Sustainability of Water Resources: Impacts and Management (Water Science and Technology Library, 116) 3031134664, 9783031134661

Citation preview

Water Science and Technology Library

Basant Yadav · Mohit Prakash Mohanty · Ashish Pandey · Vijay P. Singh · R. D. Singh   Editors

Sustainability of Water Resources Impacts and Management

Water Science and Technology Library Volume 116

Editor-in-Chief V. P. Singh, Department of Biological and Agricultural Engineering & Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA Editorial Board R. Berndtsson, Lund University, Lund, Sweden L. N. Rodrigues, Embrapa Cerrados, Brasília, Brazil Arup Kumar Sarma, Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India M. M. Sherif, Civil and Environmental Engineering Department, UAE University, Al-Ain, United Arab Emirates B. Sivakumar, School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, Australia Q. Zhang, Faculty of Geographical Science, Beijing Normal University, Beijing, China

The aim of the Water Science and Technology Library is to provide a forum for dissemination of the state-of-the-art of topics of current interest in the area of water science and technology. This is accomplished through publication of reference books and monographs, authored or edited. Occasionally also proceedings volumes are accepted for publication in the series. Water Science and Technology Library encompasses a wide range of topics dealing with science as well as socio-economic aspects of water, environment, and ecology. Both the water quantity and quality issues are relevant and are embraced by Water Science and Technology Library. The emphasis may be on either the scientific content, or techniques of solution, or both. There is increasing emphasis these days on processes and Water Science and Technology Library is committed to promoting this emphasis by publishing books emphasizing scientific discussions of physical, chemical, and/or biological aspects of water resources. Likewise, current or emerging solution techniques receive high priority. Interdisciplinary coverage is encouraged. Case studies contributing to our knowledge of water science and technology are also embraced by the series. Innovative ideas and novel techniques are of particular interest. Comments or suggestions for future volumes are welcomed. Vijay P. Singh, Department of Biological and Agricultural Engineering & Zachry Department of Civil and Environment Engineering, Texas A&M University, USA Email: [email protected] All contributions to an edited volume should undergo standard peer review to ensure high scientific quality, while monographs should also be reviewed by at least two experts in the field. Manuscripts that have undergone successful review should then be prepared according to the Publisher’s guidelines manuscripts: https://www.springer.com/gp/ authors-editors/book-authors-editors/book-manuscript-guidelines

Basant Yadav · Mohit Prakash Mohanty · Ashish Pandey · Vijay P. Singh · R. D. Singh Editors

Sustainability of Water Resources Impacts and Management

Editors Basant Yadav Department of Water Resources Development and Management Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

Mohit Prakash Mohanty Department of Water Resources Development and Management Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

Ashish Pandey Department of Water Resources Development and Management Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

Vijay P. Singh Department of Biological and Agricultural Engineering Texas A&M University College Station, TX, USA

R. D. Singh Department of Water Resources Development and Management Indian Institute of Technology Roorkee Roorkee, Uttarakhand, India

ISSN 0921-092X ISSN 1872-4663 (electronic) Water Science and Technology Library ISBN 978-3-031-13466-1 ISBN 978-3-031-13467-8 (eBook) https://doi.org/10.1007/978-3-031-13467-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

Part I

Water Resources Management

1

Water: How Secure Are We Under Climate Change? . . . . . . . . . . . . . Vijay P. Singh and Qiong Su

2

Influence of Stemflow Measurement on Interception Estimation Under Eucalyptus Plantations . . . . . . . . . . . . . . . . . . . . . . . Chitra Shukla, K. N. Tiwari, and S. K. Mishra

3

25

3

Strategic Human Resources in Water Sources Development . . . . . . . Anand Verdhen

4

Water Budget Monitoring of the Ganga River Basin Using Remote Sensing Data and GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gagandeep Singh and Ashish Pandey

51

Evaluation of SWAT Model for Simulating the Water Balance Components for the Dudh Koshi River Basin in Nepal . . . . . . . . . . . . Vijay Kumar Yadav, M. K. Nema, and Deepak Khare

63

5

6

Rejuvenating Water Wisdom: A Route to Resilience . . . . . . . . . . . . . . R. N. Sankhua

7

Reliability Analysis of Water Distribution Network: A Case Study of Bole and Yeka Sub-city of Addis Ababa, Ethiopia . . . . . . . . Mitthan Lal Kansal, Bahar Adem Beker, Tadese Gindo Kebebe, and Shweta Rathi

37

79

95

8

HEC-HMS and Geo-HMS Based Flood Hazard Modeling of an Industrial Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Dhananjay Singh, S. K. Mishra, and Prabeer Kumar Parhi

9

A Stochastic Model-Based Monthly Rainfall Prediction Over a Large River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Sabyasachi Swain, S. K. Mishra, Ashish Pandey, and Deen Dayal

v

vi

Contents

10 Study of Meteorological Drought Using Standardized Precipitation Index in Chaliyar River Basin, Southwest India . . . . . 145 Mohd Izharuddin Ansari, L. N. Thakural, Quamrul Hassan, and Mehtab Alam 11 Estimation of the Function of a Paddy Field for Reduction of Flood Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Nobumasa Hatcho, Keigo Yamasaki, Okumura Hirofumi, Masaomi Kimura, and Yutaka Matsuno Part II Water Quality Management 12 Environmental Tracers in the Identification of the Groundwater Salinity—Case Studies from Northwest India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Gopal Krishan, Bhishm Kumar, M. Someshwar Rao, Brijesh Kumar Yadav, Mitthan Lal Kansal, Rahul Garg, Mohit Kumar, and Ravi Kumar 13 A Regional Case Study for Flow of Lead (Pb) and Chromium (Cr) Through Solid Waste Management System . . . . . . . . . . . . . . . . . . 199 Mayank Gupta, Amit Kumar, Sudhir Kumar, and Mahesh Kumar Jat 14 Performance Analysis of Constructed Wetland Treating Secondary Effluent Under Cold Climatic Conditions in Hamirpur (H.P.), India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Adarsh Singh, Akash Rawat, Surjit Singh Katoch, and Mukul Bajpai 15 Exploring Challenges in Effective Wastewater Treatment for Dairy Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 Anjali Bansal and Arun Kumar 16 Impacts of Agriculture-Based Contaminants on Groundwater Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Pooja Singh, Abhay Raj, and Basant Yadav Part III Irrigation Management 17 Assessment of Productivity Based Efficiencies for Optimal Utilization of Water Resources in a Command . . . . . . . . . . . . . . . . . . . 265 R. K. Jaiswal, Chanchal Kumari, R. V. Galkate, and A. K. Lohani 18 Two-Components Flow Regulating Drip Emitter—Design, Simulation and Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283 M. Raj Kumar, P. B. Ahir, Manoj K. Mondal, and K. N. Tiwari 19 An Automated Wireless Irrigation System: Without Internet Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 M. Raj Kumar, D. Mrinmoy, Manoj K. Mondal, and K. N. Tiwari

Contents

vii

20 IoT Based Automated Irrigation Management Technique for Climate Smart Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 G. T. Patle and Tshering Sherpa Part IV Impact of Climate Change 21 Sustaining Water Sources Under Climate Change—A Regional Scale Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 T. I. Eldho and Navya Chandu 22 Diagnosing the Combined Impact of Climate and Land Use Land Cover Changes on the Streamflow in a Mountainous Watershed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Srishti Gaur, Ch. Naga Tulasi Krishna, Arnab Bandyopadhyay, and Rajendra Singh 23 Understanding Adaptation Strategies to Climate Change . . . . . . . . . 359 Juna Probha Devi, Chandan Mahanta, and Anamika Barua 24 Sustainable Water Resources in Rural Areas: Impact of Land Use and Climate Change on Surface Water Groundwater Interactions at Lake Tana, Ethiopia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375 Tibebe B. Tigabu, Paul D. Wagner, Georg Hörmann, and Nicola Fohrer 25 Sustainable Urban Water Management and Development: Issues, Challenges and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401 Deepak Khare, Sourav Choudhary, and Santosh Murlidhar Pingale Correction to: Sustainable Water Resources in Rural Areas: Impact of Land Use and Climate Change on Surface Water Groundwater Interactions at Lake Tana, Ethiopia . . . . . . . . . . . . . . . . . . . . Tibebe B. Tigabu, Paul D. Wagner, Georg Hörmann, and Nicola Fohrer

C1

Editors and Contributors

About the Editors Dr. Basant Yadav is Assistant Professor at the Department of Water Resource Development and Management, Indian Institute of Technology Roorkee, India. His areas of interest are groundwater and contaminant hydrology, Water quality, Optimal remediation system design, Artificial Recharge, Rainwater harvesting, Conjunctive water use planning, Managed Aquifer Recharge (MAR) and its impacts on groundwater quantity and quality, Integration of experimental, numerical, and data-based modeling in groundwater management studies. Prof. Yadav was awarded the “Rien van Genuchten Early-Career award of Porous Media for a Green World” by the International Society for Porous Media (InterPore) for his work in the area of porous media. Mohit Prakash Mohanty is currently working as Assistant Professor at the Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee. His area of research is flood risk management, flood hazard mapping, vulnerability analysis, climate change impact assessment, population exposure, flood resilience mechanisms, hydrological modeling, design rainfall analysis for data-poor catchments, statistical downscaling, water and wastewater treatment, and bio-kinetics modeling for pollutant removal. Prof. Ashish Pandey has a Ph.D. in Soil and Water Conservation Engineering and works in the area of irrigation water management, micro-irrigation, OFD works, soil and water conservation engineering, watershed modeling, remote sensing and GIS applications, and water resources. He has completed nine sponsored R&D projects, and seven R&D projects are ongoing which have been sponsored by various organizations. Prof. Vijay P. Singh is University Distinguished Professor, Regents Professor, and Caroline and William N. Lehrer Distinguished Chair in Water Engineering at Texas

ix

x

Editors and Contributors

A&M University, USA. He was recently elected to the National Academy of Engineering class of 2022 for his wave modeling and entropy-based hydrologic and hydroclimatic theories. He has been awarded three honorary doctorates: the Ven Te Chow Award, the Merriam Improved Irrigation Award, and the Arid Lands Hydraulic Engineering Award. He is Member of 12 science/engineering academies. He has written around 1450 academic articles, 35 textbooks, 85 edited reference books, 120 chapters, and 330 conference papers. He is on the editorial boards of the Journal of Hydraulic Engineering, Water Science and Engineering and Journal of Groundwater Research, Irrigation Science, and Hydrologic Processes. His specialities include water quality, water resources, entropy theory, and copula theory. Dr. R. D. Singh has experience of working as Scientist at National Institute of Hydrology, Roorkee, for about 25 years, and retired as Director, National Institute of Hydrology, Roorkee. He has also worked as Adjunct Faculty and is presently working as Visiting Professor at the Department of Water Resources Development and Management (DWRDM) at Indian Institute of Technology, Roorkee.

Contributors Ahir P. B. Department of Agricultural and Food Engineering, IIT Kharagpur, Kharagpur, West Bengal, India Alam Mehtab Jamia Millia Islamia, New Delhi, India Ansari Mohd Izharuddin Jamia Millia Islamia, New Delhi, India Bajpai Mukul Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India Bandyopadhyay Arnab Department of Agricultural Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli (Itanagar), Arunachal Pradesh, India Bansal Anjali Department of Civil Engineering, Indian Institute of Technology Delhi, Delhi, India Barua Anamika Department of Humanities and Social Sciences, IIT Guwahati, Guwahati, Assam, India Beker Bahar Adem Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India Chandu Navya Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India Choudhary Sourav Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India

Editors and Contributors

xi

Dayal Deen Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Devi Juna Probha Centre for the Environment, IIT Guwahati, Guwahati, Assam, India Eldho T. I. Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India Fohrer Nicola Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, Kiel, Germany Galkate R. V. National Institute of Hydrology, Central India Regional Centre, WALMI Campus, Bhopal, Madhya Pradesh, India Garg Rahul National Institute of Hydrology, Roorkee, Uttarakhand, India Gaur Srishti Postdoctoral Scholar, Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Ohio, United States Gupta Mayank Department of Civil Engineering, MNIT Jaipur, Jaipur, India Hassan Quamrul Jamia Millia Islamia, New Delhi, India Hatcho Nobumasa Faculty of Agriculture, Kindai University, Nakamachi, Nara, Japan Hirofumi Okumura Rural Promotion Division, Food and Agricultural Promotion Department, Nara Prefecture, Nara, Japan Hörmann Georg Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, Kiel, Germany Jaiswal R. K. National Institute of Hydrology, Central India Regional Centre, WALMI Campus, Bhopal, Madhya Pradesh, India Jat Mahesh Kumar Department of Civil Engineering, MNIT Jaipur, Jaipur, India Kansal Mitthan Lal Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Katoch Surjit Singh Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India Kebebe Tadese Gindo Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India Khare Deepak Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India Kimura Masaomi Faculty of Agriculture, Kindai University, Nakamachi, Nara, Japan Krishan Gopal National Institute of Hydrology, Roorkee, Uttarakhand, India

xii

Editors and Contributors

Krishna Ch. Naga Tulasi Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India Kumar Amit Department of Civil Engineering, MNIT Jaipur, Jaipur, India Kumar Arun Department of Civil Engineering, Indian Institute of Technology Delhi, Delhi, India Kumar Bhishm IAEA, Vienna, Austria; NIH, Roorkee, India Kumar Mohit National Institute of Hydrology, Roorkee, Uttarakhand, India Kumar Ravi National Institute of Hydrology, Roorkee, Uttarakhand, India Kumar Sudhir Department of Civil Engineering, MNIT Jaipur, Jaipur, India Kumari Chanchal National Institute of Hydrology, Central India Regional Centre, WALMI Campus, Bhopal, Madhya Pradesh, India Lohani A. K. National Institute of Hydrology, Jal Vigyan Bhavan Roorkee, Roorkee, Uttarakhand, India Mahanta Chandan Department of Civil Engineering, IIT Guwahati, Guwahati, Assam, India Matsuno Yutaka Faculty of Agriculture, Kindai University, Nakamachi, Nara, Japan Mishra S. K. Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Mondal Manoj K. Rajendra Mishra School of Engineering Entrepreneurship, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Mrinmoy D. Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Nema M. K. National Institute of Hydrology, Roorkee, India Pandey Ashish Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Parhi Prabeer Kumar Centre for Water Engineering and Management, Central University of Jharkhand, Ranchi, India Patle G. T. College of Agricultural Engineering and Post-Harvest Technology, Central Agricultural University, Gangtok, Sikkim, India Pingale Santosh Murlidhar National Institute of Hydrology, Roorkee, India Raj Abhay Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

Editors and Contributors

xiii

Raj Kumar M. Rajendra Mishra School of Engineering Entrepreneurship, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Rao M. Someshwar National Institute of Hydrology, Roorkee, Uttarakhand, India Rathi Shweta Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India Rawat Akash Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India Sankhua R. N. Chief Engineer (South), NWDA, Ministry of Jal Shakti (WR,RD&GR), Hyderabad, India Sherpa Tshering College of Agricultural Engineering and Post-Harvest Technology, Central Agricultural University, Gangtok, Sikkim, India Shukla Chitra Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India Singh Adarsh Department of Civil Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India Singh Dhananjay Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, Uttarakhand, India Singh Gagandeep Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Singh Pooja Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Singh Rajendra Agricultural and Food Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India Singh Vijay P. Department of Biological and Agricultural Engineering and Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA Su Qiong Water Management & Hydrological Science, Texas A&M University, College Station, TX, USA; Department of Agricultural Sciences, Clemson University, Clemson, SC, USA Swain Sabyasachi Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Thakural L. N. National Institute of Hydrology, Roorkee, India Tigabu Tibebe B. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, Kiel, Germany Tiwari K. N. Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

xiv

Editors and Contributors

Verdhen Anand Nalanda, Bihar, India Wagner Paul D. Department of Hydrology and Water Resources Management, Institute for Natural Resource Conservation, Kiel University, Kiel, Germany Yadav Basant Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India Yadav Brijesh Kumar Department of Hydrology, IIT-Roorkee, Roorkee, Uttarakhand, India Yadav Vijay Kumar Indian Institute of Technology, Roorkee, India Yamasaki Keigo Rural Promotion Division, Food and Agricultural Promotion Department, Nara Prefecture, Nara, Japan

Part I

Water Resources Management

Chapter 1

Water: How Secure Are We Under Climate Change? Vijay P. Singh and Qiong Su

1.1 Introduction Water is fundamental to sustainable social and economic development and the survival of ecosystems. It is required in most human activities, including municipal, agricultural, industrial, and energy uses, and is vital for the survival of all forms of life. Global water demand has increased dramatically over recent decades due to growing population, increasing industrialization and urbanization, growing demand for food and energy, rising living standards, and changing agricultural practices. At the same time, the availability of freshwater resources is threatened by climate change, including increased air temperature, changed precipitation patterns, reduced river flow, and more frequent and intensified hydrologic extremes. Currently, there are about 1.6–4 billion people estimated to be living in water-stress basins, depending on different metrics (Gosling and Arnell 2016; Mekonnen and Hoekstra 2016). Using the water stress index (WSI), defined as the ratio of annual mean total water withdrawal to available discharge, the world population under high water stress (WSI > 40%) is estimated to be about 2.3 billion (Stenzel et al. 2021). Such high waterstressed basins are mainly located in the Middle East, the Mediterranean, India, northern China, western United States, Mexico, the west coast of South America, and southern Asia (Fig. 1.1a). The population and regions suffering from high water stress tend to greatly increase in the future projection (Heinke et al. 2019; Stenzel et al. 2021; Wada et al. 2016). For example, a further 82% of the world population V. P. Singh (B) Department of Biological and Agricultural Engineering and Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX 77843-2117, USA e-mail: [email protected] Q. Su Water Management & Hydrological Science, Texas A&M University, College Station, TX 77843, USA Department of Agricultural Sciences, Clemson University, Clemson, SC 29634, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_1

3

4

V. P. Singh and Q. Su

will be exposed to the high-water stress condition with climate change impacts alone, and the regions with high water stress will expand, as shown in Fig. 1.1b. Therefore, sustainable management of freshwater resources requires a better understanding of the likely impacts of climate change on global water security and how secure we are under climate change. The objective of this paper is to highlight water security under the impacts of climate change. The paper is organized as follows. In Sect. 1.2, water security is defined, and its associated aspects are discussed. Section 1.3 presents the availability of water, water supply, water demand, and water consumption, followed by a discussion of the global water situation in Sect. 1.4. Section 1.5 discusses the causes of water scarcity, and Sect. 1.6 discusses how we can ameliorate water scarcity. The key issues and challenges that need urgent attention and their implications for water management are discussed in Sect. 1.7. The paper is concluded in Sect. 1.8.

Fig. 1.1 Global distribution of water stress index (WSI) for (a) today (2006–2015) and (b) future scenario under climate change (2090–2099). WSI: 0–0.1% (no stress); 0.1–20% (low stress); 20– 40% (moderate stress); and 40–100% (high stress). Adapted from Stenzel et al. (2021)

1 Water: How Secure Are We Under Climate Change?

5

1.2 Water Security The concept of water security was first introduced at the World Water Forum in 2000 (World Water Council 2000). Numerous definitions of water security have been proposed since then, and these definitions were described and compared in Cook and Bakker (2012), Allan et al. (2018), and Gerlak et al. (2018). Here, we define water security as access at all times to sufficient and quality water to satisfy varied needs, which is built on three pillars: (1) demand for and use of water, including appropriate use based on the knowledge of quality water and treatment; (2) availability or supply of water, ensuring sufficient quantities of water available on a consistent basis; and (3) access to water, having sufficient resources to obtain appropriate quantities of water for satisfying needs, as shown in Fig. 1.2. In addition, there are several aspects of water security that need to be highlighted. All these three pillars of water security are heterogeneous across space and time. For the space dimension, water security should be managed and assured at (1) individual family unit; (2) village, town, district, state, and province; and (3) country, continent, and globe. Water stress across various basins within the same countries could significantly differ. For example, despite the low water stress at the country level in Australia, the United States, and South America, water stress in some basins in these countries or regions can be very severe (Fig. 1.1). Furthermore, the physical availability of water at large scales does not necessarily guarantee the accessibility of acceptable and safe use of water at local scales. For example, the low water stress

Fig. 1.2 Three pillars of water security, space and time dimensions, and other important aspects

6

V. P. Singh and Q. Su

in some African countries, as shown in Fig. 1.1, may mask the water scarcity at the household and individual levels due to insufficient water infrastructure to collect, transport, and treat water. The time variability involved in water security includes (1) seasonal differences in water supply and demand; (2) annual variations in climate and their impacts on water supply; and (3) long-term, medium-term, and short-term plans for water security. For seasonal differences, the same regions may be affected by floods in the summer and drought in other seasons. Additionally, to satisfy water demand or needs, adequate quantity and proper quantity should be maintained simultaneously, which can affect the space and time dimensions of water security. The social and cultural influences on water security are continually evolving, making it exceedingly challenging to assure water security on an operational level.

1.3 Water Supply and Demand 1.3.1 Water Availability Over 70% of the Earth surface is covered with water. However, 96.5% of the water is seawater, and about 0.9% is other saline water, which cannot be directly used by human beings (Fig. 1.3a). Only 2.5% of the water on Earth is freshwater, of which 68.7% is in the form of glaciers and permanent ice caps (Fig. 1.3b). Groundwater accounts for about 30.1% of the freshwater, and its global distribution is shown in Fig. 1.3c. Only about 1.2% of the freshwater is easily accessible to human beings in the form of wetlands, large lakes, reservoirs, and rivers (Fig. 1.3d). Freshwater is unevenly distributed in different countries (Fig. 1.4). Countries are generally considered water-stressed if their annual mean per capita freshwater availability, calculated in terms of long-term average runoff, is lower than 1700 m3 per year.

1.3.2 Water Demand and Use Generally, there are three major water uses, i.e., agriculture, domestic, and industry. Water use can be differentiated by water withdrawal and water consumption. Understanding the differences between water withdrawal and water consumption is important to properly evaluate water stress. Water withdrawal is defined as the total water removed from a water source, and part of this water can be returned to the water source, which reflects the competition among different water users. Water consumption, however, is the permanent lost of the total water from a water source, which is not available for other users locally. Water consumption is used to evaluate the impact of water use on water shortage.

1 Water: How Secure Are We Under Climate Change?

Fig. 1.3 World water resources distribution. Data from Igor (1999)

Fig. 1.4 Country-level per capita freshwater availability

7

8

V. P. Singh and Q. Su

Figure 1.5 shows the global water use in terms of water withdrawal and consumption by sectors. Agricultural sector, including irrigation, livestock, and aquaculture, is the largest water consumer, accounting for 69% of annual water withdrawal (Fig. 1.5a). Industrial sector, including primary energy production and power generation, accounts for 19% of annual water withdrawal, followed by the domestic sector, which accounts for 12%. For water consumption, irrigation alone accounts for 81% of annual water consumption (Fig. 1.5b, c). The top 7 largest water use countries consume about 48% of global water, i.e., India (13%), China (12%), the United States (9%), Russia (4%), Indonesia (4%), Nigeria (3%), and Brazil (3%). Worldwide water withdrawal by sector varies with regions (Table 1.1). Agriculture consumes the largest share of freshwater in Africa, Asia, Latin America, and Oceania, but in North America and Europe, industry is the major consumer due to the industrial-dominated economy and more efficient irrigation system. In

Fig. 1.5 Global water withdrawal and water consumption by sectors. Primary energy production includes fossil fuels and biofuel. Crops grown for biofuels is included in the primary energy production. Data from IEA (2016) and FAO, AQUASTAT (2016)

1 Water: How Secure Are We Under Climate Change? Table 1.1 The percentages of water withdrawal by sectors in different continents (%)

9 Agriculture Industry Domestic

Asia

81

10

9

Africa

82

5

13

North America

37

50

13

Latin America & Caribbean 72

11

17

Europe

21

57

22

Oceanía

60

15

25

Data from FAO (2021)

contrast, Africa, Asia, and Latin America are agricultural economies, and the irrigation systems are not always efficient. The global share of the industrial sector is likely to increase in the future due to the booming economy in countries like China, India, and Brazil.

1.3.2.1

Domestic Water Use

Adequate quantity and quality of water for domestic uses should be first considered among different water uses. Although numerous studies have indicated that an adequate amount of water is vital for human health and well-being (Howard et al. 2020; Miller et al. 2021), insufficient evidence is available to define the adequate quantity and quality of daily water required for a person to lead to a healthy life. A recommended daily water requirement for drinking, cooking, food hygiene, and basic hygiene practices, such as handwashing and face washing, is 4–5 gallons (15–19 L) per day. However, a larger water amount is usually required to support improved hygiene. Assessment of recent water use from various European countries indicates that 19–21 gallons per day (70–80 L) is adequate for the pursuit of healthy and productive life (Biswas and Tortajada 2021). Per capita water use is highly associated with water accessibility, income level, and lifestyles. For example, the average American individual uses about 100–176 gallons (379–666 L) at home per day, while the average African family uses 5 gallons (19 L) per day due to the unreliable water supply and low-income levels to afford basic access to water. The trends of daily per capita water use vary across different countries. Most countries have observed a declining trend in per capita water use recently, including the United States, all European counties, Singapore, Japan, and Australia (Biswas and Tortajada 2021). At the same time, per capita water use in other countries is increasing rapidly. For example, Middle Eastern and Northern African (MENA) countries are in absolute water scarcity situations with less than 500 m3 of renewable freshwater resources per capita per year. Of these countries, Kuwait has the least per capita water availability, which was only 27 m3 in 1970, and 9 m3 in 2001, and is

10

V. P. Singh and Q. Su

projected to decrease to 5 m3 in 2025. However, per capita water consumption in Kuwait has increased dramatically from 200 L per person per day in the 1980s to 500 L per person per day now, which is among the highest in the world (Alazmi et al. 2022).

