River Conservation and Water Resource Management 9819926041, 9789819926046

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Advances in Geographical and Environmental Sciences

Praveen Kumar Rai   Editor

River Conservation and Water Resource Management

Advances in Geographical and Environmental Sciences Series Editors Yukio Himiyama, Hokkaido University of Education, Asahikawa, Hokkaido, Japan Subhash Anand, Department of Geography, University of Delhi, Delhi, India

Advances in Geographical and Environmental Sciences synthesizes series diagnostigation and prognostication of earth environment, incorporating challenging interactive areas within ecological envelope of geosphere, biosphere, hydrosphere, atmosphere and cryosphere. It deals with land use land cover change (LUCC), urbanization, energy flux, land-ocean fluxes, climate, food security, ecohydrology, biodiversity, natural hazards and disasters, human health and their mutual interaction and feedback mechanism in order to contribute towards sustainable future. The geosciences methods range from traditional field techniques and conventional data collection, use of remote sensing and geographical information system, computer aided technique to advance geostatistical and dynamic modeling. The series integrate past, present and future of geospheric attributes incorporating biophysical and human dimensions in spatio-temporal perspectives. The geosciences, encompassing land-ocean-atmosphere interaction is considered as a vital component in the context of environmental issues, especially in observation and prediction of air and water pollution, global warming and urban heat islands. It is important to communicate the advances in geosciences to increase resilience of society through capacity building for mitigating the impact of natural hazards and disasters. Sustainability of human society depends strongly on the earth environment, and thus the development of geosciences is critical for a better understanding of our living environment, and its sustainable development. Geoscience also has the responsibility to not confine itself to addressing current problems but it is also developing a framework to address future issues. In order to build a ‘Future Earth Model’ for understanding and predicting the functioning of the whole climatic system, collaboration of experts in the traditional earth disciplines as well as in ecology, information technology, instrumentation and complex system is essential, through initiatives from human geoscientists. Thus human geoscience is emerging as key policy science for contributing towards sustainability/survivality science together with future earth initiative. Advances in Geographical and Environmental Sciences series publishes books that contain novel approaches in tackling issues of human geoscience in its broadest sense — books in the series should focus on true progress in a particular area or region. The series includes monographs and edited volumes without any limitations in the page numbers.

Praveen Kumar Rai Editor

River Conservation and Water Resource Management

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

ISSN 2198-3542 ISSN 2198-3550 (electronic) Advances in Geographical and Environmental Sciences ISBN 978-981-99-2604-6 ISBN 978-981-99-2605-3 (eBook) https://doi.org/10.1007/978-981-99-2605-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

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Land Use Land Cover Changes and Climate Change Impact on the Water Resources: A Study of Uttarakhand State . . . . . . . . . . . Ashish Mani, Deepali Bansal, Maya Kumari, and Deepak Kumar

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Remote Sensing Monitoring of Water Productivity in Agricultural Crops: A Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Chanev, I. Kamenova, and L. Filchev

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Assessment of Groundwater Quality in South Karanpura Coalfield Region, Jharkhand, India Using WQI and Geospatial Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akshay Kumar, Varun Narayan Mishra, Rahul Ratnam, Chaitanya B. Pande, and Akhouri Pramod Krishna

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Application of Wastewater in Agriculture: Benefits and Detriments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Akanksha Verma, Anshu Gupta, and Paulraj Rajamani

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A GIS-Based Flood Risk Assessment and Mapping Using Morphometric Analysis in the Kayadhu River Basin, Maharashtra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bhagwan B. Ghute and Pranjit Sarma Hydro-Chemical Characterization and Geospatial Analysis of Groundwater for Drinking and Agriculture Usage in Bagh River Basin, Central India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nanabhau S. Kudnar, Varun Narayan Mishra, and M. Rajashekhar

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A Comprehensive Review on the Impact of Climate Change on Streamflow: Current Status and Perspectives . . . . . . . . . . . . . . . . . 117 David DurjoyLal Soren, Jonmenjoy Barman, and Brototi Biswas

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Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya Using RUSLE and Geospatial Techniques . . . . . . . . . . . . . . 151 Jonmenjoy Barman and Brototi Biswas

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Hydro-Geological Investigation and Groundwater Resource Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Kuldeep Pareta

10 Myths, Architecture, and Rites: The Concept of Conservation of the Tri Danu Area in Bali in the Contemporary Struggle . . . . . . . . 201 I Putu Gede Suyoga, Ni Ketut Ayu Juliasih, and Mira Sartika 11 Impact of Land Use and Land Cover in Water Resources . . . . . . . . . 217 Deeksha, Anoop Kumar Shukla, and Nandineni Rama Devi 12 An Assessment and Management of Ecotourism Based on Water and LULC: A Geospatial Approach of Jodhpur, Rajasthan, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Rajeev Singh Chandel, Praveen Kumar Rai, Shruti Kanga, and Renuka Singh 13 A Spatiotemporal Study of Agriculture in the Chars of Brahmaputra Basin, Dhubri, Assam . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Roli Misra, Ritika Prasad, and Bratati De 14 GIS-Based Novel Ensemble MCDM-AHP Modeling for Flash Flood Susceptibility Mapping of Luni River Basin, Rajasthan . . . . . 267 Mit J. Kotecha, Gaurav Tripathi, Suraj Kumar Singh, Shruti Kanga, Gowhar Meraj, Bhartendu Sajan, and Praveen Kumar Rai 15 Geospatial Modelling for Identification of Ground Water Potential Zones in Luni River Basin, Rajasthan . . . . . . . . . . . . . . . . . . 315 Mit J. Kotecha, Gaurav Tripathi, Suraj Kumar Singh, Shruti Kanga, Bhartendu Sajan, Gowhar Meraj, and Rahul Kumar Misra 16 Hydrological Drought Analysis of Bearma Basin, Madhya Pradesh, India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Satheesh Chothodi, Kundan Parmar, Hemant Patidar, and Rahul Mishra

Chapter 1

Land Use Land Cover Changes and Climate Change Impact on the Water Resources: A Study of Uttarakhand State Ashish Mani , Deepali Bansal, Maya Kumari, and Deepak Kumar

Abstract Rapid changes in India’s Land Use Land Cover (LULC) have accelerated due to economic and industrial development, affecting the water resource availability. This study focused on the LULC changes and climate change in Uttarakhand and its potential impact on the state’s water resources. The wms layer from Bhuvan, NRSC-ISRO of 2005 & 2015 at 50 K scale were used to evaluate the LULC change dynamics in the state. Further, the ALOS DEM data at 30 m resolution was acquired to delineate the major river basins and their drainage in Uttarakhand using Arc Hydro tool of Arc GIS Software. This study indicates that Snow and Glacier Snow area has been reduced to 203.1 km2 during these 10-year periods, which resulted in the increase of the Wetlands/Waterbodies area by 156.92 km2 . Simultaneously, the Agriculture (Fallow) area decreased by 2935.21 km2 , and the Agriculture (Cropland) area increased by 2233.96 km2 . Also, there was a 311.31 km2 increase in the Builtup area. The elevation and slope were derived to understand the topography of the state. It was discovered that the Ganga, Garra, Ramganga, and Yamuna River basins experienced the most alterations as a result of high biotic pressure. This study reveals that climate change is having a detrimental impact on Uttarakhand’s snow-covered terrain. Snow-covered hill slopes facing south and south-east directions are more A. Mani (B) · M. Kumari Amity School of Natural Resources and Sustainable Development (ASNRSD), Gautam Buddha Nagar, Amity University Uttar Pradesh (AUUP), Sector-125, Noida 201313, Uttar Pradesh, India e-mail: [email protected] M. Kumari e-mail: [email protected] A. Mani · D. Bansal Wildlife Institute of India, Post Box #18, Chandrabani, Dehradun 248001, Uttarakhand, India D. Kumar Center of Excellence in Weather & Climate Analytics, Atmospheric Sciences Research Center (ASRC), University at Albany (UAlbany), State University of New York (SUNY), Albany 12226, NY, USA Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Gautam Buddha Nagar, Amity University Uttar Pradesh (AUUP), Uttar Pradesh, Sector-125, Noida 201313, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_1

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susceptible to climate change. Also, the increasing population and built-up area will jeopardise the demand and supply of water in the lower regions and will create stress on the existing water resources. Thus, for the conservation and sustainable development of the region’s water resources, a comprehensive land use policy based on integrated management of land, water, and forest resources must be developed and implemented. Keywords Land Use Land Cover (LULC) · Climate Change · Land Surface Temperature (LST) · Water Resources · GIS and Remote Sensing

1.1 Introduction Climate change and its possible impacts on water resources have become a focus of recent research. Water, the most precious natural resource on the globe, is also closely related to human necessities and requirements (Li et al. 2021) and thus bears environmental and socioeconomic values (Du et al. 2019). These values are likely to be affected to a great extent by climate change (Li et al. 2021). The world’s rapidly growing population and the economy is affecting the distribution of water resources. This critically focuses on preserving the balance between water supply and human demand. Nonetheless, water scarcity is already a major worry in several regions of the world as a result of the impact of climate change (Fedoroff et al. 2010; Hoekstra et al. 2012; Rai et al. 2021, 2022; Mishra et al. 2022). It is, therefore, critical to examine the impact of changing environmental conditions on water resources for humans in order to enable effective and efficient water planning and management for the present and future. Climate change and land use/land cover (LULC) changes are two important aspects of the changing environment that affect water cycles and resources (Vishwakarma et al. 2016; Mishra et al. 2016; Li et al. 2009; Wu et al., 2018; Shastri et al., 2020). Understanding and quantifying the processes of landscape change is required to enable sustainable management of natural resources (Attri et al. 2015). It is also vital to gain a better knowledge of the reasons of land use change in order to implement effective counter-measures. Several studies have focused on understanding the regulating factors and potential impacts of LULC variations on vegetation cover (Kafy et al. 2021), snow cover (Collados-Lara et al. 2019), and built-up area (Mishra and Rai 2014; Naikoo et al. 2020; Rai et al. 2018; Mishra & Rai 2016; Mishra et al. 2018). According to IPCC 2013, anthropogenic and biotic pressure is one of the major factors responsible for the changes in LULC, leading to alterations in Land surface and atmospheric temperature. LULC change is a manifestation of human activity that affects the sustainability of water resources globally (Mohan et al. 2011; Sterling et al. 2013) or locally (Zhao

1 Land Use Land Cover Changes and Climate Change Impact on the Water …

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et al. 2016) by transforming agricultural operations, vegetation coverage, and impervious areas in urbanised regions (Jacobson 2011). Both anthropogenic and natural forces have contributed to this shift in the LULC pattern (Kafy et al. 2021). GIS and Remote Sensing have shown to be quite useful in assessing and analysing changes in land use and land cover. Satellite-based Remote Sensing has revolutionised the study of land use and land cover change due to its capacity to give synoptic information about land use and land cover at a specific time and location. Temporal data on land use and land cover can assist detect regions of change in a region. In this context, India has seen various changes in LULC throughout the years (Nayak & Behera 2008; Rawat et al. 2013; Nayak et al. 2021) as well as its impact on temperature trends (Mohan & Kandya 2015; Mukherjee and Singh 2020) using GIS and Remote Sensing techniques. Mukherjee and Singh 2020 have demonstrated a consistent rise in surface temperatures over two Indian cities due to probable urbanisation from 2008 to 2016. Similar findings have been reported by Naikoo et al. 2020 for the Delhi-NCR. These studies highlight the potential impact of LULC variations on temperature trends throughout Indian regions was not consistent and varied across time and region. As a result, several recent research has focused on understanding the impact of LULC changes on temperature trends across different regions of India in order to monitor climate dangers related with temperature trends over Indian regions for adaptation actions.

1.2 Materials and Method 1.2.1 Study Area Profile The 27th state of India is Uttarakhand. It was carved out of Uttar Pradesh on November 9th, 2000. It has two divisions, the Garhwal division and the Kumaon division and a total of 13 districts. Dehradun is the Capital of Uttarakhand. The state lies between the two biogeographic zone viz., mainly the Himalayas and some portion of the Gangetic Plains. The Northern part of the state is covered by the Greater Himalayas, the Central part is covered by Lesser Himalayas, and the southern part is covered by the Shiwalik. The coordinate of the study area falls between Latitude (from 28°44’N to 31°28’N) and Longitude (from 77°35’E to 81°01’E). The total area of the state is 53,483.01 km2 , with elevations ranging from 172 to 7764 m. The summers are hot and the winters very cold. The yearly temperature varies from − 30 °C in winters to 45 °C in summers. As per the Census 2011, the total human population of the state is 10,086,292, out of which approximately 30% lives in urban areas. The population density is 189 persons per km2 . The study area map is shown in Fig. 1.1.

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Fig. 1.1 The study area map

1.2.2 Description of Data Sets For this Study, the satellite imagery of MODIS/Terra Land Surface Temperature/ Emissivity Daily L3 Global 1 km SIN Grid V006 for the years 2005 and 2015 has been downloaded from the source NASA EarthData (Wan et al. 2015) to calculate the Land Surface Temperature. Formula used here to convert the LST values on the MODIS LST Image DN values to Degree Celsius: Temperature (◦ C): DN ∗ 0.02 The Advanced Land Observing Satellite (ALOS) DEM at 30 m resolution (Takaku et al. 2016; Takaku et al. 2020) has been used topographical mapping. The wms LULC layer of 2005–2006 (NRSC 2006) & 2015–2016 (NRSC 2019) at 50 K scale has been obtained from bhuvan, NRSC-ISRO Geo-portal. The ERDAS Imagine software has been utilised for the image processing and the Arc Hydro tool & Spatial Analysis tools of the Arc GIS Software have been utilised for the drainage network and basin boundary delineation. Further, for the statistical analysis and GIS mapping the ArcMap software is used (Table 1.1).

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Table 1.1 Data type and date source used for present work Sr. Type of data No

Year

Source

1

MODIS/Terra Land Surface Temperature/ 2005 & 2015 Emissivity Daily L3 Global 1 km SIN Grid V006

https://lpdaac.usgs.gov/ products/mod11a1v006/

2

ALOS DEM (30 m) Resolution

https://www.eorc.jaxa. jp/ALOS/en/index_e. htm

3

Bhuvan, NRSC-ISRO Geo-portal (1: 50 K Scale) 2005–2006 & https://bhuvan.nrsc. 2015–2016 gov.in/

2016

1.3 Result and Discussion 1.3.1 Uttarakhand Profile 1.3.1.1

Elevation

The Elevation of the Uttarakhand state is ranging from 172 to 7764 m. The maximum area is having very high elevation. The elevation has a direct relation with the slope of the area. The elevation map is shown in Fig. 1.2.

Fig. 1.2 Uttarakhand elevation map

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Fig. 1.3 Uttarakhand slope map

1.3.1.2

Slope

Slope defines the steepness of the area. Here, the slope is divided into 5 classes as shown in (Fig. 1.3): (< 10°) is very gentle, (> = 10° to < 20°) is gentle, (> = 20° to < 30°) is Moderate, (> = 30° to < 50°) is steep and (>50° to 85°) is very steep. It is visible from the map that most of the area is having very steep to steep slope, also these regions are generally covered with snow. A steep slope implies that the area is having high runoff and less infiltration. The flat or gentle slope terrain possessing fertile alluvial soil along with a high water table offers favourable conditions for prominent crops such as wheat, rice, and sugarcane. The direction of the slope is towards the south and south-east of the snow-covered hills.

1.3.1.3

Major River Basins and Drainage Network

Watershed or drainage basin is an area of land where all surface water flows into a single outlet, such as a river mouth, or flows into another body of water such as the ocean or a lake. From the Arc Hydro tool we have derived the drainage network and the seven prominent river basins of the state Uttarakhand viz. Alakananda, Bhagirathi, Ganga, Garra, Ramganga, Sharda, and Yamuna. The drainage network on the map

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Fig. 1.4 Uttarakhand drainage basin map

shows the connecting relation of small order streams with the higher order streams. The drainage network pattern here is dendritic and parallel depending on the terrain. The Shiwalik and Lesser Himalaya regions of Uttarakhand are still suitably covered with diverse woods that not only catch rainwater, function as a sponge, and continue to release water in streams/rivers during the post-rainy season. The Drainage basin map is shown in Fig. 1.4.

1.3.1.4

Rainfall

Altering rainfall patterns and extreme events have been in focus since twentieth century. Due to climate change, water resources will experience increasing pressure and degradation (Mall et al. 2006). Rainfall having a direct impact on the water resources. Change in the climate or weather causes an irregular pattern in the rainfall. For this study, we have taken a IMD rainfall data of the following years: 2005 and 2015. Figures 1.5a, b and shows the district-wise rainfall pattern of the state. The clear change is visible in the last ten years. The Southern district shows the clear decline in the rainfall (mm) pattern due to climate change. This variability in rainfall pattern may lead to extreme hydrological incidents. Climatic patterns specific to total and average rainfall, number of rainy days, monsoon onset, and intervening

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prolonged dry spells are some of the important aspects that necessitate the collection of long-term data in order to develop an understanding of possible impacts of climate change on people, natural resources, Agro-ecosystems, and the economy.

1.3.2 Impact of Land Surface Temperature (LST) on Water Resources Recent study has focused on changes in land surface temperature. Many studies have discovered a link between LST and patterns of land cover change, with many discovering that the presence of vegetation and water lowered the severity of urban heat island (UHI) but that expanding urbanisation exacerbated the effect (Ogashawara and Bastos 2012). However, these changes in land cover patterns were explored qualitatively in relation to their interaction with the LST. For the present study, the MODIS/Terra Land Surface Temperature/Emissivity data of the year 2005 and 2015 has been used to prepare the maps shown in Fig 1.6. It is clearly visible from Table 1.2 that 16,273.22 km2 area has been increased in the last 10 years of LST class Very High viz. (>35 °C). Simultaneously the 13,301.1 km2 area has beendecreased of LST class Low viz. (> 5 °C to < = 15 °C), which is a matter of concern for future. Increase in LST may deteriorate water quality and availability as warmer flowing water into the streams can put additional stress on the entire aquatic ecosystem (Maimaitiyiming et al. 2014). These modifications in LST will also lead to higher atmospheric and surface temperatures, forming Heat Islands.

1.3.3 Impact of Land Use Land Cover (LULC) on Water Resources Land use is defined in terms of human activity syndromes such as agriculture, forestry, and building construction, which influence land surface processes such as hydrology and biodiversity. Land use refers to the social and economic purposes as well as the context in which lands are maintained or left unmanaged. Land cover is a biological and physical covering of the land’s surface that includes plants, bare soil, water, and/ or artificial buildings. Understanding land use and land cover (LULC) and variations in LULC over a 10-year period is a requirement and critical for conservation planning, especially in the context of human-induced changes to the terrestrial surface (Fig. 1.7). The LULC changes have been shown to have direct and indirect impacts on the various aspects of the environment (Patra et al. 2018). The most pressing issue on the planet is rising surface temperatures caused by the conversion of vegetated surfaces

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Fig. 1.5 Uttarakhand rainfall a 2005 map b 2015 map

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Fig. 1.6 Uttarakhand LST a 2005 map b 2015 map

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Table 1.2 Land Surface Temperature (LST) area change statistics Sr. No Land Surface Temperature Class 1

< = 5 °C (Very Low)

2

> 5 °C to < = 15 °C (Low)

3

Year 2005 Area in km2

Year 2015 Area in km2

Area Change in km2

5668.71

6163.28

17,693.03

4391.92

−13,301.1

> 15 °C to < = 25 °C (Medium)

9132.44

17,784.04

8651.6

4

> 25 °C to < = 35 °C (High)

18,146.75

6028.47

−12,118.3

5

> 35 °C (Very High)

Total Area

2842.08 53,483.01

19,115.3

494.57

16,273.22

53,483.01

to impermeable surfaces (Mallick et al. 2008), as well as the conversion of fallow lands and wetlands into agricultural cropland (Pal & Akoma 2009). These changes affect the solar radiation absorption, surface temperature, albedo effect, evaporation rates. This can significantly change the conditions of the near-surface atmosphere over cities (Mallick et al. 2008) and play an essential role in many environmental processes (Weng et al. 2004; Pal and Ziaul 2017). In this study, the Snow and Glacier Snow area has been reduced to 203.1 km2 during these 10-year periods shown in Table 1.3, subsequently increasing the Wetlands/Waterbodies area by 156.92 km2 . The availability of water directly affected the agricultural activity of the state. This led to (a) a Decrease in Agriculture fallow land area by 2935.21 km2 , and (b) an increase in Agriculture (Cropland) area by 2233.96 km2 . However, with an increase in agricultural lands, the built-up area also increased by 311.31 km2 during the study duration. The result shows that the LULC pattern of Uttarakhand has changed significantly during the study period. The maximum change is observed from the agricultural fallow and croplands, followed by waterbodies, snow cover, and built-up.

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Fig. 1.7 Uttarakhand LULC a 2005–2006 map b 2015–2016 map

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Table 1.3 LULC area change statistics Sr. No

1

2

3

4

Area in km2

LULC Classes

Area Change km2

Primary classes

Secondary classes

2005–2006

Agriculture

Crop land

7445.69

9679.65

2233.96

Fallow

3859.25

924.04

−2935.21

Plantation

224.83

175.21

−49.62

Barren rocky

3848.17

3236.36

−611.81

Gullied Ravinous

0

0.06

0.06

Sandy area

8.59

Scrub land

438.73

Mining

2.21

Rural

202.98

211.81

Urban

134.18

412.58

Deciduous

7628.64

6277.77

−1350.87

Evergreen/Semi Evergreen

15,801.3

16,159.78

358.48

Forest Plantation

792.97

792.18

−0.79

Scrub Forest

1115.62

2088.55

972.93

Swamp/Mangrove

0

7.17

7.17 324.74

Barren/ unculturable/ Wastelands

Built-up

Forest

2015–2016

49.25 1183.9 26.29

5

Grass/Grazing Grass/Grazing Grass/Grazing land

3479.54

3804.28

6

Snow and Glacier

Snow and Glacier Snow

7430.84

7227.74

7

Wetlands/ Waterbodies

River/Stream/ Canals

900.53

1030.5

Reservoir/Lakes/ Ponds

168.94

Total Area

53,483.01

195.89

40.66 745.17 24.08 8.83 278.4

−203.1 129.97 26.95

53,483.01

1.4 Conclusion This study explored the potential impacts of land use/land cover changes and alterating temperature on water resources in Uttarakhand, India using Remote Sensing and GIS tools. Overall analysis and interpretation inferred an increase in Land surface temperature (°C) to a great extent from 2005 to 2015 with simultaneous decrease in rainfall (mm). This resulted in decreasing the Snow cover significantly, creating pressure on the waterbodies. In turn, agricultural practices and thus the dependence of economy on agriculture got increased as evident from LULC change. On the other hand, built-up area increased during this period, creating significant biotic pressure on the natural resources. Our study highlights the importance of Remote Sensing and GIS in assessing the impacts of climate change on water resources in

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near future. Similar studies are crucial in understanding the temperature dynamics of the ecosystem for a better understanding of the biological processes taking place.

References Attri P, Chaudhry S, Sharma S (2015) Remote sensing & GIS based approaches for LULC change detection–a review. Int J Curr Eng Technol 5:3126–3137 Collados-Lara AJ, Pardo-Igúzquiza E, Pulido-Velazquez D (2019) A distributed cellular automata model to simulate potential future impacts of climate change on snow cover area. Adv Water Resour 124:106–119 Du J, Jia Y, Hao C, Qiu Y, Niu C, Liu H (2019) Temporal and spatial changes of blue water and green water in the Taihang Mountain Region, China, in the past 60 years. Hydrol Sci J 64(16):2040–2056 Fedoroff NV, Battisti DS, Beachy RN, Cooper PJ, Fischhoff DA, Hodges CN, Zhu JK (2010) Radically rethinking agriculture for the 21st century. Science 327(5967):833–834 Hoekstra AY, Mekonnen MM, Chapagain AK, Mathews RE, Richter BD (2012) Global monthly water scarcity: blue water footprints versus blue water availability. PLoS ONE 7(2):e32688 Jacobson CR (2011) Identification and quantification of the hydrological impacts of imperviousness in urban catchments: a review. J Environ Manage 92(6):1438–1448 Kafy AA, Al Rakib A, Akter KS, Rahaman ZA, Faisal A-A, Mallik S, Nasher NR, Hossain I, Ali MY (2021) Monitoring the effects of vegetation cover losses on land surface temperature dynamics using geospatial approach in Rajshahi city, Bangladesh. Environ Challen 4:100187 Li X, Zhang Y, Ma N, Li C, Luan J (2021) Contrasting effects of climate and LULC change on blue water resources at varying temporal and spatial scales. Sci Total Environ 786:147488 Maimaitiyiming M, Ghulam A, Tiyip T, Pla F, Latorre-Carmona P, Halik Ü, Sawut M, Caetano M (2014) Effects of green space spatial pattern on land surface temperature: implications for sustainable urban planning and climate change adaptation. ISPRS J Photogram Remote Sens 89:59–66 Mall RK, Gupta A, Singh R, Singh RS, Rathore LS (2006) Water resources and climate change: an Indian perspective. Curr Sci 90:1610–1626. Mallick J, Kant Y, Bharath BD (2008) Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J. Ind. Geophys Union 12(3):131–140 Mishra V, Rai PK (2016) A remote sensing aided multi-layer perceptron-marcove chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9(1):1–18. https://doi.org/10.1007/s12517-015-2138-3 Mishra VN, Rai PK, Kumar P, Prashad R (2016) Evaluation of land use/land covers classification accuracy using multi-temporal remote sensing images. Forum Geogr (Romania) 15(1):45–53 (ISSN No: 2067-4635) Mishra VN, Rai PK, Mohan K (2014) Prediction of land use changes based on Land Change Modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst Jovan Cviji´c SASA (Serbia) 64(1):111–127. https://doi.org/10.2298/IJGI1401111M Mishra VN, Rai PK, Singh P (2021) geo-information technology in earth resources monitoring and management. Nova Science Publishers, USA, ISBN: 978-1-53619-669-6 Mishra VN, Rai PK, Rajendra P, Puniya M, Nistor MM (2018) Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models. Appl Geomat (Springer). ISSN No. 1866-9298. https://doi.org/10.1007/s12518-018-0223-5 Mohan M, Kandya A (2015) Impact of urbanization and land-use/land-cover change on diurnal temperature range: a case study of tropical urban airshed of India using remote sensing data. Sci Total Environ 506:453–465

1 Land Use Land Cover Changes and Climate Change Impact on the Water …

15

Mohan K, Shrivastava A, Rai PK (2011) Ground Water in the City of Varanasi, India: present status and prospects. Quaestiones Geographicae 30(3):47–60. https://doi.org/10.2478/v10117011-0026-9.ISSN:2081-6383 Mukherjee F, Singh D (2020) Assessing land use–land cover change and its impact on land surface temperature using LANDSAT data: a comparison of two urban areas in India. Earth Sys Environ 4(2):385–407 Naikoo MW, Rihan M, Ishtiaque M (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: spatio-temporal analysis of Delhi NCR using landsat datasets. Journal of Urban Management 9(3):347–359 Nayak S, Behera MD (2008) Land use/land cover classification and mapping of Pilibhit District Uttar Pradesh India. Indian Geogr J 83:1–10 Nayak S, Maity S, Singh KS, Nayak HP, Dutta S (2021) Influence of the changes in land-use and land cover on temperature over Northern and North-Eastern India. Land 10(1):52 NRSC (2006) Land use/land cover database on 1:50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RS & GIS AA, National Remote Sensing Centre, ISRO, Hyderabad NRSC (2019) Land use/land cover database on 1:50,000 scale, Natural Resources Census Project, LUCMD, LRUMG, RSAA, National Remote Sensing Centre, ISRO, Hyderabad Ogashawara I, Bastos VDSB (2012) A quantitative approach for analysing the relationship between urban heat islands and land cover. Remote Sens 4(11):3596–3618 Pal S, Akoma OC (2009) Water scarcity in wetland area within Kandi Block of West Bengal: a hydro-ecological assessment. Ethiop J Environ Stud Manage 2(3). eISSN: 1998-0507 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 Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses. Springer Nature, Switzerland, ISBN: 978-3-03076116-5. https://doi.org/10.1007/978-3-030-76116-5 Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning. Springer Nature, Singapore. ISBN: 978-981-16-73733. https://doi.org/10.1007/978-981-16-7373-3 Rawat JS, Biswas V, Kumar M (2013) Changes in land use/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt J Remote Sens Space Sci 16(1):111–117 Sterling SM, Ducharne A, Polcher J (2013) The impact of global land-cover change on the terrestrial water cycle. Nat Clim Chang 3(4):385–390 Stocker TF, Qin D, Plattner GK, Tignor MM, Allen SK, Boschung J, Midgley PM (2014) Climate change 2013: the physical science basis. contribution of working group I to the fifth assessment report of IPCC the intergovernmental panel on climate change Shastri S, Singh P,Verma P, Rai PK, Singh AP (2020). Assessment of spatial changes of land use/ land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India. Forum Geogr XIX(1):72–79. https://doi.org/10.5775/fg.2020.063.i Takaku J, Tadono T, Doutsu M, Ohgushi F, Kai H (2020) Updates of ‘AW3D30’ ALOS global digital surface model with other open access dataset. Int Arch Photogrammetry Remote Sens Spat Inf Sci, ISPRS XLIII-B4-2020:183–189 Takaku J, Tadono T, Tsutsui K, Ichikawa M (2016) Validation of ‘AW3D’ global DSM generated from ALOS PRISM. ISPRS Ann Photogrammetry Remote Sens Spat Inf Sci III(4):25–31 Wan Z, Hook S, Hulley G (2015) MOD11A1 MODIS/terra land surface temperature/emissivity daily L3 global 1km SIN Grid V006. NASA EOSDIS Land Processes DAAC. https://doi.org/ 10.5067/MODIS/MOD11A1.006. Accessed 5 Oct 2022 Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89(4):467–483

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A. Mani et al.

Wu J, Miao C, Zheng H, Duan Q, Lei X, Li H (2018) Meteorological and hydrological drought on the Loess Plateau, China: Evolutionary characteristics, impact, and propagation. J Geophys Res Atmos 123(20):11–569 Vishwakarma CS, Thakur S, Rai PK, Kamal V, Mukharjee S (2016) Changing land trajectories: a case study from India using remote sensing. Eur J Geogr 7(2):63–73 Zhao A, Zhu X, Liu X, Pan Y, Zuo D (2016) Impacts of land use change and climate variability on green and blue water resources in the Weihe River Basin of northwest China. CATENA 137:318–327

Chapter 2

Remote Sensing Monitoring of Water Productivity in Agricultural Crops: A Review M. Chanev, I. Kamenova, and L. Filchev

Abstract Water use efficiency and water productivity are becoming increasingly important for food security. The role of sustainable food production is emphasized by UN SDGs and is of particular importance in the new decade, where also water scarcity issues are going to exacerbate due to climate change and anthropogenic factors. The chapter will review the remote sensing applications in the peer-reviewed literature published in the past 10 years and focuses on the prospective new applications. Keywords Remote sensing · Water resource · SDGs · UAV mapping · Crop yield

2.1 Introduction Water is a vital element for almost every human activity. At the beginning of the current century, scarce water resources were already in high demand for agriculture, domestic consumption, hydropower, sanitation, industrial manufacturing, transportation, recreational activities, and ecosystem services (Mohan et al., 2011; Rai et al., 2014). The needs for water continue to evolve yet with unprecedented temps (Lillesand and Kiefer 2015), as the world has finite water resources, which are under increasing stress as the human population and water demand per capita increases. Worsening resources and climate changes, water and soil resources shortage, and extreme weather events such as severe drought and flooding have been affecting global agricultural production in a negative way (Chaurasia et al. 2013; Kumar 2021). M. Chanev · I. Kamenova · L. Filchev (B) Space Research and Technology Institute, Bulgarian Academy of Sciences (SRTI-BAS), Sofia, Bulgaria e-mail: [email protected] M. Chanev e-mail: [email protected] I. Kamenova e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_2

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Water resource scarcity will lead to the variable grain production, which is considered as the source of real food crisis (Kang et al. 2017). This has provided additional impetus for the search for solutions to problems arising from the mismatch between demand and supply in terms of water quantity, quality, and timing. Increasing water productivity has been identified as one of the global challenges that require urgent attention technologies (Cook et al. 2006; Rai et al. 2018, 2021, 2022; Mishra et al. 2018, 2021). Over the past decades, the use of water productivity as an agricultural performance indicator has increased. This indicator is specified in the United Nations Sustainable Development Goals (SDGs) which stipulate that agricultural productivity should be doubled by 2030 and that water use efficiency must substantially increase (UN 2022). Water productivity is an indicator, which is a measurable property that allows users to monitor and evaluate agricultural water productivity. Remote sensing techniques are used to measure agricultural performance at high spatial and temporal resolutions (Bastiaanssen et al. 2000). Remote sensing in optical domain allows monitoring of various aspects of agricultural production, such as different biophysical and biochemical vegetation parameters, which are indicative of crop status, health, and for predicting crop yields. Open-access satellite imagery now provides near real-time data at varying spatial and temporal resolutions including: 10 m with 0.1 mg/l) has adverse effects on the taste and look of water, home use, and the structure of the water supply, whereas a

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modest amount is required for preserving human health. In the current investigation, most of the samples, Mn levels are within acceptable bounds. Mn concentrations range between 0.01 mg/l and 0.81 mg/l during the pre-monsoon, with an average concentration of 0.43 mg/l. However, after a monsoon, Mn levels range from 0.02 to 0.06 mg/l, with a mean of 0.03 mg/l. The spatial distribution map of Mn concentration shows that the maximum portion of the study area is under the permissible limit (Figs. 3.10e and 3.9f). According to the chemical analysis, the study area’s groundwater is typically alkaline throughout the year and becomes naturally hard due to the weathering of calcium-containing minerals. Due to the extensive fertilizer use for agriculture during the monsoon, manganese concentrations in most samples are higher post-monsoon than pre-monsoon.

3.4.2 Estimation of Water Quality Index (WQI) 3.4.2.1

Standard Values (V s ) and Unit Weights (W n ) of Water Quality Parameters

Water parameters are selected for this study based on their direct involvement in water quality. Computation of quality rating (qn ) and unit weights (W n ) is done by using the standards for drinking water recommended by the Indian Council of Medical Research (ICMR) and Indian Standards Institution (ISI). Twelve water quality such as pH, electrical conductivity (EC), turbidity, total alkalinity (TA), total hardness (TH), total dissolved solids (TDS), dissolved oxygen (DO), nitrate (NO3 − ), calcium (Ca2+ ), magnesium (Mg2+ ), Iron (Fe), and manganese (Mn) were selected for calculation of WQI. Table 3.4 shows the drinking water quality standards (S i ), weights (wi ), and the relative weights (W i ) assigned to each parameter used for calculating the WQI. Maximum W i , i.e., 0.128 is assigned to both NO3 − and Mn, due to their considerable impact on the WQI. The hydro-chemical parameters given in Table 3.4 are used for all 58 samples (32 pre-monsoon, 26 post-monsoon).

3.4.2.2

Estimation of Quality Rating (qi ) and WQI for Both Preand Post-Monsoon Seasons

The quality rating (qi ) for every parameter for each location was calculated by applying Eq. 3.2 and WQI values using Eq. 3.4 for both pre- and post-monsoon seasons. According to the results, pre-monsoon samples had excellent to good water quality in 68.89% of cases (31 out of 45), whereas post-monsoon samples had excellent to good water quality in 74.19% of cases (23 out of 31). However, during premonsoon seasons, 8 (17.78%) groundwater samples are very poor to UFD categories and need appropriate water treatment of these sites before usage. Due to rainfall that

3 Assessment of Groundwater Quality in South Karanpura Coalfield … Table 3.4 Water quality parameters along with standard values (S i ), weightage factor (wi ) and their corresponding relative weights (W i )

Parameters

ICMR/BIS Standard (S i )

Weight (wi )

45

Relative weight (W i )

pH

7.5–8.5

4

0.103

EC

300

2

0.051

Turbidity

5

4

0.103

TA

200

3

0.077

TH

300

2

0.051

TDS

500

4

0.103

DO

5

2

0.051

NO3 −

45

5

0.128

Ca2+

75

2

0.051

Mg2+

30

2

0.051

Fe

0.3

4

0.103

Mn

0.1

5

0.128

∑wi = 39

∑W i = 1.000

dilutes the pollutants, only 1 (3.23%) of the groundwater samples taken after the monsoon were determined to be extremely bad to UFD categories. The WQI values for pre- and post-monsoon seasons vary between 38 to 1142 and 38 to 394 in the study area. Using the IDW interpolation method, the WQI data for each sampling location were interpolated to create the WQI map for the pre- and post-monsoon seasons. Using a manual classification approach in ArcGIS, the weight values were divided into various ranges and categories, including 0–50 (excellent), 50–100 (good), 100– 200 (poor), 200–300 (very poor), and > 300 (unsuitable for drinking) to represent various water quality index zones (Table 3.5, Fig. 3.11a, b). The results of the study revealed that during pre-monsoon, 5.04 km2 (0.59%) of the study area exhibits excellent water quality and is located in very small patches in the northeastern region. About 21.75% (184.72 km2 ) area has good water quality zone in the northern, western, and central regions of the region. The study area is Table 3.5 Area statistics of the various ranges of water quality index for pre-monsoon and postmonsoon WQI

WQS

Pre-monsoon Area (Sq. Km)

Post-monsoon % Area

Area (Sq. Km)

% Area

0–50

Excellent

0.24

0.10

3.52

1.47

50–100

Good

76.84

32.03

181.98

75.86 21.14

100–200

Poor

141.26

58.89

50.72

200–300

Very Poor

18.70

7.79

2.66

1.11

> 300

Unsuitable

2.83

1.18

1.01

0.42

239.88

100.00

239.88

100.00

Total

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Fig. 3.11 Groundwater quality zone index maps for (a) Pre-monsoon (b) Post-monsoon seasons

3 Assessment of Groundwater Quality in South Karanpura Coalfield …

47

dominated by poor quality zone comprising 58.19% (494.16 km2 ) along the northern, central, and western regions (Table 3.5, Fig. 3.11a). Very poor water quality zone occupying 9.37% (79.59 km2 ) presents in central and eastern part and unsuitable for drinking (UFD) zones comprises 22.34% (189.76 km2 ) of the study area. During post-monsoon season, an excellent water quality zone covers 6.92 km2 (0.81%) of the study area. The maximum part of the study region (694.93 km2 ; 81.83%) is occupied by a good zone, whereas poor quality zones cover 16.91% (143.64 km2 ) of the study area. Very poor and UFD zone covers 2.69 km2 (0.32%) and 1.03 km2 (0.12%) of the area of mining and surrounding regions that demonstrates the ill effects of coal mining activities on the quality of groundwater (Table 3.5, Fig. 3.11b).

3.4.3 Correlation Results of correlation employing WQI values and 12 parameters (pH, EC, Turbidity, TA, TH, TDS, DO, NO3 − , Ca2+ , Mg2+ , Fe, Mn) that considered very effective water quality parameters to define any co-variation (Table 3.2). The correlation coefficient exhibits a close association between the two variables. A high correlation coefficient indicates a good and positive relationship, whereas zero and negative values denote no association or an inverse relationship between the variables. According to Sojobi (2016), correlation analysis (± 0.9 ≤ R2 ≤ 1) was considered as strong, (± 0.9 ≤ R2 ≥ 0.5) as moderate, and R2 ≤ ± 0.5 as poor. Pearson’s linear correlation matrix was generated using analyzed ions of groundwater samples collected during pre-monsoon (32 samples) and post-monsoon (26 samples) seasons (Tables 3.6 and 3.7). The obtained results during pre-monsoon indicate a strong positive correlation between TH and Ca2+ , Mg2+ , TDS, and EC. TDS strongly correlated with major cations like Ca2+ and Mg2+ signifying these are the most significant parameters for TDS (Table 3.6). This finding corroborates with (Raju et al. 2015; Avtar et al. 2013). Turbidity shows a positive association with Fe, denoting the presence of sources rich in iron due to mining activities in the ground. Similar sources for these cations are represented by the correlation between Ca2+ and Mg2+ . pH shows weak and very weak relationships with all parameters. TDS showed a strong positive correlation with Ca2+ and Mg2+ . While Mn has a very weak positive correlation with EC, TDS, Ca2+ , Fe, TH, and turbidity but a strong correlation with WQI, whereas it has very weak negative correlations with pH, DO, NO3− , and Mg2+ . WQI and Mn have a substantial correlation, suggesting that this metal strongly impacts WQI values. In the post-monsoon season, EC indicates positive relationship with TDS, TH, Ca2+ , and Mg2+ , whereas Turbidity indicates strong positive relationship with Mn, Fe, and WQI (Table 3.7). TH found strong positive correlation with Ca2+ and Mg2+ , whereas TDS shows positive relationships with them. Ca2+ shows strong positive relationship with TH, Mg2+ and moderate relationship with TDS and EC (Table 3.7). Fe also shows a strong relationship with Mn, WQI, and turbidity. The correlation matrix shows that WQI is significantly influenced by turbidity, Fe, and Mn. Finally, it

0.056

EC

EC

1.000

0.102

0.420*

− 0.108 − 0.059

− 0.193 − 0.012

Mn

WQI

0.454

− 0.175

− 0.042

0.968**

0.948**

− 0.261

− 0.097

0.692**

1.000

TH

0.492** − 0.102

*Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed)

Fe

0.192

0.059 0.088

0.904** − 0.241

0.868** − 0.217

0.766**

Mg2+

− 0.161 − 0.082

0.110

0.268

Ca2+

0.383* 0.099

− 0.079 − 0.254

NO3 −

0.197

− 0.079

0.164

0.049

− 0.025

DO

0.056

0.079

0.604**

0.922** − 0.238

TA

1.000

1.000

Turbidity

0.113

0.244

− 0.054

0.207

0.212

TDS

TH

TA

Turbidity − 0.344 − 0.274

1.000

pH

pH

− 0.332

1.000

DO

− 0.009

− 0.149

0.024

0.838**

1.000

Ca2+

− 0.155 − 0.142

0.342 − 0.033

− 0.306

− 0.180

1.000

NO3 −

0.401* − 0.098 − 0.084

0.360

0.135

0.770** − 0.055

0.530** − 0.141

− 0.361

0.026

1.000

TDS

Fe

1.000

Mn

WQI

− 0.109 0.423* 0.933** 1.000

− 0.188 0.148

− 0.046 1.000

1.000

Mg2+

Table 3.6 Pearson’s linear correlation matrix between different parameters in the groundwater of the South Karanpura coalfield during pre-monsoon (n = 29)

48 A. Kumar et al.

0.298

0.441*

0.095

0.094

DO

NO3 −

0.024

0.217

0.259

Fe

Mn

WQI

0.840**

0.947**

− 0.169 − 0.123 0.016

− 0.142 − 0.010

0.869**

0.974**

0.471

0.089

0.404

1.000

TH

− 0.131

0.589** 0.429*

− 0.146

− 0.212

0.856**

0.392

− 0.258

− 0.306

0.573** 0.222

− 0.177

− 0.134

0.333

1.000

TA

− 0.157

1.000

Turbidity

*Correlation is significant at the 0.05 level (2-tailed) **Correlation is significant at the 0.01 level (2-tailed)

0.011

− 0.015

0.197

Mg2+

0.499*

0.545

0.178

0.302

Ca2+

0.196

− 0.017

0.545**

0.611**

0.231

0.230

TH

TDS

TA

1.000

− 0.231

0.132

EC

EC

Turbidity 0.157

1.000

pH

pH

0.155

0.303

0.268

0.425*

0.359

− 0.074

0.156

1.000

TDS

0.364

0.358

0.326

0.181

0.039

− 0.134

1.000

DO

− 0.187

− 0.330

− 0.373

0.185

0.561**

1.000

NO3 −

0.039

− 0.123

− 0.157

0.734**

1.000

Ca2+ Fe

Mn

WQI

− 0.036 0.931** 0.930** 1.000

− 0.101 0.994** 1.000

− 0.163 1.000

1.000

Mg2+

Table 3.7 Pearson’s linear correlation matrix between different parameters in the groundwater of the South Karanpura coalfield during post-monsoon (n = 22)

3 Assessment of Groundwater Quality in South Karanpura Coalfield … 49

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A. Kumar et al.

is clear that the quality of the groundwater in the study area is significantly influenced by the dissolution of minerals caused by aquifer water interaction. Water quality analysis using WQI approach is very useful in demonstrating the influence of anthropogenic and natural contaminants. The GWQI zonation maps (Fig. 3.11a, b) demonstrated that anthropogenic activities such as mining, agriculture, and municipal waste discharge are the primary factors controlling groundwater quality. Rainfall serves as the primary source of recharge through seepage and infiltration in the region. An upper aquifer layer is removed during coal mining activities, increasing the probability that contaminants will seep into the groundwater reservoir. Aquifers near coal mining areas are, therefore, susceptible to pollution. On the other hand, the inflow of rainwater improves the study area’s groundwater quality by causing the dilution of toxins owing to recharge.

3.5 Conclusions Groundwater contamination is a significant problem in mining areas. The water quality was assessed by analyzing the chemical properties of groundwater samples collected during the pre (May 2017) and post-monsoon (December 2017) seasons. The chemical analysis of groundwater samples pertaining to pH ion, EC, turbidity, TA, TH, TDS, DO, NO3 − , Ca2+ , Mg2+ , Fe, and Mn were laboratory tested to evaluate their quality for drinking and domestic purposes. The results showed that during premonsoon, 71.87% of samples (23 out of 32) showed excellent to good water quality. In contrast, during post-monsoon, 76.92% of samples (20 out of 26) had excellent to good water quality. However, 5 (15.62%) samples from pre-monsoon season are very poor to UFD categories and the groundwater from these locations requires appropriate water treatment before consumption. In post-monsoon, only 1 (3.23%) groundwater sample was found to be very poor to UFD categories due to rainfall that dilutes the contaminants. The WQI maps were created employing the results of the chemical analysis of the constituents. They showed that during the pre-monsoon, 77.08 km2 (32.13%) of the study area exhibited excellent to good water quality zone, indicating that the northern and western regions of the study area. Whereas poor to very poor water quality zone comprises 159.96 km2 (66.68%) of the study area. However, due to aquifer recharging by rainfall that dilutes the contamination, most of the study area (181.98 km2 ; 75.86%) is occupied by an excellent water quality zone during the post-monsoon season. Geospatial approaches have proven to be a valuable tool in identifying the zones with variable amounts of contamination in the area for all of the groundwater quality evaluations conducted in this study. Planners and policymakers can use the spatial distribution maps produced by this analysis for various quality criteria to advise remedial actions comprehensively for sustainable water management in the studied area. Acknowledgements Authors are thankful to Central Instrumentation Facility (CIF) of Birla Institute of Technology, Mesra, Ranchi, India for providing Perkin Elmer Optical 2100DV, Inductively

3 Assessment of Groundwater Quality in South Karanpura Coalfield …

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Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) instrument for heavy metals analysis. Authors are also grateful to Mr. Jamshed Alam and Mr. Sams Raza for their kind support during groundwater sample collection in the study area.

References Abou Zakhem B, Hafez R (2015) Heavy metal pollution index for groundwater quality assessment in Damascus Oasis, Syria. Environ Earth Sci 73(10):6591–6600 Al-Hadithi M (2012) Application of water quality index to assess suitability of groundwater quality for drinking purposes in Ratmao-Pathri Rao watershed, haridwar district India. J Sci Ind Res 23:1321–1336 Ambiga K, Durai RA (2013) Use of geographical information system and water quality index to assess groundwater quality in and around Ranipet area, Vellore district, Tamilnadu. Int J Adv Eng Res Stud, 73–80. Annapoorna H, Janardhana MR (2015) Assessment of groundwater quality for drinking purpose in rural areas surrounding a defunct copper mine. Aquatic Procedia 4:685–692 Avtar R, Kumar P, Singh CK, Sahu N, Verma RL, Thakur JK, Mukherjee S (2013) Hydrogeochemical assessment of groundwater quality of bundelkhand, India using statistical approach. Water Qual Expo Health 5(3):105–115 BIS (2003) Bureau of Indian Standards specification for drinking water. IS: 10500:91. Revised New Delhi. Bordalo AA, Teixeira R, Wiebe WJ (2006) A water quality index applied to an international shared river basin: the case of the Douro River. Environ Manage 38(6):910–920 Guofeng W, Leeuw J, Skidmore AK, Yaolin L, Prins HHT (2010) Comparison of Extrapolation and Interpolation methods for estimating daily photo synthetically active radiation (PAR)–a case study of the Poyang lake national nature reserve, China. Geo-Spatial Inf Sci 13:235–242 Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306 Jha MK, Shekhar A, Jenifer MA (2020) Assessing groundwater quality for drinking water supply using hybrid fuzzy-GIS-based water quality index. Water Res 179:115867 Khan SMN, Kumar AR (2013) Geogenic assessment of water quality index for the groundwater in Tiruchengode Taluk, Namakkal District, Tamilnadu, India. Chem Sci Trans 2(3):1021–1027 Kumar A, Krishna AP (2018) Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto Int 33(2):105–129 Kumar A, Krishna AP (2021) Groundwater quality assessment using geospatial technique based water quality index (WQI) approach in a coal mining region of India. Arab J Geosci 14(12):1–26 Kumar A, Pandey AC (2013) Evaluating Impact of coal mining activity on landuse/landcover using temporal satellite images in South Karanpura coalfields and environs, Jharkhand State, India. Int J Adv Remote Sens GIS 2(1):183–197 Mishra V, Rai PK (2016) A remote sensing aided multi-layer Perceptron-Marcove Chain analysis for land use and land cover change prediction in Patna district (Bihar), India, Arabian. J Geosci 9(1):1–18. https://doi.org/10.1007/s12517-015-2138-3 Mahato MK, Singh PK, Singh AK, Tiwari AK (2018) Assessment of hydrogeochemical processes and mine water suitability for domestic, irrigation, and industrial purposes in East Bokaro Coalfield, India. Mine Water Environ 37(3):493–504 Mishra VN, Rai PK, Kumar P, Prashad R (2016) Evaluation of land use/land covers classification accuracy using multi-temporal remote sensing images. Forum Geogr (Romania) 15(1):45–53 Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management. Nova Science Publishers, USA. ISBN: 978-1-53619-669-6

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A. Kumar et al.

Oinam JD, Ramanathan AL, Linda A, Singh G (2011) A study of arsenic, iron and other dissolved ion variations in the groundwater of Bishnupur District, Manipur, India. Environ Earth Sci 62(6):1183–1195 Raju NJ, Patel P, Gurung D, Ram P, Gossel W, Wycisk P (2015) Geochemical assessment of groundwater quality in the Dun valley of central Nepal using chemometric method and geochemical modeling. Groundw Sustain Dev 1(1–2):135–145 Rao GS, Nageswararao G (2013) Assessment of ground water quality using water quality index. Arch Environ Sci 7(1):1–5 Rattan RK, Datta SP, Chhonkar PK, Suribabu K, Singh AK (2005) Long-term impact of irrigation with sewage effluents on heavy metal content in soils, crops and groundwater—a case study. Agr Ecosyst Environ 109(3–4):310–322 Rai PK, Mishra S, Ahmad A, Mohan K (2014) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci (Springer) 7:217–232. https://doi.org/10. 1007/s13201-014-0238-y Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning. Springer Nature, Singapore. ISBN: 978-981-16-73733. https://doi.org/10.1007/978-981-16-7373-3 Sánchez E, Colmenarejo MF, Vicente J, Rubio A, García MG, Travieso L, Borja R (2007) Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution. Ecol Ind 7(2):315–328 Sawyer CN, McCarty PL, Parkin GF (1978) Chemistry for environmental engineers. McGraw-Hill Book Company, New York Sener ¸ S, ¸ Sener ¸ E, Davraz A (2017) Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey). Sci Total Environ 584:131–144 Singh AK, Mondal GC, Kumar S, Singh TB, Tewary BK, Sinha A (2008) Major ion chemistry, weathering processes and water quality assessment in upper catchment of Damodar River basin. India. Environmental Geology 54(4):745–758 Sojobi AO (2016) Evaluation of groundwater quality in a rural community in North Central of Nigeria. Environ Monit Assess 188(3):1–17 Sonwane DV, Lawande SP, Gaikwad VB, Kamble PN, Kuchekar SR (2009) Studies on ground water quality around Kurkumbh industrial area, Daund, Pune District. Rayasan J Chem 12(2):421–423 Syed RQ, Edward MM, Guang Z (2002) Water works engineering Tiwary RK (2001) Environmental impact of coal mining on water regime and its management. Water Air Soil Pollut 132(1):185–199 Wellen C, Shatilla NJ, Carey SK (2018) The influence of mining on hydrology and solute transport in the Elk Valley, British Columbia, Canada. Environ Res Lett 13(7):074012 Vishwakarma CS, Thakur S, Rai PK, Kamal V, Mukharjee S (2016) Changing land Trajectories: a case study from India using remote sensing. Eur J Geogr 7(2):63–73 WHO (2006) Guideline for drinking-water quality: recommendations, vol 1. World Health Organization, Geneva, p 130

Chapter 4

Application of Wastewater in Agriculture: Benefits and Detriments Akanksha Verma, Anshu Gupta, and Paulraj Rajamani

Abstract Water is the paramount resource for sustaining life on earth. However, there was a rise in water contamination due to increased urbanization and industrialization. Water pollution contributes to the “water crisis” at the global level by decreasing the quantity as well as the quality of available freshwater resources for humans and ecosystems. Due to a lack of freshwater, wastewater application is a prevalent technique in agricultural production in many underdeveloped nations. Water shortages can take the form of physical scarcity, wherein available water is limited to satisfy the required needs, or economic scarcity, in which water is accessible but there is no infrastructure in place to provide the quantity and quality of water that is necessary (Rai et al., Applied Water Science (springer) 8:1–16, 2018; 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; Arora et al., Arora et al., Ecol Ind 128, 2021). Given the growing significance of water as a limited resource, this chapter primarily focuses on the state of water scarcity globally and in India and the major water contamination sources in water bodies. It also discusses the major pollutants present in wastewater, which include heavy metals (Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India, Ecology Environment & Conservation, 27 (February Suppl. Issue); pp. (S260–S268). ISSN 0971–765X; Mishra VN, Rai PK, Singh P (2021) Geo-information Technology in Earth Resources Monitoring and Management (edit. Book), Nova Science Publishers, USA, ISBN: 978–1–53619– 669–6; Rai et.al., 2021; Rai, PK, Mishra VN, Singh P (2022) Geospatial Technology for Landscape and Environment Management: Sustainable Assessment & Planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi. org/10.1007/978-981-16-7373-3; Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai A. Verma · P. Rajamani (B) School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India e-mail: [email protected] A. Gupta Department of Environmental Sciences, Jammu and Kashmir Higher Education Department, Jammu and Kashmir, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_4

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VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India, Ecology Environment & Conservation, 27 (February Suppl. Issue); pp. (S260-S268). ISSN 0971–765X.). The advantage of wastewater irrigation in agriculture is that it serves as a significant source of nutrients for crop growth and development. However, due to the bioaccumulation and biomagnification properties of hazardous materials like heavy metals, excessive wastewater application contaminated with physical, chemical, and microbiological contaminants would pose health risks to the native flora and fauna. As a result, there is a need to reduce the negative impacts of wastewater on agriculture, which necessitates proper water remediation and management prior to its application in agricultural fields for crop irrigation (Vishwakarma et al., Eur J Geogr 7:63–73, 2016; Arora et al, Ecol Ind 128, 2021; Rai PK, Mishra VN, Singh P (2022) Geospatial Technology for Landscape and Environment Management: Sustainable Assessment & Planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi.org/10. 1007/978-981-16-7373-3; Brototi et al., Indian J Environ Prot 42:302–315, 2022). Several methods are included to lessen the harmful health effects of wastewater on the surrounding biota, such as restricting inappropriate disposal of industrial and urban effluents and carrying out proper treatment of industrial and municipal solid waste before dumping it into the water body. Additionally, proper policy formulation should be carried out that involves cooperation from all the stakeholders. Keywords Wastewater · Urbanization · Water Scarcity · Sewage Treatment · Pollutants

4.1 Introduction Water is vital to the survival of biodiversity. It occupies 71% of the earth’s total surface, out of which 96.5% is found in seas and oceans, which are saline in nature, thus making it unfit for the consumption of living beings, while only 2.5% of it is freshwater, out of which only 0.5% is available for drinking, agriculture, industries, etc. The other 2% is present in the form of glaciers and polar ice caps, thus making it unavailable (Birajdar and Chanshetti 2017; Kumar 2019). India accounts for 18% of the global population, which is the second-largest population on Earth. The current population of 1.3 billion people is projected to increase to 1.5 billion by 2050 (Kumar 2019). By this period, urban areas will be home to more than 50% of the world’s population, and settlements will grow both in number as well as in size. Such a high rate of demographic growth, with alarming urbanization, industrialization and changing lifestyles, will lead to increased water usage and thus larger quantities of anthropogenic wastewater, consisting of complex mixtures of compounds. Currently, 15% of India’s water resources are used for household and industrial purposes, and by 2050, this percentage will have increased to 30%. It has been estimated that by 2050, the yearly water requirement of the urban population and industrial sector would be around 90 and 81 km3 , respectively (CPCB 2021). Sewage waters have previously

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been seen as a potential source of nutrients for peri-urban agriculture since they primarily included biodegradable components and plenty of plant nutrients (Minhas and Samra 2004). However, as civilization advances and lifestyles change, harmful metals and organic micro-pollutants are now being found in increasing amounts in wastewater (Minhas et al. 2022), causing water pollution. Water pollution contributes to the “water crisis” at the global level by decreasing the quantity as well as the quality of available freshwater resources for humans and ecosystems. Water stress, water shortages, and water crises are all aspects of water scarcity. This might be the result of both anthropogenic as well as natural sources. The main causes of this problem are poor resource management, a lack of government attention, and human waste (Dhawan 2017). Freshwater scarcity is currently affecting both developing countries, including India, China, and developed countries, as well as several African nations (Ganoulis 2009). Around two billion people around the world face a scarcity of clean drinking water, and about 4.5 billion do not have basic sanitation (Aniyikaiye et al. 2019). According to UN report, two-thirds of the world’s population by 2025 may confront water scarcity. Water shortages can take the form of physical scarcity, wherein available water is limited to satisfy the required needs, or economic scarcity, in which water is accessible but there is no infrastructure in place to provide the quantity and quality of water that is necessary (Ranade and Bhandari 2014), for example construction of dam on rivers for utilization of river water. River water is required for irrigation, home water supply, and industrial purposes, among other things. The river’s relevance to a large population necessitates maintaining its water quality. Domestic pollution makes for more than 85% of total pollution. The problem worsens as a result of large-scale water withdrawals, which reduces the river’s ability to dilute the pollutants present in water thus deteriorating quality and quantity of water present in rivers and canals (Sharma and Kansal 2011). Water plays a crucial role in agricultural productivity and food security. However, the quantity of fresh water present in environment is declining at an alarming rate leading to growing scarcity of water. Thus posing one of the biggest problems in achieving sustainable development.

4.2 The State of Water Scarcity 4.2.1 GLOBALLY The imbalance between the availability and demand for fresh water is referred to as "water scarcity” (Rosa et al. 2020). The correlation between population and demand for fresh water depends on a variety of factors, making it difficult to determine whether a nation has enough water to sustain both its present and future generations. As the world’s population grows and developing countries expand, their urbanization, industrialization, and human water consumption also grow annually (Jury et al. 2007). The water requirement of a nation mostly depends on whether it produces

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the food within the country or it imports from other country to feed its people, as well as how much rain it gets. Between poor and wealthy countries, there are also notable differences in residential and industrial water consumption (Jury et al. 2007). Since the 1950s, the need for fresh water has increased thrice while the supply has decreased. Three billion people would reside in nations with water scarcity or stress by 2025 as a result of population growth, up from half a billion today (Hanjra et al. 2010). The statistics by other studies on water availability and demand are disturbing: It shows by 2025, three billion more people will need to be fed with about 20% more water than is currently available (Seckler et al. 1998); and aquifers, which provide water for one-third of the world’s population, are depleting more rapidly than usual (Shah et al. 2006). In order to survive, 1.1 billion poor people around the world are compelled to drink contaminated water, making them vulnerable to a variety of debilitating and often deadly diseases that are relatively unheard in nations with safe drinking water and proper sanitary facilities. Reports have shown that the population in millions that lack access to safe drinking water in year 2000 was in Africa (300), Asia (693), Latin America and Caribbean (78), and in Europe (26) (Jury et al. 2007). The most vulnerable and underprivileged people suffer the most when there is a water scarcity, even though it may affect large regions. The major consumer of water, accounting for over 70% of total demand, is the agricultural sector, approximately 2700 km3 annually, or 9% of the world’s freshwater resources (Rosa et al. 2020). Currently, 22% of the land area is utilized for pastures and rangelands, while 12% of it is used for agriculture (Leff et al. 2004). Shiklomanov (1998) states that, the agriculture sector uses up to 2/3 of all water withdrawals globally and consumes close to 90% of it. Even though it produces over half of the world’s food supply, just 18% of agricultural land is irrigated globally (Mancosu et al. 2015).

