported in India. Out of total geographical area of 94.160 thousand km2 (73.06%), covering about 68.80 thousand km2 flood vulnerable zone in Bihar state. The North Bihar had recorded the highest number of floods during the last 30 years in the years 1978, 1987, 1998, 2004 and 2007, respectively, and witnessed its high magnitudes of flood (Kale and Pramo 1997), and most of these floods had also been happen because of breach of artificial embankments constructed which was a part of river training, and flood control measures by the State Government. Waterlogging is another problem in North Bihar that has reached grave proposition. Recurring floods combined with water-logging (due to improper drainage system), has been affecting livelihoods of people, particularly the marginalized communities. This has resulted in increasing trend of migration for shelter and work to other states. Construction of embankments to rivers like Kosi, Gandak, Bagmathi and Mahananda and their tributaries without proper drainage network to relieve farm lands on both banks is resulting in water logging in agricultural and homestead lands every year. Natural disasters, like floods, are causing massive damages to natural and human resources (Rai et al. 2018; Du et al. 2013; Youssef et al. 2011). Nowadays, multiple satellite data can be used as an effective alternative to monitor flood situations and extent in particular area (Rai et al. 2008; Brivio et al. 2002). The satellite data are widely useful to delineate the boundaries in flood-prone zones, and their mapping and assessing the spatial and temporal dynamics of land-use and landcover (Reger et al. 2007; Serra et al. 2008; Rai and Mohan 2014) in the flooded areas. The satellite data-based information enables to estimate flood under pre-flood and post-flood through modern remote sensing technology (Rahman 2006). For flood mapping, number of independent variables, which is also being called conditioning factors; act as key measures to evaluate the flood susceptibility mapping (Pradhan 2010; Pourghasemi et al. 2012; Kia et al. 2012; Billa et al. 2006; Huang et al. 2008; Tehrany et al. 2015; Merz et al. 2010). Destructive floods are most common in lower latitude regions, especially in Asia (Kundzewicz et al. 2009; Jeb and Aggarwal 2008). The geospatial techniques provided a wide range of data sources to generate the methodology (s) related to flood management (Wanders et al. 2014, Jain et al. 2005). Over this concern, multi-spectral image classification, band rationing, contextual multi-temporal classification and object-based classification are the important classification techniques used to map the land use pattern, assessment of flood and water resource management (Cleve et al. 2008; Reger et al. 2007; Serra et al. 2008; Rahman 2006; Pradhan 2010; Pourghasemi et al. 2012; Kia et al. 2012; Brivio et al. 2002). Several methods/techniques were developed by remotely sensed data to improve the accuracy of land-cover changes such as visual interpretation of satellite image

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(Jensen 2005; Shastri et al. 2020). However, during monsoon, huge cloud cover rains and haze can represent a strong constraint to the utilization of optical remotely sensed data. To resolve these problems, micro-wave remote sensing equipped with synthetic aperture radar (SAR) system may be helpful, because, penetration capacity of microwave data offered a primary tool for real-time assessment of flooded areas. The land and water contrast can be easily distinguished by SAR data (Dewan et al. 2006; Voigt et al. 2008). However, the present study was carried out to trace out the flood devasted agricultural land, soil fertility degradation, and its impact on Kharif and Rabi crops. However, constructive measures have also been suggested towards mitigating the problems which already existed at different Panchayats in Sabour blocks. With the consideration of above facts, there is wide opportunity to map the flood-affected area, cropping sequences and scope for the promotion of aquaculture (constructive measures) which would be helpful to enhance the agricultural land resources in Sabour block.

2 Material and Methods 2.1 Study Area Sabour block is situated adjoining of river Ganges, and its geographical extension was varied from 24° 30 N to 25° 06 N and 86° 30 E to 87° 07 E, respectively. It consists of TGA (Total Geographical Area) about 114.95 km2 . There are 14 panchayats are under vast alluvial plain intersected by a number of small rivers viz. Kadua and Chanan. The Ganges across from west to east provided a large deposition of alluvial soils which are locally known as Tal and Diara perceived as treeless ecology, and some patches of Ox-Bows are also found near their river beds. The climate belongs to semi-humid to humid conditions, and mean daily maximum ambient temperature in summer season is close to 43 °C, and mean daily minimum ambient temperature in winter season is 8 °C. The summer season starts from March to early June followed by rainy season starts from mid-June to September end, and winter starts from November to February (IMD 2012). The relative humidity is generally above 80% during monsoon period from July to September. Rainfall is mainly due to south west monsoon, and it is active from mid-June to September end. The average annual rainfall for the year 2018 is 2194 mm and 1600 mm in 2019, respectively (AWS, Sabour).

2.2 Satellite Images, Hardware and Software The multi-temporal satellite images of IRS P6 LISS-III were used for the visual interpretation and mapping of temporal changes of the agricultural land. Carto DEM

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Fig. 1 Flow chart for digital soil fertility mapping of available N, P2 O5 and K2 O

data have been used for the delineation of elevation and slopes. Topographical sheet (1:2,50,000 scale) was used to trace out the administrative boundary of Bhagalpur district, and the topographical sheets of G45O7 and G45O13 (1:50,000 scale) were used to delineate the village boundaries, administrative boundary, and extraction of panchayats of Sabour Block (Fig. 1). QGIS software (Version 3.10) was used for the visual interpretation of satellite images, Google image and Bhuwan data digitization, digital image processing and mapping. The documented soil survey reports and ancillary data were being used for reference purposes during validation of research findings. The mapping of land units by using visual interpretation keys (Zhang et al. 2014) was much accurate than digital classification (Figs. 2 and 3). Field survey was done during the month of May–June, 2019, and geo-referenced based random (80 soil samples) were collected from different locations, and analyzed their physic-chemical analysis as per standard procedure. Topographical maps, documented soil survey reports and ancillary data were also used for reference purposes during field survey and validation of research findings. The Normalized Difference Vegetation Index (NDVI) was used to measure the vegetative cover on the land surface over wide areas and confirmation of the treeless ecology under Tal and Diara lands (Rouse et al. 1973).

2.3 Soil Sample Collection, Processing and Its Interpretation The geo-referenced based soil samples were collected from 0–30 cm depth at various locations of the agricultural land, agricultural fallow land and horticultural crops, respectively using of GPS receiver (Model- Garmin E- Trax) to find out the fruitful results. In laboratory, all the samples were air-dried, sieved (20%) in all soil nutrient’s except soil pH i.e., all soils nutrients geographically well distributed in study areas (Table 1). Digitized map of village ponds was observed in Pargarhi, Baizalpur and Chandheri panchayats were dried up during summer, and provided a scope to recharge through flood water. (Fig. 6). Some major artificial water recharge points were also identified based on their slope gradient in Tal and Diara lands, and might be highly helpful for the promotion of aquaculture, and adaptation of Integrated Farming System to accelerate the rural economy. These artificial water recharge points or water storage points provided an opportunity to store the plenty of water after flood devastation and used for the fisheries, makhana and chest nut cultivation, and play as an alternative

97.81

20.41

67.20

N (kg/ha)

P2 O5 (kg/ha)

K2 O (kg/ha)

0.15

OC (%)

10.12

0.10

CEC

6.19

EC (dSm−1 )

Maximum

pH

Soil attribute

279.43

25.00

240.72

25.00

0.49

0.36

7.95

Minimum

67.20–279.43

20.41–25.0

97.81–240.72

10.12–25.0

0.15–0.49

0.10–0.36

6.19–7.95

Range

Table 1 Descriptive statistics of analyzed soil parameters

151.11

20.60

171.55

20.78

0.33

0.20

7.38

Mean

43.33

4.25

30.89

2.96

0.08

0.06

0.44

SD

28.57

20.81

18.01

22.30

24.99

29.52

5.95

CV

1877.00

18.04

954.38

8.76

0.01

0.00

0.19

Variance

11.91 −0.32

−2.99 0.36

2.27 −0.28

−0.39

−0.49

−0.11 −1.14

−0.74

0.52

Kurtosis 0.36

−0.91

Skewness

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Fig. 7 Map showing existing water bodies

source of income for agrarian farmers. In Rajandipur Panchayat, number of oxbows have been prevailed and used for fisheries and makhana cultivation by farmers.

4 Conclusion Visual interpreted satellite images of Landsat 8 and IRS LISS III enable to identify flood-affected panchayats and cropping sequences over flood-prone area. The northeastern part of the Diaralands and central to southern part of Tal lands were highly affected with flood because of over flow of water by the river Ganges. Flood water in Rajandipur, Shankarpur Farka, Khankita Chandheri and Baijalpur panchayats significantly affected the cropping sequences during Monsoon season. However, surplus flood water offers an opportunity to utilize as a resource potential for promotion of aquaculture after recession of flood. Apart from that, judicious application of organic and inorganic fertilizer enables to maintain the soil health and soil quality.

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Fig. 8 a Map showing Soil pH Status of Sabour Block. b Map showing soil EC status of Sabour Block. c Map showing soil OC Status of Sabour Block. d Map showing status of available nitrogen (kg/ha). e Map showing status of available Phosphorus (kg/ha). f Map showing status of available Potassium (kg/ha)

Acknowledgements Authors are thankfully acknowledged to Chairman, SSAC, BAC, Sabour for providing laboratory facility and his kind support during research work. Dr. Y. K. Singh, Department of soil science and agricultural chemistry, BAU, Sabour is also acknowledged for providing guidance and support through the research.

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Monitoring North Bihar Flood of 2020 Using Geospatial Technologies Jai Kumar and Soham Sahoo

Abstract The geospatial technique is used in monitoring the flood event of north Bihar (NB) during the monsoon season, 2020. The research work was performed to estimate the impact of a flood using multi-temporal Sentinel-1A (SAR) and moderate resolution imaging spectroradiometer near real-time (MODIS NRT) flood data over North Bihar. Binarization technique was used for extraction of flood water pixel, and the threshold was applied over the satellite image. It reported that most of the districts of north Bihar received continuous heavy rainfall (340–400 mm/day). Results revealed that there was an intense and heavy rainfall during the monsoon season, 2020. These results are important for policymakers to assess flood impacts. It can be inferred that the flood forecast using SAR data will lead to spatial accuracy and hydrological models. Keywords Satellite · Geospatial technique · Rainfall · Flood · Threshold · MODIS NRT

1 Introduction Floods are the most perilous natural disasters which have a potential to cause damage to life and property to a great extent (Rai and Mohan 2014). Floods are the result of both meteorological and hydrological abnormalities. A certain geographic region receiving above normal rainfall can result in sudden increase in water level which can result in a flood condition if the drainage system is not sufficient to discharge that much of incoming water out of that region. In other words, we can say that, when the rate of incoming water is more than the rate of outgoing water, a situation like flood may take place. Higher rainfall and run-off rate in a particular period over a region J. Kumar (B) · S. Sahoo Centre for Geospatial Technologies, Vaugh Institute of Agricultural Engineering and Technology, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_9

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create natural disasters like flash floods and landslides (Modrick and Georgakakos 2015).

1.1 Types of Flood 1.1.1. Flash flood: It takes place when excessive rainfall results in runoff and causes a quick rise in the water level of a stream. Flash floods are very frequent in dry climatic and rocky topographical areas due to short of soil or vegetation that allows heavy rains to flow over the land surface than infiltrate into the ground (https://www.usgs.gov/faqs/what-are-two-types-floods?qt-news_s cience_products=0#qt-news_science_products). 1.1.2. Coastal flood: It is a situation when a dry and low lying area gets flooded with seawater (Ramsay and Bell 2008). Coastal flood usually occurs due to rise in sea level or due to tsunamis. 1.1.3. Urban flood: It occurs due to high-intensity rainfall and insufficient drainage as well as flooding as a result of overtopping in the channels or rivers also, flooding due to high tides, etc. (Eldho et al. 2018). 1.1.4. River flood: These floods mainly occur due to larger rivers in areas with a moist climate (Nistor et al. 2019). A high-intensity rainfall for a longer duration resulting in extreme runoff as well as sometimes from melting snow originates a slower rise in water level over a larger area (https://www.usgs.gov/faqs/what-are-two-types-flo ods?qt-news_science_products=0#qt-news_science_products). 1.1.5. Ponding: It is usually the deposition of unwanted water on a flat surface or over a road just after rainfall (www.wbdg.org). In recent years, one of the most devastating floods has been recorded in countries like India (mostly in states like Uttar Pradesh, Bihar, West Bengal and Assam), Bangladesh and Nepal during August and September 2017 (Young 2017). North Bihar suffers the most with flood conditions due to more precipitation, drainage channel, topographic features and anthropogenic activities; as a result, the surplus amount of water joins the major rivers crossing the northern regions of Bihar and affects the adjoining districts (Pandey et al. 2010; Tripathi et al. 2019). Kosi River is one of the major rivers originating from the Himalayan regions of Tibet and Nepal with a merger of three tributaries, namely the Sun Kosi, the Arun Kosi and the Tamur Kosi to form the Kosi River, which flows across the districts of the northern region of Bihar starting from Madhubani, Supaul, Darbhanga, Saharsa, Purnea, Khagaria, Sitamarhi and Muzaffarpur to Katehar District which then joins the river Ganga. It is about 720 km long and covers an area of about 74,500 km2 in Tibet, Nepal and Bihar (Nayak 1996; Kosi Basin 2016). Kosi River is considered as the ‘sorrow of Bihar’ as it is responsible for floods every year in Bihar which brings destruction, loss of lives and economy with it (Rai et al. 2018). A flood being a natural hazard is an

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unavoidable situation whose extent of damage can only be monitored and appropriate steps can be taken to mitigate or reduce the adverse effects (Rai et al. 2008). The historical references signified different major flood events that occurred in North Bihar region, prominently during 1987, 1995, 1998, 2000, 2001, 2003, 2004, 2008, 2010, 2013, 2016, 2017 and 2018 (Pandey et al. 2010; Tripathi et al. 2019; Sinha et al. 2008; Singh et al. 2011; Sinha 2011). Land use and land cover (LU/LC) classification helps in acquiring information about the distribution of different classes present in the given study area. The effect of flooding occurrences over various land use/land cover (LULC) was also evaluated in various studies (Dewan et al. 2007; Kumar et al. 2011; Brody et al. 2014). Rainfall is the main meteorological input parameter which is considered to be the primary reason for flood conditions over a geographical area. Heavy precipitation during monsoon season followed by poor drainage and low infiltration/percolation rate results in maximized surface runoff. An intense rainfall during July–August 2019 resulted in severe catastrophic flooding situations in the upper catchment for lower areas, which submersed several districts of North Bihar, among which, districts like Patna, Darbhanga and Sitamarhi were highly affected by the flood. In the last decade, increased flood occurrence has broadly affected human lives, public property and the economy, which compelled the authorities to reconsider the policies, prepared for river basin management (Brody et al. 2014). PERSIANN rainfall product helps acquiring the rainfall distribution over the study area as well as the India Meteorological Department provides station-wise rainfall data all over the country. Moderate resolution imaging spectroradiometer (MODIS) despite its coarser resolution is frequently used in flood mapping in near real time over the districts of the North Bihar region (Pandey et al. 2010). Near-real-time information of flood inundation plays an important role as precise spatial data through disaster management and strategic planning to decrease flooding impact and lessen flood induced financial losses (Matgen et al. 2007). Satellites having synthetic aperture radar (SAR) are capable of penetrating through clouds and have been considered to be successful for a real extent of flood mapping in some of most important flood-prone areas and river basins, during few decades (Wilson and Rashid 2005). Flood monitoring and mapping extensively help in preparedness, prevention and mitigation operations. A cloud free spatial data is necessary to monitor flood effectively. The use of satellite product Sentinel-1A with synthetic aperture radar (SAR) facilitates in getting a clear image of the flood-prone areas which ultimately helps in monitoring flood despite huge cloud cover, intense rainfall, low visibility (haze) and less/absence of sunlight. Due to its property of being independent of cloud cover, SAR data plays a major role in providing crucial input in real-time flood damage estimation (Matgen et al. 2007). Objectives: The objective of this research work is as follows:

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(i)

To map the spatio-temporal changes in precipitation over the study area during the flood period from July to September 2020 To map and evaluate the flood inundation cover using spatio-temporal MODIS NRT flood product and Sentinel-1A (SAR) data. This study using multiple satellite data provided a perspective about the flood event occurred during the period July to September 2020 in the four districts of North Bihar.

(ii)

2 Review of Literature A research work was performed to map near real-time flood cover using multitemporal Sentinel-1A (SAR) as well as moderate resolution imaging spectroradiometer near real-time (MODIS NRT) flood data over Darbhanga District of North Bihar for the duration of August and September 2017. Binarization technique was used to extract floodwater pixels, keeping the threshold values −22.5, − 23.4, − 23.8 and −22.7 over the VH polarization image. After the comparison of results between the SAR and MODIS data, it was found that 13% of areas were submerged based on SAR data during peak flooding spell (23rd August), while overestimation by >20% was estimated using MODIS data also, more flood-affected regions was observed in the central, northern and western parts of the district because of the existence of additional water channels in those areas. The outcome of the research signified the effect of floodwater on agriculture and urban areas (Tripathi et al. 2020). The impact of the flood was assessed for the regions-Bihar and Assam due to heavy rainfall from 5 to 16th July 2019. It was found that due to severe floods, over 90 lakhs of people have been affected, with around 100 causalities in Bihar and Assam. Results revealed the significance of near real-time monitoring satellite images of flood occurrences. It was reported that some areas of Assam and Bihar obtained collective rainfall in surplus of 1200 mm from multiple intense rainy events during 5th to 16th July 2019, where the intense rainfall episodes were reported on 6th, 11th and 13th July 2019. A situation of severe flood aroused due to the discharge from Kosi and Brahmaputra rivers along with an intense cumulative rainfall during 5th to 16th July over Assam and Bihar which caused losses to life and property (Mishra et al. 2019). Similar research was performed in Bangladesh, which is known to be the most flood-affected country in the world. An effective methodology was developed for quick mapping of the flood cover and potential flood-affected regions to support a rapid and efficient response. Using Sentinel-1 images, inundation extents of the flood occurrence months were generated. Landsat 8 images were used to make the 2017 pre-flood land cover maps to spot key land cover on the ground ahead of the flooding. The total areas under flood were 2.01%, 4.53% and 7.01% for the months April, June and August 2017, respectively. Based on obtained land cover information from Landsat 8, it was found that cropland was damaged 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions due to flood (Uddin et al. 2019).

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As SAR data provides valuable disaster related information due to its cloud penetrating characteristics, a method was presented by the use of dual-polarimetric SAR imagery obtained on Sentinel-1a/b satellites. To differentiate between the pre- and post-flood images, a false-colour map was created. It worked well in the regions where standing water exists as well as provided mixed outcomes in urban areas. By the use of an external DEM, a flood depth map was also estimated and analysed the flood events of Tamil Nadu (TN) during November–December 2015 using satellite-based remote sensing technique (Jo et al. 2018). It was found that a few districts of TN received incessant multiday heavy precipitation (in the range of 200– 340 mm/day) during the second week and the first week of November and December, respectively. Heavy and extensive rainy spells throughout the second as well as last week of November and first week of December resulted in intense flooding over TN (Mishra 2016).

2.1 Site Description The study area comprises of four districts of the Northern Bihar region, namely Madhubani, Darbhanga, Supaul and Saharsa (Fig. 1). The total area of the study area including the four districts is around 9892 km2 and is spatially located between

Fig. 1 Location map of the study area

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85° 39 53.3988 E to 87° 6 17.4348 E longitude and 25° 35 45.4596 N to 26° 39 59.1192 N latitude in the Kosi River basin. The study area was considered for the flood assessment and monitoring in this research as Kosi being the major river flowing across these regions is responsible for floods most of the years and results in severe catastrophic situations mostly in these four districts. The average maximum temperature of the study area usually ranges between 39 and 44 °C, whereas the average minimum temperature ranges between 0 and 9.9 °C. The annual precipitation received is around 1310.25 mm. The study area is mostly dominated by agricultural lands, and the people rely on agriculture as a source of income. Being a flood-prone area, the agricultural lands are mainly susceptible to damage caused by floodwater. The districts consist of many active water bodies, which are responsible for flooding every year (Young 2017; Pandey et al. 2010; Singh et al. 2011; Manjusree et al. 2012; Kumar et al. 2014). The overall population of the study area inclusive of the four districts is 12,554,501, out of which a majority of the population (11,747,580) resides in rural areas while the remaining of the population (806,921) stays in urban areas (Census 2011).

3 Materials To carry out this research work, several satellite products have been used (i) Landsat 8 satellite image have been used to prepare a land use and land cover map and to distinguish various classes while evaluating the impact of the flood on each class by overlaying the flood data, (ii) for precipitation, PERSIANN-based precipitation product was used with 2 days interval that have been used to assess the spatiotemporal rainfall distribution over the four districts (iii) MODIS NRT data to monitor flood between 31st July and 4th September 2020 (iv) Sentinel-1A synthetic aperture radar images with C-band and HH-VH were used in flood monitoring and mapping as well as to compare its results with MODIS NRT. The details of primary and secondary data used are mentioned in Table 1. Software like SNAP, ERDAS Imagine and ENVI has been used to process various SAR and multispectral satellite images, respectively, while ArcGIS software was used to carry out several geospatial analyses.

3.1 Landsat 8 Landsat 8 satellite images mainly consist of 11 spectral bands out of which only 4 spectral bands were used, viz. Band-2 (Blue), Band-3 (Green), Band-4 (Red) and Band-5 (Near-Infrared). Landsat 8 has a spatial resolution of 30 m with a temporal resolution of 16 days. The satellite images were obtained from USGS website for

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Table 1 Primary and secondary data used in the study area/research Satellite

Spatial resolution

Swath

Temporal resolution

Bands

Purpose

Sentinel-1A

10 m

400 km

12 days

C-band synthetic aperture radar (Polarization: HH + HV, VV + VH, VV, HH)

Land and water monitoring (Flood monitoring)

MODIS NRT

250 m

500 m

3 days

36 bands

Flood assessment

Landsat 8

30 m

185 km

16 days

11 bands

LU/LC

PERSIANN rainfall data

0.25° × 0.25°

60° S to 60° N

1, 3, 6 hourly, daily

–

Rainfall distribution

IMD rainfall data

–

–

Monthly

–

Rainfall distribution

15th March 2020. The images were used to classify the land use and land cover features over the four districts Madhubani, Darbhanga, Saharsa and Supaul.

3.2 PERSIANN-Based Precipitation Product The Centre for Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine, is creating rainfall estimation from remotely sensed information using artificial neural networks (PERSIANN) (UCI). It uses neural network function classification to calculate an approximation of rainfall rate at every 0.25° × 0.25° pixel of the infrared brightness temperature image given by geostationary satellites. The rainfall product covers 60° S to 60° N throughout the globe. To generate global rainfall, the algorithm used here is based on the geostationary long-wave infrared imagery. The rainfall data is available at the intervals of 1 h, 3 h, 6 h, daily, monthly and on a yearly basis (https://chrsdata.eng.uci.edu/#tabPERSIANN).

3.3 IMD Rainfall Data The India Meteorological Department (IMD) gives meteorological station-wise rainfall data distributed throughout the country. In Bihar, there are 200 rain gauge stations to record daily rainfall, out of the total 1289 stations over India. To carry out this research, mean monthly rainfall data for July–September 2020 were used (Observed Rainfall Variability and Changes over Bihar State 2020).

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3.4 MODIS NRT Flood Product The moderate resolution imaging spectroradiometer near real-time (MODIS NRT) consists of 36 spectral bands having a spatial resolution of 250 m. The MODIS NRT data is provided by Dartmouth Flood Observatory (DFO). It uses infrared bands based on the object’s absorbance and reflectance properties to demarcate the floodwater (https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/ accuracy-assessment-for-image-classification.htm). To show the spatio-temporal flood inundation extent, a 3-day composite near real-time flood product has been utilized during July–September 2020.

3.5 Sentinel-1A (SAR) Satellite Data The Sentinel-1A (SAR) satellite data consists of C-band dual-polarization channels, i.e. HH, VH, with 12 days of revisit time (temporal resolution). For flood inundation mapping, the satellite product having 10 m spatial resolution was used and the multilooked level-1 GRD products were projected to UTM 1984 projection. The obtained data product was processed and geometrically corrected with speckles. The SAR data was obtained from Alaska Satellite Facility (ASF) during the flooding spells, such as 4th, 16th, 28th August and 8th September 2020 which were analysed to acquire flood inundation extent of August–September 2020 over the four districts.

4 Methodology In this research, a Landsat 8 satellite image was used to create a LULC map along with a multi-temporal dataset of MODIS NRT flood product and Sentinel-1A (SAR) satellite products were used to map and compare flood inundation extent between the two datasets. The CHRS rainfall products were also included in this study as an input meteorological parameter of the flood inundation which ultimately helps in assessing the extent of the flood. To create a land use and land cover map, the images of Landsat 8 satellite were used. The images after being obtained from the USGS platform were layer stacked, and the atmospheric correction was performed in order to reduce the noiseinduced due to atmospheric effects. Supervised classification was carried out to classify several primary classes in the study area. LU/LC map was prepared to evaluate the classes under the flood-affected zone by overlaying the flood inundation data over the LU/LC map. Accuracy assessment is carried out post-classification, and the classified images are evaluated with other accurate data or ground truth data. The ground truth data is obtained by interpreting high-resolution imagery, available classified images or GIS information layers. Producer accuracy is the map precision

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from the perspective of the producer, whereas user accuracy is the perspective of the map user (Census 2011; Humboldt State University 2019).

4.1 MODIS NRT Flood Product It was classified into two main classes, i.e. land and water bodies. To distinguish the flood water pixels in order to facilitate the flood inundation extent, unsupervised classification was performed over the spatio-temporal satellite images. In a duration gap of 3 days, a total of 9 MODIS NRT flood scenes were taken into consideration to carry out this research to illustrate the spatio-temporal changes of the flood inundation over the study area during July–September 2020. The methodology is shown in Fig. 2.

CHRS Rainfall Data

Daily Rainfall Data

MODIS NRT Data

Unsupervised Classification

Sentinel 1A

Image processing, Calibration, Speckle Filtering, Geometric Correction

Landsat 8

Atmospheric Correction

Supervised Classification Mean Rainfall data

Flood Water/ Land

Binarization Process

Classification Rainfall Distribution (in mm)

Spatio-temporal Analysis of Flood Inundation Extent

LU/LC Map

Flood Inundation Extent

Flooding/Non-Flooding Map

Agriculture/Settlement Area under Flood Inundation

Fig. 2 Flowchart of the methodology used in the research

Impact of Flood Innudation Over LU/LC

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4.2 Sentinel-1A (SAR) Satellite It product was processed, calibrated, speckle filtered and geometrically corrected as per the requirement. SNAP software was used to carry out all the required operations.

4.3 Processing of SAR Data To process the SAR data, SNAP software was used. It was radiometrically rectified and filtered to reduce speckles using the Refined Lee speckle filter. The following is the equation used in evaluating the radar backscattering coefficient (σ o ), related to SAR image brightness (β o ). σo = βo . sin α

(1)

where α = local incidence angle. σ o = the return scattering coefficient in decibels (https://chrsdata.eng.uci.edu/# tabPERSIANN) σo (dB) = 10 log 10(σo )

(2)

To get the backscattering values, it is essential to carry out the radiometric calibration of the SAR images. σ o requires incident angles of each pixel, whereas β o requires digital number values. To evaluate the digital number, the square root of the sum and the square of real and imaginary values are calculated (Manjusree et al. 2012). Following is the equation for evaluating the Sentinel-1A radar backscattering coefficient (Zwenzner and Voigt 2009):     σo (dB) = βo (dB) + 10 log 10 sin i p / sin(i c )

(3)

βo (dB) = 20 log 10(D N ) − K (dB)

(4)

where ‘ip ’ = angle of incidence for the particular pixel. ‘ic ’ = angle of incidence for the centre of the image. ‘K’ = calibration constant of the SAR image. The Sentinel-1A images were regulated and co-registered in UTM WGS 84 projection. As the input for the binarization process, sigma naught and gamma naught values were estimated. The area of interest was picked to estimate the backscatter pattern at a different location over the water body in both polarization bands VH and

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HH. For different water bodies, suitable backscatter threshold ranges were finalized in different polarizations by using the mean backscatter result through appropriate field validation. Additionally, the flood water was classified to separate, on the basis of the principle wherein backscatter energy received from the object may contain reverse relation with the incidence angle (Qidan et al. 2010). Further, the classified floodwater area is evaluated with the optically derived area.

4.4 Binarization Process This process primarily focuses on separating the satellite image pixels into two groups, i.e. background (non-flooded) and the foreground (flooded). By using the threshold technique, the flood inundated areas were obtained by fixing the range of dB values. To separate image (I (x, y)), 1-D Ostu thresholding method was adapted which consists of brighter objects from the darker background image as follows (https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-ima ges/accuracy-assessment-for-image-classification.htm). I (x, y) = {1I (x, y) > T ; 0 I (x, y) < T }

(5)

where T = Threshold value. In the present research, the threshold values −22.5, −23.4, −23.8 and −22.7 have been selected for VH band of Sentinel-1A to take out the flood water pixels from the SAR image.

4.5 CHRS PERSIANN The rainfall distribution over the study area was shown spatiotemporally using a 3-days collective CHRS satellite precipitation product acquired in TIFF format for July to September 2020. To illustrate the spatio-temporal rainfall distribution over the study area, by means of the IDW interpolation method, available points with rainfall values were interpolated in spatially (Tripathi et al. 2019).

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5 Result and Discussion 5.1 LULC Delineation of Four Districts of North Bihar The LULC map of the study area (Fig. 3) includes different classes in which the highest area is occupied by cropland of about 8716.67 km2 followed by the water body, shrubland and fallow land covering 409.71 km2 , 200.18 km2 and 199.99 km2 of the total area, respectively. The built-up area covers around 133.38 km2 of area of the total area while the least area is covered by the wasteland, i.e. 2.17 km2 (Table 2). As the area covered by water bodies is the second-highest among the other classes, a chance of flood risk maximizes. The overall accuracy of the accuracy assessment was 92.85% with a kappa coefficient of 0.9213. The built-up land showed 100% of user and producer accuracy, the classes like shrubland, water body and plantation resulted in 88.89% of both user and producer accuracy, while the least was permanent wetlands with 85.71% of both user and producer accuracy (Table 3).

Fig. 3 LU/LC map of the study area showing different classes

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Table 2 Different land use and land cover statistics over the four districts (2018) LU/LC classes

Description

Area (in km2 ) Area (%)

Crop land

This class includes various types of seasonal crops

8716.67

88.11837849

Built-up Land

This class includes the land covered by buildings and other artificial structures

133.38

1.348362313

Shrubland

These are the lands covered with woody vegetation

200.18

2.023655479

Fallow land

The land taken up for cultivation temporarily left uncultivated for one or more seasons

199.99

2.021734735

Wasteland

Sparsely vegetated land with signs of 2.15 erosion and land deformation that could be attributed to lack of appropriate water and soil management, or natural causes

0.021734735

Water bodies

This class consists of surface water, either 409.71 impounded in the form of ponds, lakes, reservoirs or flowing as streams, rivers, etc.

4.141831783

Plantation

It includes all the plantations/vegetations growing over the study area

0.735139507

72.72

Permanent wetlands These are the lands permanently remained 157.2 under moisture condition. It usually happens due to a water body nearby or any hydrological abnormalities

1.58916296

Total

100

9892

Bold indicates major area Table 3 Accuracy assessment results of LU/LC classification 2020 Producers and user accuracies (%) of land use classification of 2020 Overall accuracy = 92.85% Kappa coefficient = 0.9213 LULC classes

Users accuracy (%)

Producers accuracy (%)

Cropland

87.5

87.5

Built-up Land

100

100

Shrubland

88.89

88.89

Fallow Land

87.5

87.5

Wasteland

87.5

87.5

Water Bodies

88.89

88.89

Plantations

88.89

88.89

Permanent Wetlands

85.71

85.71

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5.2 Spatio-Temporal Precipitation Pattern Over the Study Area Based on CHRS PERSIANN Dataset To evaluate the rainfall distribution, 3 days average spatio-temporal rainfall data was taken during 28th July–7th September, 2020 over the four districts (as shown in Fig. 4). To prepare the rainfall distribution maps, the PERSIANN rainfall data was used for the given period. The results revealed that there was a maximum rainfall received during 3rd–5th August (75 mm) as well as 30th August–1st September (119.77 mm). Remarkably, it was observed that during this period, due to a high-intensity rainfall, a flooding condition originated for downstream areas. The intensity of rainfall gradually decreased during the period 6th–29th August 2020 while a sudden rise in rainfall was seen during the 30th August–1st September, which again induced a flooding situation and gradually decreased with diminishing rainfall afterwards.

Fig. 4 Rainfall distribution over the four districts during the flood condition from July–September, 2020

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5.3 MODIS NRT-Based Flood Inundation Assessment Based on the MODIS NRT flood data of July–September 2020, the flood inundation extent has been shown in Fig. 5. The results of flood inundation maps revealed that during 31st July–3rd August 2020, the area under flood was 46.81 km2 over the study area. So far the aerial flood extent is concerned; it was found that the flood inundation extent was the highest during 4th–7th August. The estimated flood inundated area was 1415.25 km2 . The monsoon arrival in Bihar was earlier in the year 2020 (which peaked during 4th August) as compared to the year 2017 (which peaked during 12th August). In the year 2020, till 2nd August, 14 districts of Bihar were affected (as per Bihar Govt. data), while as per the satellite image, on 4th August, 4 more districts were highly affected (mainly Madhubani, Darbhanga, Saharsa and Supaul). The area under flood inundation was seen to be consequently decreased with a minimum of 27.87 km2 area during 1st–4th September (Table 4).

Fig. 5 Spatio-temporal flood inundation extent over the study area using MODIS NRT flood product

150 Table 4 Date-wise flood inundation area over the study area

J. Kumar and S. Sahoo Date 31st July–3rd Aug 4th–7th Aug

Area (km2 ) 46.8125 1415.25

8th–11th Aug

209.6875

12th–15th Aug

293.4375

16th–19th Aug

118.8125

20th–23rd Aug

84.8125

24th–27th Aug

21.0625

28th–31st Aug

299.9375

1st–4th Sept

27.875

5.4 Flood Inundation Evaluation Based on Sentinel-1A (SAR) The Sentinel-1A SAR satellite product was used for the flood periods 4th, 16th, 28th August and 8th September 2020 (Fig. 6). The results showed that, during 4th August 2020, the area under the flood extent was the highest, i.e. 1572 km2 , which indicates

Fig. 6 Spatio-temporal flood inundation extent using Sentinel-1A (SAR) product

Monitoring North Bihar Flood of 2020 … Table 5 Sentinel-1A-based flood inundation over study area

Flooding date

151 Sentinel-1A-based flood area (in km+)

4th Aug

1572

16th Aug

440

28th Aug

513

8th Sept

181

Bold indicates major inundation

that the flood was at its peak during that period. As the rainfall gradually decreased, the flood inundation area decreased too. A minimum of 181 km2 area under the flood cover was estimated during 8th September 2020 (Table 5).

5.5 Impact of Flood Inundation Over the Agriculture and Built-Up Land (LULC Classes) The impact of flood inundation over the two LULC classes Cropland (Agriculture) and built-up lands were assessed by both the MODIS NRT flood data and Sentinel-1A (SAR) data. The results of both the satellites revealed that the area of built-up land under flood cover was 77.41 km2 as estimated by MODIS NRT Flood product, while Sentinel-1A (SAR) data estimated an area of 65.68 km2 under the flood extent for the same class (Table 6). Cropped land being the major LULC class covers 1601 km2 under the flood as estimated by the MODIS NRT flood product, whereas Sentinel-1A (SAR) data estimated an area of 1572 km2 under the flood inundation for the same class (Table 6). Few photographs were taken while the visit to the flood-affected places in the study area during flood spells of July–August of the year 2020 (Fig. 7). The photograph shows the devastation caused by the flood water in several built-up areas along with agricultural lands. Table 6 Statistics of the various built-up land/crop land and flood inundation (based on MODIS flood data and Sentinel-1A satellite data) Classes

Built-up land Crop land

LULC area (in km2 ) MODIS-based Inundation Sentinel-1-based composite during 31st July–31th Aug flood inundation of 4rd Aug and 01–06th Sept (in km2 ) and 08th Sept (in km2 ) 133.38

77.41

65.68

8716.67

1601

1572

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Fig. 7 Photographs of some flood-affected places in the study area

6 Conclusion The present study intends to assess the flood impact over the four districts in North Bihar that occurred during July–September 2020. The methodology adopted in the research work provided an understandable mapping of flood and its impact assessment. Based on the results obtained from the various satellite products such as Landsat 8, CHRS PERSIANN rainfall product, MODIS NRT flood product and Sentinel-1A (SAR) data, it can be concluded that geospatial technologies aids in timely information in order to make decisions in managing and monitoring disasters like flood. The total area under the cropland (agriculture) class over the study area was found to be maximum, i.e. 8716.67 km2 , while the area covered by built-up land was 133.38 km2 . The CHRS PERSIANN rainfall datasets showed the highest rainfall during 3rd–5th August (75 mm) as well as 30th August–1st September (119.77 mm); likewise assessing the MODIS NRT flood product, it was found that a maximum area under flood inundation was during 4th–7th August 2020 with 1415.25 km2 of the area which gradually diminished with a decrease in rainfall. Similarly, the Sentinel1A (SAR) data estimated an area of 1572 km2 under the flood inundation which was close to the result of the MODIS NRT flood product. The area under cropland (agriculture) was found to be highly occupied under the flood cover by MODIS NRT and

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Sentinel-1A, i.e. 1601 km2 and 1572 km2 area, respectively, while the flood inundation area covering the built-up land was 77.41 km2 and 65.68 km2 as evaluated by MODIS NRT and Sentinel-1A, respectively. Acknowledgements The authors are thankful to all the data providing agencies National Aeronautical Space Agency (NASA): NASA GES/DISC and LP-DAAC for providing essential data such as MODIS NRT Flood product and NASA ASF-DAAC for ESC Sentinel-1A synthetic aperture radar (SAR) data; Centre for Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine (UCI), for providing PERSIANN rainfall data; India Meteorological Department (IMD), Pune, for meteorological data such as rainfall distribution data; United States Geological Survey (USGS) for providing the Landsat satellite images as well as the Bihar government for their immense support by providing information and report about the study area and other essential data which helped us in carrying out this research work.