1.3.2.2

Water Use for Food Production

To satisfy the growing food demand, a large amount of water is needed to produce products and services. The concept of virtual water was first proposed by Allan (1993), which means the amount of water embedded in food products. There is a considerable variation in the virtual water required to produce different food and beverages (Table 1.2). For example, nearly 1222 L of water are required to produce one kilogram of maize. More water is needed to produce animal products, e.g., producing one kilogram of beef requires 15,415 L of water. Therefore, diet patterns significantly affect the water required to produce the food that a person needs every day. For example, for meaty American and European diets, the water requirement for food production can be as high as 5000 L per day, while for vegetarian African and Asia diets, only 2000 L per day are required. With rising living standards in the past several decades, there has been a steady increase in the demand for meat products in rapidly developing countries. For example, in China, the per capita meat consumption was only 20 kg per year in 1995, but it increased to 70 kg in 2015 (Min et al. 2015). The rise in meat consumption has resulted in high water demand for food production in China. Moreover, the shift in dietary habits is almost impossible to reverse. It is projected that an additional 407–515 km3 y−1 of water, more than the total water use in Europe, is required in 2030 compared to the 2003 level due to the increasing consumption of meat products in China (Liu and Savenije 2008). The amount of water used to grow crops varies significantly in different countries (Fig. 1.6), depending on many factors, including crop types, climate, soil types, and irrigation technologies used. There is a high potential to reduce the quantities of water required to produce agricultural products through more efficient irrigation techniques and better water management practices. For example, China has made significant improvements in increasing agricultural water use efficiency since 2010. From 1990 to 2012, water use per hectare of irrigation decreased by 22% due to the development of irrigation technology and institutional modification. The Chinese government plans to further increase water use efficiency in agricultural production in the coming decades. China’s example has significant implications for reducing the global water requirements in agricultural production in the future. As shown in Fig. 1.6, agricultural water use efficiency in major water consumers like India and Brazil is relatively lower than that in the United States and China. Therefore, the water use for food production is likely to decrease significantly in these major countries in the future with more efficient irrigation management.

1 Water: How Secure Are We Under Climate Change?

11

Table 1.2 Global average water requirement of different food and beverages (unit: L kg−1 ) Food/beverages

Water requirement

Food/beverages

Water requirement

Chocolate

17,196

Coffee

1056

Beef

15,415

Peaches

910

Pork

5988

Apples

822

Chicken

4325

Wine

822

Rice

3400

Banana

790

Egg

3267

Oranges

560

Cheese

3178

Cucumber

353

Butter

3178

Beer

296

Olives

3015

Potatoes

287

Groundnuts

2782

Lettuce

237

Mango

1800

Cabbage

237

Wheat bread

1608

Tomatoes

214

Corn

1222

Tea

108

Data from Water Footprint Network (2022)

Fig. 1.6 Average amount of water needed to grow crops in different countries

1.3.2.3

Water Use for Energy Production

The production of energy resources, including fuel production and electricity generation, requires a significant amount of water. It is estimated that 471 km3 of water withdrawal and 91 km3 of water consumption are required annually for global energy production (IEA 2016). Electricity generation alone accounted for about 10% of global water withdrawal and 4% of water consumption (IEA 2016). For countries with high energy consumption, like the United States, the proportion of water withdrawn for thermoelectric production can be as high as 41% (Dieter et al. 2018). Freshwater is required for nearly all processes of electricity production, including fuel supply (extraction, processing, and transport), operation (cooling purpose), and plant infrastructure (material inputs of power plants) (Jin et al. 2019). Hence, water use efficiency of electricity production is highly dependent on fuel types (e.g., oil,

12

V. P. Singh and Q. Su

nuclear, biomass, coal, and natural gas), cooling technologies (e.g., dry cooling), and power plant types. A global meta-analysis of water consumption of different electricity technologies by Jin et al. (2019) shows that large variations in water consumption were observed across different energy types (Fig. 1.7). Biofuel plants are the least water-efficient, with water use as high as 85,100 L MWh−1 (median of 23 plants), which is three orders of magnitude larger than other types of plants. Wind and photovoltaic plants use the least amount of water. Generally, hydropower plants are also water-efficient, but the water consumption of hydropower could be very high if the evaporative reservoir losses are included (Fig. 1.7). Accordingly, the mix of technologies deployed for electricity production can significantly affect regional and global water use. For example, for a 60-W incandescent light bulb burning for 12 h a day over one year in 111 million households (total number of U.S. homes), a fossil fuel thermoelectric power plant would consume about 3.0 km3 of water. In contrast, most of this water could be saved if all were replaced by wind plants. Energy generation contributes significantly to greenhouse gas emissions, and, therefore, accelerating the decarbonization of the energy system is important to meet global carbon mitigation goals. In this regard, negative emission technologies, such as bioenergy (liquid biofuels and solid biomass) with carbon capture and storage (BECCS), have gained increasing attention in recent years. However, bioenergy and carbon capture and storage (CCS) technologies are high water-intensive. For example, the energy consumption by biofuels (biodiesel and ethanol) only contributed to a small portion of the global energy consumption (0.25–1.4%, from 2008 to 2020), but it consumed nearly 20% of global water consumption of energy production in 2008 (Fig. 1.8). In addition, the use of CCS to capture and store carbon from thermal power plants can significantly increase water use. For example, the water consumption by plants equipped with CCS technologies increases by 29–81%,

Fig. 1.7 Water consumption over the life cycle of different energy generation types. Water consumption is on a log scale. The term n represents the number of field data and the black dots represent the average values. Adapted from Jin et al. (2019)

1 Water: How Secure Are We Under Climate Change?

13

depending on the fuel types and cooling types (Ali 2018; Chandel et al. 2011; Sharma and Mahapatra 2018; Talati et al. 2016). Currently, global land use for biofuel production is about 30 Mha, of which 30% is equipped with irrigation. A recent study by Stenzel et al. (2021) evaluated the impact of deploying BECCS on global water stress, which shows that the global population living under high water stress will increase from 2.3 billion (2005–2015) to 4.6 billion by the end of the twenty-first century, exceeding the impact of climate change. Therefore, the side effects of biofuels on global water stress require careful consideration.

Fig. 1.8 a Global energy consumption by sources (1965–2020) and b water consumption for energy production by major energy category in 2008. Data in (a) are from https://ourworldindata.org/gra pher/energy-consumption-by-source-and-region, and data in (b) is adapted from Spang et al. (2014)

14

V. P. Singh and Q. Su

1.4 Global Water Situation Local water shortages are multiplying. Current patterns of use and abuse of water are not sustainable, and the amount being withdrawn is dangerously close to the limit and even beyond. Human interference with water bodies, such as dam and reservoir construction, regulation of rivers for navigation and transport, and diversion of water for irrigation, domestic and industrial use, has dramatically altered the connectivity and capacity of rivers, leading to ecosystem health degradation and biodiversity decline. Only 37% of the global longest rivers (longer than 1000 km) remain freeflowing, and they are restricted to remote regions, such as the Amazon basin, northern parts of North America, and the Congo basin in Africa (Grill et al. 2019). In addition, the source-to-sea connections are severely interrupted in about 54–77% of long rivers (>500 km). An alarming number of rivers fail to reach the sea, including the Indus in Pakistan, the Colorado River in the southwestern United States, the Rio Grande River on the Texas-Mexico border, the Murray-Daring in Australia, the Yellow River in China, and the Amu Darya and the Syr Darya in Central Asia. For example, the Colorado River, which originates in the Rocky Mountains, is the primary surface water resource in the southwestern United States, but the streamflow in the Upper Colorado River basin declined by 16.5% from 1916 to 2014 due to the reduced snowpacks and enhanced evapotranspiration under climate change (Xiao et al. 2018). At the same time, the Colorado River is heavily regulated by more than 100 dams and has not reached the sea since 1998 due to the overuse of water. The altered flow characteristics of rivers have severe impacts on nutrient-rich sediment transport, riverside vegetation, aquatic species, and water quality. Excess pumping of water from rivers feeding the Aral Sea in Central Asia led to its collapse in 1980. The declining volume of freshwater in rivers also leads to seawater invasion in deltas, which considerably changes the balance between freshwater and seawater. Meanwhile, large-scale degradation and losses of freshwater habitats, including rivers, ponds, lakes, and wetlands, are continuing. For example, 64–71% of the world’s wetlands have been drained, damaged, or destroyed since 1900, with a more significant loss rate in inland wetlands than in coastal ones (Davidson 2014). The ecosystem decay accelerates global biodiversity loss. Major land-based habitats have lost 20% of their native species since 1900. More than 9% of domesticated breeds of animals used for food had been extinct by 2016. This loss of diversity poses a significant risk to global food security. Soil health is also changing. Soil erosion, nutrient imbalance, and soil organic carbon loss have become major global issues. Soil erosion from cropland is estimated to be 25–40 billion tons per year (FAO 2018). Together with soil erosion, a large amount of nitrogen and phosphorus pollutants are discharged into rivers, lakes, and estuaries, leading to the degradation of freshwater ecosystems (FAO 2018). The growing industrial point source discharge from developing countries also contributes to pollutant load. Nearly 1000 km3 of wastewater is generated every year, but 92% of these waste streams in low-income countries are discharged without being treated into the receiving stream waters (Lu et al. 2018). Over the past three decades, water

1 Water: How Secure Are We Under Climate Change?

15

pollution has become a severe issue in many countries in Asia, Africa, and Latin America (United Nations 2015). Groundwater is the major source for irrigation and domestic use when surface water is limited. Globally, about 800 km3 of groundwater was abstracted and consumed annually in the 2010s, which provides nearly 50% of global water used for irrigation and drinking for billions of people. India, the United States, China, Pakistan, Iran, Mexico, and Saudi Arabia are the top seven groundwater consumers, which account for 75% of the total global groundwater pumping. It is reported that 3.6 billion people in 18 countries are overpumping their aquifers, and 5–20% of global wells are at risk of running dry if groundwater levels decline by even a few meters (Jasechko and Perrone 2021). Groundwater withdrawal, combined with surface water diversion and land-use change, can explain 33% of the observed sea-level rise in the twentieth century (Sahagian et al. 1994). More than 15% of the coastal aquifers in the United States are threatened by seawater intrusion, leading to increased groundwater salinity (Jasechko et al. 2020). Large-scale intensive droughts have been observed in Australia (Tian et al. 2020; van Dijk et al. 2013), Brazil (Cunha et al. 2019), China (Yao et al. 2018), the United States (Rippey 2015), India (Mishra et al. 2019), and Russia (Cook et al. 2020) from 1960 to 2018. These severe droughts occur more frequently (He et al. 2020), which can significantly affect crop production and disrupt food stability (Kuwayama et al. 2019). In 2017, 22.4% of the world population was under high agriculture production/yields vulnerability to severe drought (FAO 2018). In countries and regions which are highly dependent on hydroelectric power, e.g., Brazil and South America, repeated brownouts were expected due to not having enough water to drive turbines (Cuartas et al. 2022). Water security is closely linked to the security of energy, food ecosystems, and soil health. There seems to be a global water crisis that is impacting supplies of food and the generation of energy and other goods.

1.5 Causes of Water Scarcity The causes of water scarcity can be attributable to the population rise, higher food and energy requirements leading to higher water requirements, economic development, changing consumption patterns changes due to rising standard of living, and climate change.

1.5.1 Demography Since the 1950s, global population has witnessed a rapid rise (Fig. 1.9a). Over the past 70 years (1950–2019), the population has increased from 2.6 billion to 7.7 billion, and water withdrawal has increased by nearly 500%. As shown in Fig. 1.9b, the demand for water increases much faster than the population and economic development.

16

V. P. Singh and Q. Su

Population is likely to rise by another 1.7–2.5 billion (22–34%) by 2050 compared with 2019. The demand for water would rise by 20–30% by 2050 under an optimistic estimation (FAO 2018), roughly 900–1400 km3 per year. In addition, not just the absolute number, the shift in dietary habits to animal-based products can make a big difference. Global food demand is projected to increase by 14–65% from 2020 to 2050, based on the estimations from different studies (Fukase and Martin 2020; van Dijk et al. 2021). If there is no change in agricultural efficiency, the world will need 14–65% more water withdrawal for agriculture to feed the extra 1.7–2.5 billion mouths. The additional water requirement is about 451–2093 km3 of water, equivalent to 28–46% of current global water use.

Fig. 1.9 a Global population growth. Data from United Nations, DESA, Population Division, World Population Prospects 2019. http://population.un.org/wpp/; and b relative change of population, water withdrawal, Gross domestic product (GDP) per capita compared to 1990. Adapted from Boretti and Rosa (2019)

1 Water: How Secure Are We Under Climate Change?

17

1.5.2 Climate Change Climate is a long-term average of weather (such as temperature, precipitation, or winds), defined as the statistical mean conditions over certain years, typically three decades. Climate change refers to changes in climate that are in excess of natural variability and attributable to human activities. Since the pre-industrial period (1980– 1990), the mean land surface air temperature increased by 1.53 °C, and this warming is not evenly distributed across the world (IPCC 2019). Climate change impacts include rising air temperature, changing hydrologic cycle, increased frequency and intensity of extreme weather events, melting of glaciers and snow, permafrost degradation, increased soil erosion, coastal degradation, sea-level rise, water quality degradation, and increased wildfire occurrence (Hurlbert et al. 2019). Climate change leads to increased risks to water availability (Elliott et al. 2014; Haddeland et al. 2014; Hagemann et al. 2013), reduced crop yield (Iizumi and Ramankutty 2016; Iizumi et al. 2018; Piao et al. 2010), higher energy demand (van Ruijven et al. 2019), loss of biodiversity (Araujo and Rahbek 2006; Bellard et al. 2012; Fitzmaurice 2021), and increased human health risks (Patz and Olson 2006). Climate change has implications for food security, water resources, energy production, ecosystem health, extreme hazards, human society, ecosphere, and biosphere. The IPCC sixth assessment report states that global surface temperature will continue to rise, and the global warming of 1.5–2 °C will be exceeded in the twenty-first century if no significant greenhouse gas emissions reduction occurs in the coming decades (IPCC 2021). Climate change can directly affect the way plants grow through increased air temperature and CO2 fertilization (Iizumi and Ramankutty 2016; Iizumi et al. 2018; Piao et al. 2010) and therefore change the water use pattern of plants (Haddeland et al. 2014; Konapala et al. 2020). Climate change can also affect plant growth by changing water quantities and quality and accelerating land degradation. In addition, trees can be impacted during heavy rain; plants can dry up of biomass during drought; crops can grow rapidly and then wilt in a warming climate. Considerable crop production loss is projected in the future, even with immediate greenhouse gas emission reductions (IPCC 2019). Climate change-induced rising water temperatures, declining river flow, and enhanced precipitation intensity may exacerbate water pollution and affect ecosystem health, biodiversity, and human health. Climate change tends to intensify the hydrological cycle, and the types of hydrological changes or impacts include (1) freshwater availability: decrease of global renewable surface water (Schewe et al. 2014) and groundwater (Portmann et al. 2013), particularly in most dry subtropical regions, but an increase is found at high latitudes; (2) freshwater storage: decrease of natural water storage due to reduced snow and ice water storage and increased evaporation from lakes, reservoirs, wetland, and shallow aquifers (IPCC 2021); (3) rainfall variability: higher frequency and intensity of heavy rainfall events in major agricultural production areas, e.g., south, east, and southeast Asia (IPCC 2021); (4) runoff variability: decreased annual mean runoff in most dry tropical regions and increased runoff in wet tropics and at high latitudes; (5) flow variability: reduced snowmelt discharge and maximum spring snow depth in regions

18

V. P. Singh and Q. Su

with snowfall; (6) extreme hydrologic events: increased global flood risks, particularly in south America, northeast, south, and southeast Asia, and tropical Africa. People at risk from floods will increase from 1.2 billion in 2010 to around 1.6 billion in 2050 (nearly 20% of the world population). The frequency and magnitude of agricultural, meteorological, and short hydrological droughts are likely to increase. In addition, heat-stressed areas are expected to expand under global warming, and arid regions are affected the most (Lickley and Solomon 2018). Coastal communities at risk will increase due to rising sea levels (Kirezci et al. 2020). People affected by coastal flooding will increase from 110 million (2010) to 190 million (2100) under low carbon emissions and 630 million (2100) under high carbon emissions. These changes or impacts will result in changes in water availability for human consumption, agriculture, and energy generation, alterations in agricultural practices, reduced yields from rain-fed agriculture, increased number of people exposed to water stress in Africa, and replacement of tropical forests with savannah in eastern Amazonia.

1.6 Ameliorating Water Scarcity An integrated water resources and water use management is fundamental to ameliorating water scarcity and ensuring water security. The integrated approach must encompass (1) supply management, (2) storage management, (3) demand management, and (4) use management. Supply and storage management refer to integrated water resources management, which requires that surface and groundwater are managed and used in a sustainable manner without harming the environment. Surface water management includes the management of water bodies, e.g., rivers, streams, ponds, reservoirs, lakes, and estuaries, and the construction of hydraulic facilities for diversion, transfer, and storage. These should be managed with the consideration of needs of different stakeholders. It is necessary to design well-formulated surface water management law or policy that clearly states water administration and individual rights and responsibilities. In this way, the law or policy can be effectively implemented. A similar law or policy is also applied to groundwater management to avoid overexploitation. Groundwater depletion has reached alarmingly high rates (22–40 mm per year) in many countries e.g., north-western India, the Texas High Plains of the United States, and the North China Plains (Jia et al. 2020). It should also consider the order of water use priorities, water pricing, and other social-economic factors to make sure that water resource is appropriately used. Since water resource is limited, it should be used sustainably. Sustainable water use management should simultaneously consider water conservation, treatment, recycling, reuse, and water use efficiency improvement. Appropriate economic incentives should be provided to make sure that all these processes can be deployed on a commercial level. Alternative sources development, e.g., seawater desalination, may be necessary to alleviate water scarcity if the technical and economic feasibility are

1 Water: How Secure Are We Under Climate Change?

19

justified. Water conservation strategies should be practiced in the agricultural, industrial, and domestic sectors, particularly in the agricultural sector. The adoption of more efficient irrigation technologies can greatly improve water use efficiency. Irrigated agriculture relies heavily on surface irrigation, e.g., border, basin, and furrow irrigation, accounting for 83% of global irrigated areas. Surface irrigation has lower water use efficiency (30–35%) (Assouline 2019) compared with sprinkler and drip irrigations (80–95%) (Sarwar et al. 2019; Zhu et al. 2016). Therefore, the potential to reduce water use in the agricultural sector is high by replacing surface irrigation with more efficient technologies like sprinklers and drip irrigation. Crop water needs depend on crop types, area, climate, soil types, and irrigation methods. Smart irrigation systems for optimizing water use must be developed to support efficient irrigation scheduling. Industrial and domestic wastewater treatment needs special attention, particularly in developing countries. Water, soil, air, and living beings are functionally interconnected with each other and form the ecosystem. These different components are connected by the hydrologic process. Therefore, integrated water management should be planned and executed at the watershed scale. Integrated water management helps keep the watershed healthy by maintaining the health of rivers, lakes, reservoirs, forests, soil, land, and the environment. Integrated watershed management entails the simultaneous consideration of (1) conjunctive use of surface and groundwater, with the consideration of freshwater availability, freshwater storage, rainfall variability, runoff variability, flow variability, flood control, and drought management; (2) pollution control; (3) land use and land cover management; (4) afforestation; and (5) environmental management. In addition, the decision-making process of the integrated management should be based on multi-objective, multi-criteria, and multi-constraint, with social, economic, political, legal, cultural, and environmental considerations. It is important that public (stakeholders) participation is involved in data gathering and processing, modeling, verification, risk and uncertainty analysis, implementation, and learning experience. General circulation models (GCMs) are the most advanced tools available to simulate the response of global climate to increasing greenhouse gas emissions. With the integration of global hydrological models (GHMs), we can understand future climate scenarios which likely affect the basins and take appropriate measures beforehand. To integrate hydrological models with ecosystem, socioeconomic, and environmental models is needed to support decision-making in integrated watershed management. The security of water, food, and energy systems require partnerships among academia, the private sector (farmers), private organization, NGOs, and provincial and central government agencies. Traditional engineering education may not be sufficient. “Train the trainers and teach the teachers” must be emphasized in the education system (Singh 2017). Wasting food must be avoided. Nearly one-third of the edible food was wasted globally, equivalent to 1.3 billion tons per year. In North America, more than 40% of the food was lost, and the per capita food waste was as high as 300 kg per year per person (Fig. 1.10). Waste food is a waste of water as well. The per capita water requirement for food in the different regions ranged from 685 to 1820 m3 per year per person in 2003, with a global mean value of about 780 m3 per year per person (Liu and Savenije 2008). Assuming a 20% food waste, the annual

20

V. P. Singh and Q. Su

Fig. 1.10 Food losses and waste in different regions. Data from FAO (2011)

water loss due to food waste is roughly 1000 km3 , equivalent to about 20% of global water withdrawn.

1.7 Key Issues and Challenges The security of water, food, energy, and ecosystem is important to sustainable development. Water is required in food and energy production, and it should be well managed to ensure the well-being of ecosystems. Therefore, water security is key to sustainable agriculture, energy production, and ecosystem security. Fundamental to water security is the integrated water resources and water use management, which requires both technical and non-technical aspects. Technical aspects are relatively easy to handle, but non-technical issues are difficult to find an acceptable solution due to conflicting interests and political considerations among different parties. Ensuring water security is further compounded by climate change. Traditional engineering projects and practices may not be sufficient to cope with the changing climate. New projects must be adapted to climate variability and changes. Existing water infrastructure facilities were designed using historical hydrologic records, which may not adapt to climate change. A systematic revision of existing infrastructure facilities is an urgent need. For example, existing flood control projects failed during large storm events (Griffis 2007; Vachaud et al. 2019). Reservoirs cannot support enough water for domestic and agricultural use during drought. The decreased natural water storage due to snow and ice melting may lead to more swings between floods and droughts and exacerbate the water supply. This indicates that the construction of dams is required to satisfy the continuing needs for water storage and flood protection (Ho et al. 2017).

1 Water: How Secure Are We Under Climate Change?

21

1.8 Conclusions From this discussion, major conclusions can be summarized as follows: 1. Water security is defined as access to sufficient and quality water to satisfy varied needs at all times. 2. Efficient irrigation management is vital to reducing global water use. 3. Negative emission technologies, e.g., bioenergy with carbon capture and storage (BECCS), may exacerbate global water stress and therefore should be carefully used. 4. Integrated water management must be managed at the watershed scale, encompassing supply, storage, demand, and use management. 5. Food waste should be avoided to save water. 6. Sustainable water use management should simultaneously consider water conservation, treatment, recycling, reuse, and water use efficiency improvement. Wastewater treatment in developing countries needs to be significantly improved. 7. Future water infrastructure construction should be adapted to climate change. The role of dams in future water management needs a comprehensive evaluation.

References Alazmi H, Mitchell G, Trigg MA (2022) Investigating water conservation strategies in Kuwait: a micro-component backcasting approach. Riyadh, Kingdom of Saudi Arabia Ali B (2018) The cost of conserved water for coal power generation with carbon capture and storage in Alberta, Canada. Energ Convers Manage 158:387–399 Allan JV, Kenway SJ, Head BW (2018) Urban water security-what does it mean? Urban Water J 15(9):899–910 Allan T (1993) Fortunately there are substitutes for water—Otherwise our hydropolitical futures would-be impossible. In: Proceedings of the conference on priorities for water resources allocation and management, pp 13–26 Araujo MB, Rahbek C (2006) How does climate change affect biodiversity? Science 313(5792):1396–1397 Assouline S (2019) A simple method to design irrigation rate and duration and improve water use efficiency. Water Resour Res 55(7):6295–6301 Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15(4):365–377 Biswas AK, Tortajada C (2021) Water Management International Pte Ltd., Singapore Boretti A, Rosa L (2019) Reassessing the projections of the world water development report. Npj Clean Water 2 Chandel MK, Pratson LF, Jackson RB (2011) The potential impacts of climate-change policy on freshwater use in thermoelectric power generation. Energ Policy 39(10):6234–6242 Cook C, Bakker K (2012) Water security: debating an emerging paradigm. Global Environ Chang 22(1):94–102 Cook ER, Solomina O, Matskovsky V, Cook BI, Agafonov L, Berdnikova A, Dolgova E, Karpukhin A, Knysh N, Kulakova M, Kuznetsova V, Kyncl T, Kyncl J, Maximova O, Panyushkina I, Seim A, Tishin D, Dotny TWO, Yermokhin M (2020) The European Russia Drought Atlas (1400–2016 CE). Clim Dyn 54(3–4):2317–2335

22

V. P. Singh and Q. Su

Cuartas LA, Cunha APMD, Alves JA, Parra LMP, Deusdara-Leal K, Costa LCO, Molina RD, Amore D, Broedel E, Seluchi ME, Cunningham C, Alvala RCD, Marengo JA (2022) Recent hydrological droughts in Brazil and their impact on hydropower generation. Water-Sui 14(4) Cunha APMA, Zeri M, Leal KD, Costa L, Cuartas LA, Marengo JA, Tomasella J, Vieira RM, Barbosa AA, Cunningham C, Garcia JVC, Broedel E, Alvala R, Ribeiro-Neto G (2019) Extreme drought events over Brazil from 2011 to 2019. Atmos-Basel 10(11) Davidson NC (2014) How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar Freshwater Res 65(10):934–941 Dieter CA, Maupin MA, Caldwell RR, Harris MA, Ivahnenko TI, Lovelace JK, Barber NL, Linsey KS (2018) Estimated use of water in the United States in 2015: U.S. Geological Survey Circular 1441, 65p. https://doi.org/10.3133/cir1441 Elliott J, Deryng D, Mueller C, Frieler K, Konzmann M, Gerten D, Glotter M, Florke M, Wada Y, Best N, Eisner S, Fekete BM, Folberth C, Foster I, Gosling SN, Haddeland I, Khabarov N, Ludwig F, Masaki Y, Olin S, Rosenzweig C, Ruane AC, Satoh Y, Schmid E, Stacke T, Tang QH, Wisser D (2014) Constraints and potentials of future irrigation water availability on agricultural production under climate change. P Natl Acad Sci USA 111(9):3239–3244 FAO (2018) The future of food and agriculture—Alternative pathways to 2050. Food and Agriculture Organization of the United Nations, Rome FAO (2011) Global food losses and food waste—Extent, causes and prevention, Rome FAO (2021) FAOSTAT database. www.fao.org/faostat/en/#data FAO, AQUASTAT (2016) AQUASTAT database. Food and agriculture organization of the United Nations. https://www.fao.org/aquastat/statistics/query/index.html?lang=en Fitzmaurice M (2021) Biodiversity and climate change. Int Community Law Re 23(2–3):230–240 Fukase E, Martin W (2020) Economic growth, convergence, and world food demand and supply. World Dev 132 Gerlak AK, House-Peters L, Varady RG, Albrecht T, Zuniga-Teran A, de Grenade RR, Cook C, Scott CA (2018) Water security: a review of place-based research. Environ Sci Policy 82:79–89 Gosling SN, Arnell NW (2016) A global assessment of the impact of climate change on water scarcity. Clim Change 134(3):371–385 Griffis FH (2007) Engineering failures exposed by Hurricane Katrina. Technol Soc 29(2):189–195 Grill G, Lehner B, Thieme M, Geenen B, Tickner D, Antonelli F, Babu S, Borrelli P, Cheng L, Crochetiere H, Macedo HE, Filgueiras R, Goichot M, Higgins J, Hogan Z, Lip B, McClain ME, Meng J, Mulligan M, Nilsson C, Olden JD, Opperman JJ, Petry P, Liermann CR, Saenz L, SalinasRodriguez S, Schelle P, Schmitt RJP, Snider J, Tan F, Tockner K, Valdujo PH, van Soesbergen A, Zarfl C (2019) Mapping the world’s free-flowing rivers. Nature 569(7755):215 Haddeland I, Heinke J, Biemans H, Eisner S, Florke M, Hanasaki N, Konzmann M, Ludwig F, Masaki Y, Schewe J, Stacke T, Tessler ZD, Wada Y, Wisser D (2014) Global water resources affected by human interventions and climate change. P Natl Acad Sci USA 111(9):3251–3256 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(1):129–144 He XG, Pan M, Wei ZW, Wood EF, Sheffield J (2020) A global drought and flood catalogue from 1950 to 2016. B Am Meteorol Soc 101(5):E508–E535 Heinke J, Muller C, Lannerstad M, Gerten D, Lucht W (2019) Freshwater resources under success and failure of the Paris climate agreement. Earth Syst Dynam 10(2):205–217 Ho M, Lall U, Allaire M, Devineni N, Kwon HH, Pal I, Raff D, Wegner D (2017) The future role of dams in the United States of America. Water Resour Res 53(2):982–998 Howard G, Bartram J, Williams A, Overbo A, Fuente D, Geere J-A (2020) Domestic water quantity, service level and health. World Health Organization, Geneva (Switzerland) Hurlbert M, Krishnaswamy J, Davin E, Johnson FX, Mena CF, Morton J, Myeong S, Viner D, Warner K, Wreford A, Zakieldeen S, Zommers Z (2019) Risk management and decision making in relation to sustainable development. In: Climate change and land: an IPCC special report on