4.2.1.1

The Country-Wise Utilization of Freshwater in Various Sectors

Among the continents, the distribution of world’s freshwater resources shows the following order: America (45%) > Asia (28%), Europe (16%), and Africa (9%) (Mancosu et al. 2015). Developing countries like India and South Africa shows higher dependency on freshwater for agriculture sector, whereas for developed countries like Europe and USA it goes to energy production and industrial sector (Fig. 4.1). The highest freshwater utilization in agriculture sector shows the following order: India (87%) > South Africa (60%) > Europe (44%) > USA (37%) (Kesari et al. 2021). The application of untreated wastewater is a common practice in low-income nations (Latin America, Asia, and Africa) (Jiménez and Asano 2008). However, middle-income nations (Saudi Arabia, Jordan, and Tunisia) use treated wastewater for irrigation (Kesari et al. 2021).

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Fig. 4.1 Sector-wise usage of freshwater in a—Europe; b—South Africa; c—USA; d—India (Kesari et al. 2021)

4.2.1.2

The Country-Wise Utilization of Treated Wastewater in Various Sectors

The percentage of treated wastewater reused for agriculture varies widely around the world (Fig. 4.2). More than 10% of people in the globe consume food produced via wastewater irrigation (WHO 2006). Volumes of recycled and treated wastewater have augmented by 10 to 29% annually in China, the USA, and Europe, and by up to 41% in Australia (Aziz and Farissi 2014). Currently, only 37.6% of India’s urban wastewater is thought to be treated. Israel uses 90% recycled water for irrigation of agricultural land, making it the largest user of treated wastewater (Kesari et al. 2021). The Falkenmark Stress Indicator (FSI), the most widely used index, divides countries into different categories according to the availability of liquid water resources per person (PWR) (surface water flow or groundwater recharge). As per FSI classification, in 1995, a total of 29 nations, home to 460 million people, experienced water stress or scarcity. However, the number has increased to 2.8 billion people within 47 countries by 2025 (Jury et al. 2007). In the foreseeable future, it is anticipated that some of the world’s most densely populated areas, including the Mediterranean, the Middle East, India, China, and Pakistan, would experience severe water shortages. Australia and sections of the USA (such as the southwest and some of the midwest) are at risk from water shortages. For instance, rainfall and runoff have significantly decreased in Australia over the past few decades, which had an impact on irrigation water allocations (CSIRO 2008). Pollution affects half of the world’s rivers and lakes,

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Fig. 4.2 Sector-wise utilization of treated wastewater among various countries (Kesari et al. 2021)

and major rivers like the Yellow, Ganges, and Colorado which do not flow to the sea for a large portion of the year due to upstream withdrawals (Hanjra et al. 2010).

4.2.2 Regionally India possesses 4% of the world’s fresh water, 80% of which goes to agriculture sector. India experiences annual precipitation of 4,000 billion cubic meters on average. In India, only 18–20% of the water is actually used because of the lack of proper water management, inadequate infrastructure, and shortfall of storage options (Dhawan 2017; Kumar 2019). In rapidly urbanizing areas, water pollution is a severe concern. Water quality and ecosystem health in the Delhi-NCR region have deteriorated due to increased industrial expansion and population growth (Chen et al. 2016). According to the NITI Aayog report published in 2018, the findings revealed that India is going to experience its “biggest” water crisis in history and that if no action is taken, demand for portable water will surpass supply by 2030. The study said nearly 600 million Indians suffer from mild to severe water crisis as a result of a lack of access to clean water, and 20,000 people die each year. By 2020, groundwater will run out in 21 cities, including Delhi, Bengaluru, Chennai, and Hyderabad, affecting 100 million

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people. Also, the India’s Gross Domestic Product will reduce by 6% by 2050 if current trends continue (Kumar 2019). Water scarcity and deterioration of water quality are worldwide issues that will become more intense as water demand rises, as well as due to the unpredictability of extreme weather patterns. As a result, water of poor quality will become a more integral part of agricultural water supply around the world, especially in water-scarce regions of developing countries like India (Pedrero et al. 2010). Domestic waste is also one of the sources of water pollution. It accounts for 50– 80% of all waste released into the environment. Over the past two decades, it has more than tripled in size, and in 2020, it was anticipated to be 26.4 km3 (CPCB 2021). Maharashtra produced the highest sewage water (3.32 km3 ) compared to the other Indian states and union territories (UT), whereas Lakshadweep (UT) produced only 0.47 × 10–2 km3 . The most sewage water is generated in India’s southern region (7.63 km3 ) followed by northern (7.33 km3 ), western (5.2 km3 ), eastern (3.83 km3 ), central (1.77 km3 ), and north-eastern regions (0.61 km3 ). Class I cities in the Ganga basin, with populations exceeding one million, and Class II towns with populations between 500,000 and one million, generate 4.16 km3 of wastewater (GOI 2017). 1093 sewage treatment facilities (STPs) only process 7.38 km3 (or 28% of the total wastewater produced), leaving the remaining 19.03 km3 (or 72% of the total wastewater) untreated. Depending on the geography, sewage treatment capacity varies substantially (Minhas et al. 2022). The Central Groundwater Board of India has designated 16.2% of the total assessed units (Mandals, Talukas, or blocks) as being “over-exploited”. Whereas it has classified an additional 14% as being at a “critical” or “semi-critical” stage. In the country’s northwest region, the majority of the overexploited blocks are present. On the other hand, the eastern region, where groundwater use is minimal, offers more potential for maximizing the advantages of groundwater use to increase crop yields. The demand management and supply enhancement strategies are necessary due to the unsustainable groundwater use for the agriculture sector to increase water use effectiveness (Dhawan 2017). According to the Ministry of Water Resources, River Development and Ganga Rejuvenation, water demand is expected to rise from 813 BCMs (Billion Cubic Meters) in 2010 to 1093 BCMs in 2025. Also, as per the WaterAid India Country Strategy 2016–2021, there are 76 million people in India who lack access to clean drinking water. All species require clean, safe, and sufficient freshwater to survive, as well as for the smooth operation of critical systems, institutions, and economies (Birajdar and Chanshetti 2017). As per the survey, over 140,000 children under the age of five die each year as a result of diarrhea infections that occur due to the contamination of water. As per the World Bank, contaminated water causes 21% of India’s infectious diseases. If adequate steps are not taken, the situation will deteriorate further (Sharma et al. 2021).

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4.3 Sources of Water Contamination The sources of water contamination can be divided into two types: point source and non-point source (Fig. 4.3). Point source refers to the contamination that comes from a single defined location, like industrial effluents or sewage treatment plants (Hill 2020), whereas, non-point sources (NPS) include runoff from urban areas (storm water discharge, municipal solid waste overflows), agriculture (animal dung, pesticides, fertilizers) (Chen et al. 2016). The main causes of water contamination are listed below: Anthropogenic activities: The rising anthropogenic activities paired with current land use patterns can increase pollution loadings into water bodies, such as nutrients and microorganisms, posing a public health risk (Carroll et al. 2006). Rainfall events, on the contrary, can exacerbate pollution loadings by allowing storm water runoff from metropolitan areas to flow into agricultural areas because of prevalent practices like using manure in the form of fertilizer and grazing and bathing of livestock near and inside water bodies (Ackerman et al. 2003). As a result of direct and indirect activity from natural and anthropogenic sources, the quality of water in water bodies, such as lakes, rivers, canals, and groundwater, is worsening day by day. Physicochemical parameters like pH, EC, TDS, DO, boron, chloride, sulfate, fluoride, nitrate, total hardness, and biochemical oxygen demand are the major factors that determine the quality of surface water. The occurrence of abovementioned parameters in water body above an allowed threshold is classified as contaminated water quality (CWC report 2020). Industrial waste and effluents: Wastes are mostly generated by industries in today’s world. Water pollution caused by unprecedented waste disposal by

Fig. 4.3 Sources of water pollution (point and non-point sources)

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industry in developing countries, in particular, requires special attention. Such types of wastes are thought to account for half of all the pollutants and hazardous waste disposed into the environment. Water quality is degraded as a result of effluents discharged into waterways, which is characterized by significant changes in total nitrogen, total suspended solids, chemical oxygen demand, total phosphorus, copper, iron, lead, nickel, and zinc (Popa et al. 2012). As a result, the environmental contamination crisis may have an adverse impact on both humans living around and aquatic species surviving in the water body (Gemeda et al. 2020). Sewage and Municipal waste: Release of sewage and municipal waste, as well as the routine practice of bathing and washing clothes in the water bodies, are all highly prevalent thus leading to deterioration of environmental and water quality (Matta et al. 2017). Abdel-Shafy and Mansour (2018) found in their study that municipal trash has a massive impact on the environment across four continents, 22 nations, and 30 cities. Municipal waste comprises of the following components: Paper (27%) > food (15%) > yard trimming (14%) > plastics (13%) > heavy metals (9%) followed by textiles, rubber, and leather (9%) and others (3%), all contributing to environmental catastrophe in municipal garbage in the United States. This shows how garbage has a detrimental impact on the environment as a result of an inefficient waste management system (Gemeda et al. 2020). Sewage discharges are a significant source of contamination, raising oxygen demand and excessive nutrients in water bodies, promoting hazardous algal blooms, encouraging eutrophication, and destabilizing aquatic ecosystems (Okoh et al. 2007). Emulsion paints: These are made up of emulsifying agents, inorganic pigments, latexes, cellulosic thickeners, extenders, organic pigments, non-cellulosic thickeners, and other ingredients. Paint wastewater is the combination of the majority of paint components with little dilution. Furthermore, organic waste, colored materials, suspended particles, and toxic contaminants like heavy metals in the resulting wastewater are due to the several chemicals used in the paint manufacturing process (Aniyikaiye et al. 2019).

4.4 Heavy Metals in Wastewater Despite the fact that heavy metals are naturally found in the earth’s crust, they are toxic in nature thus pose a health hazard to the living beings (Al-Samman 2015). Metal pollution contaminates the soil and water’s surface consequently affecting human health. International organizations have established regulations for the application of every metal to ensure that people are safeguarded from their harmful effects, especially contamination of heavy metals in drinking water (Lane and Morel 2000). The body’s metabolic processes can be hampered by heavy metals in a number of ways. The human body needs certain metals, including copper, iron, manganese, and zinc (Lane and Morel 2000). However, they would have hazardous impact on the human health if consumed in higher doses. It has been shown that the buildup

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of heavy metals in the body is harmful to human health. Arsenic, lead, aluminum, iron, mercury, and cadmium are the key heavy metals that have the potential to have negative consequences (Fu and Xi 2020). Plant biochemical and physiological activities like chlorophyll production, DNA synthesis, and photosynthesis require certain metals like Mn, Cu, and Zn. However, on contrary, heavy metals detrimental effects in high doses have long been known. For example, Zn acts as an essential component for enzymes and transcription factors required for membrane stability, auxin metabolism, and reproductive systems. Also, at large dosages, the same zinc can interfere with plant metabolism and cause a reduction in plant growth and senescence (Fontes and Cox 1998; Warne et al. 2008). Similarly, Cu concentrations above a certain threshold have also been linked to having a similar effect. Furthermore, excessive Mn levels are toxic to a wide range of plant diversity. Likewise, cell division, as well as root and stem development, are hampered by the Cr shortage (Ai et al. 2018). Physicochemical parameters such as pH, electrochemical potential, suspended particle concentration, and temperature influence metal content in water and bottom sediments. Other factors, such as passage of time and the monsoon seasons, also have an impact on them (Jabłonska et al. 2016).

4.5 Sources of Heavy Metal Pollution Heavy metals (HMs) can be introduced to the surrounding environment, including water bodies, through natural and man-made sources. Industrial activities and waste: Industrial waste, an anthropogenic source is one of the main sources of heavy metal contamination in water bodies (Suthar et al. 2009). Urban and agriculture wastewater-contaminated soil can contain heavy metals generated from increased industrial activity, and the sewage sludge that results from this contamination penetrating the soil might pose very serious dangers (Zafar et al. 2020). Biomass burning and Fertilizer application: Heavy metal-containing particles formed by industrial sites and biomass burning can be deposited directly on the soil surface, road dust, and plants foliage by wet and dry deposition (Shi et al. 2008; Luo et al. 2011). Another form of heavy metal pollution that can readily contaminate our agricultural lands by spreading in the atmosphere is aerosol particles from the combustion of fossil fuels or other sources, also the growing use of phosphate fertilisers significantly influences the soil contamination levels, resulting in high levels of heavy metals when these fertilizers are used over an extended period of time (Alloway 2012; Zafar et al. 2020). Particulate Matter Emissions: Particulate matter (PM) emission in the form of road dust, gases, and black carbon from the chimneys of industries, coal thermal power plants, burning of municipal solid waste and agricultural waste during harvest season, and vehicular pollution act as a major source of heavy metal contamination in soil and food crops, posing a threat to living beings in the surrounding area.

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Runoff Processes: Due to complete mixing and drainage of stormwater through runoff processes, heavy metals present in road dust get absorbed to the surrounding soil and further get accumulated into vegetables via root and leaf uptake, posing health hazards to humans (Bi et al. 2018). In recent years, rigorous rules have reduced the quantity of harmful heavy metals that are released into the soil. Reduced atmospheric emissions, restrictions on wastewater’s heavy metals content, and lead removal from paints and fuels are a few regulatory examples (Jócsák et al. 2022).

4.6 Effects of Heavy Metals On Humans: Metals in wastewater may enter the human body and accumulate in fatty tissues, affecting the central nervous system, endocrine system, immunological system, hematological functioning, and regular cell metabolism, among other things. Lead, cadmium, mercury, cobalt, and arsenic are non-essential metals that, at extremely low amounts, can produce mutagenic, teratogenic, and carcinogenic effects. Research has revealed that soaring prevalence of stomach cancer cases in the Van region of Turkey were linked to high levels of these metals in the sediments, crops, and vegetables in that particular region (Turkdoan et al. 2002; Bi et al. 2018). Heavy metals have well-known adverse effects on human health. For instance, some heavy metals can lead to cancer, while others can impair the functioning of neurological, skeletal, circulatory, endocrine, immunological, or enzyme systems as well as essential organs (Cherfi et al. 2015). Furthermore, there could be a possibility of a synergistic effect when combined with other pollutants, such as antibiotics (Becerra-Castro et al. 2015). On Soil and Plants: Heavy metals accumulation generally occurs in the surface soil; it may be due to their low solubility and restricted plant uptake capacity (Kim et al. 2015). However, chronic irrigation with wastewater leads to heavy metals accumulation, which further leach down thus deteriorating the groundwater quality (Becerra-Castro et al. 2015). Gupta and Paulraj (2015) also found that the application of solid waste landfill leachate leads to inhibition of growth as well as reduction in chlorophyll content of Vicia faba because leachate contains numerous pollutants including heavy metals. Accumulation of heavy metals in plants irrigated with wastewater is not homogenous, they are more concentrated in the roots than in the foliar or fruit parts (Parveen et al. 2015). According to previous studies, irrigation with wastewater results in significantly higher concentrations of heavy metals in plants than irrigation with potable water (Parveen et al. 2015; Rezapour et al. 2019). Although Rezapour et al. (2019) revealed the concentration of cadmium and copper in plants above the threshold value. However, several other studies (Parveen et al. 2015; Qureshi et al. 2016) found that the levels of heavy metals in the plants were within the acceptable ranges for human consumption. Similarly, Rai et al. (2019) found that consumption of metal-polluted food leads to the metals accumulation in bones and fatty tissues, as a result humans suffer from

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malnutrition and have compromised immune systems. Also, excessive consumption of metal-contaminated crops and vegetables can cause grave health concerns like gastrointestinal cancer, disruption in the central nervous system, and developmental delays of the fetuses in the womb during pregnancy. The detrimental impacts of the most prevalent heavy metals are listed as follows: Lead (Pb): Lead contamination has a detrimental effect on the mental development of human beings, leading to neurological and cardiovascular diseases, particularly in youngsters. Lead poisoning can also result in musculoskeletal injuries and malformations, as well as renal dysfunction, hypertension, and other immune system ailments (Trichopoulos 1997). Obiora et al. (2016) found heavy metal contamination in vegetables growing near a Pb–Zn mine was a source of various diseases, particularly lead causes Alzheimer’s disease. Arsenic (As): Higher concentrations of As in aquifers, sediments, and food crops have been linked to cancer, skin ailments, respiratory dysfunction, and a variety of other illnesses of the circulatory, digestive, hematological, renal, hepatic, nervous, developmental, immunological, and reproductive systems (Rai et al. 2019). Zinc (Zn): A high concentration of Zn in the human body can cause immune system problems by changing the amounts of high-density lipoproteins (Zhou et al. 2016). Copper (Cu): Similarly, too much copper in humans can induce liver failure and abdominal problems. Additionally, heavy metals like zinc, copper, and chromium can result in non-cancerous health concerns like cognitive abnormalities, nausea, and hepatic diseases (Liu et al. 2015). Gupta and Paulraj (2017) also found that toxicity of municipal solidwaste landfill leachate on Triticum aestivum was dependent on the concentration of Pb and Cu along with other pollutants. Chromium (Cr): In terms of stability, among the other ionic forms of Cr, Cr (VI) is the most hazardous. As a result, the earlier form is thought to have a higher risk of lung cancer than the latter one. Cadmium (Cd): Cd carcinogenicity is well documented by Itoh et al. (2014). People typically consume it as a result of food crops contaminated with cadmium, particularly rice, and it is linked to breast cancer after menopause. In research that reiterated the systemic health effects of a combination of lead and cadmium instead of the effects of individual metals, Cui et al. (2004) discovered kidney damage in a majority of individuals who ate foods polluted with numerous different metals, rather than the impact of individual metals.

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4.7 The Advantages and Demerits of Wastewater Irrigation in Agriculture 4.7.1 Effect of Wastewater Application Water acts as one of the most important requirements for optimum quality as well as quantity of crop growth, and it has always been a source of concern for farmers. The Indian economy is based on agriculture, and India is a land of farmers (Matta et al. 2017). The benefits of using wastewater in agriculture are due to its composition which consists of water and suspended as well as dissolved particles in 99:1 ratio respectively (Hanjra et al. 2010). On an oven dry weight basis, the solid part typically contains degradable organics (40–50%), inert materials (30–40%), persistent organics (10–15%), and other chemicals (5–8%) (Ofori et al. 2021). Emerging countries are increasingly turning to sewage effluent as a source of manure, as well as a suitable fertilizer that serves to promote the growth and development of plants (O’Riordan 1983). Farmers are primarily concerned with the direct benefits of wastewater utilization in agriculture, such as increased farm productivity, low-cost availability of water, effective effluent disposal, nutrient source, organic matter, etc. However, they are ignorant of the negative consequences of wastewater irrigation, such as heavy metal pollution of soils and crops, and health-related issues. Depending on the amount and kind of elements present in contaminated canal water, long-term use of poor-quality water can cause the soil to become less productive or even barren (Verma et al. 2012). Due to the presence of detergents and soaps, sewage effluents are often alkaline in nature and have high levels of biological oxygen demand and significant amounts of critical plant nutrients including Mg, N, Ca, P, K, S, and micronutrients. The three primary plant nutrients, N, P, and K are commonly found in wastewater generated from cities. The concentration of N in the wastewater, however, could be significant if it contains untreated wastewater discharges from distilleries and agricultural industries (Ofori et al. 2021). On the contrary, there are many potentially dangerous compounds in wastewater, like heavy metals such as Pb, Cu, Mn, Ni, and Zn. As evidenced by the exploration of many toxic metals in soil and crops that are irrigated with industrial effluent-contaminated water, heavy metals can build up in the tissues of plants at levels above the permitted levels, putting living beings at risk who consume these crops (Patil et al. 2012).

4.7.2 Advantages of Wastewater Irrigation in Agriculture 4.7.2.1

Source of Macro and Micronutrients

Wastewater irrigation contributes significant amount of nutrients to the soil. Elements such as phosphorus, nitrogen, potassium, manganese, zinc, iron, and copper may be present in wastewater due to its complex mixture (Qadir and Scott 2009). Ganjegunte

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et al. (2017) noted an increase in the nutrients content of the soil during a six-year irrigation period. Both nitrate and potassium levels showed increase in concentration; earlier the nitrate concentrations ranged from 269 to 321 mg/L, whereas after the application of wastewater for irrigation, increased nitrate concentrations have been observed which ranged from 910 to 2271 mg/L. Additionally, there was a fourfold rise in potassium levels. However, in other studies, using treated wastewater to irrigate the soil and land resulted in a considerable improvement in the soil’s fertility levels in terms of nitrogen, phosphorus, potassium, and micronutrients (Galavi et al. 2010; Singh et al. 2012).

4.7.2.2

Organic Matter

Reusable wastewater could perhaps be a valuable and advantageous source of organics for soils to support the growth of plants, since wastewater may contain more organic matter in it as compared to other water sources (Ofori et al. 2021).

4.7.2.3

Impacts on Plant Growth

Treated wastewater is a desirable irrigation source due to its nutritional content. It provides nutrients to encourage plant growth and satisfies the agricultural water needs for crop production (Lu et al. 2015). Although the availability of nutrients generally promotes plant growth, there is a risk of plant toxicity when there is an oversupply of nutrients.

4.7.2.4

Improved Nutrients Availability and Uptake

When treated wastewater is applied, both macro and micronutrients are made accessible for plant absorption. Both root shape and nutrient content at the root surface influences how well plants absorb nutrients. In contrast to their elemental or uncharged forms, the majority of nutrients are absorbed in the ionic form (Jones and Jacobsen 2005). According to Aziz and Farissi (2014), the presence of nitrogen (N) in treated wastewater in the form of nitrates and ammonium makes it readily absorbable by plants. Phosphorus and potassium, which are readily available to plants, are largely present as orthophosphate and potassium ions, respectively. As a result, by providing nutrients to plants in a form that makes them easily absorbable thus treated wastewater encourages growth and production. For example, research found that irrigation with wastewater and the addition of nitrogen in the form of nitrates improved the output of lettuce by up to 50% (Vergine et al. 2017b).

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Economic Impacts (Farm Expenditure and Income)

Since wastewater supplies the crops with macronutrients (N, P, and K) as well as with micronutrients (such as phosphorus, potassium), farmers can lessen the chemical fertilizer usage and save money on fertilizer expenditures. Wastewater irrigation can cut the use of chemical fertilizers by 45% in the cultivation of wheat and 94% in alfalfa (Balkhair et al. 2013). In Tiznit, Morocco, the farmers’ income and level of living were significantly elevated by reusing treated wastewater for crop cultivation (Malki et al. 2017). Vergine et al. (2017b) assessed and calculated the possible cost savings of wastewater irrigation for tomato farming and reported a saving of e280/ ha.

4.7.2.6

Increased Crop Production

Crop cultivation demands a regular water supply in order to meet crop’s water needs and prevent water stress. Water stress negatively alters the crops’ morphological and physiological processes and reduces their harvestable yields (Sadras et al. 2016). Wastewater irrigation gives farmers access to water, in addition to supplying nutrients to boost crop output. Due to wastewater’s climatically independent nature, water for crop cultivation may always be available to farmers (unlike rainfall) (Verlicchi et al. 2012). As a result, farmers are able to produce steady, better agricultural harvests and maintain the ideal levels of soil moisture in their fields or farms.

4.7.3 Demerits of Wastewater Irrigation in Agriculture According to the WHO, roughly 80% of all human ailments are caused due to intake of contaminated water (Ravish et al. 2021). In untreated wastewater, toxic chemicals and harmful microbes proliferate, posing a threat to human health by disease transmission (Onweremadu et al. 2008). Furthermore, cytotoxic and genotoxic nature of wastewater on human embryonic kidney cells has been reported by Verma et al (2022a, b). While examining water quality, biological, physical, and radiological components are all taken into account. Chemical composition is the most commonly used criterion for assessing water quality (Verma et al. 2012). Following are the detrimental effects of wastewater irrigation.

4.7.3.1

Water Pollution

Surface water and groundwater could potentially be contaminated by wastewater. Physical, chemical, and microbiological contaminants can all be found in wastewater.

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Chemical contaminants include inorganic compounds such as heavy metals; nanoparticles and organic pollutants consist of pharmaceuticals, personal care products, pesticides, polyaromatic hydrocarbons (PAHs), endocrine disruption compounds, and disinfection by-products (Fatta-Kassinos et al. 2011; Verma et al. 2019; Verma et al. 2022a, b). Whereas, microbiological contaminants include schistosomas, helminths, protozoans, bacteria, and viruses (Jaramillo and Restrepo 2017). These microbiological, physical, and chemical pollutants may enter the water bodies such as lakes, ponds, and groundwater through irrigation process. This may have a negative impact on the water ecology, especially for aquatic fishes, due to the endocrine disruption caused by some of these toxins (Schacht et al. 2016).

4.7.3.2

Impacts on Soil Microbes

The microbial communities that live in soil are essential to its ecosystem. These Communities are built upon a complex web of interactions between soil’s physicochemical characteristics and biological constituents (Becerra-Castro et al. 2015). These communities may be harmed and the soil’s fertility and productivity could be negatively impacted by irrigation with treated wastewater. Numerous factors including climate, soil, and wastewater qualities, contribute to these intricate variations in soil microbiology (Lopes et al. 2015).

4.7.3.3

Soil Salinization

The deposition of salts or species that are soluble in water is referred to as salinization of the soil. Anions such as sodium (Na), magnesium (Mg), iron (Fe), or calcium (Ca) or cations are examples of soluble species (chloride). Wastewater irrigation may cause both short and long-term salinization because of the salts (cations and anions) it contains (Ofori et al. 2021).

4.7.3.4

Increased Sodium Adsorption Ratio (SAR)

The quantity of magnesium, sodium, and calcium ions in the irrigation water or soil can be used to determine the salt adsorption ratio (SAR) (Oster et al. 2016). It is recognized as a measure for the sodicity of soil because of its strong relationship to the exchangeable sodium percentage (Oster et al. 2016). Compared to soils irrigated with freshwater, SAR is higher in soils that get wastewater irrigation (Zema et al. 2012).

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Public Health Impacts

In contrast to potable water used for household purposes, treated and untreated wastewater may contain bacteria, toxic substances like heavy metals, or organic pollutants (Yi et al. 2011). Any irregularity in handling these toxic pollutants and pathogens could lead to significant hazard on consumers’ health and farmers. Through the intake of contaminated food and inhalation through the respiratory system, pathogens and contaminants can enter the human body (Singh et al. 2012; Yi et al. 2011).

4.7.3.6

Trace Organic Compounds (TOCs) and Microplastics (MPs) Contamination

These are groups of substances or compounds that pose a known or alleged harm to both people and the environment. Endocrine disrupting substances (EDS), personal care products, medications, and their transformation products or metabolites, and other substances are included in the category of TOCs (Fatta-Kassinos et al. 2011). Some of the sources of these compounds in wastewater include personal care and pharmaceutical products (PPCPs) that are marketed for sale both commercially and at retail stores (Tong et al. 2011; Diaz-Sosa et al. 2020). Human excreta and the discard of unnecessary drugs are a couple of the ways that TOCs reach the wastewater drain (Tong et al. 2011). Microplastics (MPs), a different group of emerging pollutants that are similar to EDS, are reported in reclaimed water (Blair et al. 2017). MPs are small pieces of plastic with a diameter of less than 5 mm that are made from packaging and textiles materials (Kay et al. 2018).

4.7.3.7

Plant Toxicity

Water stress, which may be caused by pollution of water, has negative affect on the plant’s growth and development, crop productivity and yields. Water stress can manifest itself in a variety of ways in plants, including morphological changes in leaves, repercussions on the growth and development of plant root and shoot systems, physiological changes by limiting photosynthetic activity by reducing CO2 inflow, carboxylation, electron transport chain activities of the chloroplasts in the mesophyll, and biochemical changes by altering SOD and CAT activity in plant tissue (Özy˙i˘g˙it et al. 2009). Gupta and Paulraj (2016) found that Triticum aestivum treated with lower concentrations of landfill leachate stimulated germination as well as growth whereas higher concentration inhibited the process in a time and dose-dependent manner. Excessive nutrients can lead to plant toxicity, which affects the quality and growth of plants (McCauley et al. 2009). It has been demonstrated that irrigation with wastewater increases soil and plant trace element levels (Galavi et al. 2010; Kalavrouziotis et al.2012). The accumulation of such substances may result in toxicity when concentrations increase above the given permissible limit. Hussain et al. (2019) claim that

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the soil properties, plant absorption capacity, and wastewater treatment composition all have an impact on the accumulation of metals. Fe, Ni, Cr, Cu, Cd, Pb, and Zn are the commonly accumulating elements (Becerra-Castro et al. 2015).

4.8 Conclusion In this article, we have reviewed the scarcity of water at the global level as well as regionally, particularly in India. Country-wise utilization of fresh water in various sectors, including agriculture, industry, domestic, energy, livestock, and aquaculture, has been discussed. Considering the water scarcity worldwide, the need for utilization of treated wastewater for reuse in agriculture has been explored country wise. Sources of water pollution and contamination are required to be explored, and both point and non-point sources of water pollution have been discussed. Due to water scarcity and water stress, countries are increasingly turning to wastewater for agriculture, as it has many direct benefits. But at the same time, farmers in particular and people in general need to be made aware of the long-term negative consequences of wastewater irrigation. It has been widely researched that heavy metals in wastewater are of serious concern, causing detrimental effects on health of living organisms as well as on the environment. Therefore, this article has explored both the benefits and negative impacts of wastewater irrigation, including the biomagnification of heavy metals, phytotoxicity, and long-term impacts on food chains. It is imperative to recognize the options to reduce the problems arising due to water scarcity, and the reuse of treated wastewater is apparently one of the options. But proper policies need to be formulated to guide the application of wastewater in agriculture, aquaculture, and many other fields. This would safeguard the health and livelihood of farmers, consumers, and the environment in general. So, policies and practices to encourage the safe reuse of wastewater should be encouraged.

References Abdel-Shafy HI, Mansour MS (2018) Solid waste issue: Sources, composition, disposal, recycling, and valorization. Egypt J Pet 27(4):1275–1290 Ackerman D, Weisberg SB (2003) Relationship between rainfall and beach bacterial concentrations on Santa Monica Bay beaches. J Water Health 1(2):85–89 Ai TN, Naing AH, Yun BW, Kim CK (2018) Overexpression of RsMYB1 enhances heavy metal stress tolerance in transgenic petunia by elevating the transcript levels of stress tolerant and antioxidant genes. BioRxiv: 286849 Alloway BJ. ed (2012) Heavy metals in soils: trace metals and metalloids in soils and their bioavailability (Vol. 22). Springer Science & Business Media Al-Samman T (2015) Effect of heavy metal impurities in secondary Mg alloys on the microstructure and mechanical properties during deformation. Mater Des 65:983–988

4 Application of Wastewater in Agriculture: Benefits and Detriments

71

Aniyikaiye TE, Oluseyi T, Odiyo JO, Edokpayi JN (2019) Physico-chemical analysis of wastewater discharge from selected paint industries in Lagos, Nigeria. Int J Environ Res Public Health 16(7):1235 Arora A, Pandey M, Mishra VN, Kumar R, Rai PK, Costache R, Punia M, Di L (2021) Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecol Ind 128:107810 Aziz F, Farissi M (2014) Reuse of treated wastewater in agriculture: solving water deficit problems in arid areas. Annales of West University of Timisoara. Series of Biology 17(2):95 Balkhair KS. El-Nakhlawi FS, Ismail SM, Al-Solimani SG (2013) Treated wastewater use and its effect on water conservation, vegetative yeild, yield components and water use efficiency of some vegetable crops grown under two different irrigation systems in western region, Saudi Arabia. Eur Sci Journal 9(21) Becerra-Castro C, Lopes AR, Vaz-Moreira I, Silva EF, Manaia CM, Nunes OC (2015) Wastewater reuse in irrigation: a microbiological perspective on implications in soil fertility and human and environmental health. Environ Int 75:117–135 Bi C, Zhou Y, Chen Z, Jia J, Bao X (2018) Heavy metals and lead isotopes in soils, road dust and leafy vegetables and health risks via vegetable consumption in the industrial areas of Shanghai, China. Sci Total Environ 619:1349–1357 Birajdar SN, Chanshetti UB (2017) The physicochemical evaluation of tubewell water quality of Osmanabad City. Maharastra State, India Blair RM, Waldron S, Phoenix V, Gauchotte-Lindsay C (2017) Micro-and nanoplastic pollution of freshwater and wastewater treatment systems. Springer Science Reviews 5(1):19–30 Brototi B, Singh A, Rai PK, Kumar J, Walker S (2022) GIS based study of reclamation of degraded semi-arid soil: A case study of Rajsthan from India. Indian J Environ Prot 42(3):302–315 Carroll SP, Dawes LA, Goonetilleke A, Hargreaves M (2006) Water quality profile of an urbanising catchment–Ningi Creek Catchment Central Water Commission report (CWC report) (2020) Water Quality Monitoring of Canals, August, Ministry of Jal Shakti, Government of India Chen Y, Zhao K, Wu Y, Gao S, Cao W, Bo Y, Shang Z, Wu J, Zhou F (2016) Spatio-temporal patterns and source identification of water pollution in Lake Taihu (China). Water 8(3):86 Cherfi A, Achour M, Cherfi M, Otmani S, Morsli A (2015) Health risk assessment of heavy metals through consumption of vegetables irrigated with reclaimed urban wastewater in Algeria. Process Saf Environ Prot 98:245–252 CPCB (2021) National Inventory of Sewage Treatment Plants. Central Pollution Control Board, Delhi - 110032, India (Accessed on 20th May, 2021). CSIRO (Australia) (2008) Water availablity in the Murray-Darling Basin: a report from CSIRO to the Australian Government. CSIRO. Cui YJ, Zhu YG, Zhai RH, Chen DY, Huang YZ, Qiu Y, Liang JZ (2004) Transfer of metals from soil to vegetables in an area near a smelter in Nanning. China. Environment International 30(6):785–791 Dhawan V (2017) Water and agriculture in India. In Background paper for the South Asia expert panel during the Global Forum for Food and Agriculture (Vol. 28). Diaz-Sosa VR, Tapia-Salazar M, Wanner J, Cardenas-Chavez DL (2020) Monitoring and ecotoxicity assessment of emerging contaminants in wastewater discharge in the City of Prague (Czech Republic). Water 12(4):1079 Fatta-Kassinos D, Kalavrouziotis IK, Koukoulakis PH, Vasquez MI (2011) The risks associated with wastewater reuse and xenobiotics in the agroecological environment. Sci Total Environ 409(19):3555–3563 Fontes RLF, Cox FR (1998) Zinc toxicity in soybean grown at high iron concentration in nutrient solution. J Plant Nutr 21(8):1723–1730 Fu Z, Xi S (2020) The effects of heavy metals on human metabolism. Toxicol Mech Methods 30(3):167–176

72

A. Verma et al.

Galavi M, Jalali A, Ramroodi M, Mousavi SR, Galavi H (2010) Effects of treated municipal wastewater on soil chemical properties and heavy metal uptake by sorghum (Sorghum bicolor L.). J Agric Sci 2(3): 235 Ganjegunte G, Ulery A, Niu G, Wu Y (2017) Effects of treated municipal wastewater irrigation on soil properties, switchgrass biomass production and quality under arid climate. Ind Crops Prod 99:60–69 Ganoulis J (2009) Risk analysis of water pollution. John Wiley & Sons Gemeda FT, Guta DD, Wakjira FS, Gebresenbet G (2020) Physicochemical characterization of effluents from industries in Sabata town of Ethiopia. Heliyon 6(8):e04624 GOI. 2017. Discharge of Sewage Into Ganga River. Lok Sabha Unstarred Question No. 1530. Ministry of Water Resources, River Development and Ganga Rejuvenation, Govt. of India, New Delhi, India (Accessed on 29th July, 2021) Gupta A, Paulraj R (2016) Relationship between physicochemical parameters and toxicity of leachate from municipal solid waste landfill site in Delhi. In Geostatistical and geospatial approaches for the characterization of natural resources in the environment, Springer, Cham, pp 361–367 Gupta A, Paulraj R (2017) Leachate composition and toxicity assessment: an integrated approach correlating physicochemical parameters and toxicity of leachates from MSW landfill in Delhi. Environ Technol 38(13–14):1599–2160 Gupta A, Rajamani P (2015) Toxicity assessment of municipal solid waste landfill leachate collected in different seasons from Okhala landfill site of Delhi. J Biomed Sci Eng 8(06):357 Hanjra MA, Qureshi ME (2010) Global water crisis and future food security in an era of climate change. Food Policy 35(5):365–377 Hill MK (2020) Understanding environmental pollution. Cambridge University Press Hussain A, Priyadarshi M, Dubey S (2019) Experimental study on accumulation of heavy metals in vegetables irrigated with treated wastewater. Appl Water Sci 9(5):1–11 Itoh H, Iwasaki M, Sawada N, Takachi R, Kasuga Y, Yokoyama S, Onuma H, Nishimura H, Kusama R, Yokoyama K, Tsugane S (2014) Dietary cadmium intake and breast cancer risk in Japanese women: a case–control study. Int J Hyg Environ Health 217(1):70–77 Jabło´nska-Czapla M, Noco´n K, Szopa S, Łyko A (2016) Impact of the Pb and Zn ore mining industry on the pollution of the BiałaPrzemsza River, Poland. Environ Monit Assess 188(5):1–16 Jaramillo MF, Restrepo I (2017) Wastewater reuse in agriculture: A review about its limitations and benefits. Sustain 9(10):1734 Jimenez B, Asano T (2008) Water reclamation and reuse around the world. Water Reuse: Int Surv Curr Pract, Issues and Needs 14:3–26 Jócsák I, Knolmajer B, Szarvas M, Rabnecz G, and Pál-Fám F (2022) Literature review on the effects of heavy metal stress and alleviating possibilities through exogenously applied agents in Alfalfa (Medicago sativa L.). Plants 11(16): 2161 Jones C, Jacobsen J (2005) Plant nutrition and soil fertility. Nutrient Management Module 2(11):1– 11 Jury WA, Vaux HJ Jr (2007) The emerging global water crisis: managing scarcity and conflict between water users. Adv Agron 95:1–76 Kalavrouziotis IK, Koukoulakis P, Kostakioti E (2012) Assessment of metal transfer factor under irrigation with treated municipal wastewater. Agric Water Manag 103:114–119 Kay P, Hiscoe R, Moberley I, Bajic L, McKenna N (2018) Wastewater treatment plants as a source of microplastics in river catchments. Environ Sci Pollut Res 25(20):20264–20267 Kesari KK, Soni R, Jamal QMS, Tripathi P, Lal JA, Jha NK, Siddiqui MH, Kumar P, Tripathi V, Ruokolainen J (2021) Wastewater treatment and reuse: a review of its applications and health implications. Water Air Soil Pollut 232(5):1–28 Kim HK, Jang TI, Kim SM, Park SW (2015) Impact of domestic wastewater irrigation on heavy metal contamination in soil and vegetables. Environ Earth Sci 73(5):2377–2383 Kumar R (2019) Emerging challenges of water scarcity in India: the way ahead. Int J Innov Stud Sociol HumIties 4(4):6–28

4 Application of Wastewater in Agriculture: Benefits and Detriments

73

Lane TW, Morel FM (2000) A biological function for cadmium in marine diatoms. Proc Natl Acad Sci 97(9):4627–4631 Leff B, Ramankutty N, Foley JA (2004) Geographic distribution of major crops across the world. Glob Biogeochem Cycles 18(1) Liu X, Tang L, Yang L, Zhang X, Wang L, Yu F, Liu Y, Chen Q, Liu D (2015) Genotoxicity evaluation of irrigative wastewater from Shijiazhuang city in China. PLoS ONE 10(12):e0144729 Lopes AR, Becerra-Castro C, Vaz-Moreira I, Silva MEF, Nunes OC, Manaia CM (2015) Irrigation with treated wastewater: potential impacts on microbial function and diversity in agricultural soils. Wastewater reuse and current challenges, pp 105–128 Lu Y, Song S, Wang R, Liu Z, Meng J, Sweetman AJ, Jenkins A, Ferrier RC, Li H, Luo W, Wang T (2015) Impacts of soil and water pollution on food safety and health risks in China. Environ Int 77:5–15 Luo C, Liu C, Wang Y, Liu X, Li F, Zhang G, Li X (2011) Heavy metal contamination in soils and vegetables near an e-waste processing site, south China. J Hazard Mater 186(1):481–490 Malki M, Bouchaou L, Mansir I, Benlouali H, Nghira A, Choukr-Allah R (2017) Wastewater treatment and reuse for irrigation as alternative resource for water safeguarding in Souss-Massa region, Morocco. European Water 59:365–371 Mancosu N, Snyder RL, Kyriakakis G, Spano D (2015) Water scarcity and future challenges for food production. Water 7(3):975–992 Matta G, Srivastava S, Pandey RR, Saini KK (2017) Assessment of physicochemical characteristics of Ganga Canal water quality in Uttarakhand. Environ Dev Sustain 19(2):419–431 McCauley A, Jones C, Jacobsen J (2009) Plant nutrient functions and deficiency and toxicity symptoms. Nutrient Management Module 9:1–16 Minhas PS, Samra JS (2004) Wastewater use in peri-urban agriculture: impacts and opportunities. Wastewater use in peri-urban agriculture: impacts and opportunities Minhas PS, Saha JK, Dotaniya ML, Sarkar A, Saha M (2022) Wastewater irrigation in India: Current status, impacts and response options. Sci Total Environ 808:152001 Mishra VN, Rai PK, Singh P (2021) Geo-information Technology in Earth Resources Monitoring and Management (edit. Book), Nova Science Publishers, USA, ISBN: 978–1–53619–669–6. 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, Meteorological Application, 27(1):1–16. ISSN: 1469–8080. https://doi.org/10.1002/met.1863 Obiora SC, Chukwu A, Davies TC (2016) Heavy metals and health risk assessment of arable soils and food crops around Pb–Zn mining localities in Enyigba, southeastern Nigeria. J Afr Earth Sc 116:182–189 Ofori S, Puškáˇcová A, R˚užiˇcková I, Wanner J (2021) Treated wastewater reuse for irrigation: Pros and cons. Sci Total Environ 760:144026 Okoh AI, Odjadjare EE, Igbinosa EO, Osode AN (2007) Wastewater treatment plants as a source of microbial pathogens in receiving watersheds. African Journal of Biotechnology 6 (25). Onweremadu EU (2008) Physico-chemical characterization of a farmland affected by wastewater in relation to heavy metals. Journal of Zhejiang University-SCIENCE A 9(3):366–372 O’Riordan EG, Dodd VA, Tunney H, Fleming GA (1983) The chemical composition of sewage sludges. Ireland Journal of Agriculture Research 25:239–249 Oster J, Sposito G, Smith C (2016) Accounting for potassium and magnesium in irrigation water quality assessment. Calif Agric 70(2):71–76 ˘ ˙IT ˙I˙I, AKINCI S¸ (2009) Effects of some stress factors (aluminum, cadmium and drought) ÖZY˙IG on stomata of Roman nettle (Urtica pilulifera L.). NotulaeBotanicae Horti Agrobotanici ClujNapoca 37(1):108–115 Parveen T, Hussain A, Someshwar Rao M (2015) Growth and accumulation of heavy metals in turnip (Brassica rapa) irrigated with different concentrations of treated municipal wastewater. Hydrol Res 46(1):60–71 Patil PN, Sawant DV, Deshmukh RN (2012) Physico-chemical parameters for testing of water-a review. Int J Environ Sci 3(3):1194

74

A. Verma et al.

Pedrero F, Kalavrouziotis I, Alarcón JJ, Koukoulakis P, Asano T (2010) Use of treated municipal wastewater in irrigated agriculture—review of some practices in Spain and Greece. Agric Water Manag 97(9):1233–1241 Popa P, Timofti M, Voiculescu M, Dragan S, Trif C, Georgescu LP (2012) Study of physicochemical characteristics of wastewater in an urban agglomeration in Romania. The Scientific World Journal Qadir M, Scott CA (2009) Non-pathogenic trade-offs of wastewater irrigation. In: Wastewater irrigation and health, pp 127–152 Qureshi AS, Hussain MI, Ismail S, Khan QM (2016) Evaluating heavy metal accumulation and potential health risks in vegetables irrigated with treated wastewater. Chemosphere 163:54–61 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 Rai PK, Lee SS, Zhang M, Tsang YF, Kim KH (2019) Heavy metals in food crops: health risks, fate, mechanisms, and management. Environ Int 125:365–385 Rai VK, Kumar P, Singh A, Prasad D, Rai PK (2021a) GIS based analysis of distribution of heavy metal pollutants in India: sources, toxicity and their mitigation. Pollution Research 40(1):237– 243. ISSN 0257–8050 Rai PK, Mishra VN, Singh P (2021b) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book), Springer Nature, Switzerland, ISBN: 978–3–030–76116–5. https://doi.org/10.1007/978-3-030-76116-5 Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi.org/10.1007/978-981-16-7373-3. Ranade VV, Bhandari VM (2014) Industrial wastewater treatment, recycling and reuse. ButterworthHeinemann Ravish S, Setia B, Deswal S (2021) Groundwater quality in urban and rural areas of north-eastern Haryana (India): a review. ISH Journal of Hydraulic Engineering 27(2):224–234 Rezapour S, Atashpaz B, Moghaddam SS, Damalas CA (2019) Heavy metal bioavailability and accumulation in winter wheat (Triticum aestivum L.) irrigated with treated wastewater in calcareous soils. Sci Total Environ 656:261–269 Rosa L, Chiarelli DD, Rulli MC, Dell’Angelo J, D’Odorico P (2020) Global agricultural economic water scarcity. Science Advances 6(18): eaaz6031 Sadras VO, Villalobos FJ, Orgaz F, Fereres E (2016) Effects of water stress on crop production. Principles of agronomy for sustainable agriculture. Springer, Cham, pp 189–204 Schacht K, Chen Y, Tarchitzky J, Marschner B (2016) The use of treated wastewater for irrigation as a component of integrated water resources management: reducing environmental implications on soil and groundwater by evaluating site-specific soil sensitivities. In: Integrated water resources management: concept, research and implementation. Springer, Cham, pp 459–470 Seckler D, Amarasinghe U, Molden D, de Silva R, Barker R (1998) World water demand and supply, 1990 to 2025: scenarios and issues. Research Report 19. Shah T, Singh OP, Mukherji A (2006) Some aspects of South Asia’s groundwater irrigation economy: analyses from a survey in India, Pakistan. Nepal Terai and Bangladesh. Hydrogeology Journal 14(3):286–309 Sharma D, Kansal A (2011) Current condition of the Yamuna River: an overview of flow, pollution load, and human use. Yamuna River: a confluence of waters, a crisis of need Sharma R, Kumar A, Singh N, Sharma K (2021) Impact of seasonal variation on water quality of Hindon River: physicochemical and biological analysis. SN Applied Sciences 3(1):1–11 Shi G, Chen Z, Xu S, Zhang J, Wang L, Bi C, Teng J (2008) Potentially toxic metal contamination of urban soils and roadside dust in Shanghai. China. Environmental Pollution 156(2):251–260 Shiklomanov IA (1998) World water resources a new appraisal and assessment for the 21st century; United Nations Educational, Scientific and Cultural Organization (UNESCO): Paris, France

4 Application of Wastewater in Agriculture: Benefits and Detriments

75

Singh PK, Deshbhratar PB, Ramteke DS (2012) Effects of sewage wastewater irrigation on soil properties, crop yield and environment. Agric Water Manag 103:100–104 Suthar S, Nema AK, Chabukdhara M, Gupta SK (2009) Assessment of metals in water and sediments of Hindon River, India: impact of industrial and urban discharges. J Hazard Mater 171(1– 3):1088–1095 Tong AY, Peake BM, Braund R (2011) Disposal practices for unused medications around the world. Environ Int 37(1):292–298 Trichopoulos D, Lipworth L, Petridou E, Adami HO (1997) Epidemiology of cancer. Cancer, principles and practice of oncology Türkdo˘gan MK, Kilicel F, Kara K, Tuncer I, Uygan I (2003) Heavy metals in soil, vegetables and fruits in the endemic upper gastrointestinal cancer region of Turkey. Environ Toxicol Pharmacol 13(3):175–179 Vergine P, Lonigro A, Salerno C, Rubino P, Berardi G, Pollice A (2017a) Nutrient recovery and crop yield enhancement in irrigation with reclaimed wastewater: a case study. Urban Water Journal 14(3):325–330 Vergine P, Salerno C, Libutti A, Beneduce L, Gatta G, Berardi G, Pollice A (2017b) Closing the water cycle in the agro-industrial sector by reusing treated wastewater for irrigation. J Clean Prod 164:587–596 Verlicchi P, Al Aukidy M, Galletti A, Zambello E, Zanni G, Masotti L (2012) A project of reuse of reclaimed wastewater in the Po Valley, Italy: Polishing sequence and cost benefit analysis. J Hydrol 432:127–136 Verma A, Meena R, Maurya A, Gaharwar US, Rajamani P (2019) Identification, quantification and in-vitro genotoxicity of major polyaromatic hydrocarbons produced by sugarcane fly ash emitted from Sugarmill. J Environ Prot 10(10):1244–1261 Verma A, Gaharwar US, Priyadarshini E, Rajamani P (2022a) Metal accumulation and health risk assessment in wastewater used for irrigation around the Agra Canal in Faridabad. India. Environmental Science and Pollution Research 29(6):8623–8637 Verma A, Singh Gaharwar U, Maurya A, Rajamani P (2022b) Cytotoxic and genotoxic assessment of wastewater on HEK293 cell line. Indian J Biochem Biophys (IJBB) 59(5):558–564 Verma OP, Khanan B, Shukla S (2012) Determination of physico-chemical characteristics of four canals of Allahabad region and its suitability for irrigation. Adv Appl Sci Res 3(3):1531–1537 Vishwakarma CS, Thakur S, Rai PK, Kamal V, Mukharjee S (2016) Changing Land Trajectories: A Case Study from India Using Remote Sensing. Eur J Geogr 7(2):63–73 Warne MSJ, Heemsbergen D, Stevens D, McLaughlin M, Cozens G, Whatmuff M, Broos K, Barry G, Bell M, Nash D, Pritchard D (2008) Modeling the toxicity of copper and zinc salts to wheat in 14 soils. Environ Toxicol Chem: Int J 27(4):786–792 WHO-World Health Organization (2006) Guidelines for the safe use of wastewater, excreta and greywater. In: Wastewater use in agriculture, 2nd ed. WHO-World Health Organization Yi L, Jiao W, Chen X, Chen W (2011) An overview of reclaimed water reuse in China. J Environ Sci 23(10):1585–1593 Zafar-ul-Hye M, Naeem M, Danish S, Fahad S, Datta R, Abbas M, Rahi AA, Brtnicky M, Holátko J, Tarar ZH, Nasir M (2020) Alleviation of cadmium adverse effects by improving nutrients uptake in bitter gourd through cadmium tolerant rhizobacteria. Environments 7(8):54 Zema DA, Bombino G, Andiloro S, Zimbone SM (2012) Irrigation of energy crops with urban wastewater: effects on biomass yields, soils and heating values. Agric Water Manag 115:55–65 Zhou H, Yang WT, Zhou X, Liu L, Gu JF, Wang WL, Zou JL, Tian T, Peng PQ, Liao BH (2016) Accumulation of heavy metals in vegetable species planted in contaminated soils and the health risk assessment. Int J Environ Res Public Health 13(3):289

Chapter 5

A GIS-Based Flood Risk Assessment and Mapping Using Morphometric Analysis in the Kayadhu River Basin, Maharashtra Bhagwan B. Ghute

and Pranjit Sarma

Abstract Recently, flood is one of the most destructive and frequent natural disasters of climate change effects facing the world, it destroys people’s lives and environmental assets. As a result, an increase in anthropogenic activities which puts pressure on the river channel and causes a change in river morphology. These hazards can hamper efforts to escape poverty and set back the development gain of the region. Remote sensing and GIS can provide a better understanding of its spatial extent for delineating flood risk assessment and mapping by developing models. The main aim of this study is to identify and map flood risk areas in the Kayadhu river basin, Maharashtra. Without flood risk analysis opportunities could be reduced and the impact on development may be undermined. The drainage network, morphometric analysis, and collected data were processed in a GIS environment. Criteria for the selection of the derived morphometric parameters based on their direct relationships with surface runoff. According to the findings, the Kayadhu river basin was categorized into three zones based on flood risk assessment such as high risk, moderate risk, and low risk. Flood forecasting and management practices could be useful for decision-makers to mitigate disaster risk in the future. Keywords Flood risk · Kayadhu river · Morphometric analysis · GIS-based mapping · Maharashtra

B. B. Ghute (B) Department of Geology, Toshniwal Arts, Commerce and Science College, Sengaon Dist. Hingoli, Maharashtra 431542, India e-mail: [email protected] P. Sarma Department of Geography, Guwahati University, Guwahat, Assam 781014, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_5

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5.1 Introduction Flooding is a natural part of the hydrological cycle and it became a serious problem worldwide causing loss of life, displacement, and economic damage also affecting more than 170 million people each year in the world (Gao et al. 2019; Flores et al. 2020; Hagos et al. 2022). In India, on average every year, 75 lakh hectares of land are affected, 1600 lives are lost and the loss of public utilities amounts to 1805 crores of Indian rupees (Panigrahi and Suar 2021). Hence, floods can be regarded as one of the most costly natural hazards in developing and developed countries all over the world (Brito and Evers 2016). In many parts of the world, extreme precipitation is projected to increase with global warming, even in areas where average rainfall is declining (Rana et al. 2014). From a hydrological perspective, flooding occurs when the watershed experiences an unusually intense or prolonged water input event and the resulting stream flow rate exceeds the channel capacity (Ogarekpe et al. 2020). A primary cause of the flooding is excessive rainfall. There are many other causes attributed to human activity such as land degradation; deforestation of watersheds; urban sprawl and increasing population density along river banks (Duan et al. 2016; Prasad et al. 2016; Singh et al. 2021) Besides, inadequate land use planning, zoning, and management of floodplain development. Inadequate drainage, especially in urban areas, and poor management of runoff from river reservoirs (Rai et al. 2014; Danumah et al. 2016). In order to mitigate the impact and risk assessment, a set of flood reduction measures need to be taken because such extreme events would have a large impact on the growing economy of India. Flood hazard mapping and analysis, which identifies the most vulnerable areas based on physical characteristics indicative of foraging trends, is one of the most important parts of the early warning system or method to prevent and mitigate future flood situations. Flood hazard mapping is an important part of flood-prone land use planning and mitigation strategies (Hagos et al. 2022). Flood management is necessary not only causes enormous damage to society, but also necessary for land management. This is not technically feasible without a meaningful flood hazard and risk map of the region (Kia et al. 2012; Bhatt et al. 2014). Now traditional approaches for flood mapping are usually narrow, due to insufficient data for example rainfall-runoff modeling methods, watermarks on buildings, numerical models etc., are not suitable for compressive river and flood analysis. (Tehrany et al. 2015; Ullah and Zhang 2020; Rai et al. 2021; Mishra et al. 2021; Rai et al. 2022). These methods are expensive, time-consuming, and not often available for small watershed level, especially in India. Now, researchers using a variety of methods for risk assessment such as artificial neural network (ANN), frequency ratio (FR), analytical hierarchy process (AHP), morphometric analysis, etc. (Vogel 2016; Rahman et al. 2019; Adnan et al. 2019; Ogarekpe et al. 2020). For the present study morphometric analytical method was used. Morphometric analysis is the quantitative analysis of surface landforms and quantifies all the hydrogeological parameters of the watershed. It plays a significant role in understanding terrain features, infiltration-runoff, erosion, sediment transportation,

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flooding, mass movement, and tectonics in the watershed (Rana et al. 2014; Asfaw and Workineh 2019; Nones 2019; Ogarekpe et al. 2020; Ghute and Babar 2021; Rawat et al. 2021). Today advances in science and technology, especially in remote sensing and GIS which is a powerful tool and provides different data sources for flood management, assessment, and its forecast (Panhalkar and Jarag 2017; Dandapat and Panda 2017; Vojtek and Vojteková 2019; Vanama et al. 2021). This study investigated the detailing the flood risk assessment of Kayadhu river basin and investigated linear, aerial, and relief parameters of the river basin.

5.2 Study Area Kayadhu river basin is located eastern part of Deccan Basaltic Province (DBP), Maharashtra, India. Kayadhu River originates from Ajanta hill ranges near to Angarwadi village in Risod taluka of Washim district and flows through Sengaon, Hingoli, and finally drains into the Penganga river (Ghute and Md. Babar 2020). The maximum and minimum elevations are 605 and 390 m above mean sea level. The Kayadhu river is of seventh order with a dominant dendritic drainage pattern. The Kayadhu River covers an area of 2158.38 km2 and the basin is confined between N19°22'' , E77°40'' in the South East and N 20°00'' , E76o 40'' North West (Fig. 5.1). The study area has dry and tropical climate with hot summer and mild winter with humid SW monsoon season. The average rainfall in the basin for the last twenty years is 872.29 mm. The maximum temperature is 42.6 °C and minimum 10.6 °C. The land use and land cover of study area classified into agricultural land, waste land, forest, settlement, and waterbodies and plays a significant role to understand the current landscape (Ghute and Md. Babar 2020; Ghute et al. 2022) Geologically, the river basin comprises simple (aa type) and compound (pahoehoe type) basalt flows and these lava flows nearly horizontal lava formations. These flows have been formed due to fissured type of lava eruption during the late Cretaceous to early Eocene period (Duraiswami et al. 2008; Kaplay et al. 2017).

5.3 Methodology Advancement in remote sensing and GIS technology. The morphometric analysis was carried out by using Arc GIS 10.6 software and it became much easier than the traditional method (Rai et al. 2017; Choudhury et al. 2019). The Digital Elevation Model (DEM) of the study area was obtained by using Shuttle Radar Topographic Mission (SRTM). The DEM is digital representation of cartographic information in raster format having 90 m resolution. In addition, satellite data of Indian Remote Sensing (IRS P6 LISS-IV) False Colour Composite (FCC) format and Survey of India (SoI) toposheets maps (with scale 1:50,000) were used to delineate the drainage basin parameters.

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Fig. 5.1 Location map of the study area

In the present study, based on methods described previously, quantitative morphometric parameters were determined. These morphometric parameters were classified into three categories according to their orientation in space such as linear, areal, and relief features for 1, 2, and 3 dimensional features study respectively (Alqahtani and Qaddah 2019). The three categories of morphometric parameters were calculated to delineate the river basin area based on formulae by numerous authors applied and summarized as shown in Table 5.1.

5 A GIS-Based Flood Risk Assessment and Mapping Using … Table 5.1 Morphometric parameters along with the formulae

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Parameters

Formulae

Reference

Description

Stream order (S u )

Hierarchical rank

Strahler (1964)

Linear parameter

Stream number (N u )

Nu = N1 + N2 + · · · + Nn

Schumm (1956)

Linear parameter

Horton (1945)

Linear parameter

Stream Length L u = L 1 + (L u ) L2 + · · · + Ln Bifurcation ratio (Rb )

Rb = N u /(N u + Schumm 1) (1956)

Linear parameter

Form factor (F f )

F f = A/L b 2

Horton (1945)

Areal parameter

Circularity ratio (C)

C = 4 π A/P2

Strahler (1964)

Areal parameter

Compactness index (c)

c = 0.2821 P/ √ A

Horton (1932)

Areal parameter

Drainage density (Dd )

Dd = L u /A

Horton (1945)

Areal parameter

Drainage texture (T d )

Td =

∑ Nn/P

Smith (1950) Areal parameter

√ E b = [ (A/π )]/ Schumm Lb (1956) ∑ Stream F s = Nn/A Horton frequency (F s ) (1945) Elongation ratio (E b )

Areal parameter Areal parameter

Lamniscate ratio (K)

K = L b 2 /4A

Chorley et al. (1957)

Areal parameter

Basin relief (H r )

Hr = H − h

Hadley and Schumm (1961)

Relief parameter

Relief ratio (Rr )

Rr = H r /L b

Schumm (1963)

Relief parameter

Ruggedness number (Rn )

Rn = H 1 X Dd

Melton (1957)

Relief parameter

5.4 Morphometric Analysis and Input Data The Kayadhu River basin having area 2158.30 km2 and having perimeter 306 km. Based on morphometric parameters described with formulae in Table 5.1. To delineate the flood risk assessment parameters were calculated and classified into three categories. 1. Linear parameters, such as Stream order (S u ), Stream number (N u ) Stream Length (L u ), and Bifurcation ratio (Rb ) (Table 5.2).