References Brody S, Blessing R, Sebastian A, Bedient P (2014) Examining the impact of land use/land cover characteristics on flood losses. J Environ Plan Manag 57:1252–1265. https://doi.org/10.1080/096 40568.2013.802228 Building Envelope Design Guide—Roofing Systems | Whole Building Design Guide. www.wbd g.org. Retrieved 11 Aug 2016 Census (2011) Districts of Bihar. https://www.census2011.co.in/census/state/districtlist/bihar.html Dewan AM, Islam MM, Kumamoto T, Nishigaki M (2007) Evaluating flood Hazard for land-use planning in greater Dhaka of Bangladesh using remote sensing and GIS techniques. Water Resour Manag 21:1601–1612. https://doi.org/10.1007/s11269-006-9116-1 Eldho TI, Zope PE, Kulkarni AT (2018) Urban flood management in coastal regions using numerical simulation and geographic information system. In: Integrating disaster science and management, pp 205–219. https://doi.org/10.1016/b978-0-12-812056-9.00012-9 ESRI. Accuracy Assessment for Image Classification. https://desktop.arcgis.com/en/arcmap/latest/ manage-data/raster-and-images/accuracy-assessment-for-image-classification.htm Humboldt State University (2019) Accuracy metrics. http://gis.humboldt.edu/OLM/Courses/GSP_ 216_Online/lesson62/metrics.html#:~:text=Producer’s%20Accuracy%20is%20the%20map,gro und%20is%20classified%20as%20such. Jo MJ, Osmanoglu B, Zhang B, Wdowinski S (2018) Flood extent mapping using dual-polarimetric Sentinel-1 synthetic aperture radar imagery. Int Arch Photogramm Remote Sens Spat Inf Sci 42(3):711–713 Kosi Basin (2016) Water resources information system of India. Archived from the original on 1 June 2016. Retrieved 7 Apr 2016 Kumar A, Pandey AC, Hoda N, Jeyaseelan AT (2011) Evaluating the long-term urban expansion of Ranchi urban agglomeration, India using geospatial technology. J Indian Soc Rem Sens 39:213– 224. https://doi.org/10.1007/s12524-011-0089-z Kumar R, Jain V, Prasad BG, Sinha R (2014) Connectivity structure of the Kosi megafan and role of rail-road transport network. Geomorphology 227:73–86. https://doi.org/10.1016/j.geomorph. 2014.04.031 Laur P, Bally P, Meadows P, Sanchez J, Schaettler B, Lopinto E, Esteban D (2003) Derivation of the backscattering coefficient in ESA ERS SAR PRI products Manjusree P, Kumar LP, Bhatt CM, Rao GS, Bhanumurthy V (2012) Optimization of threshold ranges for rapid flood inundation mapping by evaluating backscatter profiles of high incidence angle SAR images. Int J Dis Risk Scienc 3:113–122. https://doi.org/10.1007/s13753-012-0011-5

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Matgen P, Schumann G, Henry JB, Hoffmann L, Pfister L (2007) Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int J Appl Earth Observ Geoinf 9:247–263. https://doi.org/10.1016/j.jag. 2006.03.003 Mishra AK (2016) Monitoring Tamil Nadu flood of 2015 using satellite remote sensing. Natural Hazards. https://doi.org/10.1007/s11069-016-2249-5 Mishra AK, Meer MS, Nagaraju V (2019) Satellite-based monitoring of recent heavy flooding over north-eastern states of India in July 2019. Nat Hazard. https://doi.org/10.1007/s11069-019-037 07-zs Modrick TM, Georgakakos KP (2015) The character and causes of flash flood occurrence changes in mountainous small basins of Southern California under projected climatic change. J Hydrol Reg Stud 3:312–336. https://doi.org/10.1016/j.ejrh.2015.02.003 Nayak J (1996) Sediment management of the Kosi River basin in Nepal. In: Walling DE, Webb BW (eds) Erosion and sediment yield: global and regional perspectives. Proceedings of the Exeter Symposium July 1996. IAHS Publishing no. 236, pp 583–586 Nistor MM, Rai PK, Dugesar V, Mishra VN, Singh P, Arora A, Kumra VK, Carebia IA (2019) Climate change effect on water resources in Varanasi district, India. Meteorol Appl, pp 1–16. https://doi.org/10.1002/met.1863 Observed Rainfall Variability and Changes over Bihar State (2020) Climate research and services. India Meteorological Department, Ministry of Earth Sciences. Met Monograph No.: ESSO/IMD/HS/Rainfall Variability/04(2020)/28 Pandey AC, Singh SK, Nathawat MS (2010) Waterlogging and flood hazards vulnerability and risk assessment in indo Gangetic plain. Nat Hazards 55:273–289. https://doi.org/10.1007/s11069010-9525-6 PERSIANN. Centre for Hydrometeorology and Remote Sensing. https://chrsdata.eng.uci.edu/#tab PERSIANN Qidan Z, Liqiu J, Rongsheng B (2010) Exploration and improvement of Ostu threshold segmentation algorithm. In: 2010 8th world congress on intelligent control and automation. IEEE, Jinan, China, pp 6183–6188 Rai PK, Mohan K (2014) Remote sensing data & GIS for flood risk zonation Mapping in Varanasi District. Forum Geogr J (Romania) 13(1):25–33. http://dx.doi.org/https://doi.org/10.5775/fg. 2067-4635.2014.041.i. ISSN No: 2067-4635 Rai PK, Nathawat MS, Anurag N (2008) Temporal behavior of waterlogged area using multitemporal satellite data, Deccan Geographer. J Deccan Geogr Soc 46(2):67–74. ISSN: 0011-7269 Rai PK, Mishra VN, Singh P (2018) Hydrological Inferences through morphometric analysis of Lower Kosi River Basin of India for water resource management based on remote sensing data. Appl Water Sci (springer) 8(15):1–16. https://doi.org/10.1007/s13201-018-0660-7.ISSN:21905495 Ramsay D, Bell R (2008) Coastal hazards and climate change. A guidance manual for Local Government in New Zealand. Ministry for the Environment, New Zealand. ISBN 978-047833118 Singh SK, Pandey AC, Nathawat MS (2011) Rainfall variability and Spatio temporal dynamics of flood inundation during the 2008 Kosi flood in Bihar state, India. Asian J Earth Sci 4:9–19. https://doi.org/10.3923/ajes.2011.9.19 Sinha CP (2011) Climate change and its impacts on the wetlands of North Bihar, India: climate change and wetlands. Lakes Reserv Res Manage 16:109–111. https://doi.org/10.1111/j.14401770.2011.00457.x Sinha R, Bapalu GV, Singh LK, Rath B (2008) Flood risk analysis in the Kosi river basin, North Bihar using multi-parametric approach of analytical hierarchy process (AHP). J Indian Soc Rem Sens 36:335–349 Tripathi G, Parida BR, Pandey AC (2019) Spatio-temporal rainfall variability and flood prognosis analysis using satellite data over North Bihar during the August 2017 Flood Event. Hydrology 6:38. https://doi.org/10.3390/hydrology6020038

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Tripathi G, Pandey AC, Parida BR, Kumar A (2020) Flood inundation mapping and impact assessment using multi-temporal optical and SAR satellite data: a case study of 2017 flood in Darbhanga District, Bihar, India. Water Resour Manage 34:1871–1892. https://doi.org/10.1007/s11269-02002534-3 Uddin K, Matin MA, Meyer FJ (2019) Operational flood mapping using multi-temporal Sentinel-1 SAR images: a case study from Bangladesh. Rem Sens 11(13):1581 What are the two types of floods? USGS. https://www.usgs.gov/faqs/what-are-two-types-floods? qt-news_science_products=0#qt-news_science_products Wilson BA, Rashid H (2005) Monitoring the 1997 flood in the Red River Valley using hydrologic regimes and RADARSAT imagery. Can Geogr 49:100–109 Young WAST (2017) Flood risk assessment and forecasting for the Ganges-Brahmaputra-Meghna River basins. World Bank Zwenzner H, Voigt S (2009) Improved estimation of flood parameters by combining space based SAR data with very high resolution digital elevation data. Hydrol Earth Syst Sci 13:567–576. https://doi.org/10.5194/hess-13-567-2009

Climatological and Meteorological Disasters

A Review of Tropical Cyclone Disaster Management Using Geospatial Technologies in India Ananya Sharma and Thota Sivasankar

Abstract Tropical cyclones (TCs), also known as typhoons or hurricanes, are among the most destructive weather phenomena which is commonly observed between 5° and 25° latitudes on both sides (N–S) of the equator. In this context, remote sensing can be a cost effective, accurate and potential tool for mapping, analysing and mitigating the multiple impacts caused by TCs using high to moderate spatial and temporal resolution satellite imagery. It can be utilised in providing essential information for evacuation, relief and the management during post-disaster. For tropical cyclone tracking and monitoring, a multiplicity of geospatial techniques is taken into consideration. For instance, Indian Meteorological Department (IMD) delivering operational cyclone forecast to India along with other neighbouring countries through Dvorak technique using INSAT imagery. Apart from cyclone tracking and near real-time monitoring, early warning satellite and radar systems are the best possible solutions for the assessment of cyclone risk reduction, following major adaptation strategies in major cyclone prone areas, and consequently help in reducing the loss of lives as well as infrastructure damages.

1 Introduction Tropical cyclones are the most obscure and rigorous meteorological phenomena at the global level, its effects are closely on the coastal regions that create a high loss of economy, human health and life. Tropical cyclones occur mostly within the Atlantic region and have an effect on the Caribbean and tropical cyclone conjointly originates in various parts of the ocean and might have an effect on coastal regions of North American nation, South-east Asia, North-east Australia and also the Pacific Islands. The low depressions that form within the ocean will have an effect on Asian countries like India, Bangladesh, North-west Australia, and some elements of East Africa and also the ocean islands like Mauritius and Madagascar (Gray 1998). When the common wind speed of the A. Sharma · T. Sivasankar (B) Geographic Information Systems (GIS) Area, NIIT University, Neemrana, Rajasthan, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_10

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Table 1 Represents classification of tropical cyclones and their region of occurrence around the globe

Classification of tropical cyclones

Region of occurrence

Hurricanes

Atlantic and North-east Ocean

Typhoons and Super Typhoons

North-west ocean

Severe Tropical Cyclone

South-west Pacific and South archipelago Ocean

Severe Cyclonic Storm

North ocean Tropical Cyclone, South-west Indian Ocean

storm reaches at 74 mph then by the given criteria from WMO, the tropical cyclones are categorised into various types on the basis of their region of occurrence, as shown in Table 1.

1.1 Indian Sub-basin and Its Characteristics a.

b.

Bay of Bengal: The Bay of Bengal which is settled in the north-east of the Indian Ocean and it is greatly responsible for the formation of a number of the strongest and deadliest tropical cyclones within the world. The basin is abbreviated BOB by the Indian Meteorological Department (IMD), the official Regional Specialised Meteorological Centre of the basin. The Bay of Bengal’s coast is shared among India, Bangladesh and Myanmar. The foremost intense cyclones within the bay was the 1999 Odisha cyclone (Gupta 2006). The deadliest cyclone within the bay is that the 1970 Bhola cyclone whereas the costliest is 2020 Amphan cyclone. Arabian Sea: The {Arabian Ocean |Arabian Sea} settled within the northwest of the ocean. IMD and RSMC are monitoring the tropical cyclone to the various Arabian Sea coastal ocean countries like: India, Yemen, Oman, UAE, Iran, Pakistan, Sri Lanka, Maldives and East African region (Kalsi 1999). In summers, robust winds blow from the south-west to the north-east direction and deliver rain to the Indian landmass throughout the winter over the Arabian coast, and these lower depression conditions become favourable for process of cyclogenesis (Table 2).

1.2 Movement of Cyclones in Bay of Bengal In the month of October, the south-west monsoon retreats over the Indian Ocean. The ocean surface temperatures become high, wind shear additionally remains as low. Usually, the amount of cyclones forming within the Bay of Bengal are more in counts. Generally, these cyclones begin as low-pressure areas or remnants of Pacific

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Table 2 Criteria followed by Meteorological Department of India (IMD) to classify the lowpressure systems in the Bay of Bengal and in the Arabian Sea as adopted by World Meteorological Organisation (WMO 2013) Types of disturbance

Associated wind speed in the circulation

Low-pressure area

Less than 17 knots (12). The fire

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Fig. 2 Schematic diagram shows the methodology for fire susceptibility map

danger levels are assigned to each IGBP classes based on maximum frequency and are shown in Table 1, and fire danger map is shown in Fig. 3. Topography is an important physiographic parameter that affects the speed and action of the wind and thus affects the propagation of fire (Rothermel 1983). Topography is the fire environment triangle’s most stable parameter, and it is simpler to forecast its effect on other parameters weather and fuel. Slope, aspect and elevation are topographic variables that are influencing the forest fire danger.

2.4 Slope Fire Danger Index Slope map was generated from SRTM DEM and divided into 5° interval for further analysis. In general, fire moves up the slope faster than down, and slope is the key factor in any fire danger studies (Chuvieco and Congalton 1989; Jaiswal et al. 2002). Fire danger levels are assigned to different slope intervals based on the fire frequency and are shown in Table 2. It is evident from Table 2 that fire frequency is higher in below 5° and minimum above 40°, which is contrary to the general fire spread hypothesis (Chuvieco and Congalton 1989). After the field visit to Uttarakhand state forests, it was concluded that the higher litter content with the driest condition and longer sunlight period obtained down the slope were the main cause of the higher forest fire incidents, which will support surface fires (Babu et al. 2016b). The exact reason for higher

178 Table 1 Fire danger levels assigned to IGBP classes

S. Singh and K. V. Suresh Babu IGBP classes

Max frequency

Danger level

Evergeen needleleaf forest

2

Very low

Evergeen broadleaf forest

2

Very low

Deciduous needleleaf forests

0

Very low

Deciduous broadleaf forests

3

Very low

16

Very high

Closed shrublands

0

Very low

Open shrublands

0

Woody savannas

11

Mixed forests

Very low High

Savannas

8

Moderate

Grasslands

5

Low

Permanent wetlands

0

Very low

Cropland

11

High

Urban and built-up

2

Very low

Cropland/natural vegetation mosaics

5

Low

Snow and ice

0

Very low

Barren

1

Very low

Water bodies

0

Very low

number of fire incidents on the lower slopes (45

1

Very low

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Fig. 4 Slope fire danger index

Table 3 Fire danger levels assigned for different aspect classes

Aspect class

Max frequency

Danger class

Flat

1

North

3

Very low Very low

Northeast

5

Low

East

6

Low

Southeast

8

Moderate

South

16

Very high

Southwest

14

Very high

west

10

High

Northwest

4

Low

North

2

Very low

2.6 Elevation Danger Index Elevation is a key factor in forest fire danger analysis because it affects the temperature and precipitation (Chuvieco and Congalton 1989). Elevations are divided into

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Fig. 5 Aspect fire danger index

different ranges to know the vulnerability of forest fires, and Table 4 shows the fire danger levels assigned to elevation ranges based on maximum frequency. Table 4 Fire danger levels assigned for elevation ranges

Elevation (m)

Max frequency

Danger class

0–300

10

High

300–600

10

High

600–900

16

Very high

900–1200

14

Very high

1200–1500

8

Moderate

1500–1800

10

High

1800–2100

10

High

2100–2400

7

Moderate

2400–2700

4

Low

2700–3200

2

Very low

>3200

0

Very low

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Fig. 6 Elevation fire danger index

From Table 4, it was clear that fire frequency is ranging from high to very high in the elevations below 1200 m and very low in the elevations above 2700 m. The main cause of the higher fire frequency was anthropogenic activity at altitudes below 1200 m, and the presence of pine forests was responsible for the highest fire frequency at altitudes between 1500 and 2100 m. Fire frequency is minimum above the altitudes of 3200 m because the temperature is cold frigid, and Fig. 6 shows the elevation fire danger index.

2.7 MODIS Global Disturbance Index (MGDI) MODIS global disturbance index is useful to detect most of terrestrial ecosystem disturbances based on the ratio of land surface temperature (LST) and enhanced vegetation index (EVI) (Mildrexler et al. 2007, 2009) and can be downloaded from the Numerical Terradynamic Simulation Group (NTSG) web portal. If there is a fire, LST increases to the maximum and EVI decreases to the minimum, leading to a rise in MGDI values, and Table 5 represents the fire danger levels assigned for MGDI (Mildrexler et al. 2007), and Fig. 7 shows the MGDI map.

Forest Fire Susceptibility Mapping for Uttarakhand … Table 5 MGDI fire danger levels

183

S. No.

MGDI

Danger class

1

0.02–0.36

Very low

2

0.36–0.68

Low

3

0.68–1.32

Moderate

4

1.32–1.64

High

5

1.64–4

Very high

Fig. 7 MODIS global disturbance index (MGDI)

3 Results and Discussion The studies performed by different researchers (Jaiswal et al. 2002; Dong et al. 2005; Heikkilä et al. 2010; Eskandari and Chuvieco 2015) randomly assigned fire danger levels to each parameter based on the hypothesis of the fire spread without taking into account the characteristics of the study area and historical forest fire information. Thus, to estimate forest fire danger, the analytical hierarchical process (AHP) approach was used. In this study, fire danger levels are assigned to each parameter based on the maximum fire frequency; hence, forest fire susceptibility map was generated from the integration of all the above-mentioned individual fire

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Fig. 8 Forest fire susceptibility map overlaid with the fire hotspots of 2018

danger indices and MODIS global disturbance index. Forest fire susceptibility map was categorized into fire danger classes viz. very low (≤5), low (6–10), moderate (11–15), high (16–20) and very high (>20). MODIS fire hotspot data (MCD14) of 2018 and 2019 fire season were used to estimate the accuracy of fire susceptibility map, and Figs. 8 and 9 show the fire susceptibility map overlaid with corresponding year fire hotspot data. Accuracy was calculated in most of the studies based on the number of fire hotspots in various classes of fire danger maps (Akther and Hassan 2011; Adab et al. 2013; Babu et al. 2016a, 2019, b; Mitri et al. 2017) and assumed that fires fell in very low to moderate classes that were considered as fire is not predicted and otherwise predicted. Total number of fire hotspots are counted in various fire danger classes and shown in Table 6, and accuracy was calculated using Eq. 1 (Babu et al. 2016). Total number of fire incidences in High and very high danger classes Total number of fire incidents in all classes ∗ 100 (1)

Accuracy =

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Fig. 9 Forest fire susceptibility map overlaid with the fire hotspots of 2019

Table 6 Fire hotspots fell in fire danger classes for the years 2018 and 2019

Fire danger class

Number of fire points 2018

2019

Very low

12

6

Low

37

32

208

159

1321

1280

224

152

Moderate High Very high

The accuracy of the generated forest fire susceptibility map was found 85.73% and 87.91% for the years 2018 and 2019, respectively. Therefore, forest fire susceptibility map accurately predicts the forest fire danger over study area.

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4 Conclusions This study shows the mapping of forest fire susceptibility using satellite datasets, i.e. MODIS TERRA and AQUA land cover type (MCD12Q1) and SRTM DEM and MODIS global disturbance index. IGBP land use/land cover (IGBP) product is derived from the MCD12Q1 and is used to IGBP LULC danger index. Topographic parameters slope, elevation and aspect are generated from SRTM DEM. Fire danger levels are assigned to each parameter, i.e. IGBP land use/land cover, slope, aspect, elevation based on the maximum forest fire frequency estimated from the forest fire hotspots of 2010–2017. The MODIS global disturbance index is added to the abovementioned fire danger indices to generate the forest fire susceptibility map. Fire danger levels are divided into five classes: very low, low, moderate, high and very high, and the accuracy of fire susceptibility map is estimated based on the forest fire hotspots. The accuracy of the forest fire susceptibility map was 85.73% and 87.91% for the years 2018 and 2019, respectively. Acknowledgements Authors would be thankful to Dr. Arijit Roy, IIRS, Dehradun and Dr. P. Ramachandra Prasad, IIIT Hyderabad for their support during the research. The authors acknowledge the MODIS Science team for the Science Algorithms, the Processing Team for producing MODIS data and the GES DAAC MODIS Data Support Team for making MODIS data available to the user community, NASA Earthdata team and FIRMS websites for free MODIS TERRA datasets and active fire data.

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Roy PS (2003) Forest fire and degradation assessment using satellite remote sensing and geographic information system. In: Satellite Remote sensing and GIS applications in agricultural meteorology, p 361 van der Werf G, Randerson J, Giglio L, Collatz J, Kasibhatla P, Morton D, DeFries R (2010) The improved global fire emissions database (GFED) version 3: contribution of savanna, forest, deforestation, and peat fires to the global fire emissions budget. In: EGUGA, p 13010

Investigation of Indian Summer Monsoon Rainfall Relationship with the Bay of Bengal Sea Surface Temperature and Currents Harshita Saxena and Vivek Kumar Pandey

Abstract We made an analytical effort to find out the relationship between the Bay of Bengal (BoB) daily JJAS mean area-averaged sea surface temperature (SST) anomaly and daily JJAS mean area-averaged sea surface currents (SSC) to the Indian summer monsoon rainfall. The TRMM SST dataset of 2001 to 2013 (i.e., 1586 days) at for the region 6° N to 22° N and 80° E to 94° E and sea surface current dataset of surface currents from diagnostic model (SCUD) for the years from 2001 to 2008 was used in this analysis. We have also observed the intra-seasonal variability in daily JJAS mean area-averaged SST and daily JJAS mean area-averaged SSC over BoB. The statistical measure such as standard deviation and temporal variability of JJAS daily anomalies of SST and SSC over BoB was applied on TRMM and SCUD datasets. We focused our study on the JJAS season of the Indian monsoon rainfall (IMR) and BoB daily JJAS averaged mean SST anomaly and BoB daily JJAS averaged mean SSC anomaly. We observed high variability in the coastal region in comparison with the interior ocean and intra-seasonal mode in daily JJAS averaged mean SST anomaly. Similarly, we observed the large variability in the off east coast of the Indian basin and smaller in the interior ocean. Intra-seasonal characteristics observed in the daily JJAS averaged mean sea surface current anomaly too. The correlation coefficients between BoB daily JJAS averaged mean SST anomaly and IMR index are lies between −0.17 and +0.17 and correlation coefficient in case of BoB daily JJAS averaged mean SSC anomaly versus IMR index lies between −0.20 and +0.20 which indicate a poor correlation between them. This result shows that there is no significant effect of long-term daily JJAS averaged mean anomaly of these two parameters of the BoB on the IMR (JJAS). Keywords Indian monsoon rainfall · Intra-seasonal · Bay of Bengal · Correlation coefficient · Sea surface temperature · Sea surface current

H. Saxena · V. K. Pandey (B) K. Banerjee Centre of Atmospheric and Ocean Studies, University of Allahabad, Prayagraj, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_12

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1 Introduction The Bay of Bengal (BoB) is located in the north-eastern part of the Indian Ocean (~6° N to 22° N and 80° E to 94° E), and it is the largest bay in the world (surface area: ~2 × 106 km2 ). To the north, it is bordered by Bangladesh, to the east by Myanmar and to the west by India and the island of Sri Lanka. The Andaman and Nicobar Islands along the eastern boundary separate the bay from the adjacent Andaman Sea. The BoB annually receives large amounts of freshwater through river discharges and heavy precipitations, especially during the summer monsoon season (estimated inputs over 200 km3 ). Some of its major rivers include the Ganges, the Brahmaputra, the Krishna, and the river Mahanadi. This water produces a warm, low-salinity and high nutrient and oxygen-rich water layer to a depth of 100 m that stretches over a distance of 1500 km. There are many instances of years with drought (weak monsoon) and flood (strong monsoon) during which Indian region receives deficient or excess seasonal rainfall, respectively. Even within a season, there is considerable variation, both in space and time, in the rainfall over Indian region. The intra-seasonal variation is characterized by “active” periods of high rainfall and “break” periods with weak or no rainfall. The intra-seasonal variability of the summer monsoon has a tremendous socio-economic impact on India, especially in the fields of agriculture. The atmospheric heating associated with the convection plays a critical role in sustaining the monsoon winds, and the rainfall associated with it, not only over the bay but also over the Indian subcontinent, maintains a low-salinity surface layer. The monsoon currents extend over the entire basin, from the Somali coast to the eastern Bay of Bengal. They do not, however, come into being, or decay, over this entire region at a given time. Different parts of the currents form at different times, and it is only in their mature phase that the currents exist as trans-basin flows. The winds that blow over the Indian Ocean are the main forcing function and reverse twice during the year. Gadgil et al. (1984) state that in monsoon regions, the seasonal migration of the inter-tropical convergence zone (ITCZ) is manifested as a seasonal reversal of winds. Most of the summer monsoon rainfall over India occurs owing to synoptic and large-scale convection associated with the continental ITCZ. A study by Webster et al. (1998) described the processes of monsoon, its predictability, and the prospects for prediction. In the Arabian Sea, the strong overturning and mixing lead to lower SST, and weak convective activity, which in turn, leads to low rainfall and runoff, resulting in weak stratification that can be overcome easily by the strong monsoon winds. Thus, in both basins, there is a cycle with positive feedback, but the cycles work in opposite directions. This locks monsoon convective activity primarily to the bay (Shenoi 2002). Shankar et al. (2002) state that the monsoon currents are the seasonally reversing, open-ocean currents that flow between the Arabian Sea and the Bay of Bengal, the two wings of the north Indian Ocean. The summer monsoon current (SMC) flows eastward during the summer monsoon (May–September), and the winter monsoon current (WMC) flows westward during the winter monsoon (November–February). Assemble data of ship drifts, winds and Ekman drift, and

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geostrophic currents derived from altimetry and hydrography have been used to observe the climatological seasonal cycle of the monsoon currents. The oceanic general circulation model (OGCM) has been used to simulate these currents and estimate their transports, and a layer reduced-gravity model is also used to investigate the processes. Shenoi (2002) states that the analysis of the heat budgets of the near-surface Arabian Sea and Bay of Bengal shows significant differences between them during the summer monsoon (June–September). In the Arabian Sea, the winds associated with the summer monsoon are stronger and favor the transfer of heat to deeper layers owing to overturning and turbulent mixing. In contrast, the weaker winds over the bay force a relatively sluggish oceanic circulation that is unable to overturn, forcing a heat budget balance between the surface fluxes and diffusion and the rate of change of heat in the near-surface layer. The weak winds are also unable to overcome the strong near-surface stratification because of a low-salinity surface layer. This leads to a shallow surface mixed layer that is stable and responds quickly to changes in the atmosphere. An implication is that SST in the bay remains higher than 28 °C, thereby supporting large-scale deep convection in the atmosphere during the summer monsoon. Monsoonal winds are main contributor for the currents in BoB. The BoB exhibits seasonal reversing monsoon circulation along with depressions, severe cyclonic storms, and comparatively low-saline surface waters due to the large amount of river runoff from the Indian subcontinent. Strong surface circulations embedded with counter-rotating gyres have been noticed (Poterma et al. 1991). The steadiness and strength of the gyres and currents in the BoB seem to depend on the development and latitudinal shift of the north equatorial current and the monsoon current. Dube et al. (1990), where, through numerical simulations, they reported that the interannual variability of the upper-layer thickness of the central Arabian Sea has a good correlation with ISMR. The entire BoB has no significant correlation between ISMR from January to May indicating that the Arabian Sea may play a more prominent role in the variability of ISMR rather than BoB. The significant correlation between SST and ISMR in southeastern Indian Ocean starts in June (after the onset of the SW monsoon). One reason for this could be that the southwest monsoon enters India through the Arabian Sea. The BoB is a region of large freshwater input, high sea surface temperature, and variable monsoonal forcing. Field data (Shetye et al. 1996) provide an improved picture in the western bay during the northeast monsoon, which peaks during January to March. During this period, the anticyclonic circulation of the upper ocean intensifies in the northern bay; to the south, the north equatorial current (NEC), the south equatorial counter current (SECC), and the south equatorial current (SEC) are well developed. The cycle of seasonal currents is well understood that the monsoonal winds are the main contributor. However, recent observations from satellites reveal a character inside the main seasonal current. In the present study, aim was to investigate the intraseasonal variability in the BoB JJAS mean SST and SSC anomaly and its relationship with Indian summer monsoon rainfall (ISMR).

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2 Data Description and Methodology SST and Precipitation: We have used SST and precipitation data of tropical rainfall measuring mission (TRMM) for the years from 2001 to 2013. The TRMM, a joint mission of NASA and the Japan Aerospace Exploration Agency, was launched in November 27, 1997, to study rainfall for weather and climate research, carrying five instruments, one of which is the TRMM microwave imager (TMI). TMI is a multichannel, dual polarized, conical scanning passive microwave radiometer designed to measure rain rates over a wide swath. TMI also measured SST and TMI-based SSTs were the first satellite microwave SSTs available and have proven to be of great value to many areas of research. Surface Currents: We have used daily currents data of surface currents from diagnostic model (SCUD) for the years from 2001 to 2008. Ocean currents are one of the variables that are most difficult for observations, both in situ and remote. Satellitederived sea level topography and surface winds are utilized to optimize coefficients of the SCUD diagnostic model. The SCUD is a new IPRC product, providing daily global maps of ocean surface velocities, including both pressure and wind-driven components. Methodology: We have used grid analysis and display system (GrADS) to plot the variables and estimate the standard deviation and correlation coefficients. First, we have extracted JJAS data of SST, precipitation, and currents for every year from daily data. We have used daily data instead of monthly data as it shows the clear variability. A group of anomalies can be analyzed spatially, as a map, or temporally, as a time series. Results and Discussion: We used TRMM and SCUD data to make an analytical effort to find out the intra-seasonal variability in the BoB daily JJAS mean areaaveraged SST anomaly and JJAS mean area-averaged SSC anomaly and also to get a possible relationship between these JJAS mean averaged parameters to the ISMR using the TRMM SST dataset 2001–2013 (i.e., 1586 days) at for the region 6° N to 22° N and 80° E to 94° E and SSC dataset of SCUD for the years from 2001 to 2008. Figure 1 shows the SST variation observed by TRMM dataset for year 2001 to 2013 (i.e., 1586 days) for the region of 6° N to 22° N and 80° E to 94° E. The JJAS daily SST anomaly standard deviation in northeast BoB is maximum, i.e., the dispersion/variability is maximum in northeast BoB which is of the order of 0–8 °C or more than this and other regions, the JJAS daily SST anomaly standard deviation remains low as there is river runoff of freshwater, the standard deviation in JJAS SST anomaly is higher in some region and lower in other regions which depend on the fluctuation of river runoff due to the variability of rainfall in the JJAS season and also the distance from the off coast region from the coast. Tropical and equatorial Indian Ocean temperature, salinity, current, surface wind variability and Indian Ocean Dipole (IOD) characteristics were studied using the RAMA mooring and GODAS dataset for recent past ((Pandey and Singh 2010a, b; Pandey and Kurtakoti

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Fig. 1 Standard deviation in JJAS daily SST using TRMM dataset for the years 2001–2013

2014; Pandey 2018; Agrawal and Pandey 2017a, b) and along with this the changing relationship of ENSO-IOD and ENSO-IOD and its impact on ISMR and temperature and salinity variability of tropical Indian Ocean, its relationship with ENSO, IOD and wind were studied using the model and observational dataset (Agrawal and Pandey 2017a, b). Figure 2 shows the area daily JJAS SST anomalies variability. The positive and negative amplitude values are measure of area daily JJAS SST anomalies changes over a period, thus, the variability in area daily JJAS SST anomalies for every year. The maximum anomaly is observed 1 to −0.9 °C. There are so many frequent high and low peaks observed in a year which implies the existence of the intra-seasonal variation in the area daily JJAS SST anomalies of the BoB. The intra-seasonal oscillations have been found in SST of northern BoB which was found to be controlled by freshwater from rivers and rain during monsoon (Sengupta and Ravichandran 2001). Roxy and Tanimoto (2007) found that the SST over the Indian Ocean influencing the intra-seasonal variability of the ISMR. Senan et al. (2003) observed the intra-seasonal mode in the equatorial Indian Ocean zonal currents. Some recent developments prediction of monsoon-related intra-seasonal variability (ISV) is reviewed and found that prediction of ISV provides a method for extended range probabilistic prediction of synoptic activity as ISV strongly modulates the synoptic activity in the tropics (Goswami et al. 2011). In addition, the BoB (referred to hereafter as the bay) also receives large quantity of freshwater by excess precipitation and river runoff over evaporation (Rao and Sivakumar 2003). A study by Sengupta et al. (2001) finds the negative/positive SST anomalies generated by fluctuations of net heat flux at the ocean surface that moves northward following regions of active/suppressed convection. Such coherent evolution of SST, surface heat flux and convection suggest that air-sea interaction might be important in monsoon ISO. This large freshwater flux into the bay makes the waters in the near-surface layer less saline and maintains

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Fig. 2 Variability of daily JJAS SST anomalies average (spatial) over Bay of Bengal for the years 2001–2013

strong haline stratification (Shetye et al. 1996). This leads to the formation of an intermediate “barrier layer” (BL), the layer between the base of the mixed layer and the top of the thermocline (Lukas and Lindstrom 1991; Vinayachandran et al. 2002; Rao and Sivakumar 2003; Thadathil et al. 2007). BoB spans in the tropical monsoon belt, and its environment is strongly affected by monsoons, storm surges, and cyclones. Coastal currents are responsible for the transport and dispersal of these biological, chemical, and geological tracers in the water. Intra-seasonal variability was found in SST in the northern Indian Ocean. During summer, this part of ocean exhibits significant atmospheric ISV associated with active and break phases of the monsoon in the 30–90 days band, and also the signature of the SST variability is observed in the atmospheric variability (Vialard et al. 2011). The inter-annual variations in Indian continental rainfall during the ISMR can be usefully represented by two regional rainfall indices. Indian rainfall is concentrated in two regions, each with strong mean and variance in precipitation: the Western Ghats (WG) and the Ganges-Mahanadi Basin (GB) region. Inter-annual variability of rainfall averaged over each of the two regions (WG and GB) is uncorrelated; however, the rainfall over these two regions together explains 90% of the interannual variance of All-India rainfall (AIR). The lack of correlation between WG and GB rainfall suggests that the different mechanisms may account for their variability.

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Fig. 3 Correlation between IMR index and BoB SST anomaly for the years 2001–2013 (i.e., 1586 days)

Figure 3 shows the correlation between IMR index and JJAS mean SST anomaly during 2001–2013 for region 6° N to 22° N and 80° E to 96° E. The maximum and minimum correlation range exists that are −0.17 to +0.17, which is not a significant correlation. It indicated that the ISMR rainfall has no significant relationship with the mean JJAS anomaly of the BoBSST, where, through numerical simulations, it is reported that the temperature anomaly of the Arabian Sea has a good correlation with ISMR may be due to the onset of the monsoon signature found in the Arabian sea (Dube et al. 1990). Thus, the BoB JJAS SST mean anomaly has no significant effect on the ISMR while the Arabian Sea JJAS anomaly do this. The monsoon prediction studies improved after the Webster et al. (1998) which presented two explanations to predict the monsoon difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydro-dynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. The Indian summer monsoon (ISM) intra-seasonal oscillations (MISOs) induce intra-seasonal SST variability primarily through surface heat flux forcing, contributed by both shortwave radiation and turbulent heat flux, and secondarily through mixed layer entrainment (Li et al. 2016). Goswami et al. (2003) state that the active and break phases of the ISM are characterized by enhancement and decrease of precipitation over the monsoon trough region. Figure 4 shows the standard deviation in JJAS anomaly of SSC from SCUD for the years 2001 to 2008 for the region of 4° N to 26° N and 80° E to 100° E. The JJAS daily sea surface current anomaly standard deviation in off east coast of Indian in BoB is maximum, i.e., the dispersion/variability is maximum in northeast BoB which is

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Fig. 4 Standard deviation in JJAS anomaly of sea surface currents from diagnostic model (SCUD) for the years 2001–2008

of the order of 0.4 m or more than this. The JJAS daily sea surface anomaly standard deviation remains low in other part of the BoB. As there is river runoff of freshwater, so, the standard deviation in JJAS sea surface current anomaly is higher in some region and lower in other regions which depend on the fluctuation of river runoff due to the variability of rainfall in the JJAS season and also the distance from the off coast region from the coast. The effect of mixing of the salinity over various layers and during span of the ENSO in the Arabian Sea and BoB has been studied and found that the pronounced dilution in the salinity observed in the summer monsoon season in the BoB. A study by Rao et al. (2006) observed evolution of a mini-cold pool (MCP) off the southern tip of India (STI), and its intrusion into the south-central BoB during the summer monsoon season shows pronounced cooling episodes on intra-seasonal time scale with differences in their number, intensity, duration, and spatial extent. This variability that changes from year to year appears to be primarily determined by the corresponding variability in the upwelling driven by the divergence in the nearsurface (Ekman + geostrophic) circulation and wind-induced mixing. The signature of this cooling carried by the summer monsoon current (SMC) is seen with reduction in intensity in the south-central BoB limited mostly only to south of about 10° N. In the background of slow cooling caused by SMC, the cooling episodes of different amplitudes occur in the south-central BoB suggesting that spatially variable wind forcing is responsible for producing these episodes simultaneously on intra-seasonal time scale. Figure 5 shows the area-averaged daily JJAS sea surface current anomalies variability. The positive and negative amplitude values are measure of area-averaged daily JJAS sea surface current anomalies over a period, thus, the variability in areaaveraged daily JJAS sea surface current anomaly for every year. The maximum anomaly is observed 0.06 to −0.11 m/s. There are so many frequent high and low

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Fig. 5 Variability of SSC anomalies average over Bay of Bengal in JJAS using currents data of surface currents from diagnostic model (SCUD) for the years from 2001 to 2008

peaks observed in a year which implies the existence of the intra-seasonal variation in the area average daily JJAS sea surface current anomaly of the Bay of Bengal. Figure 6 shows the correlation between IMR Index and JJAS mean sea surface current anomaly during 2001–2013 for region 6° N to 22° N and 80° E to 96° E. The maximum and minimum correlation ranges exist are −0.2 to +0.2 which is very poor correlation. It indicates that the ISMR rainfall have no significant relationship with the mean JJAS anomaly of the BoB sea surface currents. Thus, the BoB JJAS sea surface current mean anomaly has no significant effect on the ISMR while the Arabian Sea JJAS anomaly do this. A sub-seasonal scale study of ISMR has been done to find out the active and break spell of the monsoon and to find the timing of this break that profoundly impact the agriculture in the Indian subcontinent (Vecchi and Harrison 2002). Kumar et al. (2019) have historically simulated the Indian summer monsoon rainfall (ISMR) for 1982–2006 using the regional climate model (RegCM) version 4.6 at 25 km horizontal grid resolution under which they found that JJAS (June–July– August–September) seasonal mean summer rainfall intensity over Indian landmass is increasing for the second decade (1997–2006) compared to first decade (1982– 1991) under historical scenario. Li et al. (2017) provided an analysis of variability of SST, precipitation, and salinity stratification during the ISMR done using satellite

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Fig. 6 Correlation between IMR index and BoB sea surface anomaly in JJAS for the years 2001– 2008 in JJAS using currents data of surface currents from diagnostic model (SCUD)

observations and climate forecast system reanalysis (CFSR) and performing ocean general circulation model (OGCM). Indian summer monsoon (ISM) intra-seasonal oscillations MISOs in the eastern Arabian Sea (EAS) achieve the largest intensity in the developing stage (May–June) of the ISM, and thus, the Arabian Sea anomaly has profound effect on the Indian summer monsoon rainfall.

3 Conclusion The intra-seasonal variability was found in the BoB daily area average daily JJAS SST anomaly for period 2001–2013 and area average daily JJAS SSC for period 2001–2008. An effort was made to establish a relationship between the BoB JJAS mean SST anomaly and JJAS mean SSC anomaly to the ISMR index, to measure the affinity of these changes to the ISMR. The intra-seasonal variability observed in the BoB area-averaged daily JJAS SST anomaly and area-averaged daily JJAS SSC anomaly is assessed by the standard deviation and well as temporal variability of area-averaged JJAS daily anomalies of SST and area-averaged JJAS daily SSC using different statistical techniques on TRMM and SCUD datasets. We observed higher dispersion in both data in the coastal region in comparison with the interior ocean. The correlation coefficient between area average daily JJAS mean SST anomaly versus Indian monsoon rainfall; and in case of daily JJAS mean SSC anomaly versus ISMR index is very low which shows a very poor relationship between these two parameters of BoB and IMR. Thus, we observed that there is no significant effect of BoB mean JJAS SST anomaly and mean JJAS SSC anomaly on the ISMR.

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Acknowledgements The authors would like to thank Tropical Rainfall Measurement Mission (TRMM) and Surface Currents from Diagnostic Model (SCUD) data sources for providing data.

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Biological Disasters

Climate Change and Its Impact on the Outbreak of Vector-Borne Diseases Vanya Pandey, Manju Rawat Ranjan, and Ashutosh Tripathi

Abstract Global climate change is nowadays a widespread phenomenon. Earth’s climate is changing and this affects not only the temperature, rainfall, and weather patterns, but is also expected to stimulate the emergence and spread of several infectious diseases. This applies to both climate changes as a whole as well as the individual factors such as temperature, rainfall, humidity, etc. Changes in climate and weather patterns impact the survival, distribution, reproduction of disease pathogens, vector, and hosts as well as the availability of transmission environment. Diseases that are new to the field of medicine are appearing. These include Ebola, Lyme disease, Hantavirus, etc. while the global concern is the climatic impact on vector-borne diseases, such as Malaria, Chikungunya, Dengue, Yellow Fever, and Zika. However, the observed data is too short, the impact of climate independent factors too great, with lack of understanding of the degree to which climate affects the disease patterns, a small amount of uncertainty still remains on this subject. This review examines the possible effects of changing climate on vector-borne diseases, the relation between various climatic variables and pathogen/vector as well as the mitigation measures against vector-borne disease risks with respect to Dengue, Chikungunya, and Zika, respectively. Keywords Climate change · Vector-borne diseases · Mosquito · Dengue fever · Chikungunya · Zika

V. Pandey · M. R. Ranjan · A. Tripathi (B) Amity Institute of Environmental Sciences, Amity University, Noida, Gautam Buddha Nagar, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_13

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1 Introduction 1.1 Background: Climate Change Owing to the varied interactions between the ice core, sea, biosphere, atmosphere, and solar energy, the global climate system has been stable for decades. There is actually a trend of accelerated global warming triggered by increasing greenhouse emissions. All through the twentieth century, the global temperature amplified by approximately 0.3–0.6 °C, which proves that these changes due to global warming have anthropogenic origin (Mirski et al. 2011). Climate change is a significant change in the average weather patterns (that have come to define Earth’s local, regional and global climate) over a significant period of time. Climate change includes the changes in climatic factors (such as precipitation, humidity, and temperature), extreme-weather events (La Nina, El Nino), and meteorological hazards (floods, droughts, heat waves, and cold waves). Climatic factors can alter disease (infectious) dynamics by affecting the pathogens, vectors/hosts, and environmental transmission routes. The extreme weather events are known as a value of climate variable which goes beyond a threshold range of observed data values (although these events are rare, their frequency and intensity have been gradually increasing which indicates a significant aspect of changing climate). They often come with dramatic changes in one or more climatic factors/variables.

1.2 Vector-Borne Diseases Many pathogens are passed and transmitted by intermediate hosts/vectors for completion of their life cycle and diseases caused by such pathogens are called vector-borne diseases (Rai and Nathawat 2017). Three components essential to these diseases are: an infectious agent (pathogen), host (or vector), and environment of transmission (Fig. 1). For the successful transmission, reproduction, and survival of vector, host and pathogen, suitable climatic conditions are required. They also have a very significant effect on the worldwide hydrological cycle, thus increasing the intensity, frequency, and duration of droughts, heavy precipitation, and flooding. Whereas the metrological hazards like droughts and floods act as indirect factors of climate change affecting the infectious diseases. Vector-borne diseases are sensitive to weather and climate, including effects of temperature on mortality rate of vectors, effect of humidity and temperature on the activity of vector and host finding, effects of temperature on vector development rate through stages, and effect of precipitation on accessibility and availability of breeding habitat for insect vectors. The climate also has indirect effects on the occurrence of vectors by determining the habitat qualities and the availability of animal hosts for vector blood meals (Rai and Nathawat 2014; Ogden 2017) (Table 1).