1 Water: How Secure Are We Under Climate Change?

23

climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. IPCC, Cambridge University Press, Cambridge. IEA (International Energy Agency) (2016) Water energy Nexus: excerpt from the world energy outlook, 2016. IEA, Publications, Paris Igor AS (1999) State Hydrological Institute and United Nation Educational, Scientific and Cultural Organization Iizumi T, Shiogama H, Imada Y, Hanasaki N, Takikawa H, Nishimori M (2018) Crop production losses associated with anthropogenic climate change for 1981–2010 compared with preindustrial levels. Int J Climatol 38(14):5405–5417 Iizumi T, Ramankutty N (2016) Changes in yield variability of major crops for 1981–2010 explained by climate change. Environ Res Lett 11(3) IPCC (2019) Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems IPCC (2021) Climate change 2021: the physical science basis. In: Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press. I Jasechko S, Perrone D (2021) Global groundwater wells at risk of running dry. Science 372(6540):418 Jasechko S, Perrone D, Seybold H, Fan Y, Kirchner JW (2020) Groundwater level observations in 250,000 coastal US wells reveal scope of potential seawater intrusion. Nat Commun 11(1) Jia XY, Hou DY, Wang LW, O’Connor D, Luo J (2020) The development of groundwater research in the past 40 years: a burgeoning trend in groundwater depletion and sustainable management. J Hydrol 587 Jin Y, Behrens P, Tukker A, Scherer L (2019) Water use of electricity technologies: a global metaanalysis. Renew Sust Energ Rev 115 Kirezci E, Young IR, Ranasinghe R, Muis S, Nicholls RJ, Lincke D, Hinkel J (2020) Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st century. Sci Rep-Uk 10(1) Konapala G, Mishra AK, Wada Y, Mann ME (2020) Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat Commun 11(1) Kuwayama Y, Thompson A, Bernknopf R, Zaitchik B, Vail P (2019) Estimating the impact of drought on agriculture using the us drought monitor. Am J Agr Econ 101(1):193–210 Lickley M, Solomon S (2018) Drivers, timing and some impacts of global aridity change. Environ Res Lett 13(10) Liu J, Savenije HHG (2008) Food consumption patterns and their effect on water requirement in China. Hydrol Earth Syst Sc 12(3):887–898 Lu L, Guest JS, Peters CA, Zhu XP, Rau GH, Ren ZJ (2018) Wastewater treatment for carbon capture and utilization. Nat Sustain 1(12):750–758 Mekonnen MM, Hoekstra AY (2016) Four billion people facing severe water scarcity. Sci Adv 2(2) Miller JD, Workman CL, Panchang SV, Sneegas G, Adams EA, Young SL, Thompson AL (2021) Water security and nutrition: current knowledge and research opportunities. Adv Nutr 12(6):2525– 2539 Min S, Bai JF, Seale J, Wahl T (2015) Demographics, societal aging, and meat consumption in China. J Integr Agr 14(6):995–1007 Mishra V, Tiwari AD, Aadhar S, Shah R, Xiao M, Pai DS, Lettenmaier D (2019) Drought and Famine in India, 1870–2016. Geophys Res Lett 46(4):2075–2083 Patz JA, Olson SH (2006) Climate change and health: global to local influences on disease risk. Ann Trop Med Parasit 100(5–6):535–549 Piao SL, Ciais P, Huang Y, Shen ZH, Peng SS, Li JS, Zhou LP, Liu HY, Ma YC, Ding YH, Friedlingstein P, Liu CZ, Tan K, Yu YQ, Zhang TY, Fang JY (2010) The impacts of climate change on water resources and agriculture in China. Nature 467(7311):43–51

24

V. P. Singh and Q. Su

Portmann FT, Doll P, Eisner S, Florke M (2013) Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environ Res Lett 8(2) Rippey BR (2015) The U.S. drought of 2012. Weather Clim Extreme 10:57–64 Sahagian DL, Schwartz FW, Jacobs DK (1994) Direct anthropogenic contributions to sea-level rise in the 20th-century. Nature 367(6458):54–57 Sarwar A, Peters RT, Mehanna H, Amini MZ, Mohamed AZ (2019) Evaluating water application efficiency of low and mid elevation spray application under changing weather conditions. Agr Water Manage 221:84–91 Schewe J, Heinke J, Gerten D, Haddeland I, Arnell NW, Clark DB, Dankers R, Eisner S, Fekete BM, Colon-Gonzalez FJ, Gosling SN, Kim H, Liu XC, Masaki Y, Portmann FT, Satoh Y, Stacke T, Tang QH, Wada Y, Wisser D, Albrecht T, Frieler K, Piontek F, Warszawski L, Kabat P (2014) Multimodel assessment of water scarcity under climate change. P Natl Acad Sci USA 111(9):3245–3250 Sharma N, Mahapatra SS (2018) A preliminary analysis of increase in water use with carbon capture and storage for Indian coal-fired power plants. Environ Technol Inno 9:51–62 Singh VP (2017) Challenges in meeting water security and resilience. Water Int 42(4):349–359 Spang ES, Moomaw WR, Gallagher KS, Kirshen PH Marks DH (2014) The water consumption of energy production: an international comparison. Environ Res Lett 9(10) Stenzel F, Greve P, Lucht W, Tramberend S, Wada Y, Gerten D (2021) Irrigation of biomass plantations may globally increase water stress more than climate change. Nat Commun 12(1) Talati S, Zhai HB, Kyle GP, Morgan MG, Patel P, Liu L (2016) Consumptive water use from electricity generation in the Southwest under alternative climate, technology, and policy futures. Environ Sci Technol 50(22):12095–12104 Tian F, Wu JJ, Liu LZ, Leng S, Yang JH, Zhao WH Shen Q (2020) Exceptional drought across Southeastern Australia caused by extreme lack of precipitation and its impacts on NDVI and SIF in 2018. Remote Sens-Basel 12(1) United Nations (2015) UN water. Wastewater management-A UN-water analytical brief 1–52, World Meteorological Organization, Geneva, Switzerland Vachaud G, Quertamp F, Ha PTS, Dung TNT, Thong N, Loc LX, Tuan NA, Gratiot N (2019) Flood-related risks in Ho Chi Minh City and ways of mitigation. J Hydrol 573:1021–1027 van Dijk AIJM, Beck HE, Crosbie RS, de Jeu RAM, Liu YY, Podger GM, Timbal B, Viney NR (2013) The millennium drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour Res 49(2):1040–1057 van Dijk M, Morley T, Rau ML, Saghai Y (2021) A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat Food 2(7):494 van Ruijven BJ, De Cian E, Wing IS (2019) Amplification of future energy demand growth due to climate change. Nat Commun 10 Wada Y, Florke M, Hanasaki N, Eisner S, Fischer G, Tramberend S, Satoh Y, van Vliet MTH, Yillia P, Ringler C, Burek P, Wiberg D (2016) Modeling global water use for the 21st century: the water futures and solutions (WFaS) initiative and its approaches. Geosci Model Dev 9(1):175–222 Water Footprint Network (2022). https://waterfootprint.org/en/resources/interactive-tools/productgallery/ World Water Council (2000) Ministerial declaration of the Hague on water security in the 21st century. World Water Council: Hague, The Netherlands Xiao M, Udall B, Lettenmaier DP (2018) On the causes of declining Colorado River streamflows. Water Resour Res 54(9):6739–6756 Yao N, Li Y, Lei TJ, Peng LL (2018) Drought evolution, severity and trends in mainland China over 1961–2013. Sci Total Environ 616:73–89 Zhu XY, Peters T, Neibling H (2016) Hydraulic performance assessment of Lesa at low pressure. Irrig Drain 65(4):530–536

Chapter 2

Influence of Stemflow Measurement on Interception Estimation Under Eucalyptus Plantations Chitra Shukla , K. N. Tiwari, and S. K. Mishra

2.1 Introduction Eucalyptus is among few forest species cultivated over a huge land area (~20 million hectare) throughout the world (Iglesias-Trabado and Wistermann 2008). The area of Eucalyptus plantations has expanded greatly in India, Brazil, China, South Africa and Australia which accounts for more than 50% of the total cultivated area (Shi et al. 2012). The large-scale eucalypts plantation in many parts of the world raised concerns over the environmental and social aspects of plantations. One of the major concerns is hydrological effects of eucalypts afforestation. Further, it also raises concern over their effect on local water resources (Almeida et al. 2010). The interception loss in the natural and planted forested areas is an important process in the catchment water budget (Neto et al. 2012). The rainfall that hits plant surfaces is temporarily retained and ultimately evaporates into the atmosphere or canopy rainfall interception loss or makes its way to the ground either by falling as drops (drip) or by flowing down branches and stems (stemflow). The rain that does not hit the plant surface is called free throughfall and, together with drip, is often referred to as throughfall (David et al. 2005). Leite (1996) reported a linear decrease in throughfall with the increase in planting density of eucalyptus cultivated in Minas Gerais State (Brazil). The stemflow is extremely important for precipitation and nutrient re-distribution in arid environments (Li et al. 2008) and for agricultural and forest ecosystems (Levia and Frost 2003). Subsequently, in an impervious urban landscape, the stemflow can be important for the hydrology and nutrient availability. C. Shukla (B) · K. N. Tiwari Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India e-mail: [email protected] S. K. Mishra Department of Water Resource Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_2

25

26

C. Shukla et al.

The canopy interception is the difference between gross rainfall and the amount of rain that passes through the canopy (Xiao et al. 2000; Barbier et al. 2009). Further, the canopy interception losses are mainly dependent on precipitation and its variation, canopy coverage, leaf area index, and forest types. The process of canopy interception is highly dependent on factors like type of rainfall event (magnitude, intensity and duration), type of tree species and its canopy structure, and the antecedent weather (Crockford and Richardson 2000). Most studies of canopy interception have been made in natural or managed forest systems, where upto 50% interception has been measured (Schellekens et al. 1999). The interception losses due to Eucalyptus planation varies with planation region. Cuartas et al. (2007) studied the interception loss in a year for a 245 typical tree species of Central Amazonia having normal rainfall of 13.3% and compared it with 22.6% of gross rainfall in a dry year. The losses account for 10–34% of annual rainfall in the regions like Australia, India, and Israel depending on the amount of rainfall (Calder 1986; Feller 1981). Interception losses were also affected by the age of forests. Vertessy et al. (1998) observed that the annual interception losses due to Eucalyptus peaked at 450 mm (26% of rainfall) at age of 30 years, and declined to 260 mm (17% of rainfall) at the age of 240 years. This study presents measured throughfall (TF) and stemflow (SF) values obtained against 106 nos. of storm events. Further, it estimates the interception loss using insitu measurement of TF and SF data. As SF is small quantity in comparison to TF, question arises weather it’s measurement affects interception estimation or not? Various fraction of incident rainfall (i.e., 5, 7.5, and 10%) has been taken as SF to estimate interception loss. This allows to discover the impact of SF on estimation of interception loss.

2.2 Methodology 2.2.1 Description of the Study Area and Plantations Characteristics The study was conducted at a micro watersheds of 3.3 ha area covered with Eucalyptus plantations planted in the year of 1990 in the experimental farm of Department of Agricultural and Food Engineering (AgFE), Indian Institute of Technology (IIT) Kharagpur, West Bengal. It is situated at 22° 19′ 10.97′′ latitude and 87° 18′ 35.87′′ longitude on lateritic terrain with sub-humid climate. It has moderately flat topography and is situated at an altitude of 48 m above mean sea level (MSL). The soil texture of the study area up to 0.5 m depth is sandy loam and from 0.5 to 2.0 m, it is sandy clay loam. The mean minimum and maximum temperatures are 12 °C and 46 °C, respectively and mean relative humidity ranges from 35.5 to 90.5%. The rainfall depth data was recorded using already existing Simon’s non-recording rain gauge. Based on last 10 years data the average annual rainfall of the study area ranges from 1200 to 1500 mm and about 80% of this rainfall occurs during June

2 Influence of Stemflow Measurement on Interception Estimation …

27

to October (Santosh and Tiwari 2019). Various other physiological parameters such as LAI, crown radius, crown area, and tree height were measured in situ. Plant canopies can be well characterized using LAI, which is a dimensionless quantity. LAI is defined as the green leaf area per unit ground surface area (m2 m−2 ). LAI was measured by LAI 2000, which basically estimates LAI based on the amount of solar radiation falling on a wide-angle optical sensor. This instrument uses a theoretical relationship between leaf area and canopy transmittance. It uses five sensors which simultaneously measure blue range light intensities (320–490 nm) in five concentric Field of Views (FOVs) centered at zenith angles of 7, 23, 38, 53 and 68 degrees. It calculates canopy gap fraction using ratio of solar radiation above and below the plant canopy, which eventually shows the light penetration probability (LI-COR, 1992). LAI was measured thrice (beginning, mid and end of the monsoon season) every year (i.e., 2017–18). LAI was measured at multiple points under each plantation canopy crown exactly above each of the TF collection receptacle units.

2.2.2 Throughfall Measurements It is assumed that Eucalyptus canopy crown areas are circular and the middle of the tree stem is the centre of the circle. The crown radius is the distance from the centre of the tree stem to the outer edge of the canopy crown. The mean canopy diameter is assumed as double of the average of four main directional (i.e., East, West, North and South) canopy crown radii. Assuming that tree canopy is a circle, its diameter can be divided into a number of representative annuli. At every 500 mm distance from the centre of tree stem, receptacles placed under the tree are shown in Fig. 2.1. Each representative annulus has specific LAI. Total 5 receptacles were placed under Eucalyptus. Two representative plantations of average canopy characteristics were selected for TF sampling. Assuming the radius of the stem is rs and r1 , r2 , r3 . . . rn are the radii of annular circles from the centre of tree stem (Fig. 2.1). The area of annular circles of different radii is estimated as follows: Area of the annul = a1 = π(r1 )2 , Area [ 2 first2 circular ] ′ annul of the second circular annul = a2 = π r2 − r1 , Area of the third circular ] ] = [ [ 2 a3′ = π r32 − r22 , and thus, Area of nth circular annul = an′ = π rn2 − rn−1 . Let the average depth of TF received in equidistant receptacles in first annul at radius r1 from centre of tree stem either side be d 1 , in second annul at distance r2 from the centre of tree stem d 2 . Similarly, in nth receptacles at distance rn bed n . The weighted TF from a tree can thus be estimated by using Eq. (2.1): TF =

a1 × d1 + a′2 × d2 + a′3 × d3 + · · · a′n × dn a1 + a′2 + a′3 + · · · a′n

(2.1)

where, a1 , a2′ , a3′ , and an′ are area of annuli of representative canopy circle. TF resulting from total rainfall (R), termed as relative throughfall (TFr) can be expressed

28

C. Shukla et al.

Stem radius r1, r2, r3 … … rn - Radii of annular circles from the centre of tree stem

Fig. 2.1 Tree canopy circle with representative annuls

in percent (%) as: TFr =

TF × 100 R

(2.2)

Plastic cylinders (receptacles) of 2 L capacity were used to collect TF for measurement. Sampling design includes a series of TF receptacles placed along the tree canopy diameter both sides of the tree stem. The diameter of funnel was the same as that of the diameter of non-recording type Simon’s raingauge. These receptacles were placed vertically 350 mm above the ground level in the slot provided in a stand made up of mild steel rod (Fig. 2.2). TF samples were collected manually, which took approximately 2–3 h soon after each rainfall event using a graduated cylinder having accuracy of 1 ml. Rainfall data were collected soon after each rain event and also at 08.30 a. m. for the rain occurred during night. The rainfall events occurred at an interval of three and more hours were treated as individual rainfall events. This period is assumed to be sufficient for drying out the canopy. TF was measured for 106 rainfall events. All TF receptacles were emptied after each measurement. Rainfall and corresponding TF recorded during the years 2017 and 2018 were grouped in three classes for frequency analysis. The class interval was decided such that the number of classes were less than or equal to five times the logarithm of the number of samples. The relative frequency was estimated as the percentage of the total number of rainfall events. Probability values were used to analyze the variation at significance level (α) = 0.05.

2 Influence of Stemflow Measurement on Interception Estimation …

12.7 cm

29

(b)

50 cm

35 cm

(a) Fig. 2.2 a Schematic of throughfall (TF) sampling setup with receptacles and positioning; b a field view of the throughfall (TF) and Stemflow (SF) measuring setup

2.2.3 Stemflow Measurement Stemflow (SF) was collected in a ring shape collecting tray was fixed around the plant stem at a height of 500 mm above the ground connected with the collecting bottle through a PVC tube. The collected water in bottle is measured in cylindrical flask of volume 0.02 m2 after each storm in terms of SF volume. The equivalent SF depth of each selected tree was measured by dividing the collected SF volume by the CPA (Shachnovich et al. 2008). The presence of projecting crowns is important because evaporation of intercepted water from such crowns is more rapid than from nearby lower crowns. Such projecting crowns also increase the variability of throughfall and stemflow volumes. The standard method of estimation of crown projection area (CPA) is to project the edges of the crown to a horizontal surface (Delphis and Levia 2004) in which crown are projected from four directions of tree. The crown radius was measured as the distance from the center of the tree bole to the edge of the crown. To obtain the best estimate of mean crown projection area, radius of the canopy crown was measured at four main directions (East, West, North and South) from the tree stem. Average radius of the all four directions was used to estimate CPA considering plant canopy as a circle. Then after, stemflow depth calculated using the following formula: SF =

Vsf CPA

(2.3)

where SF = Stemflow (mm) Vsf = Stemflow volume (mm3 ) CPA = Crown Projection area (m2 ). Relative stemflow (SFr) is the percentage of incident rainfall (R) obtained as SF. It can be estimated as: SF(% ) =

SF × 100 R

(2.4)

30

C. Shukla et al.

2.2.4 Estimation of Interception Loss Interception loss is greatly affected by the type of species, tree structure, and specific characteristics such as leaf area index. Many of the studied showed that larger stand density and basal area corresponded with smaller interception losses. Interception was calculated by water balance approach i.e. the difference between total rainfall and the amount of water that reaches the ground: I = R − (TF + SF)

(2.5)

where I = Rainfall interception (mm) R = Rainfall (mm) TF = Throughfall (mm) SF = Stemflow (mm). Net rainfall (RN ) represents the fraction of gross rainfall (GR) rainfall which appears in ground after satisfying the above ground losses i.e. sum of TF and SF by tree stands. Rn = GR − I

(2.6)

where Rn = Net rainfall (mm). I = Interception loss (mm).

2.2.5 Results and Discussion Study was conducted in a watershed of 3.3 ha area containing 15,500 plants of average crown area of 2 m2 and average height of 16.4 m. The average leaf area index (LAI) was 2.8 m2 m−2 . A total amount of 1 741 mm from 106 rainfall events was observed during monsoons of the year 2017 and 2018, in which 40 rainfall events observed from the year 2017, and 66 from 2018 (Table 2.1).

2.2.6 Estimation of Interception Loss Using Measured TF and SF: Rainfall Partitioning in Relation to Incident Rainfall (R) Relative throughfall ranged from 50–87%, with mean 67% and coefficient of variation (CV) 12%. However, TF contributed as huge portion of incident rainfall, CV

2 Influence of Stemflow Measurement on Interception Estimation …

31

Table 2.1 Storm events characteristics during the year 2017 and year 2018 Year

Storm events (nos.)

Rainfall (mm)

Mean (mm)

SD (mm)

CV (%)

Range (mm)

2017

40

699.8

17.5

12.8

73.3

56.0

2018

66

1041.7

10.7

12.9

81.8

56.8

106

1741.5

16.4

12.9

78.5

56.8

2017 and 2018

SD—Standard deviation, CV—coefficient of variation

was relatively low in TF measurement. Whereas, relative stemflow ranged from 2.4– 12.4% with mean 7.6% and CV 32%. Interception loss was observed 10–39% with mean 25% and CV 29%. These results matched perfectly with the range suggested by Calder (1986) for interception losses from Eucalyptus in Australia, India, and Israel (i.e. 10–34% of annual rainfall). Average net rainfall was observed 75% with CV 10%. It was observed CV observed high in relative SF is a result of variation involved with Eucalyptus height and stem diameter. This ensures the influence of stemflow in estimation of interception loss. In total, during the 106 rainfall events of 1741 mm, minimum 61% of R and a maximum 90% of R reached the forest floor via TF and SF (i.e., net rainfall), and the remaining 25% of R were intercepted and subsequently returned to the atmosphere through evaporation (Table 2.2). Similar results (i.e., interception as 22% of rainfall) were obtained by White et al. (2002) in the dry lands of Western Australia. Xu and Zhang (2006) reported interception loss for Eucalyptus (E. globulus) as 22% of rainfall in Nilgiris, India with annual rainfall of 1150 mm. Vertessy et al. (1998) noted the impact of plantations age on interception losses, as 26% at age 30, and declined to 17% at the age of 240 years. The plantation in this study were 27–28 years old and interception (i.e., 25%) obtained in the current study matches with results obtained by Vertessy et al. (1998). Distribution of relative TF, relative SF and I (% or R) is shown in Fig. 2.3. It can be seen that TF and SF increases with increase in R whereas I decreases with increase in rainfall magnitude. Significant variation (P < 0.05) at 95% confidence interval was observed between the mean of rainfall depth and mean of TF depth, SF depth and I depths for Eucalyptus. Strong positive correlation was found between Table 2.2 Average relative throughfall (TF: R), relative stemflow (SF: R) and interception loss (I:R), net rainfall (Rn:R) observed under Eucalyptus during the year 2017–18 Parameters

TF (%)

SF (%)

I (%)

Rn (%)

Mean

66.9

7.6

25.5

74.5

Maximum

87.0

12.4

38.9

89.7

Minimum

50.0

2.4

10.3

61.1

SD CV (%)

8.0

2.4

7.4

7.4

11.9

31.9

29.2

10.0

32

C. Shukla et al.

R, TF, SF and Rn depths (Table 2.4). Thus it can be inferred that precise estimation of both TF and SF is important to estimate I under Eucalyptus. Further, net rainfall is that portion of incident rainfall which contributes to the soil moisture and surface runoff. From Table 2.3, it can be seen that approximately 75% of the total rainfall come to the soil surface which can be further utilized. Linear relationship was observed between interception and rainfall depths with an average slope of 25% (Fig. 2.4). Thus, interception under Eucalyptus was 25% of incident rainfall. A relationship (I = 0.25 R ± 0.01, r2 = 0.83 and r = 0.91) can be directly used to estimate interception under Eucalyptus plantation.

Fig. 2.3 Variation in relative throughfall (%), relative stemflow (%) and interception loss (%) against rainfall events occurred during the monsoon of year 2017–18

Table 2.3 Correlation coefficients of rainfall with throughfall (TF), stemflow (SF), interception (I) and net rainfall (Rn) depths under Eucalyptus R (mm) R (mm)

TF (mm)

SF (mm)

I (mm)

Rn (mm)

1

TF (mm)

0.99

1

SF (mm)

0.87

0.84

1

I (mm)

0.91

0.84

0.76

1

Rn (mm)

0.99

1.00

0.87

0.84

1

2 Influence of Stemflow Measurement on Interception Estimation …

33

Table 2.4 Single factor ANOVA test for interception estimated using SF as a fixed portion of incident rainfall (i.e., I5 , I7.5 and I10 for 5, 7.5 and 10% of incident rainfall respectively) Variation from I (%)

P value

F values

F critical

I5 (SF as 5% of R)

0.01

5.96

6.75

I7.5 (SF as 7.5% of R)

0.9

0.01

3.88

I10 (SF as 10% of R)

3.59 × 10–09

37.89

3.88

P value–probability value and F value from F-tests statistic, F, was named in honor of Sir Ronald Fisher Note The F-statistic is simply a ratio of two variances. Since, F-statistic greater than the F-critical value is equivalent to a p-value less than alpha and both mean that mean values observed under different plantations are varying significantly

18

Interception (mm)

16 14

I=a*R+b Equation -0.00117 ± 0.231 Intercept 0.25123 ± 0.0110 Slope 226 Residual Sum of Squar 0.91 Pearson's r 0.83 R-Square (COD) 0.83 Adj. R-Square

12 10 8 6 4

I (mm) Linear fit line

2 0 0

20

40

60

Rainfall (mm) Fig. 2.4 Linear relationship between interception depths (mm) at varying magnitude of rainfall depths (mm)

2.2.7 Estimation of Interception Loss Using Measured TF and a Fixed Value of SF (i.e., I5 , I7.5 and I10 for 5, 7.5 and 10% of Incident Rainfall Respectively) The distribution of measured SF and SF taken as fixed percentage of incident rainfall (5, 7.5 and 10% of R) presented in Fig. 2.5. It is visible from the figure that fixed SF percentage values were relatively smaller than the measured SF against the same magnitude of incident rainfall, especially in small rainfall events (i.e., 40 mm). SF for rainfall events more than 40 mm magnitude may produce good estimate while taking as a percent

34

C. Shukla et al.

Fig. 2.5 Variation in measured Stemflow (mm) with Stemflow as fixed percentage of rainfall (i.e., 5, 7.5 and 10%) against the incident rainfall

of R. From Table 2.4, it is visible that mean I estimated using SF7.5 (SF as 7.5% of R) showed no significant difference from the mean I estimated using measured SF. Thus, SF as 7.5 of R, can be used for Eucalyptus plantation in absence of SF data. As these estimated were established using rainfall events of 2.8–60 mm rainfall, this SF percent may vary when used beyond this range of rainfall events. In addition, I5 and I10 showed significant variation from mean I estimated using measured TF and SF values (Table. 2.4). This emphasis the need of taking an appropriate percent of rainfall (in this case 7.5% of R) as fixed SF value based on the type of plantation and the variability rainfall events. Figure 2.6 also indicated that all the other parameter of linear fit was same for I5 , I7.5 and I10 except the constant (i.e., intercept of linear fit). These linear fit constants were 0.29, −0.41 and −2.91 for I5 , I7.5 and I10 respectively. The linear fit constant value was relatively closer to zero for I7.5 which further approve the suitability of 7.5% of R as SF for best estimation of I in absence of stemflow data.