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2. Areal parameters, such as Form factor (F f ), Circularity ratio (C), Compactness index (c), Drainage density (Dd ), Drainage texture (T d ), Elongation ratio (E b ), Stream frequency (F s ), and Lamniscate ratio (K) (Table 5.3). 3. Relief parameters, such as Basin relief (H r ), Relief ratio (Rr ), and Ruggedness number (Rn ) (Table 5.4). The calculated parameters from spatial data were converted into raster format to produce various maps that spatially represent various datasets which contributing flood risk assessment. Table 5.2 Linear morphometric parameters of the Kayadhu river

Stream order (S u )

Stream number (N u )

Stream length (L u )

1

3818

2470.57

2

990

909.05

Bifurcation ratio (Rb ) 3.86 4.25

3

233

526.62 3.95

4

59

249.94

5

12

99.8

6

2

43.61

4.92 6 2 7

1

Total

5115

66.73 4366.32

Table 5.3 Areal morphometric parameters of the Kayadhu river Form Circularity Compactness Drainage Drainage Elongation Stream Lamniscate factor ratio (C) index (c) density texture ratio (E b ) frequency ratio (K) (F f ) (Dd ) (T d ) (F s ) 0.18

0.29

1.8581

Table 5.4 Relief morphometric parameters of the Kayadhu river

2.02

16.71

0.48

2.37

1.640

Basin relief (H r ) Relief ratio (Rr ) Ruggedness number (Rn ) 0.18

0.29

1.86

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Fig. 5.2 Drainage network of Kayadhu river

5.5 Result and Discussion The Kayadhu basin and its drainage network with stream order are shown in Fig. 5.2. The estimated morphometric parameters in respect of linear, areal, and relief are computed in Table 5.2.

5.6 Linear Parameters 5.6.1 Stream Order (Su ) The determination of stream order is essential to understand the drainage characteristics of the catchment area (Horton 1945). For the present basin study, stream ordering was adopted from Strahler (Strahler 1964). This scheme of ordering is hierarchical and shows the position of stream in the hierarchy of tributaries. Discharge and flow velocity are directly proportional to stream order (Costa 1987). The results reveals that Kayadhu river is a seventh-order basin and mainly consisting basaltic hard rock terrain lithology having compact nature therefore high frequency of first-order streams was observed in hilly areas Table 5.2 and Fig. 5.2.

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5.6.2 Stream Number (Nu ) and Stream Length (Nl ) The total number of stream segments for particular order are known as stream number (Horton 1945). It is a very important hydrological feature of the catchment that provides important information about surface runoff factors. In general, the number of streams gradually decreases as the stream order increases (Bhat et al. 2019). Possible flash flooding has been reported in the catchment with a high number of first-order streams after heavy rainfall expected to imply faster peak flow and also indicate the permeability and infiltration characters (Rai et al. 2018; El-Rawy et al. 2022). The total number of a stream number and stream length were calculated for the catchment, that includes total 5115 streams, of which 3818 streams were identified in first order with length 2470.57 km, 990 streams in second order with length 909.05 km, 233 streams in third order with length 526.62 km, 59 streams in fourth order with length 249.94 km, 12 streams in fifth order with length 99.8 km, 2 streams in sixth order with length 43.61 km and 1 stream in seventh order with length 66.73 km (Table 5.2).

5.6.3 Bifurcation Ratio (Rb ) The bifurcation ratio (Rb ) is the ratio of a number of stream given order to the number of streams in the subsequent higher order (Schumm, 1956). It plays a crucial role for controlling basin carrying capacity and related flood potentiality (Joji et al., 2013; Galil Abdel Hamid Hewaidy et al., 2021). The Rb of drainage basins typically varies from 2 to 5 indicate the geological formations do not disrupt the drainage pattern and on the other hand, low Rb reveals basin is structurally less disturbed. According to Chorley (Chorley, 2019) had noted that the lower the Rb higher the risk of flooding in particular part and not the entire basin. The Rb of study area ranges from 2 to 6. The average Rb is 3.56 (Table 5.2) and the seventh-order stream shows a low value 2 that particular stream order liable to flooding.

5.7 Areal Parameters 5.7.1 Form Factor (Ff ) The form factor (F f ) is the numerical guide used to represent the basin shape and also forecast flow intensity of a given basin. The F f is defined as the ratio of basin area to basin length square (Horton, 1945). The F f ranging from o for elongated basin to 1 for a circular basin. In Table F f value is 0.18 (Table 5.3) which indicates basin

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has elongated shape, with peak discharge occurring over a long duration and makes more sensitive for flood (Erena and Worku 2018; Galil Abdel Hamid Hewaidy et al. 2021).

5.7.2 Circularity Ratio (C) Circularity ratio (C) is the ratio of basin area to the area of the circle having the same circumference as the perimeter of the basin (Strahler 1964). The C expresses basin shape, rate of infiltration, climate, relief, and the time needed for excess water to reach the basin outlet (Altaf et al. 2013; Erena and Worku 2018). When value of C is equal to 1, basin is circular and elongated basin have C value is 0. The C value of Kayadhu river basin 0.29 (Table 5.3) which indicates basin is elongated, mature topography with a low infiltration rate that can cause flood risk.

5.7.3 Compactness Index (C) Compactness index (c) is defined as the ratio of the perimeter of a drainage basin to the circumference of the circle having the same as the drainage basin. The c of the watershed is associated with climate, vegetation, lithology and provides insights of infiltration characters of watershed (Ogarekpe et al. 2020). The c value 1 indicates that the basin have circular nature while >1 indicates basin is deviated from its circular nature (Altaf et al. 2013). The value of c is 1.85 therefore basin has great deviation from the circular nature. On the basis of this it will take the longest time of concentration before peak flow occurs.

5.7.4 Drainage Density (Dd ) The drainage density (Dd ) is the ratio of the total length of streams in all orders to the basin area (Horton 1945). According to Horton, Dd is an expression of tightness of streams spacing and drainage efficiency inside a basin. The Dd of the study region varies from 0–1, 1–2, 2–3, and 3–4 (Fig. 5.3) and average Dd value of the basin is 2.02 km/km2 (Table 5.3). The Dd values are normally high in the region where subsurface material is impermeable, sparse vegetation and mountainous relief with fine drainage texture leads to highly dissected basin with flood volumes situations to rainfall events and vice versa (Pallard et al. 2009; Galil Abdel Hamid Hewaidy et al. 2021).

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Fig. 5.3 Drainage density in the study area (after Ghute and Md. Babar 2020)

5.7.5 Drainage Texture (Td ) Drainage texture (T d ) depends on several factors such as climate, rainfall, relief, infiltration capacity, rock type, vegetation, and the stage of drainage development (Das and Pardeshi 2018; Ogarekpe et al. 2020). The T d is the ratio sum of the total number of streams to the perimeter of the basin (Smith 1950). Compact and resistant rock shows coarse texture while area underlain by soft or permeable rock shows fine texture (Sreedevi et al. 2009). Kayadhu river basin (16.71) exhibit very fine drainage texture (Table 5.3).

5.7.6 Elongation Ratio (Re ) The elongation ratio (Re ) is the ratio of diameter of a circle of the same area as the drainage basin to the basins maximum length (Schumm 1956). It is used to assess shape and flooding condition of the basin (Erena and Worku 2018). The Re values ranges from 0 to 1, where 0 value is considered to be highly elongated shape while 1 value is considered to be the circular shape of the basin (Alqahtani and Qaddah 2019). In this study, elongation ratio is 0.48 (Table 5.3), indicating basin belongs to elongated categories with moderate relief.

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5.7.7 Stream Frequency (Fs ) Stream frequency (F s ) is the total number streams of all orders per unit area (Horton 1945). F s is an important parameter related to permeability, infiltration capacity, relief, and runoff pattern of the drainage basin (Rai et al. 2018; Ogarekpe et al. 2020). The F s of drainage basin value is 2.36 km2 (Table 5.3) which controlled by lithology and specifies the textures of stream network. The basin shows high value due to the underlying igneous rock resulting in impermeability and high runoff during rainfall.

5.7.8 Lemniscate Ratio (K) Lamniscate ratio (K) is used to calculate slope and real watershed shape. It is the (Chorley et al. 1957). The K values 2 (Bhat et al. 2019; Ogarekpe et al. 2020). The K value of the basin was 1.64 (Table 5.3) and that means the basin is closest to the elongation.

5.8 Relief Parameters 5.8.1 Basin Relief (Hr ) Basin relief (H r ) is the elevation difference between the lowest and highest elevation in the basin (Hadley and Schumm 1961). The H r is important geomorphic factor to understand the erosional properties of the terrain. It plays significant role in landform development, surface and subsurface water flow and drainage development (El-Fakharany and Mansour 2021). The basin has moderate (H r −215 m) basin relief as shown in Table 5.4. The basin indicates along the hill slopes the surface runoff is dominant and impermeable.

5.8.2 Relief Ratio (Rr ) The relief ratio (Rr ) is defined as the ratio of basin relief to the basin length (Hadley and Schumm 1961). It is dimensionless quantity which measures the overall steepness of drainage basin. Rr is an indicator of the intensity of the erosion process operating on the basin slope (Dodov and Foufoula-Georgiou 2005). The Rr of Kayadhu river

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basin is 1.96 m/km (Table 5.4), generally it is a low to moderate value due to igneous (resistant) basement rock and low degree of basin slope (Rai et al. 2014, 2018). The lower part of the basin faces flooding due to maximum discharge and flat terrain.

5.8.3 Ruggedness Number (Nr ) Ruggedness number (Nr) is the product of maximum basin relief and drainage density (Melton 1957). The N r value basin indicates susceptible area for soil erosion (Zaz and Romshoo 2012; Altaf et al. 2013; Rai et al. 2014). In the present study shows the ruggedness number is 0.43 (Table 5.4), which indicates the basin has low relief with less runoff potential.

5.9 Development of Flood Risk Assessment Map In the present study, the flood risk assessment map of Kayadhu river basin was obtained by using Arc GIS. The map of study area was created based on results of linear, areal and relief parameters such as stream order (S u ), stream number (N u ), stream length (L u ), bifurcation ratio (Rb ), form factor (F f ), circularity ratio (C) compactness index (c) drainage density (Dd ) drainage texture (T d ) elongation ratio (E b ), Stream frequency (F s ), lamniscate ratio (K), basin relief (H r ), relief ratio (Rr ) and ruggedness number (Rn ) (Table 5.1). The morphometric parameters have a direct relationship with surface runoff and infiltration. The basin was categorized into three zones namely: high risk area (35.09%), moderate risk area (40.91%) and low risk area (23.96%). High risk zone is primarily concentrated along the main river channel and lower reaches of river. The high risk zone is distinguished by flat areas with moderate to low slope, lower elevation, and low drainage density and all of which are significant variable conditions for flood risk assessment map. The low risk zone, primarily located along upper reaches of river basin and were distinguished by their steep slope, higher elevation, and low drainage density (Fig. 5.4).

5.10 Conclusion and Recommendations The flood risk assessment is a strategic planning tool for effectively reducing flood risk and damage, despite the fact that it cannot be avoided. The flood risk mapping of Kayadhu river basin was assessed by using GIS-based linear, areal, and relief morphometric parameters and field measurements. These parameters describe the geometry, topography, and hydrological behavior and its deep impacts on the flood susceptibility in the downstream part of the basin. The dynamics of river morphology

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Fig. 5.4 Flood risk assessment map of the Kaydhu river

and morphometry are the results of natural processes and anthropogenic interference in the study area. The results of calculated and estimated morphometric parameters were used to construct a flood risk map of Kaydhu river basin. The resultant map shows that 35.09% of the study area is at high risk of flooding. This resulting map can therefore serve as a guide for decision-makers regarding possible precautionary measures. Better land use planning and flood risk management under climate change. Strict measures must be taken against uncontrolled urbanization and occupation of areas close to rivers and where waterways are clogged, and policy makers must implement this. The following recommendations must be taken into consideration to avoid impact of flood hazards: 1. Avoid establishment of any construction in the vicinity of river channel as well as keep the river channel clean and free from the waste. 2. Construct the alternative barriers to impound the water at the upstream part of river basin so it will reduce the flooding hazards and also enhance the groundwater recharge. 3. Establish early warning system and regular monitoring of flood susceptible area. 4. Create environmental awareness in all stake holders, including local community and minority groups and they should be involved in the management plan throughout implementation to evaluation. Acknowledgements The author is thankful to Swami Ramanand Teerth Marathwada University, Nanded for providing the financial support and also thanks to anonymous reviewers for taking the time and efforts necessary to review the manuscript.

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References Adnan MSG, Dewan A, Zannat KE, Abdullah AYM (2019) The use of watershed geomorphic data in flash flood susceptibility zoning: a case study of the Karnaphuli and Sangu river basins of Bangladesh. Nat Hazards 99(1):425–448. https://doi.org/10.1007/s11069-019-03749-3 Alqahtani F, Qaddah AA (2019) GIS digital mapping of flood hazard in Jeddah-Makkah region from morphometric analysis. Arab J Geosci 12(6):199. https://doi.org/10.1007/s12517-019-4338-8 Altaf F, Meraj G, Romshoo SA (2013) Morphometric analysis to infer hydrological behaviour of Lidder Watershed, Western Himalaya, India. Geogr J 2013:1–14. https://doi.org/10.1155/2013/ 178021 Asfaw D, Workineh G (2019) Quantitative analysis of morphometry on Ribb and Gumara watersheds: implications for soil and water conservation. Int Soil Water Conserv Res 7(2):150–157. https://doi.org/10.1016/j.iswcr.2019.02.003 Bhat MS, Alam A, Ahmad S, Farooq H, Ahmad B (2019) Flood hazard assessment of upper Jhelum basin using morphometric parameters. Environ Earth Sci 78(2):54. https://doi.org/10.1007/s12 665-019-8046-1 Bhatt GD, Sinha K, Deka PK, Kumar A (2014) Flood hazard and risk assessment in Chamoli District, Uttarakhand using satellite remote sensing and GIS techniques. Int J Innov Res Sci Eng Technol 3(8):15348–15356. https://doi.org/10.15680/IJIRSET.2014.0308039 Chorley RJ, Malm DEG, Pogorzelski HA (1957) A new standard for estimating drainage basin shape. Am J Sci 255(2):138–141. https://doi.org/10.2475/ajs.255.2.138 Chorley RJ (ed) (2019) Introduction to physical hydrology. Routledge. https://doi.org/10.4324/978 0429273339 Choudhury D, Das K, Das A (2019) Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. Egypt J Remote Sens Space Sci 22(2):203–218. https://doi.org/10.1016/j.ejrs.2018.05.004 Costa JE (1987) Hydraulics and basin morphometry of the largest flash floods in the conterminous United States. J Hydrol 93(3–4):313–338. https://doi.org/10.1016/0022-1694(87)90102-8 Dandapat K, Panda GK (2017) Flood vulnerability analysis and risk assessment using analytical hierarchy process. Model Earth Syst Environ 3(4):1627–1646. https://doi.org/10.1007/s40808017-0388-7 Danumah JH, Odai SN, Saley BM, Szarzynski J, Thiel M, Kwaku A, Kouame FK, Akpa LY (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire). Geoenvironmental Disasters 3(1):10. https://doi. org/10.1186/s40677-016-0044-y Das S, Pardeshi SD (2018) Morphometric analysis of Vaitarna and Ulhas river basins, Maharashtra, India: using geospatial techniques. Appl Water Sci 8(6):158. https://doi.org/10.1007/s13201018-0801-z de Brito MM, Evers M (2016) Multi-criteria decision-making for flood risk management: a survey of the current state of the art. Nat Hazard 16(4):1019–1033. https://doi.org/10.5194/nhess-161019-2016 Dodov B, Foufoula-Georgiou E (2005) Fluvial processes and streamflow variability: interplay in the scale-frequency continuum and implications for scaling. Water Resour Res 41(5). https:// doi.org/10.1029/2004WR003408 Duan W, He B, Nover D, Fan J, Yang G, Chen W, Meng H, Liu C (2016) Floods and associated socioeconomic damages in China over the last century. Nat Hazards 82(1):401–413. https://doi. org/10.1007/s11069-016-2207-2 Duraiswami RA, Bondre NR, Managave S (2008) Morphology of rubbly pahoehoe (simple) flows from the Deccan Volcanic Province: Implications for style of emplacement. J Volcanol Geoth Res 177(4):822–836. https://doi.org/10.1016/j.jvolgeores.2008.01.048 El-Fakharany MA, Mansour NM (2021) Morphometric analysis and flash floods hazards assessment for Wadi Al Aawag drainage Basins, southwest Sinai, Egypt. Environ Earth Sci 80(4):168. https:/ /doi.org/10.1007/s12665-021-09457-1

5 A GIS-Based Flood Risk Assessment and Mapping Using …

91

El-Rawy M, Elsadek WM, De Smedt F (2022) Flash flood susceptibility mapping in Sinai, Egypt using hydromorphic data, principal component analysis and logistic regression. Water 14(15):2434. https://doi.org/10.3390/w14152434 Erena SH, Worku H (2018) Flood risk analysis: causes and landscape based mitigation strategies in Dire Dawa city, Ethiopia. Geoenvironmental Disasters 5(1):16. https://doi.org/10.1186/s40 677-018-0110-8 Flores AP, Giordano L, Ruggerio CA (2020) A basin-level analysis of flood risk in urban and periurban areas: a case study in the metropolitan region of Buenos Aires, Argentina. Heliyon 6(8):e04517. https://doi.org/10.1016/j.heliyon.2020.e04517 Galil Abdel Hamid Hewaidy A, Abu El Hassan M, Salama A, Ahmed R (2021) Flash flood risk assessment of Wadi Degla basin protected area, East of Maadi, Cairo, Egypt based on morphometric analysis using GIS techniques. Hydrology 9(3):66. https://doi.org/10.11648/j.hyd.202 10903.12 Gao L, Tao B, Miao Y, Zhang L, Song X, Ren W, He L, Xu X (2019) A global data set for economic losses of extreme hydrological events during 1960–2014. Water Resour Res 55(6):5165–5175. https://doi.org/10.1029/2019WR025135 Ghute BB, Babar SM (2021) Morphotectonics of Kayadhu River Basin in Washim-Hingoli-Nanded Districts, Maharashtra: a spatial analysis. In: Dubey CS, Shandilya AK, Singh VK, Bhatt SC (eds) Geological and geo-environmental processes on earth. Springer Natural Hazards. Springer, Singapore, pp 305–313. https://doi.org/10.1007/978-981-16-4122-0_20 Ghute BB, Md. Babar S (2020) An approach to mapping groundwater recharge potential zones using geospatial techniques in Kayadhu River Basin, Maharashtra. Indian J Agric Res. https:// doi.org/10.18805/IJARe.A-5477 Ghute BB, Shaikh MB, Halder B (2022) Impact assessment of natural and anthropogenic activities using remote sensing and GIS techniques in the Upper Purna River basin, Maharashtra, India. Model Earth Syst Environ. https://doi.org/10.1007/s40808-022-01576-3 Hadley RF, Schumm SA (1961) Sediment sources and drainage basin characteristics in Upper Cheyenne River Basin. US Geological Survey Water-Supply Paper 1531-B, 198. Hagos YG, Andualem TG, Yibeltal M, Mengie MA (2022) Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia. Appl Water Sci 12(7):148. https://doi.org/10.1007/s13201-022-01674-8 Horton RE (1945) Erosional development of streams and their drainage basins: hydrophysical approach to quantitative morphology. Bull Geol Soc Am 56:275–270 Horton RE (1932) Drainage-basin characteristics. Trans Am Geophys Union 13(1):350. https://doi. org/10.1029/TR013i001p00350 Joji VS, Nair ASK, Baiju KV (2013) Drainage basin delineation and quantitative analysis of Panamaram watershed of Kabani river basin, Kerala using remote sensing and GIS. J Geol Soc India 82(4):368–378. https://doi.org/10.1007/s12594-013-0164-x Kaplay RD, Babar MD, Mukherjee S, Vijay Kumar T (2017) Morphotectonic expression of geological structures in the eastern part of the South East Deccan Volcanic Province (around Nanded, Maharashtra, India). Geol Soc Lond Spec Publ 445(1):317–335. https://doi.org/10.1144/SP4 45.12 Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci 67(1):251–264. https://doi.org/10.1007/s12665-011-1504-z Melton M (1957) An analysis of the relations among elements of climate, surface properties, and geomorphology. Technical Report 11, Department of Geology, Columbia University, New York Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management (edit. Book). Nova Science Publishers, U.S.A., ISBN: 978-1-53619-669-6 Nones M (2019) Dealing with sediment transport in flood risk management. Acta Geophys 67(2):677–685. https://doi.org/10.1007/s11600-019-00273-7

92

B. B. Ghute and P. Sarma

Ogarekpe NM, Obio EA, Tenebe IT, Emenike PC, Nnaji CC (2020) Flood vulnerability assessment of the upper Cross River basin using morphometric analysis. Geomat Nat Haz Risk 11(1):1378– 1403. https://doi.org/10.1080/19475705.2020.1785954 Pallard B, Castellarin A, Montanari A (2009) A look at the links between drainage density and flood statistics. Hydrol Earth Syst Sci 13(7):1019–1029. https://doi.org/10.5194/hess-13-1019-2009 Panhalkar SS, Jarag AP (2017) Flood risk assessment of Panchganga River (Kolhapur District, Maharashtra) using GIS-based multicriteria decision technique. Curr Sci 112(4):785. https:// doi.org/10.18520/cs/v112/i04/785-793 Panigrahi GS, Suar D (2021) Resilience among survivors in the aftermath of the 2018 Kerala flood: an avenue toward recovery. Int J Disaster Risk Reduct 64:102477. https://doi.org/10.1016/j.ijdrr. 2021.102477 Prasad AS, Pandey BW, Leimgruber W, Kunwar RM (2016) Mountain hazard susceptibility and livelihood security in the upper catchment area of the river Beas, Kullu Valley, Himachal Pradesh, India. Geoenvironmental Disasters 3(1):3. https://doi.org/10.1186/s40677-016-0037-x Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RMA, Shufeng T (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ 3(3):585–601. https://doi.org/10.1007/s41748-019-00123-y Rana A, Foster K, Bosshard T, Olsson J, Bengtsson L (2014) Impact of climate change on rainfall over Mumbai using distribution-based scaling of global climate model projections. J Hydrol Regional Stud 1:107–128. https://doi.org/10.1016/j.ejrh.2014.06.005 Rawat A, Bisht MPS, Sundriyal YP, Banerjee S, Singh V (2021) Assessment of soil erosion, flood risk and groundwater potential of Dhanari watershed using remote sensing and geographic information system, district Uttarkashi, Uttarakhand, India. Appl Water Sci 11(7):119. https:// doi.org/10.1007/s13201-021-01450-0 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. Available at https://doi.org/10.5775/fg.20674635.2014.041.i Rai PK, Mishra VN, Mohan K (2017) A study of morphometric evaluation of the Son Basin, India using geospatial approach. Remote Sens Appl: Soc Environ 7:9–20. https://doi.org/10.1016/j. rsase.2017.05.001 Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book). Springer Nature, Switzerland, ISBN: 978-3-030-76116-5. https://doi.org/10.1007/978-3-030-76116-5 Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book). Springer Nature, Singapore. ISBN: 978-981-16-7373-3. https://doi.org/10.1007/978-981-16-7373-3 Rai PK, Mishra S, Ahmad A, Mohan K (2014) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci (Springer), 7:217–232. (ISSN: 21905495). DOI https://doi.org/10.1007/s13201-014-0238-y Rai PK, Chandel RS, 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 8(1):15. https://doi.org/10.1007/s13201-018-0660-7 Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India. Ecol Environ Conserv 27(February Suppl. Issue):S260–S268 Schumm SA (1956) Evolution of drainage systems & slopes in Badlands at Perth, New Jersey. Bull Geol Soc Am 67:597–646. https://doi.org/10.1130/0016-7606(1956)67[597:EODSAS]2. 0.CO;2 Schumm SA (1963) Sinuosity of alluvial rivers in the Great Plains. Bull Geol Soc Am 74:1089–1100. https://doi.org/10.1130/0016-7606(1963)74%5b1089:SOAROT%5d2.0.CO;2 Smith KG (1950) Standards for grading texture of erosional topography. Am J Sci 248(9):655–668. https://doi.org/10.2475/ajs.248.9.655

5 A GIS-Based Flood Risk Assessment and Mapping Using …

93

Sreedevi PD, Owais S, Khan HH, Ahmed S (2009) Morphometric analysis of a watershed of South India using SRTM data and GIS. J Geol Soc India 73(4):543–552. https://doi.org/10.1007/s12 594-009-0038-4 Strahler A (1964) Quantitative geomorphology of drainage basins and channel networks. In Chow V (ed) Handbook of applied hydrology. McGraw Hill, New York, pp. 439–476 Tehrany MS, Pradhan B, Mansor S, Ahmad N (2015) Flood susceptibility assessment using GISbased support vector machine model with different kernel types. CATENA 125:91–101. https:/ /doi.org/10.1016/j.catena.2014.10.017 Ullah K, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. PLOS ONE 15(3):e0229153. https://doi.org/10.1371/journal.pone.0229153 Vanama VSK, Rao YS, Bhatt CM (2021) Change detection based flood mapping using multitemporal Earth Observation satellite images: 2018 flood event of Kerala, India. Eur J Remote Sens 54(1):42–58. https://doi.org/10.1080/22797254.2020.1867901 Vogel R (2016) Methodology and software solutions for multicriteria evaluation of floodplain retention suitability. Cartogr Geogr Inf Sci 43(4):301–320. https://doi.org/10.1080/15230406.2015. 1039587 Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11(2):364. https://doi.org/10.3390/w11020364 Zaz SN, Romshoo SA (2012) Assessing the geoindicators of land degradation in the Kashmir Himalayan region, India. Nat Hazards 64(2):1219–1245. https://doi.org/10.1007/s11069-0120293-3

Chapter 6

Hydro-Chemical Characterization and Geospatial Analysis of Groundwater for Drinking and Agriculture Usage in Bagh River Basin, Central India Nanabhau S. Kudnar , Varun Narayan Mishra , and M. Rajashekhar

Abstract The present study aims to analyze the groundwater qualities in Bagh River basin using hydro-geochemical characterization and geospatial techniques. The rainfall distribution and groundwater qualities were analyzed. In the pre-monsoon season, the water quality of the river is found good, but in the monsoon season, the water quality is changed due to the effect of all several factors such as turbidity, organic matter, animal litter, etc. The water quality index (WQI) suggests that 72% of the total area has excellent and good quality water; and about 28% is characterized by having poor quality water. The concentration of Arsenic, Iron, Chromium, Aluminium, metallic elements is found near Gondia town, Amgaon, Salekasa, Deori, and Goregaon towns in the river basin. In general, 53% of the total annual fertilizer consumption in the river basin is used in the Kharif season, while 44% of the fertilizers are used in the Kharbi season. Due to the unrestricted use of chemical fertilizers and water, the growth of crops is affected. Among chemical fertilizers, farmers use urea, super phosphate, and potash or combined fertilizers. But due to the lack of micronutrients in these fertilizers, the nutritional balance of the crops is disturbed, and this affects the crop yield. Chemical fertilizers immediately meet the nutrient requirements of crops but deteriorate the soil texture. For this, it is recommended to give these fertilizers as a supplement to organic fertilizers. It can be used to determine the nature of rainfall in other districts and for disaster management and future planning. The present investigation indicates significant dominance of agriculture and drinking water for the groundwater chemistry in the river basin. Keywords Geospatial analysis · Groundwater · Bagh River basin · GPS N. S. Kudnar Department of Geography, C. J. Patel College Tirora, Gondia, India V. N. Mishra (B) Amity Institute of Geoinformatics and Remote Sensing (AIGIRS), Amity University, Noida, India e-mail: [email protected] M. Rajashekhar Department of Geology, Yogi Vemana University, Kadapa, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_6

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6.1 Introduction Watershed management is the most appropriate use of a watershed’s natural resources, particularly land and water, to meet its needs without causing significant damage. This management is mainly important in terms of land and water conservation and it helps in proper land use, soil fertility, and water conservation. It is also used for local flood control, to stop the accumulation of silt and to increase the productivity of the available soil (Mishra et al. 2014; Rai et al. 2017a, b, 2018; Kushwaha et al. 2022; Narsimlu et al. 2015; Patel et al. 2013; Kudnar and Rajasekhar 2020). Water is one of the essential needs of human being. The fresh and clean water is very important for human health. Several stomach disorders and diseases may rise due to polluted water. There may be various reasons of pollution and poor water quality. Water quality is degrading from various sources of pollution such as factory’s waste, domestic waste, city sewage, solid waste, etc. These sources are causing water pollution at a larger extent. The soil, silt, waste, dirt mixes with the rainwater, and the water becomes polluted (Alkindi et al. 2022; Rai et al. 2018; Karande et al. 2020; Bouteraa et al. 2019; Andualem et al. 2020). Apart from this, the water is getting polluted due to several causes like bathing of animals in the river, washing of drug spray pumps, release of different chemicals, direct release of spent wash of factories, chemically stained clothing immersion, etc. Due to many such things, the water becomes muddy and polluted. Chemical fertilizers run off and pollute water. Due to this changes in the chemical mixed with the water and new harmful chemicals are formed due to which the water becomes polluted (Bouaicha et al. 2017; Kadam et al. 2020; Kudnar 2018; Kumar and Krishna 2021). Water is polluted due to the chemicals that man is making and using today. Pests, diseases, and control chemicals cause a lot of water problems. Some chemicals are deliberately added to water to kill insect larvae, unwanted fish, and aquatic plants. Chemical drugs are used to control pests and diseases on crops. Traces of these drugs have also been found to have leached into ground water through rainwater or given water. It means that even underground water is getting polluted due to this chemical, it has to be said that this is the biggest danger (Mohan et al. 2011; Chaurasia et al. 2013, 2014; Meena et al. 2017; Rajasekhar et al. 2020; Zolekar et al. 2021). There are examples all over the world where all the water sources within 8–10 kilometers around chemical factories have been polluted and the polluted water has harmed crops, animals, and humans. When the water of many places was tested, traces of DDT and other chemicals were found in it. Therefore, everyone should contribute to prevent water pollution. Farmers should take care not to release pest-disease-control drugs, chemicals, wash pumps, etc. Chemical factories or other factories should not discharge their waste water into rivers and treat it and use it for agricultural plants. Some of the best examples of this are provided by milk dairies. Today, the condition has arisen everywhere that drinking water should be used for drinking only after purification. It is best to heat the water and use it for drinking only

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after purifying it according to the water test report. Even in the case of pesticides that are sprayed for pest control, care should be taken that they do not contaminate ground water through the soil or pollute surface water bodies (Rajasekhar et al. 2019). Water pollution is totally caused by man-made not natural process. Therefore, water pollution can be reduced by the participation of public and awareness campaigns. The main objective of this research is to investigate the quality of water in the small catchment area of the Bagh River basin and to find out the source of pollution and its effect on the agricultural sector, and drinking water.

6.2 Study Area The Bagh River is a major tributary of the Wainganga river (Kudnar 2020a, b; 2017; Kudnar et al. 2022), its basin latitude is 20° 45' 0'' N to 21° 45' 0'' N latitude and longitude is 80° 00' 0'' E to 80° 45' 0'' E. This river originates near Vandra village in Chichgad hill range in Gondia district. The total length of this river is 130 kilometers, and the total area of this river is 2876.9 km2 and the total height of this river basin is between 282 m and 835 m. Pangoli flows on the left bank of this river, Ghisari river, and Dev river flow on its right bank. The average rainfall is found to be 1323.91 mm which is the average from 1971 to 2019. In this rainfall pattern, the highest average rainfall occurs in the northern part of this river basin which is 1379.66 mm at Amgaon and the lowest rainfall occurs at Saleksha in the east with the lowest i.e., 1291.56 mm. The Bagh river basin is located at a distance from the coast, so the climate here is heterogeneous. In this place, the month of May is hot summer and the month of January is bitter cold. From June to September, rainfall is received from the south-west monsoon winds. Rainfall is higher in the hilly areas. Rainfall increases from west to east. Rainfall pattern is found between 150 to 200 cm in the North-Eastern part while 125 cm in the rest of the region. The location map of the study area is shown in Fig. 6.1.

6.3 Data Used and Methodology The first method of ground water study is the study of ground water by stratigraphic or landform method. In this way, rivers, streams, streams, streams, mountains, hills, lowlands, slopes of land, rocks and their structure seen in rivers or mountains, soil on the ground, soil depth, trees, bushes, grasses, plants growing on the ground, cracks in the ground and The information is collected by observing all the weather factors (Srivastava et al. 2013; Bisen et al. 2022; Dongare et al. 2013; Mishra and Rai 2016; Rajasekhar et al. 2021; Zolekar 2018). All these elements have an internal relationship and are related to each other. This survey is called Integrated Water Management. Bagh river is a tributary of Wainganga river and the integrated use/management options of ground water and surface water were explored by studying the area of this

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Fig. 6.1 Location map of Bagh River basin

N. S. Kudnar et al.

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river. The type of catchment area, its gradient and how much flow is likely to occur in that area were numerically arranged. Rainwater patterns from 1971 to 2013 are shown (Kudnar et al. 2022; Mishra et al. 2018). The slope of the entire catchment area is very low and its outline leads to flat land. Therefore, there are complementary conditions for water to evaporate, and the texture of the soil and the underlying geological structure is important. Based on rainfall, flat land and soil depth, texture, the water that can be stored on the ground and the water that can be stored under the ground is estimated and it is used by the farmers in that area. Another method is the study of geological formations and their structures. If the availability of groundwater is to be estimated based on the background of timely rainfall, but changed cropping patterns, the study of geological structure and its characteristics is necessary. The Bagh River area is mainly located between Bagh River and its tributaries Ghisri River, Dev River, and Panguli River. Agriculture is practiced in this region. Between 2011 and 2020, the groundwater level here went down. Before 2013, groundwater levels in these regions were very good. But due to increasing population, human needs also increased, so humans used additional chemical fertilizers in agriculture to increase agricultural production and grow different crops. Due to chemical fertilizers and pesticides, the properties of agricultural water have changed and agriculture has become infertile (Reddy et al. 2013; Salunke et al. 2020; 2021; Kudnar et al. 2021; Bisen and Kudnar 2013, 2019; Pathare and Pathare 2021).

6.3.1 Ground Water Quality Groundwater data is taken from 26 wells, rivers, and ponds in the Bagh River basin (Table 6.1) taken in pre-monsoon period 2011 and analyzed by taking 08 different perimeters. The parameters analyzed, include pH, Electrical Conductivity (EC), Total Alkalinity (TA), Total Hardness (TH), and Fluoride (F). The sample collection, preservation, storage, transportation, and analysis by the Weighted Arithmetic Index method and GIS methods.

6.3.2 Groundwater Sampling and Physico-Chemical Analysis A total of 26 water samples were taken from different villages through Groundwater Survey and Development Agency. This water was separated according to different characters. A database has been created by collecting water samples to check groundwater quality. Factors like pH, EC, TDS, TA, F- , Hardness, Do, Na+ , etc. have been analyzed in each of these samples. Various methods have been used in the chemical evaluation of groundwater for several decades. These points represented in the triangle fields are projected further into the central diamond field, which gives the general character of the water. Pesticides, and fertilizers used by spraying give good results if they are properly absorbed

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Table 6.1 GPS location of groundwater sample stations. S. Station No.

Longitude

Latitude

S. Station No.

Longitude

Latitude

1.

Dhudwa

80.3445664

21.5286765

14. Gorre

80.4650903

21.2606391

2.

Binora

80.3292059

21.5819716

15. Pandhari

80.523997

21.3593793

3.

Suryatola

80.172987

21.4647911

16. Kachargarh 80.6075802

21.2882547

4.

Birsi

80.27426119 21.50346751 17. Zaliya

80.42153631 21.37017605

5.

Mohali

80.253735

80.51584978 21.32111774

21.2784231

18. Nimba

6.

Purgaon

80.204401

21.3493171

19. Salegaon

80.33659241 21.15377009

7.

Sukhapur

80.1157503

21.3540298

20. Gotabodi

80.3345753

21.0995841

8.

Ghoti

80.20862944 21.33602808 21. Hardoli

80.40840621 21.2178619

9.

Thana

80.308857

21.3539886

22. Khampura

80.5773205

21.2049405

10. Bhosa

80.3240453

21.4790548

23. Domatola

80.4730263

21.1902266

11. Borkanhar

80.3986198

21.3305599

24. Nawatola

80.4846249

21.2793665

12. Kumbhartoli 80.38046126 21.35745088 25. Paldongri

80.4575814

21.4676115

13. Sakharitola

80.414676

21.5112841

80.40620625 21.25511524 26. Awa

by the crop. However, most of the farmers do not pay attention to the quality of water for spraying. Industrial chemicals, polluted city water, etc. are mixed in the water source, making it unusable. Its pH is more than 7 (from 8 to 10). Disintegration of the substance mixed with such water occurs quickly. This process is called ‘alkaline hydrolysis.’ This reaction occurs rapidly in solutions with pH between 8 and 9. Absorption of such solutes by crops is low. The desired results are also not obtained, so the pH of the water used for spraying should be between 6 and 6.5 so that the solution is well absorbed by the crop.

6.4 Results and Discussion Bagh river basin is the economic backbone of Gondia and Balaghat district. Hence, urban, and industrial centers like Gondia, Birsi, Goregaon, Amgaon, Deori, and Lanjji come in this valley. Also, there are 174 villages in the total length of 130 km of this basin. The 2011 census shows that 7.5 lakh people live in this basin. Of course, there is no doubt that it has definitely increased in the last 11 years. Now, with so many urban settlements, industrial areas, and villages in the basin, pollution in the river is high. Natural resources will be consumed, transported, processed, and used in agriculture in these areas, waste and waste materials, excreta, domestic sewage, metal and chemical mixed sewage, heat generated from combustion of fuel used for processing, smoke and dust. Various chemical fertilizers, pesticides, fertilizers, and artificial seeds, new plants produced from them, noise, smoke, dust, and oil leakage caused by means of transport. This is where underground water wells and traditional

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ponds are found to be increasing in water pollution. A little deeper study of this river pollution reveals that a total of 23 MLD of sewage from human settlements and industries along its banks enter the river. These government figures do not seem to include water drawn from private wells and bores and reflected in sewage. Out of this total 23 MLD of sewage, 90 percent of sewage is generated from the cities of Gondia, Amgaon, Sale KasaDeori Goregaon, and 164 villages. Industrial effluents from tobacco industry in Gondia town, MIDC area and Macchi market, mutton market, vegetable Mandai, and other small industries in Amgaon town have become an integral part of the city’s sewage. As these human settlements are right on the banks of the Bagh River, naturally all this sewage gets mixed directly into the river. This is because of our culture.

6.4.1 Ground Water Quality The problem of drinking water and cultivable water is increasing continuously in the Bagh river area. In many areas, the ground water (Table 6.2) is not fit for drinking and not even for agriculture. Similar is the situation in most parts of Gondia, Goregaon, Amgaon, and Lanji. The underground water in Saalekasa and Deori is suitable for agriculture, but the water level is continuously falling here. The groundwater level in Aleva has fallen to at least 285 meters. At most 5408 is that much (Fig. 6.2).In this area, from where the canal and the minor pass, there is sweet water in the surrounding area but in other places there is salt water. The electrical conductivity of underground water in Kahi village is more than 5000, which is not fit for drinking or even farming. In Kahi villages like Satona, Surya Tola, and Birsi, the water around the village is also bad. Drinking water is being brought to these villages by pressing a pipe line from near a canal or a minor. At the same time, water campers are being kept in many houses. TDS is expressed in units of mg per unit volume (mg/litre) or also referred to as parts per million (ppm). TDS is generally not considered a primary pollutant (as such it is not known to be associated with health effects). It is used to check whether the water is pure or potable and TDS also indicates whether it contains chemical contaminants. The full form of TDS with respect to water is Total Dissolved Solids. It means total dissolved salt in Hindi. Simply put, the amount of impure particles dissolved in water. The taste or hardness in water is due to these dissolved particles. These particles are present in the form of inorganic salts as well as some amount of organic matter. Now the question arises that from where do all these particles or impurities come in the water? In fact, pure water is basically colorless, odorless, and tasteless. But before reaching our home, it comes through chemicals present in mountains, rivers, air, and water bodies. In this process, all kinds of particles get dissolved in the water. The chemicals used in water purification treatment plants can get into our water. The water coming through the soil containing fertilizers or salts also makes it hard, it is found in the riverbanks. Water containing 200 to 300 ppm (parts per million) can be said to be good or ideal for drinking. But even amounts

6.60

0.06

7.5

2.32

5123.0

2044

429

1165

2.04

672.5

3.60

pH

EC

TDS

TA

TH

F-

Na+

Do

9.80

680.0

2.10

1195

534

2184

5408.0

8.92

Maximum

Source Agriculture Department of Maharashtra

6.20

30

105

140

285.0

Minimum

Parameter

Range

Table 6.2 Measurement of groundwater qualities

292.94

10574.2

28.89

10192

8214

23749

47033.3

304.83

Sum

7.5113

271.133

0.7408

261.33

210.62

608.95

1205.982

7.8162

Mean

0.13880

29.8098

0.10903

37.256

22.402

78.660

167.9913

0.08440

0.86683

186.1620

0.68091

232.665

139.901

491.232

1049.1054

0.52707

Std. Deviation

0.751

34656.275

0.464

54133.228

19572.243

241308.471

1100622.112

0.278

Variance

0.803

0.446

0.655

2.130

1.282

2.212

0.258

−0.682

−1.038

5.959

0.296

4.802

6.454

0.455

−0.294 2.448

Kurtosis

Skewness

102 N. S. Kudnar et al.

6 Hydro-Chemical Characterization and Geospatial Analysis …

Fig. 6.2 Electrical Conductivity (EC) map of study area

103

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N. S. Kudnar et al.

up to 500 ppm per liter are considered safe to drink. By the way, hard water above 500 ppm or 500 mg/l can also be used for drinking, but it will not taste good. Such water is called salt water. The highest TDS of water in these river areas is 2184 and the lowest is 140 (Fig. 6.3), but in general the TDS of water is found to be increased near industrial areas and also in agriculture where chemical fertilizers and pesticides are used, the TDS of water is higher. Water hardness (Fig. 6.4) is defined as the total concentration of calcium and magnesium ions in it. These two ions are living and are most commonly found in natural waters. That is, the hardness of water can be defined as the sum of all polyvalent captions in it, although the global hardness of water is more important in magnesium posters. Its pH (Fig. 6.5) is more than 7 (from 8 to 10). Disintegration of the substance mixed with such water occurs quickly. This process is called ’alkaline hydrolysis’. This reaction occurs rapidly in solutions with pH between 8 and 9. Absorption of such solutes by crops is low. The desired results are also not obtained, so the pH of the water used for spraying should be between 6 and 6.5 so that the solution is well absorbed by the crop. The rate of alkaline hydrolysis depends on the solubility of the active ingredient in water, temperature, humidity and the added fungicides, soluble fertilizers, and growth hormones. Especially in insecticides, this action happens faster and more. This degradation activity is measured by measuring the time it takes for the pesticide’s effectiveness to decrease to half. When considering water quality, one has to consider the purpose for which the water is being used. Water is used for various reasons like drinking, consumption, swimming, and agriculture. Accordingly, the required quality of water changes, basically the quality of water is determined by the dissolved or insoluble elements mixed in it. Water pollution due to many reasons makes it unsafe for drinking or for various daily uses.

6.4.2 Chemically Stained Clothing—Immersion in Water Due to many such things, the water becomes muddy and polluted. Chemical fertilizers run off and pollute water. Due to this changes in the chemical mixed with the water and new harmful chemicals are formed from it, causing the water to become polluted. Increasing industrialization, urbanization, and population have increased pollution in major rivers. It is affecting the environment as well as the health of citizens. Therefore, while there is an opinion that it is necessary to prevent and conserve river pollution, it has come to light that the most polluted river stretches in the country are in Maharashtra. This year this number has reached 56. Rivers in our country are known as life lines. At the same time, rivers are also seen as a mirror of the country’s culture and civilization. Indian culture, society, rulers, and saints have likened rivers to mothers. But, increasing industrialization, urbanization, and population have increased pollution in major rivers. They have dried up biologically and this is affecting the environment as well as the health of the citizens. Maharashtra has the highest number of 56 polluted river-belts in the country. A river is healthy,

6 Hydro-Chemical Characterization and Geospatial Analysis …

Fig. 6.3 Total Dissolved Solids (TDS) map of study area

105

106

Fig. 6.4 Total Hardness (TH) map of study area

N. S. Kudnar et al.

6 Hydro-Chemical Characterization and Geospatial Analysis …

Fig. 6.5 pH level map of study area

107

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life-giving if it is connected to its surroundings, the forest near the source, the grass on the bank, the sand near the bend. Riparian vegetation is part of the river itself. These ’riparian’ zones reduce the intensity and velocity of floods. By improving water quality, the river retains water. But, due to lack of planning of rain water, lack of this aspect can be seen everywhere today, not a single river is clean. Every river, stream is polluted by industry, agriculture, and sewage. As this polluted water seeps into the ground water, the water in the wells and taps is not clean. So drinking this water is causing various diseases. On the one hand there is no water and what is there is also polluted. For increasing agricultural production, use of hybrid and improved seeds of crops, chemical fertilizers, crop protection measures, and different irrigation facilities are used. Nutrients like Nitrogen, Phosphorus, Phosphorus, etc. are widely used for the growth of crops from the soil. Different types of bacteria present in the soil help make these nutrients available to the crops. The importance of this bacterium for the long-term health of the soil is unique. In general, 53% of the total annual fertilizer consumption in the river basin is used in the Kharif season, while 44% of the fertilizers are used in the Kharbi season. Due to the unrestricted use of chemical fertilizers and water, the growth of crops is affected. Among chemical fertilizers, farmers use urea, super phosphate, and potash or combined fertilizers. But due to the lack of micronutrients in these fertilizers, the nutritional balance of the crops is disturbed and this affects the crop yield. Balanced use of integrated fertilizers should be used for sustainable agriculture. Fertilizers should be given only after examining the mother while giving fertilizers. Soil health and sustainable crop production are closely related. Improving soil health by improving physical, chemical, and biological properties of soil will definitely help in making today’s agriculture sustainable along with soil health.

6.4.3 Benefits of an Integrated Nutrient Management Approach Considering the production of nutrients in the next 25 years, approximately 1.4 lakh tons of nitrogen, phosphorus, and phosphorus can be obtained from crop residues every year. Apart from this, the secondary and micro nutrients required by the crops are provided. There is also a significant increase in the number of bacteria. Agnostic substances are slowly available from organic manure from decomposed residues and are available to pinks as required. It increases the amount of organic matter in the soil and the crop yield. Out of the total soil elements, 16 elements/nutrients are mainly required for good crop growth. Fertilizer recommendations are made by looking at the amount of available nutrients such as nitrogen, phosphorous, and palash in the soil and the requirement of the crop. Adequate and efficient use of chemical fertilizers is the need of the hour. While determining this requirement, it is determined by considering the area of crops in the district, the requirement of elements such as nitrogen,

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phosphorous, palash, index, use of fertilizers in the last three years, etc. Also, the determined requirement of nutrients will be met by bio-fertilizers and organic fertilizers. Considering this, the balance requirement is determined by the number of chemical fertilizers. Farmers should apply the amount of fertilizers as per recommendation only after pulverizing the soil in the field. Farmers should use nitrogen, phosphorus, and palash in the ratio of 4:2:1. Fertilizers should always be applied with two teaspoons of fertilizer or near the roots. Nitrogen fertilizers, micronutrient fertilizers, liquid fertilizers are not wasted if sprayed. It also had definite uses for increasing production. Application of fertilizers through drip irrigation increases the efficiency of fertilizers. Applying the quantity of fertilizers according to the condition of the crops. As an alternative to compound fertilizers, it is necessary to prepare compost at home using straight fertilizers. Along with chemical fertilizers, organic, biological, green, and compost care can save on fertilizers. Besides, the use of organic manures, green manures, and composts along with the use of fertilizers under integrated manure management helps to increase the usefulness of chemical fertilizers. Excessive use of chemical fertilizers affects the crops. The grains, vegetables, and grains produced from it contain traces of chemical fertilizers and these grains, legumes, and vegetables are not fit for consumption. For the purpose of increasing production, we have identified high-yielding varieties. Their production is abundant, but the natural flavor of the product is gone and its nutrients are also reduced. Everyone has noticed this now. Every food has its own taste. But when we start doing chemical farming, that taste is lost. All types of crops and fruit trees and all types of vegetation require nitrogen, phosphorous, phosphorus, and other 16 types of micronutrients. They are naturally occurring. Which plants get through land, air, and sunlight and plants get it and complete their life span and help nature and life to live and according to that nature keeps the same life i.e., keeps the number under control. But now the human population has become uncontrolled and the demand for food has increased accordingly. The cycle of nature has been disturbed and to meet the growing demand of food grains, maximum food grains are grown artificially by giving the above types of artificial fertilizers. It is the need of the hour, but it has disturbed the natural cycle/balance and degraded the land and environment and the health of humans and other animals has also deteriorated. A small amount of soil can be naturally improved which was cultivated using agricultural wastes like animal excreta etc. in earlier times. Now research is being done in that too and the fertility of agriculture is being increased. Organic and chemical fertilizers are used for agriculture. In organic fertilizers, dung, compost, vermicomposting, groundnut, safflower, linseed, nimboli, karanj oilseed meal, chicken manure, and green manure is used.

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6.4.4 Application of Organic Fertilizers Improves Soil Texture Chemical fertilizers are available in five types. 1. Nitrogen Fertilizers: Urea, Ammonium Sulfate, Ammonium Sulfate Nitrate 2. Phosphorous Fertilizers: Single Super Phosphate, Dicalcium Phosphate, Triple Super Phosphate. 3. Phosphate Fertilizers: Murate of Potash, Sulfate of Potash. 4. Compound Fertilizers: Mono Ammonium Phosphate, Diammonium Phosphate, Ammonium Phosphate Sulfate, Urea Ammonium Phosphate, Ammonium Nitrate Phosphate. 5. Mixed Fertilizers: −15:15:15, 20:20:0:13, 24:24:0, 10:26:26 etc. Soluble mixed fertilizers: 19:19:19, 12:61:0, 0:52:34, 13:0:45 etc. Consequences: Chemical fertilizers immediately meet the nutrient requirements of crops but deteriorate the soil texture. For this, it is recommended to give these fertilizers as a supplement to organic fertilizers. The question was how necessary is the use of fertilizers in agriculture. Because fertilizer is a rough definition of a substance that provides nutrients needed for agriculture. But in other words, crops absorb most of the nutrients they need from the environment. But anyway, this issue is different. It is a common observation that when the proper amount of chemical fertilizers are applied in the field, the crops look green and then pest infestation increases to a large extent. If chemical fertilizers are applied in excess, the leaves will appear scorched. Also the texture of the soil deteriorates. When fully decomposed cow dung, vermicomposting or poultry manure is applied to the field, its good effect is not immediately visible on the crop. But if the same is not completely rotted, infestation of worms like humani is seen and the crop spoils.

6.4.5 Safe Water for Drinking Although the taste, flavor, smell, color, and cleanliness of water are all indicators of water quality, physical, chemical, and biological testing of drinking water is necessary to say that water is safe for drinking. Turbidity caused by fine particles of sand, or silt in water, and foul odors caused by substances such as hydrogen sulfide can affect the acceptability of water. In the pre-monsoon period, the water quality remains good in this river area, but in the monsoon season, the water quality is changed due to the effect of all factors such as turbidity, organic matter, organic matter, and animal litter. This has affected the potable water. All objectionable elements are reduced after proper filtration of water. Also, when the water comes in contact with a clean environment and sunlight, its odor is reduced. Let us have a look at what is required for water to be potable.

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Water that looks clean on the surface is not necessarily safe to drink. Though clean water and clean water may seem close, they are different concepts. In addition to chemicals and salts, long-term harm can occur if the amount of harmful metals in the water is high from continuous use. Arsenic, Iron, Chromium, Aluminium, and metallic elements can be harmful if they are in excess. The concentration of these elements is found near Gondia town, Amgaon Sale KasaDeori, and Goregaon towns in the river bed. Also, the quantity of chemicals used for water disinfection should also be within the limits. This includes chloride, fluoride, and other chemicals. But more harmful effects are caused by the consumption of water contaminated with bacteria and viruses. Water pollution causes typhoid fever, dysentery, diarrhea, dysentery, jaundice, worms, etc. Diseases can occur. Therefore, water should be used after ensuring that it is safe to drink. Such a situation is very rare in this place because the farmers here are very poor farmers and water purification is seen in very little form in this region and hence its effect on the people here to secure it, there should be filtration and chlorination system for water storage, wells, boreholes, open wells. Otherwise, by filtering and boiling the water, its quality increases and it becomes drinkable. But this does not seem to be happening because traditional ponds, community wells, river flows are used for drinking.

6.4.6 Bagh River and Water Resources Table number one shows the distribution of rainfall in the Bagh river area and the average rainfall is found to be 1323.91 mm which is the average from 1971 to 2019 (Table 6.3). In this rainfall pattern, the highest average rainfall occurs in the northern part of this river basin which is 1379.66 mm at Amgaon and the lowest rainfall occurs at Saleksha in the east with the lowest i.e., 1291.56 mm. The average monsoon rainfall in this river basin is about 100 mm and is highest in the north and lowest in the east. Among these, the maximum rainfall is 1263.64 mm over Amgaon station and 1170 mm over Saleksa station. Even during the post-monsoon period, the average rainfall is higher above Deori rainfall station i.e., in the southern part of the study area and is 65.73 mm. While less rainfall is in the north, it is found on an average of 60.35 mm. Rainfall in this river basin is very low in winter with an average rainfall of 33 mm. In this pattern, the rainfall during the pre-monsoon season is also very low and averages about 24 mm. The reason for low rainfall in pre-monsoon season is that this region is very far from the coast, the Arabian Sea and the Sea of Bengal are very far from this region. Also, the rain that falls is due to internal climate change and evaporation from the lake. An important reason why the natural structure of this river basin affects the distribution of rainfall is that the Chichgad hills to the south of the region have an influence on the rainfall and hence the rainfall pattern is more pronounced in the Cheezgad hills to the south. At the same time, the amount of rainfall is higher in the hills of Dari Kasa, but the rainfall is less at the nearby Saale Kasa rainfall station. The main reason for this is that the valleys east of NadiKshatra receive high rainfall over these valleys but the neighboring Salle Kasa rainfall station receives less rainfall

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because the region falls in the valley shadow zone. Along with this, the main reason why the pattern of rainfall in the northern regions of Amgaon and Gondia is more in the river basin is because of the influence of the Gangazari hills and Gaikhuri hill ranges found in these places. Dams have been constructed above this river at ShirpurKalisara and Pujaritola. Along with this, large lakes such as Shirpar, Mana Gad Lake, Rengepar Lake are found in the river area. The water of this river is used in dams and ponds for irrigation. Due to the large number of lakes here, fishing is also practiced here. More than 36.74% of the area of this river basin is covered by forest and it is mainly found in the hilly areas in the north and south. The forests here are of tropical deciduous type and they are of two types namely mixed forests and teak forests. In the forest here, trees such as teak, shisav, tendu, amla, n khair, bamboo, hirda, palas, bor, sitafal, anjan, tamarind, purple mango, etc. are found. Forest products like teak and haldu wood, firewood, bamboo, tendu leaves, mocha flowers and fruits, gum lac are obtained from the forests here. Animals such as tiger, leopard, sambar, chital, wild cat, fox, nilgai, cow, bear, antelope, birds like peacock, pheasant, pigeon, hola, wild rooster, and duck are found in the forest. Different types of soil are found in this river area such as black and fertile Kanhar soil made from silt, reddish and yellowish Shihar soil formed by oxidation from spastic limestone, coarse-textured and sand or lime Mistry, sandy soil, dark colored gray soil mixed with light limestone. Agriculture is the major occupation in this river basin and about 3/4 of the total population is dependent on agriculture. Due to high rainfall and availability of irrigation facilities, paddy is grown in large quantities. Along with paddy, the kharif season crops of tur and udi are grown in this place which is a leader in rice production. While wheat, gram, gram, and gram are rabi season crops grown here. Fresh water fishing is widely practiced here due to the large number of lakes and river beds suitable for fishing. A Fish Seed Breeding Center is functioning at Ambhora in Gondia. Paddy mills are spread all over these areas Gondia taluka has a paper mill at Jangera. Tile manufacturing factory at Rajegaon Wood cutting factory at Amgaon. Industries like palm oil extraction factory etc. are run in this area. Table 6.3 Statistical analysis of rainfall data Tahsil

Range

Minimum

Maximum

Std. Deviation

Skewness

Kurtosis

Amgaon

1670.00

755.20

2425.20

352.15

0.736

0.980

Deori

1691.90

467.60

2159.50

332.34

0.644

1.200

Gondia

1917.80

358.20

2276.00

330.00

0.225

1.527

Goregaon

1472.40

650.60

2123.00

276.81

0.672

1.318

Lanjji

1182.76

404

1185.84

333.44

2.63

7.50

Dongargarh

1368.51

680

1371.70

385.13

2.64

7.57

Salekasa

1421.00

687.00

2108.00

307.53

0.764

0.485

Source IMD, Pune

6 Hydro-Chemical Characterization and Geospatial Analysis … Table 6.4 Classification of ground water for irrigation based on EC

113

Type

EC (µS/cm)

No. of Samples

% of Samples

Low salinity water

2250

01

3.84

26

100.0

Total

The WQI suggests that 73.07% (Table 6.4) sites have excellent and good quality water; and about 26.92% sites characterized by poor quality water, which are unsuitable for drinking purposes. Arsenic, Iron, Chromium, Aluminium, and metallic elements can be harmful if they are in excess.

6.5 Conclusion The present study 73.07% sites have excellent and good quality water; and about 26.92% sites characterized by poor quality water, which are unsuitable for drinking purposes. The importance of this bacterium for the long-term health of the soil is unique. In general, 53% of the total annual fertilizer consumption in the river basin is used in the Kharif season, while 44% of the fertilizers are used in the Kharbi season. Due to the unrestricted use of chemical fertilizers and water, the growth of crops is affected. Among chemical fertilizers, farmers use urea, super phosphate, and potash or combined fertilizers. But due to the lack of micronutrients in these fertilizers, the nutritional balance of the crops is disturbed and this affects the crop yield. Chemical fertilizers immediately meet the nutrient requirements of crops but deteriorate the soil texture. For this, it is recommended to give these fertilizers as a supplement to organic fertilizers. It can be used to determine the nature of rainfall in other districts and for disaster management and future planning. The present investigation indicates significant dominance of agriculture and drinking water in the groundwater chemistry in river basin. Conflict of Interest The authors declare no conflict of interest.