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Fig. 1 Climate change, human infectious diseases, and human society (Wu et al. 2016)

Table 1 Examples of vector-borne diseases likely to be sensitive to climate change (Haines et al. 2006)

Vector

Major diseases

Mosquitoes

Malaria, Filariasis, Dengue fever Yellow fever, West Nile river

Sandflies

Leishmaniasis

Triatomines

Chagas disease

Ixodes ticks

Lyme disease, tick-borne encephalitis

Tsetse flies

African trypanosomiasis

Black flies

Onchocerciasis

Snails (intermediate host)

Schistosomiasis

2 Vector: Mosquito Human beings have been plagued by mosquitoes since time immemorial. Every year millions of deaths are caused by mosquitoes due to their ability to carry, transmit and spread the pathogen effectively. Mosquitoes are well-known vectors of human as well as animal diseases (Rai et al. 2013c). Dengue, Chikungunya, Zika, and Yellow Fever are some of the arboviral diseases transmitted to humans by mosquitoes. As with poikilotherms, the body temperature of mosquitoes is not constant, thus the temperature of the environment along with other environmental factors affects the life of mosquitoes to a great extent. This is also because of their short life cycle. Hence, they depend on several tactics to lower down this thermal stress. They (i) respond quickly to new opportunities in terms of overcoming barriers (dispersal) to colonize new habitats, (ii) expand into new areas as conditions become

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suitable for their population, (iii) dynamic interactions and competitive exclusion between two species. This is the reason they are able to colonize a large number of habitats and thus their distributions are highly dynamic in space and time. For example, mosquitoes are found in the entire world (except in Antarctica) and can live in a broad variety of habitats, from tundra to tropical forests to urban and rural areas. But in general, they use natural or artificial container that holds water. This can include ponds, tree holes used tires, plastic buckets, etc. The tendency of Aedes aegypti and Aedes albopictus to colonize used tires may be due to the fact that these species natively belong to the forest and primarily breed in tree holes, which are somewhat similar to the tires, as the dark color and the dark interior of the same provides an attractive place of rest or oviposition site for Aedes sp. (Tedjou et al. 2019). The spread of diseases—Dengue, Chikungunya, Zika has become a health concern globally (in the light of major recent spread events), and it has been already predicted that climate change will have an impact on the mosquitoes’ distribution, which will allow them to bring new pathogens to naïve populations. Chikungunya, Dengue Fever, and Zika are mosquito-borne diseases transmitted to humans by the two most common mosquitoes—Aedes aegypti (Figs. 2 and 3) Aedes albopictus (Fig. 4). These two species have spread worldwide at lower as well as middle latitudes in recent decades, providing more and more new vectors for transmission of ‘forest diseases’ to humans, especially in peri-urban areas. Both of these mosquitoes are generally much more active and bite in the daytime in full sunlight, unlike other mosquitoes. Aedes aegypti is the most common mosquito transmitting diseases. It is believed that Aedes aegypti was transported from Africa to other parts of the world. Aedes albopictus (the Asian tiger mosquito), native of Asia, was first recorded in early 2000s in Cameroon, Central Africa. Aedes aegypti has a high vectoral capacity, i.e., it acts as a very effective transmitter of pathogens in nature. In general, it lives in close proximity to humans, while Aedes albopictus is less likely to live as close or spread the disease (although it is a potential vector of all the three diseases mentioned above). Aedes aegypti is more extensively distributed. Fig. 2 Aedes albopictus (Gathany 2006) Centers for Disease Control and Prevention’s Public Health Image Library (PHIL)

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Fig. 3 Aedes aegypti (Gathany 2006) Centers for Disease Control and Prevention’s Public Health Image Library (PHIL)

Fig. 4 Two adult Aedes albopictus mating (James Gathany, Centers for Disease Control, and Prevention)

It shows a broader distributional potential across tropical and subtropical regions like Australia, Africa whereas, Aedes albopictus is broader potential for distribution across temperate climates like Europe and the United States (Fig. 5). The relative roles of these species in transmission of dengue appear to be unequal. A new review shows Aedes albopictus does not seem to cause large outbreaks of dengue and that Aedes aegypti appears as the primary carrier of dengue-sized transmission. However, these imbalances may not hold true for other diseases transmitted by Aedes, such as Chikungunya, which is readily transmitted by Aedes albopictus, at least in some cases. ENM (ecological niche models) based on future conditions showed correlations between the overall distribution patterns of the conditions of the future and of today; however, in the western United States there was an extension of the northern range to include parts of Southern Canada for Aedes albopictus in 2050 and 2070. Future versions also anticipated Ae. albopictus expanding further to the East to occupy much of Europe in both periods of time. In East Australia, Aedes aegypti was projected to extend to the South in 2050 and 2070 (Kamal et al. 2018). The actual global distribution of these species overlaps due to repeated active invasions by both Aedes albopictus and Aedes aegypti. Since both species exploit the same ecological niches and resources (larval habitat, source

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Fig. 5 Aedes aegypti and Aedes albopictus possible distribution (present-day climatic conditions). Blue areas indicate suitable environment and grey as unsuitable (Kamal et al. 2018)

of blood), Aedes aegypti and Aedes albopictus have competitive interactions which result in one species being replaced by another in a given environment. Indeed, multiple studies conducted around the world have shown improvements in distribution and abundance of the native species after Aedes albopictus was introduced. It is likely that this competitive phenomenon is in progress in Cameroon, where both species use the same types of resources. Competition mechanisms are not well understood, but several authors agree that it could occur at the pre-imaginal stage and that the main driving forces may be several factors such as temperature, precipitation, response to symbionts, parasites, and chemical interferences that delay growth. By contrast, two decades after a competitive displacement at Florida (USA) location, co-existence among Aedes aegypti and Aedes albopictus was described (Tedjou et al. 2019). Such shared larval environments and changes in distribution and abundance of Aedes albopictus and Aedes aegypti after the establishment of the other species indicates that there is a competitive displacement between them. While it appears that Aedes albopictus is the superior larval competitor, yet Aedes aegypti is the vector species responsible for the majority of dengue outbreaks. A thorough understanding of the current geographical distribution of these species, which has been the focus of several recent studies, is therefore to be obtained quite a bit. A predictive view of their distributional capacity in the coming decades is therefore very useful in view of the environment and climate changes already occurring on the future distribution of mosquitoes during their global invasions (Campbell et al. 2015).

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3 Direct Effects of Weather and Climate The occurrence of vector-borne diseases spans across the tropics, subtropics, and the temperate climatic zones. Amid certain few exceptions, vector-borne diseases do not occur in the world’s colder climatic (polar) zones (Rai and Nathawat 2013a). The extent to which the transmission of vector-borne disease occurs in a particular area is determined by two major factors: (i) the presence of vector capable of transmitting the disease (relative abundance of vector), and (ii) presence of a parasite. Any factor that in turn influences these two determinants directly affects the transmission event. The factors that influence the reproduction, growth rate, and longevity of the vectors are the direct results of changes in precipitation, wind patterns, humidity patterns, global, regional, local temperature resulting from anthropogenic climate change (Fig. 6). All these factors would thus be associated with changes in vector density.

4 Indirect Effects of Weather and Climate Indirect effects of climate change include changes in vegetation, its cover, and agricultural practices which have an indirect impact on disease transmission (Fig. 7). These are mainly due to changes in temperature and rainfall patterns. Their association with increased or decrease vector density either stimulates or inhibits the transmission of disease. For example, irrigated lands (such as paddy fields) provide a suitable breeding ground for several vectors. Malaria and schistosomiasis are the two vector-borne diseases that are particularly impacted by improvements in irrigation methods and irrigated field distribution. Areas with heavy pesticide use often experience resistance between vectors to the pesticide and insecticide used, with significant implications for the transmission of the disease.

Fig. 6 Direct effects of climate change on disease vectors

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Fig. 7 Indirect effects of climate change on disease vectors

Another indirect effect of climate change is associated with rise in sea-level resulting in coastal flooding. They are known for the expansion of vectors as well as their habitats. Standing water caused by either heavy rainfall or coastal flooding acts as the new breeding sites for mosquitoes. Flooding initially may flush out all the breeding sites but as the water recedes, these sites come back leading to a massive outbreak. For example, an earthquake and subsequent flooding in Costa Rica’s Atlantic region in 1991 and flooding on the Dominican Republic in 2004 led to a massive Malaria outbreak (World Health Organization, 2020. Humanitarian Health Action: Flooding and communicable diseases fact sheet). Also, the proliferation of lagoons due to coastal flooding containing brackish water may also influence the availability of habitat and may either encourage or discourages the vector species depending on whether they prefer brackish water or fresh water. Another indirect factor affecting disease transmission due to anthropogenic climate change is drought and desertification, including the expansion of global desert belts. This is imagined to decrease the vector-borne transmission. This is because breeding by vector often depends upon a moist environment and drought conditions strictly affect the longevity of vector by decreasing it. Several other factors indirectly leading to spread of vector-borne diseases are Urbanization, poverty, human migration, trade, etc. (Fig. 8).

5 Pathogens and Climate Change Pathogen refers to a wide range of agents for the disease, including viruses, bacteria, fungal germs, and fungi. The impact of climate change on pathogens can be direct, affecting the survival, reproduction, and life cycle of the pathogens, or indirect, affecting the habitat, environment, or competitors of the pathogens. As a result, it will alter not only quantity but also the geographical and seasonal distributions of pathogens Global climate change, such as global warming, may directly or indirectly influence other factors, with implications for the arbovirus epidemiology and evolution. Changes in the nature and ecology of the viruses that follow environmental changes linked to climate change-such as access to new hosts,

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Fig. 8 Factors related to global change leading to vector-borne diseases (Sutherst 2004)

including vertebrates and mosquitoes—will impact the purification of selection with corresponding effects on the diversity of viruses. Global warming, for example, may speed up the decline in sylvatic ecosystems, which is already occurring as a result of increasing human population and growing agricultural use. It would also increase interaction between the sylvatic and urban cycles, thereby increasing the potential for sylvatic viruses to re-emerge into urban cycles and cause human disease. New climatic conditions are likely to produce novel vectors and host species with unknown effects on viral evolution and epidemiology, which will inevitably impact selection purification and viral diversity restrictions. Some of the most important environmental factors affecting the pathogens are: Temperature, humidity, and CO2 level (Rai and Nathawat 2013b).

5.1 Temperature Temperature might have an effect on the disease through affecting the life cycle of pathogens (bacteria, virus, parasite, etc.). In general, development rate of a pathogen accelerates as the temperature rises. A pathogen first needs an optimum temperature range to survive and develop as well. For example, the optimal temperatures for DENV transmission lie between 28 and 32 °C.

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While the maximum temperature of 22–23 °C and minimum temperature of 25– 26 °C for development of mosquito and Japanese Encephalitis Virus (JEV) transmission, play a key role in ecology of JEV, increased temperature can increase the mortality rates for some pathogens. The development of malaria parasite (Plasmodium falciparum and Plasmodium vivax) ceases when temperature exceeds 33– 39 °C. Second, increasing temperature might also influence the reproduction and incubation period (EIP) of pathogens. EIP for P. falciparum, for example, decreases at 25 °C from 26 days at 20 °C to 13 days. On the contrary, lower ambient environmental temperature is likely to extend EIP, which in turn can decrease disease transmission, such as dengue, since fewer mosquitos may live long enough. Third, prolonged warm weather periods will increase the surface temperature of water sources which can provide an ideal climate for microbial and algal bloom reproduction cycles.

5.2 Humidity and Rainfall Climate change may also cause a shift in rainfall, that influences the spread of waterborne pathogens. Rainfall plays a significant role in the development of pathogenic water-borne diseases. Rainy season is also known to increase the fecal pathogens because heavy rainfall may cause sediments in water to stir up, allowing faucal microorganisms to accumulate in water. Unusual precipitation after a long drought, however, may lead to an increase in pathogens, triggering an outbreak of the disease. Droughts lead to low river flow, causing the concentration and accumulation of effluent water-borne pathogens. Shift in moisture also impacts infectious disease pathogens. Airborne infectious disease diseases such as flu appear to be immune to humidity.

5.3 CO2 Increasing concentrations of CO2 in many ways may affect the production of pathogens. It has been shown that higher concentrations of CO2 can stimulate microbial development, which leads to more intensive production of fungal spores. Increasing CO2 levels, however, can also cause physiological changes in the host organism which can increase its resistance to pathogens.

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5.4 Wind Wind is an important factor that affects airborne disease pathogens. Literature has indicated a strong link between the association/attachment of dust particles and the survival/transport of viruses. During Asian dust storms (ADSs), the presence of desert dust in the atmosphere has been reported to be associated with increased concentration of cultivable bacteria, growable fungi, and fungal spores (Chen et al. 2010). Studies have further indicated that infectious disease viruses are transmitted through the ocean by powder particles that can promote viral transmission between distant hosts.

6 Pathogen Transmission Eating and living behavior of Aedes albopictus, Aedes aegypti make them effective human disease vectors. An insect or an arthropod is known to be an effective vector of viruses if it shows infection and spread pathogen naturally. For Aedes aegypti and Aedes albopictus, this holds true as they meet the requirements for arboviruses mentioned above. While Aedes aegypti is commonly known to be more capable vector for many pathogens, including Chikungunya, Dengue, Zika, Yellow Fever, and Malaria, Aedes albopictus has the ability to vector several of viruses that are vectored by Aegypti but with much-decreased competence but with a heightened ability to extend its range to comparatively cold areas. There are two main factors which decides the ability of Aedes and Albopictus species to vector these pathogens: if the pathogen is capable of infecting the mid-gut cells and whether it is capable of spreading it to another host for the vector. Temperature also affects both the factors, also mosquito development, and EIP. Climate change after effects allow development of pathogens more quickly in these species and spread into newer, larger areas, delivering and transmitting pathogens with them.

6.1 Dengue Virus Complex The Dengue Virus Complex (DENV) is a member of the Flaviviridae family (Refer to Fig. 9). The DENV causes more cases annually than any other arbovirus. The DENV mainly infects primate species and mosquito species primarily in the genus Aedes. It is transmitted to human by the same primary vectors, Aedes aegypti and Aedes albopictus. The DVC (refer to 4 serotypes that lead to DF) has become a global concern. Dengue infection can cause dengue fever (DF), dengue hemorrhagic fever (DHF) as well as shock syndrome (DSS).

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a

b

c

Fig. 9 Structure of DENV—Dengue Virus a, CHIKV—Chikungunya Virus b and ZIKV—Zika Virus c, Modis et al. (2017)

This is the fastest spreading arbovirus according to the World Health Organization; figures suggest that 390 million individuals are actually diagnosed annually, while 3.9 billion people are still at risk. The serotypes might cause various severities of the virus in different individuals. This ranges from serious dengue to hemorrhagic fever to asymptomatic, the former usually arising after several serotypes have been compromised, which may also lead to death or even coma. Vaccines are being created to avoid dengue outbreaks, with one currently on the market, although it is advised to receive the vaccine only for those people who are seropositive. Because of this vaccine’s limited use, vector control remains the primary method to prevent spread of this virus. Throughout the 1950s and 1960s, eradication was accomplished in the America in an effort to eliminate yellow fever outbreaks, but return of Aedes aegypti during 1970s resulted in endemic dengue outbreaks. Since the 1970s, the prevalence of dengue has developed from nine countries to be endemic in more than 100. It takes between about 5–33 days at 25 °C, for virus to multiply, mature, and go to salivary glands before the mosquito can spread virus to another human. Aedes aegypti is presently the only primary vector whereas Aedes albopictus acts as a vector in areas where Aedes aegypti is not present. Lambrechts et al. have shown that a higher Diurnal Temperature Range (i.e., 20 °C)—observed autumn temperate climates and spring—has a harmful impact on survival and competence of Aedes aegypti compared to 10 °C DTR, usually observed summer temperature levels (Reinhold et al. 2018). As discussed earlier, temperature influences blood-feeding patterns and survival of Aedes aegypti, and possibly Aedes albopictus that would potentially both have an effect on pathogen transmission.

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Aedes aegypti is endophilic and endophagic, allowing this species to disperse to regions outside its natural temperature, which means it will take dengue to newer regions, making forecasts of dengue dispersion a challenging challenge. Couret and Benedict studied the effect of temperature on the evolution of Aedes aegypti which showed the positive impact of higher temperatures on the developmental rate. An increase in temperature will lead to greater dissemination, and increased transmission of viruses. Watts et al. found that for DEN2 in Aedes aegypti, the duration of extrinsic incubation (EIP)—time period between mosquitoes consuming an infected blood-meal to transfer the disease to next host decreases by several days at higher temperatures, possibly allowing quicker transmission (implications of global warming) (Reinhold et al. 2018). An experiment was conducted by an author with similar results on the DEN2 and DEN4 serotypes. Aedes aegypti was found to have less spread of dengue at lower temperatures. Since Aedes albopictus has the capability to thrive in colder climates, carry pathogens even during winters because of diapause, and having exophagic tendencies, (Brady et al. 2014) concluded that extended lifespan of Aedes albopictus leads to extension of its vector potential beyond the vector potential of Aedes aegypti. Brady et al. (2014) stressed that longer life span of Aedes albopictus allows their vector potential to surpass Aedes aegypti. Another author did, however, notice the Aedes albopictus has the same sensitivity to dengue as Aedes aegypti and may have a higher viral load but DEN2 and DEN4 are not found in the saliva in high numbers, indicating a lower transmission potential for these serotypes. Aedes albopictus is not known to cause dengue in regions where Aedes aegypti is already present, though it has been shown to transmit it in the laboratory. Aedes albopictus is considered to be primarily a secondary vector for the conservation of dengue in non-urban areas. Because of this, Aedes albopictus as a dengue vector, compared to Aedes aegypti, is considered less important. Mathematical modeling revealed a strong reliance on the occurrence of dengue seasonal variations. Even this seasonality is presumed since only vectors remain, which is typically weaker during warm and rainy seasons. Thu et al. however found the spread of dengue within Aedes aegypti increased with humidity, more than 60% preferred humidity, and preferred a Ta range of around 24–31 °C. This shows that the amplification of the mosquito virus tends to be optimum under conditions even beneficial to mosquitoes (Reinhold et al. 2018). Thu et al., however, observed that the dengue transmission within Aedes aegypti increased in humidity by more than 60% with desired humidity and supported a total range of around 24–31 °C. This shows that increase of the mosquito virus appears to be optimal under conditions also favorable to mosquitoes (Reinhold et al. 2018).

6.1.1

Temperature and Transmission

Temperature is an important component of DENV ecology, seen from its many contacts with other components of the disease system. Mostly, temperature increases

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are associated with a faster rate of viral replication inside the vector and a shorter period of extrinsic incubation (EIP; the time required for DENV to be transmissible to another host after initial mosquito infection). Four principal DENV serotypes are known: DENV-1, DENV-2, DENV-3, and DENV-4. Different writers have had conflicting results. For both DENV-1 and DENV4, one demonstrated that the time between feeding and virus detection in salivary glands for Aedes aegypti was reduced from 9 days at 26 °C, and from 28 °C to 5 days at 30 °C. One more specifically proved the EIP for DENV-2 virus in mosquitoes that they rely on temperature by allowing infected mosquitoes to feed on monkeys. They found that the EIP was of 7 days at 32–35 °C temperatures and about 12 days at 30 °C, whereas there was no virus transmission within 25 days at 26 °C. One author detected the end of the EIP as the moment when the virus was found in the mosquito while another identified it as the moment the mosquito was transmitting the virus.

6.2 Chikungunya Virus Chikungunya is of the genus Alphavirus of Togaviridae family (Refer to Fig. 9), is commonly found in Africa and Asia, though in recent years it has spread and caused outbreaks in more than 60 countries. It was first isolated from the serum of a female patient suffering from joint pains. Neither the simians nor the vectors show clinical evidence of virus infection. Nonetheless, mosquitoes infected by infected monkeys after taking a blood meal exacerbate the virus, which, it has been proposed, may be transmitted to the eggs that are then deposited in the forest. It is believed that Chikungunya Virus (CHIKV) may survive in these eggs for long periods of time, in common with certain other arboviruses. While instances of Chikungunya are comparatively few in numbers as compared to dengue, outbreaks can be devastating in the naïve populations. The disease is difficult to follow, often thought to be Dengue because of their common distribution, symptoms, and vectors. Dengue and Chikungunya lead to flu-like symptoms such as fatigue and diarrhea but, except for a recent epidemic in La Réunion, Chikungunya can lead to extreme joint pain and unusually contributes to death. One possibility is that the virus can live in wildlife species, traveling in epizootic waves. This is because of interaction between virus genetics, Ambient Temperature (Ta), and mosquito which means that Ta plays a major function in Chikungunya transmission. The 2007 outbreak revealed Aedes albopictus having the capability to cause disease in regions it can spread, and there is the possibility that venereal and transovarial transmission within mosquitoes result in disease surge (Reinhold et al. 2018). There is currently no antiviral vaccine available to avoid Chikungunya, therefore control of vector and protection from bites of mosquitoes are few important methods

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to avoid transmission of disease. The spread of the disease to new and higher latitudes over the last decade is currently a cause for concern.

6.3 Zika Virus Zika virus comes under flavivirus family (Refer to Fig. 9) and bear a resemblance to dengue so much so that often it is called as “fifth dengue serotype,” as serotypes are dependent on the cross-neutralization of antibodies, and it has the ability to deactivate dengue antibodies. Aedes aegypti and other Aedes species are the main vectors of Zika in the Americas but can also be transmitted by sexual activity and also from mother to fetus. The illness has been attracting interest in recent years as linked with microcephaly and other defects of babies born to mothers who were infected with Zika during birth. Mostly, 1 in 5 people infected with Zika show symptoms, including muscle and joint pain, congestion, fever, rashes, and other flu symptoms. Zika was first reported in Uganda in 1952 among humans, and was mainly widespread in African and Asian countries until 2007. The first occurrence outside endemic areas occurred on Yap Island, and it continued to French Polynesia from there, causing a big outburst in 2013 and 2015. Unfortunately, there is no evidence of how Zika is evolving at various temperatures that means the only possible way to monitor its possible effect and spread is by understanding more about its vectors. Some researches although have observed other species of Aedes, including Aedes vexans, that may vector Zika, however still low possibility of vector competence other than Aedes aegypti, such as Aedes albopictus, that can dramatically alter the extent of effects Zika could have. Zika virus currently does not have a vaccine or cure so vector control remains important.

7 Climate and Vector 7.1 Temperature Aedes aegypti and Aedes albopictus have been capable of spreading their geographical range in the world considerably. Climate change is very much expected to affect the range of these two species over the past 30 years and several studies have shown that. Several studies have also highlighted the effect on co-occurrence and inter-species rivalry between Aedes aegypti and Aedes albopictus (Reinhold et al. 2018). Climate change is expected to change the distribution of vectors and/or the expansion of geographic ranges of mosquitoes with possible health effects on human populations

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Fig. 10 Ta (ambient temperature) affects the mosquito development (blue), host-seeking and blood-meal intake activity (red), as well as pathogen development and transmission (purple). Hence, ambient temperature affects species geographic repartition, spatial distribution of mosquito, population dynamics (green). (Reinhold et al. 2018)

and other animals due to increased pathogen transmission rates, including dengue and Zika. An author also estimated that areas with ideal environmental conditions for the production of Aedes are in the sense of climate change. The ambient temperature being the most vital abiotic factors affect the physiology, behavior, and also insect survival (Fig. 10). In general, the internal temperature of insect varies as poikilotherms depends on the temperature of the surrounding environment. Insects face multiple threats including changes in metabolism, desiccation, and loss of mobility due to local, frequent, and seasonal thermal variations. But insects can function only within a temperature range; likelihood of mortality increases beyond that range. The performance range may be influenced by several endogenous factors including the physiological state or age of the insect. The range of activity is species-specific. While some have a broad temperature range at which they can function (i.e., generalists), other species have a narrower range to sustain their activity (i.e., specialists).

7.1.1

Fight Activity, Host-Seeking, and Blood-Feeding

Since mosquitoes travel from in and out of shelters and encounter seasonal thermal variations, they might also experience a substantially different temperature that can influence their behavior, including host-seeking. For Aedes aegypti, 10 °C is the lower limit for temperature, below which the mosquitoes are torpid and cannot even move.

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Rowley and Graham find female Aedes aegypti tethered together were able to fly sustainably between 15 and 32 °C, whereas flight was possible at high temperatures such as 10 and 35 °C for some time. The optimum flight temperature was calculated to be at 2 °C in terms of length and distance flying, but overall, flight efficiency of the female Aedes aegypti were better below 27 °C. The maximum speed that was observed was at 32 °C/50% humidity (34.1 m/min). It is important to stress that the mosquitoes could fly at 10 and 35 °C. The authors highlighted that flying at lower temperature enables species to become active during cool hours of the day (i.e., early morning and late afternoon) (Reinhold et al. 2018). However, in Aedes albopictus changes in flight activity and host-seeking behavior due to temperature is comparatively less known. It has now been concluded that mosquitoes bite at an optimum temperature during which they are most active. In Aedes albopictus, the shortest gonotrophic cycle has been shown to occur at 30 °C (3.5 days). The authors reported the largest cycles at this temperature during the female’s lifetime.

7.1.2

Ecology and Dispersion

Optimum and Survival Temperatures It has been shown that both the availability of food and the density are factors to consider in combination with temperature for affecting the rate of larval development and survival. At higher temperatures (30 vs. 21 °C), the development time is much shorter and is related to density and availability of food. An author found out at 32 °C, the time taken by the larvae to complete their development was optimal and that death at 14 and 38 °C was too significant. 36 °C was the maximum temperature at which complete development occurred. . In a wider range of Ta, Aedes albopictus can grow and survive. It has an ideal temperature of 29.7 °C. It has been showed to evolve entirely between 15 and 35 °C, and to live longer in both females and males at lower temperatures (15 vs. 35 °C). Different findings have been observed across studies for albopictus, depending on area of source populations studied and their resistance to cold (Reinhold et al. 2018).

Phenology and Population Dynamics Aedes albopictus usually occur in temperate as well as tropical regions and is capable of becoming an egg or an adult over winter. Additionally, they might also lay eggs in subtropical areas that will hatch but would not enter diapauses. The survival ability of various Aedes aegypti and Aedes albopictus strains were evaluated by (Hawley et al.) in, Indiana (United States). While no strains of Aedes aegypti survived, and strains of Aedes albopictus spread to North American and Asian environments, thus

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concluding albopictus had a higher survival rate than tropical strains (Reinhold et al. 2018). In addition, southern strains had low potential to lie dormant within the USA than the same from northern areas of the world. This underscores Aedes albopictus good capability to rapidly adapting to new thermal conditions and spreading to colder regions. Ae. aegypti is found mainly in tropical and subtropical regions but is not entirely bound by external temperatures, becoming the most cosmopolitan species of insect vectors. Importantly, diurnal and seasonal variations of the Ta affect both species growth, density, and dispersion. Carrington et al. took a complex approach Model to estimate below which thermal limit an Aedes aegypti mosquito population can survive and note that both small and large DTRs can affect population dynamics. Although Ta significantly affects population dynamics, rainfall and drought also influence mosquito intensity and dispersion in both tropical and temperate areas (Reinhold et al. 2018).

7.2 Humidity and Rainfall Precipitation and rainfall provide the necessary habitat for the aquatic stages in life cycle stages of mosquitoes. Pots that are ubiquitous in urban environments are also a major shelter for them. Not surprisingly, Aedes sp. increased in number during monsoon rain. Rainfall is very essential in creation and maintenance breeding sites. In Puerto Rico, for example, near San Juan, it was found that greater precipitation was correlated with increased Aedes sp. However, heavy rainfall can wash out the sites meant for breeding thus decreasing population of vectors (Barrera et al. 2011). Mosquito ranges are also affected during El Nino and La Niña conditions, influenced by the wet and dry conditions that follow. Dry conditions, can although expand the range of vectors in urban areas. Kearney et al. (2009) analyzed improvements in the Aedes aegypti distribution in Australia as a result of climate change and concluded mosquito habitat would likely broaden as individuals adapt to a drier atmosphere due to improved household water supply. For example, the risk of Dengue from Australia comes not from the direct effects of climate change but from the protective steps that people take to reduce its impact.

7.3 Interaction Between Temperature, Precipitation, and Vector Habitat Where temperature influences the vector’s rate of development, its mortality, behavior and controls the replication of virus inside the mosquito; variation in precipitation affects the availability of habitat for larvae and pupae of Aedes aegypti and Aedes albopictus nonetheless.

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Indirectly, the impact on land cover and land use of rainfall, temperature, and humidity can either stimulate or decrease the vector growth. Higher precipitation levels combined with elevated temperatures often contribute to increased humidity, and correlated high humidity with increased Aedes aegypti feeding behavior, the production of life, and larvae. When water present in pots and containers evaporates, immature mosquitoes will increase in number, improving competition and deterring future laying of eggs. An author who had educated and researched immature Aedes aegypti at various laboratory densities were found that high densities caused slower growth, lower body mass was higher mortality. Mortality was found to be density-dependent for Aedes aegypti is a period of development between the egg and the second instar larval. Likewise, it was observed that the younger instars experienced the greater delay in development while there was competition between larvae. Complete evaporation at the greatest extreme will result in total larvae and pupae mortality (Southwood et al. 1972). However, it was seen later that much of the density dependence shown in these experiments were the result of nutritional tension in the containers. This indicates that precipitation has less impact on density-dependent mortality within the ecosystem than nutritional rates. Climate change will also affect how human interacts with the environment, altering its utilization and affecting the extent of the mosquito population and the structure of the ecosystem.

8 Mitigtion Measures 8.1 Managing Vector-Borne Diseases Through Vector Control There are several chemical, biological, trapping and management strategies which are accessible for controlling disease vectors (Rai et al. 2012). The incidence of vector-borne diseases (especially mosquito-borne diseases) can be greatly reduced by combination of better conditions of living, reducing the use of water, mass vaccination, and relocation of piggery to avoid mosquito from feeding, and to minimizing human contact. For example, the instance of Japanese Encephalitis in East Asia is decreasing.

8.1.1

Pesticide Methods

Entomo-pathogens were found to be successful against larvae of mosquitoes once they were used as biopesticides, however could not produce spores bearing toxin on the surface of water and demonstrate sufficient recycling capacity, thus have very

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little potential as bio-control agent. For example, making use of Bacillus thuringiensis and Bacillus sphaericus as biological pesticide in Managua, Nicaragua, decreased malaria incidence by 90% between 1996 and 2000 and comparable rates in the Hubei province of China.

8.1.2

Biological Methods

In certain situations, biological management has the ability to provide vector species with low-cost and long-term protection that will be viable and sustainable in a number of circumstances. Natural treatments for vector-borne diseases are superior to the use of artificial chemicals which are vulnerable to failure due to the resistance, are expensive and difficult for administration and also have adverse side effects on humans and the environment. Bio-control of mosquitoes using their natural enemies is a well-known activity that specially focuses on predatory fish. Progress was achieved against Aedes aegypti, a dengue fever vector, using inundative releases of the Mesocyclops aspericornis in different conditions including gold mines and Australian wells. Biological regulation does, however, pose a threat to the local fauna if not properly assessed before implementation.

8.1.3

Preventative Methods

Preventive steps are preferred and require the construction of water reservoirs in order to prevent shallow water areas which are ideal for breeding mosquitoes. Sound construction and management of dam spills and irrigation canals will reduce the occurrence of vector-borne diseases. Alternating wet dry rice irrigation is accredited by reducing the occurrence of both Malaria and Japanese encephalitis in areas where mosquitoes in the rice fields breed vector. Since there is no vaccine or vaccination available for dengue, control mosquitoes are the only way to minimize dengue incidence worldwide.

8.1.4

Environmental Management Techniques and Trapping Methods

Environmental management strategies for controlling malaria for prolonged periods include preventive measures consisting of forest clearing, river boundary adjustment, marsh drainage, oil application to open water sources, and screening of houses. Combination of nets and quinine are also often used in addition to these methods. A combination of environmental management techniques, for example, greatly reduced malaria cases in copper mines in Zambia for a period of 20 years by 70–95%. Potential proposed vector management approaches include environmental friendly insecticides, novel vector control techniques, and educational programs for health care workers.

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8.2 Managing Vector-Borne Diseases by Aiming at Pathogens This approach to control vector-borne diseases deals with treatment of infection of the pathogens by utilizing vaccines or certain drugs and preventing them.

8.2.1

Chemotherapy

Chemotherapy has for decades provided the basis for controlling vector-borne diseases, together with vector control. Chemotherapy is a broad field. However, it is important to note that there have been some significant contributions from drug use as a component of the integrated drug and vector control programs, but the effectiveness of these drugs is minimal. The two key limitations are that people of developing countries are unable to bear the expense of modern medicines, and also pathogens have a high degree of potential to build up resistance to most commonly used drugs. Above everything, for example, malaria has become immune to any medication currently available in Asia.

8.2.2

Vaccines

Vaccines are a preventive answer to medical professionals. For example, Japanese encephalitis vaccine in relation to vector control has been apparently successful in East Asia. Mass child vaccination formed the basis for the system. However, vaccines till now have made known little promise, for a number of reasons, against protozoan-pathogens such as Trypanosoma or Plasmodium, metazoan parasites such as Schistosoma, or viruses such as DENV. Protozoa are especially complicated to monitor with vaccines because of their ability to change their antigens and thereby prevent detection by the immune system of the host. Although innate immunity protects from acute malarial infections, however we are away from practical vaccines for several years and the infrastructure required distributing vaccines all over Africa. Loss of innate immunity following new drug treatments indicates the downside faced by health care professionals in leaving patients without the innate immunity that actually prevents them from acute infection. There is no vaccine for leishmaniasis yet and a new vaccine research program is under way, originally aimed for cutaneous leishmaniasis and using a variety of molecular approaches.

9 Climate and Health So far, we have been looking at first-order (improved monitoring, responsiveness, drug and vaccine production, increased provision of clinical, public health services)

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and second-order solutions (improved prediction, integration of health surveillance, collaboration of environmental sciences, and remote sensing and GIS epidemiology). The third level is prevention, and is based on policies for the environment and energy. Restauration of forests and wetlands (‘the sponges and kidneys of nature’) is required to reduce climate risk, change or not, sustain the population, establish renewable energy sources and increase energy efficiency. Providing basic public health infrastructure like housing, refrigeration, and cooking requires a lot of energy Meeting energy requirements with non-polluting sources would be the first step towards using the limited resources of Earth rationally as well as reducing global warming. It will take resources to address all of those levels. Just as technology development funds were needed to settle the Montreal Protocol on ozone-depleting chemicals, there is now a need for substantial financial incentives to propel clean energy technologies into the global market. International funding is also required to protect common resources, such as fisheries, and for disease vaccines and drugs that lack lucrative global markets. Visions of the world can shift abruptly. Even as we may underestimate the true price of ‘business as usual’, so we may underestimate the economic benefits that the energy transition provides. A distributed network of non-polluting energy sources will help reverse mounting public health environmental assaults and provide the scaffolding to create clean, sustainable, and safe growth in the century ahead of us.

9.1 Adaptation Control Measures for Preventing Vector-Borne Diseases In order to allow them to be proactive in their work rather than reactive, a priority must be set for responses to changes in timing and severity of transmission of diseases under global change. This includes a detailed research on epidemiology and biogeography of each disease that is at risk in the local region. High-risk regions and populations need to be established to concentrate attention on improving the current systems for disease management. To make them more versatile and adaptable to a changing setting, all the strategies and methods need to be modified. This, rather than a product-based approach, needs a more factual-based and realistic approach. Climate change adaptation includes a combination of monitoring, analysis of field findings based on basic understanding of the mechanism involved, and modification of current management activities to accommodate changes in geographic distribution, seasonal numbers, and vector timing. Provision of defense for the most vulnerable human populations will remain a priority, without the acquired immunity of the pathogen concerned. The next move is to have targeted population’s vulnerability interventions, achieved by incorporating adaptation methods into social vulnerability interventions with the adaptive potential of the impacted populations. The susceptibility of urban

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communities in developing countries to dengue fever and dengue hemorrhagic fever is very likely to increase as rising population densities in megacities outstrip the authorities’ capacity to construct adequate public health infrastructure. In the case of malaria, resistance to drugs and insecticides increased irrigation in agriculture, and global warming, each has the potential to increase community vulnerability in developing countries. This would increase the incidence of airport diseases by promoting travel between developing and developed countries. The main measures to reduce the susceptibility of several different communities are: (i)

(ii)

(iii) (iv) (v) (vi) (vii) (viii)

To lift up living standards in countries (developing) with a view to protect populations and thus, in turn, reducing the risks to residents of developed countries. Reintroduce a preventive strategy as a basis for the treatment of vector-borne diseases. All of this necessitates continuous surveillance and maintaining vector breeding sites, avoiding interaction with housing vectors and changes in patterns of human behavior. Build community adaptive capacity to be further versatile and sensitive to varying circumstances. Adapting a systemic method to compensate for unintended and expected political, economic, and environmental effects of human behavior. To adopt a knowledge-based approach to vector-borne disease management, based on complete decision-making systems. Control urban and agricultural surface water to-breeding sites for insect, mollusks, and crustacean disease vectors; Prevent invasions of ancient forests. Build community adaptive capacity to be more versatile and sensitive to changing circumstances; The solution to reducing community vulnerability to climate change’s health impacts is to improve current services for public health and prevention programmes. Sustainable vector-borne disease management approaches must either build or maintain herd immunity, or take sustainable preventive action. Only improved living standards can sustainably minimize exposure to environmental health threats in the longer term.

9.2 Sustainability and Renewability Recent and continuing trends of economic development, with existing energy sources, contribute more to climate change than population growth. The world population increased nearly four-fold throughout the twentieth century, while carbon dioxide emissions rose about 12-fold at the same time. Development in populations in low- and middle-income countries would pose significant challenges for potential greenhouse gas emissions if economic growth is focused on the utilizing of fossil fuels.

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While the Kyoto Protocol is an important diplomatic effort to involve countries in developing policies to minimize greenhouse gas emissions, some of the main adverse impacts will not be impacted by the limited goals in the protocol. For example, to prevent the carbon dioxide concentration from reaching the doubling of the pre-industrial concentration of 275 parts per million, it would need emission cuts of more than two-thirds, assuming a population of 9 billion by 2050. The Intergovernmental Panel on Climate Change (IPCC) reports that a 60% reduction in emissions is needed to reduce atmospheric concentrations of greenhouse gases. The industrialized nations, which have benefited so much from fossil fuels, should take the lead, and their proportional reductions will need to be much greater than the less developed nations in order to converge on a much lower level of emissions distribution and more equitable. Approximately 2 billion people lack energy access and therefore suffer seriously ill health. Around half of the world’s population cooks daily with conventional biomass fuels (e.g., dung, crop residues, wood, and charcoal), resulting in exposure to very high concentrations of indoor air contaminants and considerable time spent harvesting wood or other fuel, and the associated cost of opportunity, especially for women. In particular, the barriers to their adoption concern the electricity costs produced in this manner. However, substantial evidence exists that dams for the generation of hydropower can have adverse effects, like impacting the vector-borne disease distribution.

10 Conclusion Global climate change is a vast arena encompassing mostly all human endeavors. Rates of change are accelerating for all phases of human and environmental action. This leads to multiple occasions for unforeseen or amplified threats from vector-borne diseases, due to interactions between several types of alterations such as climate, unplanned expansion of megacities, and agricultural intensification. Contemporary shifts in concentrations of greenhouse gases, ozone levels, cryosphere, ocean temperatures, land use and land cover also threaten the stability of our period, the Holocene. The total air pollution produced by the combustion of fossil fuels and the reduction of forests are a destabilizing force in the heat budget of the Earth. Examining the full life-cycle of fossil fuels also exposes layers of injury. To the direct health effects of air pollution and acid precipitation must be added environmental damage from their mining, refining, and transport. Returning CO2 to the atmosphere through combustion reverses the biological cycle by which plants have drawn down atmospheric carbon and created oxygen and ozone, allowing the earth to cool down and protect sufficiently to sustain animal life. The risk of transmitting vector-borne diseases to developed nations from developed countries is rising because globalization increasingly increases the transport of goods

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and individuals around the world. Major complexities about some of the threats are there but the dynamics are apparent for others and the outcomes well expected. It would be adequate to stimulate community globally for rebuilding public health services, re-establish prevention strategies, and exploit resources to reduce the risk of spreading these diseases. All of this requires a systemic method with complete and predictive fact-based models. Adaptation initiatives must be socially and biologically sustainable and should be capable of sustaining success in times of severe environmental changes. Globalization in improving healthy living standards can play an important role in reducing vulnerability of vulnerable communities by delivering programs that improve health and well-being, rather than material products.