2.2.8 Conclusions Eucalyptus is able to intercept an average of 25% of incident rainfall (R). Therefore, only 75% of total rainfall reached to the ground surface of watersheds plated with Eucalyptus. Study showed that I is linearly related with R depths and shows varying pattern depending rainfall magnitude. All the parameters namely rainfall, net rainfall, interception were found strongly correlated which emphasis the need of accurate measurement of throughfall (TF) and stemflow (SF). It was found that the variation was significantly higher in SF measurements than in TF measurements. Among the different percentage of rainfall taken as fixed value of SF, 7.5% of incident rainfall was found more appropriate for Eucalyptus plantations. This value (7.5% of rainfall

Interception (%) estimated using stemflow as a fixed portion of incident rainfall

2 Influence of Stemflow Measurement on Interception Estimation …

35

45 40 35 30

SF fixed as 5% of R

25 20 15 10 5

45

y = a + b*x Equation I at SF (5% of R I at SF (7.5% of R) I at SF (10% of R) Plot 2.09 ± 0.85 - 0.41± 0.85 - 2.91 ± 0.85 Intercept 1.02 ± 0.03 1.02 ± 0.03 1.02 ± 0.03 Slope 0.95 0.95 0.95 Pearson's r 0.91 0.91 0.91 R-Square (COD) 0.91 0.91 0.91 Adj. R-Square

10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 4045

40 35

40 35

SF fixed as 7.5% of R

30

30

25

25

20

20

15

15

10

10

SF fixed as 10 % of R

5

5 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40

10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40

Interception (%) estimated using measured stemflow

Fig. 2.6 Linear relationship between interceptions (%) estimated using measured SF and interception values obtained using a fixed percentage of rainfall as SF (i.e., I5 , I7.5 and I10 for 5, 7.5 and 10% of incident rainfall respectively)

as SF) may help in deeper understanding of the soil moisture destitution in the Eucalyptus watersheds. This study may play a significant role in water budgeting and hydrological studies for watersheds with Eucalyptus. These data may be useful to the agricultural and forestry policy makers to take more precise and suitable decisions regarding management of hydrological parameters.

References Almeida AC, Siggins A, Batista TR, Fonseca S, Loos R (2010) Mapping the effect of spatial and temporal variation in climate and soils on Eucalyptus plantation production with 3-PG, a process-based growth model. For Ecol Manag 259:1730–1740 Barbier S, Balandier P, Gosselin F (2009) Influence of several tree traits on rainfall partitioning in temperate and boreal forests: a review. Ann for Sci 66(6):602 Calder IR (1986) Water use of eucalypts—A review with special reference to South India. Agric Water Manag 11(3–4):333–342 Crockford RH, Richardson DP (2000) Partitioning of rainfall into throughfall, stemflow and interception: effect of forest type, ground cover and climate. Hydrol Process 14:2903–2920 Cuartas LA, Tomasella J, Nobre AD, Hodnett MG, Waterloo MJ, Munera JC (2007) Interception water partitioning dynamics for a pristine rainforest in Central Amazonia: marked differences between normal and dry years. Agric for Metrol 145:69–83 David J, Valente F, Gash JHC (2005) Evaporation of intercepted rainfall. In: Anderson M (ed) Encyclopedia of hydrological sciences. Wiley, New York, pp 627–634

36

C. Shukla et al.

Delphis F, Levia J (2004) Differential winter stemflow generation under contrasting storm conditions in a southern New England broad-leaved deciduous forest. Hydrol Process 18:1105–1112 Feller MC (1981) Water balances in Eucalyptus regnans, E. obliqua, and Pinus radiata forests in Victoria. Aust Forest 44(3):153–161 Iglesias-Trabado G, Wilstermann D (2008) Eucalyptus universalis. Global cultivated eucalypt forests map 2008. Version 1.0.1. https://www.git-forestry.com. Accessed 14.05.12 Levia DF, Frost EE (2003) A review and evaluation of stemflow literature in the hydrologic and biogeochemical cycles of forested and agricultural ecosystems. J Hydrol 274:1–29 Leite FP (1996) Growth, water, nutritional and light relationships in Eucalyptus grandis stands at different population densities. M.Sc. thesis, p 90, Universidade Federal de Viçosa, Minas Gerais, Brazil Li XY, Liu LY, Gao SY et al (2008) Stemflow in three shrubs and its effect on soil water enhancement in semi-arid loess region of China. Agric for Meteorol 148:1501–1507 Neto AJS, Ribeiro A, Lopes DDC, Sacramento Neto OBD, Souza WG, Santana MO (2012) Simulation of rainfall interception of canopy and litter in eucalyptus plantation in tropical climate. For Sci 58(1):54–60 Santosh DT, Tiwari KN (2019) Estimation of water requirement of banana crop under drip irrigation wit h and without plastic mulch using dual crop coefficient approach. In IOP conference series: earth and environmental science 344. https://doi.org/10.1088/1755-315/344/1/012024 Schellekens J, Scatena FN, Bruijnzeel et al (1999). Modelling rainfall interception by a low land tropical rainforest in north eastern Puerto Rico. J Hydrol 225:168–184 Shachnovich Y, Berliner PR, Bar P (2008) Rainfall interception and spatial distribution of throughfall in a pine forest planted in an arid zone. J Hydrol 349(1–2):168–177 Shi Z, Xu D, Yang X, Jia Z, Guo H, Zhang N (2012) Ecohydrological impacts of eucalypt plantations: a review. J Food Agric Environ 10:1419–1426 Vertessy RA, Watson F, O’Sullivan SK, Davis S, Campbell R, Benyon R, Haydon S (1998) Predicting water yield from mountain ash forest catchments. Cooperative Research Centre for Catchment Hydrology, Canberra, Industry Report No. 98(4):38 White DA, Dunin FX, Turner NC, Ward BH, Galbraith JH (2002) Water use by contour-planted belts of trees comprised of four Eucalyptus species. Agric Water Manag 53(1–3):133–152 Xiao QF, McPherson EG, Ustin et al (2000) A new approach to modeling tree rainfall interception. J Geophys Res Atmos 105:29173–29188 Xu DP, Zhang NN (2006) Progress of ecological effects of Eucalyptus plantation. Guangxi for Sci 35(4):179–201

Chapter 3

Strategic Human Resources in Water Sources Development Anand Verdhen

3.1 Introduction Water is life. Water may exist without a human being but a human can’t remain alive without water. We need water for every event in our life, that’s why we searched its sources (precipitating solid or liquid, flowing or stored) and settled nearby all around the springs, lakes, streams, rivers etc. which were regulated by nature and its God Indra. Our ancestors, as depicted in Veda, were worshiping air, water, earth, sky and fire sources and their Gods. They used to keep these clean and fresh. It is quite well known that Indra was not good but He had the power to regulate water sources and now human being tries to regulate and exploit manyfold against the natural laws. The hydrological study is the key to simulating natural laws. Bloschl et al. (2019) analyzed the 230-community raised unsolved problems in hydrology and summarised it in 23 titles out of which problem statements no. 18 and 23 are concerned with the topic of this study. Item number 18th states, ‘How can we extract information from the data available on human and water systems to conform the building process of sociohydrological models and conceptualization? And item no. 23rd asks, ‘What is the role of water in mitigation, urbanisation, and the dynamics of human civilization, and what are the implications for contemporary water management? Education is essential for humans to understand, innovate, analyze, plan, design and act efficiently to manage the sources effectively having high morale and team spirit for the society, relationships and natural resources; assuring sustainability at Spatio-temporal scales. But, increased population, needs and greed have disrupted value education, its rationality and relevance towards water sources and their drivers, A. Verdhen (Former Scientist at SASE (DRDO) & NIH Roorkee, WRE/Assoc. Prof. at CWRS (PU), GM at ICT Pvt. Ltd. Delhi and Prof. at DCE, Gurgaown), Bihar Sharif, India A. Verdhen (B) Vill+PO-Sadarpur, Nalanda, Bihar 811101, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_3

37

38

A. Verdhen

leading to regional and global scarcity, socio-economic crisis and climate uncertainties (Verdhen 2019). It has a great impact on the water sources in terms of precipitation, surface and groundwater flows and storage sustainability or certainty. It is primetime to establish a unified cum relevant quality education system to achieve equitable and sustainable development goals (SDGs). National Water Policy (MoWR 2002, 2010) laid down specific policy prescriptions for optimal development of the water resources. Studies and investigations are to be undertaken through integrated basin-wise (Intra & Inter Sub-basin) development at war footing. Kathpalia and Kapoor (2006) discussed quality water supply for the people below the poverty line. To keep the cost minimum, ground reserves are needed to be maintained. The springs and glaciers of the Himalayas keep the flow perennial in the rivers during the spring and summer are now receding due to the climate change scenario. Thus, a new storage scheme would have to take care of the flow deficiency. The analysis is difficult due to the lack of an observational network, especially in transboundary catchments. Indeed, to assign the statistics for designating an area of scarcity a detailed and sound technical criterion is desired regarding availability and risk involved with space and time. As an example, northeastern states including Bengal, Bihar, UP and the state of Assam are not only prone to receive waterlogging and frequent floods but are also prone to recurrent drought and famine. To have a non-monsoon period water supply and all-weather freshwater, created surface storage or aquifer (unconfined and confined) waters are the last options. Conjunctive use of surface and ground waters is very good for the health of the sources, utility and outcome but issues and recommendations are remained to be appreciated and implemented officially (Prasad and Verdhen 1990; Vincent and Dempsey 1991; Prasad et al. 1992). Nature’s work is integrated and conjunctive. It recharges and discharges conjunctively. The Dudhada Jal Vikas Samiti, filled up the canal network twice in the year 2000 to recharge the wells sufficiently. Sakthivadivel and Nagar (2004) planned water harvesting structures to understand the hydrology and hydrogeology of the area. Lakhaoti unlined Branch canal for Kharif irrigation system hoped to recharge sub-surface reservoir for use during the non-monsoon dry season. After the commissioning of this system in 1987–88, groundwater level went down within 1984–1988 from 10 to 12 m started rising except for years 1994–95 having low monsoon rain, where Madhya Ganga canal caries 234 cumecs discharge from Raolighat barrage across the Ganga and feed Upper Ganga canal and this Lakhaoti Branch canal (Sakthivadivel and Chawla 2004). Effective utilization and efficient management of water resources is essential for socio-economic development. The National water policy has provided sufficient guidelines to develop the water source as a resource. Several plan proposals and detailed project reports are still at the level of evaluation in the paucity of funds, regional issues and environmental clearance. An individual dweller/user or agency has to know their watershed and aquifer basins’ potential and status to have a fair share and participation. Since 2000, the International Association of Hydrologist (IAH) and UNESCO’s International Hydrological Programme (IHP) have established Internationally Shared (transboundary) Aquifer Resource Management (ISARM) program,

3 Strategic Human Resources in Water Sources Development

39

so case studies under different conditions will be needed (Puri 2003; Puri and Aureli 2005; Verdhen 2010a). Using Distributed Hydrological Models many researchers suggested further investigations on input data, melt-algorithm and coefficients (Rao et al. 1991; Jain et al. 2009; Kuras et al. 2011; Verdhen 2013; Verdhen et al. 2016). Remote sensing and GIS helped in land-use changes, hydrological features and spatially distributed hydrology and hydraulic model development (Forsythe et al. 2012; Verma et al. 2012). Review (Momblanch et al. 2019) on the change impacts on the Himalayan water referring twenty-first century studies, having 5–10% variability on modeling of the Himalayan water sources, restrict the confidence towards a solution. Past centuries’ studies concluded to have their Benchmark observation stations and models suitable to Indian Himalayas (Rao et al. 1987, 1991; Verdhen and Prasad 1993; Verdhen et al. 2011, 2013). Not only global change, but local changes also affect a lot. Near Pancheshwar temple on Mahakali river under the joint venture of the Government of India and Nepal proposed Dam of 814 m length and 311 m height with full storage level of 680 m (RL) is assessed to submerge 123 villages and their land (WAPCOS 2015). People having experience of not getting compensation or settlement here and there are worried about their future if not assured up to their satisfaction. The situation of overexploitation of resources means using blindly, without comprehensive study and planning or having vested interest leading to the death of sources, polluting the source and resources, wasting the precious resources, conserving but not managing scientifically or errorfree operation for safe, justified and equitable distribution including safe drainage, not carrying the assignment with responsibilities or not giving due importance of the role of a water source during infrastructure development. Various organizations working without co-ordination for the same piece of problems and objectives, getting training just to update tools and techniques without any common sense or beneficial implementation, discontinuing the best-suited programs and personalities, half-hearted restoration etc. are resulting in investment for bad than good. Who all are responsible for the tragic condition of resources and their sources? Certainly, human beneficiaries either to acquire resources, get pay and perks or be engaged in blind cum shared water business. It is prime time to assess the system, institutions, persons involved, their psychology and effectiveness to facilitate and empower the strategic human resources. Therefore, the overall objectives of the studies are to identify the role of human resources and education to have safe and sustainable water sources ensuring quantity and quality through strategic human resources over the capital-intensive unnatural practices, and establishing a uniform and eco-friendly approach satisfying planned SDGs.

3.2 Materials and Methodology Water in nature acquires all the physical states and is naturally available in the country to regain the regime by the grace of God. The human settlement flourished

40

A. Verdhen

and developed having a water source nearby. It cared first and civilization established, now human has to care water sources by conserving the quantity and quality without wasting and polluting and by augmenting it through forestry and stopping tree cutting. Every individual normally holds 70% water in the body by weight and engaged in 70% of activities related to water, consequently has experience and skills to enjoy doing business with 70% confidence but for the counterproductive or emerging challenges above 70%, the role left only for the specialized 100% to provide guidance and solution, if any. The study methodology is based on the experience gained in the water sources analysis and application covering village and Indian regions from childhood to the phases of schools, colleges and interaction with people in various fields of the society, working with central government, semi-central government, universitybased research and teachings in colleges, carrying design and consultancy along with corporate sectors, having national and international projects, assisting organizations and hankering after justice being jobless. Study materials are records, techniques, e-resources and review of the literature including own research, design, development and academic work. It is not wrong that one ounce of experience is heavier than one ton of theory. Doing good with nature, sources and resources depend upon human behavior, experience and exposure, education and opportunity. Individual honesty with integrity, devotion and team spirit may be the ingredient of strategic human resources to manage the sources. The paper highlights the dimensions of water sources in search of several such personalities to do justice with land, sources and societies. Quality education is essential as all the 17 essential target goals have been circulated globally to achieve by 2035. It is difficult to achieve anyone if not planned properly even after a lapse of 7 years. A strategic framework has been developed and key goals have been innovated to serve the purpose of all the goals. It is interesting to note that it is concerned with water and qualified human resources. What type of education will be more relevant under prevailing trends for the public or private with the fact that natural renewable resources basically belong to the public but may have the owners as well?

3.3 Results and Discussion A river has its natural basin boundary consisting of sub-basins covering various administrative, inter-geological and international boundaries. The inter-basin or transboundary river studies are more challenging than intra-basin. Indian National Water Policy has schedules for the hydrological unit planning taking into account the conjunctive use of surface and groundwater sources including environmental considerations (MoWR 2002; Verma et al. 2016). However, Himalayan river basins, at their upper catchment, lack snow and meteorological observations or data sharing. Various available operational hydrological models have been tested and intercompared for their suitability and applications under Indian conditions (NIH 1988; Verdhen et al. 2012) and found SRM (Snowmelt Runoff Martenic Model) and HEC (Hydrologic

3 Strategic Human Resources in Water Sources Development

41

Engineering Centre, USA models) are best suited for simulation and prediction. However, for better, reliable and official operations, the own Indian conditioned model should be developed (Verdhen and Prasad 1993; Verdhen et al. 2013, 2014), instead of learning, updating and leaving. Wubet et al. (2007) used mean monthly data of the mainstream gauges to estimate overall mean annual flow, is such model is efficient than a hand calculator or excel sheet, to perform this computation? Rapid urbanisation has obstructed the surface flow and depleted sub-surface water sources (Verdhen 2014). Climate change is significant since the 1980s due to the rising trend of maximum and the mean temperature, decrease in snow precipitation, rise in liquid/rain precipitation, upward marching of snowline and glacier tongs, earlier snowmelt and advance in glacier melt, the emergence of more mountain lakes, glacier lake outburst, etc. (Verdhen 1989, 2009a, b, 2010b; Verdhen et al. 2011; Verdhen et al. 2012). Deforestation and desertification are the major cause of climate degradation and precipitation anomaly, which can be restored through massive tree plantations, afforestation and protecting trees (Verdhen 2020). School children have more adaptability and awareness towards climate change and can protect their future by plantations (Bangay 2016). The groundwater recharge and aquifer potential developed during monsoon (June to September) and later through seepage of the tank, reservoir, snow and glacier melt induced perennial streams and artificial recharge failed to meet the demand from groundwater sources. Conjunctive use of surface and groundwater sources can reduce the stress and improve the quality of one by other to increase the culturable land, cropping intensities, and productivity due to certainty of supply, recharge, reclamation of land and reduction in alkalinity (Prasad and Verdhen 1990; Vincent and Dempsey 1991). The current national water policy recommends a safe yield policy supported by GW recharge and water budget to restore groundwater reserve and water table facing overexploitation. Case studies of the Kosi and Gandak projects and work under IDRC, USAID, IMMI/IWMI and Government of India conducted during the early 1990’s serve commendable guidelines for conjunctive use strategy to improve irrigation efficiency (Vincent and Dempsey 1991; Prasad et al. 1994; Verdhen and Prasad 1996; Wilson and Anderson 2006). MODFLOW like mathematical Models can be considered good but the data requirement is massive. In most cases boundary of the study area does not coincide with the natural boundary (Keshari 2010). The sharing of water among stakeholders is equally critical. Legal issues may arise, as it pertains to local and natural issues. Recently, the country has witnessed differences among the states of Karnataka and Tamil Nadu. Indian Water Resources Society reviewed and presented a critique on the effectiveness of the Bhakhra dam and the agreement to utilize it fully (Rangachari 2005). The name of the Ministry of Water Resources extended with specific assignments, River Development and Ganga Rejuvenation. Now, it is a Department under the union Ministry of Jal Shakti. After catering the flow of the River Ganga, Upper to Lower Ganga canal systems, Kanpur storage and barrage to their tributaries leaves the downstream meager non-monsoon environmental flow polluted through the wasted source of the flow generated by cities. Huge investment and massive treatment plants including death tolls of agitating

42

A. Verdhen

Ganga Bachao Samiti have not done as well as Corona cleanings but the situation reversed again in the second wave of Corona criminations. Restoration of quantity and quality of all the water sources with ongoing and emerging utilisations are complex and tough but are essential and not impossible with zero investment. Therefore, plan and policy matter. Planning like Railways network, National highways and Power grid connectivity, Inter-basin water transfer grid or River interlinking is rarely feasible proposition, because the water flows under gravity. The conflict-ridden project consists of 14 links as the Himalayan rivers component and 16 links as peninsular rivers component (Gopalakrishnan 2009). The central government had invited consent, comment and suggestion from all the concerned states, experts and stakeholders in order to achieve a feasible solution. Inter-basin transfer entirely depends on the consensus amongst all the concerned (Iyer 2009). We may need to pay the high economic and environmental costs of river inter-linking, as incomplete or inoperative (due to lack of water) SutlejYamuna link canal turns as a damaging route of floodwater. The severity of flood can’t be relieved by lifting and transporting through the limited capacity of the canal to the places, in general experiencing floods during monsoon. The performance of existing irrigation projects is not good enough. Operational and maintenance deficiencies, incomplete minor canals and watercourses, and neglected potential command area resulted in groundwater dependency. Since the 1990s, the thrust on irrigation development is meager. In the wave of big rivers interlinking projects and, in an effort to make these feasible by NWDA, the medium and minor projects of water utilization remained neglected to realize. Further 2003 movements to double the power through coal consuming cum environment polluting thermal projects in 10 years at the cost of Hydropower and conducting consent approving seminar on a plan, policy and new act with registration fees of Rs 3000 for thermal and Rs 30,000 for hydro may be tough to justify. NTPC encroached on some portion of NHPC as well. Success of all the major to minor hydraulic projects concerning hydropower, irrigation, drainage (side/cross) etc. depends essentially on the correct and complete hydrological and hydraulics input, but giving least weightage with or without key personal or team leader in the capital-intensive short duration customary projects are tragic. The impacts are obvious in terms of flooding, submergence and failure in meeting the long-term sustainable objectives. Percentage of non-monsoon (eight months) to the annual flow of a Himalayan basin can be considered as a combined component of rain, drain, ground and glacier spawn flow and envisaged as ‘Base Verdhen Flow (BVF)’, which is 14–17%, distributed over the non-monsoon period under the constraints of storage, recession and depletion. During monsoon, it may increase twice to thrice, i.e., 30–40%. Summing up annually, it goes up to 55%. The Kamala and the Bagmati, being snow and glacier-free basins, show only 7–8% (Verdhen 2010a). CGWB (2018) monitors groundwater levels (GWL) 4 times in a year (Apr. and May as pre-monsoon, August and November as monsoon and January as postmonsoon) at 23,000 stations (16,500 open dug wells of 12–15 m and 6500 piezometers set at 50–300 m depth, spread over India) and analyzed the records through its GIS and Groundwater Estimation and Management System (GEMS). Piezometer

3 Strategic Human Resources in Water Sources Development

43

head of deep aquifer fluctuation over a decade (2008–2017) is relatively less than open well water level below the ground surface but overall, out of analyzed stations, almost equal numbers of rising and fall has been observed with 0–2 m (35%), 2–4 m (10%) and above 4 m (5%). Annual pre-monsoon to post-monsoon (2017–18) water level remains depleting for 20% and gaining in about 80% with 45% up to 2 m and only 15% above 2 m. The major source of groundwater resources, recharge is about 58% and 9% of the annual replenishable GW from monsoon rain and non-monsoon rain, respectively. The rest of the 33% is shared by canal seepage, irrigation, tanks and ponds like water sources and conservation structures. Counterproductive development realized since last 3 decades. Prevailing trends of education are high cost and low-quality. The increasing trend of global warming and climate uncertainty (Verdhen 2009a, b, 2010b) since last 3–4 decades is not natural but due to lack of scientific and ethical education which left them to exploit the resources and consume fuel, forest, fodder and grains heavily without caring the society, mother earth and future generation to come. Higher education helps in research and development while school and community education help in founding behaviour (Bangay 2016). The children are needed to be trained about conserving and protecting the water sources and forest wealth from very early from their childhood by parents and local society. Being author’s village within Jamindari bund between two seasonal rivers and experiencing flood twice each year followed by drought, having no conventional irrigation system and problems of mobility and agricultural activities including farmers’ miseries decided in school time to solve the recurring problems. Naturally, a person affected by hydrological hazards and lack of water supply problem will be genuinely devoted to solving basic or emerging problems, protecting the water sources and managing these resources for the masses having qualifications and opportunities to act, while the person employed professionally to discharge duty for the shake of power, position and perks and never faced or realized the importance of this sector may not be suitable strategic human resources in water sources sustainable development. Ministry of water resources of states and center including their institutions and Ministry of Environment and Forestry are engaged with assessing, developing and managing basin, intra basin, inter-basin and rivers water sources, while Institutions and Authority of PWD, Municipalities, PHED, Agriculture are engaged in encroaching water bodies, overexploiting, wasting, regulating carelessly and polluting the water sources. Nevertheless, the Ministry of Highways and Surface Transport is also responsible for constraining the waterways, creating submergence, landslides, floods and other fluid flow hazards due to capital intensive, speedy and contractual design and construction. The human resources of water source academics, simulation and management are suffering from complexes, working without team spirit, grabbing the credibility, credits and careers of others for their vested interest and sustainability. So, in this situation, hope for justice with water source and its security and development in future for the societies are apprehensive. The implementation of the following 17 SDGs (2030 agenda) related to education and water has been agreed upon by 173 UN member countries in September 2015. The SDGs (Fig. 3.1) concerning (G1–End poverty everywhere;

44

A. Verdhen

Fig. 3.1 UN Illustration of the 17 SDGs (After Motton et al. 2017)

G2–End hungry, promote agriculture; G3-Healthy lives and well beings; G4-Quality education; G5-Women empowerment; G6-Water and sanitation; G7-Affordable energy; G8-Economic growth and employment; G9-Industrialization and innovation; G10-Reduce inequality within countries; G11-Cities and human settlement; G12-Consumption and production; G13-Control climate change and impact; G14Development of marine resources, G15-Restore terrestrial ecosystem and forest; G16-Access to justice and development; G17-Global partnership and finance for development) all together are interrelated. Motton et al. (2017) discussed interconnection and conflicts between targeted goals quoting examples. Action for zero hunger in Sub-Sahara Africa interacts positively with G1, G3 and G4, while some food production conflicts with G7 and G13. Similarly, water and sanitation (G6) underpin G1, G2, G3, G4, G7, G8 and G10. Motton et al. (2017) have realized that If we go on exploiting the resources without care then we will be breaking the implicit contract we have with future generations and will be remembered as the generation who knows too much and did too little. The SDGs are universal (Fig. 3.1), whereas the MDGs (Millennium Developmental Goals) were intended for action in developing countries. The UN resolution refers to five areas of critical importance (Motton et al. 2017), known as 5 ‘P’s: People (Goals1-6), Prosperity (Goals 7–12), Planet (Goals 13–15), Peace (Goal 16), and Partnerships (Goal 17). SDG4 having lifelong learning as its 10th agenda item ensures inclusive and equitable quality education and is critical to attaining G5, G12 and G13 with a little scope for G3 and G8 as well (English and Carlsen 2019). Scientists and researchers are capable to cover these goals, having qualifications and skills to make people productive. Why people are crazy to have more than sufficient with intense intention to be superior and facilitated than others? The problem is primarily lying behind the failure of systems/society/government which were responsible to provide equal and sharable access to all with peace of mind and qualified livelihood. The message of