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References Alkindi KM, Mukherjee K, Pandey M et al (2022) Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches. Environ Sci Pollut Res 29:20421–20436. https://doi.org/10.1007/s11356-021-17224-9 Andualem TG, Hagos YG, Kefale A et al (2020) Soil erosion-prone area identification using multicriteria decision analysis in Ethiopian highlands. Model Earth Syst Environ. https://doi.org/10. 1007/s40808-020-00757-2 Bisen DK, Kudnar NS (2013) A sustainable use and management of water resource of the Wainganga river basin: a traditional management systems. J Contrib. https://doi.org/10.6084/m9.figshare. 663573.v1 Bisen DK, Kudnar NS (2019) Climatology, SaiJyoti Publication, Nagpur.pp-1-211. Bisen DK, Kudnar NS et al (2022) Geo-spatial modeling in the assessment of environmental resources for sustainable water resource management in a semi- arid region: a case study of Bhandara District, India. Int J Sci Res Sci Eng Tech 9(4), 286–299. https://doi.org/10.32628/ IJSRSET229445 Bouaicha F, Dib H, Belkhiri L, Bouaicha F, Manchar N, Chabour N (2017) Hydrogeochemistryand geothermometry of thermal springs from the Guelma region Algeria. J GeolSoc India 90:226. https://doi.org/10.1007/s12594-017-0703-y Bouteraa O, Mebarki A, Bouaicha F, Nouaceur Z, Laignel B (2019) Groundwater quality assessment using multivariate analysis, geostatistical modeling, and water quality index (WQI): a case of study in the Boumerzoug-El Khroub valley of Northeast Algeria. Acta Geochim 38:796. https:/ /doi.org/10.1007/s11631-019-00329-x Chaurasia J, Rai PK, Singh AK (2013) Physico-chemical status of groundwater Near Varuna River in Varanasi City, India. Int J Environ Sci (Integrated Publication Association) 3(6):2114–2121 Chaurasia J, Rai PK, Mishra PK (2014) Study of Macro nutrient and physical status of soil in a Part of ‘Varuna River’ in Varanasi, India. Int J Environ Sci (Integrated Publication Association) 4(4):468–471. https://doi.org/10.6088/ijes.2014040400003 Dongare VT, Reddy GPO, Maji AK et al (2013) Characterization of landforms and soils in complex geological formations—a remote sensing and GIS approach. J Indian Soc Remote Sens 41:91– 104. https://doi.org/10.1007/s12524-011-0195-y Kadam AK, Umrikar BN, Sankhua RN (2020) Assessment of recharge potential zones for groundwater development and management using geospatial and MCDA technologies in semiarid region of Western India. SN Appl Sci 2:312. https://doi.org/10.1007/s42452-020-2079-7 Karande UB, Kadam A, Umrikar BN et al (2020) Environmental modelling of soil quality, heavymetal enrichment and human health risk in sub-urbanized semiarid watershed of western India. Model Earth Syst Environ 6:545–556. https://doi.org/10.1007/s40808-019-00701-z Kudnar NS (2017) Morphometric analysis of the Wainganga river basin using traditional & GIS techniques. PhD thesis, RashtrasantTukadoji Maharaj Nagpur University, Nagpur, pp 40–90 Kudnar NS (2018) Water pollution a major issue in urban areas: a case study of the Wainganga river basin. VidyawartaIntMultidiscip Res J 2:78–84 Kudnar NS (2020a) GIS-based assessment of morphological and hydrological parameters of Wainganga river basin, Central India. Model Earth Syst Environ 6:1933–1950. https://doi.org/10. 1007/s40808-02000804-y Kudnar NS (2020b) GIS-based investigation of topography, watershed, and hydrological parameters of Wainganga River Basin, Central India. Sustain Dev Pract Geoinfo, Scrivener Publishing LLC, pp 301–318. https://doi.org/10.1002/9781119687160.ch19 Kudnar NS, Rajasekhar M (2020) A study of the morphometric analysis and cycle of erosion in Waingang¯a Basin, India. Model Earth Syst Environ 6:311–327. https://doi.org/10.1007/s40808019-00680-1 Kudnar NS, Diwate P, Mishra VN et al (2022) Spatio-temporal variability and trend analysis of rainfallin Wainganga river basin, Central India, and forecasting using state-space models. Theor ApplClimatol. https://doi.org/10.1007/s00704-022-04168-4

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Kudnar NS, Padole MS et al (2021) Traditional crop diversity and its conservation on-farm for sustainable agricultural production in Bhandara District, India. Int Journal of Scientific Research in Science, Engineering and Technology, 2394-4099, 8-1:35–43. https://doi.org/10.32628/IJS RSET207650 Kumar A, Krishna AP (2021) Groundwater quality assessment using geospatial technique based water quality index (WQI) approach in a coal mining region of India. Arab J Geosci 14:1126. https://doi.org/10.1007/s12517-021-07474-9 Kushwaha N, Elbeltagi A, Mehan S et al (2022) Comparative study on morphometric analysis and RUSLE-based approaches for micro-watershed prioritization using remote sensing and GIS. Arab J Geosci 15:564. https://doi.org/10.1007/s12517-022-09837-2 Meena NK, Prakasam M, Bhushan R et al (2017) Last-five-decade heavy metal pollution records from the Rewalsar Lake, Himachal Pradesh, India. Environ Earth Sci 76:39. https://doi.org/10. 1007/s12665-016-6303-0 Mishra VN, Rai PK (2016) A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9:249. https://doi.org/10.1007/s12517-015-2138-3 Mishra VN, Rai PK, Mohan K (2014) Prediction of land use changes based on Land Change Modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst, Jovan Cviji´c” SASA (Serbia), 64(1):111–127. https://doi.org/10.2298/IJGI1401111M Mishra VN, Rai PK, Prasad R, Punia M, Nistor MM (2018) Prediction of spatiotemporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India using Geospatial approach: a comparison of hybrid models. Appl Geomat 10(3):257–276 Mohan K, Shrivastava A, Rai PK (2011) Ground water in the city of Varanasi, India: present status and prospects. Quaest Geogr 30(3):47–60. https://doi.org/10.2478/v10117-011-0026-9 Narsimlu B, Gosain AK, Chahar BR et al (2015) SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environ Process 2:79–95. https://doi.org/10.1007/s40710-015-0064-8 Patel DP, Gajjar CA, Srivastava PK (2013) Prioritization of Malesarimini-watersheds through morphometric analysis: a remote sensing and GIS perspective. Environ Earth Sci 69:2643–2656. https://doi.org/10.1007/s12665-012-2086-0 Pathare JA, Pathare AR (2021) Watershed prioritization for soil and water conservation in Darna River basin: a PCA approach. Sustain Water Resour Manag 7:49. https://doi.org/10.1007/s40 899-021-00531-x Rajasekhar M, Gadhiraju SR, Kadam A et al (2020) Identification of groundwater recharge-based potential rainwater harvesting sites for sustainable development of a semiarid region of southern India using geospatial, AHP, and SCS-CN approach. Arab J Geosci:13–24. https://doi.org/10. 1007/s12517-019-4996-6 Rajasekhar M, Sudarsana Raju G, Siddi Raju R (2019) Assessment of groundwater potential zones in parts of the semi-arid region of Anantapur District, Andhra Pradesh, India using GIS and AHP approach. Model Earth Syst Environ 5:1303–1317. https://doi.org/10.1007/s40808-01900657-0 Rajasekhar M, Sudarsana Raju et al (2021) Multi-criteria land suitability analysis for agriculture in SemiArid region of Kadapa District, Southern India: Geospatial Approaches. Remote Sens Land 5(2):59–72. https://doi.org/10.21523/gcj1.2021050201 Rai PK, Chaubey PK, Mohan K, Singh P (2017a) Geoinformatics for assessing the inferences of quantitative drainage morphometry of the Narmada Basin in India. Appl Geomat (Springer):1– 23. https://doi.org/10.1007/s12518-017-0191-1 Rai PK, Mohan K, Mishra S et al (2017b) A GIS-based approach in drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci 7:217–232. https://doi.org/10.1007/s13201-0140238-y Rai PK, Chandel RS, Mishra VN et al (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 8:15. https://doi.org/10.1007/s13201-018-0660-7

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Reddy GP, Sarkar D, Prasad J, Ramamurthy V (2013) Geospatial modeling in assessment of biophysical resources for sustainable land resource management. Trop Ecol 54(2):227–238 Salunke VS, Lagad SJ et al (2021) A geospatial approach to enhance point of the interest and tourism potential centers in Parner Tehsil in Maharashtra, India. Int J Sci Res Sci, Eng Technol 8(1):186–196. https://doi.org/10.32628/IJSRSET218136 Salunke VS, Bhagat RS et al (2020) Geography of Maharashtra, Prashant Publication, Jalgaon, pp 1–229 Srivastava PK, Han D, Ramirez MR et al (2013) Machine learning techniques for downscaling SMOS satellite soil moisture using MODIS land surface temperature for hydrological application. Water Resour Manage 27:3127–3144. https://doi.org/10.1007/s11269-013-0337-9 Zolekar RB (2018) Integrative approach of RS and GIS in characterization of land suitability for agriculture: a case study of Darna catchment. Arab J Geosci 11:780. https://doi.org/10.1007/ s12517-018-4148-4 Zolekar RB, Todmal RS, Bhagat VS et al (2021) Hydro-chemical characterization and geospatial analysis of groundwater for drinking and agricultural usage in Nashik district in Maharashtra, India. Environ Dev Sustain 23:4433–4452. https://doi.org/10.1007/s10668-020-00782-2

Chapter 7

A Comprehensive Review on the Impact of Climate Change on Streamflow: Current Status and Perspectives David DurjoyLal Soren, Jonmenjoy Barman, and Brototi Biswas

Abstract The study of water resource management and the effect of climate change on the water resource is very crucial. The present paper presents a review of 77 articles from 2009 to 2020 across different continents. The papers were short-listed based on Author(s) and Journal, Purpose of study, Sample (origin), Method applied, and key findings. The articles are grouped as cluster 1 to cluster 4 based on topics (i) Climate change effects on runoff (77.92%), (ii) Anthropogenic and climate change impact on runoff (14.28%), (iii) Effects of climate change on runoff and ecosystem (2.59%), (iv) Effects of climate change on streamflow and hydropower (2.59%), and (v) Effects of climate change on streamflow and sediment yield (2.59%). The study was focused on understanding the impact of climate change on the streamflow domain. The highest number of articles based on the topic was published from the continent of Asia (51%). North America, Europe, Africa, South America, and Oceania published 15, 14, 8, 4, and 1, of the articles, respectively. The findings of the study indicate that global runoff/discharge has decreased due to temperature rise brought on by climate change. The present work will provide a suitable framework to gain knowledge about the present condition of climatic impact on runoff. It will provide a robust idea about the suitable technique of analysis and future development of research in this field. Keywords Climate change · Runoff · Streamflow · Anthropogenic activity · Water resource

7.1 Introduction Climate change is currently a very important concern during the present time. It poses a threat to both bio- and non-bio-resources. It resulted in numerous changes in the environmental and socioeconomic fields including water resource and discharge D. D. Soren · J. Barman · B. Biswas (B) Department of Geography & RM, Mizoram (Central) University, Aizawl, Mizoram, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_7

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flows (Croitoru & Minea 2014; Rai et al. 2019, 2021, 2022; Mishra et al. 2021). The streamflow is impacted by climate change globally (Su et al. 2016). The primary cause of climate change is an increase in temperature, which is closely related to the cycle of water resources (Bronstert et al. 2002). Many models and climatic change projections have been developed by the scientist for watershed management and future climatic prediction with hydrological responses (Jung et al. 2012; Pourmokhtarian et al. 2012; Boni et al. 2013; Biswas et al. 2019). Various studies have been conducted on modeling scenarios that highly rely on streamflow regimes while estimating the changes in hydrological response on both local and global scales (Döll and Zhang 2010; Fung et al. 2011). The worsening effect of climate change and anthropogenic activity increased a worldwide water crisis that has been focused on global hydrological research (IPCC 2007; Kumar et al. 2020).According to the Fourth Assessment Report of the Intergovernmental Panel on Climate Changes (IPCC). The intensity and frequency of precipitation and temperature variations will rise due to climate change and anthropogenic impacts (Parry et al. 2007). Regional and worldwide distribution of water resources, both spatially and temporally, is strongly influenced by climate change and changes in the land cover spurred by human activity in the twentieth century. (Scanlon et al. 2007; Solomon et al. 2007; Ling et al. 2011) Some of the studies stated that with increasing temperature streamflow increased (Rai et al. 2014, 2017; Nijssen et al. 2001; Arnell 2003; Wang and Hejazi 2011). On the contrary high temperature could accelerate evaporation and transpiration of plants that reduce runoff (Frederick and Major 1997) and some studies stated as climate change decreased stream flow (Yilmaz and Imteaz 2011; Chang et al. 2014; Rai et al. 2017a, b, 2018). The change of runoff (either increase or decrease) consequently influences sediment yield and its temporal-spatial distribution (Zhang and Wang 2007). It is widely recognized that one of the key factors that will be affected by climate change is the availability of water. The basic concepts of water resource planning encompass streamflow and hydrological process analysis. This investigation as the present work has an immense bearing for the society. For the sake of future management of runoff conditions, the study of the intensity and magnitude of climate change has greater importance to decision-makers (Chang et al. 2014). Climate change impact on runoff and discharge is an important study domain, thus a proper review of this topic can formulate a sustainable knowledge to proceed further research interest in ‘climate change impact in stream-flow domain’.

7.2 Methodology To conduct the research, 77 articles from 2009 to 2020 were reviewed. In the 1st step, the articles were thoroughly reviewed and simultaneously grouped into clusters 1 to cluster 5 based on topics (i) Climate change effects on runoff, (ii) Anthropogenic and climate change impact on runoff, (iii) Effects of climate change on runoff and

7 A Comprehensive Review on the Impact of Climate Change …

119

ecosystem, (iv) Effects of climate change on streamflow and hydropower, and (v) Climate change impact on streamflow and sediment yield. In the 2ndstep, the clustered articles were grouped based on the year of publication. In the 3rd step, the articles were summarized systematically including the author’s name and year, the purpose of the work, methodology, and key findings. The investigation of climate change on the effect on streamflow was aided by the prepared database.

7.3 Overview of Important Literature The increase in global average air temperature and an increase in potential evaporation have significantly affected the hydrological cycle. This in turn has affected the streamflow (IPCC 2007). Al-Faraj et al. (2014) studied climate change as the reason for drought occurrence. The volume of runoff is an important concern to mitigate drought phenomena in the downstream area. Drought and Climate put adverse pressure on water resource management and considerably increase the level of water paucity in downstream countries. Müller Schmied et al. (2012) asserted that the main cause of the shift in mean annual runoff is climate change. Climate change’s effects on discharge/runoff calibration were investigated by Githuiet al. (2009), Phanet al. (2011), Zhang et al. (2012), Li et al. (2015), Oliveira et al. (2017), Qiu et al. (2019), Huo et al. (2013) and Hagemann et al. (2013) who used Soil and Water Assessment Tool (SWAT) as model validation. Gupta et al. (2011) applied the global circulation model and SCS model to estimate the effect of climate change on runoff. Stream and lake eutrophication might be substantially impacted by climate change in terms of phosphorus transfer and collaboration with changes in nutrient loading which may strengthen eutrophication symptoms in lakes (Jeppesen and Kronvang 2009). The hydrological model was used to determine the change of runoff induced by both human-caused and natural factors (Lei et al. 2014). Meng and Mo (2012) argued that annual potential evaporation was more significant than annual precipitation to reduce the runoff. The insight was given to annual runoff change and makes a future runoff change prediction. Based on the future prediction hypothesis empirical equation are developed and validated in the different watersheds. Narsimlu et al. (2013) found that climate change study is helpful to the conservation of soil water management planning, crop conservation, and drought tolerance crop management planning (Mishra and Rai 2014). Climate change also influences river ecosystems, species abundance, and ecosystem services (Schneider et al. 2013). Chen and Chen (2014) applied sensitivity analysis that detects the climate change effect on runoff. This method can provide valuable insights on how the hydrological components will respond to future climate change. Tang et al. (2012) studied the changing nature of streamflow by applying the Variable Infiltration Capacity (VIC) model with the increasing trend of temperature change and change in streamflow that harmed water

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management and ecology. Döll and. Zhang (2010) studied the effect of climate change on river flow regimes and freshwater ecosystems and concluded that by the end of the 2050s, climate changes will affect the ecological characteristics of the river.

7.4 The Trend of Article Publication and Journals Of the 77 articles reviewed from the period 2009 to 2020, the highest number of articles was found to be in the domain of the effect of climate change on runoff/ discharge which accounted for 18.18% in the year 2014 (Table 7.2). The papers had mostly appeared in Water, Hindawi Publishing Corporation Scientific World Journal, Hydrol. Earth Syst. Sci, Quaternary International, Journal of Hydrology, Front. Earth Sci, Science of the Total Environment, Water Resources Research, Front. Earth Sci, TheorApplClimatol, International Journal of Water Resources Development, Journal of Geophysical Research: Atmospheres, Climatic Change, J. Earth Syst. Sci. From 2011 to 2013 the same numbers of articles were taken that contained 11.69% of articles in each year. The lowest number of articles (2.60%) were studied for the years 2009 and 2010 (Table 7.2). The articles were grouped into five clusters (Table 7.1) as detailed in Methodology. Cluster 1 covered 77.92% of the articles that were reviewed. 14.28% of the articles belonged to Cluster 2 (to understand the anthropogenic influence on runoff). Cluster 3–5 underlined the impact of climate change on runoff, ecosystem, streamflow, hydropower, and sediment. This covered 2.5% of the reviewed articles. Out of 77 articles, highest percentage of the articles was published in the Journal of Climatic Change, and Water Resour. Res (7% of each). 6% of the articles were published in the Journal of Hydrology followed by 4% each in the journals of Hydrol. Earth Syst. Sci, Quaternary International, and Water. This was followed by 3% articles in the journals of Science of Total Environment, Hydrol. Process and Water Resour Manage. Journalsof Int. J. Climatol, Hydrol. Process, Hydrological Sciences Journal, Nat Hazards, TheorApplClimatol, and J. Earth Syst. Sci published 2% articles in each of the journals, while the other journals published only 1% articles in each (Table 7.2). An increasing trend of article publication based on the topic of review was found in the journals of Climatic Change, Water Resour. Res, and Journal of Hydrology (Fig. 7.1).

7.5 Country of Origin The study covered every continent of the globe with the highest number of articles published from the continent of Asia (51%) and in this continent, a dominant number of studies were conducted from China (26 publications), followed by India (6 publications). Nepal, Turkey, Iraq, Taiwan, and Vietnam had 1 publication each. North America had the 2nd highest publications with 15% of the articles, of which

Author(s) and Journal

Purpose

Githui et al. (2009) Int. J. Climatol.

Jeppesen and Kronvang (2009) J. Environ.

Roderick (2011) Water Resources Research

Yang and Yang (2011) Water Resources Research

1

2

3

4

Climate change projections, General Circulation Model, Hydrological NAM model

Soil and Water Assessment Tool

Method

Climate Elasticity

Murray-Darling Basin, Budyko-type equation Southeast Australia

Denmark

Western Kenya

Sample

To examine the Hai river basin, China climate elasticity of runoff and to assess the effects of climate change on annual runoff

To study relating variations in runoff to variations in climatic conditions and catchment properties

To assess Climate change effects on runoff and potential adaptations

To assess the climatic impact on runoff change

Cluster 1: Climate change effects on runoff

SL No

Table 7.1 Summary of literature review

(continued)

Evaporation elasticity, precipitation elasticity, and catchment characteristics were very sensitive to climate change. 1% change in precipitation leads to 1.6–3.9% change in runoff, and 1 °C of temperature change leads 2% decrease in runoff

Climate change was attributed to a 10% change in precipitation resulting in 26% change in the runoff which was predicted by using Intergovernmental Panel on Climate Change AR4 climate model

Climate change has a deep impact on phosphorus (P) transport in streams and on lake eutrophication

Assessed the potential future climatic changes Streamflow response was not sensitive only to temperature but also depends on population growth and land cover

Key findings

7 A Comprehensive Review on the Impact of Climate Change … 121

To assess the effect Songhua river basin, of climate change on China mean annual runoff

To study the impact of Climate change on Runoff

To assess the impact Euphrates Basin, of climate change on Turkey runoff

To assessing the streamflow sensitivity to temperature increases

Meng and Mo (2012) Hydrol. Process

Gupta et al. (2011) J Indian Soc. Remote Sens.

Yilmaz and Imtear (2011) Hydrological Sciences Journal

Tang et al. (2012) Global and Planetary Change

6

7

8

9

Salmon River Basin, Idaho

India

Wallonia, Belgium

To study hydrological response to climate change

Bauwens et al. (2011) Hydrol. Earth Syst. Sci.

5

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Physically based model was used to understand water–soil–plant relation with climate change impact. The projected climate change trend was a 10% decrease in evapotranspiration could lead to a decrease of 17% of streamflow

Key findings

Variable Infiltration Capacity (VIC) Model

Mann-Kendal Test, Spearman’s rho Tests, Distributed Models, Regional Circulation Model

Soil Conservation Service Curve Number Method

(continued)

The result shows decrease of streamflow with increasing temperature. The increase of temperature 2 and 3 °C decrease streamflow 2–6 and 3–8%, respectively

Futuristic climate change database was produced using various hydrological models The projected model shows a 34, 13, 10, and 28% runoff decline in summer, spring, winter, and autumn, respectively

Future prediction analysis indicated decrease of runoff all over India with climate change. Significant runoff reduction was found on Subarnarekha river, the lower parts of Ganga, upper parts of the Mahanadi, and Bahamani-Baitrani river

Schreiber Equation, Changing the nature of climatic variables Penman-Monteith (P-M) like temperature and wind speed increased Equation, General Circulation evapotranspiration and runoff Model

Physically Based Model

Method

122 D. D. Soren et al.

To evaluate the impact of climate change on mean annual runoff and river flow regimes

Döll and Schmied (2012) Environ. Res. Lett.

Leppi et al. (2012) Climatic Change

Wu et al. (2012) To inspect the Quaternary International climatic effects of the three gorges reservoir and simulation of runoff

Zhang et al. (2012) Journal of Hydrology

Zhang et al. (2012) Journal of Hydrologic Engineering

10

11

12

13

14

A global overview

Sample

Yangtze River Basin, China

Key findings

Non-parametric Tests (Mann-Kendall, and Spearman), Time Series Cross-Correlation Analysis

Regional Climate Models, Double Nested Method

Shapiro-Wilk test, Mann-Kendall Test

(continued)

Increase of annual runoff, both regional shortage of water and flood occurrence was accelerated owing to global warming

The significant break point of annual runoff change was examined in 1969. Forest harvesting had a significant role in dry season and annual runoff (increase of 38 mm/year)

A significant increase in temperature and a decrease in precipitation over the three gorges area

The result shows a negative correlation between temperature and discharge. A significant decline of discharge was found over the last half-century

Water GAP Global Hydrology Anthropogenic factors reduced river Model (WGHM) discharge Mean annual runoff will increase by 10% on 50% of the global land area

Method

To examine Huaihe River Basin of Variable Infiltration Capacity hydrologic China Model simulation to explore the impacts of climate change on runoff

To study the effect of forest harvesting and climatic variability on runoff

Yangtze River, China

To study the impacts Central-Rocky of climate change on Mountain, US stream discharge

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

7 A Comprehensive Review on the Impact of Climate Change … 123

To examine the California impact of climate change on hydrology and water quality through watershed modelling approach

To study how climate change modified river flow regimes

Luo et al. (2013) Science of the Total Environment

Schneider et al. (2013) Hydrol. Earth Syst. Sci.

Arnell and Gosling (2013) Journal of Hydrology

16

17

18

To study the impacts A world view of climate change on river flow regimes

Europe

Jianzhuangcuan Catchment Shaanxi Province, China

To assess the impact of climate change on streamflow in a typical debris flow watershed

Huo et al. (2013) Environ Earth Sci.

15

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

WaterGAP3 indicated that snow cover will be reduced for the boreal climate zone. River flows will likely be lower in the 2050s

Decrease in streamflow was predicted with reduced precipitation in summers while increase in streamflow during winter Modified SWAT model incorporated with CO2 impacts and future streamflow prediction

Future climate change scenario generated from 2020 to 2030 It was concluded that the climate of the study area would become warmer

Key findings

(continued)

Coupled Model The model scenario (Hadley Centre Intercomparison Project Phase HadCM3) shows a 47% significant increase 3 of annual runoff across Equator, eastern Europe, Canada, and high-latitude Siberia, and a decrease of runoff (37%) in the Mediterranean, Central America, Brazil, and central Europe

Global Hydrology Model WaterGAP3

Soil and Water Assessment Tool

Soil and Water Assessment Tool

Method

124 D. D. Soren et al.

To assess the impact Upper Soˇca River of projected climate basin, Slovenia change on the hydrological regime

To examine the future climate change impacts on water resources

Janža (2013) Nat Hazards

Narsimlu et al. (2013) Water Resour Manage

Crossman et al. (2013) Journal of Great Lakes Research

Al-Faraj et al. (2014) Water

20

21

22

23

To examine the Diyala river basin sensitivity of surface shared between Iraq runoff to drought and Iran and climate change

To study the impact Lake Simcoe of climate change on watershed, Canada hydrology and water quality along with management strategies

Upper Sind River Basin, India

A world view

To examine the Climate change impact on available water resources

Hagemann et al. (2013) Earth System Dynamics

19

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Meteorological Drought Severity, Streamflow Drought Index, Rainfall-Runoff Model

Global Circulation Model

Soil and Water Assessment Tool, Sequential Uncertainty Fitting Algorithm (SUFI2)

Distributed Hydrological Model MIKE SHE

Global climate and hydrology models

Method

(continued)

Decrease in water resources by 17.30% annually was projected owing to climate change

The projected IPCC scenario shows increased precipitation during winter and an increase in temperature in summer throughout the twenty-first century

The result shows a predicted increase in the annual streamflow in mid-century and end-century by 16.40% and 93.50, respectively

The study is based on future predictions (2011–2040, 2041–2070, 2071–2100). Future projection shows an increase of average temperature −0.9, 2.3, 3.8 °C, respectively, thereby reducing runoff and groundwater recharge

The projected result shows a 10% decrease in water resources in parts of Europe, the catchments of the Euphrates/Tigris in the Middle East, Murray in SE Australia, Zhu Jiang in southern China

Key findings

7 A Comprehensive Review on the Impact of Climate Change … 125

To examine the impact of climate change on water resources

Giang et al. (2014) Hindawi Publishing Corporation Scientific World Journal

Aich et al. (2014) Hydrol. Earth Syst. Sci.

Lei et al. (2014) Journal of Hydrology

Lin et al. (2014) Front. Earth Sci.

Oni et al. (2014) Science of the Total Environment

24

25

26

27

28

Upper Ca River Watershed in Southeast Asia

Sample

To examine uncertainty assessments and hydrological implications of climate change

To explore changes in runoff and eco-flow

Southern Ontario

Dongjiang River of the Pearl River Basin, China

To study impact of Haihe River Basin, climate change and Northern China vegetation dynamics on runoff in the mountainous region

To study the impact African river basins of climate change on streamflow

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Statistical Downscaling Model, Physically-Based Semi-Distributed Rainfall-Runoff Model

Mann-Kendall test, Pettitt-Mann-Whitney Change-Point Statistics, Indicators of Hydrologic Alteration

Community Land Model, River Transport Model

Soil and Water Integrated Model

Soil and Water Assessment Tool

Method

(continued)

Human activities increased the differences in integrated hydrological responses

The trend analysis depicts the increasing rate of annual median flow from 1957 to 2010 with a significant change point between 1970 and 1974 due to climate change and the construction of reservoirs

The climatic variables of precipitation, solar radiation, air temperature, and wind speed counted as 56, 14, 13, and 5%, respectively, which accounted for the overall reduction in the annual runoff since 1960

Result showed statistical correlation between precipitation and runoff in Nile valley, and some African regions

The result indicated an increase of temperature by 3.4 °C and evaporation in the 2090s. The discharge will increase in the wet season and will decrease in the dry season at a rate of ±25%

Key findings

126 D. D. Soren et al.

To study the climatic impact on runoff based on an integrated water balance model

To study the climate Yellow River Basin, change and China probabilistic scenario of streamflow extremes

Croitoru and Minea (2014) Theor Appl Climatol.

Yates (2014) International Journal of Water Resources Development

Yang et al. (2014b) Journal of Geophysical Research: Atmospheres

31

32

33

To study the impact of climate changes on river discharge

To study the effects of climate fluctuations on runoff

Chen and Chen (2014) Front. Earth Sci.

30

Mulberry Basin in Arkansas, USA

Moldavia region, Romania

Kaidu River in northwestern, China

An assessment of Hai River Basin, error analysis of the China Budyko hypothesis on the contribution of climate change to runoff

Yang et al. (2014a) Water Resources Research

29

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Artificial Neural Network Downscaling Models

WatBal Model, Priestley Taylor Method

Mann-Kendall Test, Sen’s Slope

Mann-Kendal Test

Budyko Hypothesis, Mann-Kendall test

Method

(continued)

The result showed a decreasing trend of streamflow in the Alpine region and predicted an extreme change of streamflow in the future

Increase of temperature by 1 °C caused reduction of precipitation by 2%. Change of precipitation ±10 and ±20% produced ±12 and ±23% changes in runoff, respectively

The result shows an increase of summer precipitation resulting in increased river discharge in 80% of the rivers. The opposite was found in winter owing to reduced precipitation

The result shows an increasing trend of precipitation and runoff over the past 50 years

Negative correlation between precipitation and potential evaporation

Key findings

7 A Comprehensive Review on the Impact of Climate Change … 127

To estimate the climate change on peak discharge variability

Climatic Change

Goulden and Bales (2014) J. Earth Syst. Sci.

Stagl and Hattermann (2015) Water

Vano et al. (2015) Water Resources Research

34

35

36

37

Upper Kings River basin, Southern California

Rhine river, Germany

Sample

To study the Columbia River basin Seasonal hydrologic and its adjacent responses to climate coastal drainages change

To study the impacts Danube River of climate change on catchment, Europe the hydrological regime

To study mountain runoff vulnerability with increased evapotranspiration and vegetation expansion

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

The result shows that runoff reduction in summer and increase in winter

Climatic projection shows in 2100 evaporation could increase 28% and river flow will decrease 26%

With an increase of temperature by 4 °C, streamflow reduced by 6% and a 20% decrease in precipitation leads to a 30% lower peak flow

Key findings

(continued)

Variable Infiltration Capacity The runoff decreasing rate was more in (VIC) land-Surface Hydrology warm season by about 74%. Runoff Model increased in the cold season at the rate of 26%

Soil and Water Integrated Model

Climate Projections

RHINEFLOW Model

Method

128 D. D. Soren et al.

Su et al. (2016) Climatic Change

40

To examine the impact of climate change on streamflow

Zhang et al. (2015) To study the changes Huaihe River Basin, eastern China Quaternary International in extreme climate events in eastern China

39

Method

Yangtze River Basin, China

General Circulation Model, An analysis of variance (ANOVA)

Penman-Monteith Method, Surface Humid Index, Crop Moisture Index, Keetch and Byram Drought Index, Crop-Specific Drought Index, daily Water stress index, Moisture Deficit Index, Agricultural Reference Index

Upper Baitarani River Soil and Water Assessment Basin of Eastern India Tool

To assess Climate Change Impact on Water Balance Components

Uniyal et al. (2015) Water Resour Manage

38

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

(continued)

The projected result shows an increased temperature all over the basin. The simulated result shows a 69% increase in runoff

The count of the annual rainy day declined; the regional average value of wet events increased by 0.0118 times/year. The regional extreme drought events had a much negative tendency about 0.0127 times/year

Change in temperature from 1 to 5 °C caused reduction in the surface runoff by 2.5–11%, respectively. While increasing the nature of rainfall 2.5 and 15% suggested increasing runoff from about 6.67–43.42% Change in evapotranspiration by 5.05–11.88% rise would change groundwater recharge by 8.7–23.15%, respectively

Key findings

7 A Comprehensive Review on the Impact of Climate Change … 129

To examine the Projections of climate change effects on discharge and inundation

Sorribas et al. (2016) Climatic Change

Pumo et al. (2016) Science of the Total Environment

Wang et al. (2016) Journal of Hydrology

41

42

43

Guyanese and Brazilian shields, and the Amazon plain

Sample

To study the multiple elasticities of runoff to climate change and catchment characteristics alteration

Thirty River Basin across China

To study the climate Italy change effects on the hydrological regime

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

The projected result shows the increasing trend of precipitation, increased discharge and inundation of river in western and central Amazonia and Peruvian floodplains. The projected results are decreasing discharge in the eastern basin along with decreasing inundation in the Amazon basin

Key findings

Mann-Kendall Test, Pettitt Test

(continued)

Among the climatic variables, precipitation played 48.98% role in runoff. Evapotranspiration and land-use change contributed to a negative impact on runoff. Among 30 catchments, 19 were detected with change points with 10% level of confidence and change point was between the year of 1970s and 1980s

Flow Duration Curves, ModABa hydrological model’s projection General Circulation Models, of 2090 depicts an increase of temperature ModABa Hydrological Model and reduction of precipitation by about 13% and reduction of annual runoff by 10 and 20% for the future projections at 2055 and 2090, respectively

General Circulation Model

Method

130 D. D. Soren et al.

Berton et al. (2016) Journal of Hydrology: Regional Studies

Chang et al. (2017) Nat Hazards

Radchenko et al. (2017) Water Resources

Donnelly et al. (2017) Climatic Change

45

46

47

48

Central Asia

To examine the Europe impacts of climate change on European hydrology

To study Climate Change Impacts on Runoff

To study the impact Weihe River Basin, of climate change on China runoff and uncertainty analysis

Key findings

General Circulation Model

General Circulation Model

TOPMODEL, M-GLUE method, Mann-Kendal Test

(continued)

The result shows increasing trend of temperature closely associated with runoff change. More instance of runoff change was found at 1.5–3 °C temperature

The simulated result shows significant decreases of summer runoff by 12–42%, and an increase of winter runoff by 44–107%

The result of the RCP4.5 and RCP8.5 scenario shows runoff will decrease 13.3–27.7%, respectively

The result shows that positive correlation between annual discharge and precipitation

JULES a Land Surface Model, Assessed mean and low hydrological states Multi-Segment Bias at 4 °C global warming scenario for the Correction (MSBC) Method European region. The study predicted increasing nature of water cycle at higher levels of warming. There were remarkable projected decreases of low flows in the future

Method

To examine the Merrimack Watershed, Principal Component changing climate USA Analysis, Factor Analysis, increases discharge Mann-Kendal Test and attenuates its seasonal distribution

To study high-end Europe climate change impact on runoff and low flows

Papadimitriou et al. (2016) Hydrol. Earth Syst. Sci.

44

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

7 A Comprehensive Review on the Impact of Climate Change … 131

To analysis the climate change impacts on streamflow seasonality

Eisner et al. (2017) Climatic Change

Shanka (2017) Environ Pollut Climate Change

Das and Nanduri (2018) To evaluate the potential climate Hydrological Sciences change impact on Journal monsoon flow

Lv et al. (2019) Scientificreports

49

50

51

52

To study the effects of climate and catchment characteristic change on streamflow

To study Climate Change Impacts on Runoff

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Yellow River, China

Wainganga River Basin, India

Budyko-type equation

Machine Learning Technique

Statistical Downscaling Model, General Circulation Model, Soil and Water Assessment Tool

General Circulation Model

a Multiple

Gidabo Basin, Ethiopia

Method

Sample

(continued)

The streamflow changed 26.87 mm between the period 1978–2010. Runoff altered 91.27% due to change of watershed characteristic and 6.50% due to climate change

The result predicted a significant decrease in monsoon affecting streamflow

The result shows a significant increase in runoff in the summer season and a high amount of decrease of runoff in the winter season

The result shows a declining trend of streamflow in the Tagus basin and an increase of winter streamflow in the Rhine basin

Key findings

132 D. D. Soren et al.

To examine the impact of climate change on streamflow hydrology

Worqlul et al. (2018) Water

Kelaiya et al. (2019) International Journal of Bio-resource and Stress Management

Catena

Qiu et al. (2019) Journal of Hydrology

53

54

55

56

Upper Blue Nile Basin, Ethiopia

Sample

To study the impact Miyun Reservoir of climate change on Watershed, China watershed systems

To assess the climate Koshi River Basin, change impact on Nepal the hydrology

To asses the water Bhadar River Basin, balance components India

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Soil and Water Assessment Tool

Soil and Water Assessment Tool

Soil and Water Assessment Tool

Global Circulation Models

Method

(continued)

The result shows that during the middle, near and future period annual runoff change were −22.17, −30.14, −3.97%, respectively. The decrease of runoff is associated with an increase in temperature and evapotranspiration

The study predicted the climate of 2030–2080. The predicted result shows a decrease in streamflow by 8.5% during the twenty-first century

The result shows that annual evapotranspiration, runoff, rainfall was 252.9, 243.67, and 670 mm, respectively. The annual average rainfall accounted for 36.37% of the annual average runoff

The result predicted an increase of maximum and minimum temperature by 3.6–2.4 °C by the end of the twenty-first century. With increasing temperature, evapotranspiration will increase by 7.8%. The hydrological model indicates that streamflow increases by 64% in the dry season and decreased by 19% in the wet season

Key findings

7 A Comprehensive Review on the Impact of Climate Change … 133

Srinivas et al. (2020) Stochastic Environmental Research and Risk Assessment

Koch et al. (2020) Climatic Change

59

60 Pajeú watershed in north-eastern Brazil

Ganges River Basin, India

61

Wang and Hejazi (2011) To quantify the United States relative contribution Water Resources of the climate and Research direct human impact on mean annual streamflow

Cluster 2: Climate change and human impact on runoff

To study climate change and water resource management

To study the hydroclimatic river discharge and seasonal trends assessment

Water Evaluation and Planning system

Method

Budyko-type equation

Soil and Water Integrated Model

Mann-Kendall Test, Sen’s Slope

Yellow River of China Standardized Precipitation Index, Mann-Kendal Test,

Bingfei et al. (2020) J. Geogr. Sci.

58

To examine the differential changes in precipitation and runoff discharge

To examine the Densu River Basin, hydrologic response Ghana to climate change

Oti et al. (2020) Heliyon

57

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

(continued)

The study stated that climate and human activities, have a strong impact on streamflow. Climate exhibits an 18% increase in streamflow. In the arid region climate- and human-induced changes were more severe than other regions

The study stated that the semi-arid region of Brazil was prone to drought

The study was predicted for 2030, 2040 and 2050. It stated that a gradual decrease of precipitation leads to significant decrease in river discharge at the rate of 15–21%

The result shows that 1989 was the change point of runoff, when 14% of streamflow reduction occurred. Variation of precipitation was not strictly consistent with runoff

The result indicated that with an increase of temperature by 8.23%, there is a decrease of 17% in the precipitation. The increase of temperature and reduction of precipitation results in 58.3% water resource reduction

Key findings

134 D. D. Soren et al.

Wang et al. (2012) To study the role of Quaternary International climate change and human activities on the runoff

To assess of impact Huifa River Basin, of climate change Northeast China and human activities on runoff

To quantify the impact of climate variability and human activities on runoff changes

Zhang et al. (2012) Water Resour Manage

Wang et al. (2012) Hydrol. Process

63

64

65

Haihe River basin, China

Yangtze River, China

To quantify the United States relative contribution of the climate and direct human impacts on mean annual streamflow

Wang an (2011) Water Resour. Res

62

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Mann-Kendall Test, Precipitation–Runoff Double Cumulative Curves Method and Pettitt’s Test

Soil and Water Assessment Tool

Cumulative Anomaly and the Slope Change Catio of Cumulative Quantity (SCRCQ)

Budyko-type equation

Method

(continued)

The study quantified climate variability and human activities on runoff response across three river basins. The climatic elasticity analysis shows an average decrease of runoff by 38.33% due to climatic variables across the basins. Human activity resulted in about 61.66% runoff change across the three basins

Reconstruction of annual runoff from 1965 to 2005 based on calibration and validation of baseline period between 1956 and 1964 The result indicates correlation between climatic variables (precipitation and temperature) and runoff coefficients

The impact of human activities on runoff change was about 90%, while climatic variables influenced by about 10%

Mean annual streamflow increased due to climate change in most of the watersheds. The impact of climate change on annual streamflow was 18%. The human-induced impact on mean annual runoff was heterogeneous in different watersheds

Key findings

7 A Comprehensive Review on the Impact of Climate Change … 135

Chang et al. (2014) To study the impact Weihe River Basin, China Quaternary International of climate change and human activities on runoff

Jiang et al. (2015) Journal of Hydrology

67

68

To study the impacts Weihe River, China of climate change and human activities on runoff

Kaidu River Basin, North China

To quantify the effects of climatic variability and human activities on runoff

Chen et al. (2013) Theor Appl Climatol.

66

Sample

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Budyko-type equations, Mann-Kendall Test

Variable Infiltration Capacity Hydrological Model

Mann-Kendall Test, Mann-Kendall–Sneyers Test, Hydrologic Sensitivity Analysis Method

Method

(continued)

The result of the study shows that human activity and climatic factors both were the driving factors to reduce the runoff

Study showed a decline of 35% in the runoff of the river basin since the baseline decade (1956). The percentage of runoff change caused by climate change in 1970, 1980, 1990, and 2000 was 36, 28, 53, and 10%, respectively. The percentage of runoff change caused by human activity was 64, 72, 47, and 90%, respectively

The runoff trend was divided into a natural period (1960–1993) and a human-induced period (1994–2009) and the change point in annual runoff was identified as the year 1993. A significant increase of runoff was recorded and it was concluded that climatic variability was the main contributing factor (90.5%) followed by and human activity (9.5%)

Key findings

136 D. D. Soren et al.

To investigate the causes of change in runoff

To Assess the Northern Taihang response of runoff to Mountain, China climate change and human activities

Wang et al. (2017) Appl Water Sci.

Wang et al. (2018) J. Earth Syst. Sci.

70

71

Gushan River

Döll et al. (2010) Hydrol-Earth-Syst-sci

To assess the impact A global overview of climate change on freshwater ecosystems and river flow alterations in a global-scale

Cluster 3: Climate change effects on runoff and ecosystem

72

Method

Runoff reduction owing to spring snowmelt and winter snowfall was 59 and 18%, respectively. The significance of annual runoff and precipitation was detected in 1982. The contribution of climate change and human activities on runoff change was 37.5 and 62.5%, respectively

The result shows a reduction of runoff by 52.4 mm during the study period of 1980–2013 and climate change contributes about 38.5% and human activity contributes 61.5% of total runoff

Alteration of the climatic variable was the prime concern of runoff change, during the year 2003 to 2013. Climate change has resulted to a 66–97% reduction of runoff while anthropogenic interventions attributed to about 4–34% reduction

Key findings

(continued)

Water GAP Global Hydrology Flow alteration and their spatial-temporal Model magnitudes were computed

Mann-Kendall test, Pettitt change-point statistics, Elasticity Coefficient Method

Hydrological Simulation Approach

Lower Zab River, Iqra Pettitt, Precipitation-Runoff Double Cumulative Curve, Mann-Kendal, Multi-Model Combination Technique

Mohammed et al. (2016) To assess various Stoch Environ Res Risk models for predicting Assess anthropogenic interventions and climate variability on surface runoff

Sample

69

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

7 A Comprehensive Review on the Impact of Climate Change … 137

To study ecosystem scenario with respect to climate and hydrological alterations

Fekete et al. (2010) Global Biogeochem Cycles

73

A global overview

Sample

Wagner et al. (2017) Environ Earth Sci.

75

To examine the impacts of climate change on streamflow and hydropower generation

Alpine region

To assess the climate Southeastern Brazil change impacts on streamflow and hydropower potential

76

Phan et al. (2011) Water Resources

To study the impact Northern Viet Nam of climate change on Stream discharge and sediment yield

Cluster 5: Climate change impact on streamflow and sediment yield

Oliveira et al. (2017) Int. J. Climatol.

74

Cluster 4: Climate change impact on streamflow and hydropower

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

Soil and Water Assessment Tool

Hydrological Modelling

Soil and Water Assessment Tool

Global Circulation Model, Water Balance/Transport Model, Millennium Ecosystems Assessment

Method

(continued)

The result predicted for the 2050s as change in the rate of sediment load and stream discharge to be 11.4 and 15.3%, respectively, with discharge increasing with sedimentation during the wet season

The result indicates a decrease in runoff in some seasons

The result shows a significant decrease of runoff in all seasons and a resultant decrease in hydropower from 6.1 to 58.6% throughout the twenty-first century

The future climatic consequence to alternative environmental management policies

Key findings

138 D. D. Soren et al.

To examine the runoff and sediment yield variations in response to precipitation changes

Li et and Gao (2015) Water

77

Loess Plateau, China

Sample Soil and Water Assessment Tool

Method

The result shows sediment yield and runoff increased by 11.54 and 18.36%, respectively, when precipitation increased by 10% and sediment yield and runoff decreased by 10.05 and 13.36%, respectively, when precipitation decreased by 10%

Key findings

Note a Multiple represent the basin set includes Rhine and Tagus in Europe, upper Amazon in South America, upper Mississippi in North America, Lena, Ganges, upper Yellow, and the upper Yangtze in Asia, the Blue Nile and Niger in Africa, and Darling in Australia

Purpose

Author(s) and Journal

SL No

Table 7.1 (continued)

7 A Comprehensive Review on the Impact of Climate Change … 139

140

D. D. Soren et al.

Table 7.2 Journal and quantity of article Sl No

Journal

Count

Sl No

Journal

Count

1

Water Resour. Res.

7

21

Global and Planetary Change

1

2

Climatic Change

7

22

Journal of Hydrologic Engineering

1

3

Journal of Hydrology

6

23

Earth System Dynamics

1

4

Hydrol. Earth Syst. Sci.

4

24

Journal of Great Lakes Research

1

5

Quaternary International

4

25

Hindawi Publishing Corporation Scientific World Journal

1

6

Water

4

26

Front. Earth Sci

1

7

Science of the Total Environment

3

27

International Journal of Water Resources Development

1

8

Hydrol. Process

3

28

Journal of Geophysical Research: Atmospheres

1

9

Water Resour Manage 3

29

Stoch Environ Res Risk Assess

1

10

Int. J. Climatol.

2

30

Journal of Hydrology: Regional Studies

1

11

Hydrol. Process

2

31

Appl Water Sci

1

12

Hydrological Sciences 2 Journal

32

Water Resources

1

13

Nat Hazards

33

Environ Pollut Climate Change

1

2

14

Theor Appl Climatol

2

34

Scientific reports

1

15

J. Earth Syst. Sci.

2

35

International Journal of Bio-resource and Stress Management

1

16

J. Environ

1

36

Catena

1

17

Hydrol-Earth-Syst-sci

1

37

Heliyon

1

18

Global Biogeochem. Cycles

1

38

J. Geogr. Sci

1

19

J Indian Soc Remote Sens

1

39

Stochastic Environmental Research and Risk Assessment

1

20

Environ. Res. Lett.

1

Total

77

the United States had 9 publications, and Canada and Ontario had 1 each. The third highest publication on the topic of study is attributed to Europe (14%). Out of 77 articles undertaken for the present study, 5 studies were based on a global overview, one study was conducted on Iraq and Iran (both as one study region), and 4 studies were conducted on a continental scale, i.e., Southeast Asia, Asia, Africa, and Alpine region. The details of the studies undertaken have been presented in Table 7.3 and Fig. 7.2.

7 A Comprehensive Review on the Impact of Climate Change …

141

Fig. 7.1 a List of journal and number of papers published. b Quantity of journal based on year

7.6 Summary of Findings The works highlighted how runoff alteration will be impacted by climate change over time. Runoff changes by 26% with a 10% change in precipitation (Jeppesen and Kronvang 2009). Climate change was indicated by a significant increase in the temperature, and a drop in precipitation having a major impact on runoff (Wu et al. 2012; Yates et al. 2014; Pumo et al. 2016). The result shows that an increase in temperature by 4 °C reduced streamflow which leads to a 30% lower peak flow. The relationship between temperature and runoff/discharge was adverse (Leppi et al. 2012). The change of temperature (1 °C) caused a reduction (2%) of runoff change (Yang and Yang 2011) and the increase in temperature by 2 and 3 °C decreased streamflow by 2–6 and 3–8%, respectively (Tang et al. 2012). The changes in land use and cover also had a detrimental effect on runoff (Wang et al. 2016). The projected result for future water resources shows a change in groundwater recharge by 23.15%

142

D. D. Soren et al.

Table 7.3 Distribution of article Sl No

Country

Count

Sl No

Country

Count

1

China

26

16

Romania

1

2

United States

9

17

Germany

1

3

India

6

18

Italy

1

4

A global overview

5

19

Nepal

1

5

Europe

4

20

Ghana

1

6

Brazil

3

21

Iraq

1

7

Ethiopia

2

22

Taiwan

1

8

Kenya

1

23

Vietnam

1

9

Denmark

1

24

a Iraq

1

10

Australia

1

25

b Southeast

1 1

and Iran

11

Belgium

1

26

c Africa

12

Turkey

1

27

d Asia

13

Slovenia

1

28

e Alpine

f Multiple

14

Canada

1

29

15

Ontario

1

Total

Asia

region

1

1 1 77

Note a Iraq and Iran contained both countries; b Southeast Asia, c Africa, d Asia, e Alpine region covered continents as study region, f Multiple represent Rhine and Tagus in Europe, upper Amazon in South America, upper Mississippi in North America, Lena, Ganges, upper Yellow, and the upper Yangtze in Asia, Blue Nile and Niger in Africa, and Darling in Australia as a study region

Fig. 7.2 a Journal distribution based on country. b Journal distribution based on the continent

7 A Comprehensive Review on the Impact of Climate Change …

143

(Uniyal et al. 2015; Biswas et al. 2018) and a reduction in both runoff and groundwater recharge (Hagemann et al. 2013; Papadimitriou et al. 2016). Over the period, different human activities have different effects on runoff change (Chang et al. 2014). The anthropogenic role in runoff reduction is more dominant than the climatic factors (Wang et al. 2012, 2017, 2018; Chang et al. 2014). In Northern Vietnam and China’s Loess Plateau, the SWAT model was used to examine how climate change would impact sediment output (Phan et al. 2011; Li et al. 2015).

7.7 Future Research The work can lead the researchers in the areas of regional climate change models, affects of climate change on agriculture, the economy, ecosystems, and hydropower production, development of decision support systems to lessen the negative effects of climate change, and the implementation of policy.

7.8 Conclusion The study was conducted as a review of the effect of climate change on stream flow. The majority of research shows that hydrological responses were directly influenced by climate change. The result of the study shows a negative association between climate change and runoff. The projected result of various studies attributed a decrease of runoff in near future. The decreasing trend of runoff was heterogeneous across the different study regions. The prime focus of this study was to assess the impact of climate change on water resources and utilize the result for future water resource management practice. This review paper explained 77 articles across the various continents and explored how climate change influenced runoff/ discharge and considered a few papers of the human impacts on runoff/discharge for better understanding. This study can inspire further research on the subject and provide knowledge regarding the use of proper models and policy recommendations for researchers. Declaration Ethics approval and consent to participate: NA Consent for publication: NA Availability of data and material: Presented in the main paper Competing interests: There are no Competing interests Funding: No funding received Authors’ contributions: The 1st and 2nd authors have helped in data analysing. The 3rd author did the editing. Acknowledgements: Not applicable.

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References Aich V, Liersch S, Vetter T, Huang S, Tecklenburg J, Hoffmann P, Koch H, Fournet S, Krysanova V, Müller EN, Hattermann FF (2014) Comparing impacts of climate change on streamflow in four large African river basins. Hydrol Earth Syst Sci 1–14. https://doi.org/10.5194/hess-18-13052014 Al-Faraj F, Scholz M, Tigkas D (2014) Sensitivity of surface runoff to drought and climate change: application for shared river basins. Water 6:3033–3048. https://doi.org/10.3390/w6103033 Allen M, Ingram J (2002) Constrains on future changes in climate and the hydrologic cycle. Nature 419:224–232 Arnell NW (2003) Relative effects of multi-decadal climatic variability and changes in the mean and variability of climate due to global warming: future streamflows in Britain. J Hydrol 270:195–213 Arnell N, Gosling S (2013) The impacts of climate change on river flow regimes at the global scale. J Hydrol 351–364. https://doi.org/10.1016/j.jhydrol.2013.02.010 Bauwens A, Sohier C, Degre A (2011) Hydrological response to climate change in the Lesse and the Vesdre catchments: Contribution of a physically based model (Wallonia, Belgium). Hydrol Earth Syst Sci 15(6):1745-1756 Berton R, Driscoll C, Chandler D (2016) Changing climate increases discharge and attenuates its seasonal distribution in the northeastern United States. J Hydrol Regional Stud 164–178. https:/ /doi.org/10.1016/j.ejrh.2015.12.057 Biswas B, Ratnaprabha J, Nilima T (2019) Rainfall Distribution and trend analysis for upper Godavari Basin, India, from 100 years record (1911–2010). J Indian Soc Remote Sens. https:// doi.org/10.1007/s12524-019-01011-8 Biswas B, Jain S, Rawat S (2018) Spatio-temporal analysis of groundwater levels and projection of future trend of Agra city, Uttar Pradesh, India. Arab J Geosci 11:278. https://doi.org/10.1007/ s12517-018-3577-4 Bingfei H, Chao J, Jianxin SO (2020) Differential changes in precipitation and runoff discharge during 1958–2017 in the headwater region of Yellow River of China. J Geogr Sci 30(9):1401– 1418. https://doi.org/10.1007/s11442-020-1789-5 Boini N, Ashvin KG, Baghu RC (2013) Assessment of future climate change impacts on water resources of Upper Sind River Basin, India Using SWAT model. Water Resour Manag 27:3647– 3662. https://doi.org/10.1007/s11269-013-0371-7 Bronstert A, Niehoff D, Burger G (2002) Effects of climate and land-use change on storm runoff generation: Present knowledge and modelling capabilities. Hydrol Process 16:509–529 Carey M, Baraer M, Mark B, French A, Bury J, Young K, McKenzie J (2013) Toward hydro-social modeling: Merging human variables and the social sciences with climate-glacier runoff models (Santa River, Peru). J Hydrol 1–11. https://doi.org/10.1016/j.jhydrol.2013.11.006 Cauvy-Fraunié S, Andino P, Espinosa R, Calvez R, Jacobsen D, Dangles O (2015) Ecological responses to experimental glacier-runoff reduction in alpine rivers. Nat Commun 1–5. https:// doi.org/10.1038/ncomms12025 Chang J, Wang Y, Istanbulluoglu E, Bai T, Huang Q, Yang D, Huang S (2014) Impact of climate change and human activities on runoff in the Weihe River Basin, China. Quat Int 1–11. https:// doi.org/10.1016/j.quaint.2014.03.048 Chang J, Zhang H, Wang Y, Zhang L (2017) Impact of climate change on runoff and uncertainty analysis. Nat Hazards 88:1–19. https://doi.org/10.1007/s11069-017-2909-0 Chen H, Fleskens L, Baartman J, Wang F, Moolenaar S, Ritsema C (2020) Impacts of land use change and climatic effects on streamflow in the Chinese Loess Plateau: a meta-analysis. Sci Tot Environ 703:134989–135003. https://doi.org/10.1016/j.scitotenv.2019.134989 Chen Z, Chen Y (2014) Effects of climate fluctuations on runoff in the headwater region of the Kaidu River in northwestern China. Front Earth Sci 1–10. https://doi.org/10.1007/s11707-0140406-2

7 A Comprehensive Review on the Impact of Climate Change …

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Chen Z, Chen Y, Li B (2013) Quantifying the effects of climate variability and human activities on runoff for Kaidu River Basin in arid region of northwest China. Theor Appl Climatol 111:537– 545. https://doi.org/10.1007/s00704-012-0680-4 Croitoru, A-E, Minea I (2014) The impact of climate changes on rivers discharge. Theor Appl Climatol 1–14. https://doi.org/10.1007/s00704-014-1194-z Crossman J, Futter MN, Oni SK, Whitehead PG, Jin L, Butterfield D, Baulch HM, Dillon PJ (2013) Impacts of climate change on hydrology and water quality: future proofing management strategies in the Lake Simcoe watershed, Canada. J Great Lakes Res 39:19–32. https://doi.org/ 10.1016/j.jglr.2012.11.003 Daba M, You S (2020) Assessment of climate change impacts on river flow regimes in the upstream of Awash Basin, Ethiopia: based on IPCC Fifth Assessment Report (AR5) Climate Change Scenarios. 1–22. https://doi.org/10.3390/hydrology7040098 Danneberg J (2012) Changes in runoff time series in Thuringia Germany—Mann-Kendall trend test and extreme value analysis. Adv Geosci 31:31–56. https://doi.org/10.5194/adgeo-31-49-2012 Das J, Nanduri U (2018) Assessment and evaluation of potential climate change impact on monsoon flows using machine learning technique over Wainganga River Basin, India. Hydrol Sci J. https:/ /doi.org/10.1080/02626667.2018.1469757 Delcour I, Spanoghe P, Uyttendaele M (2015) Literature review: Impact of climate change on pesticide use. Food Res Int 68:7–15. https://doi.org/10.1016/j.foodres.2014.09.030 Döll P, Zhang J (2010) Impact of climate change on freshwater ecosystems: a global-scale analysis of ecologically relevant river flow alterations. Hydrol Earth Syst Sci 14:783–799. https://doi. org/10.5194/hess-14-783-2010 Döll, P., & Schmied, H. M. (2012). How is the impact of climate change on river flow regimes related to the impact on mean annual runoff? A global-scale analysis. Environmental Research Letters, 7(1), 014037. Donnelly C, Greuell W, Andersson J, Gerten D, Pisacane G, Roudier P, Ludwig F (2017) Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Clim Change 143:13–26. https://doi.org/10.1007/s10584-017-1971-7 Eisner S, Flörke M, Chamorro A, Daggupati P, Donnelly C, Huang J, Hundecha Y, Koch H, Kalugin A, Krylenko I, Mishra V (2017) An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins. Clim Change 141:401–417. https://doi.org/10.1007/s10 584-016-1844-5 Fekete B, Wisser D, Kroeze C, Mayorga E, Bouwman L, Wollheim W, Vörösmarty C (2010) Millennium ecosystem assessment scenario drivers (1970–2050): climate and hydrological alterations. Globl Biogeochem Cycles 1–12. https://doi.org/10.1029/2009GB003593 Frederick KD, Major DC (1997) Climate change and water resources. Clim Change 37:7–23 Fung F, Lopez A, New M (2011) Water availability in +2 degrees C and +4 degrees C worlds. Philos Trans R Soc A Math Phys Eng Sci 369(1934):99–116. Giang PQ, Toshiki K, Sakata M, Kunikane S, Vinh TQ (2014) Modelling climate change impacts on the seasonality of water resources in the Upper Ca River Watershed in Southeast Asia. Hindawi Publ Corp Sci World J 1–15. https://doi.org/10.1155/2014/279135 Githui F, Gitau W, Mutua F, Bauwens W (2009) Climate change impact on SWAT simulated streamflow in western Kenya. Int J Climatol 29:1823–1834. https://doi.org/10.1002/joc.1828 Goulden M, Bales R (2014) Mountain runoff vulnerability to increased evapotranspiration with vegetation expansion. Dep Earth Syst Sci Univ Calif Irvine 111(39):14071–14075. Retrieved from www.pnas.org/lookup/suppl/doi:10. Gupta P, Panigrahy S, Parihar JS (2011) Impact of climate change on runoff of the major river basins of India using global circulation model (HadCM3) projected data. J Indian Soc Remote Sens 39(3):337–344. https://doi.org/10.1007/s12524-011-0101-7 Hagemann S, Chen C, Clark DB, Folwell S, Gosling SN, Haddeland I, Hanasaki N, Heinke J, Ludwig F, Voss F, Wiltshire AJ (2013) Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst Dynam 4:129–144. https://doi.org/10.5194/esd-4-129-2013

146

D. D. Soren et al.

Hamdi R, Termonia P, Baguis P (2011) Effects of urbanization and climate change on surface runoff of the Brussels Capital Region: a case study using an urban soil–vegetation–atmosphere-transfer model. Int J Climatol 1959–1974. https://doi.org/10.1002/joc.2207 Hofstra N (n.d.) Quantifying the impact of climate change on enteric waterborne pathogen concentrations in surface water. Sciverse Science Direction 471–480. https://doi.org/10.1016/j.cosust. 2011.10.006 Huo A, Li H (2013) Assessment of climate change impact on the stream-flow in a typical debris flow watershed of Jianzhuangcuan catchment in Shaanxi Province, China. Environ Earth Sci 69:1931–1938. https://doi.org/10.1007/s12665-012-2025-0 IPCC (2007) Climate change 2007. The Physical Science Basis Report AR4. Janža M (2013) Impact assessment of projected climate change on the hydrological regime in the SE Alps, Upper Soˇca River basin, Slovenia. Nat Hazards 67:1025–1043. https://doi.org/10.1007/ s11069-011-9892-7 Jeppesen E, Kronvang B (2009) Climate change effects on runoff, catchment phosphorus loading and lake ecological state, and potential adaptations. J Environ Qual 38:1930–1941. https://doi. org/10.2134/jeq2008.0113 Jiang C, Xiong L, Wang D, Liu P, Guo S, Xu C-Y (2015) Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters. J Hydrol 326–338. https://doi.org/10.1016/j.jhydrol.2014.12.060 Jung IW, Moradkhani H, Chang H (2012) Uncertainty assessment of climate change impacts for hydrologically distinct river basins. J Hydrol 466:73–87. https://doi.org/10.1016/j.jhydrol.2012. 08.002. Kelaiya J, Rank PH (2019) Assessment of water balance components of bhadar river basin using SWAT model. Int J Bioresour Stress Manag 10(2):181–184 Koch H, Silva AC, Liersch S, Azevedo JG, Hattermann F (2020) Effects of model calibration on hydrological and water resources management simulations under climate change in a semi-arid watershed. Clim Change 163:1247–1266 Kumar J, Biswas B, Walker S (2020) Multi-temporal LULC classification using hybrid approach and monitoring built-up growth with Shannon’s entropy for a semiarid region of Rajasthan, India. J Geol Soc India 95:626–635. https://doi.org/10.1007/s12594-020-1489-x Kwadjk J, Middelkoop H (1994) Eastimation of impact of climate change on the peak discharge probability of the river RHINE. Clim Change June 1–26. Lei H, Yang D, Huang M (2014) Impacts of climate change and vegetation dynamics on runoff in the mountainous region of the Haihe River basin in the past five decades. J Hydrol 511:786–799. https://doi.org/10.1016/j.jhydrol.2014.02.029 Leppi J, DeLuca T, Harrar S, Running S (2012) Impacts of climate change on August stream discharge in the Central-Rocky Mountains. Clim Change 112:997–1014. https://doi.org/10. 1007/s10584-011-0235-1 Lettenmaier DP, Wood EF, Wallis JR (1994) Hydro-climatological trends in the continental United States1948–88. J Clim 7:586–607 Li T, Gao Y (2015) Runoff and sediment yield variations in response to precipitation changes: a case study of Xichuan watershed in the Loess Plateau, China. Water 7:5638–5656. https://doi. org/10.3390/w7105638 Lin K, Lin Y, Xiaohong C, Fan L (2014) Changes in runoff and eco-flow in the Dongjiang River of the Pearl River Basin, China. Front Earth Sci 1–11. https://doi.org/10.1007/s11707-014-0434-y Ling HB, Xu HL, Shi W, Zhang Q (2011) Regional climate change and its effects on the runoff of Manas River, Xinjiang, China. Environ Earth Sci 64(8):2203–2213 Luo Y, Ficklin D, Liu X, Zhang M (2013) Assessment of climate change impacts on hydrology and water quality with a watershed modeling approach. Sci Tot Environ 450: 72–82. https://doi.org/ 10.1016/j.scitotenv.2013.02.004 Lv X, Zuo Z, Ni Y, Sun J, Wang H (2019) The effects of climate and catchment characteristic change on streamflow in a typical tributary of the Yellow River. Sci Rep 9:14535 https://doi. org/10.1038/s41598-019-51115-x