References Barrera R, Amador M, MacKay AJ (2011) Population dynamics of Aedes aegypti and dengue as influenced by weather and human behaviour in San Juan, Puerto Rico. Plos Neglected Trop Dis 5(12):e1378 Brady OJ, Golding N, Pigott DM et al (2014) Global temperature constraints on Aedes aegypti and Aedes albopictus persistence and competence for dengue virus transmission. BMC 7:338 Campbell LP, Luther C, Moollanes D, Ramsey JM, Danis-lozano R, Peterson AT (2015) Climate change influences on global distributions of dengue and chikungunya virus vectors. Royal Soc 370(1665):20140135 Chen PS, Feng TT, Lin CK, Yang CY, Chan CC, Young CY, et al (2010) Ambient influenza and avian influenza virus during dust storm days and background days. Environ Health Perspect 118:1211 Gathany J (2006) Aedes aegypti. Centers for Disease Control and Prevention’s Public Health Image Library (PHIL) Haines A, Kovats RS, Lendrum DC, Corvalan C (2006) Climate change and human health: impacts, vulnerability and mitigation. Lancet 367(9528):2101–2109 Kamal M, Kenawy MA, Rady MH, Khaled AS, Samy AM (2018) Mapping the global potential distributions of two arboviral vectors Aedes aegypti and Aedes albopictus under changing climate. PLoS ONE 13(12):e0210122 Kearney M, Shine R, Porter WP (2009) The potential for behavioural thermoregulation to buffer “cold-blooded” animals against climate warming. PNAS 106(10):3835–3840 Mirski T, Bartoszeze M, Bielawska-Drozd A (2011) Impact of climate change on infectious diseases. Pol J Environ Stud 21(3):525–532 Modis, Coudera, Weaver (2017) Immunology of viral infections with a high impact in Mexico: Dengue, Chikungunya, and Zika. Elsevier 19(7):198–207 Ogden Nicholas H (2017) Climate change and vector-borne diseases of public health significance. FEMS Microbiol Lett 364(19):186 Rai PK, Nathawat MS, Onagh M (2012) Application of multiple linear regression model through GIS and remote sensing for malaria mapping in Varanasi District, INDIA. Health Sci J (Int J Nurs Res Rev) 6(4):731–749. ISSN 1108–7366E. Available at http: www.hsj.gr/volume6/issue4/6414 Rai PK, Nathawat MS, Rai S (2013a) Using the information value method in geographic information system (GIS) and remote sensing for malaria mapping; a case study from India. Informat Prim Care (University of Surrey, U.K.), 21(1):43–52. Available at: https://doi.org/10.14236/jhi.v21 i1.38. ISSN 1475-9985

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Rai PK, Nathawat MS (2013b) GIS in health care planning: a case study from India. Forum Geogr J (Romania) 12(1):152–163. ISSN No: 2067-4635. Available at: https://doi.org/10.5775/fg.20674635.2013.180.d Rai PK, Nathawat MS (2013c) Application of GIS & statistical methods to select optimum model for malaria susceptibility zone and verification of the susceptibility methods by area under curve (AUC): a case study, scientific annals of “Alexandru Ioan Cuza” University of Iasi—Geography series (SciGeo J Romania) 59(2):73–94. ISSN: 1223-5334. Available at: http://www.analegeo. uaic.ro/index.php/SciGeo/issue/view/20 Rai PK, Nathawat MS (2014) Utilization of health care services in Varanasi district: a geographical analysis. Geog Malays J Soc Space (online) 10(2):14–33. ISSN: 2180-2491. Available at: http:// www.ukm.my/geografia/v1/?cont=v&item=2&art=449&ver=loc Rai PK, Nathawat MS (2017) Geoinformatics in health facility analysis. Springer International Publishing, Switzerland ISBN (Hard copy): 978-3-319-44623-3. eBook ISBN: 978-3319-44624-0. http://www.springer.com/gp/book/9783319446233. https://doi.org/10.1007/9783-319-44624-0 Reinhold J, Lazzari C, Lahondère C (2018) Effects of the environmental temperature on Aedes aegypti and Aedes albopictus mosquitoes: a review. MDPI 6;9(4):158 Southwood T, Murdie G, Yasuno M, Tonn R, Reader P (1972) Studies on the life budget of Aedes aegypti in Wat Samphaya, Bangkok, Thailand. Bull World Health Organ 46(2):211–226 Sutherst RW (2004) Global change and human vulnerability to vector-borne diseases. Am Soc Microbiol 17(1):136–173 Tedjou AN, Kamgang B, Yougang AP, Njiokou F, Wondji CS (2019) Update on the geographical distribution and prevalence of Aedes aegypti and Aedes albopictus (Diptera: Culicidae), two major arbovirus vectors in Cameroon. PLoS Negl Trop Dis 13(3):e0007137 World Health Organization (2020) Flooding and communicable diseases fact sheet. https://www. who.int/hac/techguidance/ems/flood_cds/en/. Accessed on 1 July 2020 Wu X, Lu Y, Zhou S, Chen L, Xu B (2016) Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Elsevier 86:14–23

Human Health Hazards and Risks in the Agriculture Sector Dimple, Jitendra Rajput, Indu, and Manoranjan Kumar

Abstract In India, agriculture contributes together with allied sectors to being the largest source of livelihoods. Farmers face a variety of biological, respiratory, noise damage, skin disorders, some cancers, chemicals related to environmental and safety problems, musculoskeletal injuries, etc. In India, there are 120 fatalities in agriculture every day. Climatic transition causes increased concern among farmers because it results in crop damage and resulting in low productivity. Drought and extreme flood events can form ecological disruptions impacting agricultural products and human health. On-farm, skin cancer is a problem because farmers spend long hours in the Sun. Most farmworkers are frequently exposed to chemicals. When they neglect to take adequate care, it may result in sickness or even death. Tractors, thresher, harvester, etc. noises are called agricultural noise which is also a major concern toward the health hazards of farmers. When the body heats more heat than it can bear, heat stress occurs. The possibilities of heat stress increase from high temperatures, high humidity, bright sunlight, and workloads. In this chapter, we are discussing the health hazards of farmers or people who work in agriculture due to agricultural activities, and to reduce or eliminate these hazards, we have tried to explain safety measures, government policies, etc. Farmers can reduce health hazards by following certain safety measures, such as sometimes a tarp or a canopy can shade a work area. Before, during, and after work, drink plenty of water and start wearing cooling vests, which are ice garments or frozen gel inserts. Give time to adapt to the workload and heat. Those are used to working in the Sun are less vulnerable to heat stress. To be adapted, work in the heat for several days in a row for around 2 h of light work a day; then slowly raise the work time and workload for the next several days. When workers Dimple (B) Soil and Water Engineering Department, CTAE, MPUAT, Udaipur, Rajasthan 313001, India J. Rajput Division of Agricultural Engineering, ICAR-IARI, New Delhi 110012, India Indu Division of Crop Improvement, ICAR-IGFRI, Jhansi, Uttar Pradesh 284003, India M. Kumar ICAR-Central Research Institute for Dryland Agriculture, Hyderabad, Telangana 500059, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_14

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are subject to noisy sounds constantly, they will have regular hearing checks. The test, called an audiogram, can show signs of loss of hearing. Once finding a hearing loss, take action to reduce exposure, thereby preventing more ear damage. Keywords Agriculture sector · Health hazard · Farm machineries · Agriculture fatalities

1 Introduction India is an agricultural nation where over half of the population relies on agriculture and associated sectors such as animal husbandry, forestry, and fisheries. This framework remains the key driver of earning sustainable income in the rural sector. The majority of the Indian people are explicitly or implicitly reliant on the farming sector. Some are actually linked with the cultivation, and some other persons are involved in doing business with any of these products. According to 2018, the farming sector involved more than 50% of the country’s labor population and added 17–18% to India’s gross domestic product (GDP) (Sunder 2018). As per the latest research, the farming sector is the ostensibly key source of livelihood generation for 58% of India’s population (Web search 1). According to the statistics provided by the directorate of economics and statistics (DES), Government of India (GoI), 2020 the output of grain production for the year 2019–2020 fourth advance predictions is 296.65 million tons which are enhanced as compared to (2018–2019) 285.21 million tons. This is indeed a promising indicator of an Indian economy from the agricultural domain. According to the International Labor Organisation (ILO), farming hires approximately 1.3 billion people globally, losing atleast 170,000 farm employees each year with an estimated worldwide fatality rate of 13.07 per 100,000 full-time equivalents (ILO 2019). International statistics indicate that agriculture labors are twice more likely to die on the job as opposed to other labor fields (ILO 2019). In the U.S.A, the total farming fatality rate was recorded as 22.8 per 100,000 full-time equivalents, which is around seven times more than the fatality rate for all the other industries (BLS 2017). From 2000 to 2018, Pennsylvania lost a total of 526 persons in farm and agriculture accidents. The objective of this chapter is to outline various health hazard risks linked to different agricultural activities and environment and also, suggests precautionary considerations to promote ways to protect farm workers at work place.

1.1 Agriculture Allied Sectors and Their Risks Rural Indian farmers make India the world’s second-largest market for livestock. Over half of the country’s population and a quarter of its agricultural GDP are dependent on it. Consequently, continued progress is hampered by mortality, sickness, scarcity

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of amenities, and care for livestock. There is a frightful debate about the danger of rearing livestock activities on human health (HH) and also about the quality of control measures to mitigate these types of hazards. Perils do have occurrences of disease infections, in specific zoonoses, as well as the excessive use of antibiotic medicines in the animal husbandry sector leading to the rise of antibiotic resistance and its dissemination from domesticated animals to human beings. Periodically, a severe outbreak of infectious diseases happens in animals. Sometimes, these types of diseases transfer from animals to humans and can worsen HH and even are fatal. For example, in Netherlands, there was a Q fever epidemic from 2007 to 2010, because of this epidemic about ten people have died. Eventually, the outbreak was stopped by extensive goat culling (Roest et al. 2011), Nipah virus infection in 1999, severe acute respiratory syndrome (SARS) in 2002, highly pathogenic avian influenza (HPAI) caused by H5N1, from which approximately 200 people have died since 2004. Due to increased livestock and human population growth, shifts in livestock production, the advent of agro-food industries and market developments across the globe, and significant difference in individual transportation, world communities of disease risk are predominantly experienced by population groups, among themselves and other animal clans. Thus, it is not shocking that 3 out of 4 emerging pathogens affecting people have emerged from animals or animal products over the past ten years (Taylor et al. 2001). On October 16, 2019, the Department of Animal Husbandry and Dairy (DAHD) issued a census report on the livestock population. The data showed a 4.6% rise in the livestock population of India, graphically represented in Fig. 1. Livestock’s addition of 26% agricultural GDP. It is a risky business to raise cattle, sheep, or poultry, especially if you do not own a herd or flock, but only one or a few animals. The biggest peril is a disease. This can decrease meat or milk production and, in the worst-case scenario, result in the death of the animals (Singh 2015). Livestock and fish are involved in the passive as well as active transfer to humans of a variety of parasites and diseases. If physical contact is prevented by adequate clothing, especially gloves, the hazards of passive transfer of pathogens through the handling of live fish during production, harvesting, and processing can be reduced. Livestock population in India

Fig. 1 Livestock population growth in India

540

536

(Million)

535 530 525 520 515

512

510 505 500 2012

2019

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In the aquaculture industry, many physical risk factors exist. In the course of their jobs, farmhands and other aqua farm laborers are vulnerable to multiple damages. The following is the list of physical perils: (1)

(2)

(3)

Noise: Feedmill workers (particularly those operating in developing countries with locally manufactured machines) are exposed to fierce noise. Ojok (1995) related the following adverse effects to noise: hearing defects, hearing loss, and mental exhaustion. Injuries: Farmworkers are vulnerable to multiple accidents, including a sting from the spines of fish (this happens during the handling of fish without proper protection devices). This can cause serious pains which could lead to infection with tetanus or witlow. Cuts, sprains, fractures, etc.: These accidents may be caused by sharp tools/objects.

1.2 Literature Review Many studies have shown higher rates of workplace accidents in the agriculture sector than in the manufacturing sectors some are listed below. Karkkainen (2002) indicated that in the food and agricultural sectors, the greatest risks exist. He characterizes the second most common cause of asthma as dust emitted from flour and animal feed mills. Snake bites, crab clawing, and fish bites are hazards to which workers are exposed in earthen pond fish farms, especially when they do not use proper protective gear. Rautiainen and Reynolds (2002) mentioned farming being one of the most harmful sectors in the USA, in which the death risk was 22/100,000 employees in the 1990s. Franklin et al. (2001) reported that in Australia’s farming deaths were recorded as 20.6/100,000 workers. Gite and Kot (2003) to have quantifiable information on farm incidents, a survey study was conducted in villages of India’s 4 states namely, Punjab, Orissa, Madhya Pradesh (MP), and Tamil Nadu over accidents occurred during the period 1995–99. The restricted data acquired showed that farm injury fatalities were 21.2/100,000 workers/year. They have found that the primary sources of accidents were farm machineries-like farm tractors, chaff cutters, threshers, and other means, namely snake biting, flooding in wells/ponds, fall in open wells/pond, and thunder lightning. Kumar and Dewangan (2009) a study did to characterize the nature, extent, causes, severity, and financial consequences of agricultural operations related to workplace accidents in the state of Arunachal Pradesh, India. Report of accidents for the years of 6 between 2000 and 2005 were collected and analyzed. The agricultural injury accident rate was 6.39 per 1000 labors per year.

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2 Health Hazards During Agricultural Operations/Activities Baker et al. (1984) reported that other than farm machinery injury rates declined by 79%, but agricultural machinery injuries rates rose by 44% between 1930 and 1980. Agricultural accidents (50.2/1000 workers) were more prevalent than industrial casualties (26.8/1000 workers) (Lundqvist and Guastafsson 1992). Agricultural operations are conducted in a potential health risk setting owing to elevated dust, atmospheric temperature, noise, vibration, chemical, and biological agents. Working at a farm under the dusty situation was identified as a causative agent of respiratory disease in the sixteenth century, is a significant source of respiratory morbidity and mortality amongst farm workers (Schenkar 2000). Farm operations consume huge energy and thus the farmer operates at a high heart rate and higher respiration rate, which increases dust inhalation at working place (Christensen et al. 1992). It was observed that while performing threshing operation, workers are theoretically subjected to the spiked concentration of dust exceeding the desirable level. The analysis demonstrated that reparable substances ( 0.5. It is consistent with the data if CFI and TLI ≥ 0.9, CMIN/df ≤ 0.2, RMSEA ≤ 0.08. To ensure the goodness-of-fit of the model, using the process of determining evidence of model misspecification which identifies any parameter that has been incorrectly specified. To this aim, modification indices (MIs) are used. These indices show the extent to which the hypothesized model is appropriately described. If the parameters were to be freely estimated in a following run, then the value of M.I. in Amos program represents the expected drop in overall χ 2 value. Only parameters with

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Table 3 Rotated factor loading in exploratory factor analysis Rotated factor

Variable

Factor loading coefficients

Rotated factor

Variable

Factor loading coefficients

Adaptation intention (F1)

D5.3 D5.4 D5.5 D5.13 D5.9 D6.10 D6.8 D9.7

0.857 0.768 0.767 0.658 0.657 0.471 0.453 0.446

Adaptation assessment (F2)

D4.3 D4.2 D1.1.1 D4.1 D4.5

0.984 0.675 0.659 0.537 0.437

Subjective norm (F3)

D8.3 D8.4 D8.2

1.013 0.732 0.626

Adaptation assessment (F4)

D2.1.3 D2.1.2 D2.1.4

0.848 0.764 0.606

Risk perception of climate change (F5)

D1.1.4 D1.1.5

0.872 0.801

a M.I. value above 10 are considered, since MI values follow 10 have no significant impact on the overall model fit. After calibrating the CFA model, the Chi-square/degree of freedom (χ2 /df) of 1.339 is acceptable (N = 243). All of recorded results are the comparative fit index CFI = 0.989, the goodness-of-fit GFI = 0.969 the adjusted goodness-of-fit AGFI = 0.941, the root mean square error of approximation RMSEA = 0.037, and PCLOSE = 0.740. These indices satisfy the conditions above, therefore, the scale model fits with the extracted data. Figure 2 indicates that the normalized and non-normalized weights are statistically significant (>0.5) (Fig. 3).

3.5 Structural Modeling The structural modeling section of the study considers the significant relations steps between latent variables after the CFA results (Fig. 4). Factor ‘Adaptation assessment’ (F2) affects significantly and negatively the factor ‘Adaptation intention’ (F1) (the structural path estimation = 0.669, p = ***). Factors Subjective norm (F3), risk perception of climate change (F5) do not have an impact on the Adaptation Intention (F1) (Table 4). As indicated in Sect. 2, a structural model considers the relations between latent variables (factors). In the survey, the structural model identifies the link between factors and especially the impact of each factors on the adaptation intention to climate change of ethnic people in Van Chan. CFA model allows to conclude that the hypothesized model fits the sample data well. Figure 4 shows the structural model for climate change adaptation intention of local people in Van Chan, Yen Bai.

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Fig. 3 Calibration results of CFA model

Fig. 4 Structural model for climate change adaptation intention of the surveyed population in the Van Chan district of the Yen Bai Province, Vietnam (Loads of paths of the alternative structural model determining the composite reliability model)

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Table 4 Standardized maximum likelihood parameter estimates Dimensional impact

Estimation

p

F1

←

F2

0.669

***

F1

←

F3

0.034

0.713

F1

←

F5

−0.026

0.824

Note ***p < 0.001

Table 5 Test of bootstrapping Bootstrap 500 Parameter

Estimate

Critial ratio

F1

←

F2

0.669

1.714

F1

←

F3

0.034

−0.600

F1

←

F5

−0.026

−2.000

3.6 Bootstrapping 500 bootstrap samples are randomly drawn from the original sample. The bootstrap tests the null hypothesis (H 0 ) by calculating the critical ratio CR. The null hypothesis is accepted at a significance level at 5% if the CR value is less than 1.96. The test results in Table 5 confirm that the estimations of the SEM model are robust. The theoretical SEM model is acceptable.

3.7 Multi-group Structural Analysis The SEM result suggests a structural model for climate change adaptation intention of the farmers on the Van Chan mountain and for each of ethnic groups Kinh, Tay, Thai, and Mong in Van Chan, Yen Bai. Figure 5 shows the positive relationships between the Adaptation assessment (F4), Subjective norm (F3), Risk perception of climate change (F5) on Adaptation Intention (F1). Among which, Adaptation assessment, Subjective norm are positively related with the adaptation intention, while risk perception of climate change is negatively correlated with the adaptation intention. Hence, the higher the Adaptation assessment of the ethnic group and the greater the Subjective norm, the more likely they intent to adapt and vice versa. This suggests that the farmer is concerned about the effectiveness of the adaptation options that have been or will be implemented. In addition, the more detailed the current situation assessment and the practical adaptation ability in the locality, the more the adaptation intention to climate change improves. The analysis also shows that the intention to implement adaptation measures to climate change does not depend to a large extent on the risk perception of climate change and natural disasters. This

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Adaptation assessment 0.669

Adaptation intention Subjective norm

0.034

-0.026

Risk perception of climate change

Fig. 5 SEM model of Van Chan, Yen Bai

also means that increasing this factor leads to a decrease of the adaptation intention among farmers and vice versa. In addition, the multi-group structural analysis assesses and compares the structural model of the effects on the factor F1 (the adaptation intention of climate change for 4 ethnicities of the Van Chan, Yen Bai province. Two methods, the partial invariability method (the factor loadings are supposed to be equal between groups) and the variability method, are used.     is The first step tests the null hypothesis (H 0 ) 1 = 2 = . . . = n , where the population covariance matrix, and n is the number of groups. To test the factor invariance, one determines a baseline model for each group separately. Then, tests for the equivalence of parameters are conducted across groups at each of increasingly stringent levels. The results of the test for multi-group invariance are shown in Table 6. When it comes to the Kinh ethnic group, no factors affect factor F1 (the adaptation intention). However, this factor is impacted by factor F2 (the adaptation assessment) for the Thai, Mong, and Tay. Particularly for the Tay ethnic group, the factor F1 is also affected by F5 (the risk perception of climate change). This allows expecting that if the factor F5 is improved and enhanced, the adaptation intention to climate change also increases and vice versa. Tay farmers in the study area grow rice, maize, and sweet potato. Because most of the fields are in the plains, they are more vulnerable to heavy rains, floods, landslides, and cyclones (Fig. 6).

F2

F3

P5

←

←

←

F1

F1

F1

Dimensional impact

Table 6 Test of multi-group

−0.024

0.224

0.185

Estimation

Kinh

0.88

0.131

0.441

P

0.1

−0.183

0.762

Estimation

Thai

0.616

0.349

0.015

P

−0.136

−0.178

1.594

Estimation

Mong

0.693

0.455

0.03

P

−1.028

0.187

2.181

Estimation

Tay

0.051

0.339

0.008

P

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(a) Kinh

(b) Thai

(c) Mong

(d) Tay

Fig. 6 SEM model for 04 ethnic groups (Kinh, Thai, Mong, Tay) in Van Chan, Yen Bai

4 Conclusions Climate change increases the sensitivity and awareness of people to environmentalrelated issues. The community perception of climate change and other environmentrelated issues remain weak in Vietnams northern mountains (Huynh et al 2014; Nguyen and Hens 2019). People are often passive and not fully aware of the impacts of climate change. As a result, they often underestimate natural disasters, have no response plan and are lack in sufficiently prepared for the impacts of climate change. However, facing an increase in frequency and intensity of extreme weather events and natural disasters, their awareness and perception of the risks, trends, and impacts of climate change and the accompanying actions changed to minimize the adverse effects of climate change and take advantage of the opportunities it offers for shifting community resilience. The study is finding out which adaptation intentions to climate changes farmers operating in Van Chan, Yen Bai have. The SEM results lead to a structural model for climate change adaptation intention of the local people of the Van Chan mountain and for each of ethnic groups Kinh, Tay, Thai, and Mong in the Van Chan moutain. Communities pay more attention to the effectiveness of the adaptation measures. The result also shows that the intention to implement adaptation measures of the community to climate change do not depend to a large extent on the risk perception of climate change impacts in Van Chan. However, these results differ among ethnic groups when analyzing the multi-group structure. This provides the basis for both local and community group structural models.

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The results suggest that in high-risk areas as the mountains, communities need pay more attention to risk information on climate change. Solutions for training, education, and propaganda to raise public awareness are effective instruments to mitigate the impacts of climate change in the study area. In addition, with support from the government and NGOs, communities send fast access to updates of weather changes such as heat waves, cold snaps, storms, floods, and landslides and ways to improve the community resilience. In the future, a disaster management communitybased model will be useful for local sustainable development.

References Arbuckle Jr, Prokopy LS, Haigh T, Hobbs J, Knoot T, Knutson C, Loy A, Mase AS, McGuire J, Morton LW, Tyndall J, Widhalm M (2013) Climate change beliefs, concerns, and attitudes toward adaptation and mitigation among farmers in the Midwestern United States. Clim Change 117(4):943–950 Dasgupta S, Laplante B, Meisner C, Wheeler D, Yan J (2007) The impact of sea level rise on developing countries: a comparative analysis. World Bank policy research working paper 4136 Eckstein D, Hutfils ML, Winges M (2018) Global climate risk index 2019. Briefing paper, Germanwatch e.V. Office Bonn, pp 35 Fankhauser S (1996) The potential costs of climate change adaptation. In: Smith J, Bhatti N, Menzhulin G, Benioff R, Budyko MI, Campos M, Jallow B, Rijsberman F (eds) Adapting to climate change: an international perspective. Springer, New York, NY, USA, pp 80–96 Huynh NT, Lin W, Ness LR, Occeña-Gutierrez D, Tran XD (2014) Climate change and its impact on cultural shifts in East and Southeast Asia. Chapter 12. Springer Science + Business Media Dordrecht, pp 245–302 IPCC (2007) Climate change 2007: synthesis report. Contribution of working groups I, II and III to the fourth assessment report of the intergovernmental panel on climate change. In: Core Writing Team, Pachauri RK, Reisinger A (eds) IPCC, Geneva, Switzerland, p 104 ISPONRE (2009) Vietnam assessment report on climate change. Institute of Strategy and Policy on Natural Resources and Environment (ISPONRE), Hanoi, pp 15–32 Kane SM, Shogren JF (2000) Linking adaptation and mitigation in climate change policy. Clim Change 45(1):75–102 MONRE (2016) Climate change and sea level rise scenarios for Vietnam. Ministry of Natural Resources and Environment, Hanoi, p 170 (in Vietnamese) Nguyen AT, Hens L (2019) Human ecology of climate change hazards in Vietnam. Springer International Publishing, p 174 Nistor MM, Ronchetti F, Corsini A, Cheval S, Dumitrescu A, Rai PK, Petrea D, Dezsi S (2017) Crop evapotranspiration variation under climate change in South East Europe during 1991–2050. Carpathian J Environ Sci 12(2):571–582 Pielke RA (1998) Rethinking the role of adaptation in climate policy. Glob Environ Chang 8(2):159– 170 Pittock B, Jones RN (2000) Adaptation to what and why? Environ Monit Assess 61(1):9–35 Rai PK, Mohan K (2014) Remote sensing data & GIS for flood risk zonation mapping in Varanasi district. Forum Geogr J (romania) 13(1):25–33 Rai PK, Nathawat MS, Anurag N (2008) Temporal behavior of waterlogged area using multitemporal satellite data. Deccan Geogr (j Deccan Geogr Soc) 46(2):67–74 Rai PK, Mohan K, Kumra VK (2014) Landslide hazard and its mapping using remote sensing & GIS techniques. J Sci Res 58:1–13. Faculty of Science, Banaras Hindu University. (ISSN No. 0447-9483)

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Rai PK, Singh P, Mishra VN, Shahi AP, Singh A, Sajan B (2019) Remote sensing based analysis on environmental and climatic impact on resources in Alaska region: a review has been accepted in (ed. Book). In: Nistor MM (ed) Alaska: social, economics, and environment, 2nd edn. NOVA Science Publisher, New York Rejesus RM (2013) US agricultural producer perceptions of climate change. J Agric Appl Econ 45(4):701–718 Santibánez-Andrade G, Castillo-Argüero S, Vega-Pena EV, Lindig-Cisneros R, Zavala-Hurtado JA (2015) Structural equation modeling as a tool to develop conservation strategies using environmental indicators: The case of the forests of the Magdalena river basin in Mexico City. Ecol Ind 54:124–136 Singh P, Sharma A, Sur U, Rai PK (2020) Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali Beri region, Himachal Pradesh, India. Environ Dev Sustain 1–20.https://doi.org/10.1007/s10668-020-00811-0. ISSN: 1573-2975 Smit B, Wandel J (2006) Adaptation, adaptive capacity and vulnerability. Glob Environ Chang 16(3):282–292 Smit B, Burton I, Klein RJT, Street R (1999) The science of adaptation: a framework for assessment. Mitig Adapt Strat Glob Change 4:199–213 Smith K (1996) Environmental hazards: assessing risk and reducing disaster. Routledge, London, United Kingdom, p 389p Sur U, Singh P, Rai PK (2021) Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India is accepted in environment, development and sustainability. Springer VCG (Van Chan Government) (2016a) Report on natural hazard prevention 2015. Yen Bai, 14 pages (in Vietnamese) VCG (Van Chan Government) (2016b) Van Chan Statistic year book 2015. Yen Bai, 350 pages (in Vietnamese)

Environmental Degradation and Disaster

Role of Space-Borne Remote Sensing Technology for Monitoring of Urban and Environmental Hazards Akshar Tripathi and Reet Kamal Tiwari

Abstract The rapid increase in urban population has on one handled to a remarkable increase in demand for dwelling spaces, and on the other hand has led to unprecedented resource competition. With better medical, educational and employment opportunities in urban areas, there is a population exodus from nearby towns and villages towards larger urban centres. This migration trend is more noticeable in developing countries like India, where there is a shift from agriculture to the service sector as a significant employer and GDP contributor. This leads to the growth of cities and metropolis, both in a planned and unplanned manner, leading to a rapid change in Land Use/Land Cover. However, all this is not without cost. A cost which the urban dwellers are paying in terms of environmental and urban hazards like— urban subsidence, land use/land cover change, urban heat island development, urban flooding and increased air and water pollution. Proper monitoring of these hazards is needed for timely response in case of disaster and control and mitigation of any tragic event. This would be highly beneficial in terms of planning for future civic facilities, rescue provisions and emergency services, in addition to taking timely precautionary measures. Physical monitoring of urban hazards and its causes requires lots of expertise and sophisticated equipment, which is both complex and timeconsuming. Remote sensing being a non-evasive tool is highly beneficial for urban and environmental hazard monitoring. With a multitude of remote sensors operating in various active–passive and regions of electromagnetic spectra, space-borne remote sensing has proved to be highly beneficial for urban land use/land cover change, urban air quality and pollution monitoring, urban flood modelling and urban heat island mapping, besides many more other potential hazards and their causes. This chapter focusses on some of the applications of space-borne remote sensing from optical, SAR and thermal sensors. The various applications discussed are a part of real-time research conducted in areas of urban land subsidence monitoring and mapping, urban A. Tripathi (B) · R. K. Tiwari Department of Civil Engineering, Indian Institute of Technology (IIT) Ropar, Rupnagar, India e-mail: [email protected] R. K. Tiwari e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_18

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land use/land cover mapping, urban heat island mapping, Urban flood run-off estimation and urban air pollution monitoring, using various satellite data. The chapter covers different regions and urban centres spread across various regions of India hence shows the geographical diversity of application of remote sensing technology. Keywords Remote sensing · Urban hazards · Land subsidence · Urban heat islands · Urban flood run-off

1 Introduction With the increasing human population, there is an unprecedented and uncontrolled utilisation of natural resources (Zebker et al. 2000). All this contributes to waste on the one hand and different types of environmental pollution on the other (Lobell et al. 2015). All this going unchecked leads to several potential problems that humanity is facing and is soon to face (Aslan et al. 2016). With the increase in the prosperity and development of modern industries, the service sector is slowly becoming the major contributor to the gross domestic product (GDP) of many countries (Falkenmark and Lundqvist 1998). Moreover, this service class dwells in cities and metropolises, roughly defined as mega urban centres (Curran et al. 2002). This rapid urbanisation also is taking tolls on nature. With expanding urban sprawl and town planning mainly confined to papers, the agricultural land shortage is already on the way (Sharma et al. 2016). To cater to the food requirements of an increasing population, more and more forest land is being cleared annually and converted into agricultural lands (Chameides and Walker 1973; Tripathi and Tiwari 2020). For countries like India where most of the population is engaged in agriculture and allied activities yet contributing to only a small fraction of its GDP, it becomes clear that proper scientific agriculture is yet a dream and environmental hazards slowly creeping in (Islam and Wong 2017; Tripathi et al. 2018a). A hazard is not a calamity, but a potential source of damage or harm for a place, organisation or an individual. With the environment already under stress and global climatic change and pollution already significant realities, there are several hazards which our urban environments face and often result in calamities (Zhang and Zhou 2016). The causes for these are both natural and anthropogenic. Whilst natural causes are beyond control, anthropogenic hazards can be avoided by causing any damage (Hoa et al. 2019). The major problems that urban centres worldwide and including India face are— floods, sea-level rise, climatic change, groundwater level depletion, urban land subsidence, air pollution and many others (Shafri and Taherzadeh 2012; Xu et al. 2011). For damage reduction and hazard mitigation, proper and regular monitoring of potential causes and threats is especially important (Tripathi and Kumar 2017). This requires regular research activities to be carried out and eradication of causes or at least taking precautionary measures promptly. This requires expertise and sophisticated equipment (Patil et al. 2016).

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With the advancement of science and technology, remote sensing is an inevitable tool that has eased human effort and saved time in several functional research domains (Uysal et al. 2015). This is true for the areas that demand extensive survey-based studies. Remote sensing is defined as the science and technology of information extraction of a feature or object from a distance without coming in physical contact (Tripathi and Kumar 2017; Ruwaimana et al. 2018). Remote sensing is of many types based on sensor type and based on the platform on which the sensor is mounted. Based on sensor type, remote sensing is based on the region of the electromagnetic spectra in which a sensor is sensitive and can be classified into two broad categories— active and passive remote sensing (Xu and Jin 2007). Active remote sensing is when the remote sensor has its energy source and illuminates the target and captures the return signal which helps in retrieval of information about the target, with which the incident signal came back after the interaction. Common examples of active remote sensors are—RADAR/Microwave sensors and LiDAR (Light Detection and Ranging) (Holzner and Bamler 2002; Wang et al. 1993). Passive remote sensors on the other hand are reflectance-based and depend upon the solar illumination of the target object (Dwivedi et al. 1999; Patel et al. 1995). Optical and thermal remote sensing are forms of passive remote sensing as they capture the reflected sunlight from objects in various regions of the electromagnetic spectra (Dadhwal et al. 2002; Jayaprasad et al. 2008). Based on the platform type, remote sensing can be defined into three types— terrestrial, airborne and space-borne or satellite-based remote sensing (Pal et al. 2007; Saif et al. 2008). Terrestrial and airborne remote sensing have been there since a long time, but it was only after the launch of Landsat-1 in the early 1970s that space-borne remote sensing came into the picture and with the launch of different remote sensing satellites with various optical, thermal and microwave sensors aboard, satellite-based remote sensing has grown leaps and bounds (Panigrahy et al. 2005). From atmospheric pollution monitoring to flood and glacial analysis, from agriculture and soils to urban planning and mapping, space-borne or satellite-based remote sensing has been applied in almost every research branch (Tapete et al. 2012; Zeni et al. 2011). With the advent of machine learning in research fields, satellite remote sensing has found a new paradigm shift (Patel et al. 2009). The availability of freely available satellite datasets from a wide range of spaceborne sensors made it a cost-effective and time-saving tool for urban hazard monitoring, mapping and mitigation (Seiler et al. 2009). This chapter aims to discuss certain applicable domains and case studies in the context of hazards in urban environments where space-borne remote sensing has been widely applied and has given encouraging results.

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2 Urban Flood Mapping and Estimation of Run-Off Using Space-Borne Microwave Remote Sensing Floods are a yearly catastrophe that causes massive damage to life and property in many areas of India, especially Northern India which is home to some of the world’s mightiest rivers (Gupta et al. 2003). Governments have made many efforts for flood mitigation one after the other, yet nothing concrete seems to come out as a solution (Anusha and Bharathi 2019; Mishra et al. 2010). There are problems both at execution level and planning level for developing countries like India where people tend to settle first and planning comes after (Gupta et al. 2013; Katimon et al. 2003). This leads to large chunks of populations often residing in areas which are in flood-prone areas, dangerously close to water bodies (Rosser et al. 2017). Remote sensing helps in minimising flood impact and disaster control and in pre-planning and evacuation measures. With the help of remotely sensed data from satellites (Tripathi and Tiwar 2019a), flood maps can be prepared to aid the local administrative units in marking the threatened areas and safe zones to relocate the populations before the onset of flood seasons (Tsyganskaya et al. 2018). Space-borne remotely sensed data also helps suggest mitigation measures for flood scenarios like the selection of sites for reservoir construction (Demir and Kisi 2016). However, since floods in India occur mostly in monsoon months with dense cloud cover most of the time, space-borne optical remote sensors being passive sensors were opaque to cloud cover; hence, flood mapping relied upon low-flying aerial remote sensing, which was costly and was in limited use (Kale et al. 1994; Sanyal and Lu 2005; Sharma and Singh 2015; Viterbo and Betts 1999). It is here when the microwave or SAR (Synthetic Aperture RADAR) remote sensing comes into use. Most space-borne imaging RADAR sensors are active sensors and have penetration abilities. Hence, they are weather independent and have no cloud cover effect and have all-weather data availability (Rahman and Thakur 2017). Coherence-Based Flood Inundation Mapping Coherence is the property of electromagnetic waves wherein the two waves (here incident and backscattered waves) are identical in all properties with a constant phase difference (Mishra and Singh 1999). For a SAR remote sensor, the electromagnetic waves are emitted from the sensor, and they are received after interaction with the target object or feature (Ulaby et al. 1986). These received waves are termed as backscatter waves. For permanent targets like built-up areas, the incident and backscattered waves are mostly coherent. Hence, these areas give high values of coherence (close to 1). Coherence values vary from 0 to 1 (Keydel 2007). For temporary or rapidly changing features like crops and soil, the coherence values are usually close to 0. For flood mapping, coherence-based thresholding is a tried and tested method wherein built-up areas show coherence loss in SAR satellite imagery (Carin et al. 1998). This is due to flooding of such areas. Areas still showing high values of coherence are masked out, and this gives the flood inundation map which is

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extremely helpful in planning connectivity to effected areas, sensing in rescue teams, planning a relocation and many more. The coherence equation is given by Eq. (1) (Li and Bethel 2008). γi,k

  E imgi · imgk =  2  2 E imgi img∗k

(1)

where, ‘i’ and ‘k’ represent the master and slave images, respectively. The master image is acquired by the SAR sensor on the first pass, whilst the slave image refers to all images acquired in successive acquisitions (Tripathi and Kumar 2017).

3 Materials and Methods 3.1 Site Description There are three study areas with different applications of remote sensing datasets for mapping, analysis and monitoring of different urban environmental hazards. Rupnagar Rupnagar is a district located in the cradle of India’s first green revolution— The state of Punjab. It is the headquarters of Rupnagar division and is a semi-urban setup. With large but concentrated urban settlements, surrounded by extensive agricultural lands. Rudrapur Rudrapur city in Uttarakhand is the headquarters of Udham Singh Nagar district and is known for its vast industrial complex. The increasing industries hub is attracting large chunks of the population in terms of skilled and unskilled migrant labour from nearby areas. This is increasing stress on the meagre amenities available in Rudrapur. Rudrapur is located between 28.980 N and 79.400 E. Dehradun Dehradun city, the makeshift capital of Uttarakhand state in India, was chosen as the city area. Dehradun lies in the beautiful Shivalik hills of the mighty Himalayas and is a popular tourist destination. Ever since the separation of Uttarakhand from the state of Uttar Pradesh in 2000, Dehradun city has expanded many folds and has seen the development of apartment culture at a rapid pace. This is all due to the increase in population over the years, most people who have migrated in search of better employment opportunities. The study areas are shown in Fig. 1.

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Fig. 1 Location map of the study area—a Sentinel-2 FCC for Rupnagar, b Sentinel-2 FCC for Dehradun and c Aerial imagery of Rudrapur

3.2 Methods Digital elevation model (DEM) based run-off estimation from space-borne remotely sensed SAR data for 2019 flood scenario of Rupnagar. Space-borne SAR data also helps in the estimation of run-off by DEM Generation. For this, a technique termed as SAR Interferometry is used. SAR Interferometry or InSAR is defined as a method of SAR image acquisition by a satellite sensor, where two or more successive SAR imageries are acquired in same geometry and along the same path and of the same area, over a time gap (Carin et al. 1998). This helps in detecting changes if any that have come up in the topography of the area in the period. This technique also helps in mapping the general elevation and topography of an area, termed as digital elevation model (DEM) (Shitole et al. 2017; Veci 2016). This DEM map is used further for run-off estimation which is extremely helpful for preventing urban areas from flooding and planning for diversion of excess run-off from urban areas (Dwivedi et al. 2016). The Rupnagar DEM is shown in Fig. 2. Pre-processing The Sentinel-1 SAR data sets were split to delineate the study area of Rupnagar in Punjab. Thereafter, the datasets were Deburst since the acquisition in Sentinel-1 is carried out in burst mode. This step is followed by calibration where DN values of each pixel are related with the SAR Backscatter coefficient. Since the SAR imageries have different spatial resolutions in range and azimuth directions, hence, to generate square pixels, multi looking is performed on the data. Following this, the pixel coordinates are then related to the actual ground co-ordinates. The resultant image is terrain corrected.

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Fig. 2 a Sentinel-1 SAR imagery with Rupnagar watershed draped over, b DEM map of Rupnagar watershed showing elevation in metres

Interferometric Processing Two or more such pre-processed time-series images are then co-registered with an older image usually being the master and newer time frame image as the slave image. This is followed by interferogram generation where the co-registered product is multiplied by the complex conjugate of itself. After that by unwrapping the interferometric phase to prevent the phase from swapping itself to 0, every time it completes a 2π phase cycle. This unwrapped phase was then used to estimate the height and generation of the DEM map. Further, this DEM map was utilised in the SCS-CN method as explained below for watershed delineation and estimation of run-off in Rupnagar Watershed. A detailed methodology flow diagram is shown in Fig. 3. SCS-CN Method for Run-Off Estimation The following set of equations represent the SCS-CN method for run-off estimation (Amutha and Porchelvan 2009; Mishra et al. 2008). P = Q + F + Ia

(2)

P F = S (Q − Ia )

(3)

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Fig. 3 Methodology flow diagram for run-off estimation using SCS-CN Method from Sentinel-1 SAR generated DEM

Here, P represents the total rainfall in millimetres, I a is the abstraction in milometres, Q is the peak run-off and S is maximum retention of soil. Maximum abstraction is related to peak retention by assuming I a = 0.2S. Therefore, maximum run-off is given by QP in millimetres (Soulis and Valiantzas 2012). QP =

(P − 0.2S)2 (P − Ia )2 = (P − Ia + S) (P + 0.8S)

(4)

where S=

254400 − 254 CN

(5)

As explained above, coherence-based flood inundation mapping was carried out for Rupnagar from Sentinel-1 satellite SAR data. The flood inundation map was overlaid on google earth as shown in Fig. 4. The Rupnagar floods showed a peak on the 18th of August when the maximum area of the district was submerged. As shown in Fig. 1, the soil conservation service-curve number (SCS-CN) method was applied to estimate the run-off based on the DEM. The SCS-CN method is one of the most common methods for run-off estimation widely used by remote sensing scientists for a long time (Geetha et al. 2008). It is highly robust and easy to use a method with high accuracy. The SCS-CN method implies that the run-off of an area is dependent upon the land use type, hydrological soil group (HSG) of soil and the and the antecedent condition of soil moisture (AMC)

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Fig. 4 Flood map by overlaying the coherence threshold mask on Google earth for Rupnagar (Punjab), India on—a the 6th of August 2019, b the 31st of August 2019 and c the 18th of August 2019

(Mishra et al. 2004; Mishra and Singh 2003). As a prerequisite, land use/land cover map for Rupnagar was prepared from Sentinel-2 Standard FCC using supervised pixel-based classification, the details of which are beyond the scope of this chapter. The LULC map for Rupnagar is shown in Fig. 5.

Fig. 5 Land use/land cover (LULC) map—a Rupnagar district, b Rupnagar watershed as used in run-off estimation

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Fig. 6 Principle of differential SAR interferometry (Gade and Stoffelen 2019)

Urban subsidence mapping using differential SAR interferometry (DInSAR) from Sentinel-1 satellite SAR data for Rudrapur. Differential SAR interferometry (DInSAR) Regarding Fig. 6, let there be RADAR positions as A1 and A2 on passing over an area and B as baseline separation. The horizontal angle of tilt is A. A2’ be another antenna position for the pass over position 3 and baseline B’ about A1 and tilt angle A’ in terms of the horizontal. It is assumed that there was some ground subsidence at point P when the satellite was passing between position 2 and 3. The change in position of P can easily be determined using the DInSAR principle (Confuorto et al. 2016; Zeni et al. 2011). In Fig. 6, ρ, (ρ + δρ) and (ρ + δρ  ) are path lengths for three different satellite passes. Phase difference for passes 1 and 2 is given by (Mutanga et al. 2012). φ=

4π − δρ λ

(6)

where δρ is slant range difference between passes 1 and 2. Considering A2A1P of A1PA2, we have a cosine relationship as shown below (Chameides and Walker 1973). cos (90 − θ + δ) =

B · B + ρ · ρ − (ρ + δρ) · (ρ + δρ) 2βρ

(7)

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Or, 2Bρ cos (90 − θ + δ) = B · B + ρ · ρ − (ρ + δρ) · (ρ + δρ)

(8)

2Bρ cos (90 − (θ − a)) = B 2 + ρ 2 − ρ 2 − 2ρ ∗ δρ δρ2

(9)

Or,

Since δρ 2 is exceedingly small, hence can be neglected. Hence rearranging Eq. (9) (Finlayson-Pitts and Pitts 1993). δρ = B sin(θ − α) +

β2 2ρ

(10)

For space-borne SAR remote sensing geometry, parallel ray approximation can be used owing to the long distance of the sensor from the ground. Hence Eq. (5) changes to δρ = B sin(θ − a) = B

(11)

where BII is baseline component parallel to look direction. Substituting the value of δρ. φ=

4π 4π ∗ B sin(θ − a) = ∗ BII λ λ

(12)

Similarly, for passes 1 and 3, the phase difference would be φ=

 4π  4π ∗ B  sin θ − a  = ∗ BII λ λ

(13)

where BII’s is the baseline component parallel to B’ direction. Dividing Eq. (13) by Eq. (14). φ BII = φ BII

(14)

Due to the RADAR wavelength being constant, the above equation is independent of the terrain. Here, it is assumed that the subsidence occurred between passes 2 and 3 coherently. This causes an additional change of phase in RADAR to look direction; hence, modified phase change equation is as follows (Garg and Manchanda 2009; Lingam et al. 2019)

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φ =

 4π   ∗ BII + δρ λ

(15)

Phase difference responsible for ground subsidence is calculated by Eq. (16) after differentiating above equations and scaling the interferometric phase differences (also called interferogram normalisation). Component of ground subsidence in RADAR look direction is as follows (Belward and Skøien 2015; Greaver et al. 2012). δφ =

4π ∗ δρ λ

(16)

Application of DInSAR for Urban Subsidence Mapping Using Sentinel-1 SAR Satellite Data As mentioned in the previous sections that DInSAR technique using space-borne remotely sensed SAR data is incredibly helpful for tracking and measuring any changes that occur in the topography of an area over a period (Mccormack et al. n.d.). This is incredibly helpful in tracking the land surface subsidence, structural deformations and many more. For this, there are two important steps—unwrapping of the phase of the backscattered microwave radiation and secondly, relating the unwrapped phase to displacement/subsidence (Molan 2018). The phase of the backscattered microwave swaps to 0, every time it completes a full cycle. To have continuity in the phase pattern, phase unwrapping is done. After that this unwrapped phase of the backscattered microwave radiation is related to subsidence/displacement as shown by the following equation (Mccormack et al. n.d.). Land Subsidence =

(φunw ) × λ −4π cos θ

(17)

Here, φ unw is the unwrapped phase, λ is the wavelength, and θ is the angle of incidence as shown in Eq. 17. Successful Applications The DInSAR technique has been applied worldwide for several cities. In India as well, the technique has been able to successfully map the land surface subsidence. Today, many towns and cities in India are facing acute water shortage. This leads to unchecked boring activities by people, and with the increase in the human population, these practises are on the rise. Overutilisation of groundwater by tube wells and private boring is leading to drying up of underground aquifers. This is leading to subsidence in major urban centres of the world and Indian towns are no exception to this. The problem is more acute in towns like Rudrapur in Uttarakhand which is located far from any water body and is a mix of industrial and agricultural township. The water demand is ofcourse much more than what would be needed for domestic needs. A major chunk of the water is used by industries and agriculture and ground boring and tapping water from sub-surface aquifers is the only way out.