3 Strategic Human Resources in Water Sources Development

45

Fig. 3.2 Verdhen’s Framework of methodological implementation and monitoring of SDGs

success and satisfaction lies within WWWW (Wisdom, Women, Water and Weather). The Verdhen Model of Sustainable Development Goals is the solution framework (Fig. 3.2) and the opportunity to achieve 17 committed SDGs. The role of (G4, G5, G6, G14, G15 and G16) quality education, women involvement, water and marine resources development restoring ecosystem, forest and justice including G17 are paramount to lead the success of G1, G2, G3, G7, G8, G9, G10, G11, G12 and G13 implementations. Consumer (G12) and Climate (G13) became hungry (G2) and thirsty and need to minimize it to zero. Poverty (G1) and inequality (G10) are prime paradoxes and need to be reset to be zero. Subsequently, finance (17), settlement & justice (G11 & G16) would expedite gaining fine health (G3), good production (G12) and congenial ecosystem (G15). All the four G1, G2, G10 and G13 can be minimized through G4, G5 and G6 (quality education, human empowerment and water sources development). Goals G4-9 have a critical role to eradicate poverty but G4-6 require G8 (employment). Goals G6-7 (water-energy) affect all but affected by G9-11 (industrialization, inequality, and insecure city). G9 unbalancing G10-11 affects G6-7, if not planned properly. Water experts & Women of Field experience and Quality education ensure G4-6 and success of SDGs using ‘Verdhen’s model’ and Indian philosophy on G3, 12 & 15 (health, consumption and ecology) through WWWW (Wisdom, Women and Water with Weather). People have to have a water vision to address the water problem at present and in the future to face. Even rain-rich area requires water conservation and harvesting irrespective of hill and plain, Meghalaya or Kerala. Low rain areas, Mizoram and Rajasthan have a long tradition of water harvesting, conservation and land reclamation. The conservation of water is not to hoard or block, but to plan and control

46

A. Verdhen

wastage in such a way at individual or community, state or national levels so that it may be available all the year round at least minimum if not optimum for all the purposes, especially fresh drinking water. In the flood-afflicted area, even during the monsoon season, the availability of drinkable water becomes a chronic problem due to pollution and contamination of resources. Nal-jal yojna is highly appreciable but the uncontrolled supply and wastage may damage the aquifer shortly. Flood and drought-proofing including land reclamation require infrastructure, adequate control and safe conservation measures. To increase water availability there is a need of the hour to adopt water harvesting and construct structures for recharging and recycling of water. Rainwater harvesting means collecting roof-top rainwater and storing it overhead tank or underground for later use and to augment groundwater. It can be also stored through quarries, channels, off channels, contour channels, reservoirs, ponds etc., by reducing the evaporation losses and adopting water use efficient drip and sprinkler irrigation. General awareness to be created on methodology to conserve involving local communities. Industrialists have to follow and adopt the policy of effluent treatment and recycling. Need has to be curtailed and wastage has to be stopped and realized. Participation of women in the affairs of Gram Panchayat can focus the attention on water conservation, as rural women do all the household work from cooking to cleaning, clothes washing, solid wastes disposal, care children and animals and also collecting water required for drinking and bath. Rural people still understand the importance of water conservation and feel the need for paain (channel), aahar (longitudinal storage) and pond renovation. In urban areas wishful wastage of freshwater is common and non-stop in multi-star hotels including posh area-based flush, flowers and fountains. Communities and municipalities have to play a greater role in the water conservation drive by adopting non-financial control measures on wastage (Verdhen 2007a, b). Therefore, the urban drainage system should be designed for the 50–100 years return period of rainstorm including the effect of climate variability, if any. The city should be freed from drainage congestion and no flow over the road either from the sewage water or rain intensity of lesser than 50/100 years return period. The stormwater and sewage water system should not be mixed by separating networks from each other. Cleaning, maintenance and monitoring should be the routine feature. Anybody putting the garbage or blocking/restraining the flow of drain/channel should be penalized. The concerned authority should be held responsible to bear the damage on account of submergence (Verdhen 2007a, b; Singh and Kesari 2007; Verdhen 2017; Beyond India 2017). Mass Movement is imperative to raise the awareness, alertness and responsibility of individuals and society and requires the development of low-cost, nonpolluting decentralized septic tanks in rural and urban (slum and posh) areas. Scientific management of solid and liquid wastes is the solution to keep the surface and groundwater sources free from contamination and pollution load even during monsoon (Verdhen 2011).

3 Strategic Human Resources in Water Sources Development

47

3.4 Conclusions There is a need of the hour for mutual cooperation among co-basin to share and manage watersheds, water sources and observations for the analysis and equitable developmental activities. A physically distributed model like SHE, Mike 11 and MODFLOW having GIS environment are good for academic and limited application at present. HEC-HMC, HEC-RAS, SRM or own national robust cum sustainable hydrology and hydraulic model, free and secure for various purposes is more valuable and warranted. Genuine, scientific and technical intra-basin and basin-wise full conservation and development of water sources should be taken initially (Verdhen 2009a, b, 2016) before the complex inter-basin transfer of surplus, if any, including unmanageable flood, if feasible, leaving scarce precious lean flow protecting the riparian right and environmental flow. Overexploitation of groundwater is to be avoided. Water harvesting, secure recharge and plantations must be expedited. Cleaning, maintenance and monitoring should be routine features. Anybody putting garbage or blocking/restraining the flow/channel should be penalized. The safety of the embankment and actions to study the unexpected rise in the HFL of the adjoining channel/river should be undertaken by the appropriate authority. The success of SDGs lies within WWWW (Wisdom, Women and Water with Weather). Verdhen’s model of sustainable solution framework may help to achieve committed SDGs through G4, G5, G6 with support of G-11, 16 & 17 in eradicating G-1, 2, 10 & 13 (poverty, hunger, inequality and climate issues) and achieving health (G3), production (G12) and ecosystem (G15). Strategic Human Resources are to be facilitated as they are proved to be the key drivers of water sources and sustainable development. Acknowledgements The author acknowledges the experience gained, the support of friends and mentors from Snow and Avalanche Study Establishment, Manali, National Institute of Hydrology, Roorkee, Centre for Water Resources Studies, Patna, ICT Pvt. Ltd., Delhi, IIT Delhi and Dronacharya College of Engineering, Gurgaon. Further, he wishes to thank Er. Aarsh Verdhan for manuscript proofing. The author is happy to acknowledge the help extended by the organiser of ICWSS21.

References Bangay C (2016) Protecting the future: the role of school education in sustainable development—An Indian case study. Int J Dev Educ Learn 8(1). https://doi.org/10.18546/IJDEGL.8.1.02 Beyond India (2017) Engineers are now in forefront to technically eradicate the problems behind the urban flooding. Complete Digital Media Solution, http://beyondindia.in/2017/06/17 Bloschl G, Bierkens FB, Chambel A, Cudennec C, Destouni G, Fiori A, Kirchner JM et al (2019) Twenty-three unsolved problems in hydrology (UPH)—A community perspective. Hydrol Sci J 64(10):1141–1158. https://doi.org/10.1080/02626667.2019.1620507

48

A. Verdhen

CGWB (2018) Ground water year book India 2017–2018: Central Ground Water Board, Dept. of Water Resources, River Development and Ganga Rejuvenation, Ministry of Jal Shakti, Govt. of India, Faridabad English LM, Carlsen A (2019) Lifelong learning and the sustainable development goals (SDGs): probing the implications and the effects. Int Rev Educ 65:205–211. https://doi.org/10.1007/s11 159-019-09773-6 Forsythe N, Fowler HJ, Kilsby CG, Archer DR (2012) Opportunities from remote sensing for supporting water resources management in village/valley scale catchments in upper Indu Basin. Water Resour Manage 26:845–871. https://doi.org/10.1007/s11269-011-9933-8 Gopalakrishnan M (2009) India’s concepts on large scale inter basin water transfer. In: Menon MGK, Sharma VP (eds) Sustainable management of water resources; emerging S & T issues in South Asia. INSA, New Delhi, pp 33–55 Iyer RR (2009) River linking project: is it good science? In: Menon MGK, Sharma VP (eds) Sustainable management of water resources; emerging S & T Issues in South Asia. INSA, New Delhi, pp 69–79 Jain SK, Goswami A, Saraf AK (2009) Role of elevation and aspect in snow distribution in western Himalaya. Water Resour Manage 23:71–83. https://doi.org/10.1007/s11269-008-9265-5 Kathpalia GN, Kapoor R (2006) Charting a new course: a strategy for sustainable management of India’s water resources in the 21st century. In: Alternative Futures and Danish Books, New Delhi, pp 6–15 Keshari AK (2010) Review of ground water assessment methodology in perspective of hydrological analysis of UP Sodic land reclamation project. In: Proceedings of workshop (23–24 February) on ground water estimation, CGWB, Delhi and CED, IIT Delhi, Delhi, pp 42–48 Kuras PK, Alila Y, Weile M, Spittlehouse D, Winkler R (2011) Internal catchment process simulation in a snow dominated basin: performance evaluation with spatiotemporally variable runoff generation and groundwater dynamics. Hydrol Processes 25:3187–3230 Momblanch A, Holman IP, Jain SK (2019) Current practice and recommendations for modelling global change impacts on water resource in the Himalayas. Water 11(1303):1–27. https://doi.org/ 10.3390/w11061303 Motton S, Pencheon D, Squires N (2017) Sustainable development goals (SDGs) and their implementation. Br Med Bull 124:81–90. https://doi.org/10.1093/bmb/ldx031 MoWR (2002 and 2010) National water policy. Ministry of Water Resources, Govt. of India, pp 1–12 NIH (1988) Hydrologic models for mountainous area. Report TN-33 (Verdhen et al. 1988), NIH, Roorkee, India Prasad T, Verdhen A (1990) Management of conjunctive irrigation in alluvial regions—Issues and approaches. In: International conference at AIT, Bangkok in 1990, (op cit) Prasad T, Verdhen A, Sinha RS, Verma AK (1992) Conjunctive management of surface water and groundwater for improving the performance of the Gandak project in Its middle and lower reaches. In: Proceedings of the workshop on IIMI-India collaborative research in irrigation management, New Delhi, 13–14 Feb 1992, pp 79–96 Prasad T, Verdhen A, Gayawali D, Dixit A (1994) Co-operation for international river basin development: case of the Kosi basin (A joint paper of CWRS & RONAST study teams). In: Kirby C, White WR (eds) Proceedings of international seminar on integrated river basin development. Wiley, New York, pp 493–502 Puri S (2003) Transboundary aquifers: international water law and hydro-geological uncertainty. Water Int, IWRA 28(2):276–279 Puri S, Aureli A (2005) Transboundary aquifers: a global program to assess, evaluate, and develop policy. Gr Water 43(5):661–668 Rangachari R (2005) Unravelling, ‘Unravelling of Bhakra’ a critique. Indian Water Resources Society, IWRS, IIT Roorkee, India, pp 1–37

3 Strategic Human Resources in Water Sources Development

49

Rao NM, Rangachary N, Kumar V, Verdhan A (1987) Some aspects of snow cover development and avalanche formation in the Indian Himalaya. In: Proceedings of symposium on avalanche formation, movement and effects, at Davos in 1986, IAHS Publ. No. 162: 453–462 Rao NM, Bandopadhya BK, Verdhen A (1991) Snow hydrology studies in the Beas basin for developing Snowmelt Runoff Model. J. Institution of Engineers (India) CE 72, pp 92–102 Sakthivadivel R, Chawla AS (2004) Using monsoon river flows to recharge groundwater: an experiment in India. IWMI, Colombo, Sri Lanka Sakthivadivel R, Nagar RK (2004) Private initiative for ground water recharge: case of Dudhada village in Saurashtra. Water Policy Research, IWMI-TATA Water Policy Program, pp 1–6. http:// www.iwmi.org/iwmi-tata Singh SK, Kesari P (2007) Managing runoff in Delhi through rain water harvesting. In: Proceedings of All India seminar on urban storm water drainage system: challenges and solutions: Institution of Engineers, Delhi Center, pp 41–48 Verdhen A (2016) Intra and inter basin linking of rivers in water resources management. CSIR J Sci Ind Res 75:150–155 Verdhen A, Prasad T (1993) Snowmelt runoff simulation models and their suitability in Himalayan conditions. In: International symposium on snow and glacier hydrology, held in November 1992 at Kathmandu, Nepal. IAHS Publ. No. 218:239–248 Verdhen A, Chahar BR, Sharma OP (2014) Snowmelt modelling approaches in watershed models: computation and comparison of efficiencies under varying climatic conditions. Water Resour Manage 28:3439–3453. https://doi.org/10.1007/s11269-014-0662-7 Verdhen A, Prasad T (1996) Problems and prospects of conjunctive use of surface and ground waters for irrigation in the command of a river diversion scheme. In: Proceedings of conference on HYDRO-96, ISH, Pune and CED of IIT Kanpur, pp 270–278 Verdhen A, Chahar BR, Sharma OP (2011) Climate change impact on snow and glacier hydrology simulation of a Himalayan watershed. in: Proceedings of the international perspective on water and environment (IPWE-2011), EWRI of ASCE, Singapore Verdhen A, Ashwagosha G, Bhutiyani MR, Chahar BR (2012) Spatio-temporal temperature reconstruction reliability and change detection. In: Proceedings of the international symposium on cryosphere and climate change (ISCCC-2012), SASE, Manali, India, C028:1–7 Verdhen A, Chahar BR, Sharma OP (2013) Snowmelt runoff simulation using HEC-HMS in a Himalayan watershed. In: Proceedings of world environmental & water resources congress 2013, EWRI of ASCE, May, Ohio Verdhen A, Chahar BR, Sharma OP (2016) Winter precipitation and snowpack-melt with temperature and elevation. J. Hydrol. Current Res 7(2):1000245(1–11). https://doi.org/10.4172/21577587.1000145 Verdhen A (1989) Hydrological study in a glaciated mountain stream. In: Proceedings of the national meet on Himalayan Glaciology, DST, New Delhi, India, pp 33–42 Verdhen A (2007a) Process, planning and design of water conservation. In: Proceedings of seminar on water resources day: water conservation. Institution of Engineers (I), Assam State Canter, Guwahati, held on 30 May, 2007a Verdhen A (2007b) Assessment, planning and design of urban storm water drainage system. In: Proceedings of All India seminar on urban storm water drainage system: challenges and solutions. Institution of Engineers, Delhi Center, pp 24–32 Verdhen A (2009a) Inter and intra basin integrated water resources management for optimal development. In: Proceedings of national seminar (29–30 October) on River Hydraulics, CE Dept. Maharshi Markandeshwar University, Mullana and ISH, Pune Verdhen A (2009b) Trend and impact of climate change, global crisis and Himalayan hydrology. In: International conference (TIMS-09) on climate change & sustainable management of natural resources, ITM Universe, Gwalior, MP Verdhen A (2010a) Hydrological investigation challenges of transboundary watershed aquifer in the Himalayan region. In: International conference on transboundary aquifers, challenges and new directions (ISARM-2010a), 6–8 Dec, UNESCO, Paris, p 92 (1–10)

50

A. Verdhen

Verdhen A (2010b) Climate change impact on Himalayan gender and hydrology. In: International conference (IASC-TIMS-2010b) on climate change & sustainable management of natural resources, ITM Universe, Gwalior, MP Verdhen A (2011) Water conservation and decentralized waste management for sustainable supply and sanitation. In: Conference on water resources day, Assam State Centre, Institution of Engineers (India), Guwahati, India Verdhen A (2013) Snow and Glacier melt simulation for hydrology in a Typical Himalayan Watershed. Ph.D. thesis, IIT Delhi, Delhi, India. Verdhen A (2014) Science and security for changing water quantity and quality with rapid urbanization and variability in climate and society. In: Proceedings of the 11th Kovacs Colloquium on hydrological sciences and water security: past, present and future, Paris, France, and IAHS Publ., pp 365 Verdhen A (2017) Improved drainage system is the key for the urban flood management. In: Proceedings of All India seminar on urban flood management: challenges & strategies in India, Institution of Engineers (I), Delhi State Centre, Delhi, pp 16–27 Verdhen A (2019) Quality education for sustainable and eco-friendly development. In: International conference on sustainability education (ICSE 2019), Organised at India Habitat Centre, Delhi, 9–10 Sept 2019. Verdhen A (2020) Trees and forest conservation-cum-afforestation to cope with climate uncertainty. In: Liticia & Shit et al (eds) Chapter-28 of Book on forest resources resilience and conflicts. Elsevier Inc. Verma RK, Murthy S, Verma S, Mishra SK (2016) Design flow duration curves for environmental flows estimation in Damodar river basin, India. Appl Water Sci 7(3):1283–1293. https://doi.org/ 10.1007/s13201-016-0486-0 Verma S, Verma RK, Singh A, Naik NS (2012) Web-based GIS and desktop open-source GIS software: an emerging innovative approach for water resources management. Advances in Computer Science, Engineering and Applications, ICCSEA, Springer-Verlag, AISC 167, Dordrecht, The Netherland, pp 1061–1074 Vincent L, Dempsey P (1991) Conjunctive water use for irrigation: good theory, poor practice. In: Vincent L (ed) Irrigation management network. Overseas Development Institute (ODI), Regent’s College, London NW1 4NS WAPCOS (2015) Executive summary of Pancheshwar multipurpose project’s detailed project report. Pancheshwar Development Authority (Bi-national Entity of India and Nepal), Ministry of Water Resources, River Development and Ganga Rejuvenation, Ministry of Jal Shakti, Govt. of India and Ministry of Energy, Govt. of Nepal Wilson L, Anderson S (2006) Development of a conjunctive use management plan for the central Platte valley-task 3A: case studies. E-Resources\JOBS\626-Central NE NRD-NPPD\Conjunctive Management Case Studies, Lee Wilson and Assoc., INC, pp 1–4 Wubet FD, Awulachew SB, Moges SA (2007) Analysis of water use on a large river basin using Mike BASIN model—A case study of the Abbay river basin, Ethiopia

Chapter 4

Water Budget Monitoring of the Ganga River Basin Using Remote Sensing Data and GIS Gagandeep Singh and Ashish Pandey

4.1 Introduction River systems across the globe are the lifelines for millions of people residing in their catchments, providing fresh water for drinking and agricultural purposes. Apart from supporting various aquatic and terrestrial ecosystems, rivers also facilitate transportation and hydropower generation. River basin management is crucial for water allocation and sharing between regions and states of a country or in a river basin located across different countries. The Transboundary Freshwater Dispute Database (TFDD, 2018) has identified 263 international transboundary river basins (see Fig. 4.1). These transboundary river basins occupy nearly 47% surface area of the Earth (excluding Antarctica) and carry about 60% of the global river discharge (Baranyai 2020). Almost 40% of the total world population lives in the transboundary basins spread over at least two countries (Wolf et al. 1999). This map has been adopted from the ‘Transboundary Freshwater Dispute Database’: Product of the Transboundary Freshwater Dispute Database, College of Earth, Ocean, and Atmospheric Sciences, Oregon State University. Additional information about the TFDD can be found at: http://transboundarywaters.science.oregon state.edu.” However, the water availability per person in Asia and the Pacific region is the lowest globally as it accommodates and suffices 60% of the global population with only 36% of the global water resources (APWF 2009). Monitoring water availability in a basin is a very crucial requirement for efficient river basin management. Factors like basin hydrology, ecology, weather as well as climate govern water availability. Accurate delineation of the watersheds and stream network on the basis of slope and terrain is an essential requirement for effective river basin management. Furthermore, G. Singh (B) · A. Pandey Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_4

51

52

G. Singh and A. Pandey

Fig. 4.1 Spatial representation of the transboundary river basins in the world

the prime requirements for monitoring water availability in a river basin are the observation, modeling, and information retrieval of the water budget components. The most significant water budget components contributing to the river flow in a basin are precipitation, evapotranspiration, infiltration, surface water, groundwater storage, and runoff. Remote sensing and geographical information system (GIS) have tremendous potential and applicability to be a valuable tool for water resources management (Pandey et al. 2022). Precipitation, evapotranspiration, streamflow, and terrestrial water storage change are balanced to represent the terrestrial water budget (Abolafia-Rosenzweig et al. 2021). Researchers have demonstrated effective use of remotely sensed datasets in conjunction with the land surface models (LSMs) for assessing the global historical water budget (Zhang et al. 2018; Pan et al. 2012) and produce a reliable estimate of the water cycle. The remote sensing and modeling data offer some advantages which make these products highly useful in water budget assessment (Himanshu et al. 2017; Dhami et al. 2018). The remotely sensed data provides near-global to global coverage, which is impossible with spatially nonuniform in-situ measurements. Remote sensing technology enables to capture of data at practically inaccessible locations on the earth’s surface. The earth system models offer a unique combination of ground-based and remote sensing observations resulting in frequent and repeated observations of water budget components. These models also provide datasets for parameters that are not directly observed by the satellites. The most game-changing advantages are that all these data products are freely downloadable and are available in near-real-time continuously for more

4 Water Budget Monitoring of the Ganga River Basin …

53

than a decade now. Moreover, these data products can be employed to monitor the water budget for basins that are sparsely gauged or where data access is restricted. Ganga River Basin in India is one such basin where the discharge data is restricted for public use (Singh & Pandey 2021). This paper focuses on employing remote sensing-based data to obtain river basin networks and consequently assessing surface water budget components in the Ganga River Basin (GRB). The objectives of this study are to estimate seasonal (wet and dry season) water budget components for the entire GRB as well as at the sub-basin level by employing remote sensing datasets and GLDAS 2.1 model data.

4.2 Study Area and Methodology 4.2.1 Study Area The Ganga River basin (GRB) is a transboundary basin shared by India, Nepal, and Bangladesh, which makes it a very vital resource for Asia. The Ganges originates situated in the Himalayan Mountain state of Uttarakhand in India at Gomukh, the terminus of Gangotri Glacier. It runs for a distance of over 2500 km before joining the ocean at the Bay of Bengal. The catchment area of the river basin constitutes 26% of the entire landmass of India and is thus labeled as the largest river basin in the country. It extends between 73.39°E and 89.75°E longitudes and 21.55°N and 31.46°N latitudes (Fig. 4.2). In India river, Ganga flows across 11 states (Fig. 4.2), namely, Uttarakhand, Himachal Pradesh, Delhi, Haryana, Rajasthan, Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand, Chhattisgarh, and West Bengal. However, in this study entire GRB with a catchment area of 1,027,095 km2 is considered for which the basin boundary was downloaded from the HydroSHEDS database. Also, sub-basin boundaries were obtained from the HydroSHEDS level 5 classification scheme, which divides GRB into 14 sub-basins, as shown in Fig. 4.2.

4.2.2 Data Sources All the datasets used in this study are remote sensing-based data products and downloaded from various sources, as presented in Table 4.1. GLDAS model operates on a global scale and solves for the interaction of mass, energy, and momentum between the surface and atmosphere by integrating remote sensing and surface-based observations in the land surface models. It provides uniformly gridded ready to use data products on the water budget components.

54

G. Singh and A. Pandey

Fig. 4.2 Study area showing the location and extent Ganga River Basin and its sub-basins

GLDAS also hosts data not directly observed by the satellites viz. runoff, evapotranspiration, and snow water equivalent. In this study, satellite-based precipitation data product from Integrated Multi-satellitE Retrievals for GPM (IMERG) was employed. Evapotranspiration dataset based on MODIS vegetation index, thermal infrared (TIR) bands of MODIS, Landsat-8, and global geostationary satellites was used.

4.2.3 Methodology The water-budget equation is simple and universally adaptable. The basis of the equation rests on a few assumptions on mechanisms of movement of water and its storage (Healy et al. 2007). A basic water budget for a watershed can be expressed as: P R + Q in = E T + ΔS + Q out where PR is precipitation, Qin is the discharge flowing into the watershed, ET is the evapotranspiration, ΔS is the change in water storage, and Qout is the discharge flowing out of the watershed. All these components derived from remote sensing

4 Water Budget Monitoring of the Ganga River Basin …

55

Table 4.1 Details of the data products and their sources Dataset

Spatial resolution

Temporal resolution

Data source

IMERG precipitation

0.1° × 0.1°

30-min, daily, monthly

Giovanni https://giovanni.gsfc. nasa.gov/giovanni/

MODIS ET

500 m

8-daily, annual

Application for extracting and exploring analysis ready samples (AρρEEARS) https://lpdaacsvc.cr. usgs.gov/appeears/

GRACE-FO

1.0° × 1.0°

Monthly

JPL GRACE Tellus https://grace.jpl.nasa. gov/

GLDAS 2.1 Precipitation, evapotranspiration, runoff, terrestrial water storage

1.0° × 1.0°

3-hourly, monthly

GES DISC https://daac.gsfc.nasa. gov/

stream network, watershed, and sub-basin boundaries

–

–

Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS) https://hydrosheds.org/ downloads

observations as well as GLDAS 2.1 model were freely downloaded from the data sources listed in Table 4.1. In this study, two different approaches have been employed to examine and compare dry and wet season water budget components of GRB in 2019. Figure 4.3a shows the detailed methodology adopted using remote sensing-based datasets of IMERG precipitation, MODIS Evapotranspiration (ET), and GRACE Terrestrial Water Storage (TWS) anomalies to derive sub-basin wise and overall seasonal water balance for GRB. Figure 4.3b shows the detailed methodology adopted to obtain water balance using the water budget components extracted from GLDAS 2.1 model. In the northern part of India, the dry season spans between March and May, while the wet season stretches from June to September.