7 A Comprehensive Review on the Impact of Climate Change …

147

Ma H, Yang D, Tan SK, Gao B, Hu Q (2010) Impact of climate variability and human activity on streamflow decrease in the Miyun Reservoir catchment. J Hydrol 389:317–324. https://doi.org/ 10.1016/j.jhydrol.2010.06.010 Meng D, Mo X (2012) Assessing the effect of climate change on mean annual runoff in the Songhua River basin, China. Hydrol Process 26(7):1050–1061 Mohammed R, Scholz M, Nanekely AM, Mokhtari Y (2016) Assessment of models predicting anthropogenic interventions and climate variability on surface runoff of the Lower Zab River. Stoch Environ Res Risk Assess 1–18. https://doi.org/10.1007/s00477-016-1375-7 Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management (edit. Book). Nova Science Publishers, USA, ISBN: 978-1-53619-669-6 Mishra VN, Rai PK, Mohan K (2014) Prediction of land use changes based on Land Change Modeler (LCM) Using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst Jovan Cviji´c” SASA (Serbia) 64(1):111–127 https://doi.org/10.2298/IJGI1401111M. Narsimlu B, Gosain A, Chahar B (2013) Assessment of future climate change impacts on water resources of Upper Sind River Basin, India Using SWAT Model. Water Resour Manage 27: 3647–3662. https://doi.org/10.1007/s11269-013-0371-7 Nijssen B, Schnur R, Lettenmaier DP (2001) Global retrospective estimation of soil moisture using the variable infiltration capacity land surface model, 1980–93. J Clim 14:1790–1808 Ning T, Li Z, Liu W (2016) Separating the impacts of climate change and land surface alteration on runoff reduction in the Jing River catchment of China. CATENA 147:80–86. https://doi.org/ 10.1016/j.catena.2016.06.041 Oliveira V, Mello C, Viola M (2017) Assessment of climate change impacts on streamflow and hydropower potential in the headwater region of the Grande river basin, Southeastern Brazil. Int. J. Climatol 1–19. https://doi.org/10.1002/joc.5138 Oni S, Futter M, Molot L, Dillon P, Crossman J (2014) Uncertainty assessments and hydrological implications of climate change in two adjacent agricultural catchments of a rapidly urbanizing watershed. Sci Tot Environ 473:326–337. https://doi.org/10.1016/j.scitotenv.2013.12.032 Oti, JO, Kabo-bah A, Ofosu E (2020). Hydrologic response to climate change in the Densu River Basin in Ghana. Heliyon. https://doi.org/10.1016/j.heliyon.2020.e04722 Papadimitriou L, Koutroulis A, Grillakis M, Tsanis I (2016) High-end climate change impact on European runoff and low flows—exploring the effects of forcing biases. Hydrol Earth Syst Sci 20:1785–1808. https://doi.org/10.5194/hess-20-1785-2016 Parry ML, Canziani OF (2007) Technical summary climate change: impacts, adaptation and vulnerability, contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change In: Parry M, Canziani O, Palutikof J, van der Linden J, Hanson C (eds) Cambridge University Press, Cambridge, UK, pp 23–78 Peng D, Qiu L, Fang J, Zhang Z (2016) Quantification of climate changes and human activities that impact runoff in the Taihu Lake Basin, China. Hindawi Publ Corp Math Probl Eng 1–8. https:// doi.org/10.1155/2016/2194196 Phan D, Wu C, Hsieh S (2011) Impact of climate change on stream discharge and sediment yield in northern Viet Nam. Water Resour 38(6):827–236. https://doi.org/10.1134/S0097807811060133 Pourmokhtarian A, Driscoll CT, Campbell JL, Hayhoe K (2012) Modeling potential hydrochemical responses to climate change and increasing CO2 at the Hubbard Brook Experimental Forest using a dynamic biogeochemical model (PnET-BGC). Water Resour Res 48(13):1–14. https://doi.org/ 10.1029/2011WR011228 Pumo D, Arnone E, Francipane A, Caracciolo D, Noto L (2017) Potential implications of climate change and urbanization on watershed hydrology. J Hydrol 554:80–99. https://doi.org/10.1016/ j.jhydrol.2017.09.002 Pumo D, Caracciolo D, Viola F, Noto L (2016) Climate change effects on the hydrological regime of small non-perennial river basins. Sci Tot Environ 542:76–92. https://doi.org/10.1016/j.scitot env.2015.10.109

148

D. D. Soren et al.

Qiu J, Shen Z, Leng G, Xie H, Hou X, Wei G (2015) Impacts of climate change on watershed systems and potential adaptation through BMPs in a drinking water source area. J Hydrol 223–235. https:/ /doi.org/10.1016/j.jhydrol.2019.03.074 Qiu J, Shen Z, Hou X, Xie, H, Leng G (2020) Evaluating the performance of conservation practices under climate change scenarios in the Miyun Reservoir Watershed, China. Ecol Eng 143 105700 Radchenko I, Dernedde Y, Mannig B, Frede H-G, Breuer L (2017) Climate change impacts on runoff in the Ferghana Valley (Central Asia). Water Resour 44(5):1–24. https://doi.org/10.1134/ S0097807817050098 Rai PK, Mishra S, Ahmad A, Mohan K (2014) A GIS-based Approach in Drainage morphometric analysis of Kanhar River Basin, India. Appl Water Sci (Springer) 7:217–232. https://doi.org/10. 1007/s13201-014-0238-y Rai PK, Mishra VN, Mohan K (2017a) A study of morphometric evaluation of the son basin, india using geospatial approach. Remote Sens Appl Soc Environ 7: 9–20. https://doi.org/10.1016/j. rsase.2017. Rai PK, Chaubey PK, Mohan K., Singh P (2017b) Geoinformatics for assessing the inferences of quantitative drainage morphometry of the Narmada Basin in India. Appl Geomatics (Springer) 1–23. https://doi.org/10.1007/s12518-017-0191-1 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 Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book). Springer Nature, Switzerland, ISBN: 978-3-030-76116-5. https://doi.org/10.1007/978-3-030-76116-5. Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book). Springer Nature, Singapore. ISBN: 978-981-16-7373-3. https://doi.org/10.1007/978-981-16-7373-3 Rai PK, Singh P, Mishra VN, Singh A, Sajan B, Shahi AP (2019) Geospatial approach for quantitative drainage morphometric analysis of Varuna River Basin, India. J Landsc Ecol 12(2):1–21 Roderick M (2011) Introduction to special section on Water Resources in the Murray? Darling Basin: Past, present, and future. Scanlon T, Caylor K, Levin S, Rodriguez-Iturbe I (2007) Positive feedbacks promote power-law clustering of Kalahari vegetation. Nature 449:209–2012 Schneider CLaizé CL, Acreman MC, Flörke M (2013) How will climate change modify river flow regimes in Europe? Hydrol Earth Syst Sci 17:325–339. https://doi.org/10.5194/hess-17-3252013 Shanka, A. S. (2017). Evaluation of climate change impacts on run-off in the Gidabo River Basin: Southern Ethiopia. Environment Pollution and Climate Change, 1(03), 1-6. Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed, district Almora, India. Ecol Environ Conserv 27(February Suppl. Issue):259–266 Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (2007) Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, p 996 Sorg A, Bolch T, Stoffel M, Solomina O, Beniston M (2012) Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Clim Change 2:725–732. https://doi.org/10.1038/NCL IMATE1592 Sorribas MV, Paiva RC, Melack JM, Bravo JM, Jones C, Carvalho L, Beighley E, Forsberg B, Costa MH (2016) Projections of climate change effects on discharge and inundation in the Amazon basin. Clim Change 1–16. https://doi.org/10.1007/s10584-016-1640-2 Srinivas R, Singh AP, Dhadse K, Magner J (2020) Hydroclimatic river discharge and seasonal trends assessment model using an advanced spatio-temporal model. Stoch Environ Res Risk Assess 1–16. https://doi.org/10.1007/s00477-020-01780-6

7 A Comprehensive Review on the Impact of Climate Change …

149

Stagl J, Hattermann F (2015) Impacts of climate change on the hydrological regime of the Danube River and its tributaries using an ensemble of climate scenarios. Water 7:1–14. https://doi.org/ 10.3390/w7116139 Su B, Huang J, Zeng X, Gao C, Jiang T (2016) Impacts of climate change on streamflow in the upper Yangtze River basin. Clim Change 1–14. https://doi.org/10.1007/s10584-016-1852-5 Tang C, Crosby B, Wheaton J, Piechota T (2012) Assessing streamflow sensitivity to temperature increases in the Salmon River Basin, Idaho. Glob Planet Change 88: 32–44. https://doi.org/10. 1016/j.gloplacha.2012.03.002 Uniyal B, Jha MK, Verma AK (2015) Assessing climate change impact on water balance components of a river basin using SWAT model. Water Resour Manage 29:4767–4785. https://doi.org/10. 1007/s11269-015-1089-5 Vano J, Nijssen B, Lettenmaier D (2015) Seasonal hydrologic responses to climate change in the Pacific Northwest. Water Resour Res 1–18. https://doi.org/10.1002/2014WR015909 Wagner T, Themeßl M, Schüppel A, Gobiet A, Stigler H, Birk S (2017) Impacts of climate change on stream flow and hydro power generation in the Alpine region. Environ Earth Sci 76(4):1–24. https://doi.org/10.1007/s12665-016-6318-6 Wang D, Hejazi M (2011) Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States. Water Resour Res 47:1–16. https://doi.org/10.1029/2010WR010283 Wang G, Zhang J, Li X, Bao Z, Liu Y, Liu C, He R, Luo J (2017). Investigating causes of changes in runoff using hydrological simulation approach. Appl Water Sci 7:2245–2253. https://doi.org/ 10.1007/s13201-016-0396-1 Wang J, Gao Y, Wang S (2018) Assessing the response of runoff to climate change and human activities for a typical basin in the Northern Taihang Mountain, China. J Earth Syst Sci 1–15. https://doi.org/10.1007/s12040-018-0932-5 Wang M, Qin D, Lu C, Li Y (n.d.) Modeling anthropogenic impacts and hydrological processes on a Wetland in China. Water Resour Manage 2743–2757. https://doi.org/10.1007/s11269-0109577-0 Wang S, Yan M, Yan Y, Shi C, He L (2012) Contributions of climate change and human activities to the changes in runoff increment in different sections of the Yellow River. Quat Int 282:66– 77.https://doi.org/10.1016/j.quaint.2012.07.011 Wang W, Zou S, Shao Q, Xing W, Chen X, Jiao X, Yu Z (2016) The analytical derivation of multiple elasticities of runoff to climate change and catchment characteristics alteration. J Hydrol 541:1042–1056. Worqlul AW, Dile YT, Ayana EK, Jeong J, Adem AA, Gerik T (2018) Impact of climate change on streamflow hydrology in headwater catchments of the Upper Blue Nile Basin, Ethiopia. Water 1–18. doi:doi:https://doi.org/10.3390/w10020120 Wu J, Gao X, Giorgi F, Chen Z, Yu D (2012) Climate effects of the Three Gorges Reservoir as simulated by a high resolution double nested regional climate model. Quat Int 282:27–36. https:/ /doi.org/10.1016/j.quaint.2012.04.028 Yang H, Yang D (2011) Derivation of climate elasticity of runoff to assess the effects of climate change on annual runoff. Water Resources Research, 47(7) Yang H, Yang D, Hu Q (2014a) An error analysis of the Budyko hypothesis for assessing the contribution of climate change to runoff. Water Resources Res 50:9620–9630. https://doi.org/ 10.1002/2014WR015451 Yang T, Wang X, Yu Z, Krysanova V, Chen X, Schwartz F, Sudicky E (2014b) Climate change and probabilistic scenario of streamflow extremes in an alpine region. J Geophys Res Atmos 119:8535–8550. https://doi.org/10.1002/2014JD021824 Yates D (2014) WatBal: an integrated water balance model for climate impact assessment of river basin runoff. Int J Water Resour Dev 12(2):121–140. https://doi.org/10.1080/079006296500 41902 Yilmaz AG, Imteaz MA (2011) Impact of climate change on runoff in the upper part of the Euphrates. Hydrol Sci J—Journal des Sciences Hydrologiques 56(7):1265–1279.

150

D. D. Soren et al.

Zhang JY, Wang GQ (2007) Research on impacts of climate change on hydrology and water resources. Science Press, Beijing, China, pp 1–9 Zhang JY, Wang GQ, Pagano TC, Liu CS, He MR, Liu LI (2012) Using hydrologic simulation to explore the impacts of climate change on runoff in the Huaihe River basin of China. J Hydrol Eng 1–28. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000581 Zhang W, Shaoming P, Cao L, Cai X, Zhang K, Xu Y, Wei X (2015) Changes in extreme climate events in eastern China during 1960–2013: a case study of the Huaihe River Basin. Quat Int 380:1–13. https://doi.org/10.1016/j.quaint.2014.12.038

Chapter 8

Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya Using RUSLE and Geospatial Techniques Jonmenjoy Barman and Brototi Biswas

Abstract The first step in reducing the risk from natural hazards is susceptibility mapping. One of the main issues in the Himalayan Dooars Plain area is soil washing. The goal of this study is to identify the soil degradation severity zone in the West Bengal district of Alipurduar’sDima River basin. The process of erosion is a complex system depending upon different socio-environmental conditions. To analyze soil erosion, the well-known model of RUSLE was used, considering the “R, K, C, LS, and P. The R factor or rainfall data was obtained from IMD, Pune. The K factor was computed from soil database, collected from FAO, India. The study area comes under the influence of monsoonal rains. After applying RUSLE, it was found that most of the area fell under the very low severity class, covering 66.2 percent, followed by a low susceptible area covering 21.29 percent, followed by a medium severity class covering 8.57 percent, a high (3.02 percent) and a very high severity class covering 0.99 percent of the basin. The finding of the study highlighted that soil erosion from vegetation-covered areas is about 1761.96 ton/hec/yr followed by barren land/others (1440.45 ton/hec/yr), tea garden (619.32 ton/hec/yr), agricultural land (454.90 ton/ hec/yr), and buildup areas (0.90 ton/hec/yr), respectively. The authors are optimistic that the current work will be useful in several facets of ecological preservation, watershed management, and rural sustainable planning. Keywords Soil erosion · RUSLE · Dima River · Watershed · Himalaya

8.1 Introduction Sustainable watershed management is nothing without proper assessment of soil degradation (Balasubramani et al. 2015; Singh et.al. 2021; Rai et.al. 2021; Mishra et.al. 2021; Rai et.al., 2022), which is a serious environmental issue nowadays. J. Barman · B. Biswas (B) Department of Geography and Resource Management, Mizoram University, Aizawl 796004, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_8

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Worldwide climate change, global warming, and rainfall variability are behind it. The definition of soil erosion is the breakdown of soil structure and its transportation from one place to another. (Hembram et al. 2019). As a result, the nutrients in the topsoil have been steadily decreasing. About 84% of the global land area has been affected by soil erosion (Opeyemi et al. 2020; Rai et.al. 2021, 2022; Mishra et.al. 2021; Brototi et.al., 2022). It has been estimated that the world soil loss ranges between 12 and 15 t ha1 yr 1 (Ashiagbor et al., 2013), and India shares 5334 m-tones annually (Prasannakumar et al. 2011). Since the 1930s, scholars have been grappling with the problem of soil erosion, and numerous models have emerged to gauge the extent of soil loss (Pan and Wen 2014). Frequent rainfall and undulating topography cause significant soil erosion in the Dooars region (Mandal and Sharda 2013). UNEP (1997) reported that among the individual types of soil erosion, sheet and rill erosion affect around 34% of the agricultural land in the Himalayan foothill region. The Dooar region has been identified as a severe to extremely severe region of soil erosion susceptibility (Mandal and Sharda 2013). The term “susceptibility” can be defined as the possibility of an event occurring (Dai and Lee 2001). Different models have been invented by scholars to identify erosion susceptibility. Most commonly used models have been categorized into physical basis, empirical, and semi-empirical categories (Pan and Wen 2014; Gayen et al. 2019). These models, such as “Soil and Water Assessment Tool (SWAT), universal soil loss equation (USLE), Limburg Soil Erosion Model (LISEM), Chemical Runoff and Erosion for Agricultural Management System (CREAMS), Water Erosion Prediction Project (WEPP), European Soil Erosion Model (EUROSEM), and revised universal soil loss equation (RUSLE),” etc., have been used by the researchers multiple times. Among them, RUSLE is the most dominant model widely used owing to its flexible nature, which can easily be integrated with GIS. The model is a refined version of the "Universal Soil Loss Equation (USLE) of the UDSA’s (United States Department of Agriculture)", which was intended to forecast average yearly soil loss from a specific field slope under a specified land use management scheme. When most of the models suffer from insufficient input data (Pan and Wen, 2014), RUSLE can estimate soil erosion only from the product of rainfall erosive (R) factor, soil erodibility (K) factor, cover management (C) factor, slope length and slope steepness (LS) factor, and support practice (P) factor. Initially, this model was developed for the judgment of soil loss from cultivated land. At present, it is suitable for different environmental settings. The physical environment, which is a function of the interaction between climate, terrain, and ground surface cover, controls the soil erosion process (Gupta and Chaudhuri 2018; Nistor et.al. 2019). The main intention of the current study is to prepare a soil erosion spatial distribution zone in the Dima River basin of Alipurduar district. A few works have been carried out by various scholars in the Bengal Dooar region. Chakraborty and Mukhopadhyay, 2015 reported the average river bank erosion for the period 1990– 2014 was 6.93 meters/ year in Duduya-Rehti watershed of Darjeeling district, West Bengal (having similar geographical condition as Dima River basin). According to Jana (1997), extensive farming methods in the plain lands and the hilly forested areas of North Bengal have resulted in significant landslides and soil loss in the river basins of North Bengal. Gupta and Chaudhuri (2018) reported about 2.66 t ha–1

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y–1 topsoil material is decadent in the Panchanoi basin due to land use change. The novelty of the present study is that it is based on a modified RUSLE model as well as emphasis has been given to the weighting system of each controlling factor in the AHP and AHP-VIKOR models. The authors are hopeful that the study will be helpful for hydrologists, rural planners, civil engineers, etc., for proper watershed management.

8.2 Materials and Methodology 8.2.1 Study Area The Dima is a small tributary of the Kaljaniriver. It is a transboundary river that originates in the Bhutan hills and flows through the Alipurduar district of West Bengal, passing through the Buxa Tiger Reserve Forest. It meets the Kaljaniriver near Alipurduar town, WB. Geographically, the river basin propagates from 26°48' 15"N to 26°30' 15"N latitude and 89°28' 30"E to 89°35' 30"E longitude, having an area of 240 km2 (Fig. 8.1). The scenic beauty of the tea garden and the Dima River bridge attracts a lot of tourists every year. The study region is under the dominance of the SW monsoon, having an average annual rainfall of 3599 mm while temperatures range between 21° and 32° C in the summer period and 11° to 22° C in the winter period (Pai et al., 2014). The entire area is covered with quartz and phyllite of Proterozoic age, sandstone, shale, clay, and conglomerate of Pliocene age, calcareous sand, clay, sand, and silt of Pleistocene-Holocene age, sandstone and shale with minor coal of Permian age, clay, sand, and silt of Meghalayan age, and feebly oxidized sand, clay, and silt of Holocene age. The older alluvium was formed during the Pleistocene and Pleistocene-Holocene periods. Similarly, the fresh alluvium was formed throughout the Holocene and Meghalayan epochs. Except for the northern hilly dissected section, the entire study area comes under the alluvial plain. Being plain land, intensive agriculture practices are found within the basin. The river Dima plays a considerable role in the daily lives of local people. It can be said that it is the lifeline of the Buxa tiger reserve forest.

8.2.2 RUSLE Model RUSLE is a computerized and improved version of the USLE model (Universal soil loss equation). The model RUSLE is an explorative model that is extensively used for monitoring and measuring the average annual soil erosion (Chuenchum et al. 2020). The model can forecast annual soil erosion due to sheet and rill sheet erosion. (Marondedze and Schütt, 2020). The RUSLE is formulated by using Eq. 8.1.

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Fig. 8.1 Location of study area. (a) location of West Bengal in India, (b) Location of Dima River basin in West Bengal, (c) FCC composition of Landsat 8 in Dima basin

Sl = R × K × L S × C × P

(8.1)

where, Sl is annual average soil erosion (t ha − 1 every year), R is rainfall erosivity (MJ ha − 1 every year), K is soil erodibility (t ha h MJ − 1 mm − 1), C is cover management (dimensionless), LS is amount of slope length and slope steepness (dimensionless), and P is magnitude of support practices (dimensionless). (a) Rainfall erosivity (R) factor—The R factor is the numerical measure of the ability of the rain to erode the soil (Lee and Heo, 2011). It is not only used in soil erosion modelling but also in different fields of natural hazard modelling (e.g., flood, sediment yield, water quality). According to Renard et al., 1997, rainfall erosivity is the product of average long-term total rainfall energy and maximum 30-min rainfall intensity. The R is formulated according to Wischmeier and Smith, 1978 as follows (Eq. 8.2). R=

12 ∑ i=1

1.735 × 10(1.5log10 ( Pi / p)−0.08188) 2

(8.2)

8 Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya …

155

The rainfall erosivity (R) map for the entire Dima basin has been prepared by using the IDW interpolation algorithm in ArcGIS 10.5 (Fig. 8.2a). (b) Soil erodibility (K) factor—The K factor can be defined as the resistance power of soil against soil loss by rain (Kebede et. al. 2021). It is not only affected by the soil texture but also by the organic matter and soil structure (Stone and Hilbornn 2012). In the present study, HWSD soil database is collected from FAO which contains percent of topsoil sand, silt, clay, and organic carbon. The soil erodibility is formulated according to Chadli 2016 as Eqs. 8.2 to 8.7 (Fig. 2b). K usle = f csand × f cl−si × f orgc × f hisand

(8.3)

Fig. 8.2 Conditioning factors (a) R factor (rainfall erosivity), (b) K factor (Soil erosivity), (c) LS factor (Slope steepness and length), (d) C factor (Crop management), (e) LULC (land use/land cover), and (f) P factor (Supporting practices)

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) ( f csand = 0.2 + 0.3 × exp−0.256×ms(1−msilt/100)

(8.4)

f cl−si = ( msilt /(mc + msilt ))0.3

(8.5)

) ( f orgc = 1 − 0.25 × orgc + exp3.72−2.95× orgc

(8.6)

( f hisand = 1 −

0.7 ×

1−ms 100 1−ms 100

) + exp−5.51+22.9×(1−ms/100)

(8.7)

where, Kusle is the soil erodibility (K) factor, Ms is the percentage of sand, Msilt is the percentage of silt, Mc is the percentage of clay, and Orgc is the percentage of organic matter. (c) Slope length and slope steepness (LS) factor—The LS factor is the measurement of the role of landscape on rill and sheet erosion (Balasubramani et. al. 2015). To evaluate the LS factor, an ASTER 30 m digital elevation model was used with some pre-processing such as fill and shrink was performed before the final run. Several steps have been adopted to assess the L and S factor. The processes below were followed to assess the LS factor (Eq. 8.8–8.12) (Fig. 2c). F=

sin( slope × 0.01745)/0.0896 3 × power (sin( slope × 0.01745), 0.8) + 0.56 M = F/(1 + F)

power (( flowaccumulation + 900), (M + 1)) − power ( flowaccumulation , (M + 1)) L= power (30, (M + 2)) × power (22.13, M) ( S = Con

(8.8) (8.9)

(8.10)

) T an(slope × 0.01745) < 0.09, (10.8 × Sin(slope × 0.01745) + 0.03) (16.8 × Sin(slope × 0.01745) − 0.5) (8.11)

LS = “L” ∗ “S”

(8.12)

(d) Cover management (C) factor—The C factor describes the influence of plants on soil erosion. Plants can reduce the rate of overflow and shield the pores on the surface. It is used to measure the efficacy of various crop and soil management strategies in obstructing soil loss (Chadli 2016). Different methodologies have been used by scholars to prepare the C factor (Bhattacharya et al. 2021; Mallick et al. 2014). In the going study, NDVI has been adopted from Landsat 8 OLI using Eq. 8.13.

8 Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya …

NDVI = (NIR − Red)/(NIR + Red)

157

(8.13)

The NDVI value spread between −1 to 1. Healthy and dense vegetation is represented by 1 and no vegetation e.g., bare land is represented by 0. Similarly, -1 represents the waterbodies. C factor for the Dima River basin is measured with the help of NDVI as done by Gupta and Kumar 2017; Gayen et al. 2019 in the Eq. 8.14. [ C = exp −a ∗

NDVI β − NDVI

] (8.14)

where α and β are two units less parameters, represented as 2 and 1, respectively, taken in consideration of the shape of the curve related to NDVI and the C factor (Fig. 2d). (e) Support practices (P) factor—It is the reflection of the soil conservation agricultural techniques adopted in relation to soil erosion and is defined as the ratio of soil loss between straight-row farming along the slopes and that of various contour farming techniques (Gupta and Kumar 2017; Pan and Wen, 2014). The P-value ranges from 0 to 1, where 0 stands for higher conservation techniques and practices adopted and value 1 stands for no supporting conservation techniques being adopted. Different land-use land cover classes are developed from Landsat 8 OLI by using the SVM algorithm (Fig. 8.2e). A report has been prepared for the year 2022 containing the classes-built up area, tea garden, agricultural land, vegetation cover, water body, and barren land to compute the “P” value(Fig. 8.2f).

8.3 Result The popular mode of RUSLE was used to calculate soil erosion susceptibility zonation in the Dima river basin in the Dooars Himalayan region in this study. The RUSLE is a modified version of the USLE. The five conditioning factors, namely R, K, LS, C, and P factors, are integrated in a GIS environment for analysis of the soil erosion susceptibility. The “R” factor ranges between 13,772 to 13,271.1 MJ mm ha − 1 h − 1 year − 1. The study area is influenced by the monsoonal rains. The annual average rainfall is 3859.62 to 5427.96 mm for the period 2015 to 2020 (Fig. 8.3). The erosiveness of rainfall rises from north to south. Similarly, the “K” factor is determined from FAO soil statistics considering the percentage of sand, silt, clay, and organic carbon contained in the topsoil, which varies from 0.104 to 0.169 (Table 8.1). The LS factor, ranging between 0.03 and 22.45, increases toward the north. The Ls factor increases with an increasing slope and elevation. The “C” factor has a significant role in soil erosion. NDVI is a popular band ratio index that indicates vegetation health. The “C” factor has been prepared from NDVI, which ranges between 0.1 and 1.19. The possibility of soil erosion is highest between the “c” factor values of 0.30 and 0.60. “P” is another important factor in determining the soil erosion rate. A land use and

32.7

82.1

36.4

BD

RD

BE

sand % topsoil

DOMSOI

37.2

6.7

30.3

silt % topsoil

Table 8.1 “K” factor of each soil groups

26.4

11.3

37.1

clay % topsoil

1.07

0.27

3.28

OC % topsoil

0.2

0.2

0.2

fcsand

0.85

0.74

0.79

fcl_si

0.99

1

0.97

forgc

1

0.7

1

fhisand

0.17

0.1

0.15

k_Factor

158 J. Barman and B. Biswas

8 Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya …

159

Table 8.2 “P” factor of each land use/land cover LULC

Area in Sq.KM

“P” factors

Build up area

13.74

0

Agricultural land

21.75

0.6

Tea garden

20.93

0.4

Vegetation cover

174.03

1

Barren/Others

7.30

1

Water body

2.45

0

land cover map has been used to compute the “P” factor. The weight of the “P” factor is obtained from literature review and expert opinion view (Table 8.2). Vegetation cover and barren lands have been imparted value 1. Similarly, the lowest value is imparted to built-up areas and water bodies. The soil erosion mapping is done after the integration of different parameters in the GIS environment. According to the RUSLE model, merely 0.99 percent of the total region falls under the “very high susceptible” soil loss area, which is mainly concentrated in the northern dissected hilly area. The region is prone to soil erosion owing to its dissected hilly terrain, agricultural practices, and high LS factor. Due to the high elevation, a high susceptible area of 3.02% was found primarily in the northern dissected hilly and valley areas. Moderate and low-susceptible areas covered 8.57% and 21.29%, respectively. This category was distributed along the river banks, paleo-channels, and small ephemeral channels. The very low susceptible area covered 66.22% of the total area, mainly covering the southern plain region (Table 8.3).

8.4 Discussion In 1998, a significant change in the flow of the Dima River caused extensive soil loss, damaging the teak plantations in the Buxa forest area (Das 2012). According to Khalid and Patel (1999), channel modification of the river has impacted 400 hectares of land area. The region has numerous tourist attractions, compelling land users to alter their land uses, thereby triggering soil erosion (Sunlu, 2003). Land use and land cover are the main contributing factors to soil erosion (Table 8.4). Six different land use and land cover classes have been identified. According to the RUSLE model, vegetation cover ranks first in soil erosion among the various identified classes, followed by barren land and tea gardens. Deforestation and intensive agricultural practices are promoting soil erosion. Some small hamlets, namely GaroBasti and Atiyabari tea garden hamlet, are situated inside the forest area. The vegetation cover over the years in the study area has become quite sparse owing to rapid deforestation, rendering the topsoil uncovered in many places. Further vegetation cover (both sparse and moderate) is the primary land cover of the study area. Thus vegetation cover ranks first in terms of spatial extent of soil erosion in the study area. Geomorphologically, the area has four

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Fig. 8.3 Spatial distribution of soil erosion severity classes

major physiographic divisions, namely: highly dissected hills and valleys; alluvial plain; flood plain; and water bodies. Maximum soil erosion takes place from the high dissected hills and valleys, followed by erosion along the water bodies (Table 8.5) according to the RUSLE model. About 0.99 percent of the area under study falls under very high soil erosion severity, which is geographically located between the northern

8 Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya …

161

Table 8.3 Area covered by each severity class Class

Area in sq.km

% Of area

Very low

158.19

66.22

Low

50.86

21.29

Medium

20.47

8.57

High

7.22

3.02

Very high

2.37

0.99

Table 8.4 Estimated soil erosion from different land use land cover by RUSLE model LULC Build up area

Area in Sq.KM 13.742219

Soil erosion (ton/hec/yr) Min

Max

Mean

STD

0

3286.445

0.895519

37.60954

Agricultural land

21.75271

0

9614.576

454.9062

737.9326

Tea garden

20.925605

3.788722

9679.633

619.3266

663.5672

0

30,252.51

1761.96

2568.343

Vegetation cover

174.029725

Barren/Others

7.29514

17.67524

14,467.55

1440.451

1858.512

Waterbody

2.451137

0

0

0

0

parts of the Buxa Tiger Reserve forest. Soil erosion occurs suddenly when there is a change in slope and a high concentration of sand, silt, and clay soil. In contrast, the upper, middle, and lower Sivalik consist of cobble, pebble, and boulder, mudstone, and sandstone, respectively, that work as resistance behind erosion. It is found that soil erosion is high between the slope values of 4 to 10 degrees. Soil erosion is also caused by tectonic activity and rapid changes in river courses. Soil erosion affects three main resources, locally called “3 T”, namely timber, tea, and tourism. Both sides of the very high susceptible area belong to high susceptibility, whereas the northern high hilly region belongs to moderate susceptibility due to the high concentration of boulders and hilly vegetation cover. The southern part of the basin falls under low and very low susceptibility due to the low slope and low elevation of the area. A few light village-level management projects were implemented in the area. Soil erosion in the Dooars foothills is caused by both natural and anthropogenic activities. Not only is soil erosion a major issue in the region, but flooding is also a major barrier to sustainability. Changes in slope are the main cause of soil erosion. In that case, some barrage can be implemented to check the water flow. Embankment work as resistance to soil erosion can be used to protect natural resources. Development of eco-tourism is the best alternative source of income and anti-deforestation should be the best way toward sustainable watershed management.

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Table 8.5 Estimated soil erosion from different geomorphological units by RUSLE model Geomorphology

Area in Sq.KM

Soil erosion (ton/hec/yr) Min

Max 14,461.2

Alluvial Plain

170.5

0

Flood Plain

8.88

0

Highly Dissected Hills and Valleys

45.86

0

Waterbodies-Other

15

0

Mean

STD

677

817.07

525.27

913.71

30,252.5

4470.99

3590.24

15,417.4

917.28

1472.15

9242.54

8.5 Conclusion The present research is based on the Dima River basin (a river basin in North Bengal) being severely affected by soil loss. The mean annual rainfall of the Dima River basin is 3859.62 mm to 5427.96 mm, which can be categorized as a very high rainprone area. Previous studies on the Darjeeling Himalayan area have reported that the average river bank erosion rate is 6.93 m per year. As a result, the primary goal of this research is to compose a soil erosion susceptibility zonation map of the Dima River basin based on the RUSLE model. The novelty of the present study is that it is based on a modified RUSLE model. In the present work, the soil erosion susceptibility has been divided into five severity classes: very low, low, medium, high, and very high susceptibility zone. The authors are hopeful that the current study will aid scholars in applying the techniques used in soil erosion-prone areas with similar objectives. Further, the delineated zones will be useful to the hydrologists, rural planners, civil engineers, etc., for proper watershed management of the erosion-prone regions of North Bengal.

References Ashiagbor G, Forkuo EK, Laari P, Aabeyir R (2013) Modeling soil erosion using RUSLE and GIS tools. Int J Remote SensGeosci 2(4):1–17 Balasubramani K, Veena M, Kumaraswamy K, Saravanabavan V (2015) Estimation of soil erosion in a semi-arid watershed of Tamil Nadu (India) using revised universal soil loss equation (rusle) model through GIS. Model Earth Sys. and Environ. 1(3):1–17. https://doi.org/10.1007/s40808015-0015-4 Bhattacharya, R. K., Chatterjee, N. D., Acharya, P., & Das, K. (2021). Morphometric analysis to characterize the soil erosion susceptibility in the western part of lower Gangetic River basin, India. Arab. J. Geosci, 14(6), 1–22.https://doi.org/10.1007/s12517-021-06819-8. Brototi B, Singh A, Rai PK, Kumar J, Walker S (2022) GIS based study of reclamation of degraded semi-arid soil: A case study of Rajsthan from India. Indian J Environ Prot 42(3):302–315 Chadli K (2016) Estimation of soil loss using RUSLE model for Sebou watershed (Morocco). Modeling Earth Systems and Environment 2:1–10. https://doi.org/10.1007/s40808-016-0105-y Chuenchum P, Xu M, Tang W (2020) Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang-Mekong River by using the modified RUSLE model. Int. Soil Water Conserv. Res. 8(3):213–227

8 Soil Erosion Susceptibility in Dima River Basin of Dooars Himalaya …

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Dai FC, Lee CF (2001) Terrain-based mapping of landslide susceptibility using a geographical information system: a case study. Can Geotech J 38(5):911–923 Das BK (2012) Losing biodiversity, impoverishing forest villagers: Analysing forest policies in the context of flood disaster in a National Park of Sub Himalayan Bengal. Institute of Development Studies, India Gayen A, Pourghasemi HR, Saha S, Keesstra S, Bai S (2019) Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms. Sci Total Environ 668:124–138 Gupta S, Chaudhuri S (2018) Application of GIScience to Assess the Actual and Potential Soil Loss of Panchanoi Basin, Darjeeling, West Bengal. J. Indian geomorphol 6 Gupta S, Kumar S (2017) Simulating climate change impact on soil erosion using RUSLE model− A case study in a watershed of mid-Himalayan landscape. J Earth Syst Sci 126(3):1–20 Hembram TK, Paul GC, Saha S (2019) Comparative analysis between morphometry and geoenvironmental factor based soil erosion risk assessment using weight of evidence model: a study on Jainti river basin, eastern India. Environ. Process. 6(4):883–913 Kebede YS, Endalamaw NT, Sinshaw BG, Atinkut HB (2021) Modeling soil erosion using RUSLE and GIS at watershed level in the upper beles. Ethiopia. Environ. Challen. 2:100009 Khalid MA, Patel S (1999) Ecological assessment of habitat loss due to boulder/bed material deposit in rivers of Buxa Tiger Reserve. A Re-search Report, Department of Forests, Govern-ment of West Bengal. Lee JH, Heo JH (2011) Evaluation of estimation methods for rainfall erosivity based on annual precipitation in Korea. J Hydrol 409(1–2):30–48 Mallick J, Alashker Y, Mohammad SAD, Ahmed M, Hasan MA (2014) Risk assessment of soil erosion in semi-arid mountainous watershed in Saudi Arabia by RUSLE model coupled with remote sensing and GIS. GeocartoInt 29(8):915–940 Mandal, D., &Sharda, V. N. (2013). Appraisal of soil erosion risk in the Eastern Himalayan region of India for soil conservation planning. Land Degrad Dev., 24(5), 430–437.https://doi.org/10. 1002/ldr.1139 Marondedze AK, Schütt B (2020) Assessment of soil erosion using the RUSLE Model for the Epworth district of the Harare Metropolitan Province. Zimbabwe. Sustainability 12(20):8531 Mishra, V.N., Rai, P.K., Singh, P. 2021. Geo-information Technology in Earth Resources Monitoring and Management (edit. Book), Nova Science Publishers, U.S.A., ISBN: 978–1–53619–669–6. 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, Meteorological Application 27(1):1–16. https://doi.org/10.1002/met.1863 Opeyemi Dr AO, Adewunmi Dr BI, Oluwaseyi Dr AI (2020) Physical and chemical properties of soils in Gambari Forest Reserve near Ibadan. South Western Nigeria. J. Bioresour. Manag 7(2):7 Pan J, Wen Y (2014) Estimation of soil erosion using RUSLE in Caijiamiao watershed, China. Nat. Hazards, 71(3), 2187–2205.https://doi.org/10.1007/s11069-013-1006-2 Prasannakumar V, Shiny R, Geetha N, Vijith HJEES (2011) Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala. India. Environ Earth Sci 64(4):965–972 Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book), Springer Nature, Switzerland, ISBN: 978–3–030–76116–5. https://doi.org/10.1007/978-3-030-76116-5. Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: Sustainable assessment & planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi.org/10.1007/978-981-16-7373-3. Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India, Ecology Environment & Conservation, 27 (February Suppl. Issue); pp S260–S268 Stone RP, Hilborn D (2012) Universal Soil loss equation. Ministry of Agricutlure, Food and Rural Affairs. Ontario. Queens Printer, Order No. 12–051:48–60

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Sunlu U. Environmental impacts of tourism. In:Camarda D. (ed.), Grassini L. (ed.). Local resources and global trades: Environments and agriculture in the Mediterranean region. Bari: CIHEAM, 2003. p. 263–270. (Options Méditerranéennes: Série A. SéminairesMéditerranéens; n. 57). Conference on the Relationships between Global Trades and Local Resources in the Mediterranean Region, 2002/04, Rabat (Morocco). http://om.ciheam.org/om/pdf/a57/040 01977.pdf United Nations Environment Programme (1998) 1997 United Nations list of protected areas (Report No. 24). UNEP-WCMC. https://www.unep.org/resources/report/1997-united-nationslist-protected-areas Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning (No. 537). Department of Agriculture, Science and Education Administration

Chapter 9

Hydro-Geological Investigation and Groundwater Resource Estimation Kuldeep Pareta

Abstract Water scarcity is an everlasting phenomenon in Udaipur despite the several man-made lakes. Due to indiscriminate usage, the stage of groundwater development in the Ayad River basin, Udaipur has reached 101.88%. The study is focussed on the hydrological, hydro-geological investigation, and estimates, projects the groundwater resource by innovating and extensive usage of secondary data collected from USGS, GSI, CGWB, SOI, NBSS&LUP, and WRD Rajasthan. Rainfall (1901–2021), LULC, soil, geology, geomorphology, drainage, river–lake link has been analysed based on satellite imagery, DEM, toposheet, and other secondary data. The area has a good aquifer inside the hard rock formations phyllite, schist, gneiss, and quartzite which is predominantly formed in weathered, fractured, and jointed rocks. The average depth of groundwater in various rock formations ranged from 3.9 m to 16.3 m. Based on behaviour of groundwater flow recharge and discharge zones has been identified. Rama and Iswal situated in NE of basin are the best recharge zone, while Amberi, Bedla, Badgaon, Sukher, Dhinkli, and Udaipur situated in the central of basin are discharge zones. The average annual groundwater resource (2011–2020) has been estimated based on GEC-1997 method, which is 127.81 MCM. Based on mini-max rainfall data and deficit / surplus reserves, a mathematical relationship has been established, and the same has been used to project the availability of groundwater. Large RHS, water conservation, reuse-recycle measures have been recommended for sustainable management of groundwater reserves. Awareness campaigns and training on these measures may be helpful to stop the decline in water levels and justified use. Keywords Hydrology · Hydrogeology · Groundwater resource · Ayad River · Remote sensing and GIS

K. Pareta (B) DHI (India) Water & Environment Pvt. Ltd (DHI), New Delhi, India e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_9

165

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9.1 Introduction Water resources have been essential for human survival on the planet and the functioning of nature since the beginning of time (Rai et al. 2017, 2018, 2019; Priscoli 2000; Singh et al. 2021). The main source of life on earth is water, it is abundantly supplied by nature. However, it is challenging to obtain this resource in sufficient amounts, and its quality and quantity are dwindling quickly (Sirhan et al. 2011). One of the major challenges of the twenty-first century is still seeing good quality, sufficient quantities of water in urban and peri-urban areas to meet the needs of communities and ecosystems with unregulated urbanization fueled by internal migration and population expansion (Anomohanran 2015). Many people rely on groundwater exploration and exploitation to provide adequate, good-quality water, and that has increased extensively because of awareness and technology (Lawrence et al. 2012). Although groundwater is a resource that may be annually replenished, but its availability is not constant with time and space (CGWB 2006). With time, the rapid pace of urbanization and the expanding population have greatly strained surface water resources and deteriorated the quality of groundwater (Carpenter et al. 1998; Sharma et al. 2018; Rai et al. 2021, 2022; Mishra et al. 2021). In India, the requirement for groundwater has grown significantly over the past several years to fulfil domestic and industrial needs as well as to improve agricultural productivity (Karanth 2008). About 85% of rural drinking water comes from groundwater sources (IDFC 2013). According to Sharma et al. (NIH) 2008, the total annual water resources of India is 1960 Km3 , out-of-that utilizable water resource is 1140 Km3 (690 Km3 from surface water and 450 Km3 from groundwater). The present utilization is 750 Km3 (500 Km3 from surface water and 250 Km3 from groundwater). The predicted demand for 2025 is 1050 Km3 , which means that by that time, all the water resources would have to be used. The semi-arid state of Rajasthan is particularly vulnerable (Rajasthan Water Assessment 2013). It has 10% of India’s area but only about 1% of the water resources. Due to limited surface water resources in Rajasthan, groundwater has become an important source for supplying domestic, agricultural, and industrial water needs (Rajput et al. 2020). Udaipur city is a growing urban area, which now is the sixth largest city in Rajasthan and known as the city of lakes in India. During the last decade, the groundwater has already dried up in Udaipur city particularly the valley fills near Ayad River around Kanpur, most shallow tube wells and wells run dry throughout the summer (CGWB 2013). The available literature was used in this work to review the methodologies for hydrological, hydro-geological investigation and groundwater resource estimation, and to discuss the best way to estimate the groundwater resource of Ayad River basin, Udaipur. Several studies have suggested various approaches for groundwater resource estimation in Udaipur and Ayad River basins. Such as Dani (2009) investigated the GW exploration using electrical resistivity method in Ahar (Ayad) River basin, Udaipur. He has analysed the electrical resistivity data and explored the potential groundwater in the study area. Pareta (2013) has attempted to work on sustainable GW management using RS/GIS. He calculated the annual GW increment, annual GW

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draft, total GW balance, and identified the dark zones where GW draft is more than the dynamic potential. Samar and Vaishnav (2014) analysed the GW level pattern of Ahar (Ayad) River basin by using statistical method. They have investigated the hardrock aquifers of Ahar River basin at 50 sites. Pareta and Pareta (2015) have worked on Berach River basin and estimated the GW balance and reserves using geospatial technology. They have used GEC-97 method and calculated the GW potential for the year 2014. According to them, net annual GW availability was 786.56 mcm, GW draft was 379.29 mcm, and total GW potential was 1165.85 mcm. Machiwal et al. (2017) have attempted to work on Ahar (Ayad) River catchment and determined aquifer parameters in hard-rock by conducting 19 pumping tests. They have analysed preand post-monsoon GW levels data at 50 sites and found that the distribution of the aquifer parameters and recharge indicated that the northern portion of the catchment with high ground elevations (575–700 m msl), high specific yield (Sy: 0.08–0.25) and transmissivity (T: 600 m2 / day) values acted as recharge zone. Sinha et al. (2018) have worked on delineation of GW potential zones in Udaipur district using RS/GISbased multi-criteria decision-making technique and selected eight criteria to identify the GW potential zones. Pradeep et al. (2018) have attempted to work on estimation of GW recharge through soil moisture balance method at CTAE farm, Udaipur. The average recharge has been observed that 32.9 mm, i.e., 6.9% of the average annual rainfall. Rathore et al. (2018) has worked on shrinking of water resource in Udaipur by using SOI toposheets, multi-temporal satellite imageries from 1972 to 2014, and mapped the water resource in the Udaipur district, and they have found that the lakes/ponds/reservoirs are continuously shrinking. Shyam et al. (2022) have worked to assess the GW reserves of Udaipur district using geospatial techniques. They have estimated the annual dynamic GW reserves (637.42 mcm), and total GW draft (639.67 mcm). The deficit GW reserves are 2.25 mcm/annum from an average rainfall of 627 mm; hence the stage of groundwater development is 100.67% and categorized as over-exploited. The main objective of the present study is to analyse the biophysical—geomorphic, hydrological, and hydro-geological characteristics as well as groundwater resource estimation, projection, and sustainability management of groundwater reserves. Additionally, this study is also insinuating the innovative and extensive use of secondary source data in hydrological and hydro-geological investigation.

9.2 About the Study Area The Ayad River basin extends from 24° 50' 16” N to 24° 27' 46” N and 73° 31' 44” E to 73° 59' 44” E and covers an area of 1206.75 Km2 . Administratively, the Ayad River basin fall into 4 tehsils (Girwa—58.48%, Mavli—19.85%, Vallabh Nagar— 6.97%, Gogunda—5.94%) of Udaipur district and 1 tehsil (Nathdwara—8.76%) of Rajsamand district. The Ayad River is also known as Ahar River, which originates from the hills of Gogunda in the north-west of Udaipur and travels through for 68.45 km before joining the Vallabh Nagar lake in the eastern part of Udaipur

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Fig. 9.1 Location map of Ayad River Basin, Udaipur

(Fig. 9.1). The Ayad River is the major river flowing through Udaipur, and it is seasonal in nature and is on the peak of its youth during monsoons. The Ayad River is a tributary of the Berach River. Journey of Ayad River to Bey-of-Bengal is: Ayad River = > Berach River = > Banas River = > Chambal River = > Yamuna River = > Ganges River = > Bay-of-Bengal.

9.3 Data Used and Their Sources The present study is based on secondary data such as rainfall, topography, digital elevation data, land use land cover, soil, landform, geology, geomorphology, aquifer data, groundwater level, and groundwater draft which have been compiled from different sources, e.g., Water Resource Department (WRD) Rajasthan, Survey of India (SoI), United States Geological Survey (USGS), National Bureau of Soil Survey and Land Use Planning (NBSS&LUP) Udaipur, Geological Survey of India (GSI), Ground Water Department Rajasthan, Central Ground Water Board (CGWB). The list of data used and their sources are given in Table 9.1. The abominated data can be accessed from the project website: http://udaipur.dhiindia.com.

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Table 9.1 List of data used and sources S. No

Data type

Period

Sources

1

Survey of India (SoI) Toposheet at 1:50,000 Scale

2006

Toposheet No.: 45H/09, 10, 11, 13, and 14 Source: http://www.soinakshe.uk. gov.in

2

Landsa-7 ETM + , and Landsat-9 OLI-2 Satellite Imageries with 30 m Spatial Resolution

2011, 2021

USGS Earth Explorer Source: http://earthexplorer.usgs.gov

3

Topography/Digital Elevation Data (DEM) Data with 30 m Spatial Resolution

2014

Shuttle Radar Topography Mission (SRTM), USGS Earth Explorer Source: http://earthexplorer.usgs.gov

4

Soil Texture Data at 1:250,000 Scale

2016

National Bureau of Soil Survey and Land Use Planning, Regional Centre, Udaipur Source: http://www.bhoomigeopor tal-nbsslup.in/

5

Geological Data at 1:50,000 Scale

1999–2001

Geological Survey of India (GSI) Source: http://www.portal.gsi.gov.in

6

Daily Precipitation Data

1901–2021

Water Resource Department, Govt. of Rajasthan Source: https://water.rajasthan.gov. in

7

Daily Evapotranspiration (ET) with 0.25O × 0.25O Spatial Resolution

2000–2021

Giovanni, NASA. (GLDAS_ CLSM025_DA1_D v2.2) Source: https://giovanni.gsfc.nas a.gov

8

Monthly River Discharge Data for Gambhiri and Berach River

1964–1990

Water Resource Department, Govt. of Rajasthan Source: https://water.rajasthan.gov. in

9

Depth to Water Level (DTWL) Data (Pre-Post Monsoon)

2011–2020

Ground Water Department, Jodhpur (Rajasthan) Source: https://phedwater.rajasthan. gov.in

10

Aquifer Potential Zone Map of Udaipur District

2013

Ground Water Department, Rajasthan Source: https://phedwater.rajasthan. gov.in

9.4 Methodology SRTM DEM of 30 m spatial resolution has been used to derive the general topographic characteristics (such as slope, landforms) of the area, and it has been updated by using Survey of India topographical map of 1:50,000 scale. Landsat satellite imageries have been used in this study, which is very useful for the preparation

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of LULC maps, and the updation of soil, geology, and geomorphology data. Rainfall data from 1901 to 2021 (121 years in total) has been collected from WRD Rajasthan which has further analysed with mean, standard deviation (SD), coefficient of variation (CV), and Mann–Kendall’s test was performed for rainfall trend analysis. Residual mass curve has been prepared to analyse the annual groundwater increment condition. Drainage network of the area has been extracted from SRTM DEM. Thereafter, based on drainage network, drainage density has been generated and correlated with topography, lithology, precipitation, and vegetation coverage. Author has also analysed river and lake link in the Ayad River basin. Depth-to-water-level (DTWL) data of 45 water monitoring stations from 2011 to 2020 (pre- and post-monsoon) has been acquired from Central Ground Water Board (CGWB), which are well distributed in Ayad River basin. Thus, these datasets have been analysed and extracted from the groundwater flow and water level fluctuation. Subsequently, the best recharge zone as well as discharge zone has been identified. Author has estimated the groundwater resource by using groundwater level fluctuation and specific yield method, and rainfall infiltration factor method recommended by GEC-1997. Annual groundwater draft, overall stage of groundwater development, projected groundwater reserves have also analysed. Flow diagram of the overall methodology is shown in Fig. 9.2.

9.5 Results and Discussion 9.5.1 Biophysical and Geomorphic Characteristics 9.5.1.1

Land Use Land Cover Analysis

Various traditional classification schemes incorporate one or two parameters to classify the image (e.g. spectral differentiation, texture) and the focus is the physical property of the image (Mishra and Rai, 2016). These approaches are efficient with low to moderate spatial resolution satellite images. Visual image processing was used to prepare land use land cover (LULC) maps of the Ayad River basin for the year 2011 and 2021. Landsat-7 ETM + (Enhanced Thematic Mapper Plus) satellite imagery of 20th November 2011 with 30 m spatial resolution, and Landsat-9 OLI-2 (Operational Land Imager-2) satellite imagery of 9th November 2021 with 30 m spatial resolution have been used for preparation of LULC maps. The Landsat data processing done for Ayad River basin for 2011 and 2021 has been divided into two steps: Land Use Land Cover Classification: (i) acquisition of Landsat imageries and generation of seamless image mosaic for 2011and 2021, (ii) geo-rectification using GCPs and subsequent resampling of the images into a common coordinate system and pixel size of 30 m, (iii) image subset creation for highlighting area of interest,

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Fig. 9.2 Overall methodology of the present study

(iv) LULC classification (level 2) using visual image interpretation, and (v) accuracy assessment for the LULC thus generated. Change Detection and Conversion Matrix: (i) post-classification method of image change detection was implemented, (ii) images of two vintages (2011 and 2021) were classified to LULC classification scheme, (iii) detection of LULC changes between the year 2011 and 2021 in the Ayad River basin both in quantitative and qualitative terms using techniques of RS and GIS, and (iv) conversion matrix was created to better understand the changes in LULC in terms of direction of change (which land use encroached on which). LULC data for the year 2011 and 2021 have been prepared, and LULC statistics have been presented in Table 9.2. The LULC maps of Ayad River basin for 2011 and 2021 are shown in Fig. 9.3.

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Table 9.2 Land use land cover (LULC) statistics of Ayad River basin for the Year 2011 and 2021 S. No

LULC classification (Level-1)

Area in 2011

Area in 2021

Changes (from 2011 to 2021)

Km2

Km2

Km2

%

%

%

1

Built-up area (urban)

61.71

5.11

75.17

6.23

+13.46

+01.12

2

Built-up area (rural)

10.40

0.86

15.85

1.31

+05.45

+00.45

3

Agricultural crop land

210.38

17.43

185.47

15.37

−24.91

−02.06

4

Agricultural fallow land

563.96

46.73

598.50

49.60

+34.53

+02.86

5

Vegetation

91.05

7.55

87.81

7.28

−03.24

−00.27

6

Scrub land/barren land

95.79

7.94

83.53

6.92

−12.26

−01.02 −01.23

7

Forest area

142.34

11.80

127.47

10.56

−14.88

8

Water bodies

18.07

1.50

20.52

1.70

+02.45

+00.20

9

River/Drain/Canal

13.04

1.08

12.44

1.03

−00.60

−00.05

1206.75

100.00

1206.75

100.00

Total

9.5.1.2

Soil Texture

For detailed analysis of soil material and texture in the basin, a published soil map of Ayad River basin has been collected from National Bureau of Soil Survey & Land Use Planning (NBSS&LUP). The soil map has geometrically registered to the base data to match Landsat-9 OLI-2 satellite imagery. The geo-referenced soil map has been used to assist in visual classification of satellite imagery for obtaining soil categories. Survey of India (SoI) toposheets (1: 50,000), Landsat-9 OLI-2 satellite imagery, SRTM (DEM) data have been used for updation of soil categories. The final vector data layer has been stored in a geo-database which has amenable to spatial analysis. More than one-third (34.49%) of Ayad River basin is covered by less permeable black clayey soil, other than this soil, brown loamy soil covered 33.02% of area, brown gravelly loam soil covered 20.8% of area, red gravelly loam hilly soil covered 9.77% of area, while red loamy soil covered only 1.92% area of Ayad River basin. With reference to soil depth, approx. 47% of area was covered by deep / moderately deep soil (>50 cm), 29%, and 23% of area was covered by extremely shallow soil (50 cm). Geology and lithology found in the area are ranging in age from Archaean to Upper Proterozoic; and belong to three geological cycles—Bhilwara, Aravalli, and Delhi supergroups. A very detailed geomorphological map has been developed by visual image interpretation of satellite imagery, DEM data, toposheets, geological map (lithology and structural) with limited field check. The map has been classified into thirteen broad geomorphic units, and 63 micro-geomorphic units. Based on 121 years (1901–2021) rainfall data analysis, the average annual rainfall of the area is 640 mm, and residual mass curve is also indicating the better condition for annual groundwater increment. The drainage pattern in that basin is mostly dendritic and sub-dendritic and drainage density is ranging between 0.46 and 3.45 (Km/Km2). Eight artificial lakes are linked together in a chain in the saucer-shaped valley of Udaipur, which is the first historic river-linking project in Udaipur and it helped the city to meet its water needs. The area has a good aquifer inside the hard rock materials such as phyllite, schist, gneiss, and quartzite, which is predominantly formed in weathered, fractured, and

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jointed rock formations. The average water level (bgl) in gneiss, limestone, phyllite, phyllite/schist, and dolomitic-marble formation are 12.30 m–5.62 m, 16.35 m– 9.48 m, 10.06 m–3.97 m, 15.13 m–6.29 m, and 13.64 m–8.38 m, respectively. Water level fluctuation of the area is ranging between 0 m and 21.2 m over the period (2011– 2020), while low water level fluctuation is indicating the overexploitation of the area. Based on behaviour of groundwater flow recharge and discharge zones has been identified—north and north-east area of the basin (area around Rama, Iswal) is the best recharge zone. While the area around Amberi, Bedla, Badgaon, Sukher, Dhinkli, and Udaipur which is situated at central in the basin is the discharge zone. Based on GEC-1997 method, the average annual groundwater resource (2011–2020) of the area is 127.81 MCM. CGWB 2019 has measured the annual groundwater withdrawal for all uses, which was 131.45 MCM. Overall stage of groundwater development is 101.88%, and it has over-exploited. Based on mini-max rainfall data and deficit / surplus reserves, a mathematical relationship has been established, and the same has been used to project the availability of groundwater. Large rainwater-harvesting structures, water conservation, reuse-recycle measures, and current groundwater management have also been recommended for sustainable management of groundwater reserves.