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Time-series TerraSAR-X data were first pre-processed where the DN values were related with SAR backscatter in a step called calibration. This was followed by terrain correction where the pixel co-ordinates of the SAR image were correlated with the actual ground co-ordinates. Further interferometric processing was carried out as explained before. The unwrapped phase was then used to calculate the vertical displacement and urban subsidence map was generated. A detailed methodology flow diagram is shown in Fig. 7. Urban Heat Island (UHI) Mapping from Space-Borne Remotely Sensed Data Urban heat island is defined as urban areas that experience much warmer temperatures than the nearby areas. This happens due to excessive cutting of trees and erecting concrete jungles in place (Liu et al. 2012). This age of skyscrapers has reduced the urban green belts to a considerable extent, leading to a decline in rainfall and higher temperatures (Tiwari et al. 2017). Moreover, there is little on the part of the efforts of Urban reforestation. UHIs are slowly emerging as the areas of pollution concentration and fresh air are deficient, causing many health issues for the dwellers (Deepthi et al. 2018). UHIs are perhaps the most potent urban hazards that modern cities are experiencing on a day-to-day basis (Ahluwalia et al. 2016). Remotely sensed satellite data is a helpful, cost-effective and time-saving technique that helps map UHIs without any sophisticated-on field equipment (Shafri and Taherzadeh 2012). Thermal remote sensing that operates in the 3–5 μm and 8–15 μm region of the electromagnetic spectrum is incredibly helpful in the estimation of Land Surface Temperature (LST). The areas with high LST are deemed as UHI. Space-borne remotely sensed satellite data from Landsat 8 was used for generation of LST map. The Landsat 8 has band number 10 as a thermal band. The thermal band was used for the generation of top of atmosphere temperature. Based on the TOA calculated, the brightness temperature is estimated. Thereafter, the normalized

Fig. 7 Methodology flow diagram for urban subsidence mapping

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differential vegetation index (NDVI) is calculated. NDVI values range from −1 to 1 (Antognelli n.d.). NDVI =

(NIR − R) (NIR + R)

(18)

From NDVI, proportional vegetation index and error were calculated. From the standard error, and brightness temperature, LST were calculated using the following equation (Ishimwe et al. 2014; Liu Yun 2011).  LST = (BT/(1 + 0.00115 ×

BT 1.4388

 × ln(error)

(19)

Here, BT is the brightness temperature. The areas showing high values of LST with surrounding areas experiencing lesser temperature are termed as UHIs. A detailed methodology flow diagram is shown in Fig. 8.

Fig. 8 Methodology flow diagram for UHI mapping

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4 Results and Conclusion 4.1 Results Run-off Estimation for 2019 Rupnagar Flood Scenario The surface run-off, usually in urban areas depends upon the soil properties, retention capacity, texture of soil and the LULC of the study area. Most of the studies that used SCS-CN method for run-off modelling use optical data which has often cloud cover constraint in monsoon, using entirely remotely sensed SAR data gives penetration ability through cloud cover and better temporal data availability. The coherencebased threshold for flood mapping is another way in which, SAR remotely sensed data has been extraordinarily successful. A run-off map was prepared based on the estimated run-off using SCS-CN Method (Tripathi et al. 2021). The minimum runoff was estimated at 100.7 mm and maximum run-off was at 353.08 mm on the 18th of August 2019, as shown in Fig. 9. Urban Subsidence Mapping of Rudrapur Using TerraSAR-X Remotely Sensed SAR Data Using SAR data from German Space Agency DLR’s satellites—TerraSAR-X, successful mapping of land subsidence was carried out as shown in Fig. 10 (Tripathi et al. 2018c). Figure 10 shows the land subsidence measurements. Positive values represent elevation in buildings that have come up because of some sort of construction. Similarly, urban land subsidence was studied for Chandigarh Tri-city region as well, using Sentinel-1 C-band SAR data for 2018–19, as shown in Fig. 11 (here measurements are in millimetres) (Tripathi and Tiwari 2019b). Urban Heat Island (UHI) Mapping from Space-Borne Remotely Sensed Data for Dehradun City Based on the methodology (Fig. 8), a UHI map was prepared from the LST calculated using Landsat-8 imagery as shown in Fig. 12.

5 Conclusion Remote sensing is a non-evasive and state of the art technology for monitoring and assessing hazards in the urban environments. With the changing lifestyle, there is widespread exploitation of natural environments. With cities and urban areas becoming the engines of the economy, urban areas must develop sustainably. Remote sensing can genuinely help this smart and sustainable development in several waysfrom, giving an early alarm for hazards and upcoming disasters to aiding in planning for relief measures. With new satellites and sensors and machine learning used

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Fig. 9 Estimated run-off map for Rupnagar watershed on the 18th of August 2019

together with remotely sensed data from space, the future is exceptionally good in this field for budding researchers and users. Smart city development programme from the taxi system to live location tracking of food deliveries has been made possible through remote sensing and geographical information systems (GISs).

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Fig. 10 Google earth overlay of urban land subsidence map for Rudrapur (Uttarakhand) showing land subsidence up to 2.46 cm per year (2017–18), (Tripathi et al. 2018c)

Fig. 11 Google earth overlay of land surface subsidence for Chandigarh Tri-city region during 2018–19 with maximum surface subsidence of 17.82 mm

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Fig. 12 LST Map of Dehradun city showing urban heat islands marked in a circle

Acknowledgements This chapter was supported by Geomatics Lab, Department of Civil Engineering, Indian Institute of Technology (IIT) Ropar, Rupnagar, Punjab, India.

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Urban and Environmental Hazards Kriti Varma, Vaishali Srivastava, Anjali Singhal, and Pawan Kumar Jha

Abstract The ever-rising urbanization and economic aspirations of humans have led to the increased vulnerability of humans to future hazards. The prominent urban and environmental hazards that have emerged in the past few decades include pollution, floods, earthquakes, and the urban heat island effect. The potential risks in the future can be attributed to two factors, population rise and subsequent increase in the ‘built’ environment. It is quite natural that the increasing population would generate more waste, need more land for residential, industrial, healthcare, education, and other purposes. The rapid developmental and economic activities in the urban areas have aggravated environmental pollution through the discharge of untreated industrial waste, domestic and municipal effluents, and toxic industrial and vehicular emissions. Another most severe environmental hazard, in recent times, floods, is prevalent in the South and South-East Asian countries. The process of urbanization comprises of construction of roads and buildings, by removing vegetation and soil. Such constructions lead to the replacement of permeable soil with impermeable surfaces, causing decreased infiltration of water to the ground and increased runoff to the surface water bodies, aggravating the frequency and impact of the flood causing inundation of land and human settlements amongst others. Also, unplanned concrete construction activities in urban areas have exposed the urban population to significant seismic hazards worldwide. The urban towns, capital cities, and business centers have surpassed their carrying capacity, disturbing the seismic activity. The sudden release of accumulated tectonic energy, when it strikes densely populated urban centers causes an earthquake, rendering most of the urban inhabitants either dead or homeless, disconnected, and deprived of their basic needs. Another devastating hazard of the modern period is the ‘urban heat island effect’. Studies of urban climate suggest that significant difference prevails in the ambient air temperature of cities and their adjoining rural areas, giving rise to the urban heat island effect. This is mainly due to the emissions from industries and concrete infrastructure occupying K. Varma · V. Srivastava · P. K. Jha (B) Centre of Environmental Studies, University of Allahabad, Prayagraj, India A. Singhal Department of Botany, University of Allahabad, Prayagraj, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_19

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the urban areas. This study deals with these urban environmental hazards taking into account their causes, impacts, frequency of occurrence together with mitigation and management policies/practices worldwide and in the Indian context, in an attempt to save life, property, and environment as well. Keywords Environmental hazards · Flood · Mitigation · Urban heat island · Urbanization · Vulnerability

1 Introduction The process of urbanization is a complex one, characterized by economic development, change in land use pattern, migration of population, and reform in lifestyle (Wang et al. 2019). At the beginning of the twentieth century, only 15% of the population used to live in cities. At present, about half of the world’s population lives in urban areas, which is roughly 2.8% of the total land of Earth (Millennium Ecosystem Assessment 2005). The growing percentage of the urban population in India is summarized in Table 1. Also, inhabitation in urban areas is expected to reach 5 billion or 60% by 2030 (Population Reference Bureau 2007). An increase in urban populations has led to urban sprawl, particularly in developing countries. It is estimated that the urban population in developing nations will grow at a rate of 2.3% annually between the years 2000 and 2030 (United Nations 2004). The limited coping and adaptive ability of the developing countries enhances the impact of the hazards (García-Soriano et al. 2020). This growth in urbanization has put immense pressure on natural resources and the areas of their extraction to meet the demands of the urban population, resulting in adverse impacts on the environment and its components (Chen 2007). Growing urbanization brings new challenges to mitigate the risk of natural disasters, rendering the urban population more vulnerable to adverse impacts of disasters. Natural disasters include phenomena related to meteorological, biological, geophysical, hydrological, and climatological aspects (Rudiarto et al. 2018). The period between 1999 and 2000 witnessed some large-scale disasters accounting for the relation between urbanization and natural disasters. These disasters include earthquakes in the densely Table 1 Total and percent increase in the urban population of India from 1961–2011

Year

Total population

Urban population (%)

1961

439,234,771

17.97

1971

548,159,652

19.91

1981

683,329,097

23.34

1991

846,421,039

25.71

2001

1,028,737,436

27.78

2011

1,210,193,422

31.16

Source (Ministry of Home and Urban Affairs 2020)

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urbanized region of Turkey killing 17,000 people, leaving 44,000 people injured and destroying 300,000 homes; the floods of Venezuela destroying 230,000 houses; cyclones in Orissa killing over 100,000 people and rendering 8,000,000 people homeless; heavy rains followed by cyclones in Mozambique, affecting over 1,000,000 inhabitants with direct impacts of floods as well as indirect health impacts in the form of widespread cholera and dysentery (Sanderson 2000). The occurrence and death tolls due to disasters around the world between the period of 2000–2019 are presented in Fig. 1. The high amount of impervious land cover in urban areas reduces the stormwater infiltration and enhances the radiative heat, further worsening the impact of flood and heat risk respectively (Fahy et al. 2019). The floods emerged as the major environmental threat during the past two decades as a result of changed land use and climatic patterns, followed by storms, earthquakes, and extreme temperatures giving rise to heat stress conditions and the urban heat island effect. Despite the occurrence of floods with maximum frequency, the death tolls due to floods were minimum during the past 20 years. However, loss of life was most prominent due to earthquakes followed by storms, and extreme temperatures. The major problem with disasters in the urban environment is their vast and intense impact due to the highly dense agglomerations in urban areas. The deadliest disasters of the world and the number of people affected by them during the past decades are presented in Table 2. This study encompasses some of the major disasters caused as a consequence of urbanization and its associated developmental activities. The most common and widespread consequence of rapid urbanization faced by the environment is in the form of pollution. The exploitation and overuse of natural resources to fulfill the growing needs of the urban population have resulted in the degradation and pollution

Fig. 1 Percentage occurrence and mortality due to extreme temperature, earthquakes, storms, and floods in India as compared with the world, during 2000–2019. Source (United Nations Office for Disaster Risk Reduction 2020)

322 Table 2 The ten deadliest disasters of the world during 2000–2019

K. Varma et al. Disaster

Location

Year

Population affected

Earthquake and Tsunami

Indian Ocean

2004

226,408

Earthquake

Haiti

2010

222,570

Storm

Myanmar

2008

138,366

Earthquake

China

2008

87,476

Earthquake

Pakistan

2005

73,338

Heatwave

Europe

2003

72,210

Heatwave

Russia

2010

55,736

Earthquake

Iran

2003

26,716

Earthquake

India

2001

20,005

Drought

Somalia

2010

20,000

Source (United Nations Office for Disaster Risk Reduction 2020)

of the environment. The other common hazards occurring due to urbanization and associated activities include earthquakes, floods, and the urban heat island effect, which significantly affect the health and function of living systems and the ecosystem as a whole.

2 Environmental Pollution 2.1 Types and Sources Urbanization and associated activities consequently cause pollution of air, water, soil, noise, and so on. It has led to an alteration of both the biotic and abiotic components of the environment, not only within or near the urban areas but has also affected the distant areas. The source of pollution may either be a point source, where pollutants are released from an identified source, or a non-point source, where pollutants are released from multiple sources making it difficult to identify (Vallero 2014). With the growing population and subsequent developmental activities, urbanization is bound to increase in the coming decades, further changing the pattern, nature, and pace of pollution, affecting environmental and ecological sensitivities on a larger scale. Also, there exist regional variations in ecosystems owing to a different climate, geomorphology, and vegetation that allow a coalition of non-point pollution sources into certain hotspots or source regions including urban agglomerations (Grimm et al. 2008). Air, water, soil pollution, and waste disposal pose hazards in urban areas of the developing world (Wu et al. 2020a, b). The urbanization-related activities release pollutants either in the air causing air pollution through primary or secondary pollutants or generate a humongous amount of toxic or non-toxic waste that, if disposed on land, cause soil pollution, and the runoff or leaching or release of sewage

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Fig. 2 Venn diagram showing urban pollution as a combination of air, water, and soil pollution. Source (Karn and Harada 2001; Mireles et al. 2012; Liang et al. 2019)

or other industrial effluents into water bodies result in water pollution. As it is quite evident that the components of the environment viz. atmosphere, hydrosphere, and lithosphere, have a dynamic inter-dependent relationship, hence the pollution of one component, directly or indirectly, affects the other environmental components as well. The causes of urban pollution are depicted in Fig. 2. Urbanization has provided a boost to the global economy but has subsequently put immense pressure on air quality. The pollution of air varies with differences in geographical regions, given the nature and pattern of the urbanization process (Xia and Gao 2011). The problem of air pollution is more prominent in developing countries of South-East Asia and West Africa, where urbanization is taking place at a rapid pace, whereas the developed countries such as North America and Europe are relatively less polluted (Wang et al. 2020). Emission of carbon, industrial smoke, dust, fossil fuel combustion, and automobile exhaust from urban expansion contributes to air pollution by adding excessive amounts of oxides of carbon, sulfur, nitrogen, PM10, PM2.5, polyaromatic hydrocarbons (PAHs), and a variety of secondary pollutants to the atmosphere (Liang et al. 2019). Such pollutants can result in grave consequences of global warming, photochemical smog, climate change, acid rain that tend to impact the health of plants by hampering photosynthetic and transpiration rate; animal and human health by causing several respiratory, cardiovascular, and neurological problems; thereby affecting the health of the ecosystem as a whole. Soil, being the basic component of the terrestrial ecosystem, not only provides the physical base for yielding food, fodder, fiber, and fuel but also acts source and sink

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for greenhouse gases and is an integral part of the biogeochemical cycles (Yaalon, 2000). Soil pollution by several toxic substances, non-biodegradable materials, and heavy metals as a result of intense urbanization has emerged as a serious problem in most developing countries (Mireles et al. 2012). The residence time of pollutants in the soil acts as the major factor influencing soil pollution, followed by road and population density (Peng et al. 2013). The accumulation of pollutants in soil from wastewater, municipal effluent, agrochemical runoffs, smelter, and incinerator discharge degrade the soil quality reducing yield and quality of crops, affecting the health of animals, humans, and the ecosystem (Hu et al. 2013). Thus, urbanization results in the transformation of natural land to urban land affecting soil resources and food security through various direct as well as indirect impacts (Chen 2007). The growing urbanization has impacted the water sector through increased demand for water and severe pollution of available water resources. The population pressure in urban regions accompanied by massive domestic needs and ever-growing agricultural demand has further deteriorated the already substandard water quality (Ren et al. 2014). This depletion in quantity and quality of water resources has emerged as the topmost concern around the world, particularly in the Asian region (Karn and Harada 2001). Urbanization has also led to the change of source, process, and sink of urban non-point source pollution (Yang et al. 2003). The lack of adequate and proper sewage systems to facilitate the growing urban population also adds to the water pollution as untreated domestic discharge and other non-point pollution sources are released into the water bodies (Zhao et al. 2013). These pollutants can either directly harm human, plant, and animal health, or indirectly harm their health by the processes of eutrophication, bioaccumulation, and biomagnification, thereby affecting ecological and environmental health as well.

2.2 Air Pollution Air pollution resulted in the mortality of 8793 × 103 person yr−1 in the world with the highest death count was observed in East Asia (3112 × 103 person yr−1 ) followed by South Asian countries (2809 × 103 person yr−1 ) (Lelieveld et al. 2020). In the year 2017, air pollution resulted in the death of 1·24 million people in India out of which 54% of death was attributed to ambient air pollution and the remaining to indoor air pollution (Balakrishnan et al. 2019). National Ambient Air Quality Standards (NAAQS) has defined six major pollutants, as ‘criteria pollutants’ that include CO, Pb, NO2 , oxides of sulfur, particulate matter (PM2.5 and PM10 ), and tropospheric ozone. NAAQS set performance levels instead of suggesting equipment design to control air pollution. The Environmental Protection Agency (EPA) also assigns the responsibility of designing implementation plans to the states to attain the air quality standards in their respective regions (Grainger and Schreiber 2019). The anthropogenic emissions of short-lived pollutants and long-lived greenhouse gases have resulted in a change in global temperature patterns. The past thirty years have witnessed a tremendous increase in the concentration of tropospheric ozone

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and black carbon owing to the process of urbanization (Burney and Ramanathan 2014). The deviation from the set standards of air pollutants is responsible for several respiratory, cardiovascular, and neurological disorders in humans. This increases the risk of premature deaths in populations exposed to severe air pollution. Long-term exposure to PM10 is responsible for high mortality rates among infants (Wang and Mauzerall 2006). It also causes severe impacts on plant physiology including the development of necrotic lesions, deposition of thick layers of black dust on leaves, delayed sprouting, and hastened senescence, thereby affecting plant growth, health, and yield (Emberson et al. 2001). Apart from the impact on biotic components, air pollution also damages buildings and monuments as a consequence of acid rain. The buildings are corroded rendering them degraded and requiring repair that is a cost-intensive process (Rabl 1999). The other problem related to air pollution is the formation of urban smog that is a combination of smoke from automobile and industrial exhaust and fog, that hampers both, visibility and health (Shah and Arooj 2019). Thus, the menace of air pollution is emerging as a major threat to the ecosystem and its components, particularly in urban areas.

2.2.1

Major Impacts of Air Pollution

Air pollution has caused several adverse impacts on the biotic as well as abiotic components of the environment. Some of the major impacts of air pollution are discussed below: • Greenhouse Effect and Global Warming The greenhouse effect refers to the ability of greenhouse gases, such as water vapor, CO2 , CH4 , N2 O, that retain the infrared radiation and prevent their escape to outer space, making Earth’s atmosphere a habitable one. However, excess emission of greenhouse gases in recent years has resulted in excessive warming of the Earth, giving rise to the well-known problem of global warming (Kweku et al. 2018). In the wake of rapid urbanization practices, it is expected that the emission of greenhouse gases will further increase during the twenty-first century, resulting in a 1–3.7 °C rise in global temperature, giving a boost to global warming (Anderson et al. 2016). The major impacts of these phenomena include changed climatic patterns, thawing of glaciers, flooding of coastal areas, disruption of food supplies, increased wildfires, migration, and/or loss of biodiversity (Bush 2017; Rani et al. 2019). • Acid Rain It refers to the atmospheric deposition of acidic substances that precipitates down in the form of rain, snow, particulates, gases, and vapor, occurring mainly due to the emission of oxides of sulfur and nitrogen as a consequence of urban pollution by fossil fuel combustion, burning of wastes, automobiles, and airplanes (Burns et al. 2016). It can adversely affect human health, vegetation, aquatic and other biotas, along with monuments and sculptures. The major impacts of acid rain include the reduction of chlorophyll content of leaf by up to 6.71% per pH unit, obstructing the growth and functioning of the plants (Du et al. 2017), alteration in the soil fauna community and their vertical distribution, leading to

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change in underground functions of the ecosystem and greenhouse emissions as well (Wei et al. 2017); adverse impact on human health including irritation in eyes, nose, throat, headaches, asthma, bronchitis, and dry cough among others (Mohajan 2018); and potential damage to buildings, monuments, and sculptures of cultural and historical significance, generally composed of marble and bronze (Livingston 2016). • Tropospheric Ozone Pollution Tropospheric ozone is an important secondary pollutant that impacts the climate through radiative forcing and also by acting as an atmospheric oxidant. It also exerts prominent oxidative stress on the biosphere and indirectly impacts the climatic patterns by changing the dynamics of material and energy exchange between atmospheric and terrestrial ecosystems (Wang et al. 2017). The concentration of tropospheric ozone may vary with time and region of the globe, but it will continue to rise given the rapid increase in emission of urban pollutants (Lefohn et al. 2018). The phytotoxic nature of the troposphere has significant impacts on vegetation, obstructing their metabolic and morphological normal functions, ultimately reducing crop yields (Singh and Agrawal 2017). It also affects human health by adversely impacting the respiratory and circulatory systems (Díaz et al. 2018). • Effect on Health The rapid pace of urbanization in major parts of the world has led to some devastating effects on human health through the modern practices of industrialization, construction, and other developmental activities. These practices have resulted in increased emission of air pollutants. The growth in air pollution over the years has further aggravated several health issues including respiratory problems such as cough, asthma, bronchitis; cardiovascular problems like increased blood pressure, disturbed heart rate, cardiac arrest; vision problems, and nervous disorders among others (Nowak et al. 2018). It has also been reported that if there is an increase of 1 µg/m3 in the mean concentration of PM2.5 for a decade will increase the risk of dementia by 1.68% (Bishop et al. 2018), and is also responsible for disturbed reproductive cycles in female population resulting in problems in the menstrual cycle, increased risk of miscarriages, the high neonatal mortality rate (Gruchala et al. 2017). 2.2.2

Air Pollution: Spread in World and India

The grave issue of pollution caused by the growing and expanding urban population is not uniform around the world. It is most prominent in the speedily developing nations owing to the ever-growing developmental activities, and relatively less prominent in rich and poor nations, due to respective control and absence of pollution sources (Wen et al. 2017). The Arctic region is also not untouched by the menace of air pollution due to the anthropogenic emissions of long-lived greenhouse gases, ozone, and aerosols, which are transported mainly from Eurasia, reaching their peak concentrations in the spring and winter season (Law and Stohl 2007). The growing demand for energy in European countries to meet the requirements of urban life has led to the excessive use of fossil fuels to generate electricity. Around 20 percent of electricity production

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in Europe is dependent upon fossil fuels, releasing a generous amount of greenhouse gases and other pollutants into the atmosphere, thereby depleting the air quality (AlMulali et al. 2015). One of the countries with an extremely rapid pace of urbanization in China, with an urban population growth rate of 41.66% from the year 1978 to 2018. This growth in urbanization has resulted in a serious problem of increasing pollution. China ranks fourth from the bottom in terms of assessment of environmental quality regarding PM2.5 . Such severe pollution has taken a toll on the lifestyle and health of the people living in China (Wu et al. 2020a, b). The problem of pollution is widespread in Malaysia as well. The major sources of pollution here include stationary sources (20–25%), mobile sources (70–75%), and open burning sources (3–5%). This trend continues to increase owing to the vast increase of 26 percent in motor vehicles within a period of four years, i.e., from the year 1996 to 2000 (Afroz et al. 2003). In the recent past, winter smog in Delhi, India mainly contributed due to heavy vehicular traffic, crop stubble burning and Diwali celebrations along with metrological-induced inversion conditions has gained much public attention (Garg and Gupta 2020). The Indo-Gangetic plain, popularly known as the ‘breadbasket of India’ is also heavily polluted due to the release of aerosols, ozone, and other short-lived climatic pollutants that impact the climate and air quality (Burney and Ramanathan 2014). The city of Agra in Uttar Pradesh faced severe impacts of air pollution from year 2002–2014. The risk of mortality and morbidity during the period increased by 13.43 to 27.52% due to increased concentrations of nitrogen and sulfur oxides together with particulate matter leading to excess cases of chronic obstructive pulmonary disease (COPD), respiratory disorders, cardiovascular diseases, ultimately increasing the mortality rate of the city (Maji et al. 2017). Other cities with heavy pollution include Ahmedabad with high SPM (suspended particulate matter) levels due to rapid urbanization and industrialization. Pune, an educational and cultural city in the south-western part of India is threatened by pollution due to vehicular emissions, which compelled the Pune Municipal Corporation and Regional Transport Office to ban heavy vehicles in the commercial and residential areas of Pune. Kanpur in Uttar Pradesh is also heavily polluted mainly due to huge amounts of industrial and vehicular emissions, particularly the three-wheelers that have poor efficiency (Singh et al. 2007). Air pollution has emerged as a major concern in recent years. The rapid growth in emissions from vehicles and industries as a part of development and urbanization has resulted in increased mortality and morbidity in the capital city (Rizwan et al. 2013).

2.2.3

Air Pollution: Control and Mitigation

Several mitigation measures can be implied to mitigate the problem of urban pollution. Some of them are discussed below: • Urban Forests It consists of all the vegetation, lawns, and pervious soils in a complex and modified environment where humans are the main influencer of their kind, amount, and distribution (Dobbs et al. 2011). These forests can help in

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reducing energy consumption and also in the significant abatement of pollution in the urban areas by absorption of gaseous pollutants, mainly through leaf stomates, and also intercept the particulate matter with wind currents (Brack 2002). Many cities have now started to integrate urban forests as a part of their life for achieving urban sustainability and environmental quality, by providing unique ecosystem and environmental services that help in mitigating urban pollution (Escobedo et al. 2011). • Use of Innovative Technologies and Alternative Resources Several innovative techniques can be developed and applied for the abatement of pollution, such as catalytic/non-catalytic converters, exhaust gas recycling, evaporative loss control devices, etc. can be used to deal with the air pollutants. Switching from conventional to unconventional energy resources can also be a potential measure to mitigate pollution. The recent shift from fossil fuels to other unconventional fuels like biodiesel, CNG, battery-operated vehicles, wind energy, tidal energy, and solar energy is a positive step in this direction. Such usage of a variety of alternate energy sources has reduced the burden of fossil fuels, thereby reducing the depletion of natural resources, and subsequently abating pollution (Bhandarkar and Lecturer 2013).

2.3 Water Pollution Waterborne disease diarrhea is the major reason behind the death of children under five worldwide (15%) and in India, it is responsible for the death of five hundred thousand children each year (WHO and UNICEF 2000). The pollution caused as a result of expanding urbanization has affected both the freshwater and marine water systems alike. Most of the coastal zones on Earth are deteriorated by pollution affecting the commercial fisheries and hence deteriorating the economy (Islam and Tanaka 2004), while freshwater pollution is responsible for affecting the health of people directly consuming and using it for various domestic purposes. The huge amount of waste generated by the urban population is ultimately discharged into the water, with or without proper treatment, in a proportion that exceeds the self-purification capacity of water bodies (Goel 2006). Such discharge leads to eutrophication of water bodies reducing the amount of dissolved oxygen, threatening aquatic life. The pollution of toxic heavy metals can result in bioaccumulation and successive biomagnification, ultimately causing several health hazards to plants, animals, and humans. Humans are threatened by many diseases such as Minamata, blue baby syndrome, and other diseases mainly affecting the nervous, skeletal, digestive, cardiovascular, and reproductive systems (Sharma et al. 2014). Several directives and regulations have been laid by various organizations on national and international levels, such as the World Health Organization (WHO), Environmental Protection Agency (EPA), Central Pollution Control Board (CPCB), Bureau of Indian Standards (BIS), that are responsible for checking the number of pollutants discharged into the water bodies. The water quality standards vary depending upon the region and these organizations

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are also responsible for the revision of water quality standards from time to time, as and if required (Mushtaq et al. 2020).

2.3.1

Major Impacts of Water Pollution

The pollution of water is known to cause many widespread effects on microbial, plant, animal, and human health, thereby altering the normal structure and function of the ecosystem. Some of the major impacts are as follows: • Eutrophication The increased amount of discharge following the recent developmental trends has enriched the water bodies with nutrients, mainly nitrogen and phosphorus, ultimately leading to algal blooms (Glibert et al. 2005). These algal blooms consume most of the oxygen, suffocating the other aquatic species, leading to their deaths and thus creating ‘death zones’ that are devoid of life. Also, these algal blooms can produce neurotoxins that can potentially affect aquatic life (Conley et al. 2009). • Contamination of Food Chain The process of accumulation of pollutants in biotic systems, known as bioaccumulation, and their subsequent transfer up the trophic levels, where their effects become more pronounced by the process of biomagnification (Zenker et al. 2014). The major pollutants that get accumulated in living systems originate from pharmaceutical discharges, tanneries, cosmetic products, and other industrial effluents. These pollutants can accumulate in lower trophic levels, and their successive biomagnification can lead to the contamination of the food chain and disrupt the food web, causing grave consequences regarding the health of individuals, species, and the ecosystem as a whole (Kidd et al. 2011). • Loss of Biodiversity The pollutants may cause toxic effects on the aquatic species resulting in their mortality. The loss of one species may subsequently affect the population of other species as the components of the ecosystem is interdependent either directly or indirectly. The tolerance levels, behavioral patterns, reproductive cycles, and associated processes of aquatic species like fishes, coral reefs, and turtles are hampered by the introduction of these pollutants that may further reduce their populations ultimately resulting in biodiversity loss (Khan et al. 2016). • Impact on Health The polluted water, upon consumption by humans in direct and indirect forms, causes several water-borne diseases such as diarrhea, gastroenteritis, nausea, dysentery. It is also known to weaken the immune system, cause liver and heart diseases, reproductive problems, kidney damage, neurodegenerative disorders, bone damage, skin problems, hyperkeratosis, melanosis, leuko-melanosis, and other fatal diseases like cancer (Schwarzenbach et al. 2010). 2.3.2

Water Pollution: Spread in World and India

Water pollution has emerged as a menace in recent years on a global scale owing to the humongous amount of waste generation and improper dumping, untreated sewage discharge, industrial effluents, and agricultural run-offs. The pollution of

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water in North America is attributed to several non-point sources and transboundary movement of waste (Garricka et al. 2016). Nile river in Egypt is highly polluted, mainly by persistent organic pollutants (POPs) of industrial origin, thereby posing a hazard to human and aquatic life (Dahshan et al. 2016). The problem of water pollution is not only limited to surface water. Studies have reported that as large as 80 percent of the groundwater of China is polluted, mainly from industrial and agricultural run-off (Currell and Han 2017). The study by United Nations suggests severe water pollution in the Asia–Pacific belt, particularly in the south-Asian region including India, Bangladesh, and Nepal, where urban waste is discharged within short stretches of rivers (Karn and Harada 2001). Bangladesh, one of the most densely populated countries in the world, has a sufficient amount of water resources but is being rapidly polluted due to the development and growth of industrial, agricultural, and commercial sectors. Such pollution has put the health of the citizens at risk with an increased mortality rate due to water-borne diseases (Hasan et al. 2019). Pakistan has limited water resources ranking third among countries experiencing extreme water shortage and according to the Pakistan Council of Research in Water Resources (PCRWR), there will be no or minimal clean water available in Pakistan by 2025. At present, only 20% of the population has access to clean water, while 80% of the population relies on water contaminated by sewage, agricultural and industrial waste, resulting in 30% mortality and 80% of all diseases (Nabi et al. 2019). One of the major rivers of India, the Ganga river, is heavily polluted in physical, chemical, and microbial aspects due to the introduction of pollutants from several sources such as urban, industry, mines, domestic, agriculture, and pharmaceuticals, affecting the health of the people and the whole ecosystem of the Ganga plain (Panda et al. 2018). The main tributary of Ganga, the Yamuna river, is also threatened by nutrient enrichment and eutrophication, chiefly through anthropogenic inputs from domestic, agricultural, industrial, and urban areas occurring in the catchment areas (Sharma et al. 2017). River Tapi, flowing through Gujarat, has been facing a deterioration in water quality due to ever-increasing human interferences leading to elevated pollution load in the river (Gaur 2018). Apart from the pollution of these freshwater resources, coastal pollution is also increasing at a significant pace. The nutrient and heavy metal fluxes in the coastal waters of the Arabian Sea and the Bay of Bengal have severely polluted the coastal ecosystems, potentially affecting the diverse and unique flora and fauna of the ecosystem (Chakraborty 2017).

2.3.3

Water Pollution: Control and Mitigation

The major control and mitigation measures for reducing further deterioration of water quality are discussed below: • Artificial Wetlands Artificial or constructed wetlands can help in the treatment of industrial, agricultural, and domestic wastewaters. It can also enhance the self-purification capacity of hydrological systems. Wetlands remove pollutants by using macrophytes through the process of absorption and bioaugmentation

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(Gregoire et al. 2009). Also, the introduction of certain commercially important species in these wetlands can help in boosting the economy of the area while maintaining environmental quality. However, their effectiveness varies along with seasonality gradient, hydrological flows, and pollutant characteristics. Thus, the construction of such wetlands can help in the potential mitigation of pollution along with providing economic feasibility (Tournebize et al. 2017). • Wastewater Treatment The best way to mitigate the pollution of water bodies is to treat the pollutants at the source itself, reducing the negative impacts of the pollutants. The proper treatment of wastewater of urban areas originating from industrial, agricultural, domestic, and other commercial sectors can help in sustainable development without degrading the quality of water resources. Such treatment can be achieved by employing several physicals, chemical, or biological processes efficiently to get cleaner water resources (Salgot and Folch 2018). • Proper Monitoring and Strict Rules Although several rules, regulations, laws, and guidelines for the release, treatment, and disposal of pollutants exist in various parts of the world, yet their negligence is a major threat to the environment. The failure of manufacturers, industries, agrochemical users, and the general public to comply with the laid guidelines and regulations has resulted in the release of ecologically toxic substances in the environment, degrading the environmental health and risking the quality of life (Izah and Angaye 2016). Apart from the preexisting rules and regulations, the increasing pollution in urban areas demands adequate and timely up-gradation and proper monitoring of the regulations to check pollution and safeguard the urban environment.

2.4 Soil Pollution and Solid Waste The process of urbanization demands more land for infrastructure to accommodate the growing population and provide residential, commercial, industrial, educational, and healthcare facilities. These activities involve land-use change and intensive construction, that most of the time, renders the soil vulnerable to pollution. Soil pollution significantly affects the structure of macrofauna communities, such as earthworms, which play an important role in decomposition (Nahmani and Lavelle 2002). The pollutants that render the soil acidic, aggravates leaching, and the uptake of heavy metals by plants leading to the phenomena of bioaccumulation and biomagnification (Menon et al. 2007). The direct impacts of such pollution can be witnessed in the form of reduced crop yield, degraded crop quality, loss of soil micro/macro flora and fauna, while the indirect impacts include disturbed biogeochemical cycles, hampered soil enzyme activity, reduced moisture content leading to erosion, loss of biodiversity, and several health hazards affecting plants, animals, and humans as well (Marzadori et al. 1996; Abrol et al. 2002). The increasing population and the subsequent urbanization are bound to generate a large amount of waste, particularly in urban areas. The disposal of a such great amount of solid waste is a widespread problem, both in developing and developed

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nations. Improper disposal of waste, in turn, pollutes air, water, and soil as well. Burning of waste to reduce the volume is the most common method of waste disposal that releases toxic gases and particulate matter into the atmosphere, thereby causing air pollution. Dumping of waste on open land may contaminate the soil, attracting flies and other vectors of diseases, and spoiling the aesthetic beauty of the landscape as well. It may also cause groundwater and surface water contamination through the process of leaching and rainwater run-off respectively (Tisserant et al. 2017). The major challenge lies in the discovery of waste disposal methods that are economically sustainable, socially viable, technically feasible, legally acceptable, and eco-friendly (Abdel-Shafy and Mansour 2018).

2.4.1

Soil Pollution: Impacts

The major impacts of soil pollution and limitless waste generation are discussed below: • Climate Change Soil plays a significant role in the biogeochemical cycles and also acts as a sink for many greenhouse gases. In the twenty-first century, the degradation of soil has led to a release of 3.6 to 4.4 billion tons of CO2 in the atmosphere, thereby increasing the global temperature with a more pronounced greenhouse effect. Such changes alter the climatic patterns leading to adverse impacts on the components of the ecosystem (Rodríguez-Eugenio et al. 2018). • Water and Air Pollution The contamination of land and dumping of waste may subsequently degrade the quality of water and air as well. The leaching and runoff from contaminated soil, during rainfall, deteriorates the quality of water bodies, causing water pollution, and affecting aquatic life and human health (Cachada et al. 2017). The burning of solid waste releases toxins, particulate matter, and other harmful gases into the atmosphere that leads to the deterioration of air quality, affecting life in all aspects (Delang 2018). • Desertification The loss of vegetation due to changing land-use patterns and construction of concrete forests has degraded the quality of soil, rendering it vulnerable to erosion. Such soils are devoid of moisture, nutrients, and organic matter, making life difficult to exist, and susceptible to desertification. The past three centuries have witnessed a shrink in the wetland area by 87% due to the process of desertification, making sustenance of life extremely difficult (˙Imamoglu and Dengiz 2019). • Loss of Biodiversity The impacts of soil pollution discussed above may affect the tolerance levels, survival rates, and reproductive efficiencies of soil flora and fauna. The disturbance in soil biodiversity may, in turn, affect nutrient cycling. The loss of one species will, either directly or indirectly, affect the existence of other species, thereby creating ecological imbalance (Tiwari et al. 2020). • Effect on Health The soil forms the basis of Earth, where food crops grow. Contaminated soil would accumulate the contaminants in the food crops, which would subsequently be taken up by the primary and secondary consumers. Some of

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these contaminants may undergo successive increases in concentration by undergoing biomagnification, and hence, affect the health of species at each trophic level. The persistent and toxic nature of soil contaminants is known to cause several dermal, skeletal, cardiovascular, nervous, and pulmonary disorders (Mohammadi et al. 2020). 2.4.2

Soil Pollution: Spread in World and India

The issue of soil pollution is most prominent in China. The surficial soil of China is highly contaminated with heavy metals, such as cadmium, zinc, arsenic, copper, chromium, and zinc throughout the country (Duan et al. 2016). Soil pollution is also prominent in the Krakow region of Poland, where the metallurgical activities of the past continue to contaminate the soil and putting the health of its citizens at risk (Kowalska et al. 2016). The soil of Iran contains a significant amount of pollutants, mainly heavy metals, owing to the increase in demand for cement, and hence setting up of cement factories, for construction activities in urban areas. The surface soil contains the maximum amount of pollutants, increasing the hazard quotient through indirect oral routes of ingestion (Jafari et al. 2019). The generation of a huge amount of municipal solid waste in Saudi Arabia has resulted in improper disposal of the same. The open dumping and other improper disposal methods of solid waste have contaminated the soil to a great extent (Anjum et al. 2016). India, being one of the most populous and rapidly developing countries, is also not unaffected by the rising threat of soil pollution. The intensive agricultural practices in Bhatinda, Punjab have led to soil pollution, mainly due to the excessive use of agrochemicals (Ahmad and Pandey 2020). The Korba river basin in Chhattisgarh is continuously experiencing the impacts of soil pollution, particularly arsenic, owing to the coal-fired power plants required to fulfill the growing needs of the urban populations (Sharma et al. 2019). The indiscriminate disposal of coal fly ash from power plants has significantly altered the physical, chemical, and biological properties of the soil of Aligarh. It continues to deteriorate the soil quality of the area, affecting the morbidity and mortality rate of the region (Khan and Umar 2019).

2.4.3

Soil Pollution: Control and Mitigation

Some of the mitigation measures that can be undertaken to mitigate the increasing soil pollution and management of solid waste are discussed below: • Proper Agricultural Practices The growing population has resulted in an increased demand for food, putting more stress on intensive agriculture. Poor agricultural practices degrade the soil quality by loss of fertility, nutrients, and organic matter, rendering the soil vulnerable to pollution. The implementation of practices, such as crop rotation, protector belt, strip cropping, planting along the contours, crop residues, and use of organic composts, may help in soil conservation, thereby reducing soil pollution (Sarkar et al. 2017).

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• Proper Solid Waste Management Indiscriminate disposal of waste has emerged as a major concern in most parts of the world in recent years. The concept of reduce, reuse, and recycle must be implemented. The management of solid waste by the reduction in packaging material, manufacturing of reusable products, and recycling of waste into useful products may help in reduced waste generation. However, urban developmental practices will continue to generate more and more waste that needs proper treatment. The neutralization of acidic or alkaline waste, breakdown of biodegradable waste, segregation of waste, proper monitoring, integrated waste management practices, and proper implementation of rules and regulations play an important role in managing solid waste (Ramachandra et al. 2018). • Public Awareness and Participation The involvement of the public is of utmost importance in combating soil pollution. It can be achieved through educating them about the hazards associated with soil pollution, rewarding them for their contribution towards the environment, and awakening a sense of responsibility among them towards the environment. The combined efforts of the public and authorities can significantly abate the problem of waste management and soil pollution (Brombal et al. 2017).