4.3 Results and Discussion The remote sensing data sets were pre-processed in a GIS environment using open source QGIS 3.18 software. Figure 4.4 shows maps depicting the spatial variation of

56

G. Singh and A. Pandey

Fig. 4.3 Methodology flowchart a water budget assessment using remote sensing-based datasets; b water budget assessment using GLDAS 2.1 model data (PR = Precipitation; ET = Evapotranspiration; RO = Runoff; TWS = Terrestrial Water Storage; M-A-M = March–April-May; J-J-A-S = June-July–August-September; MOD16A2GF = Gap Filled MODIS ET data product)

water budget components over Ganga River Basin for dry (top row) and wet (bottom row) seasons of 2019. The seasonal (wet and dry seasons of 2019) spatial variation of monthly accumulated IMERG rainfall data is shown in Fig. 4.4a, b. It is visibly evident from the maximum and minimum values that there is a considerable variation in the rainfall in the dry and wet seasons of 2019. Similarly, the seasonal variation in Evapotranspiration (ET) is presented in Fig. 4.4c, d. Also, the change in water storage for wet and dry seasons is illustrated in Fig. 4.4e, f. The precipitation and ET maps presented in Fig. 4.4 were further used to obtain the difference between wet and dry seasons by subtracting the dry season raster from the wet season raster, as shown in Fig. 4.5. Figure 4.5a shows that the northern part of GRB experienced lesser rainfall in the wet season and therefore shows the lower range of change significantly in the region of Nepal and the northwestern region of India. The remaining part of the basin shows medium to high variation in the difference of rainfall observed between the dry and wet seasons of 2019. However, the ET difference map presented in Fig. 4.5b shows a contrasting spatial variation over the basin. Almost the entire basin features low to very low (negative) difference in the ET with a minimal area featuring high difference. These maps were further used to calculate zonal statistics for each of the 14 subbasins of GRB. Table 4.2 presents the basin-averaged water budget components viz. precipitation, evapotranspiration, and terrestrial water storage for the entire GRB and at the sub-basin level for the year 2019. The residuals obtained after subtracting TWS from (PR-ET) for wet and dry seasons can be attributed as seasonal discharge. Another attempt to estimate the seasonal water budget of GRB was made using the gridded datasets from the GLDAS 2.1 model. The spatial maps of the water budget components for dry and wet seasons are presented in Fig. 4.6. The maps show spatial

4 Water Budget Monitoring of the Ganga River Basin …

57

Fig. 4.4 a Accumulated precipitation map (MAM); b accumulated precipitation map (JJAS); c evapotranspiration map (MAM); d evapotranspiration map (JJAS); e change in water storage map (MAM); f change in water storage map (JJAS) (MAM: March–April–May; JJAS: June–July– August-September)

Fig. 4.5 a Seasonal precipitation difference map, b seasonal ET difference map

58

G. Singh and A. Pandey

Table 4.2 Estimates of seasonal, basin-averaged, and sub-basin level water budget components Subbasin

Area (km2 )

PRD (m3 )

PRW (m3 )

ETD (m3 )

ETW (m3 )

TWSD (m3 )

1

35,180.07

9.05E + 09

4.69E + 10

3.94E + 09

1.13E + 10

1.39E + 07 1.24E + 10

2

89,105.43

1.53E + 10

8.35E + 10

7.82E + 09

2.42E + 10

−3.10E + 09

2.52E + 10

3

166,323.00

1.59E + 10

1.72E + 11

7.76E + 09

4.35E + 10

−1.39E + 10

6.40E + 10

4

131,757.00

1.20E + 10

1.23E + 11

9.64E + 09

3.57E + 10

−1.36E + 10

2.94E + 10

5

82,063.27

1.63E + 09

8.90E + 10

3.54E + 09

1.73E + 10

−9.12E + 09

3.19E + 10

6

93,726.94

5.36E + 09

7.45E + 10

5.90E + 09

2.05E + 10

−1.08E + 10

1.63E + 10

7

130,508.00

2.48E + 09

1.43E + 11

4.07E + 09

2.53E + 10

−1.33E + 10

4.97E + 10

8

142,043.00

2.85E + 09

1.61E + 11

2.48E + 09

2.89E + 10

−1.22E + 10

4.77E + 10

9

79,415.98

4.87E + 09

4.80E + 10

4.43E + 09

1.47E + 10

−8.68E + 09

1.38E + 10

10

29,716.42

5.41E + 09

2.50E + 10

2.47E + 09

8.48E + 09

−9.97E + 08

1.17E + 10

11

977.25

2.02E + 08

9.27E + 08

1.51E + 08

3.01E + 08

−5.82E + 07

3.59E + 08

12

37,698.61

5.91E + 09

3.53E + 10

2.15E + 09

9.66E + 09

−2.56E + 09

1.58E + 10

13

6202.99

1.34E + 09

6.16E + 09

1.03E + 09

1.84E + 09

−4.28E + 08

2.24E + 09

14

2377.78

3.00E + 08

9.02E + 08

1.07E + 08

3.46E + 08

−1.34E + 08

4.96E + 08

Total volume

8.26E + 10

1.01E + 12

5.55E + 10

2.42E + 11

−8.89E + 10

3.21E + 11

Billion m3

82.60

1009.46

55.49

242.01

−88.85

320.93

PR-ET (wet) BCM

767.45

Wet season discharge BCM

446.52

PR-ET (dry) BCM

27.17

Dry season discharge BCM

115.96

TWSW (m3 )

variation of (PR-ET), total runoff (TRO), and change in terrestrial water storage derived from GLDAS 2.1 model over Ganga River Basin for dry (top row) and wet (bottom row) seasons of 2019. These maps were further used to calculate zonal statistics for each of the 14 sub-basins of GRB. Table 4.3 presents the seasonal, basin-averaged, and sub-basin level water budget components viz. precipitation, evapotranspiration, terrestrial water storage, and total runoff for the dry and wet seasons of 2019 in GRB.

4 Water Budget Monitoring of the Ganga River Basin …

59

Fig. 4.6 a P-ET map (MAM), b P-ET map (JJAS), c runoff map (MAM), d runoff map (JJAS), e change in terrestrial water storage map (MAM), f change in terrestrial water storage map (JJAS)

Tables 4.2 and 4.3 have summarized the water balance of GRB using remote sensing-based datasets. However, it can be seen that there is variation/mismatch in the water budget components estimated by the two approaches. This can be attributed to the uncertainties associated with the water budget estimation using satellite-based datasets and GLDAS 2.1 model data due to limitations in capturing and modeling the water components and other essential factors which were not taken into account like the actual river discharge, irrigation water application, groundwater extraction, and distribution. The terrestrial water storage (TWS) anomalies from GRACE & its follow-on missions have a coarse spatial resolution of 3 × 3 degrees. They thus can’t provide accurate estimates for watersheds smaller than ~ 150,000km2 . Other remote sensing products viz. MODIS ET and IMERG precipitation used in the assessment may also have significant uncertainties affecting overall water budget estimation accuracy.

4.4 Conclusions This study was conducted with a prime focus on exploring the potential of remote sensing data products and GIS-based analysis to estimate water budget components over the Ganga River Basin. Two different approaches were employed (a) using remote sensing products and (b) using GLDAS 2.1 model outputs. The total volumes

60

G. Singh and A. Pandey

Table 4.3 Estimates of seasonal, basin-averaged, and sub-basin level water budget components using GLDAS 2.1 model derived datasets Sub-basin

Area (km2 )

(PR-ET) W (m3 )

(PR-ET) D (m3 )

TWSW (m3 )

TWSD (m3 )

TROW (m3 )

1

35,180.07

2.67E + 10

4.73E + 08

1.57E + 10

7.75E + 08

9.92E + 6.17E + 09 09

2

89,105.43

2.95E + 10

−3.88E + 1.95E + 09 10

−3.25E + 09

7.87E + 5.62E + 09 09

3

166,323.00

8.91E + 10

−8.02E + 5.08E + 09 10

−6.34E + 09

3.35E + 9.15E + 10 09

4

131,757.00

5.32E + 10

−1.20E + 3.05E + 10 10

−9.43E + 09

1.71E + 4.46E + 10 09

5

82,063.27

5.44E + 10

−6.78E + 3.20E + 09 10

−5.51E + 09

1.91E + 1.85E + 10 09

6

93,726.94

3.17E + 10

−1.01E + 1.68E + 10 10

−1.06E + 10

1.13E + 4.39E + 10 09

7

130,508.00

9.65E + 10

−7.56E + 4.36E + 09 10

−5.95E + 09

4.74E + 1.11E + 10 09

8

142,043.00

1.05E + 11

−3.44E + 4.52E + 09 10

−2.24E + 09

5.24E + 7.92E + 10 08

9

79,415.98

2.14E + 10

−7.07E + 1.23E + 09 10

−6.42E + 09

6.97E + 2.30E + 09 09

10

29,716.42

1.35E + 10

1.36E + 08

9.78E + 09

−8.45E + 06

4.74E + 5.47E + 09 09

11

977.25

6.29E + 08

−1.69E + 3.04E + 07 08

−1.19E + 07

3.01E + 1.87E + 08 08

12

37,698.61

2.14E + 10

−1.18E + 1.39E + 09 10

−1.24E + 09

7.54E + 7.60E + 09 09

13

6202.99

4.11E + 09

−2.59E + 2.43E + 08 09

−1.52E + 08

1.56E + 1.20E + 09 09

14

2377.78

4.09E + 08

−1.26E + 3.02E + 06 08

−2.49E + 07

9.20E + 2.06E + 07 08

Total volume 5.47E + 11

−5.97E + 2.93E + 10 11

−5.04E + 10

2.20E + 5.05E + 11 10

Billion m3

−59.66

−50.42

219.86

547.49

293.14

TROD (m3 )

50.50

of precipitation, evapotranspiration and terrestrial water storage change in the basin for dry and wet seasons were estimated from (a) GPM IMERG, MODIS, and GRACE data and (b) GLDAS 2.1 model for the year 2019. Despite the uncertainties associated with the satellite-based remotely sensed datasets and GLDAS 2.1 model data could be employed to assess seasonal and inter-annual water budget components to get overall indications of water availability for relatively large river basins. The increase or decrease in water availability can be judiciously acted upon by the stakeholders and government agencies for efficient

4 Water Budget Monitoring of the Ganga River Basin …

61

water resources management in the basin and sub-basins. This study demonstrates the potential of satellite-based data and GIS analysis for an overall situational assessment of the water budget in the Ganga River Basin, and a similar approach can be replicated elsewhere for other large river basins across the globe. Validation of the remote sensing-based data and GLDAS model data with in-situ measurements of precipitation, discharge, and soil moisture will surely provide more profound insights regarding the accuracy of these datasets. Acknowledgements We wish to express a deep sense of gratitude and sincere thanks to the Department of Water Resources Development and Management (WRD&M), IIT Roorkee, for providing a conducive environment and resources to conduct the research work.

References Abolafia-Rosenzweig R, Pan M, Zeng JL, Livneh B (2021) Remotely sensed ensembles of the terrestrial water budget over major global river basins: an assessment of three closure techniques. Remote Sens Environ 252:112191 APWF (Asia-Pacific Water Forum) (2009) Regional document: Asia Pacific. Istanbul, Turkey, 5th World Water Forum Secretariat. http://www.apwf.org/documents/ap_regional_document_final. pdf Baranyai G (2020) Geography of transboundary river basins. In: European water law and hydropolitics. Water governance—concepts, methods, and practice. Springer, Cham, pp 9–14. https://doi. org/10.1007/978-3-030-22541-4_2 Dhami B, Himanshu SK, Pandey A, Gautam AK (2018) Evaluation of the SWAT model for water balance study of a mountainous snowfed river basin of Nepal. Environ Earth Sci 77(1):21.https:// doi.org/10.1007/s12665-017-7210-8 Healy RW, Winter TC, LaBaugh JW, Franke OL (2007) Water budgets: foundations for effective water-resources and environmental management,vol 1308. US Geological Survey, Reston Himanshu SK, Pandey A, Shrestha P (2017) Application of SWAT in an Indian river basin for modeling runoff sediment and water balance. Environ Earth Sci 76(1):3.https://doi.org/10.1007/ s12665-016-6316-8 Pandey A, Singh G, Chowdary VM, Behera MD, Prakash AJ, Singh VP (2022). Overview of geospatial technologies for land and water resources management. In: Geospatial technologies for land and water resources management, Springer, Cham, pp 1–16 Pan M, Sahoo AK, Troy TJ, Vinukollu RK, Sheffield J, Wood EF (2012) Multisource estimation of long-term terrestrial water budget for major global river basins. J Clim 25(9):3191–3206. https:// doi.org/10.1175/JCLI-D-11-00300.1 Singh G, Pandey A (2021) Flash flood vulnerability assessment and zonation through an integrated approach in the Upper Ganga Basin of the Northwest Himalayan region in Uttarakhand. Inter J Disaster Risk Reduction 66:102573. Article no S2212420921005343. https://doi.org/10.1016/j. ijdrr.2021.102573 TFDD Transboundary Freshwater Dispute Database (2018) Transboundary freshwater spatial database. Oregon State University. Retrieved from https://transboundarywaters.science.oregon state.edu/content/transboundary-freshwater-spatial-database

62

G. Singh and A. Pandey

Wolf A, Natharius J, Danielson J, Ward B, Pender J (1999) International River basins of the world. Int J Water Resour Dev 15(4):387–427 Zhang Y, Pan M, Sheffield J, Siemann AL, Fisher CK, Liang M, Beck HE, Wanders N, MacCracken RF, Houser PR, Zhou T, Lettenmaier DP, Pinker RT, Bytheway J, Kummerow CD, Wood EF (2018) A climate data record (CDR) for the global terrestrial water budget: 1984–2010. Hydrol Earth Syst Sci 22:241–263. https://doi.org/10.5194/hess-22-241-2018

Chapter 5

Evaluation of SWAT Model for Simulating the Water Balance Components for the Dudh Koshi River Basin in Nepal Vijay Kumar Yadav, M. K. Nema, and Deepak Khare

5.1 Introduction Hydrology refers to studying the earth’s water, its occurrence, circulation and distribution, water’s chemical/ physical properties, and reaction to the environment and its components. A hydrologic model simplifies a real-world system (e.g., surface water, soil water, wetland, groundwater, estuary) that aids in understanding, predicting, and managing water resources. There are numerous ways that hydrological models can be used, and most of these applications relate to providing information to support decision making for water-related development and management policies. Hydrologic models are usually classified according to the representation of physical processes, space and randomness or time. According to its physical manifestation, the classification depends on the input and parameters required for the model and the scope of physical principles defined within the model. While considering the spatial representation of the basin, it is either modelling the basin as a whole part or dividing it into spatially definitive sub-basins. Further classifications target if the randomness is integrated into the model and if the time factor is represented. Some standard computer-based models that have been developed are HEC, SWAT, Win-SRM, WetSPa, Crawford, TANK, NAM, Mike-SHE, etc., and have been extensively used. However, all the model principally requires inputs like precipitation, temperature, soil properties, topographic characteristics, vegetation, hydro-geology and other physical and biological parameters and have their unique aspects and advantages and disadvantages. In addition, the extensive use of hydrological and water quality simulation models has been increased to address the problems related to the water resources around the world, including the V. K. Yadav · D. Khare Indian Institute of Technology, Roorkee 247667, India M. K. Nema (B) National Institute of Hydrology, Roorkee 247667, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_5

63

64

V. K. Yadav et al.

effect of alternative best management practices (BMPs), the possible future impacts of climate change on the quantity and quality of streamflows (Gassman et al. 2014). The hydrologic modeling starting from rainfall to a runoff which is also referred to as rainfall-runoff modeling is one of the most classical applications of hydrology is of prime importance. During the past few decades, hydrologists and the water research community have developed significant numbers of runoff generation models. The model considers various spatial inputs such as land use, soil, topography and catchment are divided into small units, usually square or the triangulated irregular network, where the input, output, and parameters can vary spatially (Devia et al. 2015). The semi-distributed (quasi-distributed) models are the ones that contain the feature of both the distribution as well as lumped model, wherein the parameters are allowed to vary partially in spatial scale, dividing the watershed into sub-watersheds (Moradkhani and Sorooshian 2009). In this study, a semi-distributed hydrologic model SWAT has been used to model the Dudh Koshi River Basin (DKRB) with ArcSWAT extension in Arc-GIS. In view of the crucial role of DKRB in the overall sustenance of the livelihood of the Terai region of Nepal, this study was carried out to model the hydrological processes within the basin using the SWAT model to study the water balance.

5.2 Materials and Methodology 5.2.1 Study Area The Dudh Koshi River Basin (27°08′ 47′′ –28°06′ 41′′ N, 86°25′ 40′′ -87°00′ 34′′ E) is a glacier-dominated basin with glaciers covering nearly 13% of the catchment at higher elevations. It is one of the major sub-basin of the Koshi River Basin, located in the mid-hills and Himalayas of Province No.1 of Nepal (including three districts, i.e., Solukhumbu, Khotang and Okhaldhunga). It originates from the higher snowy range of the Himalayas (at 8848 m asl, i.e., Mt. Everest). It joins downstream with the Sun-Koshi River (at 335.5 m asl) Fig. 5.1. The length of the main course of the DKRB is about 90 km, covering 3673.93 km2 of catchment area U/S of the gauging station at Rabuwa Bazaar and has about 22% of the overall catchment area of Sun Koshi (Fig. 5.1). Some of the major tributaries in the Dudh Koshi River Basin are Imja Khola, Thotne Khola, Rawa Khola, etc. There is one hydrological station at Rabuwa Bazar (Station No. 670) located at 27° 16′ 00′′ N latitude, 86° 39′ 50′′ N longitude with an elevation of 460 m. Discharge at Rabuwa Bazaar station ranges from 13.5 to 5380 m3 /s during base flow and the highest flood. Although a majority of the precipitation for this region is concentrated during the monsoon months, the snow-covered and glaciated upper reaches of the basin contribute meltwater to the streamflow throughout the year. Furthermore, other spring sources in the hilly and mountain terrains provide considerable perennial water discharge in the basin.

5 Evaluation of SWAT Model for Simulating the Water Balance …

65

Fig. 5.1 Location map of Dudh Koshi River Basin in Nepal

5.2.2 Data Collection and Analysis 5.2.2.1

Spatial Datasets of Dudh Koshi River Basin

Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (90 m resolution) generated by NASA, 90 m resolution Land Cover maps of Nepal (For the year 2010) developed by the International Centre for Integrated Mountain Development (ICIMOD) and the soil data from the Soil and Terrain (SOTER) database programme, which FAO and Nepal’s Survey Department jointly compile processed and used in this study. The clipped Projected DEM of the basin, Land Use Land Cover (LULC) map, Soil map and the auto-prepared slope map in ArcGIS is shown in Figs. 5.2, 5.3, 5.4 and 5.5. The LULC map shows eight different land use classes, and it can be seen that the basin is occupied mainly by dense forests to deciduous forest, agricultural and snow-covered areas. Similarly, the SWAT Soil map shows seven different classes where Gelic Leptosols are the most dominant, and Chromic Cambisols are the least prevalent. Also, the slope map was classified into the maximum available four different classes.

66 Fig. 5.2 DEM of Dudh Koshi River Basin

Fig. 5.3 LULC Map of Dudh Koshi River Basin

V. K. Yadav et al.

5 Evaluation of SWAT Model for Simulating the Water Balance … Fig. 5.4 SWAT Soil distribution map of Dudh Koshi River Basin

Fig. 5.5 SWAT land slope distribution Map in Dudh Koshi River Basin

67

68

V. K. Yadav et al.

5.2.2.2

Hydro-meteorological Data

Historical observed daily precipitation and temperature (maximum and minimum) data for 28 years (1979–2006) were collected from nine stations owned by the Department of Hydrology and Meteorology (DHM), Govt. of Nepal, and one DHM temperature station within the proximity of the basin. Also, the other six temperature stations used as SWAT input had satellite-based (Global Weather data) data. Before using the meteorological data for the model application, it was checked for continuity, and missing rainfall and temperature records were interpolated by the ‘Inverse distance method’ and ‘Arithmetic average method’, respectively. In addition, other meteorological data such as wind velocity, solar radiation, relative humidity was simulated in the SWAT model. And during this process, Global Weather Database Stations ((https://globalweather.tamu.edu/#pubs) were merged with DHM stations. For the model calibration and validation, the daily observed flow data of the same time window had been used at the gauging station Rabuwa Bazaar (670) maintained by the DHM of Nepal.

5.2.3 SWAT Model Setup SWAT is an evaluating tool of soil and water developed by the USDA-Agricultural Research Service to investigate watersheds with surfaces going from a few hundreds of Km2 to several thousands of Km2 . A threshold area that defines the origin of a stream was used to create the stream network. The threshold value for land use, soil and slope was fixed to 15, 10, and 10%, resulting in 219 HRUs from 19 subwatersheds. Basin no. 18 (Outlet-18) has the gauging station (Rabuwa-670). In this research, ten elevation bands with an equivalent vertical distance from the mean elevation of the sub-basins centroid were set up for the snow and glacial dominated sub-basins at higher altitudes. The weather data (precipitation and temperature) were formatted in the *.txt file type. Other remaining meteorological databases were simulated by the model itself using the weather generator database for various hydrological processes. The model was run from 1982 to 2006, with three years of warm-up time between 1979 and 1981. The steps of setting up the Arc SWAT model are very briefly mentioned here: • • • • •

Preparation of the input data Watershed delineation with DEM data HRUs definition using soil, slope, and land-use data Weather data definition, write input table and The final execution of the SWAT model.

After the final execution of the model simulation, SWAT Cup was used for the calibration and validation of the Model. Moreover, a flowchart of methodology is given in Fig. 5.6.

5 Evaluation of SWAT Model for Simulating the Water Balance …

69

Fig. 5.6 Flow chart for water balance study

5.3 Results and Discussions This section provides an overview and discussion of the findings of this dissertation report, which include hydrological modelling and water balance studies in the basin area, SWAT model parameter calibration, validation, and sensitivity analysis.

5.3.1 Sensitivity Analysis According to the SWAT-CUP user manual and parameters used in previous studies on similar basins, a list of 35 possible parameters sensitive to discharge/flow was compiled, and a global sensitivity analysis was performed in SWAT-CUP, with 29 of the 35 parameters being found to be more sensitive to flow. Those sensitive parameters were initially obtained using only the discharge for the calibration time, with NS efficiency set to 0.5 as the threshold value. The sensitive parameters were identified based on the larger the absolute value of t-stat and the smaller the p-value after running thousands of simulations for different iterations for calibration in SWAT CUP for the daily time step. After getting the best possible results in calibration, 200 simulations were run to validate the flow with the same parameter’s range but different time duration.

5.3.2 Model Calibration and Validation Based on available river discharge data at Outlet-18 (Rabuwa Bazaar-670) for the observation year of 1979–2006, the SWAT model was distributed into warm-up

70

V. K. Yadav et al.

Table 5.1 Results of calibration, validation and whole period at a time on a daily time scale S. No. Statistics 1

Coefficient of determination

Notation Calibration Validation Entire period R2

2

Nash-Sutcliff efficiency

NSE

3

Percent bias

PBIAS

4

The ratio of RMSE to the standard RSR deviation

0.67 0.65 −8.5 0.59

0.65 0.63 16.2 0.61

5

p-factor

0.41

0.27

6

r-factor

0.12

0.08

0.65 0.65 5.46 0.59

period data (1979–1982), calibration data (1982–2000) and validation data (2000– 2006) for daily time scale. Also, statistical tests and graphical representation have been used for calibration and validation. The outcomes of daily simulation for the basin are given in Table 5.1. The value of the coefficient of determination (R2 ), the Nash–Sutcliffe Efficiency (NSE), the value of measured and standard observation ratio (RSR), the percentage bias (PBIAS) was obtained 0.67, 0.65, 0.59 & −8.5%, respectively, during the calibration period (1982–2000). On the other hand, during the validation process for the period (2001–2006), R square, NSE, RSR & PBIAS is 0.65, 0.63, 0.61 & 16.2%, respectively for the Basin as detailed in Table 5.5. The percentage of observed data captured within 95% prediction uncertainty (p-factor) for both calibration and validation period was found to be 0.41 & 0.27. In contrast, the average thickness of the 95PPU band (r-factor) for those periods was 0.12 and 0.08, respectively, both within a quite good acceptable range for a daily run. According to (Moriasi 2007) and (Arnold et al. 2012), if the values of the coefficient of determination (R2 ) and Nash-Sutcliff Efficiency (NSE) are greater than 0.5, and the Percentage bias is within the range of 15%, it is inferred as the model calibration, and validation result has very-good performance. The model had performed somewhat better for the calibration period in comparison to the validation period based on NS efficiency criteria. The sensitivity analysis result confirmed that the in case of daily scale, Temperature lapse rate (V_TLAPS) was the most sensitive parameter followed by Moist bulk density (V_SOL_BD). Similarly, other more sensitive parameters were SCS runoff curve number for moisture condition II (CN2), Snowfall temperature (V_SFTMP), Baseflow alpha-factor (V_ALPHA) and so on. The calibration and validation results show a good agreement between the observed and simulated data, as shown in Fig. 5.7 in daily cases for calibration, validation and the entire period. The simulated hydrograph is more or less following the observed hydrograph pattern. Base runoffs and most of the peaks are well simulated except in very few databases during the entire period. In the years 1998, 2003, 2004, and 2005, flow peaks are under-predicted, while in the years 1990 and 1999, it under-predicted the peak runoffs while maintaining the general pattern in the daily period. The relationship between observed and simulated daily discharge data is shown by the scatter map in Fig. 5.8, which shows that the slope of two sets of data

5 Evaluation of SWAT Model for Simulating the Water Balance …

71

Observed Vs Simulated Discharge at Rabuwa Bazaar Station 6000

0

50

100 4000 150 3000 200 2000

Precipitation (mm)

Discharge ( m3/sec)

5000

250 1000

300

0

350

95PPU

observed

Simulated

Avg Precipitation

Fig. 5.7 Calibration and Validation of daily discharge at Rabuwa Station (1982–2006)

Observed vs Simulated flow Simulated flow, m3/sec

Fig. 5.8 Scatter chart of observed and simulated daily flow

3200 2800 2400 2000 1600 1200 800 400 0

y = 0.6379x + 62.926 R² = 0.6481

0

1000

2000 3000 4000 Observed flow, m3/sec

5000

is 0.768, which is nearly 45°, and R2 is equal to 0.67. Though the statistical and graphical result by the model satisfied runoff simulation for both calibration and validation periods, it underestimated the runoff during extreme flow periods, which is noticed in the graphs.

5.3.3 Simulated and Observed Discharge at Rabuwa Bazaar Outlet In this study, the model was re-run in the ArcSWAT model after calibration and validation in SWAT-CUP for the same study period of 1979–2006, taking the bestfitted values of sensitive parameters obtained during calibration. The final SWAT model performance is visualized in a graphical form in which the observed daily discharge data is compared with the final simulated daily discharge data obtained

72

V. K. Yadav et al.

Fig. 5.9 Monthly average discharge hydrograph of observed and simulated data

Observed vs Simulated Monthly Average Discharge 800.00 600.00

Year:1982-2006 400.00 200.00 0.00 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Observed

Simulated

Table 5.2 Average annual observed and simulated discharge Station

Period

Averaged annual observed flow (m3 /s)

Averaged annual simulated flow (m3 /s)

Percent deviation

Rabuwa Bazaar (670)

Calibration

192.72

209.09

−8.50

Validation

242.43

203.20

16.18

Entire period

204.64

193.47

5.46

from the model. Table 5.1 shows the average annual observed and the simulated value of flow at Rabuwa Bazaar outlet for the entire period. Similarly, a graph of mean monthly flow is plotted for calibrated streamflow against simulated streamflow and presented in Fig. 5.9. Again, both hydrographs were nearly symmetrical. The figure further proved that the hydrographs follow the same monthly base flow pattern, and the lowest average monthly flow is available in March, whereas peak flow is available in August. Table 5.2 shows the observed and simulated average annual flow values at the Rabuwa Bazaar outlet for the calibration and validation periods. For the calibration, the total annual discharge for the observed and simulated cases was 192.72 cumecs and 209.09 cumecs, respectively, and for the validation, it was 242.43 cumecs and 203.20 cumecs.