References Aggarwal RK, Sharma A, Hussain, Kumar A, Pakshit P (2011) Geology and mineral resources of Rajasthan, 3rd ed. Geological Survey of India, Miscellaneous Publication 30(12):13–31. Ahnert F (1988) Modelling landform change. In: Anderson MG (ed) Modelling geomorphological systems. Wiley, Chichester, pp 375–400 Anomohanran O (2015) Hydro-geophysical and hydro-geological investigations of groundwater resources in Delta Central, Nigeria. Journal of Taibah University for Science. 9(1):57–68 ARDS (1979) Report of the ground water over-exploitation committee. Agricultural and Refinance Development Corporation (ARDA), New Delhi ASCE Manual (1987) Groundwater management. American Society of Civil Engineers, New York USA Boryczko K, Rak J (2020) Method for assessment of water supply diversification. Resources 9:87 Carpenter SR, Caraco NF, Correll DL, Howarth RW, Sharpley AN, Smith VH (1998) Nonpoint of surface waters with phosphorous and nitrogen. Ecological Applications, Wiley. 8(3):559–568 CGWB (2006) Dynamic ground water resources of India. Central Ground Water Board (CGWB), Ministry of Water Resources, Govt. of India CGWB (2009) Report of the ground water resource estimation committee, ground water resource estimation methodology. Central Ground Water Board (CGWB), Ministry of Water Resources, Govt. of India. pp 1–113 CGWB (2013) Ground water scenario, Udaipur district, Rajasthan. Central Ground Water Board (CGWB), Ministry of Water Resources, Govt. of India. pp 1–19 CGWB (2019) National compilation on dynamic ground water resources of India-2017. Central Ground Water Board (CGWB), Ministry of Water Resources, RD & GR, Ministry of Jal Shakti, Govt. of India. pp 1–329 Chatterjee R, Gupta BK, Mohiddin SK, Singh PN, Shekhar S, Purohit R (2009) Dynamic groundwater resources of national capital territory, Delhi: Assessment, development, and management options. Environmental Earth Sciences. 59:669–686

198

K. Pareta

Cooke R, Warren A, Goudie A (1993) Desert geomorphology. UCL Press, London Dani AS (2009) Groundwater exploration using electrical resistivity method for Ahar River basin, Udaipur. Geology. Dramis F, Guida D, Cestari A (2011) Nature and aims of geomorphological mapping. Developments in Earth Surface Processes. 15:13–23 GEC (1997) Report of the groundwater resource estimation committee (GEC), Ministry of Water Resources, Government of India, New Delhi. p 107 Gupta BJC (1981) Systematic geological mapping in parts of Banswara and Chittorgarh districts, Rajasthan. Geological Survey of India (GSI), FS. 1976–77. Gupta SN (1968) Systematic geological mapping in parts of Udaipur district, (T.S. 45 G/15), Rajasthan. Geological Survey of India (GSI), FS. 1966–67 Hamdan ANA, Almuktar S, Scholz M (2021) Rainfall-runoff modeling using the HEC-HMS model for the Al-Adhaim River catchment. Northern Iraq. Hydrology. 8:58 Heron AM (1953) The geology of central Rajputana. Memoirs of the Geological Survey of India. 79:1–389 Hills ES (1975) Physiography of Victoria, 1st ed. Whitcombe & Tombs, Melbourne Hipel K, McLeod A (1994) Time series modelling of water resources and environmental systems. Elsevier, Amsterdam, The Netherlands Horton RE (1945) Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Bulletin of the Geological Society of America. 56(3):275– 370 Ibrahim A, Zakaria N, Harun N, Muzamil M and Hashim M. 2020. Rainfall runoff modeling for the basin in Bukit Kledang, Perak. In: Proceedings of the International Nuclear Science Technology and Engineering Conference (iNuSTEC 2020), Selangor, Malaysia. 1106:12–33 IDFC (2013) Rural development network. India Rural Development Report 2012:13 Karanth KR (2008) Groundwater assessment, development, and management. Tata McGraw-Hill Publishing Company, New Delhi Kendall M (1975) Rank correlation methods. Charles Griffin, London, UK Kwon M, Kwon HH, Han D (2020) A hybrid approach combining conceptual hydrological models, support vector machines and remote sensing data for rainfall-runoff modeling. J Remote Sens 12:1801 Langbein WB (1947) Topographic characteristics of drainage basins. USGS Water Supply Paper 968C:127–157 Lawrence AO, Ojo TA (2012) The use of combined geophysical survey methods for groundwater prospecting in a typical basement complex terrain: case study of Ado-Ekiti southwest Nigeria. Research Journal in Engineering and Applied Sciences. 1(6):362–376 Mishra VN, Rai PK, Mohan K (2014) Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India, Journal of the Geographical Institute, Jovan Cviji´c” SASA (Serbia), 64 (1):111–127. https://doi.org/10. 2298/IJGI1401111M. Mishra V, Rai PK (2016) A remote sensing aided multi-layer perceptron-marcove chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian Journal of Geosciences 9(1):1–18. https://doi.org/10.1007/s12517-015-2138-3. Machiwal D, Singh PK, Yadav KK (2017) Estimating aquifer properties and distributed groundwater recharge in a hard-rock catchment of Udaipur. India. Applied Water Science. 7:3157–3172 Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3):245–259 Montgomery DR, Dietrich WE (1989) Source areas, drainage density, and channel initiation. Water Resource Research. 25:1907–1918 NABARD (1984) Review of methodologies for estimation of ground water resources in India. National Bank for Agriculture and Rural Development Narasimha Prasad NB (2003) Assessment of groundwater resources in Nileshwar basin. Journal of Applied Hydrology. Vol. XVI, pp 52–60

9 Hydro-Geological Investigation and Groundwater Resource Estimation

199

Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book), Springer Nature, Switzerland, ISBN: 978–3–030–76116–5. https://doi.org/10.1007/978-3-030-76116-5. Rai PK, Chaubey PK, Mohan K, Singh P (2017) Geoinformatics for assessing the inferences of quantitative drainage morphometry of the Narmada Basin in India. Applied Geomatics (springer) 1–23. https://doi.org/10.1007/s12518-017-0191-1 Mishra VN, Rai PK, Singh P (2021) Geo-information technology in Earth resources monitoring and management (edit. Book), Nova Science Publishers, USA., ISBN: 978–1–53619–669–6. Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi.org/10.1007/978-981-16-7373-3. Pareta K, Pareta U (2012) Quantitative geomorphological analysis of a watershed of Ravi river basin, H.P. India. Int J Remote Sens & GIS. Vol. 1(1), pp 47–62 Pareta K, Pareta U (2015) Ground water balance and resource estimation using geospatial technology. In: Esri International User Conference, San Diego, CA, pp 1–18 Pareta K (2013) Sustainable ground water management in Kakund watershed of south Bharatpur district (Rajasthan) using remote sensing and GIS Techniques. In: 14th ESRI India User Conference, New Delhi. pp 1–10 Paron P, Claessens L (2011) Makers and users of geomorphological maps. In: Smith MJ, Paron P, Griffiths J (eds) Geomorphological Mapping: methods and applications. Elsevier, London, pp 75–106 Pradeep CM, Bhakar SR, Machiwal D, Singh PK, Jain HK (2018) Estimation of groundwater recharge for the study area. Int J Curr Microbiol App Sci 7:745–751 Priscoli J (2000) Water and civilization - using history to reframe water policy debates and to build a new ecological realism. Water Policy. 1:623–636 Raja Rao CS, Gupta BD, Iqbaluddin PBC, Basu KK, Dutta AK (1971) Precambrian stratigraphy of Rajasthan—a review. Records of the Geological Survey of India. 101(2):60–79 Rajasthan Water Assessment (2013) Potential for private sector interventions. International Finance Corporation, World Band Group. p 137 Rajput H, Goyal R, Brighu U (2020) Modification and optimization of DRASTIC model for groundwater vulnerability and contamination risk assessment for Bhiwadi region of Rajasthan, India. Environ Earth Sci 79:1–15 Rathore NS, Rathore D (2018) Shrinking of water resource in Udaipur, Rajasthan (India) basin using multi-temporal satellite data. Int J Water Resour Arid Environ 7(2):160–171 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. Applied Water Science (springer) 8(15):1–16. https://doi.org/10.1007/s13201-018-0660-7 Rai PK, Singh P, Mishra VN, Singh A, Sajan B, Shahi AP (2019) Geospatial approach for quantitative drainage morphometric analysis of Varuna River Basin. India, Journal of Landscape Ecology 12(2):1–21 Rai PK, Mishra S, Ahmad A, Mohan K (2014) A GIS-based Approach in Drainage morphometric Analysis of Kanhar River Basin, India. Applied Water Science (springer) 7:217–232. https:// doi.org/10.1007/s13201-014-0238-y Rathore NS (2010) Water resource management: a study of the world’s first man-made river links, river diversions and micro-watershed of Udaipur basin. Pacific Business Review 2(4):76–82 Rosengren N (1984) Sites of geological and geomorphological significance in the Shire of Otway. Department of Conservation, Forests and Lands Sharma A, Singh P, Rai PK (2018) Morphotectonic analysis of Sheer Khadd River basin using geospatial tools. J Spat Inf Res 26(4):405–414 Singh A, Rai PK, Deka G., Biswas, B., Prasad, D., Rai, V.K. 2021. Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India, Ecology Environment & Conservation, 27 (February Suppl. Issue); pp. S260–S268.

200

K. Pareta

Sahu RL (1995) Final report on the geology of Vindhyan Supergroup of south-eastern Rajasthan. Geological Survey of India (GSI), FS. 1994 Samar K, Vaishnav J (2014) Pattern of groundwater levels by statistical method; an investigation: a case study of Ahar River basin, Udaipur. Lambert Academic Publishing. pp. 1–52. Shah T, Molden D, Sakthivadivel R, Seckler D (2001) Global groundwater situation: Opportunities and challenges. Econ Pol Wkly 36:4142–4150 Sharma KD, Kumar S (2008) Hydrogeological research in India in managing water resources. In: Ahmed S, Jayakumar R and Salih A. (eds) Groundwater Dynamics in Hard Rock Aquifers. Springer, pp 1–19. Shekhar S, Purohit R and Kaushik YB. 2009. Groundwater management in NCT Delhi. Technical paper on Ground water. In: 5th Asian Regional Conference of INCID, Vegan Bhawan, New Delhi, India. Shekhar S (2006) An approach to interpretation of step drawdown tests. Hydrogeol J 14:1018–1027 Shin S, Park H (2018) Achieving cost-efficient diversification of water infrastructure system against uncertainty using modern portfolio theory. J Hydroinf 20:739–750 Shyam M, Meraj G, Kanga S, Sudhanshu Farooq M, Singh SK, Sahu N, Kumar P (2022) Assessing the groundwater reserves of the Udaipur district, Aravalli Range India, using geospatial techniques. Water 14(648):1–26 Sinha AK, Kumar V, Singh PK (2018) Delineation of groundwater potential zones using remote sensing and GIS techniques: A case study of Udaipur district, Rajasthan. India. Journal of Pharmacognosy and Phytochemistry. 7(4S):265–273 Sinha-Roy S, Malhotra G, Mohanty M (1998) Geology of Rajasthan. J Geol Soc India, Bangalore. p 278 Sirhan A, Hamidi M, Andrieux P (2011) Electrical resistivity tomography, an assessment tool for water resource: case study of Al-Aroub Basin, West Bank. Palestine. Asian Journal of Earth Sciences. 4(1):38–45 Strahler AN (1957) Quantitative analysis of watershed geomorphology. Transactions of the American Geophysical Union 38(6):913–920 Wali SU, Abubakar IM, Umar MA, Shera AA (2020) Reassessing groundwater potentials and subsurface water hydro-chemistry in a tropical Anambra basin, southeastern Nigeria. J Geophys Res 2:1–24 Wiedenbeck M, Goswami JN, Roy AB (1996) An ion microprobe study of single zircons from the Amet granite, Rajasthan. J Geol Soc, India. 48:127–137 Yue S, Pilon P, Cavadias G (2002) Power of the Mann-Kendall and Spearman’s Rho tests for detecting monotonic trends in hydrological series. J Hydrol 259:254–271 Zhang Y, Chiew FHS, Zhang L, Li H (2009) Use of remotely sensed actual evapotranspiration to improve rainfall-runoff modelling in southeast Australia. J Hydrometeorol 10:969–980

Chapter 10

Myths, Architecture, and Rites: The Concept of Conservation of the Tri Danu Area in Bali in the Contemporary Struggle I Putu Gede Suyoga, Ni Ketut Ayu Juliasih, and Mira Sartika

Abstract The Tri Danu area (Lake Beratan, Buyan, and Tamblingan) has mythical stories, architectural physical artifacts, and periodic rites, which are an effort to conserve the sacred Tri Danu area. This study focuses on these three aspects as an individual and collective knowledge of the local community living around the Tri Danu area. The values, norms, and belief systems of the people in these myths have driven a socio-eco-religious practice that supports lake conservation programs, but is under pressure from today’s market ideology. This study is a qualitative study with an interpretive descriptive approach. The analysis of data and information is based on Pierre Bourdieu’s theory of generative structuralism and other theoretical conceptions that are used eclectically. The results of the study show that a number of knowledge in the context of preserving the ecology of Tri Danu are wrapped in mystical and magical stories. The mythology is the episteme of the local community which underlies the way of thinking, speaking, and behaving toward the sacred area of the temple and the radius of lake conservation. The socio-religious approach in an effort to preserve the Tri Danu conservation area is under pressure in various aspects of life. The struggles in the realm of today’s life are categorized into conservative, progressive, and adaptive groups. The three perspectives are based on ecological ideology, market ideology, and sustainability ideology. The mechanism of compromise and normalization is an adaptive and solution option for the middle way of sustainable preservation of the Tri Danu function. Community praxis occurs in a socio-economic-ecological pattern. Keywords Tri Danu · Conservation concept · Sustainability I. P. G. Suyoga Bali Design Institute and Business, Tukad Batanghari St. No. 29, Denpasar 80225, Indonesia N. K. A. Juliasih Indonesia Hindu University, Sangalangit St. Penatih, Denpasar 80238, Indonesia M. Sartika (B) Chakra Kultural Foundation, Tebet Barat, Jalan Prof. Dr. Soepomo SH, No. 23, Jakarta 12810, Indonesia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_10

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10.1 Introduction The interaction of living in harmony with the settlers in the lake area is also built not only on the “ecosystem” aspect, but also the “humanism” aspect, and the “divinity” aspect of parhyangan. The culture of the people who live in the Tri Danu area (Lake Beratan, Lake Buyan, and Lake Tamblingan), has myths as a sociocultural product that contains the same ecoreligious values, norms, and ideologies. Historical travel, geographical proximity, and the equality of cultural codes underlie the common knowledge based on myths that are believed to be true. Myth became a vehicle for conveying ideological ideas that were effective in the agricultural era. The ecological ideology is applied as a convention in the form of behavior patterns, oral and written legal rules. There are another physical form of cultural artifacts includingsacred spaces, sacred sitres and a number of temple architectures of the Tri Danu area. The worship sites and architecture are enlivened by periodic rites (temple festivals). Rituals are integrated with the agricultural cycle in the subak system (agricultural water management organization), water conservation (springs, streams, and lake waters), forest conservation, and places of worship within the territory of traditional villages in the Tri Danu area. These collective knowledge become part of social discourse. Social discourse in the form of mythology of naga gombang and naga rakrik, kayu larangan, alas angker “sacred forest,” and others. These things are forms of discourse articulated by intellectual actors as local elites on various occasions of social interaction. This articulation drives social practice, which underlies the mindset, perception, and behavior of the people living in the Tri Danu area. The articulation of local wisdom has grown and developed as a concept of everyday life which has become a form of body training from an early age. This form of body discipline through knowledge has become cultural capital, social capital, symbolic capital, economic capital, and has even become the individual and collective habitus of settlers in the Tri Danu area. Habitus and capital become a kind of ecological ideology which have implications for the preservation of lake ecosystems and real efforts to conserve resources in the Tri Danu conservation area. At present, specifically for Lake Buyan and Tamblingan, supervision is very strict considering their status as part of a conservation area under the direct management of the central government ministry (Jakarta) through the Central Natural Resources Conservation Agency (BKSDA). On the other hand, Lake Beratan is not a conseravation area and under the Tabanan Regency Environmental Service. This means that specifically, the Lake Beratan area is the responsibility of the local government of Bali. This area is developed and managed wisely (paying attention to water quality standards and carrying capacity of the area) by the region, namely, Tabanan Regency. Along with the development of market ideology which has also touched various aspects of the daily life of the people living in the Tri Danu area, it is inevitable that it will also have an effect on increasing pressure on this ecological ideology which was established before. A number of struggles between the socio-eco-religious and the contemporary market ideology are interesting to study. That is, by placing it

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as a conventional, progressive, and adaptive compromise of the local community, especially in maintaining sustainability and efforts to preserve the continuity of Tri Danu functions in a sustainable manner.

10.2 Method This study is a qualitative research with descriptive analysis. Primary data was extracted through purposive interviews with a number of competent informants, namely, Bendesa Adat (Traditional Village Leader), Prebekel (National Village Leader), Forest Officer/Police, Farmer, Menega Danu (lake fisherman), and visitors (tourists and Hindu worshipers). Secondary data was extracted from various sources of literature, journals, and mass media news. These data were then analyzed with the theory of generative structuralism from Pierre Bourdieu. The theory of generative structuralism from Pierre Bourdieu covers has several important concepts, namely, habitus, capital, domain, and practice. The theoretical idea is inspired by a generative formula, namely, crossing. The formulation is (Habitus X Capital) + Domain = Practice (Harker et al. 2009). The way to read this theoretical formulation in the context of the humanities is to place habitus as a group of “habits of daily life” that have been integrated with systems of belief, religion, values, norms, philosophy of life, and other ideological complexities. Furthermore, habitus is crossed with four capitals, namely, economic, cultural, social, and symbolic capital in a realm (social place or space), then it will produce or may not produce a sociocultural practice. Economic capital concerns material and financial wealth, cultural capital includes ownership of knowledge, diplomas, and cultural codes. Social capital in the form of social relations, colleagues, friendship, brotherhood, and symbolic capital includes nobility, descent, rank, position, and all other forms of symbolic respect (Bourdieu 2016; Haryatmoko 2016). Local elites, especially those living in the Tri Danu area, play a very important role in controlling, strengthening, playing with, or converting a number of these capitals, especially symbolic capital and cultural capital in the form of knowledge in myths based on a mystical and magical storyline in the setting of lake waters and the area around the lake. This knowledge is captured and through discourse, it is disseminated in the middle of the social sphere in accordance with their respective interests, of course, to fight for an ideology. Thus, the idea of ideology can refer to the pro-environmental or pro-ideology of today’s market. The individual and collective habitus of the people living in the Tri Danu area who still have strong mystical and magical values has become a fertile ground for the development of mythological transformation practices in an effort to preserve and preserve the Tri Danu area. Likewise, his various struggles with the carriers of market ideology today are described in this study.

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10.3 Results and Discussion Tri Danu consists of three lakes in the ancient volcanic caldera of the Beratan volcano, the Bedugul highlands, namely, Lake Beratan, Buyan, and Tamblingan. Administratively, Lake Beratan is in Tabanan Regency with an area of 379 hectares and a depth of 35 m. Lake Buyan with an area of 360 hectares and a depth of 87 m, while Lake Tamblingan has an area of 110 hectares and a depth of 90 m, are both in Buleleng Regency. The Tri Danu area map is shown in Fig. 10.1. The topography of the area varies from flat, rather steep to very steep with an altitude between 1210 and 1350 m above sea level. According to the climate classification by Schmidt and Ferguson, this area is included in type A with an average rainfall of 2000–2800 mm/year and air temperatures ranging from 11 to 25 °C. Meanwhile, according to forest type, this area is a type of montane tropical rain forest (plateau), the condition of the area is always wet and has a relatively high diversity (Distributor Team 2005). Lake Beratan, Buyan, and Tamblingan do not have rivers either as fillers (inlets) or as outlets, so they are only filled with springs that are around them, or those that come from rainwater flowing in the catchment area. Thus, Tri Danu includes the characteristics of a confined basin lake (KLHK 2020). Besides the term Tri Danu,

Fig. 10.1 Pleiades Satellite Image (2014) of the Tri Danu Region. Source Sutomo et al. (2019). Bedugul from Space. LIPI Press

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the term twin lakes or dwi danu is also popular to refer to the existence of Lake Buyan and Tamblingan, which are located next to each other. The existence of Tri Danu is a source of raw water for springs on the island of Bali. In addition, Tri Danu also has ecological, economic, social, and cultural functions. Ecological function is related to the function of the lake as a habitat for organisms, as well as controlling soil balance and microclimate (Arora et al. 2021). Social functions include being an open space where people carry out social interactions. Meanwhile, the cultural function is related to the function of the lake as a sacred area in accordance with the concept of the sacred hierarchy of territorial space in the Hindu tradition called the tri mandala (main, middle, and lowly mandala). The concept of the tri mandala places the Tri Danu in the main position of the mandala because it is located in the upstream part, namely, the most sacred area as a holy place (offering center). The economic function of Lake Beratan, Buyan, and Tamblingan is related to the function of the lake as a source of water for agricultural irrigation for several subak (agricultural water management organizations), fisheries, local and international tourism in Bali (Sudji 2015). Communities around Tri Danu have used the land around the lake as seasonal agricultural land, especially horticulture, including animal husbandry and fisheries. The use of sustainable pesticides in the Tri Danu area does not rule out the possibility of causing bioaccumulation which is then accompanied by biomagnification which will have a negative impact on the lake (Manuaba 2008). Multisector utilization and activities around the lake area cause the condition of the lake ecosystem to experience increasingly severe degradation. Uncontrolled exploitation of lakes as a natural resource has caused various problems, such as pollution, damage to natural resources, loss of natural resources, which results in a decrease in environmental quality (Mishra et al. 2018; Nistor et al. 2019; Singh et al. 2021). The decline in water quality can be caused by sediment content originating from erosion or the content of materials or compounds from industrial and agricultural wastes (Rai et al. 2021; Mishra et al. 2021; Rai et al. 2022). The increase in the need for the use of natural resources has led to excessive management of natural resources, which has had an impact on disrupting the balance of the water system and the ability to produce land with marked increases in erosion, sedimentation, and enrichment of aquatic nutrients, especially nitrogen and phosphate (Sulistyanto et al. 2018; Rai et al. 2021; Mishra et al. 2021; Rai et al. 2022). It seems that the decline in environmental quality in the Tri Danu area cannot be separated from the lack of attention paid to knowledge of traditional conservation concepts that have irrational or magical nuances. Traditional conservation knowledge in the form of oral traditions, myths, and the meaning of the existence of physical artifacts, architectural worship, and periodic rituals that have been carried out by the community. This is in line with the influence of the development of global society which pays more attention to the rational aspect. This progressive thinking, which is only fulfilling the needs of today’s life, is evident from a number of research reports which show a decrease in the environmental quality of the Tri Danu area. The Lake Beratan area is reported to have experienced degradation. The results showed that: (1) The distribution of Lake Beratan water quality both horizontally

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and vertically in the study area was classified as moderate (class 2), because several parameters were above the drinking water quality standards, including: turbidity, pH, NH3, F, Fe, DO, BOD, COD, and Coliform bacteria. The results of calculating the water quality status of Lake Beratan show that Lake Beratan has been lightly, moderately, and heavily polluted for each sample location. The causes of the damage to the function of Lake Beratan are sedimentation, water quality, changes in land use (agriculture, settlements, and tourism facilities), and increased water use (Atmaja 2019). The Lake Buyan area is also reported to have experienced degradation. The Bali Natural Resources Conservation Center (2012) states that Lake Buyan has lost 10% of its initial area and has silted up to 10% of its initial depth. The Provincial Government of Bali in 2015 stated that Lake Buyan was experiencing degradation in the form of siltation/eutrophication, pollution of hazardous chemicals/heavy metals from agro-industrial activities, decreased water level, and reduced fishing communities (Manuaba 2008). The results of the study also show the condition of Lake Buyan as reported by Purnama (2016), that Lake Buyan has experienced quite high environmental degradation, which was caused by changes in land use to residential and agricultural areas, pollution of chemicals from both household and industrial waste. Furthermore, Suyasa et al. (2017) also reported that Lake Buyan water was polluted with phosphor with a content of 4.353 mg/L–5.936 mg/L and sulfide with a content of 0.302 mg/L–0.960 mg/L, and the BOD content exceeded the class III water quality standard in the 2016 Bali Governor Regulation. The BOD content which exceeds the standard limit indicates that the organic load from garbage and sewage has polluted the waters of Lake Buyan. The waters of Tamblingan Lake have previously been reported to have experienced pollution from seasonal agricultural/horticultural activities and their use as a source of domestic water needs (Sudji 2015). The current condition of the Tamblingan Lake area is relatively better, because of its location which is relatively far from settlements and the awareness of the residents of the surrounding community to commit to maintaining its sustainability through reactivating existing traditional institutions, namely, jaga wana and jaga teleng (Head of Munduk Village, interview July 2022). In addition, geographically the Lake Tamblingan area is isolated within the Bedugul Natural Tourism Park area, when compared to Lake Beratan and Buyan (KLHK 2020). Various problems in the Tri Danu area originate from anthropogenic factors, due to land conversion for agricultural activities and development on the lake’s borders. Change of function of agricultural land to non-agriculture. Changes in the agricultural sector, which was originally an annual crop to become an annual crop farming. This greatly affects the resistance to rainwater overflow, the root absorption system, and the speed of the lake’s sedimentation rate. That is, increasing the potential for siltation and nutrient enrichment of lake waters due to leaching by erosion. The characteristics of the Tri Danu are in the form of a confined basin that has no outlet, so that all the waste that enters the lake will continuously accumulate and affect the quality of the lake water (KLHK 2020). In addition, traditional knowledge related to conservation

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is increasingly fading, and the lack of role of existing traditional institutions that function to maintain the sustainability of the Tri Danu area. The description above directs an understanding of the importance of the functions of Lake Beratan, Buyan, and Tamblingan, and for the life of the Balinese people, ecologically, economically, socially, and culturally. However, on the other hand, it experiences degradation which reduces the said four functions. Strategic efforts should be made to identify and revitalize conservation concepts and control patterns that are most appropriate to empirical conditions. The focus of this study explores from the standpoint of local cultural praxis, namely, from the people who live around the Tri Danu water area, especially regarding conservation knowledge and the meaning contained in a number of non-physical artifacts (myths, rites, and customary law) and physical artifacts (sites and temple architecture).

10.3.1 Tri Danu Myth The term myth in a very broad sense can refer to traditional stories. Myths are sacred stories that usually explain how the world and humans came to be as they are today. The study of myths is known as mythology. Mythology can cover the story of the creation of the world to the origin of a nation (Armstrong 2006: 23). Myth is similar to ideology because mythology will appear like universal truths that are presented in the rotation of people’s everyday reasoning memories (Barker 2000: 93), so that it becomes collective knowledge. The mythology in this study is specifically explored from the myths that become the collective knowledge of the settlers in the Tri Danu area that are relevant to conservation efforts or based on ecological ideology. There are a number of myths related to efforts to preserve the environment in the Tri Danu area, namely, (1) forbidden wood, (2) naga gombang and naga rakrik, (3) duwe animals, and (4) soan kuning and soan besi, one by one is described as follows:

10.3.1.1

The Myth of Kayu Larangan (Forbidden Woods)

The myth of forbidden wood reveals the local people’s belief in the prohibition of cutting down trees, which are forbidden wood or wood protected by the kingdom. If you want to cut down a tree, you have to get permission from the customary village. Violators who cut haphazardly without the knowledge of the traditional village or violate the tradition of prohibited timber as protected wood will be cursed by the ancestors and subject to fines and customary sanctions. Most of the inscriptions found in the lake area in Bali state that Balinese kings from the tenth to twelfth centuries had a concern for saving forests and lakes. Both of these areas are understood to have an important position to underlie Balinese civilization. In the manuscripts of Balinese inscriptions I and II found in the villages on the edge of the lake, the rules for land use around the lake are clearly written, even

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the people of the ancient Balinese era had land divisions or zoning. It is stated in the manuscript that the division of land was for agricultural land, grass fields for fodder, and land for planting wood for building materials in detail on the inscription made of copper. In addition, it also mentions areas for breeding, breeding horses, and the amount of taxes imposed. They know that certain types of grass for animal feed can outperform the roots of certain trees. The trees around the lake will gradually dry up if grass is planted in the same area, which has a very high water absorption capacity (Goris 1986). If based on Bourdieu’s thinking, their knowledge of the characteristics of land, plants, and animals shows the strong ownership of cultural capital. This knowledge is still held firmly and passed down to the present generation. Only dead wood can be taken from the forest or has fallen down on its own. Even then, the knowledge of traditional village administrators or forest officials who are called jaga wana and jaga teleng must be known. The guardian of the community groups tasked with guarding the forest around the temples in the Lake Buyan and Tamblingan areas. Meanwhile, jaga teleng is responsible for protecting and caring for the waters of the lake. Jaga teleng is a group of lake fishermen or bendega danu. These officers are still part of the social praxis in the Dalem Tamblingan Traditional Catur Desa (Four Villages), namely, Munduk, Gobleg, Gesing, and Uma Jero (Bendesa Pancasari, interview July 5, 2022). The existence of jaga wana and jaga teleng officers, and a social system that is still in effect is a form of social capital in Bourdieu’s thought. The cultural capital and social capital have been transformed into symbolic capital because its existence is not merely a myth with mystical and supernatural nuances. However, it becomes a form of disciplining the social body. Its existence is obeyed and respected, like an ideology that underlies the collective actions of settlers in the Tri Danu area. Ownership of cultural, social, and symbolic capital is a real practice of local communities in preserving forests and lakes. They really understand its existence as a source of life or economic capital.

10.3.1.2

The Myth of Naga Gombang and Naga Rakrik

The myths of the naga (dragon) gombang and naga (dragon) rukrik are popular folklore in the Tri Danu area because the setting of the story concerns the existence of sites on Lake Beratan, Buyan, and Tamblingan (Chandrawan 2015). The myths of the dragon gombang and dragon rakrik tell of two powerful dragons that live in the lake area. The two of them have often fought over their supernatural powers for years, resulting in the destruction of the natural environment and the misery of other living things. Both of them have great supernatural powers so that the battle is even, neither loses nor wins. After nature was badly damaged, the gods who resided at Pucak Mangu and Pucak Sangkur mountains were worried about the sustainability of nature in the future. The two gods descended from the sky into the midst of the two dragons’ battle. The gods of Pucak Mangu subtly then asked where the magic points of the two dragons were. They were asked to tell the truth if they wanted to win the duel. Then, each

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dragon shows its power before the gods. Naga Gombang claims that his supernatural powers are in the outrigger kekolongan (vocal cords), while dragon Rakrik says his supernatural powers are located at the tip of his tail. After hearing the location of each magic power, the dragons again attacked each other. The Gombang Dragon managed to peck the tip of the Rakrik Dragon’s tail, whereas the vocal cords on the Gombang Dragon’s neck were successfully bitten by the Rakrik Dragon. The two dragons finally collapsed. When he saw that Naga Gombang was seriously injured and Naga Rakrik’s corpse was lying all over Lake Beratan, Dewa Pucak Mangu ordered Naga Gombang to perform meditation at sapta patala (seventh layer of the underworld). Before heading to the hermitage, Naga Gombang met his wife and told her what happened. At the end of the story, he advised that if Naga Gombang wanted to meet, he would move his body, then the whole nature would shake or quake, so his wife suggested hugging the joints (base of the building) and saying it alive! Naga Gombang then said goodbye and headed for the hermitage. After passing soan kuning on the west bank of Lake Buyan, head southwest into the cave toward sapta patala. The soan is in the form of a water basin below the surface of the lake and where the Naga Gombang hermitage is now, the Goa Naga Loka Temple is being built (Bendesa Pancasari, interview, July 5, 2022). The mythology of the stories of Naga Gombang and Naga Rakrik is the knowledge of the local community about the existence of mountains and forests around the lake (Pucak Mangu and Pucak Sangkur), knowledge of water (lake bodies and river flows in lakes/soan), knowledge of the land (land) with natural phenomena (Juliasih et al. 2022). This local wisdom shows the ownership of cultural capital, social capital, and symbolic capital in Bourdieu’s terminology. These three capitals complement the mystical and magical habitus of the local community. The meeting of capital with mystical and magical habitus in the realm or struggles of life of the people living in the lake area has supported the occurrence of social practices. In this case, the collective practice of respecting and maintaining the function of mountains, forests, springs, rivers, lake waters, soan (river under lake water), and land so that life is in harmony with other creatures for the continuation of its function or sustainability.

10.3.1.3

Myth of Duwe Beast

The myth of the duwe beast tells of the existence of a number of fauna that live in the waters of the Tri Danu area, such as ulam agung “big fish,” crocodiles, and dragons. The appearance of a number of duwe animals is unpredictable, only occasionally, and can only be seen by certain people. The existence of the duwe animal is highly trusted by the local community, so they obey not to speak rudely, spit, urinate, or defecate in the waters of the lake. If it is violated, it will have fatal consequences for the life of the perpetrator (Bendesa Pancasari, interview July 5, 2022; Juliasih et al. 2022). The myth of the duwe animal is a source of individual and collective knowledge for the settlers in the Tri Danu area. Ownership of cultural capital in the form of local

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wisdom has become a guide and control for people’s behavior toward lake waters. This also includes social capital and symbolic capital which is strengthened by the strong conservation ideology behind the mythical packaging. Mystical and magical habits that are still strongly internalized within each individual, have cumulatively collaborated with cultural, social, and symbolic capital. The combination has formed a collective practice of preserving water resources in the Tri Danu waters with all their biodiversity.

10.3.1.4

The Myth of Soan Kuning and Soan Besi

Soan is the name of the local community for water bodies below the surface of the lake. There are two large soan flowing from the shores of Lake Buyan, namely, soan kuning and soan besi. The soan kuning stream is located on the west bank, while the black soan besi is on the east bank of Lake Buyan. The myth of soan kuning is closely related to Naga Gombang’s journey to his hermitage in Goa Naga Loka. Likewise, soan besi is often associated with the passage of magical beasts. This is why the two soans are sacred and their sanctity is maintained. The uniqueness of the fish and plants that live in the soan kuning water flow has a yellow tint, while those that live in the soan besi have a black color. If the soan is “contaminated,” then a Pakelem ceremony must be carried out using white ducks and other offerings (Bendesa Pancasari, interview, July 5, 2022; Juliasih et al. 2022). The myths of the two soans are the individual and collective knowledge of the settlers in the Lake Buyan area or what Bourdieu calls cultural capital. Cultural capital with very strong symbolic value. It is symbolic capital that shapes perceptions and directs social behavior. Cultural, social, and symbolic capital has complemented the collective habitus of the local community. This is what drives the practice of the community to try to protect themselves not to throw away waste, garbage, urine, or activities that will pollute the lake waters.

10.3.2 Temple Architecture, Sites, and Rites The Tri Danu area, in accordance with its position in the upper reaches of the island of Bali, is a sacred area and locus for the construction of physical artifacts of temple architecture. A number of ancient sites and temples in the Lake Beratan area, namely, Ulun Danu Beratan Temple, Penataran Agung Temple, Dalem Purwa Temple, Sacred Coral/Garden Sites, Prajapati Temple, Puncak Mangu Temple, Luhur Pucak Bukit Sangkur Temple, Luhur Pucak Terate Bang Temple, Batu Meringit Temple, Pucak Sari Temple, Pucak Candi Mas Temple, Pucak Bedugul Temple, Pucak Kayu Sugih Temple, Pucak Taman Sebatu Temple, Mertha Sari Temple, Tirtha Mampeh Waterfall Site, Rejeng Besi Jurang Site, Puseh Sengawang Temple, Candi Lebah Temple, Alas Metaun Forest Site, and Gumi Kambang Land Site (Head of Candikuning Village, and Jro Mangku Ulun Danu, interview September 1, 2022).

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A number of sacred sites and temple architecture are also found in the Lake Buyan area, namely Ulun Danu Bulian Temple, Luwur Sari Temple, Gunung Anyar Temple, Tajun Temple, Beji Yeh Mas Temple, Soan Kuning Spring, Soan Besi Spring, and the Meringgit Stone Site. The existence of temple architecture and sacred places means that all components of society take real action to protect the sanctity of the lake environment in general, as the headwaters of the island of Bali. One of them is by maintaining forests and springs as sacred temple locations and sources of survival. This is done by building a joint commitment to preserving the lake through belief in the truth of the existing Tri Danu myths and balanced with maintaining cleanliness, planting trees in several landslide hill locations, increasing community environmental knowledge, improvement of physical waste disposal systems, and buildings. It is also fundamental for the younger generation to reintroduce traditional conservation concepts that have been forgotten in today’s life, through local content in education in schools (Bendesa Pancasari, interview, July 5, 2022). Likewise, in the Tamblingan Lake area, there are a number of sacred sites and temple buildings, namely Ulun Danu Tamblingan Temple, Tirtha Mangening/ spring Temple, Dalem Tamblingan Temple, Endek Temple, Gubung Temple, Subak Sanghyang Kangin Temple, Subak Sanghyang Kauh Temple, Pengukiran Temple, Penimbangan Temple/dolmen site, Embang Temple/stone throne site, and Pande Catur Lepus Temple/metal mining site. Symbolic rites in the form of offering ceremonies are carried out daily and periodically at sites and temples scattered in the forest area and on the banks of the Tri Danu waters. Rituals are carried out individually or in groups by the local community (temple administrators and traditional village residents), spiritual groups that perform tirtayatra, local government, and provincial government. The existence of sites, a number of temples, and the meaning of these ritual activities is that the community does not stop at the symbolic level, but is balanced with practical actions to maintain the radius of the sanctity of the temple and its environment. One way is to reactivate traditional institutions such as jaga wana and jaga teleng, reforestation activities, socialization of local wisdom to the younger generation, building the commitment of all stakeholders, and joint-supervision (Bendesa Munduk, interview, August 31, 2022). Periodic rituals include piodalan (temple festival) once every 210 days cycle, aci pakelem manca warna (a year), aci pakelem utamaning utama (five years), nangluk merana (during an epidemic), ngaturang suwinih (post-harvest), mapag toya (beginning of the planting season), wana kertih (forest sustainability), danu kertih (lake sustainability), and the tawur agung panca balikrama (nature main purification ceremony). This tawur agung refers to the Purana Bhuana Bangsul manuscript, which states that catur danu, which consists of Lake Batur, Beratan, Buyan, and Tamblingan is the upstream or center of Balinese life. The panca balikrama ceremony was held at Lake Beratan on June 16, 2011. The ceremony was aimed at asking for kerahayuan jagat (the universe’s) “prosperity and all its contents.” The meaning of all these ceremonies is to direct the community to protect the sacred area of the lake with real behavior, namely, by keeping the environment clean, preventing it from being polluted by waste, planting annual trees, and gradually returning to organic farming (Head of Candikuning Village and Bendesa Pancasari, interview July 5, 2022).

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10.3.3 Present Struggle In general, there are three mindsets and perspectives in today’s life, namely, conservative, progressive, and adaptive. First, a conservative point of view is promoted by a strong society based on ecological ideology. The structure of their meaning of the existence of the lake as a source of life “amerta” is strictly guarded with a strong belief in the Tri Danu myths. The existence of the lake in the sense that the function of the Tri Danu area is maintained so that it is sustainable in customary or traditional ways, knowledge-based in the packaging of Tri Danu myths or environmental ethics in oral tradition, belief in the existence of sacred sites, the construction of a number of worship architectures, and periodic rites. The behavior of the local community is also based on traditional legal rules in traditional villages, namely, awig-awig, especially pawos palemahan (environmental division), which is also carried out collectively and individually by members of traditional villages. Traditional rules as local wisdom are also respected by nonHindu settler communities in the Tri Danu area by synergizing with traditional village organization officials, government officials, and other stakeholders. In this first perspective, the use of the Tri Danu waters area is only limited to controlled domestic needs and religious activities. The existence of Tri Danu with all its resources is seen as the ownership of cultural capital, social capital, and symbolic capital. The conversion of these three capitals into economic capital through exploitation is very limited with strict supervision from all components of the traditional village. If understood, in general, the conservative mindset that underlies the praxis of the community in understanding the function of the lake refers to the socio-religious-ecological pattern. The stability of this pattern, in the development of global relations through unlimited information and communication interactions, has influenced the perspectives and behavior of the settlers in the Tri Danu area and its surroundings, which are limited to fulfilling current or progressive needs, regardless of long-term effects for the sustainability of the Tri Danu area. Such a point of view is placed as a second perspective. Second, a progressive perspective places the community more carrying the fulfillment of momentary needs or the needs of daily life as the main orientation. This group of people refers to, or is more driven by market ideology. The structure of their meaning of the existence of resources in the Tri Danu area sees more as natural resources that can be utilized optimally. Development or excessive utilization to support the needs of the agricultural industry, tourism services, and trade in horticultural products that are potential and are growing rapidly in the region (Bedugul). The area of Lake Beratan, Buyan, and Tamblingan is placed as a source of fulfilling life welfare and material wealth, or in Bourdieu’s view, it is maximally converted into economic capital. Thus, the praxis of the community understands the main function of the Tri Danu area only in socio-economic patterns.

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Such a socio-economic praxis has reduced the function of lakes, in general, degraded ecological functions by increasing sedimentation, eutrophication, and exceeding quality thresholds. Likewise, it can threaten its socio-religious function as a sacred temple area with a number of sacred sites. Awareness of these matters has led to the revitalization of local wisdom in the form of myths that once lived in oral tradition in the community, namely, related to the concept of magical guardians of the Tri Danu area (gombang dragon and rukrik dragon mythology, duwe beast, haunted pedestal, forbidden wood, and others). Reinterpreting the existence of temple sites and architecture that are scattered in the Tri Danu area with a number of rituals carried out by the community so far, showing that existing temples as architectural worship and rituals that are carried out are not limited to symbolic activities, but are interpreted as part of real actions to care for the environment to protect the Tri Danu conservation area from excessive daily activities. The sanctity radius of the temple and the sacred forest site is meant to protect it from the touch of the profane activities of the local people who live around it and to give an early warning to guard the thoughts, words, and behavior of outsiders who visit the Tri Danu area. All of these things can empirically protect the Tri Danu area so that it continues to exist naturally, namely, maintaining forest cover with all its biodiversity, protecting springs and watercourses, supporting air, water, and soil quality conditions so that they remain at the required quality standards, and other aspects of the ecosystem. Thus, an adaptive point of view is built on the basis of the ideology of sustainability in society as a third perspective. Third, adaptive perspective. This public perspective is based on the ideology of sustainability. Compromise and normalization mechanisms are adaptive and solutive choices for a middle way to sustainably preserve the functions of the Tri Danu. Efforts were made with several excellent programs, such as revitalization and strengthening local wisdom, based on mythology which has proven successful in maintaining the existence of the lake during the traditional era. Rereading these myths to reveal new meanings that are relevant to the millennial era is absolutely necessary. This is urgently done to direct the community to real practical environmental improvement. The implementation of symbolic rituals, which are periodically carried out in the Tri Danu area, is balanced with concrete actions through actions to conserve the lake and its surroundings. Replanting coffee trees on agricultural land belonging to the community’s coastal forests, in line with the return of the trend of increasing market demand. The planting of coffee trees is combined with superior advocates who will also become shade trees for the coffee plants beneath them (Bendesa Pancasari, interview, 5 July 2022). Likewise, the development of the camping ground rental area has also indirectly hardened the land. Land processing activities will decrease gradually compared to horticultural farming which requires loose soil. Loose land is relatively more prone to erosion during the rainy season. Erosion-borne materials can increase the sedimentation and eutrophication of lake “silting.” The trend of the rapid development of nature tourism in the Tri Danu area also needs to be addressed wisely by stakeholders, both government and private parties. The involvement of all parties to understand the vision and mission of the conservation of the Tri Danu area, in addition to the

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government, must also involve the participation of all villagers, farmers, fishermen, youth, forest rangers, teleng guards, tourism actors, and visitors to the Tri Danu area. Thus, this third mindset, namely, the adaptive perspective, puts forward the ideological foundation of sustainability. In order to conserve the function of the Tri Danu area, the understanding is that lakes as natural resources can be used optimally below the quality standard threshold. Exploitation of land in buffer zones for agricultural, settlement, tourism, and other supporting functions within the limits of their carrying capacity. The rehabilitation program, normalization of the Tri Danu area, and waters are carried out in conjunction with an integrated domestic and agricultural waste management program. Participatory pattern and synergy of all components of society and government (across sectors/agencies) according to the role of each lake stakeholder. That is, there is a compromise between socio-economic-ecology as a pattern of community praxis. This will gradually encourage the achievement of quality standards set by the Natural Resources Conservation Agency and of course the sustainability of the Tri Danu area in the future. The revitalization program in the form of traditional socio-ecological-religious praxis in the form of mythology, sacred sites, worship architecture, and rites or symbolic artifacts, needs to explore new meanings according to the present, namely, those that are relevant to socio-economic-ecological patterns that carry the ideology of sustainability. This “irrational” based program also needs to be balanced and accelerated through a conservation program with the latest “rational” based approach, namely, measurable statistical data on water quality conditions and the Tri Danu area, integrated and holistic cross-sectoral programming (conservation agencies, agriculture, settlements, tourism, population, and customs), and community empowerment with other stakeholder commitments in a sustainable manner.

10.4 Conclusions Preservation knowledge contained in a number of myths, the existence of sacred sites, temple architecture, and Tri Danu periodic rites, constitutes cultural capital, social capital, and symbolic capital. The ownership of the three capitals has been converted into economic capital by the settlers in the Tri Danu area. In the traditional era, capital ownership synergized with their habitus and realm of social struggle and formed a praxis that put forward socio-religious-ecology. Things are very different from the perspective of a progressive society with a market ideology. The praxis that occurs only in socio-economic patterns is in understanding the function of the Tri Danu area. The struggle in the realm of today’s life, broadly speaking, there are three ideological-based mindsets and perspectives, namely, conservative, progressive, and adaptive. These three perspectives are based on ecological ideology, market ideology, and sustainability ideology. Compromise and normalization mechanisms are adaptive

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and solutive choices for a middle path for the sustainable preservation of the functions of the Tri Danu area. Community praxis is described in a socio-economic-ecological pattern.

References Armstrong K (2006) A short history of myth. Knopf, Canada Arora A, Pandey M, Mishra VN, Kumar R, Rai PK, Costache R, Punia M, Di L (2021) Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecol Ind 128:107810 Atmaja DM (2019) Conservation of the function of Lake Beratan based on geographic information systems in the Bedugul highlands of Bali. Dissertation. Postgraduate Doctoral Study Program in Economics at Sebelas Maret University (UNS). Barker C (2000) Cultural studies: theory and practice. Sage, London Bourdieu P (2016) The arena of cultural production: a study of cultural sociology. Kreasi Wacana, Yogyakarta Chandrawan IBG (2015) Cosmology of Hindu communities in the Tri Danu area in environmental preservation. Dharmasmrti J 14(27):23–35 Distributor Team (2005) Bali province conservation area. Bali KSDA Unit. Goris R (1986) Translation of Balinese inscriptions I and II. No Publisher Harker R, Mahar C, Wilkes C (2009) (Habitus X Capital) + Domain = Practice. The most comprehensive introduction to the thought of Pierre Bourdieu. Jalasutra, Yogyakarta Haryatmoko. 2016. Dismantling the certainty regime. PT. Canisius, Yogyakarta Juliasih NK, Widyantari AAASS, Arsana IN, Suyoga IPG (2022) The myth of Danu Bulian: source of knowledge for conservation of Lake Buyan, Pancasari, Sukasada, Buleleng, Bali. Proceedings of the 9th international conference on interreligious and intercultural studies (ICIIS). Hindu University of Indonesia Denpasar, Bali, 30th Sept 2022 KLHK (2020) Management plan for Lake Buyan-Tamblingan for Bali province. Directorate General of Watershed Control and Protection Forest. Directorate of Inland Water Damage Control, Jakarta Manuaba IBP (2008) Chlorine-Organic pesticide contamination in Lake Buyan water Buleleng Bali. J Kimia (j Chem) 1(2):39–46 Mishra VN, Prashad R, Rai PK, Vishwakarma AK, Arora A (2018) Evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multisensor remote sensing data. Earth Sci Inf 12(1):71–86. https://doi.org/10.1007/s12145-0180369-z Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management (edit. Book), Nova Science Publishers, USA, ISBN: 978–1–53619–669–6 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. Meteorological Appl 27(1):1–16 https://doi.org/10.1002/met.1863 Purnama SG (2016) Eurotrophication and impact on the surrounding environment: the case at Lake Buyan (module). Public Health Study Program, Faculty of Medicine, Udayana University, Denpasar Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book), Springer Nature, Switzerland, ISBN: 978-3-030-76116-5. https://doi.org/10.1007/978-3-030-76116-5. Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: Sustainable assessment & planning (edit. Book), Springer Nature, Singapore. ISBN: 978-981-16-7373-3. https://doi.org/10.1007/978-981-16-7373-3

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Sudji NW (2015) Bali province Tri-Lake area development policy (Beratan, Buyan, Tamblingan). Proceedings of the Symposium on analysis of the carrying capacity and accommodation capacity of water resources in the Tri-Lake Beratan, cultural, and Tamblingan regions of Bali Province. UPT Plant Conservation Center for Botanical Gardens Eka Karya Bali-LIPI, Denpasar Sulistyanto P, Suwardi S, Tyas P (2018) Study of transported sediment levels and nutrient materials (N and P) in the Tulis River, Central Java. Proceedings of the UMS IX Geography National Seminar. ISBN: 978-602-361-137-9 Suyasa WB, Mahendra MS, Adnyana WS (2017) Report on community socio-economic research and water quality of Lake Buyan, Pancasari Village, Buleleng Regency, Bali. Master of Environmental Science Study Program, Postgraduate Program at Udayana University Denpasar, Denpasar Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India, Ecol Environ Conserv 27(February Suppl. Issue):S260–S268.

Chapter 11

Impact of Land Use and Land Cover in Water Resources Deeksha, Anoop Kumar Shukla , and Nandineni Rama Devi

Abstract The assessment of the influence of land use cover on river water quality is a prerequisite for long-term river basin planning and management. Non-point source (NPS) contamination from agricultural watersheds has recently considerably deteriorated the water quality of major rivers. Due to the intricate interconnections of various impacting elements, water quality management remains a difficulty. Even though the impacts of land use types on water quality have earned a great deal of attention, handful of researchers have tried to define the connection between water body attributes and water quality, and the relationships between water bodies and other land use types on water quality have earned a lot of interest. In this work, we have tried to give an idea on the impact of Land use Land cover (LUCL) on the water resources, along with various modeling and monitoring techniques used for the same. As a result, we tried to put forward various techniques to reduce the impact of various LULC on the water resources along with future scope of work that could bring in value addition in the domain of water resource-related research. People dealing with hydrology, land use designers, watershed supervisors, and decision-makers require precise data on the ecological reaction to land use change in order to plan long-term water management programs and ecosystem services. As a result, policymakers, designers, and users must design methods to ensure long-term viability of the watershed hydrology in order to protect agricultural activities and urban planning and management systems inside the catchment region. Keywords Land use Land cover (LULC) · Water resources · Water quality · Water management Deeksha · A. K. Shukla (B) · N. Rama Devi Manipal School of Architecture and Planning, Manipal Academy of Higher Education, Manipal, Karnataka, India e-mail: [email protected] Deeksha e-mail: [email protected] N. Rama Devi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_11

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11.1 Introduction Many factors within a watershed, particularly climate change and land use can influence the lengthy spatial and temporal variability of hydrological parameters such as runoff water, moisture levels, evaporation and transpiration (ET), groundwater, and streamflow (Deng et al. 2015; Nistor et al. 2017, 2018). Land cover and land use are very unpredictable, especially in developing countries with agricultural economy and rapidly expanding populations (Mishra and Rai 2016; Mishra et al. 2021; Rai et al. 2021, 2022; Sulamo et al. 2021). Land cover refers to the terrain and biophysical aspects of the ground atmosphere, such as plants, water, animals, soil, and constructions due to anthropogenic activity (Lambin et al. 2003). Fast population increase and unequal distribution, inadequate irrigation techniques, rapid urbanization/industrialization, large-scale deforestation, and inappropriate land use practices have resulted in the depletion and degradation of both surface and groundwater resources (Brototi et al. 2022; Giri and Qiu 2016; Jaiswal et al. 2003). The primary impact of land use/cover change is anticipated to influence sub-basin hydrologic responses, water availability, and quality of streamflow. Changes in land use land cover (forest land becoming built-up or agricultural land, for example) have a substantial influence on runoff water, groundwater recharge, erosion, and sediment transfer, according to research (Arora et al. 2021; Piao et al. 2007; Rai et al. 2022; Singh and Rai 2017; Shastri et al. 2020a, b; Singh et al. 2021). Scattered water pollution is challenging to evaluate and regulate as it results from several interconnections among the water cycle and land use and land cover (LULC) patterns instead of a single clearly identified and treated discharge site. As a result, the linkages between LULC patterns and water quality rely on a variety of parameters, including geographical and temporal dimensions (i.e., spatial extent and temporal duration), watershed features, landscape composition and structure, land use intensity, and seasonal fluctuations (Xiao et al. 2016). Understanding the interactions between changes in land use hydrology, and management of water resources is critical because they have a major and profound influence on the quality and quantity of water (Balthazar et al. 2015; Rai et al. 2017). Various land use practices, as an essential feature of anthropogenic activity, can have an impact on water quality, either directly or indirectly (Chang et al. 2021; Lintern et al. 2018; Mishra et al. 2016, 2018a, b). Various land usage types endanger water resources to varying levels, with urban and agricultural areas, are now the main reason for global water contamination. The most prominent sources of diffuse contamination in freshwater systems are agricultural and urban discharges (Ferreira et al. 2019; Kumar Shukla et al. 2018; Shukla et al. 2020). Grassland and woodland reduce water runoff, soil erosion, and consequently contaminants transported into water bodies (Xu et al. 2019). Changes in LULC due to growing water demand, population expansion, and climate change are projected to have a crucial impact on water supplies (Mello et al. 2020). Studies have revealed that urbanized land, like cities as well as farms, could produce higher particles and dissolved substances. Crops, for example, can increase the amount of nitrogen and phosphorus in water,

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but biodegradable contamination can be created by industrial and domestic effluent released from developed land activities. There is a substantial amount of knowledge on the impact of land use practices on water bodies (Mainali and Chang 2018; Ramesh et al. 2019). However, there is currently a paucity of comprehensive understanding of the qualitative and quantitative connections between land use patterns and water utilizing numerous statistical analytic approaches. As a result, to achieve effective watershed management centered on water security, a scientific judgment call process is essential (Azevedo-Santos et al. 2017). Some research looked at the connections among land usage, land cover, and water quality in different types of bodies of water (Chiang et al. 2021; Deng 2020; Wei et al. 2020). Several river quality indicators have been found to be significantly connected with urban land, including dissolved oxygen, chemical oxygen demand, ammonianitrogen, total nitrogen, and total phosphorous, with buffer sizes ranging from 500 to 100 m (Deng 2020). Other land use categories have been discovered to have a major impact on water quality in a variety of ways. For example, chemical oxygen demand, ammonia-nitrogen, total nitrogen, and total phosphorous are considerably adversely connected with forest land, grassland, and marsh; farmed land, on the other hand, is strongly favorably correlated with all of the aforementioned water quality metrics (Wei et al. 2020). Development events, via diverse processes, remain sources of water quality degradation in many water bodies (Camara et al. 2019). This process affecting the water quality is shown in Table 11.1 Agriculture is the largest cause of non-point source pollution that degrades water grade and reduces dissolved oxygen concentrations (Srinivas et al. 2020). Due to its intricate nature (Giri and Qiu 2016), NPS contamination is a major worry. It includes farmland and urban land events, deforestation, and other ecological concerns. From the literature, we understand that there are three types of water resources, (1) saltwater, (2) surface water, and (3) groundwater. In this chapter, we shall look into the impact of land use on surface water. To understand the impact of LULC on surface water, it is critical for us to understand the impact of the LULC through the lens of water quality. This will give us a better picture of the harm that is caused by anthropogenic activities on the water resources. The connections between water resources and land use are intricate (Weatherhead and Howden 2009). Rainfall characteristics, topography, soil, hydrogeological properties, and river flow are all factors in the hydrological procedures which convert net rainfall to river flow and water recharge (Anderson and Burt 1978). Water delivery to groundwater and river channels in any given catchment is governed by a number of different processes. The resultant stream flows might include features from several hydrological flow routes, such as direct runoff, shallow through-flow with a few-day holding time, and groundwater base flow with a dwelling period of decades or longer. An essential and complex role in this interaction is played by spatial and temporal variability. Strong small-scale differences in water quantity and quality are caused by the spatial variation of topography, geology, soils, and land use, whereas floods and droughts are caused by the temporal heterogeneity of these factors.

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Table 11.1 Crucial processes affecting water quality Category

Parameters

Significant process within Water body affected water body

Physical

Temperature, electrical conductivity (EC), total suspended solids (TSS), turbidity, total dissolved solids (TDS) etc.

Gas exchange with atmosphere

Mostly rivers and lakes

Volatilization

Mostly rivers and lakes

Chemical

Biological

Heavy metals, pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), nitrate, etc.

Coliform bacteria, sea urchin etc.

Absorption/ desorption

All water bodies

Heating and cooling

Mostly rivers and lakes

Diffusion

Lakes and groundwater

Photodegradation

All water bodies

Acid-base reactions

All water bodies

Redox reactions

All water bodies

Dissolution of particles

All water bodies

Precipitation of minerals

All water bodies

Ionic exchange

Groundwater

Primary production

Surface water

Microbial die-off and growth

All water bodies

Decomposition of organic Mostly rivers and lakes matter Hydrological Water level depth (m), velocity (m/s), flow direction. Discharge, etc.

Bioaccumulation

Mostly rivers and lakes

Dilution

All water bodies

Evaporation

Surface water

Percolation and leaching

Groundwater

Suspension and sitting

Surface water

11.1.1 Methods Used to Assess Water Resources From the literature, we understand that the direct impact of LULC changes is seen in water resources wherein, it is measured through the lens of the quality of the water. We have various models to measure the quality of water. A significant portion of the fundamental information needed to comprehend and model surface water movement is becoming more accessible. Even a few years ago, it would have been impossible to produce topographical and land use detail using aerial photography and satellite imagery. Ground truth checking, however, is still necessary and costly. There are plenty of surface flows and generally reliable historical data on the weather, but very few long-standing archives of water quality and very few small-scale observations. It is mainly challenging to quantify and validate aquifer properties. The majority of soil data comes from surveys that were conducted decades ago. As a result, modeling outcomes frequently rely on significant interpolation and assumptions. Understanding and calculating the uncertainty is crucial.

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Experimental research has typically been conducted at a small scale. This implies that its findings are likely to consider small context factors instead of landscapewide characteristics. At a broader scale, the different local traits are merged into a complicated mosaic and homogenized into the watershed reaction. The size of the person or the group influence, as well as the extent in which it is related to the stream channel across the landscape, define how much any component or combination of components influences the overall reaction. Numerous findings offer dependable magnitude estimates of individual or group impacts. The tags “land use” and “land management” need to be clearly distinguished from one another. Many worries about the quantity and quality of water resources that are sustainable relate only tangentially to specific land uses and are more directly related to land management techniques. Although the distinction is subtle, it is nonetheless significant. There are various factors affecting the water quality such as the impact of agricultural activities and the impact of urbanization (Giri and Qiu 2016). To measure these impacts, we have indices to measure the quality of water contamination potential index (CPI), it is a measure used to quantify the impact of any action that generates toxic or hazardous waste, and it is calculated as the product of waste material amount and hazard index (Seeboonruang 2012). Water quality index (WQI), is a quantitative indicator for assessing water quality. It provides a single score by merging the subindex values of each worry pollutant (Fox 2014). Figure 11.1 shows the schematic diagram of the water quality assessment. There are indices used to process explanatory variables that are used to determine the water quality such as Shannon’s diversity index (SHDI) is a common indicator used to analyze the impact of land use on water quality, with the potential to examine landscape variability through time (Huang et al. 2013). Landscape development intensity (LDI) index, using this index the severity of land use disturbances within a certain area is measured (Carey et al. 2011).

Fig. 11.1 Framework showing steps involved in water quality assessment (Giri and Qiu 2016)

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Water quality is often assessed in three ways. (i) Hydrological/Water quality modeling, (ii) Statistical modeling, and (iii) monitoring are all examples of modeling. These methods are frequently used in regulatory/policymaking, planning, and experimental reasons.

11.1.1.1

Hydrological/Water Quality Modeling

Soil and Water Assessment Tool (SWAT), Hydrological Simulation Program-Fortran (HSPF), Long-Term Hydrologic, and Nonpoint Source models are examples of hydrologic/water quality models. To analyze water quality on a watershed scale, the Pollution (T-THIA) and Agricultural Nonpoint Source (AGNPS) models were utilized. SWAT has been used extensively around the world to examine water quality effects because of its superior predictive power when compared to others.

11.1.1.2

Statistical Modeling

It is a straightforward and easy-to-understand strategy that produces satisfactory outcomes. This encourages more academics and scientists to employ this technique rather than sophisticated watershed models that require extensive input data to enable model parametrization, calibration, and validation. The techniques generally used for statistical modeling are Ordinary least squares (OLS), Geographic weighted regression (GWR), Bayesian hierarchical linear regression (BHLR), Stepwise multiple regression, Linear mixed effect model, Partial least square regression (PLSR), and, Principal component analysis (PCA).

11.1.1.3

Monitoring

Monitoring stream water quality data isn’t just critical for a sustainable system, it also provides knowledge that enables the early deployment of corrective actions for a sustainable environmental system. Even though, monitoring is costly, time intensive, and ineffectual over broader areas. In the event of a broader research area, such as a watershed, modeling is recommended over monitoring. It is critical to use proper explanatory factors when building statistical models among water quality and explanatory variables. In recent years, many strategies for identifying appropriate explanatory variables have been developed. Non-spatial land use metrics, measures to depict landscape changes and their influence on water quality. Inverse distance weighted method, when measuring stream integrity, the landscape metric closest to the stream is given more weight than the landscape value farther away from the stream. Riparian zone approach, as it connects the terrestrial environment to the aquatic ecology, it plays a key role in regulating stream health (Gregory et al. 1991). Hydrological sensitive area is a small part of the watershed that

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actively contributes to runoff (Qiu 2009). Critical source area is a location in a watershed where a substantial volume of pollution is generated from a small hydrologically sensitive region.

11.2 Results and Discussions Researchers used various techniques in assessing and understanding the relationship between land use change and the water quality. Figure 11.2 shows the collective findings demonstrated unequivocally that land use activities have a major influence on water quality. Changes in land use have a significant impact on water and solute fluxes at the catchment scale, but the specific hydrological processes that are significant in the time and character of water quantity and quality distribution to the stream channel will be governed by the watershed. Runoff rates and chemical weathering can both be accelerated by variations in land use and management. Given their potential to occur quickly, especially in the presence of governing, market, or governmentfunded incentives, it is recommended that these variations were potentially additional significant in manipulating catchment responses than climate changes.

11.2.1 Effects on Catchment Yield Two mechanisms, including high rates of canopy interception evaporation and amplified soil water storage capacity, are used by mature forests to reduce peak flows. According to researchers, afforestation may have an attenuating effect on small

Fig. 11.2 Land use affecting the water quality

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rainfall events at the catchment scale, but the attenuation is likely to be minimal for large-scale events in large afforested catchments. Increased evapotranspiration caused by the expansion of energy crops would be detrimental to lowland rivers and groundwater resources.

11.2.2 Effects on Infiltration There are numerous states that have experienced numerous severe floods in the past ten years, which have an impact on water resources as well as other things. The size and scope of these floods have been attributed to agricultural practices, and the effects of agriculture on soil structure and runoff processes have significantly decreased the capacity of the land to store water, increasing runoff rates and the risk of flooding. Recharge to aquifers and subsequently groundwater resources is decreased by increased runoff and consequently decreased infiltration. Intensive animal grazing can alter the physical qualities of soil. Stocking levels that are higher have been associated with reduced soil permeability, porosity, and hydraulic properties, as well as higher soil bulk density. Intensive pasteurization and accompanying soil deterioration have been connected to increased runoff charges at the plot size, and permanent grassland infiltration rates have been demonstrated to be particularly vulnerable to cow grazing throughout the winter months. Furthermore, grazing or conversion of shrubland into better grass reduces plant height and biomass, resulting in decreased water interception, root depth, and soil porosity, which may increase runoff rates. According to the researchers, forest and tree shelter belts can assist, minimize overland flow, and boost infiltration rates.

11.2.3 Effects on Dissolved Organic Carbon and Water Color in Upland Drainage Waters The amounts of Dissolved organic carbon (DOC) are crucial in management of water resources. Removal of DOC from freshwater could add significantly to the expense of water purification from diverse sources. Improper DOC elimination results in low visual quality water, increases the danger of biological contamination of treated water, and can lead to the formation of trihalomethanes, which are potential carcinogens. A number of studies have found long-term increases in DOC concentrations in a variety of sub-boreal situations. Changes in DOC concentrations have been connected to land management changes. However, varied land management strategies have happened in different places where DOC concentrations may be mapped. This shows that no one management measure could have resulted in such broad DOC concentration increases.