3 Urban Heat Island 3.1 Causes and Formation Urban climate studies, which collectively define the urban heat island (UHI) effect, have long been concerned about the extent of the difference in the observed ambient air temperature between cities and their surrounding rural regions (Landsberg 1982). The air temperature pattern of urban areas is similar to the temperature contours of the island, hence the designation of “urban heat island” (Oke 1995). It was first discovered by Luke Howard in early 1833 and has been explored globally since then (Singh et al. 2017). The effect of weather on human health has become an increasingly important concern in recent years, given the possible impacts of global warming and the UHI due to urbanization. The warming of the climate system is unambiguous and such temperature gradient giving rise to the UHI effect has already been documented for Montreal, Paris, Mexico, and Atlanta (Sarrat et al. 2006). Two types of UHI can be distinguished according to the methods of temperature measurement: the canopy layer (UCL) heat island and the boundary layer (UBL) heat island. The UCL is made of air between the components of roughness, such as buildings and tree canopies, with an upper boundary just below the surface of the roof, while the UBL is above the former, subject to the impact of the urban surface with a lower boundary (Weng et al. 2004). The major cause of the urban heat island effect is the absorption of solar radiation by urban structures such as concrete buildings, roads, and other hard surfaces during the daytime. The absorbed heat re-radiates in the surrounding

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resulting in increased ambient night temperature (Wong and Yu 2005). The IPCC Fourth Assessment Study (AR4) clearly shows that global surface temperature has a revised 100-year linear trend (1906–2005) of 0.74 K. The warming trend has averaged 0.13 K per decade over the last 50 years and 11 of the last 12 years (1995–2006) have been among the 12 warmest years since 1850 (IPCC 2007). A warming atmosphere is likely to result in a rise in heatwave frequency and duration (Tan et al. 2010). Urbanization is responsible for the differential heating of impervious concrete surfaces and the surrounding natural landscapes. This contrast in temperature caused by the differential heating is quite prominent in forested land where vegetation is physiologically active, while it is lesser in short vegetation areas and the winter season when leaves are either lost or stressed by low temperatures (Imhoff et al. 2010). A variety of reasons can be attributed to the urban and rural temperature variance including thermal properties of the radiating surface, conversion of green areas with impervious surface reducing evapotranspiration rate of urban areas (Susca et al. 2011). Under suitable conditions, evapotranspiration creates relatively cooler areas called ‘oases’, which, under extreme conditions, generates extremely large latent heat flux causing the air above vegetation and dry areas to supply sensible heat to the vegetated regions (Taha 1997). The formation of Urban Heat Island is summarized in Fig. 3. Urban topography significantly contributes toward the ‘canyon effect’ that reduces outgoing radiation of long wavelength, increases shortwave radiation absorption, and increased roughness reducing boundary layer winds and obstructing necessary heat loss. The albedo of an object or surface refers to its capacity of reflecting light from the sun. The typical range of albedo in urban areas lies between 0.10 and 0.20, however, values exceeding this range have been recorded from many cities around the world (Dickinson 1983). High pollution levels from combustion can also be a contributing factor for increased urban albedo and re-radiation of long waves affecting the urban climate, resource use, habitability, local wind patterns, the formation of urban smog and cloud, altering precipitation rate, and increasing humidity (Streutker 2002; Kardinal Jusuf et al. 2007; Mirzaei and Haghighat 2010). This might, thus, prove to be an unsustainable factor that contributes to unnecessary use of energy for cooling the urban population and putting it at great risk concerning morbidity and mortality.

3.2 Impacts of Urban Heat Island A direct result of the UHI effect is heat stress. The detrimental effects of elevated temperatures on urban infrastructure, habitats, human health, and comfort are responsible for a variety of severe health problems and can even account for a significant number of deaths. For a rise of 10 °C in temperature in summer, energy demand will increase by 2–4% (Akbari et al. 2001). However, the worst affected people by this microclimate effect are those working outside in open areas. Heatwaves also often result in increased use of electricity and water due to elevated electricity demand

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Fig. 3 Diagram showing various factors responsible for the formation of Urban Heat Island. Source (Watkins et al. 2002; Nuruzzaman 2015)

resulting in the burning of fossil fuels subsequently releasing greenhouse gases, deteriorating the climate (Adinna et al. 2009; Mohajerani et al. 2017). The simultaneous use of air conditioners and cooling devices further worsens the condition. Although in the winter season, the increased temperature due to the UHI effect tends to give comfort to people (Nuruzzaman 2015). The effects of UHI are depicted in Fig. 4. Some of the major impacts of the UHI effect are discussed below: • Increased Temperature The elevation in temperature results in chemical reactions converting oxides of nitrogen and volatile organic compounds into ozone, ultimately giving rise to urban smog. The percent of vegetated land and biomass in urban areas is comparatively less than that of rural areas. This lack of vegetation fails to reduce the temperature through the exchange of latent heat for evapotranspiration. Thus, the temperature continues to rise in absence of vegetation and associated cooling processes such as evapotranspiration. The presence of vegetation has been observed to reduce the temperatures to 20, 8, and 5 °C in the urban cities of Tokyo, Athens, and Singapore, respectively (Bonan 2000; Bass et al. 2003). • Cooling Load It has been observed that the cooling load in urban areas is about 25% higher annually. This is because of the elevated temperatures associated

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Fig. 4 Diagram representing the detrimental effects of Urban Heat Island. Source (Nuruzzaman 2015)

with urban activities, which play a significant role in accelerating the cooling demand (Watkins et al. 2002). Albedo is also an important factor in analyzing the energy consumption in hot areas and mitigating the UHI effect and subsequently increasing the cooling loads (Taha et al. 1988). The period between 1970 and 2010 witnessed a global increase of 23 percent in cooling demand and an average consumption of energy for heating and cooling also elevated by 11% (Santamouris 2014). The shift of workplace from rural to urban areas and the increase in working hours has led to more overheating, increasing the cooling load (Kolokotroni et al. 2007). Thus, the UHI is one of the major factors for increasing the cooling load on a global scale. • Emission of Anthropogenic Heat and Energy Wastage The major source of anthropogenic heat is air conditioners, which tend to maintain a comfortable indoor thermal environment, but the heat emitted from it into the atmosphere only worsens the urban atmosphere. (Kikegawa et al. 2003; Kolokotroni et al. 2006). In modern times, most commercial buildings install central air conditioning that dissipates heat through cooling towers, however, a large number of residential buildings use window and split air conditioners which dissipate heat directly into the atmosphere through condensers. The extremely high on-coil temperature used by such cooling systems results in wastage of energy, affecting the operation and efficiency of the equipment, consuming more energy for cooling the buildings to required temperatures. Such anthropogenic heat emission and energy wastage can be reduced through adequate ventilation and vegetation (Priyadarsini 2009). • Impact on Health The heat stress due to the increased intensity of UHI has resulted in a growth in morbidity and mortality of the urban population globally. One of the examples is the European heatwave that took a toll of over 1000

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excess deaths in the year 2003. The urban population is at higher risks as the UHI effect exacerbates their vulnerability to health hazards (Vargo et al. 2016). The compact infrastructure, dense agglomerations, and urban lifestyle further elevate the intensity of UHI, rendering the urban population susceptible to health-related issues (Wong et al. 2017).

3.3 Effect of UHI on Global and Indian Level The precise shape and size of this phenomenon vary as a consequence of meteorology, geographic, and urban characteristics on a temporal and spatial basis. At present, major cities of the world are about 2 °C warmer as compared to the surrounding rural areas, while the rise in commercial and densely populated residential areas has reached from 5–7 °C (Shahmohamadi et al. 2010). Climate change has resulted in several implications in terms of energy consumption, human health, and environmental health. The heatwave proved fatal for about 800 people of Chicago in 1995, around 35,000 people in Europe in 2003, an increase of 2.3% mortality rate for each degree rise in daily mean temperature beyond 20 °C in Canada, and an increase of 5–8% mortality rate in the Netherlands (Wang et al. 2016a, b). The weekly variation also plays important role in the UHI effect as the temperatures in Oregon differ by approximately 2 °C on weekdays due to traffic density and other anthropogenic emissions as compared to the weekends (Hart and Sailor 2009). Atlanta, Georgia, has quickly become the pioneer of southeastern U.S. centers for trade, manufacturing, and transportation in the past 30 years. From 1973 to 1997, forested areas in Atlanta declined by more than 20%. Industrial growth and low-density residential areas (suburbs) also doubled during that period. The Sierra Club ranked Atlanta as the country’s ‘most sprawl-threatened area’ in 1998. Also, Atlanta lost green space to grow at a rate higher than any other metropolitan region in world history during the 1970s and 1980s, giving rise to the UHI effect (Grady Dixon and Mote 2003). The annual cooling energy and peak demand in Athens have increased significantly as a consequence of the UHI effect, stressing upon application of natural means to reduce the use of cooling energy (Hassid et al. 2000). Asian cities constitute the most expeditiously growing areas of the modern world. In Asia, the transition of a significant proportion of the population with increased intake of energy and dense urban infrastructure is reported to impact the life quality of urban residents, worsening the urban atmosphere and urban ecosystem (Tran et al. 2006). Urbanization in China has led to reduced moisture content as a result of the decline in water vapor, which enhances heat stress in urban areas. The expansion and intensification of areas facing heat stress observed in China have been observed due to the strong UHI effect with an average increase of 0.74 °C, while the peak of UHI generally reaches around the time of sunset with a temperature increase of 1.6 °C (Chen et al. 2014). The intensity of UHI in Singapore is influenced significantly by seasonal variations with maximum intensity during dry months of May to August and minimum during the months of December and January (Chow and Roth 2006).

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The rapid growth of urban built-up in Lucknow, the capital city of Uttar Pradesh has also elevated urban temperature and affected the local climate. The central part of the city witnessed the maximum rise in temperature due to a considerable rate of population growth and subsequent need for urbanization (Singh et al. 2017). The compact and densely populated city of Nagpur has also been facing the adverse effects of UHI with an increase of 1.76–4.09 °C in the temperature (Kotharkar and Bagade 2018). Urbanization has also affected the land surface temperature (LST) of Chandigarh, Punjab, where the central region of the city faces hotter temperatures as compared to the rest of the city areas because of intensive road networks, high rise buildings, and complex infrastructure. The overall UHI intensity of the city reached up to 5.2 K on average, raising the urban temperatures (Mathew et al. 2016). The UHI intensity of Delhi, the capital city of India, is reported to be highest between 13.4 and 14 °C, particularly in the built-up areas and barren lands, giving rise to higher-temperature areas called ‘hotspots’ (Sultana and Satyanarayana 2019).

3.4 Mitigation Measures The adverse impacts of Urban Heat Island can be mitigated through the measures discussed below: • Green Walls and Roof The large surface area of roofs and walls of massive urban buildings can easily incorporate vegetation. Such arrangement can help in the considerable reduction of temperature up to 2 °C by providing thermal insulation due to the process of evapotranspiration, ultimately redirecting the available energy to latent heat (Zinzi and Agnoli 2012). The daytime temperature of green roof is lower than the conventional roof, while the night time temperatures are also comparatively lower but the temperature difference between green and conventional roofs are less prominent (Li et al. 2014). This difference of 2 °C can help in conserving about 15% of cooling energy. Thus, green roofs and walls can be a potential measure for mitigating the effects of urban heat. • Cool Roof A cool roof increases the albedo of roof surfaces by elevating the solar reflectance. It has been observed that the cool roofs have resulted in considerable conservation of cooling energy and peak power. It involves the usage of white or second-generation reflective materials, thereby increasing the reflectivity of the roof to about 72%, without any extra expenditure (Akbari 2003). The cool roof is more effective in mitigating the UHI effect as compared to the green roofs when the albedo of reflective roofs reaches 0.7 or more (Coutts et al. 2013). Recent developments of cool coatings have given rise to advanced colored materials, thermochromic paints, and PCM-doped coatings that use infrared reflective pigments, enhancing the reflectivity of coated surfaces. The major advantage of these coatings is that they are environment-friendly as there is no additional waste generation as they can be applied on new as well as existing roofs (Yang et al. 2018).

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• Urban Forestry Several activities have been implied by state and local governments for mitigating the UHI effects including tree and landscape laws, incentive programs, designing and implementing policies and guidelines, zoning codes, and urban forestry initiatives (Middel et al. 2015). The practice of urban forestry was promoted by the USEPA in 2008 as a measure to mitigate urban heating (EPA 2008). Urban forestry involves the planting of trees in urban areas that moderate the climate by evapotranspiration, modification of wind patterns, and surface shading, thereby reducing temperatures by interception of incoming solar radiations. Urban forests are designed with an approach to enhance environmental quality and reduce the impacts of UHI by providing shade and cooling through evapotranspiration (Livesley et al. 2016). It is, perhaps, the most cost-effective and eco-friendly measure to mitigate the UHI effect and improve the environmental quality degraded due to urbanization and associated activities.

3.5 Case Study—New Delhi, 2014 The UHI effect continues to spread widely around the globe creating a significant temperature difference between the urban area and rural surroundings. It is influenced by several factors including urban geometry, replacement pattern of vegetation by concrete construction, and anthropogenic heat flux, which directly affect the albedo, evapotranspiration, and emissivity (Oke and Cleugh 1987). This temperature difference is more prominent during clear nights, reaching up to 7 °C higher than the UHI during the day (Lac et al. 2013). However, there are some incidences where the urban temperatures are cooler than the surrounding rural areas (Bohnenstengel et al. 2011). New Delhi, the capital city of India is expected to become the largest city in the world by 2030, in terms of urban agglomeration. Delhi has a total area of 1483 km2 , out of which 1113.65 km2 is urban accommodating a vast population of 97.5%. The area experiences UHI intensity of >4 °C during the night and recently, new areas with an intensity of 5–6 °C have become evident, which were insignificant 50 years ago (Mohan et al. 2020). Earlier, the intensity of intra-city UHI ranged between 2.8 and 3 °C in low vegetation areas, which has now reached up to 6 °C. The areas experiencing the most prominent UHI intensity include Raja Garden and Rajouri Garden in western Delhi, while low UHI intensities were observed in southern areas of Saket and Hauz Khas and the Cantonment area in south-west Delhi. The high UHI intensity can be attributed to the low cooling rate in the urban set-up (Yadav and Sharma 2018). Other studies include that of Mohan et al. (2012), which reported a temperature difference of 8.3 °C between the urban canopy and vegetated areas, while Pandey et al. (2014) reported the UHI intensity of 4–6 °C between the period of 2000–2012. Thus, with the progress of inevitable urbanization, this problem needs immediate attention and mitigation measures to avoid heat stress and associated adverse impacts on energy consumption, anthropogenic emissions, and human health.

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4 Flood Floods a perennial phenomenon in most of the river basins in India and results in losses to lives, economy, transportation, and infrastructure system. In India, 1.2% of the total geographical area is prone to floods and on average results in the death of 1600 persons and Rs. 1805 crores loss in the economy every year (NDMA 2008). Flood is defined as high water flow condition especially during monsoon season, not confined in the river channel and water overflows onto its floodplain. Urbanization further acerbates the hazard due to poor drainage facilities, encroachments on the flood pains, reduction in the wetlands, increase in the proportion of impervious surface, and change in the land use pattern (Svetlanaa et al. 2015). Besides flooding due to high precipitation during monsoon, cloud burst, dams or levees breaking, glacial lake outburst floods storm surge during cyclone and tsunamis event can also result in flooding. Climate change further increases the risk of flooding especially in the coastal areas due to sea-level rise and increase frequency of cyclone and extreme weather events (Kundzewicz et al. 2014). The urban settlements are prone to floods as a result of both physical and socio-economic factors as they are characterized by impervious surfaces that increase the speed and quantity of run-off (Debbage 2019). The South Asian countries especially Bangladesh, India, and Pakistan experience an increase in the frequency, magnitude, and extent of flood events linked with climate change (Mirza 2011). As per a report by the DTE-CSE Data Centre of the Central Water Commission (CWC), in the last 65 years (1952–2018), 109,412 people were killed due to floods in India. Additionally, 258 MHa of crops were damaged and around 81 M establishments were affected due to flood, all of which amounted to a cumulative sum of Rs 4.69 trillion (Mahapatra 2020). The location, causes, and effect of some recent flood events in India during the year 2015–2020 is given in Table 3. The worldwide share of Asian causalities far exceeds any other flood-affected region of the world (Jonkman 2005). Southeast Asia has been amongst the worst flood-affected areas. Areas especially like Vietnam, Laos, the Philippines, Cambodia, Thailand, and areas surrounding the River Mekong face the most frequent and severe flood-related disaster. As per the 2011 estimate, around 567 casualties happened in Thailand, 30 deaths in Laos, approximately 248 deaths in Cambodia, 85 death cases in Vietnam, and about 102 deaths in the Philippines, summing up to 1302 causalities in South East Asia due to flood-related phenomena like drowning, incessant downpour lashing various regions, etc. (Torti 2012).

4.1 Causes of Flood Floods are caused by the inability of rivers to contain water, from the upper catchment areas, within their banks. The reason for this situation may be vast, including several natural as well as anthropogenic causes, which are discussed below:

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Table 3 The location, causes, and effect of some recent flood events in India during 2015–2020 Year (Month)

Region

Cause of flood

Fatalities

Population affected

2020 (May, June)

Assam

Upsurge of Brahmaputra river inundating the floodplain

110

2,000,000–3,000,000

2019 (July, August, September)

Maharashtra, Karnataka, Gujarat, Rajasthan, Andhra Pradesh, Orissa, Uttarakhand, Madhya Pradesh, Bihar, Uttar Pradesh, West Bengal, Assam, Punjab

Continuous monsoonal deluges and excessive rain

200

>1,000,000

2018 (August)

Kerala

Unusual rainfall during monsoon season and sudden discharge from reservoirs

683

5,800,000

2017 (August)

Mumbai

High tide, extreme rainfall, and improper drainage

32

> 50,000

2017 (July)

Gujarat

Low-pressure systems in the Arabian Sea and Bay of Bengal causing high rainfall

94

350,000

2017 (July)

West Bengal

Continuous rainfall due to Komen cyclone

39

47,000

2017 (July)

Bihar

Torrential rain in Nepal causing a sudden increase in discharge of 8 rivers of Bihar

41

180,000

2017 (June, July)

North-East

Extreme monsoon 80 rainfall resulting in bursting of Brahmaputra river and its tributaries

>1,700,000

2016 (July)

Assam

Extreme rainfall 28 causing a surge of Brahmaputra river

1,794,554

(continued)

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Table 3 (continued) Year (Month)

Region

Cause of flood

Fatalities

Population affected

2015(November)

Chennai

Transition of low pressure into a deep depression causing heavy rainfall and massive urban flooding

42

2,000,000

Source (Singh et al. 2018; Arun et al. 2020; Majumdar et al. 2020)

(a)

Natural Causes • Snowmelt and Glacial Outbursts: Melting of snow is a gradual process, which generally does not cause floods directly. However, the large amount of bounded water contained in the glaciers may be released suddenly with melting ice blocks, giving rise to ‘Glacial Lake Outburst Floods’ (Harrison et al. 2018). The snowmelt-fed Himalayan rivers in the northern part of the country are prone to such flash floods. Such a flood occurred in the Sutlej river in 2000 causing devastating effects on life and property (Allen et al. 2016). • Cloudbursts: The sudden burst of clouds due to distinct climatic changes in some parts of the world lead to unprecedented heavy rains. Such heavy rains of short duration can also occur as a result of monsoon depressions, low-pressure circulation in coastal areas, off-shore vortices, and fluctuation in location and intensity of the monsoon period. The urban cities are more susceptible to devastating effects of this problem as they require the rapid transformation of stormwater drainage and interdependent set-ups, and an adequate understanding of their infrastructural decisions and policies (Rosenzweig et al. 2019). Such flash floods followed by torrential rains are widespread in the southern part of the Indian Himalayas. One of the most infamous incidents of such an event is the devastating Kedarnath cloudburst leading to extreme precipitation and flash flood in Uttarakhand, making fatality due to flash flood of Uttarakhand the highest in India (Pratap et al. 2020). • Monsoon Depression/Cyclones: The rainstorms caused by low-pressure systems like depressions or tropical cyclones are also responsible for the occurrence of floods. The past century witnessed over a thousand cyclones, low-pressure systems, and monsoon depressions arising from the Arabian Sea and Bay of Bengal, and crossing the Indian subcontinent, 40% of which severely affected the country. The major regions affected by extreme flooding due to such conditions include coastal regions of Assam, Orissa, West Bengal, Andhra Pradesh, and Tamil Nadu (Lakshmi et al. 2019).

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• Ice Jam Packing: Sometimes, extreme jam-packing of the river body due to excessive cold temperature creates a dam-like structure, which outbursts on melting, thus creating havoc (Redd 2017). (b)

Anthropogenic Influences • Increased Developmental Activities: Unprecedented urbanization, swelling economic aspirations, unsustainable land use planning, forest clearing for cash crops, etc. have also led to gearing up of the pace for such devastations. These problems result in the emission of greenhouse and other toxic gases, which subsequently change the climatic patterns, disturbing the monsoon cycles (Dhiman et al. 2019). It influences the river dynamics and hence contributes to flood-related disasters. Sea temperature rise influences the amount of moisture being carried by the hot air (especially, during the pre-monsoon period) above. This particularly affects coastal and low-lying areas. Chronic floods like those experienced in Venice, Italy is an outcome of sea-level surge due to unprecedented ice melting (Moftakhari et al. 2018). • Administrative Chaos: With special reference to India, transboundary water sharing disputes- like the one between India and China over the Brahmaputra river, and even in between adjoining states within India, leads to administrative chaos, thus depriving one state of its water resource, while submerging other (Bandyopadhyay et al. 2020; Ghosh et al. 2019). Thus, proper coordination for tackling the disasters is the need of the hour to safeguard life as well as property. • Failure of Proper Construction: Recent instances from Bihar, Nepal (Kosi embankment instance, 2008) depict how this short-sighted flood mitigation plan (‘levee effect’) backfires, by causing more havoc than any temporary good. Such failures due to human negligence lead to heavy flooding of the low-lying areas, significantly impacting the socio-economic and health aspects (Acharya and Hori 2019).

4.2 Flood Measurement Flood measurement is dependent on two factors viz rain intensity and rain duration. Accordingly, the following methods are deployed: (a)

(b)

(c)

Gauge Height: This helps to monitor agencies in surveilling any fluctuations in the water level of the river concerned. Post-disaster, it also helps in evaluating the extent of water upsurge (Bhatt et al. 2017). Radioisotopes and Chemical Tracers: This is used in measuring the velocity of the water body. For instance, a dye is dropped into the flowing stream, and the duration it takes to travel a specific distance is measured. Wading Rod (or Cable): It is used to measure the discharge of the stream. The amount of water that moves downstream through a specific location provides

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this data (depth, width, and calculated area). This method is not reliable in the case of fast-flowing rivers and frozen rivers. Flowmeter: This is used to measure the water velocity, for urban planning, and therefore to mitigate and limit flood vulnerability. It is also essential for studying the hydrologic cycle to establish the relationships between downpour, surface run-off, and groundwater (Lee 2017).

4.3 Scientific and Administrative Measures to Mitigate the Flood Hazard (a)

Pre-disaster Planning • Structural Measures The construction of structures such as embankments, floodwalls, flood levees, dams, reservoirs, channel improvement, diverging floodwaters, drainage improvement, and dredging of rivers are some of the measures that restrict the overflow of the river over its banks. Different structure or combination of structures can be used for flood control in different areas such as in the case of mountainous area (multipurpose dam, detention pond, and floodway), Rural area (detention pond, washland, floodway, levees), and in the Urban area (detention pond, washland, floodway, levees along with improvement in river channel) (Kim et al. 2019). These structural measures are discussed in detail in the following section. • Embankments/Levees/Floodwalls These structures are designed to prevent the flooding of areas along the river banks. In the areas where land is scarce, floodwalls can be constructed, which are inexpensive and easy to construct. Such structures are constructed to protect against floods of a particular intensity and frequency. The nineteenth-century witnessed the construction of several such structures along the bank of north Indian rivers and rivers of Orissa, Andhra Pradesh, and Tamil Nadu (NDMA 2008). • Dams and Reservoirs These are constructed to store excess water during the periods of flood, which is later released to comply with the water requirements of domestic, irrigational, power generation, and industrial purposes, post the flood period. In India, the construction of a total of 5187 large dams is proposed out of which 4839 are completed. The state of Maharashtra (1693) hosts the maximum number of large dams followed by Madhya Pradesh (899) (CWC 2013). • Channel Improvement The control of discharge capacities of rivers can be attained through the construction of channels, that carry the flood water at lower levels. The process includes straightening of the river channel, channel width widening, deepening by dredging accumulated sediment, and clearing operations to remove natural or artificial structures hindering the flow (NDMA 2008). • Diversion of Floodwater The excess of floodwater can be diverged to natural or artificial channels, within, or sometimes outside the flood plain.

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•

•

•

•

•

•

It helps in the considerable lowering of water levels in the flood zones. Some of the examples include that the Srinagar flood spill channel and supplementary drain of Delhi (Waghwala and Agnihotri 2019). Afforestation Afforestation practices lead to conservation of soil cover, thereby reducing the intensity of run-off and hence, lowering the peaks of the flood. It might not be very helpful during large floods, but it significantly reduces the siltation load in rivers (Bhattacharjee and Behera 2017). Non-structural Measures Several non-structural measures can also be taken into account for efficient mitigation of this disaster, which are discussed below: Flood Zone Mapping Curbing the situation requires its understanding. For completely deciphering the trend and extend of this hazard, a comprehensive flood zonation mapping is required. Mapping helps understand the physical attributes associated with the flood-like water level, pace, and inundation extent of the outflow. Here, the constrain lies in the expanse utility of this model. This model is viable only for a limited area as the data (rainfall, terrain, river channel, etc.) interpretation and overlapping required in this model is pretty complex (Das 2019). Flood Forecasting and Warning The prediction of the frequency, intensity, and duration of floods may prove helpful in the early mitigation of the disaster. The early forecasting may help in the evacuation of the affected region and also in taking appropriate preventive measures, thereby minimizing the damage to life and property. This can be achieved by the joint efforts of the general public, government, and disaster management authorities (Ali et al. 2020). Floodproofing It may help in mitigating the adverse effects of flooding by providing immediate relief to the people of affected flood-prone regions. It does not involve evacuation but is a combination of emergency action and structural change, mainly involving the facilities of food, shelter, clean drinking water, and other basic amenities (NDMA 2008). Socio-economic Awareness Containing the havoc like these also requires the study and awareness about the socio-economic condition of the region concerned. For instance, as mentioned above, the South Asian region is a developing region, with scarce resources, a lack of awareness, and high vulnerability (Mohanty et al. 2020). Trained Personnel and Establishments Mitigation steps also require trained personnel and establishment-related pre-planning like construction materials to be used, features of foundation to be laid (for instance, as laid by Intermediate Technology Development Group–Bangladesh), where to be established (land-use planning), etc. Arenas like capacity building, mock drills, awareness generation, and advisories, etc. by both state and non-state players, early warning technological systems (like VinAWARE program of Vietnam), the ideological transition of policymakers, donors (like United Nations, as in Vietnam), and contributions from humanitarian assistance providers too require an upsurge (Patel et al. 2020).

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Post-Disaster Mitigation • After trauma, mental and physical health alleviation; compensating (in case of dam outburst, etc.), and displacing those afflicted are the major challenges. • Post-disaster counseling and assistance require huge capital input and coordination between administration and disaster relief agencies. • Infectious diseases (especially waterborne) prevalent in the relief camps require pre-planned and sophisticated medical interventions besides research for predicting and tackling future instances (Gotham et al. 2018).

(c)

Other Measures • Interlinking of Rivers Many workers also proclaim the idea of interlinking rivers (those with the surplus to those with the deficit, by constructing artificial canals, such as the Ken-Betwa river linking project in India). Contrary to this, others do not advocate this practice, as this might turn out to be ecological havoc in long the run and might also repeat the Aral sea like episode (Karthe et al. 2017). • Transboundary Water Sharing Data Administrative measures like transboundary water data sharing (such as that between India and China for the Brahmaputra river water), Investments in structures like Dams (Hirakund Dam, India- across Mahanadi river) culverts and dykes, etc. had been taken to control floodwater (Deka et al. 2019). • River Channel Alterations Approaches like deepening and widening of river channels would do the trick in riparian areas. Diversion channels to divert excess water to temporarily store and (hence) to enhance the lag time of water reaching downstream could be practiced (Mondal and Patel 2018). • Insurance of Properties Including Crops and Livestock For developing economies like South East Asia, primary activities include agriculture and animal rearing. Insurance of these and scientific interventions like seed storage would subside the grieves of those affected (Yao et al. 2017).

5 Earthquakes An earthquake refers to the series of vibrations occurring within the earth’s crust as a result of the accumulation of elastic strain leading to the rupture of rocks in the crust. Most of the earthquakes occur at the tectonic plate boundaries, causing significant physical and chemical changes. The point where the rocks first rupture, deep in the ground, is known as the focus or hypocenter of the earthquake. The point above the hypocenter on the surface is called the epicenter, where the maximum shaking of the earthquake is experienced. The violent rupture of rocks is followed by vibrations in the form of seismic waves, that travel in all directions from the

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hypocenter, becoming weaker with distance, but causing significant damage, particularly around the epicenter (Lamsal 2017). With around 0.3 million dead and/or missing, 0.3 million people injured, and about 1.3 million left homeless, such as the intensity of the January 2010 earthquake of the Republic of Haiti, which struck the capital city of Port-au-Prince and numerous adjacent cities with the intensity of 7.0 on the Richter scale. This recent event marks the classic example of socioeconomic vulnerability, lack of preparedness and awareness, and fragile geology among others, which amounted to the havoc of this intensity (DesRoches et al. 2011).

5.1 Types and Causes of Earthquake Earth’s crust is divided into seven major and few minor tectonic plates. These plates are struck together harboring intense pressure along the fault line. When these plates slip past each other or override each other, then this suddenly releases a massive amount of energy, which subsequently translates itself into devastation over the earth’s surface (Lu et al. 2018). Other natural phenomena like volcanic eruptions, abrupt rock impaction, etc. might also trigger earthquakes of varying intensity and expanse. Besides, anthropogenic influences viz. pressure and threat imposed by impounding structures like dams (like eastern Vidarbha) and reservoirs, subsidence of roof in the case of underground (rat-hole) mining, etc. can also aggravate the situation (although with much lesser intensity and areal expanse). The detailed classification of the earthquake is given in Fig. 5.

Fig. 5 Flow chart showing the classification of Earthquakes based on different parameters. Source (Lamsal 2017)

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5.2 Effects and After Effects of Earthquake Regarded as the most unpredictable and highly devastating of all the natural disasters, earthquakes influence the larger areas, uproots life, and economy aggressively. The majority of the earthquake-related causalities result not from the tremor but, also from the fragile man-made structures erected on the vulnerable urban land. These may, in turn, be triggered due to secondary influences like landslides, Tsunami, fire, etc. (Zhu et al. 2020). Prominent effects amongst these have been mentioned underneath: • Ground Shaking Earthquake releases waves of energy, which consequently sakes the stratum as they travel, and subsequently everything structured over it (buckling and collapse of manmade structures.) causing large-scale loss of life and property (Luo et al. 2017). • Ground Failure Earthquake causes ground cracks and fissures. • Ground Slip and Tsunami In this, ground slips on either side of the fault, to release stress, thus causing Tsunami, seiche, etc. in the river body. • Landslide Based on the constituents and the steepness of the slope, landslides associated with the earthquake differ in intensity. Intense ground shaking disturbs overlying fragile landscape, thus shedding off fragile material viz rocks, mud, soil, vegetation, etc. This kind of incidence is more likely in wet and dry slopes. • Liquefaction Earthquake shaking can disturb the grains surrounded by the groundwater, rendering them loose and flowy, and make them incapable of handling weight. • Logistics Supply Chain Cut People face isolation due to interruption in the rail/rail and telecommunication connectivity, oil and gas supply, drowned accommodation, and other flood-related situations like waterborne diseases, etc. (Choudhury et al. 2016).

5.3 Distribution of Earthquakes The major earthquake site hosting the largest earthquakes includes circum-Pacific seismic belt (81% including M 9.5 Chilean Earthquake (1960) and the 1964 earthquake of Alaska M 9.2) Alpide earthquake belt (17% including 2005 M7.6 Pakistan Earthquake (2005) and the 2004 earthquake of Indonesia M 9.1) and submerged mid-Atlantic Ridge (Hayes et al. 2020a, b). About 59% of the geographical area of India is at risk of moderate or severe seismic hazard (NDMA 2007). The major earthquake events in India and their magnitude are given in Table 4. In the year 2020, the Delhi-NCR area experienced more than 20 low to medium intensity earthquakes creating panic among the residents however the occurrence of low to medium intensity earthquakes in the vicinity of Delhi is not very uncommon as it is located in the Zone IV of seismic hazard and have a history of occurrence of earthquakes with 5–6 magnitude (Gupta 2020).

350 Table 4 Mortality due to the major earthquake events in India during 1819–2005

K. Varma et al. Location

Magnitude

Year

Number of deaths

Kachchh, Gujarat

8.3

1819

1500

Assam

8.7

1897

1542

Kangra, Himachal Pradesh

8.0

1905

20,000

Bihar-Nepal

8.4

1934

10,653 4800

Assam-Tibet

8.7

1950

Koyna-Maharashtra

6.5

1967

200

Bihar-Nepal

6.6

1988

1004

Uttarkashi, Uttarakhand

6.6

1991

768

Latur, Maharashtra

6.4

1993

7635

Bhuj, Gujarat

7.7

2001

13,805

Kashmir

7.6

2005

80,000

Source (Jain, 1998; Jain and Pathak 2012; Hamilton and Halvorson 2007)

5.4 Scientific and Administrative Measures to Mitigate Hazards Associated with Earthquakes • As mentioned earlier, earthquakes are associated with faults. The pressure, friction, and geometry of the fault concerned affects the elastic wave propagation, which in turn translates to destruction Curbing measures require a deeper and clear understanding of these patterns, trends, and intensity of the site in question. • Besides, studies on the intervening rock (in the passage of wave propagation) and the associated geology, topography, and soil type needs to be undertaken, to decipher this peril, well in advance and to absorb the maximum devastation possible. • This would also help in designing disaster-resistant urban planning and smart buildings, capable enough of subsiding these shocks and associated hazards. • Improvements in structural and non-structural measures may help in risk reduction, and will subsequently ensure seismic safety. Several preparedness measures, preventive policies, and mitigation strategies at the national, state, and local levels can help in abating the disaster through proper coordination and prompt response to the situation (Bianchi et al. 2020). • Recently, the Government of India has set up a Ministry of Earth Sciences (MoES), which is a nodal ministry assigned with the responsibility of coordinating with different institutions of seismology, meteorology, earth, and atmospheric sciences to issue early warnings and predictions through proper monitoring of the seismic activity (NDMA 2007).

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• Constructing a seismic expectancy hazard map helps in anticipating the intensity, frequency, tectonic trends, attenuation curve, etc. required for the early warning system, data repository, and associated research (Shah et al. 2018). • Computer simulation of topographical datasets and observations from the past could help decipher the actual cause of the recurrent earthquakes in the hotspots etc. (Takeuchi et al. 2003).

6 Conclusions Urbanization and associated developmental activities increase environmental vulnerability in the fast-growing modern world enhancing the potential of environmental disasters. The widespread pollution in urban areas has significantly degraded the quality of air, water, soil, thereby affecting the health of biotic as well as abiotic components of the environment. The developmental activities lead to the emission of greenhouse gases, which in turn, increases the temperature of urban areas resulting in the formation of urban heat islands. Similarly, the land-use change and other associated activities have altered the climatic and seismic pattern of the Earth, giving rise to disasters such as floods and earthquakes. The adverse impacts of these environmental disasters are quite intense and prominent, particularly in the urban areas, owing to their dense structure, complex interactions, and compact settlements. The growing demands of the urban population need to be fulfilled, but sustainably, such that neither the developmental activities are hampered, nor the environmental quality is compromised. Such a sustainable approach can only be attained by the combined efforts of the general public, policymakers, and administrative authorities that would help in the understanding, preparedness, awareness, and mitigation of the concerned environmental disasters emerging in the urban environment.

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Environmental Hazards Due to Grassland Ecosystem Degradation: Perspectives on Land Management in India Kirti Avishek and Ankit Kumar

Abstract Grassland ecosystems provide valuable services such as food and fodder for cattle’s, biodiversity conservation, erosion control and carbon sequestration. In spite of having such major importance, grasslands across the globe have witnessed degradation due to overgrazing, land-use change, urbanization and industrialization resulting in increased soil loss, biodiversity loss, fodder losses and climatic changes. The objective of the article is to study grassland degradation restoration studies in India. This article highlights the significance of using remote sensing and GIS technique in grassland studies. The article is based on review of the literature published in various platforms during 1981–2019 and highlights institutions and mechanisms for restoration of grasslands in India such as the Banni and Ronda grasslands. Intensive grazing and urbanizations are identified as the driving forces to grassland degradation. Use of native species, leguminous plants, rotational grazing and seeding is mechanisms for their restoration. Keywords Grassland · Degradation · Restoration · Remote sensing · Banni · Ronda · NDVI

1 Introduction Grasslands are grass-dominated areas with few trees. Though the grasses are easily identifiable natural group of plants, but they show remarkable diversity. Sreekumar and Nair (1991) said that grass belongs to family Poaceae, which is the fourth largest family of flowering plants and has over 700 genera and probably 10,000 species. Civilizations flourished in and around grassland. India also has vast stretch of lands under grassland, which apart from providing various ecosystem goods and services, K. Avishek (B) Department of Civil and Environmental Engineering, Birla Institute of Technology, Mesra, Ranchi, India e-mail: [email protected] A. Kumar Tata Consultancy Services, Gurugram, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_20

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and also plays a key role in carbon management. The tropical grasslands of India are referred as savanna. A reconnaissance survey of the grassland of India (Dibadghao and Shankarnarayan 1973) when carried resulted in the country having five major ecosystems based on factors like vegetation composition and vegetation distribution which are primarily governed by climate, precipitation, elevation, topography, soil moisture, land use and land cover. Misra (1980) described the climax of Indian vegetation as forests or desert vegetation. The grasslands are highly dependent on the anthropogenic activities. The grassland over the last few thousand years has undergone significant changes. The western India, which is characterized by arid and semi-arid type of vegetation, was once under forests but was gradually destroyed by man for agricultural practices, and due to a reduction in the precipitation, the area turned into a desert. In the north-eastern India, the region is identified as one of the dense forest due to heavy rainfall in the region. Studies by Ramakrishnan and Patnaik (1992), suggest that the tribal activities like Jhum cultivation have been identified to convert the primary forests to secondary forests to grassland. Whyte (1957) classified Indian grasslands are divided into eight major types. The eight types are given in Table 1. As per the grassland survey by the Indian Council for Agricultural Research (Dibadghao and Shankarnarayan 1973) between 1954 and 1962, the Indian grassland is classified into five major types. They are discussed below: 1.

2.

3.

Sehima-Dichanthium: This kind of grassland is found in the central India, in the Chotanagpur plateau, the Aravali and the north Peninsular plateau. 24 species of perennial grasses, 89 of annual grasses and 129 of dicots, including 56 legumes, are found in the region. This region lies in the average ASL of 300–1200 m. Dichanthium-Cenchrus-Lasiurus: This type of grass is spread over the Punjab plain in the north-western part of the country, including the parts of Delhi, Aravali Range and parts of Haryana, Gujarat and Rajasthan. The elevation of the grassland is in the region with an ASL of 150–200 m. The region has approximately 118 species of grasses including perennial grasses, annual grasses, dicots and legumes. The grassland is considered extremely important for the survival of many species of the birds. The grass is available in the region with a total area of 436,000 km2 , between 23° and 32° N latitude. The rainfall level between the months of July and September varies in the ranges of 100 mm in the west to around 750 mm in the eastern region. The topography does not show much variation, as the region is fairly a plain. Phragmites-Saccharum-Imperata: This type of grassland covers the Gangetic plains of India which are created because of the depositional activity of the perennial river flowing from the Himalayan mountain system. The region is rich in micronutrients that support agriculture of grasses like wheat, maize and rice. The grassland extends up to the Brahmaputra valley in the east, which is also known for its rice cultivation. The western plains of Punjab and Haryana are also the region that shows such kind of vegetation. The entire area has a total area of about 2,800,000 km2 . The latitudinal extent of the grassland is between 26° and 32° N, with an elevation between 150 m in east and 300 m in

Type

Sehima-Dichanthium

Dichanthium-Cenchrus

Phragmitis- Saccharum

Bothriochloa

Cymbopogon

S. No.

1

2

3

4

5

Table 1 Grasslands of India

Dominant grasses

Cymbopogon spp

Bothriochloaodorata

Phragmitiskarka, Saccharumspontaneum, Imperatacylindrica and Bothriochloa

Dichanthiumannulatum and Cenchrusciliaris are very important fodder grasses

Sehimasulcatum, S. nervosum, Dichanthiumannulatum, Chrysopogonmontanus and Themedaquadrivalvis

Themeda, Heteropogon and Aristida

Perennials like Bothriochloapertusa, Heteropogoncontortus, Cynodondactylon and the annuals, Eragrostistennela, E. tremula, E. viscosa, E. ciliaris, Aristidiaadscensionis and Dactylocteniumaegyptium. Well-drained wet soils are characterized by Desmostachyabipinnata and Dichanthiumannulatum

Ischaemumrugosum, Eulalia trispicata, Isilemalaxum and Heteropogoncontortus. Themeda and Heteropogon are more extensive on hilly tracks

Associated grass species

(continued)

Low hills of the Western Ghats, Vindhyas, Satpuras, Aravali and Chota Nagpur

high-rainfall paddy areas of Lonavala track of Maharastra are only with dense growth of Bothriochloaodorata

Terai areas of northern Uttar Pradesh, Bihar, Bengal and Assam. Swamps of Sundarbans and Cauvery delta of Tamilnadu

Sandy loam soils of the plains of Punjab, Haryana, Delhi, Rajasthan, Saurashtra, eastern Uttar Pradesh, Bihar, Bengal, eastern Madhya Pradesh., coastal Maharastra and Tamilnadu. In dry areas of Rajasthan, Saurastra and Western Madhya Pradesh, after severe grazing, these are replaced by sparse population of annuals

Black soils of Maharastra, Madhya Pradesh, south-western Uttar Pradesh and parts of Tamilnadu and Karnataka

States

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Type

Arundinella

Deyeuxia-Arundinella

Deschampsia-Deyeuxia

S. No.

6

7

8

Table 1 (continued)

Dominant grasses

Deyeuxia, Deschampsia, Poa, Stipa, Glyceria and Festuca. Deschampsia and Trisetum spicatum extend even beyond 5000 m

Deyeuxia, Arundinella, Brachypodium, Bromus and Festuca sp

Arundinellanepalensis, A. setosa with Themedaanathera form extensive stands with sporadic growth of Chrysopogon spp

Associated grass species

Restricted to the Himalayas above 2500 m in the alpine to subartic region

Temperate regions of the upper Himalayas between 2000 and 3000 m. From Assam, Bengal through Uttar Pradesh to Punjab and Himachal Pradesh

High hills of the Western Ghats, Nilgiris, and throughout on lower Himalayas from east in Assam to west in Kashmir. On the Himalayas, between 1500 and 2000 m

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5.