5.3.4 Water Balance Study of the Basin Water balance in which changes of total water volume, inflow (precipitation, snowmelt) and outflow (evaporation, transpiration, surface and subsurface runoff) on a given area are balanced is the key force behind every process within a watershed for any study done using the SWAT model. In this study, the model was re-run

5 Evaluation of SWAT Model for Simulating the Water Balance …

73

Table 5.3 Average monthly basin value (in mm) for different water balance components Months Jan

Rainfall 18.5

Snow fall 13.7

Surf. Runoff 0.4

Lat. Flow 0.2

Water Yield 23.9

ET 11.2

PET 42.68

Feb

30.7

24.4

0.6

0.2

14.6

10.8

43.28

Mar

49.0

29.6

4.6

0.6

14.8

17.2

63.42

Apr

79.0

35.9

11.2

2.7

22.1

28.1

76.07

May

189.2

63.2

56.2

8.8

73.1

44.7

70.90

Jun

386.3

60.1

161.4

25.3

203.8

40.2

45.94

Jul

582.9

50.6

240.3

55.2

377.0

40.6

41.60

Aug

503.0

45.2

184.5

56.6

376.3

43.5

44.72

Sep

271.7

41.0

71.8

36.8

254.9

31.7

33.44

Oct

56.0

18.0

7.4

10.2

120.1

35.5

46.47

Nov

13.7

5.9

1.6

1.0

60.8

21.0

44.18

Dec

16.9

10.4

0.3

0.2

37.0

16.4

44.81

2196.9

398.0

740.2

197.7

1578.4

340.7

597.51

Total Percent Monsoon %

100.0

18.1

33.7

79.4

49.5

88.9

9.00 87.9

71.8

15.5

27.20

76.8

45.8

27.73

after calibration and validation in SWAT CUP for the same study period of 1979– 2006. Adopting the best-fitted values of sensitive parameters obtained during calibration and SWAT output results were analyzed to conduct the water balance study. The monthly breakup of different annual water balance components is presented in Error! Reference source not found (Table 5.3). From the water balance study of the basin, the average annual precipitation of the basin is 2197.19 mm. 340.77 mm (15.5%) of annual precipitation returns as average annual evapotranspiration from the basin. Water yield is the discharge available at the basin outlet and is the summation of the surface runoff, lateral flow and return flow. The total yearly water yield at the basin outlet is found to be 1578.40 mm, out of which 740.20 mm is due to surface runoff and accounts for 33.70% of total precipitation and 46.90% of total water yield. Lateral flow or subsurface flow contributes 197.70 mm (only 9.00% of total precipitation and about 12.52% of total water yield). The remaining flow is assisted by base flow originating from shallow groundwater aquifer as returned flow 462.05 mm (21.94% of total precipitation and about 30.53% of total water yield). In this way, the average annual runoff volume available at the basin at Rabuwa Bazaar outlet of the basin is found to be 6.34 BCM. Further, 158.23 mm of average yearly precipitation enters the deep aquifer, which is assumed to contribute to streamflow somewhere outside the watershed (Arnold et al. 1993). The average Curve Number of the basin is found to be 72.80, which is within the acceptable range for the mid and higher mountain region of Nepal, according to (Mishra et al. 2008). Also, from the monthly distribution of water balance components, it is found that the rainfall was highly concentrated in monsoon months, with 79.40% of precipitation

74

V. K. Yadav et al.

occurring during the four months, June to September. Similarly, with surface runoff, about 88.90% and water yield occur about 76.80% during four months of monsoon, i.e., from June to September. But the evapotranspiration is 44.7 mm in May, which is the highest.

5.3.4.1

Snowfall

Like other Snow-covered basins, the northern sub-basins of the Dudh Koshi basin is found to have a considerable part of precipitation in the form of snowfall during the winter season, which is responsible for the glacier and snow-covered area in the basin for a couple of months that contribute meltwater to the streamflow throughout the year. Table 5.4 shows the percentage of average annual snowfall over total precipitation throughout all months. It shows that more than three fourth of the precipitation occurs during the winter season in the form of snow within the basin. A maximum of 79.46% snowfall is available in February, followed by 74.11% in January. During monsoon season, though the snowfall amount is more, it is significantly less in terms of precipitation. Further, sub-basins number one to seven have contributed the snowmelt runoff, highest from sub-basin two and decreasing order from sub-basins 1, 5, 3, 4, 10, 8, 6, 7 and 11, which specifies that the catchment contributes the major share of the snowmelt runoff above 4500-m altitude. Table 5.4 Snowfall in Dudh Koshi River Basin

Months

Precipitation

Snowfall

Snow %

Jan

18.54

13.74

74.11

Feb

30.67

24.37

79.46

Mar

49.04

29.56

60.28

Apr

78.98

35.9

45.45

May

189.19

63.19

33.4

Jun

386.32

60.06

15.55

Jul

582.85

50.64

8.69

Aug

503

45.23

8.99

Sep

271.72

41.03

15.1

Oct

55.95

18.02

32.21

Nov

13.7

5.88

42.92

Dec

16.89

10.35

61.28

Total

2196.85

397.97

18.12

5 Evaluation of SWAT Model for Simulating the Water Balance …

75

Table 5.5 Average annual water balance in the sub-basins Sub-basin

Area (km2 )

Precipitation (mm)

1

143.47

2509.24

ET (mm) 47.71

Surface runoff (mm)

Water yield

1468.38

1468.38

2

144.12

2582.6

48.3

1469.29

1469.29

3

274.71

2563.21

57.6

2128.67

2209.47

4

83.53

2488.53

45.13

665.14

665.14

5

422.58

2453.19

55.35

6

43.67

2366.89

40.59

1977.6 195.71

195.71

1992.16

7

551.53

2261.09

432.47

216.12

1761.71

8

305.06

2408.01

95.11

753.65

2193.98

9

4.5

2100.91

826.23

353.13

1134.87

10

594.81

2323.69

101.76

599.33

2088.71

11

526.64

1766.57

390.6

196.71

1312.57

12

127.97

2196.81

862.82

427.49

1178.26

13

171.23

2300.79

906.23

352.69

1239.22

14

148.87

1561.27

857.1

138.03

578.73

15

53.81

2034.32

832.77

408.96

1058.94

16

75.82

2082.68

797.36

400.79

1148

1686.81

803.6

305.12

742.66

18

1.61

1963.01

814.44

375.15

1010.4

19

208.06

1595.97

789.37

273.3

672.81

17

5.3.4.2

135.7

Sub-basin Wise Water Balance Components

GIS is a potent spatial analysis tool that can visualize the different water balance components distribution in sub-basin or HRU level. This analysis provides an idea of the distribution of any water balance components like precipitation, evapotranspiration, surface runoff, lateral flow, groundwater flow or water yield within the basin. Spatial analysis allows to solve complex location-oriented problems and better understand the impact of those water balance components on different aspects. The subbasin wise distribution of precipitation, evapotranspiration, surface runoff and water yield is provided in Table 5.5, which shows that the sub-basins located at a higher altitude, like sub basins 2, 3, 1 etc., are having comparative more precipitation. The results also show that the highest and lowest runoff is caused by sub-basins 3 and 14, respectively. Sub-basins 3, 5, 2 and 1 have the highest share of total runoff available at the basin outlet, while sub-basins 11, 6 and 14 have the lowest share. Sub-basin 3 contributes 9.2% of the total volume of water available at the outlets, followed by sub-basin 8, which contributes 9.1%, and sub-basins 6 contributes 0.81%. The table also indicates that the sub-basins with higher runoff have a lower value of evapotranspiration and vice-versa, which may be because sub-basins at higher elevation have a lesser percentage of vegetation and agricultural land. This may

76

V. K. Yadav et al.

result in lesser evapotranspiration, resulting in lesser infiltration and more surface runoff. Sub-basins 6, 4, 1, 2, 5 and 3, situated at higher altitudes, have lesser evapotranspiration while remaining sub-basins located in the valleys and mid-hills have higher value evapotranspiration. Water yield was excessive in sub-basins 3, 8 and 10, but the surface runoff was excessive in sub-basins 3, 5 and 2. So, from the table, it was also clear that the water yield may not be highest for all the sub-basin with the highest runoff. The total amount of water obtained from surface runoff, baseflow, and return flow from the shallow aquifer is known as water yield. As a result, a sub-basin with a significant contribution of surface runoff does not always have an immense contribution to baseflow or groundwater flow.

5.4 Conclusion The SWAT model is successfully calibrated and validated in the Dudh Koshi River Basin using the SUFI-2 algorithm. From the present study, the SWAT model is considered a satisfying/good model for analysing water balance in a dense forest to a snow-covered area dominated basin. A few of the conclusions drawn from this study are: • The model performance was found quite well, ‘good’ according to (Moriasi et al. 2007) both statistically and graphically for daily time scale. Therefore, it can be justified that the model is appropriate for Dudh Koshi River Basin, relying on the calibration and validation result of the SWAT model. • SWAT underestimated runoff during high flow periods, which may be because the present curve number technique can’t accurately forecast runoff for a day with several storms. The SCS-CN method described a rainfall event as the amount of rain that falls in a single day, leading to underestimating runoff. Though the SWAT model underestimated the extreme flow runoff, the results are sufficient for hydrological modelling and water resource assessment in Nepal’s Dudh Koshi River and other mountainous rivers. • During the study period, the basin gets an average annual rainfall of 2197.10 mm, out of which 33.7% (740.20 mm) contributes to surface runoff, 9.0% (197.70 mm) accounts as lateral flow, and 15.5% (340.70 mm) accounts for evapotranspiration from the basin. As a result, the yearly water yield at the basin outlet is 1578.40 mm, which is 71.8%. Whereas from the monthly distribution of water balance components, it is found that 79.40% of precipitation, 88.90% of surface runoff and 76.80% of water yield occurs during monsoon months, i.e., from June to September. But the evapotranspiration is 44.7 mm in May, which is the highest. • For the calibration and validation, the observed and simulated average annual discharges were 192.72 cumecs, 209.09 cumecs, 242.43 cumecs, and 203.20 cumecs, respectively. • During the winter season, a considerable part of the precipitation occurs in the form of snowfall within the basin, with a maximum of 79.46% snowfall in February

5 Evaluation of SWAT Model for Simulating the Water Balance …

77

and 74.11% in January. However, a minimal amount of snowfall occurs in the basin during the monsoon season. • The sub-basins located at higher altitudes receive comparatively more precipitation. Further, low vegetation density and agricultural land result in low evapotranspiration, less infiltration, and more surface runoff. • The volume of about 6.34 BCM is available annually at the basin outlet. • Hence, the simulated discharge at other outlets obtained from this study may be helpful for the studying and proposing of the hydropower potential at different locations.

5.5 Recommendations • SRM and others can be used for comparing simulated snowmelt runoff from the SWAT model to determine the best representation of snowfall/melt in the basin. • The latest site-specific local soil and LULC database (as provided by ICIMOD) for SWAT modelling is recommended • Further calibration at the Rabuwa outlet could be done with another hydrological modelling other than SWAT in the basin to improve the representation of observations in this catchment.

References Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, Van Griensven A, Van Liew MW, Kannan N, Jha MK (2012) SWAT: model use, calibration, and validation. Trans ASABE 55(4):1491–1508 Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142(1–4):47–69. https://doi.org/10.1016/0022-1694(93)90004-S Devia GK, Ganasri BP, Dwarakish GS (2015) A review on hydrological models. Aquat Procedia 4:1001–1007 Gassman PW, Sadeghi AM, Srinivasan R (2014) Applications of the SWAT model special section: overview and insights. J Environ Qual 43(1):1–8. https://doi.org/10.2134/jeq2013.11.0466 Mishra BK, Takara K, Tachikawa Y (2008) NRCS curve number based hydrologic regionalization of nepalese river basins for flood frequency analysis. Ann Disaster Prev Res Inst Kyoto Univ 51(B):189–195 Moradkhani H, Sorooshian S (2009) General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis, 1–24 Moriasi DN (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations 50(3):885–900. https://doi.org/10.13031/2013.23153

Chapter 6

Rejuvenating Water Wisdom: A Route to Resilience R. N. Sankhua

6.1 Introduction Sustainable water future for India begins with a vision. With the current state of affairs, correcting measures still can be taken to avoid the crisis to be worsening. There is increasing awareness that our freshwater resources are limited and need to be protected both in terms of quantity and quality. This water challenge affects not only the water community, but also decision-makers and every human being. The surface water and groundwater resources in India play a major role in agriculture, hydropower generation, livestock production, industrial activities, forestry, fisheries, navigation, recreational activities, etc. Potential impact of global climate change on water resources includes enhanced evaporation due to warming, geographical changes in precipitation intensity, duration and frequency, together affecting the hydrological parameters such as, discharge, soil moisture, etc. Potent, twist to the growing water crisis exacerbate the situation in some of the regions of India due to growing seasonal monsoon afflictions and some states have been reeling under severe water scarcity due to demographic growth, urban development, new lifestyles and economic development aggravating water, food and energy needs. The increasing mutual trust deficit and growing rhetoric of the riparian states are fuelling water conflicts and are publicly alluded. Such pronouncements are bound to heighten existential anxieties and support permanent escalation and are further constricting any room for engagement. Each State to enshrine the Right to Water, beyond written solutions through DPRs prepared by NWDA, it is essential that practical measures are put in place to make this right a reality. The riparian states’ relations may fall apart at the seams, sans transparent execution of water sharing on bilateral and multilateral agreements. Against the backdrop of the urgency of water issues,

R. N. Sankhua (B) Chief Engineer (South), NWDA, Ministry of Jal Shakti (WR,RD&GR), Hyderabad, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_6

79

80

R. N. Sankhua

the ILR may come as a cropper without direct access to emblematic water facts and relevant, synthetic, and in-depth information about planning of the resource. Looking deep, the inclusion of the active climate actors, impacts, solutions, and initiatives to basin water and climate related events and processes cannot be just ignored. The need of the hour is to ensure more attention is given to water as a driver of change to achieve all the SDGs. Hydrologic predictions and spikes in natural events are upsetting this balance. Further, convivial dialogue between riparian states with development of a larger neighbourhood policy incorporating interests of key stakeholders, adaption of better response to mainstream climate impacts, resilience across governance systems, in-situ capacities at local-national-regional levels, and a river basin solution to region’s water sharing predicament can catch the wave.

6.2 Rainfall Variability The comparative figures of rainfall during the period 1955–2015 are tabulated in three different periods in Table 6.1. Figures 6.1 and 6.2 depict comparison of rainfall (in BCM) between (1955–1984) and (1985–2015) and comparison of rainfall (in BCM) between (1965–1984) and (1985–2015) respectively. In India, erosion is the main process that will occur to land as sea level continues to rise. As a consequence, coast-protection structures built by humans will usually be destroyed by the sea while the shoreline retreats. In some coastal areas of Asia, a 30 cm rise in sea level can result in 45 m of landward erosion. The coastal recession can add up to 500–600 m in 100 years, with a rate of between 4 and 6 m/year. In monsoon, decreasing sediment flux is generally a main cause of coastal erosion. Available evidence suggests a tendency of river sediment to further decline that will tend to worsen coastal erosion. Rainfall in India is dependent on the South-West and North-East monsoons, on shallow cyclonic depressions and disturbances and on violent local storms which form regions where cool humid winds from the sea meet hot dry winds from the land and occasionally reach cyclonic dimension. Most of the rainfall in India takes place under the influence of South-West monsoon between June to September except in Tamil Nadu where it is under the influence of North-East monsoon during October and November. However, there is considerable spatial variation in rainfall which ranges from less than 100 mm in the western Rajasthan to more than 2500 mm in North-Eastern areas. The total mean annual rainfall as calculated from IMD data in study area comes out to be 1105 mm, which is the highest anywhere in the world for a country of comparable size. The annual rainfall in India however fluctuates widely. The highest rainfall in India of about 11,690 mm is recorded at Mousinram near Cherrapunji in Meghalaya in the northeast. In this region rainfall as much as 1040 mm is recorded in a day. At the other extreme are places like Jaisalmer, in the west, which receives barely 150 mm of rain. Though the average rainfall is adequate, nearly three-quarters of the rain pours down in less than 120 days, from June to September. As much as

6 Rejuvenating Water Wisdom: A Route to Resilience

81

Table 6.1 Comparative figures of rainfall during the period 1955–2015 Sl. No.

Basins

1

Indus (within India)

2

Ganga–Brahmaputra–Meghna

Average rainfall in water year (1955–1984)

Average rainfall in water year (1965–1984)

Average IMD rainfall in water year (1985–2015)

mm

mm

mm

BCM

BCM

BCM

762

303

752

299

895

356

(a) Ganga

1069

978

1035

947

1007

914

(b)Brahmaputra

2589

551

2635

561

2330

495

(c)Barak and others

2462

126

2593

133

2625

134

3

Godavari

1122

365

1062

346

1117

365

4

Krishna

842

225

797

213

841

226

5

Cauvery

957

82

948

81

949

81

6

Subarnarekha

1412

40

1408

40

1427

40

7

Brahmani-Baitarani

1407

80

1369

78

1456

83

8

Mahanadi

1311

200

1237

189

1317

200

9

Pennar

684

38

675

38

716

40

10

Mahi

840

36

839

36

811

35

11

Sabarmati

726

25

711

24

727

25

12

Narmada

1133

117

1104

114

1045

108

13

Tapi

876

61

838

58

839

59

14

West flowing rivers from Tapi to Tadri

2664

161

2548

154

2661

161

15

West flowing rivers from Tadri 3059 to Kanyakumari

166

2929

159

2773

151

16

East flowing rivers between Mahanadi and Pennar

1081

91

1035

88

1144

97

17

East flowing rivers between Pennar and Kanyakumari

928

95

938

96

960

98

18

West flowing rivers of Kutch and Saurashtra including Luni

482

100

465

96

479

100

19

Area of inland drainage in Rajasthan Desert

317

51

305

49

302

49

20

Minor rivers draining into Myanmar (Burma) and Bangladesh

1680

57

1733

58

1812

61

Total

1123

3945

1096

3853

1105

3880

82 3500 3000 2500 2000 1500 1000 500 0

R. N. Sankhua 1955-1984 1985-2015

Fig. 6.1 Comparison of rainfall between (1955–1984) and (1985–2015) 3500 3000 2500 2000 1500 1000 500 0

1965-1984 1985-2015

Fig. 6.2 Comparison of rainfall (in BCM) between (1965 1984) and (1985–2015)

21% of the area of the country receives less than 750 mm of rain annually while 15% receives rainfall in excess of 1500 mm. Precipitation generally exceeds 1000 mm in areas to the east of Longitude 78°E. It reaches nearly to 2500 mm along almost the entire west coast and over most of Assam and sub-Himalayan West Bengal. Large areas of peninsular India receive rainfall less than 600 mm. Annual rainfall of less than 500 mm is experienced in western Rajasthan and adjoining parts of Gujarat, Haryana and Punjab. Rainfall is equally low in the interior of the Deccan plateau, east of the Sahyadris. A third area of low precipitation is around Leh.

6 Rejuvenating Water Wisdom: A Route to Resilience

83

6.3 Water Resources Potential in the River Basins of India Water problems are precarious in the population sector but, as a whole, the country has been facing a water crisis both for agriculture as well as for basic needs. Water demand is predicted to increase significantly over the coming decades. In addition to the agricultural sector, which is responsible for 70% of water abstractions nationwide, large increases in water demand are predicted for industry and energy production. Accelerated urbanization and the expansion of municipal water supply and sanitation systems also contribute to the rising localised demand. Climate change scenarios project an exacerbation of the spatial and temporal variations of water cycle dynamics, such that discrepancies between water supply and demand are becoming increasingly aggravated. The average annual water resource of the basins as reported by Central Water Commission for the period of 30 years (1985–2015) has been assessed as 1999.20 BCM and the mean annual rainfall of the basins for the same period is 3880 BCM (Table 6.2).

6.4 Per Capita Water Availability in India Water problems are precarious in the population sector but India has been facing a water crisis both for agriculture as well as for basic needs. Water demand is predicted to increase significantly over the coming decades. In addition to the agricultural sector, which is responsible for 70% of water abstractions nationwide, large increases in water demand are predicted for industry and energy production. Accelerated urbanization and the expansion of municipal water supply and sanitation systems also contribute to the rising localised demand. Climate change scenarios project an exacerbation of the spatial and temporal variations of water cycle dynamics, such that discrepancies between water supply and demand are becoming increasingly aggravated. The per capita availability of water for the country decreased from 5178 m3 /year in 1951 to 1508 m3 /year in 2015. This clearly indicates the need for water resource development and conservation, and its optimum use. Due to spatial variation of rainfall, the per capita water availability also varies from basin to basin. India has been planning to utilize this water by prolonging its stay on land by using engineering innovations such as dams and barrages.

6.5 Challenges in Water Sector The water availability in India is going to be a serious challenge due to various reasons. The most serious concern is the growing population which is likely to increase to 1.66 billion by 2050. With the increasing population, the annual food

84

R. N. Sankhua

Table 6.2 Water resources availability of Indian basins Sl. No.

Basins

Catchment area (km2 )

1

2

3

(1)

Indus (within India)

317,708

(2)

Ganga- Brahmaputra- Meghna

Average rainfall in water yeare (1985–2015) (BCM)

Water resources Availability (BCM) Average

(75% dependable)

5

6

330

45.53

37.15

4

(a) Ganga

838,803

914

509.52a

471.76

(b) Brahmaputra

193,252

495

527.28b

480

(c) Barak and others

86,335

134

86.67

68.58

(3)

Godavari

312,150

365

117.74

87.67

(4)

Krishna

259,439

226

89.04

71.43

(5)

Cauvery

85,167

81

27.67

22.62

(6)

Subarnarekha

26,804

40

15.05

12.00

(7)

Brahmani-Baitarani

53,902

83

35.65

25.00

(8)

Mahanadi

144,905

200

73.00

49.00

(9)

Pennar

54,905

40

11.02

5.95

(10)

Mahi

39,566

35

14.96

9.14

(11)

Sabarmati

31,901

25

12.96

8.92

(12)

Narmada

96,659.79

108

58.21

45.24

(13)

Tapi

65,805.80

59

26.24

21.23

(14)

West flowing Rivers from Tapi to Tadri

58,360

161

118.35

106.81

(15)

WFR from Tadri to Kanyakumari

54,231

151

119.06

106.13

(16)

EFR between 82,073 Mahanadi and Pennar

97

26.41

17.41

(17)

EFR between Pennar 101,657 and Kanyakumari

98

26.74

18.21

(18)

WFR of Kutch and Saurashtra including Luni

100

26.93

14.59

(19)

Area of inland 144,835.9 drainage in Rajasthan Desert

49

Negligible

Negligible

(20)

Minor rivers draining 31,382 into Myanmar (Burma) and Bangladesh

61

31.17

26.56

192,112

(continued)

6 Rejuvenating Water Wisdom: A Route to Resilience

85

Table 6.2 (continued) Sl. No.

1

Basins

Catchment area (km2 )

2

3

Total

3,271,953c

Average rainfall in water yeare (1985–2015) (BCM)

Water resources Availability (BCM) Average

(75% dependable)

4

5

6

3880

1999.20d

a Without

contribution from Nepal (17.24 BCM). Considering contribution from Nepal, Water Resources Availability in Ganga basin is 526.76 BCM comprising components viz. 197.22 BCM in Upper Ganga, 192.60 BCM in Lower Ganga and 136.94 BCM in Yamuna sub-basins b Without contribution from Bhutan (63.50 BCM).Water Resources Availability in Brahmaputra basin i.e. 527.28 BCM comprises components viz. 504.55 BCM in Brahmaputra and 22.73 BCM in Teesta c Excluding area of Indus above border, Lakshadweep Island and Andaman and Nicobar group of islands d Excluding contribution from Nepal (17.24 BCM) and Bhutan (63.5 BCM) e Based on IMD gridded rainfall data for 30 years (1985–2015)

requirement in the country will exceed 250 million tons by 2050. The total demand for grains will increase to 375 million tons including grain for feeding livestock by 2050. With the growth in the National GDP, at 6.8% per annum, during the period from 2000 to 2025 and 6.0% per annum, during the years 2025 to 2050, the per capita income is bound to increase by 5.5% per annum. This will increase the demand for food. While the per capita consumption of cereals will decrease by 9%, 47% and 60%, with respect to rice, coarse cereals and maize, the per capita consumption of sugar, fruits and vegetables will increase by 32%, 65% and 78% respectively, during the period from 2000 to 2050. This will create an additional demand for water. The requirement of water for livestock will rise from 2.3 BCM in 2000 to 2.8 BCM in 2025 and 3.2 BCM in 2050.

6.6 Causes of the Water Crisis in India Although India is not in the water stressed category, the real situation of per capita water availability is more serious than that depicted by the average figures. The main causes of the water crisis in India are: i.

Inadequate attention to water conservation, efficiency in use, water re-use, groundwater recharge, and eco-system sustainability. ii. Highly uneven availability of water, both in space and time, often leading to floods and droughts. iii. Rampant pollution of freshwater resources mainly by the agricultural, industrial, and municipal sources. iv. Highly unreliable municipal water supply with poor quality.

86

R. N. Sankhua

v. Laws which give unlimited ownership of groundwater to the landowner and coupled with uncontrolled use of bore-wells that has allowed extraction of groundwater at very high rates, often exceeding recharge.