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11.2.4 Effects on Nutrient Transfer to Rivers and Groundwaters For several decades, rising nitrate concentrations in rivers and groundwaters have been a source of worry throughout the developing world. A large scientific effort has been launched in recent years to assess nitrogen fluxes from farmland regions and their influence on surface and groundwater. Despite the fact that measures to decrease nitrogen transfers from land to water have been in place for almost 20 years, there have been few investigations evaluating their effectiveness. To evaluate the performance of specific Nitrate Vulnerable Zones (NVZ)s, in-field measurements of nitrate concentrations in soil water were combined with a transport model to determine the overall impact. Several limitations of these studies in assessing the effectiveness of NVZs have recently been identified by researchers, including: the lack of statistical testing to confirm effectiveness; the lack of control data; the short time period considered by the studies, which would not be sufficient to capture effects on groundwater nitrate concentrations; and the lack of controls relative to the range of explanatory variables. As a result, the success or ineffectiveness of NVZs may be overlooked, and the appropriate route to implementation cannot be determined based on the studies. It is empirically proved that land use modifications might be beneficial. According to one research (Lovett et al. 2006), major conversion of arable land to woods might lower nitrate concentrations in surface and groundwater by up to 30% by 2030. The financial expenses to farmers, however, would be many times more than the current intake treatment expenditures. Several researches have been conducted to solve this water issue. Some employed a physically based SWAT model, while others used statistical models such as multiple linear regression, spatially weighted regression, ordinary least squares, spatial lag model, and spatial error model to evaluate land use behavior in terms of pollution generation. Since topography and soil qualities stay stable over extended periods of time, climate and LULC are always viewed as the primary determinants of changes in hydrological parameters. Changes in precipitation volume and intensity have a direct influence on the hydrological parameters of the watershed, which in turn has an indirect impact on surface runoff. The quantity of contaminants in river water is greatly influenced by seasonal runoff. The impact of environmental variables and LULC on watershed water quality vary seasonally and spatially. The buffer scale gives information on the processes that occur inside a certain location, which might vary depending on surface conditions and activities throughout the year (Liu et al. 2013). Many academics have stated that farmland has a detrimental influence on water quality, but forest grassland can greatly enhance water quality (Hanief and Laursen 2017; Zhang et al. 2019). The nutrients accumulated by non-point agricultural operations are the key causes of boosting nutrient loadings to watersheds, resulting in an increased degree of eutrophication in the aquatic environment (Somura et al. 2012). More than 70% of the nitrogen and

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phosphorus transported to watersheds comes from various agricultural operations (Yang et al. 2018). Land use type has a stronger influence on surface water quality than environmental variables, showing that manmade activities have a greater impact on water quality than natural causes (Wang et al. 2022). Measures in agricultural production should be performed in line with local conditions. Agricultural production activities should be established beyond the optimal buffer zone to enhance monitoring water interventions and limit sewage outflow from fields. In addition, landscape pattern planning should be addressed to improve water quality in the watershed. The future scope of the work should also look into both mega scale and mesoscale impact of LULC on water resources, as the difficulties include uncertainty in causeeffect linkages as a result of the complexity of hydrological, climatic, biogeochemical, and anthropogenic processes occurring in time and place, which frequently results in an insufficient collection of water quality and land management information. These limits are exacerbated by the prolonged time periods required to establish patterns and account for temporal delays in water quality response.

11.3 Future Water Demands Changes in water quality are less responsive to climate change than changes in land use (Khoi et al. 2019). The majority of these factors are influenced by farming in highland areas. It is difficult to reduce massive nutrient loadings into the lake in a short period of time, especially in rural regions, because this involves the employment of sophisticated agricultural equipment and financial investment in building infrastructure, as well as behavioral changes among locals. Furthermore, the insufficient data for river water quality are a noteworthy concern for calculating intraseasonal fluctuations in water quality or identifying specific pollution sources. Thus, when addressing water resource management at the regional and sub-watershed stages, more regular water quality monitoring should be done. Nonetheless, it is obvious that enacting acceptable modifications in land use plans for specific local conditions would be critical in addressing water quality challenges, particularly during the rainy season, which can have excess outflow and high rainfall intensity. Landscape metrics are frequently used to represent water quality. According to many researches, nonpoint source pollution from agricultural regions is intimately connected to landscape structure (He et al. n.d.), and changes in nutrient concentrations and stream biological state may be described by landscape metrics (Alberti et al. 2007). Five consequences of agricultural practice modification should be examined or expected to improve the scientific information and knowledge generated by investigations (Melland et al. 2018). To begin, the investigations should identify practice change scenarios that are likely to be unsuccessful for certain metrics or losses along specific routes. Retrofitting the appropriate measure(s) to site-specific losses through known routes based on site-specific knowledge can improve water quality. Second, if changes in practice enhance water quality, the extent to which water quality objectives are likely to be met should be investigated. Third, because the monitoring duration

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and location of monitoring required rely on the parameter or indicator of improved water quality, the temporal and spatial scale of efficacy of a practice change scenario should be calculated. A fourth scientific advice is to investigate the possibility of pollutant exchange. Finally, in order to make informed judgments regarding modifying practices, the cost-benefit ratio of making practice modifications should be evaluated.

11.4 Conclusion Growth during the past years, considerable improvement has been made in the assessment and interpretation of LULC changes. Nevertheless, a great deal needs to be understood in order to accurately analyze and estimate the prospective impact of LULC change in the performance of the terrestrial ecosystem, as well as establish prerequisites for effective land use. With the improvement of processing capabilities and the accessibility of spatiotemporal data, modeling hydrology has become appealing tool for examining and analyzing the features of watersheds and how the catchment’s hydrological system performs under various LULC patterns. Hydrological modeling is a powerful method for examining relationships between watershed elements and analyzing hydrological behavior to LULC change at different geographical and time scales. In water-stressed locations, land uses that enhance evapotranspiration or fast runoff should be avoided in order to protect useable water supplies. LULC changes or agricultural practices that create runoff, floods, or soil erosion at the cost of groundwater and summertime low water levels should be avoided. As a result, promoting infiltration is indeed an essential goal in LULC planning. River basin sensitive agriculture approaches help to safeguard water systems by preserving the structure of the soil and therefore, infiltration capacity, as well as water quality by reducing dispersed contamination. Other measures, such as cross-compliance requirements, agro-based programs, and more broad guidance supply, as well as legislation, when necessary, can help to raise land administrators’ understanding of soil management approaches. The influence of climate change and model unreliability be researched more to assist policymakers in addressing developing ecological consequences in the future. Nevertheless, it is apparent that disputes can still exist among several aims and regulations influencing LULC and water resources, such as conservation of the environment, freshwater accessibility, climate change prevention and adaptation, crop production, healthier food goals, and livelihoods. The goal should be to manage opposing needs in a manner that is agreeable to all parties. Investing in information gathering and verification is becoming increasingly important in ensuring that policy and choices are evidence-based.

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References Alberti M, Booth D, Hill K, Coburn B, Avolio C, Coe S, Spirandelli D (2007) The impact of urban patterns on aquatic ecosystems: an empirical analysis in Puget lowland sub-basins. Landscape Urban Plan 80(4):345–361. https://doi.org/10.1016/j.landurbplan.2006.08.001 Anderson MG, Burt TP (1978) The role of topography in controlling throughflow generation. In Earth Surf Process 3:331–344 Arora A, Pandey M, Mishra VN, Kumar R, Rai PK, Costache R, Punia M, Di L (2021) Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecol Ind 128:107810 Azevedo-Santos VM, Fearnside PM, Oliveira CS, Padial AA, Pelicice FM, Lima DP, Simberloff D, Lovejoy TE, Magalhães ALB, Orsi ML, Agostinho AA, Esteves FA, Pompeu PS, Laurance WF, Petrere M, Mormul RP, Vitule JRS (2017) Removing the abyss between conservation science and policy decisions in Brazil. Biodivers Conserv 26(7):1745–1752. https://doi.org/10.1007/s10 531-017-1316-x Balthazar V, Vanacker V, Molina A, Lambin EF (2015) Impacts of forest cover change on ecosystem services in high Andean mountains. Ecol Ind 48:63–75. https://doi.org/10.1016/j.ecolind.2014. 07.043 Brototi B, Singh A, Rai PK, Kumar J, Walker S (2022) GIS based study of reclamation of degraded semi-arid soil: a case study of Rajsthan from India. Indian J Environ Prot 42(3):302–315 Camara M, Jamil NR, Abdullah AF bin (2019) Impact of land uses on water quality in Malaysia: a review. Ecological process 8(1). https://doi.org/10.1186/s13717-019-0164-x Carey RO, Migliaccio KW, Li Y, Schaffer B, Kiker GA, Brown MT (2011) Land use disturbance indicators and water quality variability in the Biscayne Bay Watershed, Florida. Ecol Ind 11(5):1093–1104. https://doi.org/10.1016/j.ecolind.2010.12.009 Chang H, Makido Y, Foster E (2021) Effects of land use change, wetland fragmentation, and best management practices on total suspended sediment concentrations in an urbanizing Oregon watershed, USA Chiang LC, Wang YC, Chen YK, Liao, CJ (2021) Quantification of land use/land cover impacts on stream water quality across Taiwan. J Clean Prod 318. https://doi.org/10.1016/j.jclepro.2021. 128443 De Mello K, Taniwaki RH, de Paula FR, Valente RA, Randhir TO, Macedo DR, Leal CG, Rodrigues CB, Hughes RM (2020) Multiscale land use impacts on water quality: assessment, planning, and future perspectives in Brazil. J Environ Manag 270. https://doi.org/10.1016/j.jenvman.2020. 110879 Deng X (2020) Influence of water body area on water quality in the southern Jiangsu Plain, eastern China. J Clean Prod 254. https://doi.org/10.1016/j.jclepro.2020.120136 Deng Z, Zhang X, Li D, Pan G (2015) Simulation of land use/land cover change and its effects on the hydrological characteristics of the upper reaches of the Hanjiang Basin. Environ Earth Sci 73(3):1119–1132. https://doi.org/10.1007/s12665-014-3465-5 Ferreira P, van Soesbergen A, Mulligan M, Freitas M, Vale MM (2019) Can forests buffer negative impacts of land-use and climate changes on water ecosystem services? The case of a Brazilian megalopolis. Sci Total Environ 685:248–258. https://doi.org/10.1016/j.scitotenv.2019.05.065 Fox DR (2014) Probability weighted indices for improved ecosystem report card scoring. Environmetrics 25(5):351–360. https://doi.org/10.1002/env.2285 Giri S, Qiu Z (2016) Understanding the relationship of land uses and water quality in twenty first century: a review. J Environ Manag 173:41–48. https://doi.org/10.1016/j.jenvman.2016.02.029 Gregory S, Swanson FJ, Mckee WA, Cummins KW (1991) An ecosystem perspective of Riparian Zones focus on links between land and water. BioSci 42:540–541 Hanief A, Laursen AE (2017) SWAT modeling of hydrology, sediment and nutrients from the grand river, Ontario. Water Qual Res J 52(4):243–257. https://doi.org/10.2166/wqrj.2017.014 He C, Malcolm SB, Dahlberg KA, Fu B (n.d.) A conceptual framework for integrating hydrological and biological indicators into watershed management

11 Impact of Land Use and Land Cover in Water Resources

229

Huang J, Zhan J, Yan H, Wu F, Deng X (2013) Evaluation of the impacts of land use on water quality: a case study in the Chaohu lake basin. Sci World J. https://doi.org/10.1155/2013/329187 Jaiswal RK, Mukherjee S, Krishnamurthy J, Saxena R (2003) Role of remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development—an approach. Int J Remote Sens 24(5):993–1008. https://doi.org/10.1080/01431160210144543 Khoi DN, Nguyen VT, Sam TT, Nhi PTT (2019) Evaluation on effects of climate and land-use changes on streamflow and water quality in the La Buong River Basin, Southern Vietnam. Sustainability (Switzerland) 11(24). https://doi.org/10.3390/SU11247221 Kumar Shukla A, Shekhar Prasad Ojha C, Mijic A, Buytaert W, Pathak S, Dev Garg R, Shukla S (2018) Population growth, land use and land cover transformations, and water quality nexus in the Upper Ganga River basin. Hydrol Earth Syst Sci 22(9):4745–4770. https://doi.org/10.5194/ hess-22-4745-2018 Lambin EF, Geist HJ, Lepers E (2003) Dynamics of land-use and land-cover change in tropical regions. Annual Rev Environ Res 28:205–241. https://doi.org/10.1146/annurev.energy.28.050 302.105459 Lintern A, Webb JA, Ryu D, Liu S, Bende-Michl U, Waters D, Leahy P, Wilson P, Western AW (2018) Key factors influencing differences in stream water quality across space. Wiley Interdisc Rev Water 5(1). https://doi.org/10.1002/WAT2.1260 Liu ZP, Shao MA, Wang YQ (2013) Spatial patterns of soil total nitrogen and soil total phosphorus across the entire Loess Plateau region of China. Geoderma 197–198:67–78. https://doi.org/10. 1016/j.geoderma.2012.12.011 Lovett AA, Hiscock KM, Dockerty TL, Saich A, Sandhu C, Johnson PA, Sünnenberg G, Appleton KJ (2006) Assessing land-use scenarios to improve groundwater quality: a Slea catchment study. www.environment-agency.gov.uk Mainali J, Chang H (2018) Landscape and anthropogenic factors affecting spatial patterns of water quality trends in a large River Basin, South Korea. J Hydrol 564:26–40. http://www.elsevier. com/open-access/userlicense/1.0/ Melland AR, Fenton O, Jordan P (2018) Effects of agricultural land management changes on surface water quality: a review of meso-scale catchment research. Environ Sci Policy 84:19–25. https:/ /doi.org/10.1016/j.envsci.2018.02.011 Mishra V, Rai PK (2016) A remote sensing aided multi-layer perceptron-marcove chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9(1):1–18. https://doi.org/10.1007/s12517-015-2138-3. Mishra VN, Rai PK, Kumar P, Prashad R (2016) Evaluation of land use/land covers classification accuracy using multi-temporal remote sensing images. Forum Geogr (Romania) 15(1):45–53 Mishra VN, Prashad R, Rai PK, Vishwakarma AK, Arora A (2018a) Evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multisensor remote sensing data. Earth Sci Inf 12(1):71–86. https://doi.org/10.1007/s12145-0180369-z Mishra VN, Rai PK, Rajendra P, Puniya M, Nistor MM (2018b) Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models. Appl Geomatics (Springer). ISSN No. 1866–9298. https://doi.org/10.1007/s12518-018-0223-5 Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management (edit. Book), Nova Science Publishers, USA. ISBN: 978–1–53619–669–6 Nistor MM, Petrea D, Rai PK, Dezsi S, Hognogi G (2018) Climate change impact on crop evapotranspiration in Turkey during 21st century. Meteorolol Appl 26(3):442–453. https://doi.org/ 10.1002/met.1774 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

230

Deeksha et al.

Piao S, Friedlingstein P, Ciais P, de Noblet-Ducoudre N, Labat´D, Zaehle S¨nke (2007) Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends. PNAS 104:15242–15247. Qiu Z (2009) Assessing critical source areas in watersheds for conservation buffer planning and riparian restoration. Environ Manage 44(5):968–980. https://doi.org/10.1007/s00267-0099380-y Ramesh T, Bolan NS, Kirkham MB, Wijesekara H, Kanchikerimath M, Srinivasa Rao C, Sandeep S, Rinklebe J, Ok YS, Choudhury BU, Wang H, Tang C, Wang X, Song Z, Freeman OW (2019) Soil organic carbon dynamics: impact of land use changes and management practices: a review. Adv Agron 156:1–107. https://doi.org/10.1016/bs.agron.2019.02.001 Rai PK, Chaubey PK, Mohan K, Singh, P (2017) Geoinformatics for assessing the inferences of quantitative drainage morphometry of the Narmada Basin in India. Appl Geomatics (Springer), 1–23. https://doi.org/10.1007/s12518-017-0191-1 Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book), Springer Nature, Switzerland, ISBN: 978–3–030–76116–5. https://doi.org/10.1007/978-3-030-76116-5 Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi.org/10.1007/978-981-16-7373-3 Seeboonruang U (2012) A statistical assessment of the impact of land uses on surface water quality indexes. J Environ Manag 101:134–142. https://doi.org/10.1016/j.jenvman.2011.10.019 Shastri S, Singh P, Rai PK (2020a) A Land covers change dynamics and their impacts on thermal environment of Dadri Block, Gautam Budh Nagar, India. J Landscape Ecol 13(2):1–13 Shastri S, Singh P, Verma P, Rai PK, Singh AP (2020b) Assessment of spatial changes of land use/ land cover dynamics, using multi-temporal landsat data in Dadri block, Gautam Buddh Nagar. India. Forum Geogr 19(1):72–79. https://doi.org/10.5775/fg.2020.063.i Shukla AK, Ojha CSP, Garg RD, Shukla S, Pal L (2020) Influence of spatial urbanization on hydrological components of the upper Ganga River Basin, India. J Hazard Toxic Radioactive Waste 24(4). https://doi.org/10.1061/(asce)hz.2153-5515.0000508 Singh S, Rai PK (2017) Application of earth observation data for estimation of changes in land trajectories in Varanasi District, India. J Landscape Ecol 11(1):5–18. ISSN: 1805–4196. https:/ /doi.org/10.1515/jlecol-2017-0017 Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India. Ecol Environ Conserv 27(February Suppl. Issue):S260–S268 Somura H, Takeda I, Arnold JG, Mori Y, Jeong J, Kannan N, Hoffman D (2012) Impact of suspended sediment and nutrient loading from land uses against water quality in the Hii River basin, Japan. J Hydrol 450–451:25–35. https://doi.org/10.1016/j.jhydrol.2012.05.032 Srinivas, R, Singh AP, Dhadse K, Garg C (2020) An evidence based integrated watershed modelling system to assess the impact of non-point source pollution in the riverine ecosystem. J Cleaner Prod 246. https://doi.org/10.1016/j.jclepro.2019.118963 Sulamo MA, Kassa AK, Roba NT (2021) Evaluation of the impacts of land use/cover changes on water balance of bilate watershed, rift valley basin, ethiopia. Water Pract Technol 16(4):1108– 1127. https://doi.org/10.2166/wpt.2021.063 Wang W, Liu C, Zhang F, Leong Tan M, Shi J, Zhang Z, Duan P, te Kung H, Xin H (2022) Evaluation of impacts of environmental factors and land use on seasonal surface water quality in arid and humid regions using structural equation models. Ecol Ind 144. https://doi.org/10.1016/j.ecolind. 2022.109546 Weatherhead EK, Howden NJK (2009) The relationship between land use and surface water resources in the UK. Land Use Policy 26(SUPPL. 1). https://doi.org/10.1016/j.landusepol.2009. 08.007

11 Impact of Land Use and Land Cover in Water Resources

231

Wei W, Gao Y, Huang J, Gao J (2020) Exploring the effect of basin land degradation on lake and reservoir water quality in China. J Clean Prod, 268. https://doi.org/10.1016/j.jclepro.2020. 122249 Xiao R, Wang G, Zhang Q, Zhang Z (2016) Multi-scale analysis of relationship between landscape pattern and urban river water quality in different seasons. Scientific Reports, 6. https://doi.org/ 10.1038/srep25250 Xu G, Ren X, Yang Z, Long H, Xiao J (2019) Influence of landscape structures on water quality at multiple temporal and spatial scales: a case study of Wujiang River watershed in Guizhou. Water (Switzerland), 11(1). https://doi.org/10.3390/w11010159 Yang Q, Almendinger JE, Zhang X, Huang M, Chen X, Leng G, Zhou Y, Zhao K, Asrar GR, Srinivasan R Li X (2018) Enhancing SWAT simulation of forest ecosystems for water resource assessment: a case study in the St. Croix River basin. Ecol Eng 120:422–431. https://doi.org/ 10.1016/j.ecoleng.2018.06.020 Zhang J, Li S, Dong R, Jiang C, Ni M (2019) Influences of land use metrics at multi-spatial scales on seasonal water quality: a case study of river systems in the three gorges reservoir area, China. J Clean Prod 206:76–85. https://doi.org/10.1016/j.jclepro.2018.09.179

Chapter 12

An Assessment and Management of Ecotourism Based on Water and LULC: A Geospatial Approach of Jodhpur, Rajasthan, India Rajeev Singh Chandel , Praveen Kumar Rai , Shruti Kanga , and Renuka Singh

Abstract Ecotourism is defined as a visit to natural and cultural heritage places that should be done to protect the environment, benefit the local community, and consist of both education and interpretation. There is always something for everyone in terms of cultural, traditional, and natural beauty when it comes to ecotourism. Sustainable management and the geospatial process provide broad scope to enhance tourism and its aspects to benefit current and future generations. The main objective is to examine how the GIS-based tourism and management planning creates a new road map for sustainable development for the tourism sector, also knowing the role of government and other stakeholders. (a) Examining the function of GIS in managementbased planning as a tool is key to a sustainable future. (b) Mapping of major utilities and creating a favorable environment for travel and tourism. (c) Assessing the ecotourism based on water and LULC, also analyzing the impact of tourism on water. Accomplishing these objectives, it consists of five main stages collection, extraction, processing, analysis, and result. The collection stage is an arrangement of raw data for further processing, whether primary or secondary. Extraction includes all the data useful in defined limits, whether spatial or non-spatial. The processing stage generates thematic maps from the data extracted from different sources. These R. S. Chandel (B) C3WR, Suresh Gyan Vihar University, Jaipur, Rajasthan, India e-mail: [email protected] P. K. Rai Department of Geography, Khwaja Moinuddin Chishti Language University, Lucknow, India S. Kanga Suresh Gyan Vihar University, Jaipur, Rajasthan, India e-mail: [email protected] R. Singh Department of Architecture, Planning and Design, IIT-BHU, Varanasi, Uttar Pradesh, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_12

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thematic maps play crucial roles in further analysis of GIS, management-based planning, and tourism-based solutions. Analysis of different maps gives a new prospect of thinking about the tourism sector in a sustainable way that what is already done, what resources we have, how much time is needed, what needs to be done, and what the future requirements are. The resulting stage clearly shows the implementation of the GIS and management planning outcome possibilities. The goals of ecotourism and tourism are similar in that they aim to connect environmental goals with economic and rural development. Ecotourism provides travellers with instructive and novel experiences, and it must be created and maintained in an ecologically conscious manner that protects the environment. GIS and management planning tool is an effective and efficient tool to satisfy the current and future needs of the tourism sector. Ecotourism development can help Jodhpur to build a distinct identity for heritage tourism. Technology integration and operations based on database, such as query creation and spatial representation, yields a variety of map types. Keywords Management planning · Sustainability · GIS · Ecotourism · LULC · Utility mapping · Water

12.1 Introduction Tourism based on nature is now one of the world’s rapidly growing sectors of the tourism industry (Arnegger et al. 2010). Tourism is “mainly concerned with the direct pleasure of some reasonably undisturbed natural event (Burton 1998).” Rajasthan is one of India’s most famous tourist destinations, with a significant place on the international tourism map for its heritage tourist attractions. It features a wide range of tourist attractions that attract both domestic and international tourists (Patuelli et al. 2016). But due to pandemic, state already suffered a lot in terms of food, clothes, and shelter after 2019. It also affects the tourism on a wide scale (Kim et al. 2015). To restore the previous situation, substantial efforts are being made by the state and central governments to boost tourism in the state, as it has enormous potential to produce jobs and wealth for the people (Scheyvens and Momsen 2008). The state has wide possibilities for ecotourism development (Tiwari et al. 2021). The state has 27 wildlife sanctuaries, 15 conservation reserves, and three national parks (Mondal et al. 2013). Besides that, four Biological Parks have been established in Udaipur, Kota, Jaipur, and Jodhpur (Chandel and Kanga 2020). Due to immense potential to redevelop the tourism sector on its own on the basis of cultural, traditional, and heritage values, Jodhpur is taken as a study area. Ecotourism is an economically, socially, and ecologically sustainable activity that connects tourists with natural and cultural landscapes in a true and consistent manner, resulting in positive exchanges between these landscapes, the community, and the visitor (Wearing 2009). It has been presented as a method to handle expanding numbers of tourists desiring a truly environmental tourism experience while lowering expenditures and optimizing the advantages related to natural area tourism under the wide banner of nature-based

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Fig. 12.1 Strategic planning for ecotourism (Mobaraki et al. 2014)

tourism (Chan and Bhatta 2021; Kanga et al. 2014). There are several definitions and types of ecotourism, but in its most accepted term, it refers to explore the natural areas which include local community, generate revenue to protect surrounding environment, contribute in the preservation of diversity by reducing visitor impact, and publicizing tourist education (Spenceley and Meyer 2012). In this way, governments and the tourist industry promote ecotourism (a type of alternative tourism) as a sustainable alternative to mass tourism (Fig. 12.1). Ecotourism is defined as a visit to natural places that should be done to protect the environment, benefit the local community, and consist of both education and interpretation (Chawla and Cushing 2007). The primary difference between tourism and ecotourism is the depth of interaction with nature (Buultjens et al. 2010); tourism is unconcerned with the well-being of local people or environmental conservation, whereas ecotourism aims to have the least possible influence on both people and the environment. Ecotourism may also encourage small communities to create jobs and improve educational opportunities (Stone and Wall 2004). Sustainable tourism is described as “tourism that meets tourists present needs, the tourism industry, and host communities without jeopardising future generations’ capacity to meet their own needs” (KC et al. 2021). Ecotourism makes efficient use of and conserves resources to ensure long-term survival (Wondirad et al. 2020). In essence, sustainable ecotourism entails minimizing negative consequences on the environment while maximizing beneficial outcomes for the visitors. On the other hand, sustainable tourism may be perceived as both type of sustainable development (i.e., development as a process) and a mechanism to accomplish the latter (i.e., development as a goal).

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12.2 Objectives of the Study (a) Examining the function of GIS in management-based planning as a tool is a key to a sustainable future. (b) Mapping of major utilities and creating a favorable environment for travel and tourism. (c) Assessing the ecotourism based on water and LULC, also analyzing the impact of tourism on water.

12.3 Study Area Description Rajasthan is a state in the northwest part of India famous for its legacy, culture, desert, sand dunes, varied wildlife, and lush forests. The geographical topography and climate in most districts of Rajasthan do not depend on agriculture; the tourist sector is regarded as a critical business in the state. The best example is adopting the “Padharo Mhare Desh” promotional slogan, which means “Welcome to My Land, Rajasthan” (Rathore and Sharma 2021). When it comes to heritage tourism, Jodhpur is one of the principal districts of Rajasthan, has a population of 3,685,681 as per census 2011, with a total area of 22,850 sq. km. in total Fig. 12.2. Jodhpur, known initially as Marwar, the Rajput monarchs’ capital, has a rich history of numerous customs still practiced today. It is a vibrant city. It pulsates with the romance, beauty, splendor, and warmth of a thriving desert metropolis that has witnessed civil wars, love relationships, intrigues, and displays of power and money. Traditional folk dance and music performances, which take place at night, are popular with visitors. It has excellent air, road, and train connections to the rest of Rajasthan. On the outskirts of the enormous “Thar desert,” this city is steeped in the state’s rich

Fig. 12.2 Location map of Jodhpur District, Rajasthan, India

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history and tradition. The Rathore lords constructed the city in 1459 AD. It contains a large Mehrangarh fort from the fifteenth century. The city’s lone twentieth-century palace is the stately Umaid Bhavan Palace (and hotel). The exquisite white marble royal cenotaphs at Jasvant Thada and Mandore, the old capital of Marwar, with its cenotaphs and gardens, are other noteworthy. The Osiyan hamlet, about an hour’s drive from the capital, is another rising destination, with 15 finely carved Jain and Brahmanical temples dating from the fifth century. Jodhpur, one of Rajasthan’s significant districts, is located in the state’s western region. The city’s average elevation above mean sea level is 250–300 m. It has a geographical area of 22,850 square kilometers and is located between the northern and eastern hemispheres. It is surrounded by four main districts, Nagaur district on the east, Jaisalmer district on the west, Bikaner district on the north, and Pali on the south. Relief structures represent the topographical characteristics of the region. The alluvial plains, a physiographic unit in Jodhpur, are divided into three main physiographic units; sand dunes, escarpments, and ridges. Sand dunes may be found in the western and north-western parts of the area. In the district, there is no year-round river. The Luni and Mithri rivers, which flow through the region near Bilara, are the only rivers in the area. Wells and tube wells are the primary sources of irrigation, aside from precipitation. Limestone, sandstone, construction stone, stone slabs, quartz, flagstones, and clays of various colors, dolomite, and red color sandstone of the Jodhpur district are among the most famous and abundant minerals found in the district. Jodhpur’s climate is incredibly warm and arid. Summer lasts from April through June, with the rainy season beginning in July and ending in mid-September. November to March is the winter season. The average annual rainfall is 366 mm; however, the amount varies throughout the year. During the monsoon season, the city receives the majority of its rain. In the summer, the temperature rises, and in the winter, it falls. The city receives 351 mm of rain on a yearly basis. Over 80% of the rainfall falls during the south-west monsoon season. The yearly rainfall varies greatly from year to year; the average rainfall was 204 mm in 2007, while 482.4 mm in the next year. The temperature goes up in the summer and falls in the winter. Winds are stronger in the summer and milder the rest of the year. Winds blow from the north or north-west throughout the winter. South-westerly and westerly winds begin to blow in March, and they grow more prevalent over the summer months. During the months of June to September, the wind tends to blow from the south to the west. In the summer, the prevalent hot wind, known locally as Loo, is a significant occurrence.

12.4 Travel Link By road, rail, and air, Jodhpur is well connected to the neighboring villages as well as India’s main cities. The city is crossed by the highways NH-114, NH-112, and NH-65. Jodhpur is the North-Western Railway’s divisional headquarters and a major railway junction in western Rajasthan. Railways link Mumbai, Kolkata, Delhi, Ahmedabad,

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Hyderabad, Barelly, Guwahati, Jabalpur, Nagpur, Trivandrum, Indore, Bangalore, Pune, Kanpur, Lucknow, Bhopal, Chennai, Dhanbad, Kota, and Jaipur to important Indian cities.

12.5 Tourist Frequency

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The flow of tourism in the study area is divided into two stages, normal stage and pandemic stage. It is clearly visible that the flow of tourism is not normal during the pandemic because of the lockdown activities in the beginning months. This pandemic already affects the world at a large scale in which India is severely affected due to its population size. India lost a lot of lives during the pandemic, due to poor management of health infrastructure and bad decision-makings of higher authorities. The most affected part of India during the pandemic is Mumbai and Delhi. Jodhpur is also affected, but not that extent due to early and strict lockdown activities. This affects tourism at a great extent, which is clearly visible in Fig. 12.3.

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Fig. 12.3 Tourist arrival trends during pre and post-pandemic time in Jodhpur. Source Statistics of Tourism 2022, Ministry of Tourism, India

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12.6 Identified Tourist Spots 12.6.1 Umaid Bhawan Palace/Hotels It is one of the world’s largest private mansions, situated in Jodhpur, Rajasthan, India. Taj Hotels maintains a portion of the palace. The name was inspired by Maharaja Umaid Singh, the grandfather of the current owner, Gaj Singh. The palace, which has 347 rooms, was the primary residence of the previous Jodhpur royal family. A museum is housed within the palace. Maharaja Umaid Singh laid the cornerstone for the building’s foundations on November 18, 1929, and the construction work was finished in 1943.

12.6.2 Mehrangarh Fort It is in Jodhpur, Rajasthan, is 1200 acres (486 hectares) in size. The structure was built in 1459 by Rajput monarch Rao Jodha on a hilltop roughly 122 m above the surrounding plain. There are many palaces within its walls that are notable for their beautiful carvings and vast courtyards, as well as a museum that houses different treasures. The city below is reached through a twisting route. The effect of the cannonball fired by the invading army of Jaipur can be seen even today at the second gate. The chhatri of Kirat Singh Soda, a soldier who died defending Mehrangarh, is located to the left of the fort. There are seven gates, including Jayapol (which means “victory gate”), which was constructed by Maharaja Man Singh to celebrate his triumphs against the forces of Jaipur and Bikaner. A Fattehpole (also known as a “victory gate”) celebrates Maharaja Ajit Singh Ji’s glorious victory against the Mughals.

12.6.3 Jaswant Thada In the city of Jodhpur, the Jasvant Thada is a cenotaph. Maharaja Sardar Singh of Jodhpur state construct the Jasvant Thada in 1899 in remembrance of his father, Maharaja Jaswant Singh II and Marwar’s ruling Rajput family use it as a cremation place. The cenotaph is made of many sheets of marble that has been beautifully sculpted. When lighted by the Sun, these sheets are exceedingly thin and glossy, giving them a pleasant glow. There are sculpted gazebos, a small lake, and a tiered garden on the grounds of the cenotaph. Three further cenotaphs can be seen on the grounds. Portraits of Jodhpur’s kings and Maharajas can be seen on Maharaja Jaswant Singh’s cenotaph.

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12.6.4 Kaylana Lake Kaylana Lake, near Bijolai Palace, is situated nearly eight kilometers west of Jodhpur. Pratap Singh created it in 1872 as an artificial lake. The lake has a surface area of 0.84 sq. km (0.32 sq. mi). Bhim Singh and Takhat Singh, two lords of Jodhpur, built palaces and gardens in this region which had been destroyed for making the Kaylana Lake. Volcanic rock outcrop surrounded the lake. Water is provided by Hathi Nehar (literally “elephant canal”), which is attached to the Indra Gandhi canal. Babool trees (Acacia nilotica) dominate the natural vegetation and Siberian cranes (migrating birds) may be spotted throughout the winter season. Kaylana Lake serves as a source of drinking water for Jodhpur and the nearby cities and villages.

12.6.5 Balsamand Lake The lake of Balsamand is located on the Jodhpur–Mandore Road, 5 km (3.1 miles) from Jodhpur. Balak Rao Pratihar, a member of the kshatriya community, founded this lake as a famous picnic site in 1159 AD. It was built to deliver water to Mandore as a reservoir. The lake is one kilometer long (0.62 mile), 50 m wide (160 feet), and 15 m deep (49 feet). Later, on the coast of Balsamand Lake, the Balsamand Lake Residence was constructed as a vacation palace. The lake is encircled by lovely green gardens with mango, papaya, pomegranate, guava, and plum plantations.

12.6.6 Mahamandaleshwar Mahadev Jodhpur’s splendor is not restricted to its beautiful forts; temples also draw visitors’ attention. There are several historic temples in Jodhpur, the oldest and most frequented of which is Mandaleshwar Mahadev, which was erected in 923 A.D. by Mandal Nath. The shrine, dedicated to Lord Shiva, is said to be the region’s oldest. Its brilliance and exquisiteness are evidenced by its wonderfully studded walls, which are adorned with some of the best paintings of Lord Shiva and Goddess Parvati. During the Mandalnath Mela, which takes place in March or April, the temple looks stunning and attracts a lot of visitors.

12.6.7 Other Tourist Spots Jodhpur and the whole of Rajasthan are filled with many heritage places which attract many tourists from around the world. This allows tourists to learn and understand the Indian culture and the old luxurious lifestyle of Rajasthan. The forts of Jodhpur

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are the most striking, magnificent, and enchanting in India. It gives a superb view of the whole “Marushthali.”

12.7 Major Water Bodies of Jodhpur Natural drainage is not of much significance from the point of view of tourism industry as none of the Rajasthani River flows for the whole year in a true sense and thus, the lakes (controlled drainage) are of prime importance. Throughout the states of former Rajputana, natural drainage was modified to bring into existence the large and small water bodies by constructing huge dams across the valleys at suitable points between two hills. These reservoirs were erected for beautifying the landscape or for the pleasure of the rulers. These days, the sweet water lakes of Rajasthan furnish enchanting scenes and natural panorama to domestic and foreign tourists. Their calm, glittering, blue waters, tranquil atmosphere, lulling breeze, boating, dividing and watching the excellent greenery of the woodland around the lakes, magnificent structural embankments, flights of steps, shrines, islands fishing and shooting around them, etc., constitute the chief lure and allurement for the tourists. Among the chief lakes are Najarji Ki Baori, Navi Baori, TapiBaori, BirkhaBaori, GorindaBaori, Raghunath Baori, Toor Ji Ka Jhalara, RajmahalJhalara, Sukh Dev Ji Ka Jhalara, Fateh Sagar, Gulab Sagar, Ranisar—Padamsar, Jojri River, Arna Jharna, Chamunda Mata Pond, Rasolai Pond, Dev Kund, Gudo Bishnoi Pond, Soor Sagar Pond, Barli Bheru Pond, Surpura Dam, Nagadari Lake, Sardarsamand Lake, Machia Lake, near Jodhpur city Fig. 12.4.

Fig. 12.4 Water-based tourist locations of Jodhpur city

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Most of the abovementioned lakes are not only the spots of scenic beauty but, also process religious sanctity. These lakes are visited and venerated by millions of domestic and foreign tourists year round.

12.8 Data and Methodology This study is based on five main stages, which are as follows; collection, extraction, processing, analysis, and result (Table 12.1; Fig. 12.5). COLLECTION: In this stage, we collect all the relevant raw data, which is required to generate the thematic map which ultimately supports the accomplishment of the objective. The main data used are spatial boundary map, district head-quarter location, Landsat 5 (TM), Landsat 8 OLI/TRS and DEM SRTM, which is downloaded freely from the USGS website, different POI’s (Point of Interest) of ATMs, banks, Table 12.1 Data sources and a list of data S/N

Used Data

Sources

1

Boundary Map

Survey of India—http://www.surveyofindia.gov.in/

2

District Head Quarters

Survey of India—http://www.surveyofindia.gov.in/

3

Landsat 5 TM30 × 30 M

U.S. Geological Survey (USGS)—https://earthexplorer. usgs.gov/

4

Landsat 8 OLI/TRS 30 × 30 M

U.S. Geological Survey (USGS)—https://earthexplorer. usgs.gov/

5

DEM SRTM 30 × 30 M

U.S. Geological Survey (USGS)—https://earthexplorer. usgs.gov/

6

Heritage Spots

Field Survey with GPS

7

Tourist arrival trends

Department of Tourism

Fig. 12.5 Methodology adopted

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health facility, hotel facilities, police stations, rail network, road network, stream network, major tourist spots, water bodies of jodhpur city has been collected using field survey method, and lastly, tourist arrival trends from the department of tourism, Jaipur, Rajasthan. Various other data have been used at both primary and secondary levels, which ultimately helps in accomplishing the main objective of this paper. EXTRACTION: This is the stage where the raw data has preprocessed for further analysis. In this, the spatial boundary has been utilized at ArcGIS platform for clipping and cropping of raw data which mainly includes Landsat 8 OLI (30 × 30 M) and DEM SRTM (30 × 30 M). This extracted data has further gone on to the processing stage. PROCESSING: In this stage, all the satellite raw data has been processed to generate a thematic map for further analysis. Existing tourist spot shows the density of tourism location in the study area and helps in understanding the current tourist frequency. Land Use shows the pattern of utilization of land resources at different levels of tourism. The LULC information provided by the GIS-based data is crucial for assessing the geographical matching between possible suitability zones and present LUC patterns (Arora et al. 2021; Mishra et al. 2018a, b, 2021; Rai et al. 2021, 2022). This data was used to determine which places are most suitable for ecotourism and if future land uses may be changed to accommodate future development, where the accessibility map shows the distance from the tourism location to the road. It clearly indicates the facilities which help the tourists to reach the destination in an efficient and effective manner.

12.8.1 Land Use and Land Cover Land use and land cover are essential parts of tourism and related activities (Mishra et al. 2016). Managing natural resources and observing environmental changes depend on the changing land use and land cover patterns (Mishra and Rai 2016; Singh and Rai 2017; Shastri et al. 2020a, b; Singh et al. 2021; Brototi et al. 2022). Land-based resources are the most valuable natural resources for any nation, and every nation must be concerned about this. Proper utilization and maintenance are essential to long-term success. LULC classification is one of the most popular ways of information extraction from satellite images. The most popular LULC classification methods are supervised and unsupervised classifications. In this study, Landsat images have been used to classify various LULC categories. The satellite images used are Landsat 5 (TM) and Landsat 8 (OLI/TIRS) Operational Land Imager/Thermal Infrared Sensor at a spatial resolution of 30 M. The path/row of the downloaded satellite images are (150/ 41,149/041, 149/042, 148/042). The required satellite images are downloaded from the USGS website (https://earthexplorer.usgs.gov/). The methodology adopted for LULC is shown above which clearly indicates the steps involved in Fig. 12.6. The preprocessing and interpretation of satellite images

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Fig. 12.6 Methodology adopted for LULC of Jodhpur (Rai et al. 2018)

are performed on (ERDAS Imagine 2019) image processing software for generating LULC maps. The raw Landsat images are first imported into ERDAS Imagine 2019 software to layer stack and generate false color composite (FCC) for both years of images. After that preprocessed Landsat 5TM and Landsat 8 OLI/TRS are classified using supervised classification under ERDAS Imagine software in order to categorize different land features. There are mainly seven major LULC classes used, which are Crop Land, Barren Land, Grass Land, Water Bodies, Forest, and Fallow Land. After carefully following all the steps, the final classified LULC maps have been obtained in Fig. 12.7., These maps undergo the accuracy assessment. On the basis of accuracy assessment, the final map has been accepted or rejected. To measure the accuracy of LULC maps interpreted from Landsat TM and OLI/TRS data, a total of 100 stratified random point pixels were produced for both the years 2005 and 2015 on the land cover map. The points were converted to KML files, to overlay data on Google Earth. This was necessary to analyze the results of classifier performance and land cover category changes. These points were further used to verify the classification accuracy. The result of accuracy assessment achieved and (OA) overall accuracy of 83.50% for 2005 and 90.00% for 2015. The (Kc ) Kappa coefficients for 2005 is 0.826 and 0.893 is for 2015. After analyzing Table 12.2, it is clearly visible that there are both positive and negative changes occurred in the LULC pattern of Jodhpur. During the year 2005– 2015, the Built-up land in the study region has increased 293.58 sq. km in 2005 to 401.06 sq. km in 2015, which accounts for 0.47% of the total study area. The Cropland has decreased from 8195.57 sq. km in 2005 to 6880.06 sq. km in 2015, which accounts for 5.80%. The Fallow Land has increased from 8295.45 sq. km in 2005 to 11,038.83 sq. km in 2015, which accounts for 12.10%. The Forest Land has decreased from 110.71 sq. km in 2005 to 108.91 sq. km in 2015, which accounts for 0.01%. The Grass Land has also decreased from 1020.83 sq. km in 2005 to 607.77

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Fig. 12.7 LULC of Jodhpur from 2005 to 2015

sq. km in 2015, which shows 1.82 percent change. The Barren Land has decreased from 4597.18 sq. km in 2005 to 3471.07 sq. km in 2015, which accounts 4.97%. The Water bodies have slightly increased from 154.19 sq. km in 2005 to 159.81 sq. km in 2015, which shows 0.02%. ANALYSIS: The use of GIS and management planning tools is merely a first step in determining which land areas are ideal for ecotourism. Regular GIS-based monitoring and management needs-based activity execution are crucial. Management planning is the process of examining a tourist objective and developing a realistic, comprehensive plan of action for achieving those goals. Finding resources, developing goal-related activities, prioritizing objectives and tasks, creating timetables, establishing assessment techniques, and identifying alternate courses of action are Table 12.2 Area and percentage change in LULC during 2005–2015 Year

2005

LULC class

Area in Sq. Km

Built-up

293.58

2015 Percentage 1.30

Area in Sq. Km 401.06

Change 2005–2015 Percentage 1.77

Area in Sq. Km 107.48

Percentage +0.47

Cropland

8195.57

36.16

6880.06

30.35

−1315.51

−5.80

Fallow land

8295.45

36.60

11,038.83

48.70

2743.38

+12.10

Forest land

110.71

0.49

108.91

0.48

−1.80

−0.01

Grass land

1020.83

4.50

607.77

2.68

−413.06

−1.82

Barren land

4597.18

20.28

3471.07

15.31

−1126.11

−4.97

Water bodies Total area

154.19

0.68

159.81

0.71

5.62

+0.02

22,667.51

100.00

22,667.51

100.00

0.00

0.00

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all part of it. Local communities may play a significant role in planning, coordinating stakeholder and community involvement, exploring alternative land uses, and performing environmental and social impact evaluations. The local community’s involvement is also recognized by the administration. Ecotourism management must also include the nature and potential of existing resources from several ecotourism maps in order to plan appropriate activities and maintain compatibility between ecotourism and the area’s original activity. This should include the avoidance of any severe conflict, particularly in locations where ecotourism is more suited than other kinds of tourism. RESULT: Ecotourism and management planning-based development must emphasize educational aspects and raise public knowledge of the need to collaboratively maintain the area’s ecology. There is a need to execute development strategies that protect the ecological and environmental integrity of existing natural resources. Environmental education and interpretation are essential for tourists to have a positive and meaningful ecotourism experience, as well as one of the primary points of difference between ecotourism and conventional vacation offerings. Ecotourism goods with successful interpretative components will encourage respect and assistance with conservation efforts, culture, and local communities. It also generates general contentment among tourists, giving the nation a positive image in comparison to others. Jodhpur is a very beautiful destination in terms of tourism especially for the foreign tourists. It is the most important industry that most of the people depend on that it is major source of earning for satisfying their daily needs. It’s heritage tourism spots give a view to historical people lifestyle. It creates an enthusiasm among the tourist to imagine such great lifestyle or be a part of it at least once. The goals of ecotourism and tourism are similar in that they aim to connect environmental goals with economic and rural development. Ecotourism provides travellers with instructive and novel experiences, and it must be created and maintained in an ecologically conscious manner that protects the environment. One of the most significant advantages of GIS and Remote sensing tool is the accuracy of the information availability across a vast region and simultaneous observation, which allows for the detection of temporal changes. The LULC and mapping of water tourism locations give a broad idea about the activities done on the land and water in recent years. GIS-based management planning and utility mapping Fig. 12.8, certainly aids in the collection, analysis, modeling, and visualization of data on a large scale, allowing the administration and stakeholders to enhance tourism and related services in more effective and efficient manner.

12.9 Impact of Tourism on Water Water is one of the most valuable resources in the tourist industry as it powers every sector, including hotels, restaurants, leisure activities, and transportation. A single day of limited water availability can have a negative impact on the brand and public

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Fig. 12.8 Utility maps for enhancing ecotourism in Jodhpur

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image of any tourist destination. Climate change is a major threat to future water availability in different areas, leading to severe economic consequences for popular tourism destinations. In many cases, tourists are often unaware of local water shortage concerns, and they cannot be relied upon to sacrifice the enjoyment of their vacation by adopting pro-environmental decisions. Improving the inherent efficiency with which the tourism sector utilizes water becomes essential not only in reducing the sector’s environmental impact but also in ensuring its continued existence through sustainable resource management. Management and research efforts have predominantly focused on tourism facilities that consume substantial amounts of water, such as swimming pools, hotels, and golf courses. Tourism is one of the largest global sectors, employing one in every eleven people and representing over 9 percent of the global GDP in 2022. With new destinations continuously emerging, it is crucial for the tourism industry to prioritize sustainable water management practices to ensure the long-term viability and success of these emerging destinations. Considering the projected annual increase of 3.3 percent in global tourist visits between 2020 and 2030, the undeniable significance of tourism in driving economic output becomes evident. Therefore, all efforts to conserve water resources must aim to maintain or even enhance, where feasible, the profitability of the industry.

12.10 Steps to be Taken to Reduce the Impact on Water The following strategies are suggested to minimize the effects of human tourism impact on the aquatic environment of Jodhpur. 1. Tourists must be redirected/restricted in order to reduce the problem of high population density in a short span of time. 2. Awareness of tourism environmental protection should be strengthened. 3. Tourism infrastructures must be scientifically planned.

12.11 Conclusion This study lays focus on current advancements in tourism using GIS, Remote Sensing, and management planning as a tool that will promote heritage-based tourism and provide a diverse experience for tourists visiting various locations in the study area. Tourists’ enjoyment demands are always changing, and their need for cultural travel is continuously increasing, which will assist the tourism sector. Diverse aspects of tourism, as well as visitor interests and preferences, are critical. Sustainable ecotourism development can help Jodhpur build a distinct character for historical tourism. Stakeholders must not overlook the tourist industry’s potential for ecotourism management planning. GIS technology opens us to a world of options for creating ecotourism maps. This combination of technology and database-based

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processes, such as query generation and spatial analysis visualization, provides benefits in the form of various sorts of maps. These maps help in finding many resources which boost the enhancement of ecotourism-based activity. This study gives a broad prospect to think what resources we have, what needs to be done, and what are the future requirements. It also clears the role of sustainability in tourism. Conflicts of Interest The author declares no conflict of interest. Funding This research received no external funding.

References Arnegger J, Woltering M, Job H (2010) Toward a product-based typology for nature-based tourism: a conceptual framework. J Sustain Tourism 18(7):915–928. https://doi.org/10.1080/09669582. 2010.485680 Arora A, Pandey M, Mishra VN, Kumar R, Rai PK, Costache R, Punia M, Di L (2021) Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics. Ecol Ind 128:107810. ISSN: 1470–160X Brototi B, Singh A, Rai PK, Kumar J, Walker S (2022) GIS based study of reclamation of degraded semi-arid soil: a case study of Rajsthan from India. Ind J Environ Prot 42(3):302–315 Burton R (1998) Maintaining the quality of ecotourism: ecotour operators’ responses to tourism growth. J Sustainable Tourism 6(2):117–142. https://doi.org/10.1080/09669589808667306 Buultjens J, Gale D, White NE (2010) Synergies between Australian indigenous tourism and ecotourism: possibilities and problems for future development. J Sustainable Tourism 18(4):497–513. https://doi.org/10.1080/09669581003653518 Chan RCK, Bhatta KD (2021) Trans-Himalayan connectivity and sustainable tourism development in Nepal: a study of community perceptions of tourism impacts along the Nepal–China friendship highway. Asian Geographer, 1–22. https://doi.org/10.1080/10225706.2021.1943470 Chandel RS, Kanga S (2020) Sustainable management of ecotourism in western Rajasthan, India: a geospatial approach. Geo-J Tourism Geo-sites 29(2):521–533. https://doi.org/10.30892/gtg. 29211-486 Chawla L, Cushing DF (2007) Education for strategic environmental behavior. Environ Educ Res 13(4):437–452. https://doi.org/10.1080/13504620701581539 Kanga S, Thakur K, Kumar S, Gupta H (2014) Potential of geospatial techniques to facilitate the tourist & administration : a case study of Shimla Hill station, Himachal. Int J Adv Remote Sens GIS 3(1):681–698 Kc B, Dhungana A, Dangi TB (2021) Tourism and the sustainable development goals: stakeholders’ perspectives from Nepal. Tourism Manag Perspect 38:100822. https://doi.org/10.1016/j.tmp. 2021.100822 Kim W, Jun HM, Walker M, Drane D (2015) Evaluating the perceived social impacts of hosting large-scale sport tourism events: scale development and validation. Tourism Manag 48:21–32. https://doi.org/10.1016/j.tourman.2014.10.015 Mobaraki O, Abdollahzadeh M, Kamelifar Z (2014) Site suitability evaluation for ecotourism using GIS and AHP: a case study of Isfahan townships, Iran. Manag Sci Lett 4(8):1893–1898. https:/ /doi.org/10.5267/j.msl.2014.6.038 Mondal K, Gupta S, Sankar K, Qureshi Q (2013) Status, distribution and conservation of leopard Panthera pardus fusca in Rajasthan. Faunal heritage of Rajasthan, India (pp. 469–479). Springer, New York. https://doi.org/10.1007/978-1-4614-0800-0_17

250

R. S. Chandel et al.

Mishra V, Rai PK (2016) A remote sensing aided multi-layer perceptron-marcove chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9(1):1–18. https://doi.org/10.1007/s12517-015-2138-3 Mishra VN, Rai PK, Kumar P, Prashad R (2016) Evaluation of land use/land covers classification accuracy using multi-temporal remote sensing images. Forum Geogr (romania) 15(1):45–53 Mishra VN, Prashad R, Rai PK, Vishwakarma AK, Arora A (2018a) Evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multisensor remote sensing data. Earth Sci Inf 12(1):71–86. https://doi.org/10.1007/s12145-0180369-z Mishra VN, Rai PK, Rajendra P, Puniya M, Nistor MM (2018b) Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models. Appl Geomatics (Springer). ISSN No. 1866–9298. https://doi.org/10.1007/s12518-018-0223-5 Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management (edit. Book), Nova Science Publishers, USA. ISBN: 978–1–53619–669–6 Patuelli R, Mussoni M, Candela G (2016) The effects of world heritage sites on domestic tourism: a spatial interaction model for Italy (pp. 281–315). https://doi.org/10.1007/978-3-319-301969_13 Rai PK, Chandel RS, 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. https://doi.org/10.1007/s13201-018-0660-7 Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book), Springer Nature, Switzerland. ISBN: 978–3–030–76116–5. https://doi.org/10.1007/978-3-030-76116-5 Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book), Springer Nature, Singapore. ISBN: 978–981–16–7373–3. https://doi.org/10.1007/978-981-16-7373-3 Rathore AS, Sharma C (2021) Emotional branding for tourist destinations for the future: a review of ICT tools. In Future of tourism in Asia (pp. 177–200). Springer Singapore. https://doi.org/ 10.1007/978-981-16-1669-3_11 Scheyvens R, Momsen JH (2008) Tourism and poverty reduction: issues for small Island States. Tourism Geogr 10(1):22–41. https://doi.org/10.1080/14616680701825115 Shastri, S, Singh P, Rai PK (2020a) A land covers change dynamics and their impacts on thermal environment of Dadri Block, Gautam Budh Nagar, India. J Landscape Ecol 13(2):1–13 Shastri S, Singh P, Verma P, Rai PK, Singh AP (2020b) Assessment of spatial changes of land use/ land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India. Forum Geogr 19(1):72–79. http://dx.doi.org/10.5775/fg.2020.063.i Singh S, Rai PK (2017) Application of earth observation data for estimation of changes in land trajectories in Varanasi District, India. J Landscape Ecol 11(1):5–18. ISSN: 1805–4196. https:/ /doi.org/10.1515/jlecol-2017-0017 Singh A, Rai PK, Deka G, Biswas B, Prasad D, Rai VK (2021) Management of natural resources through integrated watershed management in Nana Kosi micro watershed; district Almora, India. Ecol Environ Conserv 27(February Suppl. Issue):S260–S268 Spenceley A, Meyer D (2012) Tourism and poverty reduction: theory and practice in less economically developed countries. J Sustain Tourism 20(3):297–317. https://doi.org/10.1080/09669582. 2012.668909 Stone M, Wall G (2004) Ecotourism and community development: case studies from Hainan, China. Environ Manag 33(1):12–24. https://doi.org/10.1007/s00267-003-3029-z Tiwari S, Tomczewska-Popowycz N, Gupta SK, Swart MP (2021) Local community satisfaction toward tourism development in Pushkar Region of Rajasthan, India. Sustainability 13(23):13468. https://doi.org/10.3390/su132313468 Wearing, (2009) Ecotourism. Routledge. https://doi.org/10.4324/9780080940182

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Wondirad A, Tolkach D, King B (2020) Stakeholder collaboration as a major factor for sustainable ecotourism development in developing countries. Tourism Manag 78:104024. https://doi.org/ 10.1016/j.tourman.2019.104024

Chapter 13

A Spatiotemporal Study of Agriculture in the Chars of Brahmaputra Basin, Dhubri, Assam Roli Misra, Ritika Prasad, and Bratati De

Abstract Agriculture is the most important sector in Dhubri and a major contributor to the state’s economy. The Brahmaputra River flows through Assam for 650 km, traversing thousands of acres, from Dibrugarh upstream to the town of Dhubri in lower Assam close to the Bangladesh border. The Brahmaputra River in the Assam region of Dhubri is distinguished by its intricately braided channel pattern, which has produced several river islands (chars) of varied sizes and shapes. Many of these chars persist for a long time, allowing vegetation to develop and settlement to take root. The present study aims to map and analyse the current situation of agriculture as well as identify the land use and land cover changes (LULC) that have occurred on the chars within one km buffer of the Brahmaputra River over the previous 30 years, from 1992 to 2022. Landsat 5 and Landsat 9 multitemporal satellite images are used in the research. Primary data sources are also used to supplement the information. This analysis shows that in terms of land use change, habitation and agriculture are expanding while the sand bars are shrinking and being converted to agricultural land. In Dhubri’s chars, the majority of crop cultivation is done. Furthermore, it may be assumed that stable chars, the majority of which are found in this area of Assam, engage in a significant amount of agricultural activity. The findings of the study shall aid in the proper use of chars for agricultural uses and enable decision-makers better grasp the issues with the dynamics of the Brahmaputra river bank. This study can assist locals in planting crops in accordance with changes in the Brahmaputra River’s water level and can assist the agriculture department in keeping an eye on those agricultural fields. In this way, the study can support greater regional food security.

R. Misra (B) Department of Economics, Lucknow University, Lucknow, India e-mail: [email protected] R. Prasad Department of Geography, Lucknow University, Lucknow, India B. De Swastik Edustart - GIS and Remote Sensing Institute, New Delhi, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_13

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Keywords LULC · Agriculture · Chars · Brahmaputra River · Assam · Dhubri

13.1 Introduction The Brahmaputra River one of the largest alluvial rivers in the world is characterised by regular bank erosion and deposition that results in changes to the channel pattern and shifting of the bank line. Brahmaputra is the seventh-largest river in the world and is the most active and predominately braided river (Hovius 1998; Tandon and Sinha 2008). Braided rivers are those that meander around alluvial bars or islands having two or more channels. Steep slopes and sediment overloading are the main factors that cause braided rivers (Das et al. 2012; Goswami et al. 1999; Kotoky et al. 2005; Lahiri and Sinha 2012; Sarma and Phukan 2004). Other causes of braiding include erodible banks, highly fluctuating discharge and a high width-to-depth ratio (Schumm et al. 1972; Knighton 1998). At low flow stages, the bars are exposed, giving them a braided appearance. However, at high flow stages, all or some of the bars are submerged. The Brahmaputra is a transboundary river that stretches from the Himalayas to the Bay of Bengal through China (Tibet), India and Bangladesh. Its source is the Yarlung Tsangpo glacier at the Mana Sarovar lake in the Kailash range in southern Tibet. The river’s distinctive drainage pattern, varied geological setting and large sediment load make it unique (Mahanta and Saikia 2015; Mishra et al. 2021; Rai et al. 2021, 2022). The river undergoes ongoing processes of erosion and deposition along its course in an effort to achieve a new equilibrium in channel geometry and morphology due to the constantly changing nature of the flow. The Brahmaputra, one of the largest rivers in the world, is thought to produce 1028 tonnes of silt per square kilometre annually. Erosion and deposition of the river have been linked to land use and land cover (LULC) since the land cover is under constant change in a dynamic landscape constantly shaped by continuous erosion and deposition (Hutton and Haque 2003, 2004; Rai and Mohan 2014; Mishra and Rai 2016; Mishra et al. 2016, 2018a, b). Every year, from May to October, Assam has intense rainfall from the south-west monsoon, which is followed by floods. Four to five waves of intense flooding can happen per year. The state’s agricultural production is primarily unstable due to floods. As per Census 2011 of the total land mass, 98.4% is rural and Assam’s economy is largely rural by nature, where agriculture and allied activities play a very important role in its socio-economic development. As a sector, agriculture contributes significantly to the state’s economy and provides the main source of livelihood to a large proportion of its rural population, which is both rural and agrarian (Shastri et al. 2020a, b). It contributes almost 25% to the state’s GDP and provides for roughly 70% of its population (Assam Budget Analysis 2022). The flood-prone area of the state as assessed by the Rastriya Barh Ayog (National Flood Commission) is 31.05 Lakh Hectares against the total area of state 78.523 Lakh Hectares, which is about. 39.58% of the total land area of Assam (assamgov.in). From the available literature, it is inferred that there are many islands in Brahmaputra in Assam, which are shelters

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to poor marginalised community. In local parlance, these are called chars. These chars are naturally formed and deformed by the river Brahmaputra. This activity becomes more intense during floods displacing thousands of people who then move from submerged char to either a new island or towards the mainland in search of livelihood losing their land holdings and documents. It would not be out of place to mention that Assam shares porous borders with Bangladesh, Dhubri being the bordering district with Bangladesh. It is perceived that many at times, people across the border enter the state taking the river route and make their settlements in chars. There are religious, ethnic and sectarian differences and language barriers in the state between these Bengali-speaking Muslim migrants and Assamese population, which further segregate and contribute to the isolation of the displaced population due to floods. Another problem is that of entitlement as most of them do not have ownership of land (patta) in their name. So, when the lands get washed away they have no right over the reclamation of land. It is in place to mention that prior to 2015, there was no land compensation policy for those who had lost their agricultural lands due to river erosion. It is only since 2015 that the Assam government has come out with a policy related to compensation. However, majority of the population engaged in agricultural activities in chars do not have land entitlements. Keeping in view the changes in the river configuration of the Brahmaputra main channel in Dhubri, agriculture practices, this study has been undertaken with the following objectives. 1. To identify the land use and land cover changes on the chars within one km buffer of the Brahmaputra river over the past thirty years period from 1992 to 2022 using multispectral satellite data. 2. To identify, map and understand the present status of agriculture in chars from 1992 to 2022. The study has been conducted using multispectral satellite data and Google Earth Engine as well as primary data sources collected through a field survey conducted in chars of Dhubri in 2020. According to data on land use change, agriculture and settlement are growing, while the sand bar class is declining. The majority of the land in the area affected by erosion–deposition and river migration is used for agriculture. It is also obvious how river dynamics affect settlements. Increase in agricultural land indicates internal migration in the floodplains, according to the literature assessment (Singh and Rai 2017). The observed pattern of river dynamics and the consequent land use change in the recent decades have thrown newer environmental challenges at a pace and magnitude way beyond the coping capabilities of the dwellers. This study also reveals that the major crop cultivation is done in chars of Dhubri. It may be inferred that stable chars, the majority of which are found in this region of Assam, have a substantial amount of agricultural practice. Additionally, it has been noted that the double cropping system is utilised in the majority of the chars. The suitability of various crops for this study can be further explored by integrating variables like soil type, soil texture, rainfall, socio-economics, etc. To improve food security, farmers in this area must receive the proper infrastructure and incentives.