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west. The region showing such a vegetation of grasses is generally low-lying and ill-drained regions due to a low slope gradient. Themeda-Arundinella: The grassland of this type occupies the entire northern and north-western montane tract of 230,400 km2 . Manipur, Assam, West Bengal, Uttar Pradesh, Punjab, Himachal Pradesh, Uttarakhand and Jammu and Kashmir show this kind of grassland vegetation. The region lies in the Outer Himalayan or the Shivaliks, close to the Terai region, where the grasses grow. The region contains 37 major species of perennial grasses, 32 species of annual grasses and 34 species of dicots, including 9 legumes. Temperate and alpine cover: These type of grasses is available in high altitudes higher than 2100 m ASL in the temperate and cold deserts of the country. The Indian states of Himachal Pradesh, Jammu and Kashmir, Uttarakhand show this kind of vegetation. The region has wide variety of grass species including 47 perennial grasses, 5 annual grasses and 68 dicots (including 6 legumes). Due to climatic variability, this grassland is not found in any other region of the world. Singh and Misri (1993) found that in the states of Arunachal Pradesh and Nagaland, fodder is not cultivated. It has been noticed that livestock production is more efficient from cultivated fodder than from the degraded grazing lands in these regions.

2 Problem Identification Grassland degradation refers to the biotic disturbance in which grasses struggle to grow and survive due to a host of problems ranging from ecological to anthropogenic. Shankar and Gupta (1992) have classified the Indian grazing lands as fragile ecosystems. Humans have been considered as an important factor behind grassland degradation. Environmental damage, resulting from population pressure, intensifying land use and climate change, has been accompanied by a progressive depletion of natural resources. As per the Global Assessment of Human-Induced Soil Degradation (Oldeman and van Lynden 1997) concluded that ‘approximately 23% of the world’s used terrestrial area was degraded: 38% was lightly degraded; 46% moderately degraded; and 16% strongly to extremely degraded’. Papanastasis (2009) identified grasslands as the most degraded land use type in the world. Kessler and Laban (1994); Muller et al. (1998); Carrick and Krüger (2007) identified that grassland degradation is not a local problem, whereas it has turned into a global problem. Li et al. (2013) referred to grassland as the most expansive and unimproved land that supports natural vegetation such as grasses and grass like shrubs and forbs. The grassland has witnessed a visible change all across the world due to varied reasons. The grazing of animals takes place on a variety of grazing lands. True pastures and grasslands are spread over an area of about 12.04 M ha. Other grazing lands are available under tree crops and groves (3.70 M ha), on wastelands (1.50 M ha) and on fallow lands (2.33 M ha). Misra (1983) found that pastures and grasslands have often resulted from degradation and destruction of forests until savannahs are formed.

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Li (1997) found that grassland degradation is characterized by a reduction in the production of forage accompanied with reduction in the soil fertility, fragmentation of grass cover and reduction in soil compaction. White et al. (2000), Lund (2007), Wen et al. (2013) suggest that as grassland provides various ecosystem goods and services, its degradation will affect the livelihood of the pastoralists (Harris 2010) who depend on these ecosystem goods and services.

3 Grassland Degradation: India Scenario India is the seventh-largest country in terms of area that occupies 2.4% of the geographical area and supports 16% of the world’s population. Under the given circumstances, the land faces heavy pressure to deliver beyond its carrying capacity. The country exhibits great diversity in climate, flora, fauna, topography and land use. The grassland of India, unlike any other country, commands tremendous potential both ecologically as well as economically. But the grassland topography has undergone a recent change due to various factors, ranging from natural to anthropogenic. Almost 50% of the country’s population depends upon agriculture for sustenance, and this puts a heavy pressure on land to deliver. Livestock is complementary to agriculture. Thus, the land and grassland particularly face heavy pressure due to anthropogenic activities. Sen (1981) estimated that 32% of the country’s total geographical area is affected by land degradation and 25% by desertification resulting in serious implications for the livelihood and food security of the country. The grasslands are the ‘common’ lands of the community that belongs to all but controlled by none. All the types of grassland are under phenomenal pressure due to grazing. As per the study conducted by Shankar and Gupta (1992), it showed that the carrying capacity of semi-arid grassland is 1 Adult Cattle Unit (ACU) per hectare but the pressure of cattle is estimated at 51 ACU per ha. Similarly, Sahay (2015) found that overgrazing exerts huge pressure on the grassland, because as per an estimate, with a cattle population of 467 million and with a pasture land having a geographical area of 11 Million hectares, the average stands at 42 cattle per hectare, compared to a sustainable limit of 5 ACU per hectare. A study conducted by Sharma (1997) estimated that overgrazing results in an increase in soil loss from 5 to 41 times greater than the normal at the mesoscale and 3–18 times greater at macroscale. Sahu and Dash (2011) concluded that India is moving towards rapid industrialization, infrastructure development and resource utilization resulting in scarcity of water, soil and water table contamination and vegetation and resource loss. The agriculture sector of the country attempts to meet the countries food security and earns remittances through export by using new technologies. FAO (1994) found that the increase in the net sown area is supported by unbalanced use of fertilizers and improper crop rotation. A study by Hobbs et al. (2008) shows that aggressive tillage using heavy machinery for harvesting has resulted in the decline in the SOM that has the potential to impact the soil biology to a great extent.

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The Kachchh district of Gujarat falls under the arid tract of the state. Due to scanty rainfall in the region, it is suitable to have a livestock-based economy. The region has important grassland called the Banni grassland. This grassland, once referred as Asia’s finest grassland, accounted for approximately 45% of the permanent pasture and 10% of the grazing ground available in the state as per the study conducted by Parikh and Reddy (1997). In spite of its importance, the grassland is subjected to immense pressure and has been facing rampant degradation. Even though the Banni grassland has species that have adapted very well in the arid conditions. Frequent drought has rendered the soil deficient of adequate moisture and thereby facilitated a faster degradation of the grassland. A study conducted by GUIDE (1998). The grassland sustains 55% humans and 65% pf livestock and thus posing degradation due to high carrying capacity.

4 Grassland Restoration and Management Restoration ecology deals with the scientific study that aims to renew and restore, degraded, damaged and partially damaged ecosystems. Burch (1996); Hansson and Hagelfors (1998) suggest that restoration makes sense in reclaiming the lands that affect the habitat of the wild animals and plants. The theme of restoration ecology is to develop natural or semi-natural vegetation on land with habitat that is of degraded quality, but important in order to restore ecology. Packard (1997) however referred to restoration as a complex activity because it intends to reverse or mitigate the effects of human activity on the landscape. For every restoration effort, a reference ecosystem is chosen for a particular site condition to get realistic target. Lulow (2004) found that despite of the fact that grasslands are degraded or devoid of the native grasses, they still harbour relatively rich floras of native herb species, and if introduced with the similar species, they can adapt and ensure grassland restoration. Briggs and Knapp (1995), Baer et al. (2003), and Anderson (2006) identified the factors that affect management of grasslands as soil characteristics, restoration status and disturbance regime. Among the soil characteristics, Isbell et al. (2011) suggest that the role of soil nitrogen becomes significant because, as the nitrogen content increases, and it has been found that the many nitrophilous and non-native grasses also increase in dominance. These grasses are efficient in utilizing the soil nitrogen and dominate the native species. Soil characteristics that define the grassland productivity, the role of soil texture and structure also become significant. Parton et al. (1987) correlated soil texture with water holding capacity of soil, soil organic carbon and organic matter and thus play a role in defining the dominance of the species of grassland. There are however studies by Hanson et al. (2008) that show that soil texture does not define the dominance of the exotic species, but even there it has been the predictor of the dominance of the exotic species. Hobbs and Huenneke (1992) concluded that disturbance in the grassland results in changes in community composition and thereby reduces diversity in the region. Basu et al. (2019) identified that due to aridification and fires, C4 plants have expanded over Banni grasslands.

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Thus, the regulation of the grassland is of great importance in order to sustain the ecosystem goods and services from the grassland.

5 Restoration Strategies in India Agricultural & Processed Food Products Export Development Authority (APEDA 1997) reports that India has a vast stretch of grassland that supports huge population of livestock’s. India also exports cattle and is one of the largest countries in such a trade. Ruminant livestock husbandry has been a major component of Indian agricultural production systems since time immemorial. Burke et al. (1989) refer to grasslands with economic importance as they sustain world’s grazing capacity. The rural economy also benefits from animal husbandry, and the role of rural economy to generate aggregate demand is well documented, which helps the Indian economy in totality. Mythili (2003) correlated the loss of productivity in grassland in Indian context and economic losses and its role to support the livestock. This dependency of livestock on grassland often results in rampant exploitation of the grasslands as studied by Pathak and Roy (1995). A similar rise in livestock population is observed in the Hivare bazaar in the Ahmednagar district of western Maharashtra. The rise in the livestock population in the region has led to widespread exploitation of the grasslands in the region. This has rendered the grassland unworthy to be classified as grassland. Thus, grazing needs to be regulated, and grazing lands need to be identified for restoration. Rotational grazing is advisable as it provides the time to rest and reclaim the grassland itself naturally. To ensure high herbage yield and soil moisture conservation, Ahuja (1977) had recommended that contour furrowing, contour bunding and contour trenching be adopted for grassland management in India. Grazing regulations should also involve seeding period that will provide reseeding of the grass for harvesting after full growth. Shankar and Gupta (1992) specifically advised that the natural pastures of India as dominated by Sehimanervosum, Heteropogoncontortus and Iseilemalaxum, and the production can be increased from 4.1 to 7.6, 3.4 to 5.6 and 4.5 to 6.4 tonnes/ha/year by the application of nitrogen at a rate of 40 kg/ha, respectively. Removal of bushes is also an important step towards grassland reclamation as the bushes are invasive species that could affect the native species and lead to a change in the land cover ecosystem. Kaul and Ganguli (1963) have also recommended that grasslands must have 14% under edible bushes in order to get better productivity results. But despite the challenges, the Indian grassland has tremendous potential to develop and ensure high productivity. The responsibility has been very well understood, and the government along with the NGO’s has carried out investigations and suggested remedial measures. One such grassland is the Banni grassland that was degraded due to many reasons, and restoration was considered as the only way to improve its sustainability, and suitable responsibility was taken by the stake holders. NRC (1989) adopted the strategy to restore it at small scale and small patch land scale as usually done in developing countries. In order to deal with the

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grassland restoration, Gujarat Ecology Commission was formed by the Government of Gujarat, firstly to research and formulate an evidence-based remedial steps. Since overgrazing was identified as the major threat to the existence of the grassland, thus Banni grassland was protected from unchecked grazing via trench fencing. As well known that the trenches are not full proofed, the region also saw barbed fences to regulate grazing by the livestock. Bhimaya et al. (1967) and Kanodia et al. (1978) had suggested these methods as they will increase the forage production by reducing the disturbances levels. Kanodia and Patil (1983) attempted further restoration by removal of weeds such as Prosopisjuliflora from the region along with seeding of native species. Prosopisjuliflora weed to infest the region and decreas the productivity and seeding of grasses. The restoration also involved the inclusion of legumes along with the grasses as legumes fix the nitrogen in the atmosphere and increase the protein content of soil. The increase in the protein contents of soil could help in the production of a better quality fodder that could invariably support the cattle. The saline patch of the grassland was dealt separately by Gujarat Institute of Desert Ecology, Bhuj, Gujarat, for saline soil reclamation. The region and its people were confronted with water harvesting techniques. Due to concerted effects in the region, the grassland changed after some showers. After the adequate moisture was available in the soil, the growth of grass was vigorous, and the area was gradually turned lush green. Usage of fertilizers should be regulated. The appropriate soil micro as well as macronutrients should be tested in laboratory, and accordingly, the fertilizers should be added. In India, the process of urea is regulated by the GoI and is subsequently subsidized. This leads to a frequent use of urea in the soil. This has virtually affected the demand of other micronutrient fertilizers like the N, P and K. Hence, the market forces should determine the demand and supply of these fertilizers, which if used efficiently is a boon for the country in terms of more land productivity as well as the less land degradation. So, the step in the direction of Soil Health Card Scheme by the GoI is a welcome step. However, the grassland in particular does not get fertilizers specifically for their development. Thus, the grassland lacks in micronutrients that need to be supplemented adequately. Ninan (2002) advised watershed development in grasslands for improving the production gains.

6 Grassland Studies Using Geo-informatics Colwell (1983) stated that local mapping has traditionally been undertaken using aerial photography in association with the topographic sheets that make the study evidence based and tedious. With scientific advancement, Jianlong et al. (1995) and Tueller (1989) used remote sensing (RS) and geographic information system (GIS) technologies in grassland studies. RS and GIS have significant magnitude for analysis of spatial extent and temporal changes of the land use and land cover in urban as well as regional planning that also helps in decision making (Taylor et al. 1985; Jaiswal et al. 1999; Coppin et al. 2004; Hott et al. 2019). The study of grassland

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ecosystem in RS uses the response of grassland under various EM waves when sent from the satellite and received back by the sensor of the satellite, which is then sent for data analysis. Brockhaus and Khorram (1992) and Nemani et al. (1993) identified a key relationship between the vegetation properties and remotely sensed variables. For instance, the vegetation parameters like the leaf area index, basal area and canopy area have been correlated with the visible, near-infrared and middleinfrared waves. A study by Cook et al. (1989) and Jaiswal et al. (1999) showed that vegetation productivity is more strongly related to band ratios; thus, usages of normalized difference vegetation index (NDVI) and multi-level correcting (MLC) algorithms have made the grassland management more effective. GIS is also used to demarcate the spatial distribution of the species under consideration, and hence, such a scientific study is making its significance pronounced (Rai et al. 2011). There are many research works carried out in the field of grassland management using GIS. In recent times, the dynamics of land study require more scientific systems like GIS, and remote sensing data provides more synoptic coverage of the area. Supervised classification method makes the maximum likelihood algorithm available for land cover classification. The algorithm assumes pixels of same values in the same class, and spectral signatures are taken for each class, and then, classification is performed based on the signatures. The vegetation cover is determined by studying NDVI that measures the reflectance in red and infrared bands of electromagnetic spectrum. Thus, on the basis of RS and GIS, various ecosystem health has been assessed; for instance, Chen and Wang (2005) assessed grassland ecosystem, Xu et al. (2005) assessed lake ecosystem, Wu and Lou (2007) river ecosystem, and Liu and Dong (2006) assessed for city ecosystem. The benefit of using RS is widely known, and India utilizes the same by analysing the data screened by its satellites like the Landsat that is professionally managed by the National Remote Sensing Centre (NRSC), Hyderabad. The launch of Landsat in 1972 helped to monitor the natural resources and provide input to better manage the earth. The satellite is used for a host of activities like monitoring deforestation, agro ecologic zonation, monitoring climate change, wetland degradation, ocean mapping and monitoring, LULC study and many more. These technologies have been developed using rudimentary remote sensing systems, such as NOAA AVHRR, Landsat MSS and TM. Vanak (2013) studied the Banni grassland in Gujarat using RS and GIS. The data obtained from Landsat satellite were analysed using ERDAS IMAGINE 2011, Arc GIS 9.3 and extensive ground-truth surveys. The country wide map of grassland was made using the MODIS time series data 2011. The NDVI was obtained by performing unsupervised classification on the data, with a resolution of 250 m. Further, Vanak et al. (2014) used supervised classification on data helped to create land cover maps of these selected areas. The restoration depended on the analysis of LISS IV data series in three spectral bands which have a resolution of 5.8 m for accurate observation and a subsequent analysis. Lele et al. (2015) identified the invasive species in Ronda Grasslands of Kanha National Park using five decades of satellite data and NDVI technique. It was observed that Butea monosperma is the invading species in the region. Rizvi et al. (2016) used classification techniques for land use land cover classification and assessment of carbon storage potentials from different

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vegetation types. Restoration of both these grasslands is in progress using geospatial technologies.

7 Conclusions Grasslands serve the ecosystems and society in a multiple manner, and thus, degradation of these ecosystems pose a threat to sustainability and livelihood. India is the second largest populated country facing issues related to food and water insecurities, climate change and biodiversity loss. Grasslands in such instance play a vital role in maintaining healthy ecosystem, carbon sequestration and biodiversity conservation. It is observed that intensive grazing, urbanization, unawareness and lack of institutional setups for grassland research resulted in grassland degradation across India. Setting up of organizations, legal policies and research resulted in restoration of grasslands in India. Rotational grazing, land allocation for grazing, seeding, mulching and use of leguminous plants along with native grass species of grass helped in restoration of the grasslands. It is further suggested that introduction of superior quality grasses in the grassland should be promoted that could help in augmenting the natural productivity of the grassland. The high-yielding varieties of grassland are superior in facing adverse environmental considerations as well as the pest attacks. Apart from these, good quality grassland could help to expand the area under grasses as they could adapt in barren as well as degraded lands. Good examples have been seen in case of Banni and Ronda grasslands. NDVI and classification techniques are being widely used in vegetation studies and thus are faster techniques in achieving outputs that could be beneficial in grassland management. A time series analysis using remote sensing technique could provide solutions to managing grasslands.

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Assessing of Soil Erosion Risk Through Geoinformation Sciences and Remote Sensing—A Review Lachezar Filchev and Vasil Kolev

Abstract During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realize the very importance of the soil resources on which the biosphere relies for its existence. The main goal of the chapter is to review different types and structures erosion models as well as their applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in the USA and Modèle d’Evaluation Spatiale de l’ALéa Erosion des Sols (MESALES) model. These and more models are being discussed in the present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning, etc. At the end of this work, a prospectus for the future development of soil erosion risk assessment is drawn. Keywords Artificial Intelligence (AI) · Soil erosion · Assessment risk · Universal soil loss equation (USLE) · Revised universal soil loss equation (RUSLE) · RUSLE2 (revised universal soil loss equation version 2.0) · Machine learning (ML) · Geographical information systems (GIS) · Artificial neural networks (ANN) · Convolutional neural network (CNN) L. Filchev (B) Space Research and Technology Institute, Bulgarian Academy of Sciences, Bl.1 Acad. G. Bonchev St., 1113 Sofia, Bulgaria e-mail: [email protected] V. Kolev Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bl.2 Acad. G. Bonchev St., 1113 Sofia, Bulgaria e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 P. K. Rai et al. (eds.), Recent Technologies for Disaster Management and Risk Reduction, Earth and Environmental Sciences Library, https://doi.org/10.1007/978-3-030-76116-5_21

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Abbreviations AGNPS AI AISLUS ALOS ANFIS ANN ANSWERS CLSE CNN CREAMS CORINE DEM DP DNN DYRIM EI EPIC EPM FLDA GIS GEIM GPU HAND HI ImpelERO LAI LISEM LDN LMT LR LSP MARS ME MESALES ML MLP MLR MPFPR NBTree NDVI NIR

Agricultural Non-point Source Pollution model Artificial Intelligence All India Soil and Land Use Survey Advanced Land Observing Satellite Adaptive Neuro-Fuzzy Inference System Artificial neural networks Aerial Non-point Source Watershed Environment Response Simulation Chinese Soil Loss Equation Convolutional neural network Chemicals, Runoff and Erosion from Agricultural Management Coordinated Information on the Environment Digital Elevation Model Deep Learning Deep Neural Networks Digital Yellow River Model Erosivity Index Erosion Productivity Impact Calculator Erosion Potential Method Fisher’s Linear Discriminant Analysis Geographic Information System Gully Erosion Inventory Map Graphical Processing Units Height Above the Nearest Drainage Human Intelligence Integrated Model to Predict European Land Leaf Area Index Limburg Soil Erosion Model Land Degradation Neutrality Logistic Model Tree Logistic Regression Landslide Susceptibility Prediction Multivariate Adaptive Regression Splines Model Efficiency Modèle d’Evaluation Spatiale de l’ALéa Erosion des Sols Machine Learning Multilayer perceptron Multi Linear Regression Multi-parameter Fuzzy Pattern Recognition Naïve Bayes Tree Normalized Difference Vegetation Index Red and Near-Infra-Red

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PESERA QSWAT RBF RF RUSLE SDG SDR SEMMED SMUs SONN SSA SSAO-MARS ST SVM SVR SYI SWAT SWRC SWRRB USLE USPED WATEM/SEDEM WEPP WEPS WEQ WRB WSRF

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Pan-European Soil Erosion Risk Assessment Quantum Soil and Water Assessment Tool Radial Basis Function Random Forest Revised Universal Soil Loss Equation Sustainable Development Goals Sediment Delivery Ratio Soil Erosion Model for Mediterranean Regions Soil Mapping Units Self-organization Neural Network Social Spider Algorithm Social Spider Algorithm Optimized the Multivariate Adaptive Regression Splines Soil Taxonomy Support Vector Machine Support Vector Regression Sediment Yield Index Soil and Water Assessment Tool Soil Water Retention Curve Simulator for Water Resources in Rural Basins Universal Soil Loss Equation Unit Stream Power Erosion Deposition Water and Tillage Erosion Model/Sediment Delivery Model Water Erosion Prediction Project Wind Erosion Prediction System Wind Erosion Equation World Reference Base Weighted Subspace Random Forest

1 Introduction Soil erosion by water was found to be a ubiquitous site problem. The study of soil erosion is having become a severe worldwide problem. Moreover, it is a serious environmental, economic, and social problem as often met in agriculture. These are very real and at times severe issues. An effective soil erosion prediction has become an essential goal for many researchers. Soil erosion is considered a major threat to food security and causes damages both on-site and off-site, i.e., to adjacent infrastructures and surface waters. Many soil properties used for design are not intrinsic to the soil type but vary depending on conditions. In-situ stresses, changes in stresses, the presence of water, rate and direction of loading, and time can all affect the behaviour of soils.

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New challenges and policy developments after 2015 (among others, the Common Agricultural Policy (CAP), Sustainable Development Goals (SDGs)) are opportunities for soil scientists and soil erosion modellers to respond with more accurate assessments and solutions as to how to reduce soil erosion and furthermore, how to reach Zero Net Land Degradation targets by 2030. According to the Thematic Strategy for the Soil Protection in European Union (EU) (European Commission– Soil Thematic Strategy 2006), soil erosion by water is the most severe hazard for soils in Europe (Envirotech21 2021). In 2019 five mission areas centred on societal priorities and challenges have been approved in EU. One of them ‘Soil health and food’ is aiming at least 75% of all soils in each EU Member States 2030 to turn healthy, or show a significant measurable improvement for all indicators where levels are below accepted thresholds, to support the provisioning of essential ecosystem services (Veerman et al. 2020). In order to estimate this Mission defining a set of 8 fundamental indicators the fourth of which is ‘4) Soil structure including soil bulk density and absence of soil sealing and erosion’. This fundamental indicator is to be observed and estimated EU wide by the EU Soil Observatory which in turn is run by the DG JRC of EC. When estimating the soil erosion risk, it is recommended to exclude surfaces that are not prone to soil erosion such as urban areas, bare rocks, glaciers, wetlands, lakes, rivers, inland waters and marine waters. Apart from the rainfall and topographic factors, soil properties and cover management conditions influence the variation of soil loss rates. However, there are big variations in rainfall distribution and slope gradient, which lead to high fluctuations in the soil erosion risk from one place to another. The rate of the potential erosion risk is increasing with the increase of the rainfall intensity, slope gradient, and soil erodibility. Soil erosion prediction is relevant at a wide range of spatial scales, from the plot scale to the catchment scale, from the regional scale up to the continental and global scales. At different scales, different processes tend to become dominant, so that the effective focus of the models also changes. At the larger scales, topography, soil and vegetation patterns become more important. This is where remote sensing and GIS become valuable tools. Remote sensing has become a monitoring and predicting tool for environmental variables by using satellite data which have clear benefits for and can be delivered information as fast as changing surface conditions (Rai et al. 2018). Remote sensing imagery can be used in soil erosion modelling where it studies land degradation of erosion features such as gullies or vegetation impoverishment or by collect input data for simulation. Methods using satellite imagery to produce maps of vegetationrelated variables for soil erosion studies have been compared, who found that the normalized difference vegetation index (NDVI) was the most useful. Geographical Information Systems (GIS) provide an important spatial and analytical function, for geo- and spatial processing to develop the erosion model (Rai et al. 2011). Some important advantages include in soil erosion models are: • Processing of the input data to simulate different scenarios very quickly. • The ability to look at spatial variation and applied of a custom resolution;

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• Use of an animate sequence of model output across time and space. It enables objects to be viewed from all external perspectives. The use of GIS is indispensable in agroforestry research, planning and extension because agroforestry science is directly dependent upon the linkage of spatial and non-spatial information, for instance, use of spatially-distributed soil erosion models with GIS that can quantify soil loss at the landscape level and spatial erosion risk assessment can be determined. Also the GIS techniques can provide easy and timeeffective tools to map and analyze erosion input data of hydrophysical parameters. The well-known and often applicable models worldwide are USLE and RUSLE. Because they have compatibility with GIS (Millward and Mersey 1999; Jain et al. 2001; Pandey et al. 2009; Lu et al. 2004; Jasrotia and Singh 2006; Dabral et al. 2008; Kouli et al. 2009; Bonilla et al. 2010; Samanta et al. 2016)). In 1993, using RUSLE begun the development of the RUSLE2 model as uses the basic USLE equation structure to compute sediment detachment but differs greatly from the USLE in almost every other way. RUSLE 2 is similar to RUSLE, but RUSLE 2 uses new equations, a new mathematical integration procedure, new database values, and is implemented in a modern graphical user interface computer program. Almost all of the mathematical relationships in RUSLE2 have been revised from corresponding relationships in RUSLE. The main goal of soil erosion models is either predictability or explanatory. Modelling of soil erosion is the process of describing soil particle detachment, transport and deposition mathematically on land surfaces. It is also necessary to keep in mind that good predictions can only be obtained by using models if they are based on good data from field observations and measurements, and not on generated or assumed data. Soil erosion models are usually developed for particular environments, which mean they only involve erosion processes that are important in those environments based on various assumptions and simplifications. Soil erosion models have been widely applied to predict soil erosion rates and understand interactions between different environmental drivers of soil erosion processes. In the last decade years, many erosion models that have appeared includes some extension and improvements of already existing methods, new methods based on AI, as well as erosion models dependents from geography areas are constructed. There exist different classifies of the erosion models—mains, spatials, evens types, scale types, categories, structures, Artificial Intelligence (AI)—based, ML-based, and so on, which leads to appears of the big important question—Is it possible let to do a new, a global division of erosion models? It is easy to see that such a division can be—Human Intelligence (HI) and AI—based erosion models. It allows us also easily add and divide of further erosion models, which most likely should be the Artificial Intelligence—based models. Therefore, the review chapter considers most-used erosion models divided into two categories which are described in the following parts, respectively. In the ‘Soil erosion’ is considered in brief the erosion theory, some most important characteristics and divisions. In the last subchapter are discussed some of the models and conclusions are drawn.

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2 Soil Erosion 2.1 General Theory Soil erosion is the common land degradation problem worldwide because of its economic use and environmental impacts. To estimate soil erosion and to establish soil erosion management plans, many computer models have been developed and used. Soil erosion is a complex geomorphological process with varying influences of different impacts at different spatio-temporal scales. Soil erosion is the displacement of the upper layer of soil, one form of soil degradation. This natural process is caused by the dynamic activity of erosive agents, that is, water, ice (glaciers), snow, air (wind), plants, animals, and human activities. In accordance with these agents, erosion is sometimes divided into water erosion, glacial erosion, snow erosion, Soil erosion may be a slow process that continues relatively unnoticed, or it may occur at an alarming rate causing a serious loss of topsoil. The loss of soil from farmland may be reflected in reduced crop production potential, lower surface water quality and damaged drainage networks. The assessment methods of the soil erosion processes at field scale and under natural conditions is necessary to measure soil surface changes for multi-spatio-temporal scales without disturbing the area of interest and to identifies and quantifies sediment sources and sinks at the hillslope with high spatial resolution.

2.2 Erosion Types The development of soil properties for geotechnical design purposes begins with developing the geologic strata present. A geologic stratum is characterized as having the same geologic depositional history, stress history, and degree of disturbance, and generally has similarities throughout the stratum in terms of density, source material, stress history, hydrogeology, and macrostructure. The properties of each stratum shall be consistent with the stratum’s geologic depositional and stress history, and macrostructure. The main types of erosion studied by the community are: • Impact Erosion—Physical detachment of soil particles as a result of raindrop impact. • Sheet erosion—Thin, uniform wearing away of the uppermost surface layers in the soil profile. Seldom have the detaching agent, but just merely transporting soiled particles detached by raindrop impact. • Rill erosion—Follows sheet erosion. As the amount and velocity of water increases, water is now able to both detach and transport soil particles.

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• Gully erosion—As rills deepen and widen, gullies form. Simple definition: gullies are rills that are too large to be repaired with conventional tillage equipment. Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. • Channel erosion—Erosion as a result of concentrating and confining the erosive forces of water. Includes both manmade and natural channels. • Mass Wasting—Large failures usually as a result of gravitational forces. Landslides, pot-slides, slumps, debris torrents • Soil erosion is a complex geomorphological process with varying influences of different impacts at different spatio-temporal scales, the displacement of the upper layer of soil, one form of soil degradation. This natural process is caused by the dynamic activity of erosive agents, that is, water, ice (glaciers), snow, air (wind), plants, animals, and human activities.

2.3 Erosion Model Types and Structures Models are part of the erosion prediction technology, used in development of soil conservation and environmental degradation planning systems. Erosion is based on defining the relation among controlling factors and soil loss delivery soil loss from one point on the landscape to another within a given set of management conditions. The types of soil erosion models are summarized in (Wischmeier and Smith 1978). More recently an overview with a typologization of water erosion models is made by (Karydas et al. 2012). In general, models fall into three main categories, depending on the physical processes simulated, the model algorithms describing these processes, and the data dependency of the model. The category of soil erosion models: • Empirical models—They are generally referred to a simplified representation of a system and are based primarily on the analysis of field experiments and seek to characterize the response from these erosion plots using statistical inference. The computational and data requirements for such models are usually less than for conceptual and physically based models (SLEMSA, MUSLE, USLE, RUSLE etc.). They are particularly useful as a first step in identifying the sources of sediments. • Conceptual models (Beck 1987; Renschler 1996; Merritt et al. 2003)—include only a general description of catchment processes, without including the details occurring in the complex process of interactions. This allows these models to provide an indication of the qualitative and quantitative effects of land-use changes, without requiring large amount of spatially and temporally distributed input data. • Physically-based models—They are based on an understanding of the physics of the erosion and sediment transport processes and describe the sediment system as includes the laws of conservation of mass and energy, where energy can change form but total energy remains the same. They are based on the understanding of the physics of erosion processes. Example models are Soil and Water

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Assessment Tool (SWAT) (Arnold and Fohrer 2005), WEPP, Chemicals, Runoff and Erosion from Agricultural Management (CREAMS) (Knisel 1985), Aerial Non-point Source Watershed Environment Response Simulation (ANSWERS) (Beasley et al. 1980), The Three Dimensional Storm Process Based Erosion model (EUROSEM) (Morgan et al. 1998), and Agricultural Non-point Source Pollution model (AGNPS) (Young et al. 1989; Bhuyan et al. 2001; Grunwald and Norton 1999), etc. The equations are formulated for use with continuous spatial and temporal data, yet the data used in practice are often point source data to represent. The main advantage of physical models is more their relative transferability, which favours predicting soil erosion and sediment yield under different climate and physiographic land-use scenarios and their ability to consider environmental issues such as climate change. Another typologization separates them into two types of soil erosion models: • Empirical—In these models, statistical techniques are employed to examine the relationships between different components of studied systems. Empirical models can achieve accurate results. However, they are limited to conditions for model development and therefore often do not perform well when applied to other areas or other time periods. • Process-based—They explain and predict the dynamic behaviour of the system. We also have two structures of soil erosion models: • Lumped—In lumped models, contributing factors of erosion are represented by a constant value over the study area. • Distributed—In spatially-distributed models, by using of advances GIS the spatial variability achieve better representations, a possibility of a large area to be divided into small sub-units which have uniform characteristics—climate, land use, and topography, Considering the spatial and temporal domain—two scales of a soil erosion model focuses on soil erosion and transport at: • Spatial scale—It refers to the spatial extent or time span that models operate at; 1. 2. 3. 4.

Hillslope (10,000 km2 ),

• Time step—The time interval used by a model during applications: 1. 2. 3. 4.

Event, Daily, Monthly, Annual.

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The models can be classified also according to: • The spatial as: 1. 2.

Non-spatially distributed—EPIC, CREAMS, Spatially distributed—ANSWERS, AGNPS, and SWRRB.

• The evens type as: 1. 2.

Single-event—AGNPS, ANSWERS, Continuous-time scale—EPIC, CREAMS, and SWRRB.

• The scale type as: 1. 2.

Field-scale—WEPP, EPIC, CREAMS, Watershed/basin-wide—ANSWERS, AGNPS, and SWRRB.

The primary factors for soil erosion are identified as: climate, soil, topography, vegetation, and conservation practices, which have either a positive or negative effect on soil loss. These primary factors are the basis of the universal soil loss equation (USLE) model.

2.4 Some Important Erosion Model Parameters Considering the soil erosion model’s parametrization with remotely sensed data and products the most widely used vegetation index is the Normalized Difference Vegetation Index (NDVI). NDVI is one of the most relevant vegetation indices used heavily in different disciplines to estimate vegetation cover with sufficient accuracy. Many researchers used NDVI as a reliable method for geospatially estimating vegetation status. The NDVI (Karaburun 2010) express the difference between reflectance in the Red and Near-Infra-Red (NIR) portion of the electromagnetic spectrum, and by extension between the corresponding satellite bands and used in large geographic areas. For LANDSAT 8 Red portion is located in Band 4 and Near-Infra-Red (NIR) in Band 5. Therefore the index is definite from the following formula (Efthimiou and Psomiadis 2018); NDVI =

Band5 − Band4 NIR − RED = , NIR + RED Band5 + Band4

−1 ≤ NDVI ≤ 1

The index values are in the intervals: NDVI < −0.2 NDVI < 0.2 NDVI > +0.2

water bodies, the non-vegetated areas (bare soil, rock, snow, built-up areas), vegetation traces,

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represent shrub and grassland, indicate temperate and tropical rainforest.

The index has strong correlations with Leaf Area Index (LAI), total vegetation cover, or above-ground biomass and can be used to express the vegetative state in different seasons to find a soil erosion risk assessment. NDVI images can be acquired by the spectroradiometer sensors, such as on board the TERRA satellite (Alexandridis et al. 2015) and can be used to provide a good estimate of the annual soil loss. Since the finding of the soil loss of each month is compared with the mean monthly, then the month with the lowest difference was selected as the optimum single time which can be used in a soil erosion model. The important factor closely related to the NDVI index is the coefficient C. C = e−α β.NDVI , 0 ≤ C ≤ 1 NDVI

where C—the cover and management factor, α and β are the parameters that determine the shape of the NDVI curve as some researches to obtain reasonable results α = 2, β = 1. C-factor is related to the ratio of soil loss to soil loss occurring in bare soil. If the land use completely prevents erosion—C becomes 0; if there is no land use that acts as a protection against erosion, then the C becomes 1, and for the otherwise C < 1.

2.5 Basic Methods, Tools, and Standards for Soil Erosion Risk Assessment The selection of method of measurement depends on the type of erosion, the purpose, and the target accuracy of the measurement. The techniques for measuring erosion discussed in (Loughran 1989) are all appropriate and necessary for assessing soil loss.

3 Expert-Based Methods An expert-based approach to the soil erosion risk map of Western Europe is described in (De Ploey 1989) where the map produced by various experts who delineated areas where, according to their judgment, erosion processes are important. But, there exist that limitation the expert-based approach does not give us the clearly for the plan criteria. Despite that, expert-based method is a qualitative and its results depend heavily on expert judgment, it can be a good way to derive information about land degradation phenomena for the scale of the produced maps, as well as by lack of suitable quantitative data (Colonna et al. 2008).

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3.1 MESALES Model The MESALES (RMSER) is based on expert-defined empirical rules and available data for the assessment of soil erosion risk at the European scale with 25 ha minimum mapping unit for land cover and 5 ha for land cover change every 6 years (Bissonnais et al. 2002). CORINE land cover mapping provides the only consistent classification system of long-term land cover data in Europe defined in 44 classes. It is the assumption that soil erosion occurs when water that cannot infiltrate into the soil becomes surface runoff and moves downslope; thus the erosion process that is considered is erosion by overland flow. Land cover and crust formation on cultivated soils were considered as key factors influencing runoff and erosion. The soil erosion assessment in 5 classes (very low, low, moderate, high, very high) based on hierarchical decision tree (DT) classifications which are simple, requires ranking of each parameter, do not require the use of parameters that are not available at national scales, such as the USLE model, but is give priority to the human activities factors. In comparison with the CORINE erosion model the present MESALES model is much more precise and accurate uses a single decision tree whatever the land use and takes into account only two classes of land use, three climate classes and four slope classes. The main disadvantages of the French model assessing erosion risk that the final information is provided on 5 scales of risk and it is not possible to link these classes to quantitative values of erosion, nor is it possible to assess the errors associated with the results. The advantage of the MESALES model that allows realization of a single homogeneous map of erosion risk at a national scale, shows the importance of the seasonal effect on erosion, and gives us a possibility to be compared regions. Therefore, the decision tree MESALES model is applicable for large areas with expert opinion as it can be used to either simulate soil erosion sensitivity or soil erosion risk. In (Hessel et al. 2014) is made an application of the present model for 3 large areas in Europe and Morocco, using soil data from ESDB and DSMW. The newly e-SOTER database (Michéli et al. 2011) was used to evaluate whether its assessment of soil erosion sensitivity is better than existing data. The e-SOTER database consists of a spatial unit map of soil and terrain spatial units in combination with a database that gives representative soil profiles and values of soil attributes for these profiles for the units on the map. As the soil map does not contain data for these areas, MESALES is unable to generate a result as well as for application to Europe, as observed on the maps presented in JRC. Due to inconclusive expert judgment and the fact that the MESALES predicts soil erosion sensitivity, which cannot be measured in the field, in (Michéli et al. 2011) is didn’t say which results are better. In (Colmar et al. 2010) MESALES and PESERA models are used and compared to obtain of the erosion risk to evaluation map at the national scale and to call upon expert knowledge to validate, at the regional scale of Brittany. The results show that both methods improved the results and we were able to produce a new regional map of

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erosion risk for Brittany. In (Cheviron et al. 2011) are considered and compared four erosion models where it shows that the PESERA model exhibited more dynamic behaviour than the MESALES through stronger variations in model outputs and sensitivity indices. MESALES behaviours are consistent with current knowledge on soil erosion as it can be used to predict seasonal erosion rates on larger territories, provided sufficiently reliable input parameter values are available. Need to note, expected results are strongly affected by uncertainties in erodibility arising from imprecise cartography or lack of information on soil characteristics.

4 Model-Based Methods A wide variety of models are available for assessing soil erosion risk. Erosion models can be classified in a number of ways. One may make a subdivision based on the time scale for which a model can be used: some models are designed to predict long-term annual soil losses, while others predict single storm losses (event-based). Alternatively, a distinction can be made between lumped models that predict erosion at a single point, and spatially distributed models. Another useful division is the one between empirical and physically-based models.

4.1 Empirical Models 4.1.1

CORINE (Coordinated Information on the Environment) Method

CORINE model (CORINE 1992) is an empirical and an expert-based factor model having simple structure predicting soil erosion. It is spatial-explicit correctly identifying Mediterranean areas with the highest risk of erosion. It is a semi-qualitative cartographic method that involves designing and overlaying of several layersthematic maps, and it can present the spatial heterogeneity of soil erosion risk with GIS by four parameters: • • • •

Soil erodibility; Erosivity; Slope; Surface cover as essential databases;

Moreover, together with GIS and remote sensing, the model can be used to obtaining better accuracy in larger areas of a soil erosion risk and its spatial distribution. It was mostly applied in the European and Mediterranean countries, while spatial comparison of actual soil erosion risks map and field investigation. In (Zhu 2012) is described that the model consists of the four steps:

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Step 1: Soil texture, depth and stoniness layers are overlaid to form a soil erodibility map. Step 2: Use of the two aridity indices to forming the erosivity layer:  Pi2 • The Fournier index is FI = 12 i=1 P , where Pi is total precipitation in a  month, and P—total mean annual precipitation; • Bagnouls-Gaussen index BGI = 12 i=1 (2ti − Pi )ki where ti is the mean temperature for the month, ki is the proportion of the month during which 2ti − Pi > 0; Step 3: Slope classes from digital elevation model (DEM) of the study area are produced. DEM is a valuable tool for topographic parameterization. Step 4: Prepare the Landsat 5 TM imagery to obtain the land use and land cover (LULC) layer. In order to obtain the final soil erosion risk, the combination between LULC and layers with the potential soil erosion risk is considered. In a study case the Danjiangkou Reservoir region (DRR), China, considered in (Zhu 2012), is found that the CORINE model possesses two disadvantages, the first that it provides only qualitative output, which can be hard to validated, and second one that the final results have a smoothing effect that may erase some small or sparse high-risk areas. In (Tayebi et al. 2017) are considered areas within the Bonrod Zangane watershed, western Shiraz, Iran, to assess soil erosion risk for restoring and protecting. The high soil erosion risk is found at the northern and south eastern parts of the study area. The results show that CORINE model with GIS provides the most important factors for erosion risks in the watershed it can be used to delineate the soil erosion risk and also to discriminate the potential soil erosion risk areas. The CORINE model is used in (Dengz and Akgul 2005) to find the soil erosion risk in Gölbasi Environmental Protection area and its vicinity, located south of the city of Ankara, Turkey. The model combining four parameters, soil erodibility, erosivity, topography and vegetation cover consists of 6 steps each of which using different overlaying combinations of soil texture, depth, stoniness, climatic data, and LULC information. The final results show that the CORINE model is very useful, in contrast to the conventional methods which require high labour cost, time to collect data.

4.1.2

USLE (Universal Soil Loss Equation) Model

The USLE is an empirical model was designed by the United States Department of Agriculture (USDA) in 1978 to predict longtime-average inter-rill and rill cropland soil losses by water under various effects such as land use, relief, soil and climate, and guide development of conservation plans to control erosion. Although it is an empirical model, it predicts erosion rates of ungauged watersheds as well as presents the spatial heterogeneity of soil erosion. The model was based on the field measurements of soil erosion rates in agricultural areas in (Wischmeier and Smith 1978). The use of USLE model is highly acknowledged in both agricultural and hilly watersheds

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because of its beneficial points as compared to the others tool to desegregate disparate data sets and in a position to evaluate any dynamic system like soil erosion. It is the primarily favoured analytically based model used worldwide for soil erosion prediction and management, also the USLE can predict the long-term average annual soil loss associated with sheet and rill erosions. The USLE incorporates improvements in the factors based on new and better data but keeps the basis of the USLE equation. Additional research and experience have resulted in an upgrade of the USLE from the past 30 years. The USLE was enhanced by revising the weather factor, the soil erodibility factor depending on seasons, revising the gradient and length of slope and developing a new method to calculate the cover management factor. The USLE assumes that detachment and deposition are controlled by the sediment content of the flow. Erosion is limited by the carrying capacity of the flow but is not source limited. Detachment will no longer take place when the sediment load has reached the carrying capacity of the flow. USLE analysis includes R factor, K factor, LS factor, C factor and P factor values as LS factor play very important role in model. The formula of USLE is (Universal Soil Loss Equation 2021): A = R ∗ K ∗ L ∗ S ∗ C ∗ P, where A is computed soil loss, R is the rainfall-runoff erosivity factor, K is a soil erodibility factor, L is the slope length factor, S is the slope steepness factor, C is a cover management factor, and P is a supporting practices factor. In (Devatha et al. 2015) using USLE model and Remote Sensing (RS) and Geographic Information System (GIS) methods are used to estimate the annual soil loss for Kulhan watershed of Shivnath basin, sub-basin of Mahanadi basin, Chhattisgarh. Results show that the study area has gentle slope so the erosion loss is obtained with low rate and it is within acceptable limit. Soil erosion map is reclassified according to erosion risk where the study area is within the acceptable limit. Results show that soil erosion USLE model in combination with GIS is an efficient tool to handle large volume data needed for watershed soil loss studies. When the assessment model region is large and needed consider big year time period USLE method can be time-consuming. A help to reduce the calculation time significantly come by the parallelization implementation of the method. Such an implementation in (Wieland et al. 2012) is considered. In (Risse et al. 1993) shows that the USLE method overestimates erosion for low measured erosion and does not underestimate erosion for measured high erosion relative to moderate erosion. By using of USLE model and remote sensing technologies for the northern catchment of Lake Tana in (Balabathina et al. 2016) soil erosion risk showed the high influence of climatic seasonality and was high during the rainy season only. Barren land exhibited the highest soil erosion rates, followed by the croplands and plantation forest in the catchment. The soil erosion estimation was generated by multiplying the required input thematic layers of the model together in a GIS platform. The results showed that gully erosion was constrained in the steep slopes of all subcatchment areas, which could be attributed to higher steep slopes in land-use. But also it expanded significantly to the middle and lower parts of the catchment.

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MUSLE (Modified Universal Soil Loss Equation)

In (Williams 1975) is an empirical model which was developed the following revised form of the USLE using 778 storm-runoff events collected from 18 small watersheds, with areas varying from 15 to 1500 ha, slopes from 0.9 to 5.9% and slope lengths of 78.64 to 173.74 m and called it the modified universal soil loss equation (MUSLE). The MUSLE was given in the following general form: b  Sy = a Q qp ∗ K ∗ L ∗ S ∗ C ∗ P where, Sy —sediment yield (in t) on a storm basis and for the entire study watershed, Q —volume of runoff (in m3 ), qp —peak flow rate (in m3 s−1 ). K—the soil erodibility (in t ha h ha−1 MJ−1 mm−1 ), L—slope length, S—slope steepness, C—crop management, P—soil erosion factor, a and b are location coefficients. For the areas where the equation was developed, a and b were 11.8 and 0.56, respectively, for metric system units. The optimization technique suggested in (DeCoursey and Snyder 1969) was used for the development of the prediction equation and designating a and b. A disagreement with the principle of dimensional analysis of the MUSLE has been explained in (Cardei 2010). The MUSLE has been an attempt to estimate stream sediment yield for individual storms by replacing the rainfall factor with a runoff factor (Sadeghi et al. 2013). The MUSLE model produces reasonable estimates when it is applied under appropriate conditions as shows a significant difference with measured sediment yield in many watersheds. Applying the model need calibration else in the result has huge errors. But despite good results in some areas, for correctly applying the MUSLE model is strictly recommended a very careful review of the correct values and exact variables used.

4.1.4

CLSE (Chinese Soil Loss Equation)

The CLSE is an empirical model that was developed in (Liu et al. 2002) of USLE for China as estimating average annual soil loss by water on hillslope for cropland. The model predicts interrill erosion from farmland under different soil conservation practices use an empirical multiplicative equation with six factors obtained from experiment stations covering most regions of China and modified to the scale of the Chinese unit plot defined:

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A=R∗K ∗L∗S ∗B∗E∗T where the notations are: A—Annual average soil loss (t ha−1 ), R—Rainfall erosivity (MJ mm ha−1 h−1 yr−1 ), K—Soil erosibility (t ha h MJ−1 mm−1 ), S—Slope steepness dimensionless, L—Slope length factors dimensionless, B—Biological-control dimensionless factors, E—Engineering-control dimensionless factors, T—Tillage practices dimensionnes factors. Chinese soil loss equation is to predict annual average soil loss from slope cropland under different soil conservation practices. In (Liu et al. 2002) the USLE model use 30-year data was adapted in substituting the C and the P factor with three factors considering the biology, engineering and tillage practices.

4.1.5

RUSLE (Revised Universal Soil Loss Equation) Model

RUSLE is a straightforward and empirically based model that has the ability to predict long-term average annual rate of soil erosion on slopes using data on rainfall pattern, soil type, topography, crop system and management practices. RUSLE is an empirical model, designed for use at runoff plot or single hillslope scales. However, erosion rates of ungauged catchments can also be predicted using RUSLE by using knowledge of the catchment characteristics and local hydroclimatic conditions. In (Renard et al. 1997) is shown the Revised Universal Soil Loss Equation (RUSLE) model as an updated version of USLE model. In the RUSLE model, the potential soil erosion risk consists of only the multiplication of three natural factors to indicate the area under high vulnerability: • Rainfall erosivity (Nearing et al. 2017), • Soil erodibility, • Slope length and Slope steepness. RUSLE can be expressed as: A = R ∗ K ∗ L ∗ S ∗ C ∗ P, where A = average annual soil loss per unit area (t ha − 1 yr − 1), R = rainfall-runoff erosivity factor (MJ mm ha − 1 h − 1 yr − 1), K = soil erodibility factor (t ha h MJ − 1 mm − 1), L = slope length factor, S = slope steepness factor, C = cover and management factor, and = support and conservation practices factor (Panditharathne et al. 2019). The RUSLE model is well studied and it has been widely applied at different scales to estimate soil erosion loss, and to plan erosion control for different land cover categories such as croplands, rangelands, and disturbed forestlands.

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In comparison with the other methods such as CORINE, NNIPHE, GLASOD and Hot Spot approaches, as well as often uses data from Geographic Information System (GIS) the RUSLE model is considered as a leading model as gives the most detailed information on the soil erosion risks (Grimm et al. 2002; Claessens et al. 2008). Important disadvantage is that the available data for finding some of the RUSLE parameters can be a limitation for obtaining of a maximal accuracy. Moreover, using of a model method often implies uncertainties in the calculation of each factor (Van der Knijff et al. 2000). In (Fu et al. 2005) the RUSLE model has been applied at large watershed scale combined with GIS to assess soil loss using local data in the Yanhe. Because of the limitations of the RUSLE and spatial heterogeneity, more work should be done on the RUSLE R-factor and C-factor. Obtained is that the middle and southeast parts of the Yanhe watershed have more erosion than the northwest part. The main reason for soil loss is the close relationship with land use and rainfall–runoff erosivity. But, because of the limitation of RUSLE, spatial heterogeneity in the watershed and use of empirical data, there are uncertainties in the predicated value. In (Kim 2006) is analyzed the spatial distribution of’ soil erosion in the Imha watershed, to determine the Sediment Delivery Ratio (SDR) in the Imha watershed and determine the Trap Efficiency (TE) at the Imha reservoir. To estimate the gross soil erosion as well as analyze the mean annual erosion and the soil losses caused by typhoon “Maemi” in the Imha watershed is used the RUSLE model combined with GIS techniques. The model is used to evaluate the spatial distribution of soil loss rates under different land uses. The RUSLE model and GIS techniques in (Bhat et al. 2017) to determine a quantitative assessment of average annual soil loss for Micro-watershed in J&K, India is considered. The results in the micro-watershed show that the minimum rate of soil erosion is in the land areas with natural forest cover in the head water regions, higher rate of soil erosion—in areas with human intervention. Therefore this model helps to increase the prediction capability and accuracy of remote sensing and GIS-based analysis. In (Winning and Hann 2014; Lu et al. 2004) is applied the RUSLE, remote sensing, and GIS to the mapping of soil erosion risk in Brazilian Amazonia as the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). In this research is that remote sensing by Landsat 7 satellite and GIS estimation and its spatial distribution feasible with reasonable costs and better accuracy in larger areas. Additional is shown the relationships between land use and land cover (LULC), and soil erosion risks which are useful for managing and planning land use that will avoid land degradation. As shown in results, a remote sensing and GIS provide useful tools for evaluating and mapping soil erosion risk in Amazonia.

4.1.6

RUSLE 2 (Revised Universal Soil Loss Equation, Version 2.0)

With the development and application of GIS technologies and expansion of computational power and refinement of mathematical expression, estimating erosion transitioned from an empirical to a process-based model. Although RUSLE2 is empirical

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model by nature, the model consists of the newest development of mathematical representations of tillage conservation and sub-factors of crop systems. RUSLE2 improves predictive ability by identifying extreme loss event using daily inputs. RUSLE2 is the replacement for RUSLE used commonly today in the United States by government agencies for conservation planning purposes where additional has been introduced the new concept of “erosivity density” for calculating and mapping rainfall erosivity in the United State (USDA-RULSE2 2013). In (Nearing et al. 2017) is calculated erosivity density as an amount of erosivity per unit of rainfall depth, generally calculated on a monthly basis at a given location. The units of energy density are energy per unit time per unit area (MJ ha−1 min−1 ). These values are multiplied by average unit depth of precipitation (mm) for the period of interest (e.g., monthly) to give the average erosivity for the time period. Calculation of the erosivity density approach to rainfall erosivity provides better estimates of erosivity, smoother mapping across regions, better performance in mountainous areas, the capability to use shorter 15-year rainfall records, and the ability to utilize daily data in conjunction with less common 15-min rainfall data. A major advancement in RUSLE was the use of sub factor relationships to compute C factor values from basic features of cover-management systems. RUSLE2 was developed primarily to guide conservation planning, inventory erosion rates and estimate sediment delivery (RUSLE2a 2008). The equation is as USLE except it computes soil for a given day rather than an annual soil loss. The RUSLE2 contains several major enhancements including (RUSLE2b 2014): • Improved tools to develop and use annual and perennial vegetation descriptions, • Tools to estimate runoff and develop a representative runoff event sequence, • Tools to calculate and display tillage erosion on profiles with changing slope steepness, • Tools to make it easier to apply and remove permeable barrier systems commonly used on construction sites. The K-value has been made to fluctuate during the year, rather than remaining constant. The soil erodibility calculation method was also changed in order to express the soil structure sub factor. The difference between the USLE, RUSLE, and RUSLE2 results in the three methods giving different erosion estimates even when each method gives the same average annual values for each USLE factor. This difference results in as much as a 20% difference in average annual erosion values between RUSLE2 and the USLE and RUSLE. RUSLE2 displays a variety of erosion values that can be used in conservation and erosion control planning. Also, RUSLE2 can be applied in the traditional USLE way by assuming a uniform slope and that deposition ends slope length. The erosion values computed by RUSLE2 can be compared with soil loss tolerance values or other erosion control criteria just as USLE soil loss values were used. In the equation of the factor K (soil erodibility factor) is used the modified soil structure subfactor KS given by (RUSLE2a): KS = 3.25(2 − St )

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and in (Ismail and Ravichandran 2008) KS = 3.25(St − 2) where St —soil structure class. A new subfactor has been added, and the deposition equations have been extended to consider sediment characteristics and how deposition changes these characteristics. In the RUSLE2 to estimate K-factor values for mixed soils is used modified soil erodibility nomograph. RUSLE2 method works best where rainfall occurs regularly, rainfall is the dominant precipitation, and average annual rainfall exceeds 20 inches. Also can be used to estimate erosion in the special winter condition represented by the Northwest Wheat and Range Region, but in cases of snowmelt it does not explicitly estimate erosion. A modified version of RUSLE model in (Panagos et al. 2015) with 100 m resolution is constructed notation as RUSLE2015. It considers a negative impact on ecosystem services, crop production, drinking water, and carbon stocks. The impact of the Good Agricultural and Environmental Condition (GAEC) requirements of the Common Agricultural Policy (CAP) and the EU’s guidelines for soil protection can be grouped under land management (reduced/no till, plant residues, cover crops) and support practices (contour farming, maintenance of stone walls and grass margins). RUSLE2015 was found to be the most suitable modelling approach for estimating soil loss at the European scale (in terms of validation, usability, replicability, transparency, and parameterisation) which means that can be very useful tool for simulating the effects of land-use changes and land management practices on the rates of soil loss due to water erosion. As is well–known RUSLE method is a computerized version of USLE with revised estimations of its equation factors which can be implemented of graphical processing units (GPU). Such an example is realized in (Sten et al. 2016) where two new parallel algorithms for flow accumulation calculations are presented. Need to note, flow calculations are a key quantity in many surface hydrological simulations as well as in the estimation of the LS factor (RUSLE method). The final implementation shows that the calculation of RUSLE for an area of 12 km × 24 km (72 million cells, one UTM-25 mapsheet) in less than 1 s. when using binary datasets and stored topological sorts. Therefore, the GPU implementation can be used in future as a system for erosion map production in large areas even whole countries.

4.1.7

USPED (Unit Stream Power Erosion Deposition) Model

The USPED is empirical model combines the USLE parameters and upslope contributing area to estimate sediment flow and then erosion and deposition rates are computed as a change in sediment flow in the direction of steepest slope. As shown as in (Mitasova 1996, 1999; Mitasova et al. 1996) the model can predict the spatial distribution of erosion, as well as deposition rates for a steady-state overland flow with uniform rainfall excess conditions. The model was applied to the complex topography of the catchment in order to obtain quantitative information

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on the processes of soil detachment and sediment deposition. Model spatial pattern estimates are similar to those of the RUSLE-3D model, showing consistently that erosion prediction is controlled by relief and land cover. The model predicts satisfactorily the occurrence of erosion in areas where currently acute processes of erosion are taking place. However, the model fails to predict high erosion rates for areas where all arable land has already been long eroded.

4.1.8

Zheng’s Model

Zheng’s soil erosion model is described in (Zheng et al. 2008). It is an empirical model that develop a proportional function for event sediment yield prediction through analyzing the field observations at 12 small catchments over the Loess Plateau as well as updated through deriving the regression coefficient from the power function. The model can be effectively used as a management tool for the vast majority of the annual sediment yield. In order to predict event sediment yield is proposed a proportional function. The performance of the model is good for high-magnitude events, especially extreme events.

4.1.9

SYI (Sediment Yield Index) Model

The SYI (Bali and Karale 1977) is an empirical model defined as the Yield per unit area and its value for hydrologic unit is obtained by taking the weighted arithmetic mean over the entire area of the hydrologic unit by using suitable empirical equation. It considering sedimentation as product of erosivity, erodibility and areal extent was conceptualized in All India Soil and Land Use Survey (AISLUS) as early as 1969 and has been in operational use since then to meet the requirements of prioritization of smaller hydrologic units. The erosivity determinants are the climatic factors and soil and land attributes that have direct or reciprocal bearing on the unit of the detached soil material. The SYI model is developed by AISLUS in order to identify critical hydrological units over a large basin, was tested in a drainage basin of the Western Ghats mountainous zone which receives heavy rainfall. It is a well-known means of providing criteria for priority delineation in river valley projects and flood-prone rivers (AISLUS 1991). The SYI method is highly useful for prioritization of microwatersheds according to erosion impact. The erosivity is simulated with the sediment yield weightage value which is based on the assessment of the composite effect of assemblage of erosivity determinants. SYI =

n  Ai × Wi × DL i=1

Aw

× 100, for i= 1, 2, 3, · · · n,

where, Ai —area of the erosion intensity ith unit (EIMU), Wi —weightage value of the erosion intensity ith mapping unit, n—no. of mapping units, DL—delivery

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ratio, and AW – total area of sub-watershed. The range in SYI values defined by the specific characteristics of the individual basin is divided into 6 classes—very high, high, medium, low, and very low. In (Agegnehu et al. 2020) the two different empirical (RUSLE and SYI) models are compared and assessed the impact of rainfall-induced soil erosion and prioritize the sub-watersheds. Results show that two models of sub-watersheds have similar average soil loss at higher elevations. In this research are observed a strong antagonistic relationship between the elevation/slope zones and the mean sediment loss in both models. In (Gajbhiye et al. 2015) is shown a relationship between Soil Conservation Service Curve Number (SCS-CN) and SYI for the Narmada watersheds (Madhya Pradesh). The runoff curve number (CN) included as input for model development is in the interval CN = [0–100], but practically is used in CN = [40–98]. For the study area, the SYI model predicts the need to adopt the suitable soil conservation measure in the study watershed for minimizing soil erosion. The simplified AISLUS model provides available catchment parameters as shows a good match with SYI model. In (Naqvi et al. 2015) the SYI method was used to calculate soil loss in micro-watershed. The values and thematic layers were integrated as per the model as well as calculate of minimum and maximum sediment yield values and classified into four priority zones according to their composite scores. Due to the important assessments if the model needs immediate attention for the study area and its conservation.

4.1.10

G2 Model

G2 is a soil erosion model for developing monthly erosion maps at regional scale was introduced by a team from Aristotle University of Thessaloniki and DG JRC within GEOLAND 2 project is included in JRC (G2 2020). The G2 model proposes innovative techniques for the estimation of vegetation and protection factors. It is a complete, an empirical model for soil erosion rates and has evolved with time into a quantitative model mapping soil loss and sediment yield on month-time intervals, designed to run in a GIS environment. A detailed description in (Karydas and Panagos 2018) is presented where the model adopts fundamental equations from RUSLE and the Erosion Potential Method (EPM). It aimed at developing an equation to estimate the water erosion and to put up a service for regional soil erosion monitoring across Europe (Karydas et al. 2012; Panagos et al. 2014a, 2015). The G2 model clearly shows the critical seasons, hotspots and land uses which are more susceptible to erosion as the model is described by the following formula (Panagos et al. 2014b): E=

T R ×S × V l

The Dynamic factors: • E—The predicted soil amount removed from an area during a specific time period (t/ha−1 ),

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• R—The rainfall-runoff erosivity factor (modified from USLE by G2), which quantifies the impact of raindrop and runoff energy, • V—The vegetation retention factor (dimensionless, and analogous to the USLE’s C-factor), which represents the effects of all interrelated cover and management variables (developed by G2). The Static factors: • S—The soil erodibility factor (modified from USLE by JRC, 2000–5), identical to the USLE’s K-factor, which reflects the ease of soil detachment by raindrop splash or surface flow; • T—The topography factor (dimensionless and analogous to the LS-factor of the USLE), which expresses the effect of slope length and slope gradient (USLE modifications, 1996); • I—Interception of slope length (developed by G2). The formula was uptake by the community and was adopted for the whole territory of Greece. Develop and revise of the model as an effective decision-making tool is considered in (Karydas and Panagos 2018) for study of areas in the South-East Europe and Cyprus as in most cases (with exception of two cases—Crete and Cyprus), adequate field data were not available for comprehensive accuracy. The month-time step assessments improve understanding of erosion processes, especially in relation to land uses and climate change. The results from the use of G2 models show realistic features of flow conditions and consistent flow patterns. In (Halecki et al. 2018) the G2 model has been verified for the first time under Polish conditions. It was used to investigate soil erosion assessment for the monthly data in the Outer Western Carpathians, in the southern region of Małopolska Province, Poland. The result shows that the model can assess soil loss in cropland and forest-dominated land as well as in a steep-sloped agricultural basin with a variable hydrological regime. The assessing total soil eroded mass over monthly intervals enable the determination of the influence of plant growing stages on the V parameter. Therefore, due to the use of various plant stages the model is an important tool for soil and water conservation. In (Karydas et al. 2020) the model is used for the study of the Candelaro river basin in Apulia region (Italy) with Sentinel2 image scenes for the first time for erosion assessments. Sentinel-2 is a ready-to-use, image product of high quality, freely available by the European Space Agency. The results show that G2 model is a rapid, robust, and flexible mapping tool as shows an appropriate solution for erosion risk assessments in the whole Apulia region. Needed to note by using the G2 model are achieved to reveal the specific contribution of every land cover to soil loss as well as the seasonal changes of rain intensity and vegetation cover.

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4.2 Conceptual Models A catchment is represented as a series of internal storages without including the specific details of process interactions. Parameters of conceptual models have limited physical interpretability which leads to its intermediate role between empirical and physically models (Sorooshian 1991). Conceptual models can indicate the effects of land-use changes without requiring large amounts of spatially and temporally distributed input data.

4.2.1

Thornes Model

It is a conceptual erosion model (Ali and De Boer 2010) that contains a hydrological component based on a runoff storage type analogy, a sediment transport component, and a vegetation cover component. It is based on square grid cells that starts with an exponential frequency distribution within a specified time period. It is a geomorphic model that combines runoff rate, soil erodibility, the effects of topography, and vegetation protection in a simple physical equation. The Thornes erosion equation for each cell reads (Ali and De Boer 2010): Ei =

kROi2 s1.67 e0.07cc

where, Ei is the erosion rate (mm month−1 ), k—the soil erodibility coefficient, s—the slope (mm−1 ), and ci —the fraction of vegetation cover (%), ROi =

Pi rc−Si

e Pi /Di is the surface runoff (mm), Pi —the total precipitation (mm), rc– the potential water storage capacity (mm), Si is the total initial soil moisture (mm), Di is the number of precipitation days, and i = 1:12 is the time period. In (Zhang et al. 2002) the model is used for predicting global erosion rates which show that the model structure is suitable for predicting potential erosion rates at daily, monthly, and annual time scales. In (Thornes 1985, 1990) the model analyzes the competition between vegetation growth and soil erosion is constructed by combining sediment transport and vegetation protection. It contains a hydrological component based on a storage type analogy, a sediment transport component and a vegetation growth component.

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FLEX–Topo

In (Savenije 2010) a modelling approach rather than a new conceptual model structure is proposed for conceptual modelling approach enables us to design different model structures which are based on landscape units including plateau, hillslope and wetland with separate conceptualization of water balance, and constitutive equations. It is FLEX-Topo structure dramatically reducing the need for calibration by use of hydrological landscape analysis. The model uses a new topographical indicator Height Above the Nearest Drainage (HAND) as a topographic index to distinguish between landscape-related runoff processes which can be different for every climate, ecosystem, land-use system and morphological setting. The author opinion shares the fact that the elements in FLEX-Topo structure are not connected which may be one of the key raisons the model structure to be conceptually better. Figure 1 shows the three sub-systems—wetland, hillslope, and plateau as well as the limits where details they conceptual model structure that should reflect the structure of these sub-systems in the real world. It is a simple conceptual approach to hydrological modelling, where the topography linked to geology, geomorphology, soil, land use, ecosystems, climate, is uses for classification. The main advantage of an approach is its maximum simplicity, which including the observable landscape characteristics. In (Gao et al. 2014) FLEX-based approach is used for testing of four models— FLEXL , FLEXD , FLEXT0 , and FLEXT where the study area is a tailor-made hydrological model for a cold, large river basin in north-west China. The model FLEXL is lumped, the FLEXT0 and FLEXD models are semi-distributed, and FLEXT model is with the structure and parameterization as FLEXT0 . The results from the four models show that FLEXT0 and FLEXT models are better spatially transferable than others, as well as a better transferability was performed by using FLEXT . For represent the heterogeneity of hydrological functions more appropriate are FLEXT0 and FLEXT models. It means that they have a more realistic model structure and parameterization. Fig. 1 FLEX-Topo model concept; the thee sub-systems—wetland, hillslope, and plateau and its limits (after Savenije 2010)

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4.2.3

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MMF (Morgan-Morgan-Finney) Model

The MMF model developed in (Morgan et al. 1984) is a process-based (physically based model), spatially-distributed consisting of the following two phases: • A water phase—runoff volume is estimated as an exponential function of rainfall, with a consideration of vegetation interception, topography, soil water storage and routing. • A sediment phase—the model estimates rainfall splash via rainfall energy and interception and transport capacity of runoff based on runoff volume, slope gradient and crop management. The basis for development of physically-based (or process-based) soil loss model is the mass balance differential equation which in 1D a hill slope profile is: d (cq) d (ch) + +S =0 dx dt where the variables are: c—Sediment concentration (kg m−3 ), q—Unit discharge of runoff (m2 s−1 ), h—Depth of flow (m), x—Distance in the direction of flow (m), t—Time (s), S—Source/sink term for sediment generation [kg m−2 s−1 ]. Annual sediment yield is determined as the lesser of the amount of soil-particle detachment and transport capacity. The model predicts annual soil loss from hillslopes. The obtained results in (Morgan et al. 1984) are with respect to examine the effects of shifting cultivation on soil erosion in a tropical rainfall forest from Malaysia show that, except for very low and very high rates of erosion, realistic predictions over a wide range of conditions are founds. The model there is some improvements included in its revised versions (De Jong 1994; Morgan 2001; Morgan and Duzant 2008). As know well Earth observation and GIS technology enable us to extract information from satellite images and from digital elevation models (DEM) and to process vast amounts of data. This gives a possibility to be construction in (De Jong et al. 1999) modified MMF method using a cumulative sediment transport capacity algorithm, remote sensing imagery and DEM. The model estimates annual soil loss by evaluating both rainfall soil detachment and sediment transport over the soil surface. The GIS and MMF model is applied in (Tesfahunegn et al. 2014) for estimating soil erosion in the Mai-Negus catchment, northern Ethiopia where the input data include climate, topography, land use, and soil data. The MMF modelling processes erosion in two phases (Morgan et al. 1984):

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• The water phase—The water phase mainly comprises of prediction of soil detachment by rain splash as requires data for intensity of rainfall, number of rainy days, and average annual rainfall. • Sediment phase—soil erosion result from the detachment of soil particles from the soil mass by raindrop impact and the transport of those particles by overland flow. The model estimated soil loss at catchment level was compared with the surveybased measured sediment yield from the reservoir located at the outlet of the catchment. Since the results from MMF model shows a lower rate of erosion for the soil transport capacity of over land flow it follows that erosion is transport limited. Also is important to note integrate GIS is a useful tool to manage spatially distributed hydrophysical data while assessing the spatial distribution of erosion.

4.3 Physically-Based Models Physical models are based on fundamental physical equations and their solutions describe sediment and stream flows in a catchment. The models represent controlling erosion and sediment yield as well as physical characteristics, topography, geology, land use, climate, plant growth and river flow characteristics. Advantages of physically-based models in comparison with empirical/conceptual models. • more accurate extrapolation to different land use; • more correct representation of erosion/deposition processes; • application to more complex conditions including spatially varying soil properties and surface characteristics; • More accurate estimation of erosion/deposition and sediment yield on a single storm event basis. Disadvantages are such as large data requirement, lack of user-friendliness, unclear guidelines for conditions of their applicability, improper measure of reliability and lack of expression of their limitations.

4.3.1

SWAT (Soil and Water Assessment Tool) Model

It is a river basin, or watershed, process-based scale model developed by the United States Department of Agriculture–Agricultural Research Service (USDA–ARS) (Arnold et al. 1998; Srinivasan et al. 2004) to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time. It requires specific information about weather, soil properties, topography,

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vegetation, and land management practices. It is a physically-based model to estimate runoff, nutrient losses, chemical and sediment transport within the watershed scale for daily time step. SWAT is a continuous-time, physically-based hydrological model based of the soil water balance equation (Landsberg and Sands 2011) calculated as the month is divided into dR equal periods with an amount dRR rain falling as a single event at the beginning of each period, where R is the total monthly rainfall and dR the number of rainy days, and the water balance is performed separately over each period. The hydrologic routines within SWAT model account for vadose zone processes (i.e., infiltration, evaporation, plant uptake, lateral flows, and percolation), and ground water flows. Sediment transport is a function of deposition and degradation, which are determined through comparing the sediment concentration and maximum sediment concentration. But the method does not account for rejected recharge. In (Neitsch et al. 2009) is shown that the method is computationally efficient and enables users to study long-term impacts as simulation of very large basins, a variety of management strategies can be performed without excessive investment of time or money. The model is not designed to simulate detailed, single-event flood routing. Together with satellite remote sensing and GIS this model in (Mosbahi et al. 2013) show that can be very useful tools to estimate surface runoff, soil erosion, predict surface runoff generation patterns and soil erosion hazard. Also, the method is effective for identifying and prioritizing vulnerable sub-catchments. The SWAT model includes presently a variety of parameters for which there is no information available. In order to focus on the most significant ones, a systematic sensitivity analysis is needed. In (Karki et al. 2020) is considered in details the methods used for field-scale SWAT modelling and discusses the limitations and advantages.

4.3.2

QSWAT (Quantum Soil and Water Assessment Tool) Model

The QSWAT is process-based method an implementation of the SWAT in Quantum GIS (QGIS). It is an improved version of SWAT model developed in (Dile et al. 2016) as open-source software set up with Python uses various functionalities of QGIS and is installed as its plugin. QGIS is a free and open-source desktop GIS application that provides data viewing, editing and analysis capabilities. The model was input with various watershed parameters as spatial variation of topographic features along with weather parameters. QSWAT has the capability to visualize results. It can help visualize static data, and it can animate the results at model simulated time steps. It demonstrates successfully with Gumera watershed located in Lake Tana basin, tropical highland region of Ethiopia. QSWAT model has improved the processing capabilities of the SWAT and lesser processing time on Digital Elevation Models (DEMs), and better statistical and dynamical representation of outputs.

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DYRIM (Digital Yellow River Model)

DYRIM is created in (Wang et al. 2007) to be a framework to simulate the dynamic erosion, transport, and sedimentation processes at a range of spatial and temporal scales for the Yellow River. This is a process-based, physically-based spatiallydistributed and continuous model for land management and to predict soil erosion and sediment transport for Loess Plateau catchments. The sediment yield equation is derived by simple assumptions where sediment discharge would increase nonlinearly with the slope length, leading to unrealistic results as the spatial scale increases. It was used successfully to simulate the huge soil loss (Guo et al. 2015). Some improvements of the model are shown in (Guo et al. 2015) as well as the parallelization to improve its computational efficiency and parallel algorithms have been implemented on high-performance clusters in (Li et al. 2011; Wang et al. 2013; Wu et al. 2013).

4.3.4

EPIC (Erosion Productivity Impact Calculator) Model

The EPIC model (Sharply and Williams 1990; Williams et al. 1984; Williams 1990) was originally developed to determine the relationship between soil erosion and soil productivity throughout the U.S. The EPIC is a physically-based model which components include weather simulation, hydrology, erosion-sedimentation, nutrient cycling, plant growth, tillage, soil temperature, economics, and plant environment control. The EPIC model is operational and has produced reasonable results under a variety of climatic conditions, soil characteristics, and management practices. It has also demonstrated sensitivity to erosion in terms of reduced crop production. It was used for that purpose as part of the 1985 RCA (1977 Soil and Water Resources Conservation Act of USA) analysis. To simulate rainfall/runoff erosion, EPIC contains six equations—the USLE, the Onstad-Foster modification of the USLE, the MUSLE, two recently developed variations of MUSLE, and a MUSLE structure that accepts input coefficients. The approach in EPIC estimates potential wind erosion for a smooth bare soil by integrating the erosion equation through a day using the wind speed distribution.

4.3.5

LISEM (Limburg Soil Erosion) Model

The Limburg Soil Erosion Model (LISEM) (De Roo et al. 1996; De Roo and Jetten 1999) is a process-based and a distributed physically-based hydrological and soil erosion model developed for planning and conservation purposes. LISEM incorporates a number of different processes, including rainfall interception, surface storage in micro-depression, infiltration, vertical water movement through the soil, overland flow, channel flow, detachment by overland flow and transport capacity of flow. LISEM does not simulate concentrated erosion in rills and gullies; rather it simulates flow detachment only in the ponded area. This can be seen as intermediate

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between sheet and rill erosion. Processes describing sediment detachment by rainfall, throughfall and overland flow are included, in addition to the transport capacity of the flow.

4.3.6

ANSWERS (Aerial Non-point Source Watershed Environment Response Simulation)

The ANSWERS model developed in (Beasley et al. 1980) presented one of the first operational, fully spatially distributed, catchment erosion and sediment yield models. It is a physically-based model that simulates excess rainfall, the orientation of laminar and rill flow, subsurface drainage, and the removal and transport of sediment at the watershed scale, applicable to basins with an area of up to 100 km2 . The model is limited in the size of watershed it can deal with. The ANSWERS model consists of a water erosion and sediment transport model described in (Beasley et al. 1980) as: • Hydrologic. The elemental size is defined such that the pertinent hydrologic and erosional variables can be assumed to be uniform within the watershed element where conditions may vary greatly from one element to the next. A typical cross-sectional view of a small area or element within a watershed is depicted in Fig. 2. • Erosion. Soil detachment, transport, and deposition are closely related to the concurrent hydrologic processes occurring in a watershed. Detachment and transport can be accomplished by either raindrop impact or overland flow. However, the small amount of sediment transported from a field by raindrop impact was neglected. The processes described in the erosion model are shown in Fig. 3. The ANSWERS program is a comprehensive model intended to be used in quantitatively evaluating nonpoint source pollution in an ungaged watershed and in determining the relative effectiveness of alternative corrective plans. The distributed analysis also provides a characterization of the hydrologic response as well as erosion and deposition occurring throughout the watershed during a storm event. The disadvantage of the model is that it requires a large amount of input data and analyze results. Fig. 2 Profile of a watershed element (after Beasley et al. 1980)

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Fig. 3 Sediment detachment and transport (after Beasley et al. 1980)

4.3.7

CREAMS (Chemicals, Runoff and Erosion from Agricultural Management Systems)

CREAMS model in (Knisel 1985) is a physically-based and a field-scale (less than 5 ha in size) model that uses various aspects of the USLE equation, that predicts runoff, erosion and chemical transport from agricultural areas operating as well as in single storm events as in a long-term average mode. The model includes four components: hydrology, erosion, plant nutrients, and pesticides. The erosion component of CREAMS simulates detachment of soil particles and subsequent transport of these particles. Hydrologic inputs include rainfall depth, rainfall erosivity, runoff volume, and peak runoff rate. Soil–water content is calculated from a water balance that includes evapotranspiration, percolation, seepage below the root zone, and snowmelt.

4.3.8

PESERA (Pan-European Soil Erosion Risk Assessment) Model

The model has been developed from earlier models (Kirkby and Neale 1987; Kirkby and Cox 1995) based conceptual separation of precipitation into over-land runoff generation and infiltration, with a runoff threshold depending primarily on soil and vegetation properties. It is a physically-based, spatially distributed, long-term coarsescale soil erosion risk model across Europe at a spatial resolution of 1000 m in (Kirkby et al. 2008) and even 250 m in (Berberoglu et al. 2020). It combines the impacts of soil, climate, vegetation and topography and soil erosion. It is a useful tool for soil and water conservation and informing ecological restoration. The model explicitly describes the processes of hydrology, vegetation growth, erosion and their interactions and therefore has a robust theoretical basis. It has been widely applied and validated across Europe and is currently used by planners and policy makers to determine funding decisions for soil erosion protection measures on farms. The model also assists in understanding the links between different factors causing erosion as well as scenario analysis for different land use and climate. The model considering 1D hydrological balance that partitions precipitation between evapotranspiration, overland flow, subsurface flow and groundwater recharge. It has been designed to produce state-of-the-art soil risk evaluation at a European scale where soil erosion is

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estimated as the amount of sediment carried to the base of a hillside land delivered to the channel network. The model used with data for Turkey area in (Berberoglu et al. 2020) indicated that climate change increase soil erosion due to the changes in the precipitation regime and temperature rise. However, biomass increase as a result of temperature increase and precipitation in some regions will decrease soil erosion. Modelling soil erosion in the long term will enhance our understanding of the spatial variation of soil erosion to device soil conservation schemes. In (Karamesouti et al. 2016) is shown predicting post-fire soil erosion loss where RUSLE model predicted higher soil losses than PESERA probably because only considers the sediment transport processes. The model shows in (Tsara et al. 2005) a clear and explicit dependence on vegetative factors and to the specific rainfall regimes within Zakynthos, Greece, as is used for a regional diagnostic tool under a range of soil, topographic and climatic conditions for identifying the best land-use type and vegetation cover to protect hilly areas from soil erosion. Comparison between the data obtained from the PESERA model and the measured values in the various soil erosion plots generally showed a satisfactory performance by the model. In (Licciardello et al. 2009) is evaluate the sediment transport model to predicted monthly erosion rates were also calculated using observed values of runoff and vegetation cover instead of simulated values. By using the multistep approach PESERA model shows promise to predict annual average spatial variability quite well. The results show the model allows prediction of the average annual spatial variability of runoff and erosion rates quite well over a range of land-use/land management systems in two contrasting climates. The advantage of the PESERA modelling framework is that it describes runoff and sediment transport characteristics and allows a more realistic incorporation of effects of global change. Therefore this model can be used to predict runoff and erosion with reasonable accuracy based on limited input data.

4.3.9

WEPS (Wind Erosion Prediction System) Model

Soil erosion by wind is initiated when wind speed exceeds the saltation threshold velocity for a given field condition. After initiation, the duration and severity of an erosion event depend on the wind speed distribution and the evolution of the surface condition. Because WEPS is a continuous, daily, time-step model, it simulates not only the basic wind erosion processes, but also the processes that modify a soil’s susceptibility to wind erosion as assumes a flat topographical field surface. By changing the field input parameters for a specific field of interest can be compared various alternatives to control soil loss by wind. The WEPS is a computer-based model developed to provide an accurate, universal, and simple tool for simulating soil wind erosion. The strongest benefit of WEPS is its ability to provide producers system to apply different “what-if ” management scenarios to the land for developing and evaluating alternative wind erosion control practices. The WEPS is a process-based (USDA-ARS 1995; Wagner 2013; Maurer

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and Gerke 2011; Funk et al. 2004; Coen et al. 2004), daily time-step computer model that predicts soil erosion by simulation of the fundamental processes controlling wind erosion. WEPS represents new technology in wind erosion and is not merely an improvement in Wind Erosion Equation. It is designed for conservation planning and application providing the user with many elements of wind erosion including soil movement, estimated plant damage, and PM-10 emissions. WEPS replaces the predominately empirical Wind Erosion Equation (WEQ) (Woodruff and Siddoway 1965) as the wind erosion prediction tool and can predict average erosion along linetransects across the field. WEQ predicts average erosion along line-transects across the field, whereas WEPS treats the field as two-dimensional. The WEPS EROSION submodel simulates soil loss/deposition at grid points over the entire simulation region. WEPS model and calculates soil loss on a daily basis and analyze the output data to determine the times of the year when conservation treatments may be needed. WEPS model is coded in FORTRAN in (Carey et al. 1989) and last version 2012.8 was released on October 3, 2012 (Wang et al. 2010). In (Hagen 2007) is presented some updated equations in the model which also enable direct simulation of field-scale fetch effects, and the durability of protective clods, crusts, and surface roughness. Estimating erosion effects on surface immobile elements as well as the buildup of mobile soil on downwind surfaces at the end of storms allow right and fast decision-making solutions.

4.3.10

WEPP (Water Erosion Prediction Project) Model

It is a process-based, spatially-distributed model. Compared with the USLE method (Flanagan et al. 2007) which was used extensively to predict long-term average annual soil loss, it was a mature technology only applicable to detaching regions of a hillslope, and could not estimate sediment deposition or sediment delivery from fields to off-site channels or streams. Also USLE method no capabilities to estimate runoff, spatial locations of soil loss on a hillslope profile or within a small watershed, channel erosion, effects of impoundments, recurrence probabilities of erosion events, or watershed sediment yield. Developed to address all of these needs leads to the WEPP (Nearing et al. 1989) as serve empirically-based erosion prediction technologies like USLE as well as software development including GUI and integration of WEPP with GIS software. It is a physically-based model with distributed parameters that can be used in either a single event or continuous time scale and calculates erosion from rills and inter-rills, assuming that detachment and deposition rates in rills are a function of the transport capacity. A more process-based model of rainfall erosion than RUSLE2 is WEPP presented in (Tiwari et al. 2000). It is used to predict soil loss on an event basis using rainfall events that are generated stochastically over many years by a CLIGEN model (Nicks et al. 1995) but does not take account of gully erosion. Given that rainfall is not distributed evenly through time, it can be argued that the event soil loss predicted by WEPP is more realistic than predicted by RUSLE2. CLIGEN can be used as

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a weather generator for RUSLE2 to in order to predict long-term erosion under conditions where rainfalls do not occur at regular intervals during the calendar year. It is used for runoff and erosion modelling at a field or small catchment-scale (