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13.2 Study Area Dhubri is one of the plains districts of Assam. It falls in the Brahmaputra valley. Three sub-divisions constitute the present Dhubri district: Dhubri (Sadar), Bilasipara and South Salmara/Mankachar. Assam’s Goalpara and Bogaigoan districts and Meghalaya’s Garo Hills district border it on the east, West Bengal and Bangladesh on the west, Kokrajhar district on the north, Bangladesh and the state of Meghalaya on the south. This region of the world is situated between 25.28° north and 26.22° north latitude and 89.42° east to 90.12° east longitude (Fig. 13.1). The mighty Brahmaputra River, which brings both joy and sadness to the locals of Dhubri, surrounds the city on three sides. Flood is a regular phenomenon for the district which causes extensive damage almost every year. As mentioned, the existence of chars, or riverine silt islands, is one of the Brahmaputra River’s peculiar characteristics in Assam. As the River Brahmaputra approaches its terminus in the Bay of Bengal, it is seen that it runs very slowly, depositing enormous amounts of silt along the banks. Over time, these places gradually turn into habitable areas. The chars so created are fertile lands that provide the inhabitants with a livelihood through agriculture. Total geographical area of Dhubri district is 217600 hectares out of which 114541 hectares are the Gross cropped area (Land Utilization Statistics, Assam 2019–2020). Dhubri being one of the western-most districts of Assam on the bank of River Brahmaputra houses a large number of chars. These chars have the potential for agricultural expansion as the soil is fertile and productive (Best et al.

Fig. 13.1 Location map of the study area. Source Indian Geo Portal Bhuvan

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2007). Keeping in view the changes in the river configuration of the Brahmaputra main channel, around one km of buffer area along the river has been undertaken for this study. Over time, residents of the char regions have made significant contributions to the district’s socio-economic and cultural elements (District Census Handbook, Dhubri 2011).

13.3 Data Sources and Methods The present study uses multitemporal satellite images of Landsat 5 and Landsat 9 to map the changing pattern of the Brahmaputra river channel and agriculture on the chars for 1992 and 2022 (Table 13.1). Thematic mapper (TM) and Operational Land Imager (OLI) sensors have been used. The main application of these sensors TM, and OLI is in the areas of forest, agriculture, coastal, inland water resources and LULC mapping and monitoring. All the images were downloaded from the USGS earth explorer website (http://earthexplorer.usgs.gov/). In the pre-processing stage, images were layered and put on and the study area was clipped using a subset tool after converting the shape file into AOI (area of interest). Radiometric correction was done to get more clarity in the image using histogram equalisation. To prepare the LULC map from satellite imageries, a classification scheme, which outlines the LULC classes was considered. The number of LULC classes are preferred based on the requirement of a specific project for a particular application (Arora and Mathur 2001; Saha et al. 2005). Five major LULC classes were chosen for mapping the entire study area consisting of Vegetation; Water bodies; Sand bars; Built up and Agriculture (Table 13.2). The classes have been defined based on the USGS classification scheme as well as the requirement of the present study objective. In the post-processing stage, we used a supervised type of classification with the help of known ground truth points. Then, ERDAS Imagine was connected to Google Earth and the imagery was synchronised. In the next step, using a signature editor, in the supervised classification, the classification process was controlled by creating, managing, evaluating and editing signatures. After the classification scheme was created, maximum likelihood classification, one of the most widely used image classification techniques, was applied to map all land use and land cover classes. Table 13.1 Description of satellite images used in the study Data

Date

Sensor

Bands used

Resolution in metres (m) & Source

Landsat 5 08.03.1992

TM

Band 1, Band 2, Band 3, Band 4, band 5, Band 7

30 m, USGS earth explorer

Landsat 9 12.03.2022

OLI

Band 1, Band 2, Band 3, Band 4, Band 5, Band 6, Band 7

30 m, USGS earth explorer

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Table 13.2 Different LULC categories LULC category

Classes induced general description

Vegetation

Land with tree, scrubs and area under agroforestry

Water bodies

This category comprises areas with river surface water in the form of ponds, lakes, drains, etc

Sand bars

Sand deposits along the river

Built-up

This category includes urban and rural settlements, transportation, communication and recreational utilities

Agriculture

This category involves land under crops, fallow and aquaculture/ pisciculture

Source Based on the USGS classification scheme

After classifying the images, ground control points were used to check the accuracy of the classification. To evaluate the accuracy of the classified map for the years 1992 and 2022, the error matrix, one of the most frequently used accuracy assessment techniques, was chosen. An error matrix is a table that has the land cover classes in the remotely sensed data which are set as rows and the land cover classes in the reference data are set as columns. A specified area or a number of pixels with a given combination of reference data and remotely sensed land cover classifications are shown in each table cell. The diagonal line displays the pixels that have the same land cover class in both sets of data since the two sets of classes ought to be identical. Off-diagonals, which show the discrepancy between reference data and remotely sensed data, have been improperly classified. Primary data was also collected through observations. The field survey to a few chars in Dhubri was undertaken at different time periods from 2011 to 2019. It helped in gaining more in-depth information on the physical setting of the study area as well as understanding the socio-economic conditions of inhabitants, who are affected in their daily lives due to the changing pattern of the Brahmaputra River. Moreover, to supplement the primary data, secondary data has been collected from a number of books, journals, national and state-level government published reports.

13.4 Results and Discussions 13.4.1 Land Use and Land Cover Status from 1992 to 2022 Land use and land cover are two distinct terms that are often used interchangeably (Dimyati et al. 1996). Land use refers to how a portion of land is utilised, while land cover describes the materials that are present on the surface (Sabins 1987). Figure 13.2 shows land use and land cover change in different categories during the years 1992 and 2022 along one km buffer of Brahmaputra River in Dhubri,

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Assam. It also shows the spatial distribution of the major land use and land cover classes in the study area for the years 1992 and 2022. The areal extent of these LULC features is in km2 and area coverage in percent is shown in Table 13.3. Results from classified maps indicate that in 1992 area occupied by different classes; agricultural land was about 11.69%, built-up area covered 0.97%, vegetation covered 0.01%, water bodies occupied 36.06% of the area and sand bars covered most part of the study area, occupying about 51.27%. On the other hand, in 2022 about 42.06% of the area was covered by agricultural land against 11.69% area in 1992. This shows an increase in cultivated land, having the largest share of the total geographical area. This four-fold increase in agriculture is mainly due to the presence of small-scale settlement on chars that has grown gradually with time who practices subsistence agriculture. Built-up area covered 4.70% showing an expansion of it from 1992. This expansion is driven by internal migration of people from the chars to the mainland and it is perceived that there are also illegal migrants from Bangladesh who have entered the Indian state boundary through the river Brahmaputra and settled in the chars. The vegetation had a share of 0.01%. The area covered by water bodies was 28.48% of the total geographical area. The deposition of sand is more in the lower Brahmaputra region causing the river channel to shrink in this area. The accuracy assessment has been done using the kappa coefficient calculated for each classified map. Kappa coefficient is a degree of agreement or correctness among the classified map and the reference data. The accuracy assessment revealed for the year 1992 is 96% and 95% for 2022, which is proximate to Anderson’s standard of 85% (Anderson et al. 1976). The results from the error matrix provide a key platform for analysis of LULC changes.

13.4.2 Changing Pattern of Agriculture on Chars in Dhubri from 1992 to 2022 The most important activity in Dhubri is agriculture, which considerably contributes to the state’s economy and provides employment for thousands of people. Its elaborately braided channel system, which has given rise to numerous river islands (chars) of all sizes and shapes, makes the Brahmaputra well-recognised (Best el al. 2007; Sarkar et al. 2012). Due to seasonal flooding, these islands are continually changing in size, shape and direction (Lahiri-Dutt 2014; Hazarika et al. 2015). Most of these chars last for a very long time, allowing vegetation to grow and settlements to establish themselves (Rahman and Rahman 2012). The char remains flooded with water for a major part of the year, leaving the household in the lurch. Thus, people in chars live a nomadic life moving from one char to another due to massive destruction caused by foods, which submerge these chars along with their cattle, belongings and the land on which the people practice subsistence agriculture; all get washed away (Misra 2018). While islands with lower levels of stability can be used to cultivate shortterm, seasonal crops like vegetables, islands with higher levels of stability can be

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Fig. 13.2 Change in the LULC and Spatial Distribution of LULC in Dhubri District during 1992 and 2022. Source Landsat 5 (1992) and Landsat 9 (2022)

Table 13.3 The LULC distribution along 1 km. buffer of Brahmaputra River in Dhubri LULC distribution Classes

Area in km2 in 1992

Percentage of area in 1992

Area in km2 2022

Vegetation

0.03

0.01

Agriculture

59.28

11.69

227.98

42.06

259.89

51.27

134.41

24.75

Sand bars

0.01

0.97

25.50

4.70

Waterbody

182.81

36.06

154.99

28.48

Total

506.92

100

542.92

100

Built up

4.91

0.04

Percentage of area in 2022

Source Authors construction

utilised to grow horticulture and plantation crops. The great majority of these chars, however, are always empty and idle. These chars have the potential for increasing agricultural production because of the fertile and productive soil (Best et al. 2007). According to the LULC maps, the area used for agriculture has increased by about four times between 1992 and 2022. Agricultural land made for 59.28 km2 of the total area in 1992, and 227.98 km2 in 2022. As per Fig. 13.3, the total growth in agriculture area from 1992 to 2022 is 219 km2 while 21.45 km2 of area of agriculture

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Fig. 13.3 Change in area of agriculture in Dhubri from 1992 to 2022

remains unchanged. At present, the agricultural land comprises 42.06% of the total geographical area. A large portion of chars that were once barren has been turned into agricultural land. There has been an increase in the density of people in the char areas (Chakraborty 2014). The hardships of the char inhabitants who appear more as climate refugees, being a floating population who are compelled to move inside the river, changing their locations due to rising of Brahmaputra during floods and eroding of shores. Many of these people bear the brunt of being called illegal immigrants from Bangladesh because these people speak Bengali, are Muslims and bear cultural resemblance with people across the border. Figure 13.2 illustrates how chars with settlements exhibit agricultural crop indicators. Thus, it can be inferred that people staying in these chars have agriculture as the main source of livelihood. This pattern was not noted earlier when there was not much agriculture in 1992. The chars in Dhubri are also subjected to deposition due to their downstream location. When flood levels drop and the river’s flow velocity slows down, the river’s ability to clear these deposits reduces. Due to the accumulation of silt, vegetation eventually covers it. The region is good for agriculture because of this phenomenon. The chars are once again submerged in flood water during the next floods, changing the landscape or causing silt to be deposited (Chakraborty 2014). The soil is made up of alluvial deposits that have recently been deposited by a river and has little humus. The soil is acidic by nature. The region’s soil has been shown to have a variety of properties. The soil in the riverine regions is loamy to sandy loam. Some locations also have clay soil, which ranges in density from light clay to heavy clay (District Agriculture Contingency Plan for Dhubri 2012). In the char

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Picture 13.1 River Erosion of a Char area located in Dhubri

areas of Dhubri, the distribution of land ownership has been greatly distorted. Actual ownership patterns in many char areas are impossible to verify because of the lack of cadastral surveys, but according to one estimate, 10% of the population occupies around 80% of the total land (Char Area Development Authority 2004). The HDR survey data on occupation reveal that 26.6% is engaged in agriculture and allied activities (Assam Human Development Report 2014). The chars are typically unstable by nature. The size, shape and location of most of the chars change from time to time. This occurs when the area deals with the intensity of the flooding issue (Picture 13.1). The erosion problem on the chars and mainland banks of the Brahmaputra River is made worse by the floods. Due to the pressure of the high floodwater on the embankment, which washes away its weaker sections, the over bank flood breaches the embankment. The neighboring lands are inundated by floodwater, which has the consequence of heavily depositing silt and sand in the agricultural areas. During flood, the settlers of char areas shift to some other high lands to spend the period of flood and come back to their original place of abode after flood recedes. The timing and severity of floods substantially influence the agricultural pattern in Dhubri. While a modest flood is always desirable, an early flood will undoubtedly be disastrous for those who cultivate char. Low floods boost soil productivity by bringing in silt. High floods, however, make land unusable because of the issue with sand casting. Similar to how early floods severely harm jute and ahu paddy crops; they also cause loss of income and food security during the Kharif season. The cropping period is chosen, and in particular, the planting and harvesting of crops, in a way that minimises flood damages (Prashnani et al. 2019). Paddy, jute, mesta and foxtail millet are common kharif crops throughout the monsoon seasons. Ahu paddy is one of the crops grown during the Kharif season.

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Its cultivation begins in March and is usually completed in June. Amon paddy also continues simultaneously. Jute is harvested in December and is sown over a longer length of time, from April to early August. Several crops, including wheat, chenna, boro paddy, barley, sweet potatoes, mustard, seaweed, linseed, sunflower, peas, gram, soybean, coriander, garlic, onion and chillies are grown during the post-monsoon season. Typically, wheat is sown in November and harvested in March. A new variety of rice locally called Iri is also cultivated by the farmers of these areas. Although agriculture is the primary source of livelihood in the char area, they are still following the traditional methods of cultivation. Khesari, a kind of pulse and Jute (Kaisha or Kahi boon in local language) are also sown in this island. Modem scientific methods of cultivation have little impact on them. Due to the conservative nature and superstition prevalent in the society, they don’t like to use the modem techniques of cultivation. Jhai a naturally grown plant was visible throughout this char which is being used as firewood along with cow dung (Field Survey 2019). The inhabitants of the char do not grow the same crops year after year. Crop rotation is attempted to the greatest extent. The following year, jute is planted where there was formerly Ahu paddy. A plot of land utilised for Ahu is similarly employed for Rabi crop the season thereafter. The following are some significant plantation crops: bamboo, guava, mango, lemon, coconut and areca nut (Central Ground Water Board 2019– 2020). Despite having a more intensive cropping pattern and a diverse crop profile, the cultivators in the char areas are mainly subsistence farmers. A number of factors, including a skewed pattern of land ownership, the recurrent threat of floods and the accompanying erosion, sand casting issues, a lack of agricultural extension programmes, the lack of input support, weak marketing ties, poor transport and communication, and a variety of other factors, make it difficult for these people to obtain a fair price, a steady income and a sustainable way of life. These causes account for the vast majority of poverty among char dwellers. They are socio-economically backward owing to their lifestyle and community habits and habitats have not been able to keep pace with the modern society. They are not as advanced as the people of the rest of the State are. According to estimates from the Char Area Development Authority, in the years 2003–2004, over 69% of residents in Dhubri were considered to be living in poverty.

13.5 Conclusion Assam is a land of floods which is an annual feature in the state which severely damages the crop, livestock, human lives and results in massive land erosion and deposition of sand. Dhubri district as a whole is one of the worst affected districts and particularly, the char areas are the worst affected geographical areas. Based on its inherent resources and advantages, Assam has the potential to achieve high agricultural productivity and production. The study of river dynamics along with land use in Dhubri, an active floodplain of a large tropical river is analysed. According to

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the findings, island area/chars have been gradually growing over the past thirty years. The data also shows that the floodplains are increasingly being used for human activities, particularly agriculture. For site suitability analysis for agriculture expansion, it is necessary to study elements such as soil qualities, meteorological conditions and socio-economic conditions. This study will aid in the proper use of chars for agricultural uses and enable decision-makers better grasp the issues with the dynamics of the Brahmaputra river bank. The locals typically produce seasonal crops haphazardly and have no notion whether these chars are stable or unstable. This study can assist locals in planting crops in accordance with changes in the Brahmaputra River’s water level and can assist the agriculture department in keeping an eye on those agricultural fields. In this way, the study can support greater regional food security.

References Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data, Professional Paper 964. https://doi.org/10.3133/pp964 Arora KM, Mathur S (2001) Multi-source classification using artificial neural network in a rugged terrain. Geocarto Int 16(3):37–44 Assam Budget Analysis (2022) Ministry of Finance, Government of Assam, India. Assam Human Development Report (2014) Directorate of Economics and Statics, Government of Assam, India. Best JL, Ashworth PJ, Sarker MH, Roden JE (2007) The Brahmaputra-Jamuna River, Bangladesh. In: Large rivers: geomorphology and management. https://doi.org/10.1002/9780470723722. ch19 Central Ground Water Board (2019–2020) Report on Aquifer Mapping and Management of Ground Water Resources Dhubri District, Assam, Annual Action Plan. Department of Water Resources, River Development and Ganga Rejuvenation, Ministry of Jal Shakti Government of India Chakraborty G (2014) Assam hinterland: society and economy in the char areas. Akansha Pub. House, New Delhi Das AK, Sah RK, Hazarika N (2012) Bankline change and the facets of riverine hazards in the floodplain of Subansiri-Ranganadi Doab, Brahmaputra Valley, India. Nat Hazards 64:1015–1028 Dimyati M, Mizuno K, Kitamura T (1996) An analysis of land use/cover change using the combination of MSS landsat and land use map: a case study in Yogyakarta, Indonesia. Int J Remote Sens 17(5):931–944 District Agriculture Contingency Plan for Dhubri, 2012 District Census Handbook 2011 Assam, Series 19, Part XII A Flood and Erosion problems, Government of Assam. Available at https://waterresources.assam.gov. in/portlets/flood-erosion-problems Goswami U, Sarma JN, Patgiri AD (1999) River channel changes of the Subansiri in Assam, India. Geomorphology 30(3):227–244. https://doi.org/10.1016/S0169-555X(99)00032-X Hazarika N, Das AK, Borah SB (2015) Assessing land-use changes driven by river dynamics in chronically flood affected Upper Brahmaputra Plains, India, using RS-GIS techniques. Egyptian J Remote Sens Space Sci 18:107–118 Hovius N (1998) Controls on sediment supply by large rivers. In: Shanley KW (ed) Relative role of eustasy, climate, and tectonism in continental rocks (Special publication / Society for Sedimentary Geology; 59). SEPM, Tulsa, OK, pp 3–16

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Hutton D, Haque CE (2003) Patterns of coping and adaptation among erosion-induced displaces in Bangladesh: implications for hazard analysis and mitigation. Nat Hazards 29:405–421. https:// doi.org/10.1023/A:1024723228041 Hutton D, Haque CE (2004) Human vulnerability, dislocation and resettlement: adaptation processes of river-bank erosion-induced displaces in Bangladesh. Disasters 28(1):41–62 Knighton D (1998) Fluvial forms and processes: a new perspective, 2nd edn. Routledge. https:// doi.org/10.4324/9780203784662 Kotoky P, Bezbaruah D, Baruah J, Sarma JN (2005) Nature of bank erosion along the Brahmaputra River Channel, Assam, India. Curr Sci 88(4):634–640. http://www.jstor.org/stable/24110266 Lahiri SK, Sinha R (2012) Tectonic controls on the morphodynamics of the Brahmaputra River System in the Upper Assam Valley, India. Geomorphology 169:74–85 Lahiri-Dutt K (2014) Chars-Islands that foat within rivers. Shima Inter J Res Island Cult 8(2):23–38 Land Utilization Statistics, Directorate of Economics and Statistics, Government of Assam, 2019– 2020 Mahanta, C, Saikia, L (2015) The Brahmaputra and other rivers of the North East. In: Iyer RR (ed) Living rivers in dying rivers. Oxford University Press, New Delhi Mishra V, Rai PK (2016) A remote sensing aided multi-layer perceptron-marcove chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian J Geosci 9(1):1–18. https://doi.org/10.1007/s12517-015-2138-3 Mishra VN, Rai PK, Singh P (2021) Geo-information technology in earth resources monitoring and management (edit. Book). Nova Science Publishers, USA. ISBN: 978-1-53619-669-6 Mishra VN, Rai PK, Kumar P, Prashad R (2016) Evaluation of land use/land covers classification accuracy using multi-temporal remote sensing images. Forum Geogr (Romania) 15(1):45–53 Mishra VN, Prashad R, Rai PK, Vishwakarma AK, Arora A (2018a) Evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multisensor remote sensing data. Earth Sci Inform, 2019 12(1):71–86. https://doi.org/10.1007/s12 145-018-0369-z Mishra VN, Rai PK, Rajendra P, Puniya M, Nistor MM (2018b) Prediction of spatio-temporal land use/land cover dynamics in rapidly developing Varanasi district of Uttar Pradesh, India, using geospatial approach: a comparison of hybrid models. Applied Geomatics (Springer). https://doi. org/10.1007/s12518-018-0223-5 Misra, R (2018) From farmland to wasteland: a study of out migration. Indian J Labour Econ 62(4). https://doi.org/10.1007/s41027-019-00196-3 Prashnani M, Qadir A, Goswami J, Raju P (2019) Spatio-temporal study of Brahmaputra River Islands (chars) for agriculture expansion in Assam, India. ISPRS—Int Arch Photogramm Remote Sens Spat Inf Sci XLII-3/W6:429–433. https://doi.org/10.5194/isprs-archives-XLII3-W6-429-2019 Rahman MA, Rahman MM (2012) Char formation process and livelihood characteristics of Char Dwellers of Alluvial River in Bangladesh. ICSE-6, Paris, August, pp 27–31 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. Available at https://doi.org/10.5775/fg.20674635.2014.041.i Rai PK, Mishra VN, Singh P (2021) Recent technologies for disaster management & risk reductionsustainable community resilience & responses (edit. Book). Springer Nature, Switzerland. ISBN: 978-3-030-76116-5. https://doi.org/10.1007/978-3-030-76116-5 Rai PK, Mishra VN, Singh P (2022) Geospatial technology for landscape and environment management: sustainable assessment & planning (edit. Book). Springer Nature, Singapore. ISBN: 978-981-16-7373-3. https://doi.org/10.1007/978-981-16-7373-3 Sabins FF (1987) Remote sensing: principles and interpretation. W.H. Freeman and Company, New York, pp 312–320 Saha AK, Arora MK, Csaplovics E, Gupta RP (2005) Land cover classification using IRS LISS III image and DEM in a rugged terrain: a case study in Himalayas. Geocarto Int 20(2):33–40

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Sarkar A, Garg RD, Sharma N (2012) RS-GIS based assessment of river dynamics of Brahmaputra River in India. J Water Resour Prot 4:63–72 Sarma JN, Phukan MK (2004) Origin and some geomorphological changes of Majuli Island of the Brahmaputra River in Assam, India. Geomorphology 60:1–19 Schumm SA, Khan HR, Winkley BR, Robbins LG (1972) Variability of river patterns. Nat Phys Sci 237(74):75–76 Shastri S, Singh P, Rai PK (2020a) A Land covers change dynamics and their impacts on thermal environment of Dadri Block, Gautam Budh Nagar, India. J Landscape Ecol 13(2):1–13 Shastri S, Singh P, Verma P, Rai PK, Singh AP (2020b) Assessment of spatial changes of land use/ land cover dynamics, using multi-temporal Landsat data in Dadri Block, Gautam Buddh Nagar, India. Forum Geogr XIX(1):72–79. https://doi.org/10.5775/fg.2020.063.i Singh S, Rai PK (2017) Application of earth observation data for estimation of changes in land trajectories in Varanasi District, India. J Landscape Ecol 11(1):5–18. https://doi.org/10.1515/jle col-2017-0017 Socio-Economic Survey Report (2003–2004) Char Area Development Authority, Government of Assam, India Tandon SK, Sinha R (2008) Geology of large river systems. In: Gupta A (ed) Large rivers: geomorphology and management. Wiley, UK

Chapter 14

GIS-Based Novel Ensemble MCDM-AHP Modeling for Flash Flood Susceptibility Mapping of Luni River Basin, Rajasthan Mit J. Kotecha, Gaurav Tripathi, Suraj Kumar Singh, Shruti Kanga, Gowhar Meraj, Bhartendu Sajan, and Praveen Kumar Rai

Abstract Water is the most valuable natural resource on the planet and is essential for life. No living thing can exist without water. Because the overall amount of water in the cycle is constant, even if the distribution changes across time and space, the hydrologic system is termed closed. This fluctuation in distribution causes two hydrological extremes: “Floods” and “Droughts.” A large increase in the number of extreme meteorological and hydrological events has been reported because of recent climatic changes as well as major changes in land usage. As a result, rising global average temperatures, which indicate an excess of energy in the atmosphere, and the conversion of more and more natural surfaces into constructed areas are the primary causes of an increase in the number of floods and flash floods around the world. Remote sensing and geographic information systems have evolved in recent decades M. J. Kotecha · G. Tripathi · B. Sajan Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, Rajasthan, India e-mail: [email protected] B. Sajan e-mail: [email protected] S. K. Singh (B) Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur, Rajasthan, India e-mail: [email protected] S. Kanga School of Environment and Earth Sciences, Department of Geography, Central University of Punjab, Bathinda, India e-mail: [email protected] P. K. Rai Department of Geography, Khwaja Moinuddin Chishti Language University, Lucknow, Uttar Pradesh, India G. Meraj Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 P. K. Rai (ed.), River Conservation and Water Resource Management, Advances in Geographical and Environmental Sciences, https://doi.org/10.1007/978-981-99-2605-3_14

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as instruments for performing hazard management and geospatial mapping using multi-criteria decision models. Identification of conditioning elements in a specific context is critical for spatially mapping flood susceptibility. Drainage density, proximity to the river and road, land use/land cover types, topographic wetness index (TWI), amount of rainfall, stream power index (SPI), lithology, flow accumulation, elevation, slope, aspect, curvature, and normalized difference vegetation index (NDVI). The methodology used in this research aids decision-making through statistical modeling (AHP). As a result, we are discussing the integration of geospatial multi-criteria decision-making (GIS-MCDM) with statistical (AHP) modeling. The final flood susceptibility map created using the AHP-GIS technique showed that 20.8% of the area was highly susceptible to flash floods, 51.5% was moderately susceptible, and 27.7% was not susceptible to flood. Keywords Flood · Drought · Hydrological extremes · TWI · NDVI · AHP · MCDM

14.1 Introduction 14.1.1 Flash Flood Susceptibility Water is the most valuable natural resource on the planet and is essential for life. No living thing can exist without water. It is crucial to the economy, food security, and social practices. The hydrological cycle, which is a continuous process with no beginning or finish, defines its life cycle. Because the overall amount of water in the cycle is constant, even if the distribution changes across time and space, the hydrologic system is termed closed. This fluctuation in distribution causes two hydrological extremes: “Floods” and “Droughts,” both of which have an impact on agricultural activity, living conditions, and the economy (Singh et al. 2021). The people and the government are both challenged by the occurrences of these hydrologic extremes. The IPCC Working Group found evidence that recent regional climate changes have already affected many physical, biological, and human systems (Bhatt et al. 2020). A large increase in the number of extreme meteorological and hydrological events has been reported as a result of recent climatic changes as well as major changes in land usage. (Meraj et al. 2015; Meraj et al. 2018; Li et al. 2019a, 2019b; Sajan et al. 2022; Kanga et al. 2022; Rather et al. 2022; Debnath et al. 2023). As a result, rising global average temperatures, which indicate an excess of energy in the atmosphere, and the conversion of more and more natural surfaces into constructed areas are the primary causes of an increase in the number of floods and flash floods around the world (Costache et al. 2020). Flash, urban, coastal, fluvial, and pluvial floods are among the five types of floods that can occur. Floods occur when large volumes of water exceed the river’s carrying capacity, producing inundation of the river’s banks (Mishra et al. 2021; Pal and Singha 2021; Rai and Mohan 2014; Rai et al. 2021,

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2022). Natural disasters are responsible for 40% of all socioeconomic losses worldwide, according to data (Rai et al. 2021; Souissi et al. 2020). Due to its unique geo-climatic conditions, precipitation patterns, geographical features, population expansion, urbanization, and industrialization, India is one of the most flood-prone countries in the world. According to the National Flood Commission, over 40 million hectares out of a total geographical area of approximately 329 million hectares are prone to flooding (Prakash et al. 2021). Remote sensing and geographic information systems have evolved in recent decades as instruments for performing hazard management and geospatial mapping using multi-criteria decision models (Arora et al. 2021; Brototi et al. 2022; Rai et al. 2017a, b). Various methods for studying flood risk have been developed during the last two decades, including analytical hierarchy process, fuzzy logic, and genetic algorithm, among others. By carefully assessing numerous impacting elements, the analytical hierarchy process model and geospatial techniques are the simplest way to determine flood risk regions (Das 2019). Identification of conditioning elements in a specific context is critical for spatially mapping flood susceptibility. Drainage density, proximity to the river and road, land use/land cover types, topographic wetness index (TWI), amount of rainfall, stream power index (SPI), lithology, flow accumulation, elevation, slope, aspect, curvature, normalized difference vegetation index (NDVI), and other factors are frequently used as flood contributing factors (Pal and Singha 2021; Rai et al. 2014, 2017a, b). Furthermore, the methodology used in this research aids decision-making through statistical modeling (AHP). As a result, we are discussing the integration of geospatial multi-criteria decision-making (GIS-MCDM) with statistical (AHP) modeling (Dikshit et al. 2022; Doke et al. 2021). The major goal of this methodology was to create a hierarchy prototype of a multi-criteria decision-making based on the Pairwise Comparison Method (PCM), which included delimitation, and evaluation of flood susceptibility (Bui et al. 2020; Souissi et al. 2020). This hierarchy model has been used to aid decision-makers in making decisions, primarily in the area of spatial planning, to resolve issues of inundation management through a temporal and spatial analysis of flood risk, in order to reduce the impact of floods on socioeconomic and environmental processes. The use of RS and GIS to demarcate flood-prone zones is a powerful technique that has been used in numerous studies. Then, in order to estimate the flood susceptible region, an analytical hierarchy process (AHP) was utilized, which is a multi-criterion mathematical method for assigning a specific weight and rank for each factor (Souissi et al. 2020). As a result of the combination of MCDM, GIS, AHP, and sensitivity analysis, this study proposes a flood hazard potentiality mapping and assessment approach in the Luni river basin. These strategies are an appropriate method that is commonly utilized for accurate flood susceptibility mapping.

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14.1.2 What Is Flood? A flood is an excessive flow or overflow of water from a river or other comparable source that lasts for a while (Chini et al. 2018). Flooding results from the rivers’ inability to contain the enormous volumes that are pushed down from the upper catchment areas after a lot of rain (Dano et al. 2019; Das 2018; Deshpande et al. 2021). Flood refers to both the inundation of low-lying areas and the discharge of a river under conditions of extreme rainfall. It has been noted that the geography, weather, and hydrology of the regions through which the rivers pass determine the specific characteristics of river floods. Some rivers frequently alter their path, making them unpredictable. However, as a result of increased human involvement with natural processes and encroachment on floodplains and even riverbeds, the damage caused by floods has tended to worsen over time. Floods have existed for as long as the rivers and hills themselves. The Rigveda and the Old Testament’s “Deluge” are the earliest sources of information on floods. They were thought to be divine retribution against humans for the massive destruction they continued to create even today. It is understood that floods are a natural occurrence that cannot be completely stopped. Man must consequently learn to adapt to floods and, if feasible, correct ecological errors (Fig. 14.1).

14.1.3 Causes of Flood • Over a short amount of time, extraordinarily heavy rain fell in the catchment. • Massive debris clogging the riverbed, changing the path of the river as a result. • Artificial barriers to the natural flow of rivers, such as insufficient channels on railroads, bridges, or embankments, are present. • By accumulating water from heavy rainfall, areas with poor drainage systems become inundated. • An aggravating cause for the issue of water logging is the excessive irrigation water applied to command areas as well as the rise in groundwater levels brought on by seepage from canals and irrigated fields. • The issue is made worse by elements including silting of riverbeds, decreased carrying capacity of river channels, erosion of beds and banks changing river courses, blockages to flow caused by landslides, synchronization of floods in the main and subsidiary rivers, and retardation due to tidal effects. • Failure of the hydraulic system and other safety measures. • Mangrove destruction and removal of non-regenerating trees. • Forest destruction and root system removal. • Storm surge and Tsunami.

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Fig. 14.1 The Vedic Deluge. Source The Shatapatha Brahmana (I.8.1.1)

14.1.4 Types of Flood 14.1.4.1

Riverine Flood

It happens when major rivers and their tributaries overflow, resulting in a significant amount of flooding. The rivers may rise gradually and, with a modest recession, continue to surge above danger points for several weeks. It may result in significant floods. River flooding can come in three different forms: slow-onset, rapid-onset, and flash floods (Agencies and NGOs, n.d.). • Slow on-set flood: This kind of flooding happens gradually and may linger for several weeks or even months. Snowmelt or persistent, steady rain are two potential causes. It can cause significant material losses, crop damage, and significant infrastructure damage to services like electricity supply, rail linkages, and highways. People will have the chance to leave at-risk areas if rising flood levels are predicted. • Rapid on-set flood: Both the main rivers’ headwaters in the mountains and the rivers that flow to the shore are more susceptible to this kind of flooding. It often

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lasts a few days. Due to the fact that there is typically less time to take preventive measures, these floods are potentially far more deadly than slow-onset floods and can result in a higher risk of loss of life and property. • Flash flood: A stream or creek rising quickly above a designated flood level within six hours of the triggering event, or a severe and sudden discharge of high water into a normally dry area (e.g., intense rainfall, dam failure, ice jam). Nevertheless, in other regions of the nation, the real time barrier might be different. In circumstances where severe rainfall causes a swift rush of rising floodwaters, ongoing flooding can become flash flooding. 14.1.4.2

Coastal Flood

When there is a surge of water from the ocean, a bay, or an inlet onto land, there are coastal floods. Storm surges brought on by cyclones, tropical storms, tsunamis, and tidal waves are one possible manifestation of this. Along the coast, it can include vast regions.

14.1.4.3

Urban Flood

Due to inadequate drainage infrastructure, this type of flood frequently occurs in urban areas.

14.1.5 Floods in India and Rajasthan With 113 million flood-prone residents, India is one of the world’s most flood-prone nations. According to a UN assessment, India experiences an estimated 9.8 billion USD in economic losses annually as a result of disasters, of which more than 7 billion USD are attributable to floods (Tomar et al. 2021). Estimates for the amount of precipitation that falls in India each year, including snowfall, range from 4,000 billion cubic meters (BCM) to 3,000 billion cubic meters (BCM) (3,000 BCM). The south-west monsoon (summer monsoon), which lasts for roughly 100 days starting in the first week of June and ending toward the end of September, brings the most rain to the Indian subcontinent. Over the Indian land mass, the normal area-weighted rainfall during this time is 89 cm. In India, the SW monsoon season accounts for almost 80% of all yearly precipitation (National Disaster Management Authority, n.d.). About 41 million hectares, or roughly 12%, of the country’s total geographic area of 328 million hectares, are thought to be at risk for flooding. Although the south-west monsoon season, which accounts for around 80% of the total annual precipitation sees the majority of floods, inundation of inhabited areas does happen occasionally throughout the year. There are times when one region of the country is suffering

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flooding while another is gripped by a severe drought (Manual, Management, and Manual, n.d.). The state of Rajasthan is among the driest in India. Despite a general lack of precipitation, the state has seen flooding during the monsoon season in numerous locations (Manual, Management, and Manual, n.d.). Rajasthan has a history of flooding and inundations, primarily in the basins of rivers like the Luni and Chambal, despite the fact that the majority of the state receives little rain. The state is home to 13 different river basins, including the Shekhawati, Ruparail, Banganga, Gambhiri, Parbati, Sabi, Banas, Chambal, Mahi, Sabarmati, Luni, West Banas, and Sukli. These are divided into various sub-basins, the largest of which are the Luni, Banas, and Chambal basins. The flood-prone areas of Rajasthan are depicted in Fig. 14.2. These comprise significant portions of the Luni River basins and subbasins in the districts of Barmer, Pali, Sirohi, and Jalore as well as the Chambal River basins and sub-basins in the Baran, Kota, and Bundi districts. Floods are a risk in several areas of Bharatpur districts, particularly those in the River Banganga and River Ghaggar basins in Sriganganagar. Among the causes of flooding in these areas are an excessive amount of rainfall in the catchment; a sudden release of huge amounts of water from dams or water reservoirs; and breaches or damage to large reservoirs or dams, a small capacity for holding (Naghibi et al. 2015; Meraj et al. 2018; Nazmul et al. 2021; Nsangou et al. 2022; Pandey et al. 2021; Parmar et al. 2021; Pradhan et al. 2021; Ministry of Jal Shakti, Department of Water Resources 2022). There are other causes of inundation besides floods in these natural drainage systems. Numerous locations that were not traditionally prone to flooding now face a higher danger of flash floods as a result of changes in rainfall patterns (Farooq et al. 2022; Sahu et al. 2021; Saleh et al. 2022; Singh et al. 2014; Sud et al. 2023; Talha et al. 2019; Tehrany et al. 2014; Vishwanath and Tomaszewski 2018; Wang et al. 2020). The 2006 Barmer flood was a revelation that forced disaster managers and policymakers to reevaluate the dangers and vulnerability associated with floods in the State. The most vulnerable populations to flooding are those who live in the low-lying regions of the aforementioned basins (SDMP 2014) (Table 14.1). The irregular and sporadic nature of Rajasthan’s floods makes developing a system more challenging. Throughout the whole monsoon season, Rajasthan had heavy rainfall on a vast scale. However, according to the monsoon report from 2015, which shows that 29 tehsil received more than 60% of the average normal rainfall, three districts Jaisalmer, Barmer, and Jalore fall under the category of abnormal rainfall, which can lead to flash flood-like conditions (Government of Rajasthan 2015) (Fig. 14.3). The 2017 monsoon season had a lot of fluctuation in rainfall. Rajasthan’s central and western regions experienced normal and unusual rainfall, while the eastern region of Rajasthan experienced a deficiency. In Sirohi, Jalore, Pali, Barmer, and certain regions of Jodhpur, flooding or flooding-like conditions occurred. The state typically receives 594.9 mm of rainfall per year, with the monsoon season accounting for 75 to 95% of that total (Monsoon Report 2017 Water Resources Department 2017). The

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Fig. 14.2 Flood prone area map of Rajasthan. Source Flood manual Rajasthan, Disaster Management and Relief Department

devastation caused by flash floods is shown in Figs. 14.4, 14.5, 14.6, 14.7, 14.8, 14.9, and 14.10. According to the Indian Metrological Department, the Kota Zone in the South East of Rajasthan experiences an average monsoon rainfall of 762.38 mm. During the 2019 monsoon, Zone received 1363.85 mm in contrast. which is excessive by 78.92%. The Kota Zone includes the districts of Kota, Baran, Bundi, and Jhalawar (Fig. 14.11).

14.1.6 Flash Flood in Luni River Luni is a lifeless river, thus every flow of water that is worth talking about makes headlines. The situation was the same in 1979, and in fact, residents of the Luni River watershed and the rest of Western Rajasthan were eagerly anticipating even a

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Table 14.1 Basin wise Flood Prone Area in Rajasthan Sr. No

Name of basin

Name of sub basin

District name

Town/village

1

Luni

Luni

Ajmer Barmer Jalore

Ajmer City Balotra, Sindri, Guda Chitalwana, Bhawatra

2

Luni

Luni

Jodhpur

Bilada

3

Luni

Luni

Jodhpur

Kakelav, Kankani, Dudiya

4

Luni

Jojari

Jodhpur

Benar, Barilya, Kalyanpura

5

Luni

Bhundh Hemawas

Pali

Pali City, Kharchi, Gurwara

6

Luni

Sukri

Pali Jalore

Rani, Chanod Rama, Bhavrani, Debawas

7

Luni

Jawai

Jalore

Ahore, Jalore

8

Luni

Bandi

Sirohi Jalore

Pandiv, Jdwal Siynna, Bagra

9

Luni

Sngi

Jalore

Jaswatpura, Nimbawas

10

Sukli

Sukli

Sirohi

Karaunti

11

West Banas

West Banas

Sirohi

Abu Road

12

Banas

Banas

Udaipur

Udaipur city

13

Banas

Berach

Chittorgarh

Chittorgarh City, Sambhupura (continued)

brief break in the monsoon season. A 70-to-80-h period of almost nonstop torrential rain that at times reached the intensity of a veritable downpour began around 11:00 PM on Sunday, July 15, 1979. Many locations had rainfall on just one day that was nearly equal to the average yearly rainfall for the entire year. In the impacted area, the cumulative rainfall of 700 to 850 mm that some stations received in just 5 days broke all previous records. Because most of the Upper Luni basin was spared from the downpour, the tremendous runoff was immediately condensed into the streams, giving the river an extraordinary fury. On July 16, 1979, at 9:00 a.m., the water level at Pipar in Mitri-Jojri, one of Luni’s tributaries, was only 25 to 30 cm, but within a few hours, it had risen to 500 cm. In just five hours, the water level in the Luni proper at Bhavi jumped from 75 to 400 cm, and in just six hours, the Bandi River at Jetpur rose from 60 to 392 cm. The streams also reached a record-breaking final flood level. With 179.52 m above mean sea level, the railway bridge across the Luni near Luni In. holds the highest flood mark ever observed. This is located 3.35 m above ground. This year, the flood level exceeded the previous record by about 2 m (JuJy 1979 Flash Flood in the Luni 1979). The flood’s size and pace were both much above anything the populace could have imagined. This explains the extraordinary loss of the priceless lives of people and animals. 119 persons were listed as missing, and there were close to 350 fatalities. More than a lac animal perished.

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Table 14.1 (continued) Sr. No

Name of basin

Name of sub basin

District name

Town/village

14

Banas

Banas

Bundi

Khatoli, Tonk-Uniara

15

Banas

Morel

Jaipur

Jaipur, Sanganer

16

Banas

Mashi

Jaipur

Bichun

17

Mahi

Som

Udaipur

Chillyand

18

Mahi

Som

Udaipur

Jhadol

19

Chambal

Mej

Bundi

Bundi City

20

Chambal

Chambal

Kota

Kota City

21

Chambal

Chambal

Kota

Kathun

22

Chambal

Kali Sindh

Kota

Khajuri

23

Chambal

Kali Sindh

Kota

Sangod

24

Chambal

Kali Sindh

Jhalawar

Jhalawar City

25

Chambal

Kali Sindh

Jhalawar

Richwa

26

Chambal

Parwan

Jhalawar

Manohar Thana

27

Chambal

Parwati

Baran

Chhabra, Baran, Karaiahat

28

Chambal

Parwati

Baran

Baran Town

29

Banganga

Banganga

Bharatpur

Kaman, Pahi, Bharatpur, Deeg, Bayana, Roopwas

30

Sabi

Sabi

Alwar

Kotkasim, Tapukra, Patiabad

31

Shekhawadati

Mehdha

Nagaur

Kuchaman

32

Ghaggar

Ghaggar

Sriganganagar

Hanumangarh, Pilibanga, Suratgarh, Jetsar, Srivijaynagar

Source State Disaster Management Authority, Rajasthan

Since there have been numerous periods in the past when some area of the Luni Basin has experienced such spells, the phenomenon is not unique for the Basin as a whole. For instance, Pindwara experienced 476 mm of rainfall on August 31, 1973. In 1973, Bali recorded a total of 583 mm in four days, and Erinpura had 350 mm of rain every day for two straight days. In 1952, Desuri received 660 mm of rain in 8 days, and Sanchor received 93 rona of rain in 5 days. Desuri also received 475 mm in five days in August 1944. In the year 1926, Kharchi received 356 mm during the course of two days.

14.1.6.1

Damage Due to Flood in Luni River

• There have reportedly been 337 flood-related deaths, including those from house collapse and other causes, while 119 people have been reported missing.

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Fig. 14.3 Rajasthan Monsoon map of 2015. Source Water Resource Department, Rajasthan

Fig. 14.4 Canal Break due to flash flood

• There have been a total of 99,686 dead livestock heads, including cattle, camels, sheep, and goat. Among these, the Jodhpur district alone lost 61,435 heads totaling Rs. 300 lacs.

278 Fig. 14.5 Road blocked rainwara-bhinmal, kodi river

Fig. 14.6 Toll plaza, Jalore

Fig. 14.7 Sumerpur, Pali

M. J. Kotecha et al.

14 GIS-Based Novel Ensemble MCDM-AHP Modeling for Flash Flood … Fig. 14.8 Luni river flowing over railway track near Balotra

Fig. 14.9 Village dhola near sanderao, Pali

279

280 Fig. 14.10 Jalore, NCP Colony

Fig. 14.11 Flood Condition in Rajasthan during 2019 flooding event

M. J. Kotecha et al.

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• In total, 63,464 homes (both pucca and kachcha) were either totally destroyed or severely damaged. Damage incurred on this account totals Rs. 221.02 lacs in the district of Pali alone, but it is larger in the district of Barmer, at Rs. 1103.1 lacs. • Standing crops covering an area of 67,481, 4800, and 14,905 ha have all been destroyed in the districts of Pali, Barmer, and Jalore, respectively. Only in Pali and Barmer is a loss of Rs. 118.25 lacs reported. • The network of roads, bridges, causeways, and culverts was unable to withstand the force of the unforeseen floodwaters as well as the onslaught of severe rainfall. As a result, all traffic communications were entirely cut off, severely damaging the roadways as well as the cross-drainage infrastructure.

14.2 Study Area ‘Rediya randka kare, Luni lahran khaye, Baandi baapdi ka kare, Guhiya soon ghar jaye’ (Rediya makes lot of noise, Luni makes waves, Baandi cannot do any harm to anyone and Guhiya rises very fast, flooding the houses) This couplet in Marwari depicts the water flows Luni and its tributaries must have carried in eras gone by. The Thar Desert extends across a considerable portion of Rajasthan. As a result, there aren’t many rivers in the state’s western region. The sole integrated drainage basin (34,866 km2) in northwest desert India is formed by the Luni, one of the three main rivers that flow through Rajasthan. However, its primary role is acting as the first natural barrier to stop sand from spreading to the east. It divides the humid climate of the east from the aridity of the west (Moudgil 2016). The Saraswati and the Sabarmati, two branches of the Luni that have their origins close to Pushkar, unite in Govindgarh, whereupon the river acquires its name “Luni” (Fig. 14.12). The word “Luni” comes from the Sanskrit word “Lavana,” which means salt. It is thought that the town of Balotra is the sole place where its waters are sweet. The 550 m msl height of the western Aravalli range, close to Ajmer, is where the Luni River begins to flow. The River in Rajasthan travels 495 km in a south-westerly direction before it vanishes in the swampy Rann of Kutch. Between latitudes 23°41’ and 27°05’ and longitudes 71°04’ and 74°42’, the Luni Basin catchment region in Rajasthan spans 69,302 km2 . It includes portions of the districts of Ajmer, Barmer, Bhilwara, Jaisalmer, Jalore, Jodhpur, Nagaur, Pali Rajsamand, Sirohi, and Udaipur. Figure 14.11 depicts the general layout of Luni Basin (Sea 2014) (Table 14.2).

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Fig. 14.12 Location of study area

Table 14.2 Administrative setup of Luni basin Sr. No

District name

Area (sq.km)

% of Basin area

Total number of blocks

Total number of town or village

1

Ajmer

1903.5

5

4

310

2

Barmer

6623.3

17.5

5

422

3

Bhilwara

2.9

–

1

1

4

Jalore

8824.4

23.3

8

566

5

Jodhpur

3496.6

9.3

4

206

6

Nagaur

1892.5

5

4

182

7

Pali

12,375

32.8

10

960

8

Rajsamand

420.7

1.1

3

42 156

9

Sirohi

2075.1

5.5

5

10

Udaipur

182.7

0.5

2

10

Total

37,796.4

100

46

2855

Source Plate Ii et al. (2013)

14.2.1 Topography Following the general slope of the topography within the basin, the Luni river runs from northeast to southwest. Due to the northwesterly slopes of the Aravali hills, the river tends to flow first in a NW direction before turning in a SW direction and flowing into the Rann of Kutch. The presence of hills causes the highest elevations

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to reach more than 1000 m in the districts of Pali, Rajsamand, Sirohi, and Udaipur, while the lowest elevations are typically found in Barmer, Jodhpur, Jalor, Nagaur, and the western parts of Pali district, where they range from 150 to 300 m. The terrain at Barmer and Jalor is similar to that of the mean sea level (Plate Ii et al. 2013). In the Luni Basin, there are 12 sub-basins, including Bandi (770.11 km2 ), Bandi (Hemawas), 1,649.30 km2 , Guhiya 3,854.20 km2 , Jawai 2,640.50 km2 , Jojri 7,046.00 km2 , Khari 2,638.30 km2 , Khari (Hemawas), 1,098.60 km2 , Luni 42,521.10 km2 , and Mithari 1,702.70 km2 . Below is a quick summary of significant tributaries of the Luni river. • Guhiya River: The river Guhiya has its beginnings in the Pali District hills close to the settlements of Khariyaniv and Tharasani. Near the Pali District settlement of Phekariya, it enters the river Bandi. The Pali district is where the catchment is located. Raipur Luni, Radia Nadi, Guria Nadi, Lilri Nadi, Sukri, and Phunpharia Bala are the principal tributaries of Guhiya. • Khari River (Hemawas): Small streams Somesar and Khari Kherwa come together to form the River Khari. The Somesar River, formerly known as Sumer Nadi, has its beginnings in the western Aravalli range, close to the village of Somesar in the Pali District. Sumer Nadi is joined by the stream Umrawas Ka Nalla, which originates on the western Aravalli hills close to the town of Kanklawas. After flowing for around 30 km, Kotki Nadi, which comes from Dewair Reserved Forest Bhakar, joins Sumer Nadi. The river is known as Khari once it joins all of these tiny streams. After running for roughly 25 km, it joins the Bandi River downstream of Hemawas reservoir. • Bandi River (Hemawas): The Bandi River is formed when the Khari and Mithari rivers converge at the Bombadra pickup weir. After running for roughly 45 km, it merges with the Luni close to the settlement of Lakhar in the Pali District. The Pali District is where the catchment is located. • Mithari River: A confluence of nearby nallahs gives rise to the River Mithari on the south-western slopes of the Aravalli mountain in Pali District. The river travels roughly 80 km in a north-westerly direction through the districts of Jawai, Bali, and Falna before it disappears in sand plains close to the village of Sankhwal in the Jalore district. It includes portions of the Pali and Jalore Districts. • Sukri River: The minor nallahs Ghanerav Nadi, Muthana ka Bala, Magai Nadi, etc., which originate from the Aravallis in Pali and Udaipur Districts, come together to form River Sukri. It feeds Bankli Dam with its 110 km of flow in a south-east to north-west direction. Near the settlement of Samdari in the Barmer District, it enters River Luni. A portion of the Jalore, Pali, and Barmer Districts are included in this sub-basin. • Jojari River: Before joining the Luni River at the hamlet of Khejadli Khurd in Jodhpur District, the River Jojari flows for about 83 km from north to south-west, beginning in the hills near the village of Pondlu in Nagaur District. The districts of Jodhpur and Nagaur are included in the catchment. In the upper stages, a variety of little streams merge into this river.

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• Jawai River: In the Udaipur district, on the western slopes of the Aravallis, the River Jawai and its principal tributary Sukari have its beginnings. For over 96 km, the river normally meanders in a north-westerly direction before entering River Khari close to the settlement of Sayala in the Jalore District. • Khari River: The Sirohi District’s Shergaon village lies close to the south-western slopes of the Aravali hills, where the River Khari has its source. Before entering the Sukri river near Sayala village in the Jalore district, it travels northwest for about 64 km. The catchment area includes the districts of Sirohi and Jalore. Krishnawati and Kameri are the two principal tributaries of the Khari river. • Sukri River (Sayala to Luni): After meeting the Khari River, the River Jawai becomes known as the Sukri. It travels for roughly 80 km in a southwesterly direction until entering the Luni river close to the settlement of Golia. The catchment includes portions of the districts of Jalore and Barmer. • Bandi River: Kapal Ganga Nadi, which has its origins in the hills close to Seankra village, and Jaswantpura Nallah, which has its origins in the hills close to Nivaj village in Sirohi District, come together to form River Bandi. After traveling 65 km in a roughly northwesterly path, it finally disappears in the west. • Sagi River: The Jaswantpura hills in the Jalore district are the source of the river Sagi. Before joining the Luni near Gandhav hamlet in the Barmer district, it first runs for around 72 km northwest and then southwest. The only branch of the river Sagi is the Kari Nadi. The catchment includes portions of the districts of Jalore and Barmer.

14.2.2 Climatological Features 14.2.2.1

Rainfall

The annual rainfall in the Luni Basin varies spatially and ranges from 221.5 to 1,048.10 mm, with a mean of 388.20 mm. In the Luni Basin, there are typically 19 rainy days each year, ranging from 12 to 38 days.

14.2.2.2

Wind Speed

With a mean value of 4.38 km/hr, the average daily wind speed in the Luni Basin ranges from 1.9 to 7.16 km/hr.

14.2.2.3

Relative Humidity

In Luni Basin, the mean annual daily relative humidity ranges from 43.50 to 60.54%.

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14.2.3 Drainage From Rajasthan’s primary water divide, the Aravalli Hill Ranges. The only river west of Aravallis is the Luni. The streams in the remaining western Rajasthan, which make up around 60% of the state’s land area, are internally drained and flow only briefly from their site of origin before disappearing into the desert sands. The essence of Luni is that it is a transient stream with a 16-year flood cycle. The direction of drainage in western Rajasthan is west and south-west. The primary drainage is toward the north-east in the eastern Aravalli ranges.

14.3 Materials and Methodology The Luni basin in the Indian state of Rajasthan was chosen as the study region for the current endeavor. High-resolution SRTM DEM and Sentinel-2 data have been utilized to examine groundwater potential zones and the susceptibility to flash floods. Sentinel-2 data has been used to determine NDVI, land use, and land cover. The Copernicus data hub was used to download Sentinel-2 data. Following download, the satellite images were stacked and layered with particular bands to aid in the study. The maximum likelihood technique was used in a supervised manner to carry out the LULC classification.

14.3.1 Data Used Following is information on the satellite data, auxiliary data, and software utilized to achieve the study’s objectives.

14.3.1.1

Satellite Data

Monitoring and evaluation of LULC and NDVI are made easier by the quick revisit time and excellent spatial resolution of Sentinel-2 images. Sentinel 2 satellite mission consists of two satellites, Sentinel 2A and 2B, which were launched back-to-back in June 2015 and July 2016. Both sensors are equipped with MSI (multispectral instrument) and have 13 spectral bands (Red, Green, Blue, NIR, and SWIR) with high spatial resolutions of 10 ms, 20 ms, and 60 ms, respectively. The multispectral instrument has two focal planes for visible (1 m) and SWIR (1–25 m) spectral bands. Through the website https://scihub.copernicus.eu/, satellite images are obtained from the Copernicus open-access hub. Level 0, Level-1, and Level-2 satellite photos are provided by Sentinel-2 (Table 14.3).

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Table 14.3 Details about Bands of Sentinel-2

14.3.1.2

Band

Details of band

Resolution (meters)

Wavelength (μm)

1

Coastal Aerosol

60

0.443

2

Blue

10

0.490

3

Green

10

0.560

4

Red

10

0.665

5

Vegetation Red Edge

20

0.705

6

Vegetation Red Edge

20

0.740

7

Vegetation Red Edge

20

0.783

8

NIR

10

0.842

8A

Vegetation Red Edge

20

0.865

9

Water Vapour

60

0.945

10

SWIR-Cirrus

60

1.375

11

SWIR

20

1.610

12

SWIR

20

2.190

Auxiliary Data

• Administrative and basin boundary was provided by groundwater department Rajasthan • Rainfall data of last 50 years was provided by water resource department Rajasthan • Temperature, humidity, and wind speed data were provided by Indian Meteorological Department (IMD) Jaipur • The geological survey of India release the geology, geomorphology, aquifer, and depth of water level of Luni basin • Lineament of Luni basin was derived from Bhuvan India • Flood figures were provided by state disaster management authority.

14.3.2 Software The following software for image processing and map construction was used in the current study.

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ArcMap 10.3

It is one of the key elements created by Esri. It is used to make maps and conduct analyses. control geographic information. The geographic information model, analyzing, and storing databases and files are the four components of Arc Map. The program is utilized to create cartographic maps. Various layers and map components, including latitude, longitude, scale, legend, north arrow, and title, are provided by the Arc Map.

14.3.2.2

ERDAS Imagine 2014

Erdas Consider software that can convert between several image formats and has a number of change detection techniques. Different NDVI and LULC models were created in the current investigation.

14.3.2.3

MS Word

Reports must be prepared using MS Word. The Microsoft Office was created in 1988 to be used for document creation and presentation creation. One of the most popular programs within MS Office, MS Word was created by MS Office.

14.3.2.4

MS Excel

For storing and analyzing statistical data, it is a spreadsheet tool. With MS Excel, a variety of calculations and graphs may be created. Statistics are used in the software.

14.3.3 Layer Stack The act of stacking and combining bands into a single layer is known as layering. For the proper visibility of land use and cover features in the current study, we have layered the blue, green, red, NIR, and SWIR bands. Bands 2, 3, 4, 8, and 11 in Sentinel-2 satellite images are layered.

14.3.4 Methodology A study’s information is identified, chosen, processed, and analyzed using tools and processes known as methodology. It is a critical assessment of the general validity and dependability of a study. In order to reach the findings, methodology therefore uses both quantitative and qualitative data. Methodology refers to the methodical

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design of a study to guarantee valid and trustworthy results that address the goals and objectives of the investigation.

14.3.4.1

Flash Flood Susceptibility AHP Modeling

Geospatial and hydrological information characteristics are essential for identifying the research area’s flood-prone zones. Therefore, factor evaluation or determination is a crucial task for hazard mapping, including flood mapping, landslide mapping, soil mapping, gully mapping, etc. (Pal and Singha 2021). In order to reduce food danger and collect rainfall, flood risk assessment is essential. One of the many methods used for mapping food risk is the MCDM employing AHP in a GIS environment. Various studies were consulted in order to determine the primary flood evaluation criteria. The importance of fourteen factors in creating floods was discovered, and they were utilized to predict flood-prone zones. These primary criteria were divided into five categories based on their similar properties and coherence: (I): hydrological criterion: rainfall, drainage density, and SPI; (II): morphometric criterion: elevation, slope, curvature, aspect, geomorphology, and distance from rivers; (III): permeability criterion: TWI and rainfall; (IV): LU/LC dynamics criterion: LU/LC, and NDVI; (V): anthropogenic interference: distance from roads. All criteria were precisely defined and prepared as raster datasets. They were ranked according to the opinions of professionals in the fields of local administration, local professionals, meteorology, disaster management, and soil management, and then their weights were evaluated using AHP. The weightage linear combination method of AHP was used to create a final flood susceptibility map after conducting a multi-criteria analysis. Figure 14.13 shows a flow chart that illustrates how the study was conducted. Using the WGS84/UTM/Zone 43 North coordinate system, the ArcGIS 10.3 software environment analyzed all of the primary factors and sub-criteria affecting floods in the research area. The ranges of each parameter were divided into five susceptibility categories (5: very high, 4: high, 3: moderate, 2: low, and 1: very low) based on how likely they were to be in the flooding zone. To determine the susceptibility of a major criterion, the susceptibility of each individual criterion was assessed separately and then combined. Based on the following data, the major and supporting criteria were chosen and estimated (Table 14.4).

Hydrological Paradigm Three factors made up the hydrological criterion: rainfall, drainage density, and SPI. For the purpose of mapping rainfall, 46 weather stations dispersed around the Luni basin were treated as independent grids. Krigging, a type of geostatistical interpolation, was used to gather and distribute over the research area the average precipitation data for the previous 50 years. Utilizing the “line density” function of the ArcGIS software environment, the drainage network density (measured in km/km2 )

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Fig. 14.13 Flow chart for flash flood susceptibility

was calculated and mapped. The ratio of the total length of “river segments” to the entire “drained area” or drainage basin was used to estimate it. SPI is described by Jebur et al. (2014) as the rate of discharge, with power of erosion of the flowing water, inside a certain location, i.e., the workflow in a river basin, and may be given using the equation given below. SPI = (As tanβ), where As is the basin area (m2 /m3 ), and β is the radiant of slope (in degree)

19.59–31.67 2.34–12.25

Km/km2

Level

Stream Power Index (SPI)

4.1–37.3 292.5–360 0.3–0.48 hills

rad/m

(°)

km

Level

Curvature

Aspect

Distance from river

Geomorphology

Topographic Wetness Index (TWI)

(E) Permeability Paradigm

Distance from road

Level

m

Level

NDVI

(D) Anthropogenic Interference

Level

Land use land cover

>4.44

0–745