6.7 Climate Resilient Water Resources: Challenges and Opportunities (i)

(ii)

(iii)

(iv)

(v)

(vi)

Climate change is projected to reduce renewable surface water and groundwater resources significantly in most dry subtropical regions exacerbating competition for water among agriculture, industry and energy production, affecting regional water, energy and food security. Detection of change in ground water systems and attribution of those changes to climate change is rare owing to lack of appropriate observation wells and a small number of studies. Heavy rainfall is likely to become more intense and frequent during the twentyfirst century in many parts of the country, which may lead to more intense soil erosion even if the total rainfall does not increase. Due to greater variability of precipitation the seasonal reductions of water supply due to reduced snow and ice storage. Availability of clean water can also be reduced by negative impacts of climate change on water quality, for instance, the quality of lakes used for water supply could be impaired by the presence of algae-producing toxins. Changes in climate (precipitation, temperature, radiation) will affect the water demand of crops grown in both irrigated and rain fed systems. Major irrigated areas in India might experience a slight decrease in irrigation—demand, due for example to higher precipitation but only under some climate scenarios. Climate change affects hydropower generation through changes in the mean annual stream flow, shifts of seasonal flows, and increases of stream flow variability (including floods and droughts), as well as by increased evaporation from reservoirs and changes in sediment fluxes. Therefore, the impact of climate change on a specific hydropower plant will depend on the local change of these hydrological characteristics, as well as on the type of hydropower plant and on the energy demand, which will itself be affected by climate change. Climate change also impacts water quality indirectly. Many drinking water treatment plants—especially small ones—are not designed to handle the more extreme influent variations that are to be expected under climate change. This demands additional or even different infrastructure rendering water treatment very costly.

Owing to the serious concern of increasing population, which is likely to increase to 1.66 billion by 2050, the implications of water resources could be as below: 1. By 2050, with the increasing population, the annual food requirement may exceed 250 million tons in India. The total demand for grains including livestock grain demand will increase to 375 million tons by that time. Further, there will be

6 Rejuvenating Water Wisdom: A Route to Resilience

2.

3.

4.

5.

6.

7.

87

increase in the demand for food if national GDP is taken at 6.8% per annum from the year 2000 to 2025 and 6.0% per annum during the years 2025 to 2050. The per capita income would increase by 5.5% per annum. Increase in the per capita consumption of sugar, fruits and vegetables during the period from 2000 to 2050 (NCIWRD 1999) will create an additional demand for water. The requirement of water for livestock will rise from 2.3 BCM in 2000 to 3.2 BCM in 2050. The average rainfall in 4 months (June–September) is about 730.7 BCM. The water requirement for average irrigation support in all the commands in the basins remains as 482 BCM against total completed command area of about 0.586 Million km2 and rainfall of 3803 BCM excluding Andaman and Nicobar Islands and inland drainage in Rajasthan and Leh-Ladakh area. By promoting appropriate technological interventions of micro-irrigation, ‘More crop per drop’ can be achieved to improve water use efficiency in the agriculture sector. Almost 75.30% of rainfall occurs in the 4 monsoon months (June to September). The total utilizable quantity of water is 690 BCM, 432 BCM GW and in toto 1122 BCM per year. Interlinking of rivers will facilitate an addition of about 180 BCM of water. The unutilized water draining to the sea per annum is about 1166 BCM. Better water management would generate 4000 ha. more for one TMC of running water, and it could produce 5.5 tons per hectare. Thus, it would be possible to irrigate about 148 Mha of land annually to produce about 800 million tons of rice. Proper implementation actions should be devised and adopted to respond to challenges, resources and capacity. Optimization of priorities and allocation of water resources to adapt interventions, reflecting the conditions in sub-basins, should be adopted. In the water sector, the national water planning entangles the national strategy framework for water resource management. At watershed level, adaptive institutional mechanism would be vital for management, and longer-term strategies need to be devised for management incorporating fluctuations in the water availability due to climate change. Significant trade-offs are necessitated to suffice continued development, particularly in terms of the allocation between agricultural and urban-industrial water use in case of drier situations. These constraints are most likely to be experienced in some pockets of central, northern and south-western parts of India. The viable agriculture and energy production will impact on water supplies to economic and social users. It warrants having a clear planning strategy to the trade-offs between water, energy and food production. Harnessing of excess monsoon runoff for enhancing groundwater storage will increase the availability of water to meet the growing demands and mitigate damages from floods to some extent.

88

R. N. Sankhua

6.8 Traditional Water Conservation: The Promising Potential The potential of traditional water conservation is once again being recognised and debated over as because the large water supply systems built around mega-dams have proved to be extremely capital-intensive, with long gestation periods and the revival of water conservation systems is in the offing. With about 10% of country’s land area set aside for rainwater collection, most of the household water needs can be met. But a decentralised system of water management will demand a community-based system of natural resource management. This has acquired disparate connotations in different social groups. Many conservation techniques have deep historical roots in practice and policy. Most water conservation research in India continues to focus on rural practices, notwithstanding the rapid pace of urbanisation. Some analysts stress physical water use efficiency while others argue for economic efficiency, productivity, or protection. To adopt an inclusive approach of working alongside ecology, hydrology, climate, and societal constraints to bring in a mix of technologies, policies, governance to help alleviate the problem at the local level to move up the ladder. Realizing the seriousness of problem confronting water bodies, the Centre had launched the Repair, Renovation and Restoration (RRR) of Water Bodies’ scheme in 2005 with the objectives of comprehensive improvement and restoration of traditional water bodies, including increasing tank storage capacity, ground water recharge, increased availability of drinking water, improvement of catchment areas of tank commands, etc. Water conservation is a key element of any strategy that aims to alleviate the water scarcity crisis in India. Some important traditional Water conservation practices are enumerated below (Fig. 6.3). (i)

(ii)

(iii)

Bawdi/Jhalara These step-wells are grand structures of high archaeological significance constructed since ancient times, mainly in honour of kings and queens. They are typically square shaped step-wells with beautiful arches, motifs and sometimes rooms on sides. Apart from storing water for basic needs, they at times also served for water sports. Kul Kuls are diversion channels that carry water from a glacier to village. Often spanning long distances, with some over 10 km long, kuls have been around for centuries and are the lifeline of people of Spiti valley of Himachal Pradesh and in Jammu too. Kul starts at the glacier, which is to be tapped. Keeping the head clear of debris is achieved by lining the sides of Kul with stones which ensure that there is no seepage or clogging. The Kul leads to the village where the water is stored in a circular water tank. The water is drawn from here are per the need of the village. Johad one of the oldest systems used to conserve and recharge groundwater, are small earthen check dams that capture and store rainwater. Constructed in an area with naturally high elevation on three sides, a storage pit is made by

6 Rejuvenating Water Wisdom: A Route to Resilience

89

Bawdi/Jhalara

Kul

Bawari

Johad

Pangam Keni

Baoli

Nadi in Jodhpur Fig. 6.3 Traditional practices of water conservation

Ahar Pynes in Bihar

Kund

Bhandara Phad

90

R. N. Sankhua

Ramtek

Zing in Ladakh

Apatani in Arunachal

Surangam in Maharashtra Fig. 6.3 (continued)

Zabo in Nagaland

Pat in MP

Eri in TN

Virdas in Gujarat

6 Rejuvenating Water Wisdom: A Route to Resilience

91

excavating the area, and excavated soil is used to create a wall on the fourth side. Johad, a crescent shaped small check dam built from earth and rock to intercept and conserve rainwater, was thus reinvented. This helps to improve percolation and increases groundwater recharge. (iv) Bawari These are unique stepwells that were once a part of the ancient networks of water storage in the cities of Rajasthan. Water is diverted to man-made tanks through canals. The water would then percolate into the ground, raising the water table and recharging a deep and intricate network of aquifers. To minimize water loss through evaporation, a series of layered steps were built around the reservoirs to narrow and deepen the wells. (v) Zabo Zabo means impounding water. Known locally as the Ruza system, this system is a unique combination of water conservation with animal care, forests and agriculture. Mostly practised in Nagaland, Zabo is used to deal with a lack of drinking water supply. During monsoon, rainwater that falls on the hilltops is collected into the pond like structures that are carved out on the hillsides. The water is then passed onto cattle yards below from where the water enters the paddy fields rich in manure. (vi) Eri One of the oldest water conservation systems in India, Eri (tank) of Tamil Nadu is still widely used around the State. With over a third of irrigation in the State being made possible due to Eri, the traditional water harvesting system plays an important part in the agriculture. They also have other advantages such as prevention of soil erosion, recharge of groundwater, and flood control. Eri can either be fed through channels that divert river water, or rain-fed ones. They are usually interconnected to balance the water in case of excess or lesser supply. (vi) Pangam Keni The Kuruma tribe (a native tribe of Wayanad) uses a special type of well, called the Panam Keni, to store water. Wooden cylinders are made by soaking the stems of toddy palms in water for a long time so that the core rots away until only the hard outer layer remains. These cylinders, four feet in diameter as well as depth, are then immersed in groundwater springs located in fields and forests. This is the secret behind how these wells have abundant water even in the hottest summer months. (vii) Baoli Built by the nobility for civic, strategic or philanthropic reasons, baolis were secular structures from which everyone could draw water. These beautiful stepwells typically have beautiful arches, carved motifs and sometimes, rooms on their sides. The locations of baolis often suggest the way in which they were used. Baolis within villages were mainly used for utilitarian purposes and social gatherings. Baolis on trade routes were often frequented as resting places.

92

R. N. Sankhua

(viii) Khadin Khadin is a water conservation system designed to store surface runoff water for the purpose of agriculture. It entails an embankment built around a slope, which collects the rainwater in an agricultural field. This helps moisten the soil and helps in preventing the loss of topsoil. Additionally, spillways are provided to ensure that excess water is drained off. This system of water conservation is common in the areas of Jaisalmer and Barmer in Rajasthan. A dug well is usually made a bit further from Khadin to additionally take advantage of groundwater recharging that happens around the structure. (ix) Virdas Developed by the nomadic Maldhari tribes of Rann of Kutch, virdas are shallow wells dug within a natural depression (Jheel). Since the area around is very saline, when rainwater seeps down the soil, it collects over the saline groundwater due to the difference in density (rainwater being less dense). The tribesmen identify areas on basis of flow of the monsoon runoff and build these shallow wells. This smart method helps them separate freshwater from saltwater and provide water for a variety of purposes. Vegetation is planted along virdas to help protect them. (x) Surangam Surangam is a traditional water conservation system present in areas of Karnataka and Kerala. The terrain of the area makes it impossible for people living around to survive only on surface water. Thus a complex labyrinth of fine tunnels are built which constitutes horizontal wells dug in laterite rocks. The Surangam can be of varying length and can even go up to 300 m. Water is collected into a storage tank using gravitational force. Vertical shafts are provided for airflow. The population nearby depends mainly on these horizontal wells for their water requirements. They are also used to irrigate crops such as paddy and coconut. What is also important that the water from Surangam is of good quality. (xi) Ahar Pynes This is a water conservation technique indigenous to South Bihar. Due to a variety of reasons including sandy soil, temporal river flow, low groundwater levels etc., floodwater harvesting is considered as the most suitable option for the area. Ahar consists of a catchment basin embanked on three sides, at the end of a rivulet or a canal that leads from a river. Pynes are artificial channels, which were constructed to use river water for agriculture. The process starts from the river, from where the water goes to pynes and eventually lands up in an ahar. Although the system suffered under British rule, it has again been rejuvenated for agricultural purposes, especially in the district of Gaya.

6 Rejuvenating Water Wisdom: A Route to Resilience

93

(xii) Kunds/Kundis With the look of an upturned cup nestling in a saucer, these water conservation structures are built to harvest rainwater. Usually dotting the areas of Rajasthan and Gujarat, they have a saucer shaped catchment area sloping towards the centre to where the well is situated. Traditional water harvesting structures of India demonstrate the people’s unique modes and basic engineering skills intrigued with ingenuity at its best. With an extraordinary diversity of agro-ecological systems, ranging from the wide desert of Rajasthan to the cold desert of Ladakh, from the sub-temperate Himalayan mountains to the high tropical mountains in the south; interspersed are various hill and mountain ranges, plateaus and the unique Indo-Gangetic plains which are more flood-prone than any other part in the world. Water harvesting systems are fragile creations and need to be continuously monitored, maintained, and repaired.

6.9 Cooperation Continuum a Solution In an attempt to avoid the intricacies of the shared resource, the riparian states implement water development projects unilaterally within their territory, often without consultation with their neighbours as could be clearly seen in between States in Indian context that impacts at least one of its neighbours to meet existing uses in the face of decreasing relative water availability. In the absence of relations conducive to conflict management, this act becomes a flashpoint and impacts the neighbour, heightening tensions and regional instability in terms of health of dependent populations, and ecosystems. This requires years or, decades, to resolve even after water dispute tribunal is set up. This problem only worsens as the dispute intensifies. Disparities of economic development, infrastructural capacity, political orientation between regions further complicate water resources management. Navigating the competing interests of concerned States and negotiating solutions for governance frameworks defusing the conflict potential implicit looms large. Germane to this challenge, robust negotiated solutions, and governance frameworks for inter basin rivers must be anchored in the bedrock of National law governing relations among Indian States with specific regard to the water bodies they have in common. A complementary challenge is translating negotiated solutions for the governance of surface water, as well as the obligations stemming from the rules of water law that have crystallized into agreed governance frameworks, into “action on the ground” at the domestic level of the regions concerned.

94

R. N. Sankhua

6.10 Cessation Remarks Effective cooperation on a national watercourse is any action by riparian states leading to better development of the water resource to their mutual satisfaction. Integrated, predictive management with alternatives for and improvement of the multiple uses must be implanted at the level of hydrographic basins in order to decentralize management and provide opportunities for participation to users. To avert a water conflict, vacillate on multiple fronts, there is a need to work on the water-energy-food triptych, in the context of a regions to promote sustainable water resources management. In addition to comparative modes of practice, far more understanding is needed about informal processes of implementing the traditional conservation practices. Water planners in India look around for promising approaches of water management to witness more rigorous comparative analysis of exchanges of water knowledge that could be adapted for sustainable use. Let us aim to raise the concerns, participate in seeking answers and—more importantly—in pushing for answers through creative challenges transforming these into policy and so practice. A madaka, a pine, a zohad, a zabo or an iri on the top of a watershed, and a few kattas on streams or rivulets below, would go a long way in water sustainability. The water wisdom has its way in reviving India’s blue future for life to return to its normal rhythm.

References Agarwal A, Narain S (1997) Dying wisdom: rise, fall and potential of India’s traditional water harvesting systems. Centre for Science and Environment, New Delhi Bhatnagar M (2005) Proposed action plan for conservation of water bodies in Delhi. Context Built Living Nat 2(1):47–52 IPCC (2013a) Climate phenomena and their relevance for future regional climate change. Sec. 14.6, Chapter14, pp 1248–51 Iyer RR (2007) Towards water wisdom: limits, justice, harmony. Sage, Delhi Report on reassessment of water potential using space inputs (2018), CWC, New Delhi

Chapter 7

Reliability Analysis of Water Distribution Network: A Case Study of Bole and Yeka Sub-city of Addis Ababa, Ethiopia Mitthan Lal Kansal, Bahar Adem Beker, Tadese Gindo Kebebe, and Shweta Rathi

7.1 Introduction Water is necessary for the survival of human beings. Life cannot exist without water, thus, requiring proper treatment and water supply. The water distribution system (WDS) connects water sources to the links, nodes, tanks, and pumps. It involves other appurtenances to deliver the water to all the community with required pressure, adequate quantity, and standard of quality. Therefore, it is necessary to investigate how a particular water supply system satisfies its quality and quantity objectives, which is indicated through its reliability. WDS efficiency is assessed by examining its reliability. Only a few studies are found in the literature where factors are addressed: adequacy of water distribution and water source of sufficient quality (Gheisi et al. 2016). In general, reliability is the ability to perform a system’s mission over a specific period under different operating conditions (Ostfeld et al. 2002). Reliability analysis should be an integral part of water distribution systems’ planning, design, and operation (Ostfeld 2012). However, estimation of the reliability of a system is a very complex phenomenon since a large number of parameters are involved for consideration of quality and quantity of available water, pump and pipe failure rates, valve failure, pipe roughness characteristics, etc. In the last few decades, several methods have been proposed for quantitative assessment of the reliability of water distribution systems, classified as analytical and simulation methods. Several researchers suggested and adopted an analytic approach for reliability assessment of WDS, including Shamir and Howard (1981), Mays (1985), Wagner et al. (1988a, b), Mays (1989), Duan and Mays (1990), Kessler et al. (1990), Kansal et al. (1995), Xu and Goulter (1999), Tanyimboh and Templeman M. L. Kansal (B) · B. A. Beker · T. G. Kebebe · S. Rathi Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee 247667, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_7

95

96

M. L. Kansal et al.

(2000), Tanyimboh et al. (2001). The analytical method does not consider the intermediate state of partial working conditions in the network simulation. The researchers who work on simulation methods are Rowell and Barnes (1982), Xu and Goulter (1999), Germonolpoulos et al. (1986), Bao and Mays (1990), Gupta and Bhave (1992), Gupta and Bhave (1994), Gupta and Bhave (2004). A reliable water supply system will provide a safe and adequate quantity of water with the required pressure at their taps during the design period. Further, the system should ensure safety against fire by supplying a sufficient amount of water to the place. Providing reliable water to the community ensures better living standards for the community. Therefore, adequate and reliable WDSs should be a necessity in a city. However, due to the lack of community funds to implement the project, the decision-makers face problems in reliable project implementation (Wagner et al. 1988a, b). Also, the water delivery system malfunctions fully or partially (Gheisi et al. 2016). Further, it requires intensive energy to deliver water from the source to the consumers (Abdallah 2020). Such problems are essential aspects of a reliable water supply system. Ideally, water is supplied for all 24 h in a day if there is no shortage of water unless and otherwise, the water is provided in the intermittent type (Kansal et al. 1995). In the majority of the areas, water is supplied in an intermittent mode. In general performance of the system requires satisfactory components that satisfy the consumers’ demands. In finding the reliability of a real system model has to be developed. A model represents the structure in two or three-dimensional, which is the small representative. Hydraulic models play an essential role in improving water sustainability, management, and water supply policies. To achieve this goal, the modeling of WDS should simulate the network flow rate and hydraulic heads accurately and represent the water demand. Further, it models the systems in lowpressure in critical conditions and accurately simulates WDS behavior in different scenarios (Menapace et al. 2018). Geographical information systems (GIS) visualize the sources and feeders and conceptualize the entire distribution network (Roy et al. 2015). Graph theory and hydraulic efficiency integration hold the most promise for potential computationally efficient and sufficiently rigorous steps for WDN reliability evaluation (Goulter 1995). Simulation is an approximate process or operation of the system. It can measure the reliability analysis of water distribution networks analytically (Wagner et al. 1988a, b). Simulation of the WDN helps evaluate the pipeline’s pressure, heads at different nodes, and velocity in pipelines (Menapace et al. 2018). Beker and Kansal (2021) assessed the hydraulic performance of WDS using the technical performance index (pressure index and velocity index) for Dire Dawa city in Ethiopia. Further, the integrated performance index coupling uses three indexes (reliability, resilience, and vulnerability) proposed by Beker and Kansal (2022) to evaluate the overall performance of WDS. However, few studies were performed in Addis Ababa related to water supply and demand challenges (Kitessa 2021a, b). No studies are yet to assess the Ababa city network’s hydraulic and water quality reliability. Further research is necessary to properly manage water in developing countries where equitable water distribution remains a challenge (Aragaw et al. 2021; Aragaw and Mishra 2022).

7 Reliability Analysis of Water Distribution Network …

97

Finally, the main aim of this study is to understand the existing water supply system and to perform the reliability analysis of the WDN under two different conditions (i.e., all pipelines are working and when a single pipeline are under failure condition) in the Bole and Yeka sub-city of Addis Ababa, Ethiopia.

7.2 Reliability Parameters and Assessment Methods 7.2.1 Mechanical Reliability While calculating reliability, it is necessary to know the availability and nonavailability of the flow in the water distribution lines. Mechanical reliability estimation involves node-pair reliability analysis and is based on the presentation of a path set or cut set approach to developing in communication and electrical engineering. Mechanical failure (partial or complete) is expected during a WDS thought lifetime operation. Failure of the system undermines the integrity of the WDS. Failures will interrupt the operation of the WDS. Therefore, the analysis of WDS plays a significant role in the satisfaction of the nodal demand and pressure (Gheisi et al. 2016). Availability refers to the probability of the operating conditions at a time t, provided at the time zero, the component was good. Availability is expressed in the percentage of the time interval. In the cycle of the repairable components, it consists of time for operation and time for failure (Kansal 2004). It determined as Pi =

MTTFi MTTFi + MTTRi

(7.1)

qi = 1 − Pi

(7.2)

MTTF =

1 Ni *Li

(7.3)

Ni = 2.5exp[0.04(t − 0.1D)]

(7.4)

MTTR = 9.6D0.4

(7.5)

In which MTTFi = Mean Time To Failure in the ith pipe, t = age of pipe in the years, MTTRi = Mean Time To Repair in the ith pipe, D = Diameter of pipeline in mm, Ni = Breaks/km length/yr. Li = Length of pipeline in the km.

98

M. L. Kansal et al.

7.2.2 Hydraulic Reliability Hydraulic reliability is the probability of a nodal demand receiving sufficient water with required pressure at a given time and place. It measures the hydraulic performance of the WDN. Hydraulic reliability can be affected due to demand variation (spatial and temporal), inadequate system components like valves, pumps, pipes, improper interactions, etc. To supply an adequate quantity of water to the consumer, the nodal pressure should be enough to satisfy the demands of all the consumers. However, the pressure in the system is constrained due to the aging and corrosion of the pipes, improper operational control, maintenance activity, and tuberculation of pipes. Hydraulic failure occurs in distribution systems when hydraulic pressures are reduced at demand nodes. It measures the ability of WDN to provide water with desirable pressure at every demand node though some of the pipelines are failure states (Gheisi et al. 2016). Further, the hydraulic reliability of the system can be determined in two conditions: (1) When all the pipes in the system are fully operational and (2) When few (starting from a single) of the pipes are not working.

7.2.2.1

Node Flow Analysis (NFA)

A WDN may have low performance in uncertain situations like pump failure or breakages of pipes, therefore, unable to supply the water at all nodes. Water may be available at some nodes fully, partially, or no water is available at some nodes. Bhave (1981, 1991) was first defined as NFA. In a case of an available pressure (Havl ) more than the desired pressure (Hdes ) at a node, available water flow (qavl ) is equal to the required demand (qreq ); and for an available pressure less than the minimum pressure (Hmin ), no water flow is considered to be available at a node (Wagner et al. 1988a)

QAval j

7.2.2.2

⎧ 0, HAvl ≤ Hmin for j = 1, 2 . . . J ⎪ j j ⎪ ⎪ ( ) ⎪ Avl des α ⎨ Hj − Hj = Qreq , if Hmin ≤ HAvl ≤ Hdes j j j , for j = 1, 2 . . . J (7.6) j des min ⎪ H − H ⎪ j j ⎪ ⎪ ⎩ req Qj if HAvl ≥ Hdes j j , for j = 1, 2 . . . J

Node Reliability Parameter for All Pipes in Working Condition

Gupta and Bhave (1994) defined the nodal reliability parameter Rnj is the ratio of nodal volume for total outflow available to the nodal volume for the desired flow for all the states during the analysis period (Gupta and Bhave 1994). Rnj =

( Σ avl ) ( Σ avl ) Vs q s ts Σs req , for all nodes j = Σs req V q s s j s s ts

(7.7)

7 Reliability Analysis of Water Distribution Network …

99

r eq

where VSavl is available water volume at state s; VS is required volume during states; r eq q Savl is available folw rate at state s; q S denotes required water flow rate at state s; ts is a period of a state (assume equal for all nodes); j is subscript representing nodal demand; and s is subscript is the state.

7.2.2.3

Volume Reliability Parameter

The volume reliability factor Rv is calculated for the entire state for the given period. The Rv is expressed as the ratio of the total available water flow volume (amount) to the needed water flow volume for the overall WDN for all states during the analysis period (Gupta and Bhave 1994). Mathematically written as: Σ Σ s

j

Vavl js

s

j

Vjs

Rv = Σ Σ

7.2.2.4

Σ Σ s

j

qavl js ts

s

j

qjs ts

=Σ Σ

req

req

(7.8)

System Hydraulic Reliability for Normal Working Condition

The system hydraulic reliability of The WDN is calculated using the volume reliability factor, and the node reliability factor. Thus, it is determined by Eq. (7.9) Rnw = Rv Ft Fn ,

(7.9)

where, Rnw = System hydraulic reliability factor, Ft = time factor. Fn = node factor. Σ Σ Ft =

s

J ajs tjs

JT

,

(7.10)

where, Σ Ft = time factor, J = the total numberreqof demand nodes, T = period of analysis (= ts ), aj = 1, if the discharge ratio, qavl j /qj ≥ 0.5 at a node is an acceptable value., aj = 0, if the nodal discharge demand will not satisfy the nodal demand at the state if it is less than 50%„ The node factor calculated as follow it is the geometric mean at jth junctions of the node reliability factors.

Fn =

[ J ⊓

]1/J Rnj

(7.11)

J=1

The value of Rnj it should be greater than the particular values unless, and otherwise, the value of Rnw will be zero, then the value of Fn is also zero. The acceptable value for the Rnj is greater than or equal to 0.90, and the acceptable value for the Ft is greater than or equal to 0.5.

100

7.2.2.5

M. L. Kansal et al.

Nodal Hydraulic Reliability Assessment for a Pipe Failure Condition

Nodal hydraulic reliability is represented as the ratio of available supplied water to the demand node by the total required water needed at the node. Also, it is determined by multiplying the probability of occurrence of the event by the ratio of available to required water flow under the given condition. Hydraulic reliability of WDN when one pipe is failing is calculated as follows (Kansal 2004). RELHNi = P(0) ∗

Q(0) Ni supp

+

QNi reqd

P(0) =

n 

P(r)∗

r=1

n ⊓

p(i)

Q(r) Ni supp QNi reqd

(7.12)

(7.13)

i=1 n ⊓

P(r) = q(r) ∗

p(i)

(7.14)

RELHj s

(7.15)

i =1 i /= r ΣS RELHS =

J=1

In which RELHNi = Nodal hydraulic reliability; P(r) = Probability of failure of the single pipe; RELHS = system hydraulic reliability, Q(0) Ni supp = Discharge rate supplied

to the ith node at the rth link. p(i) = availability, Q(r) Ni supp = Discharge rate supplied to the ith node at the rth link, q(r) = non-availability at rth link, S = Number of demand nodes in the network.

7.2.3 Water Quality Reliability Over time, water quality reliability refers to the purity and content provided in different types of failures connected with connectivity, reachability, quality, and other concerns that affect the water quality delivered to consumers. Water quality problems may emerge due to aging, deterioration of metal, chemical reactions, leaching, deterioration in the internal part due to the formation of scale and different kinds of contaminants intrusion, dissolution Nitrification, leaching, contaminant intrusion, biofilm growth. The required chlorine dose is added to the water to react with organic and inorganic content to disinfect the entire water found in the network. Chlorine exists as a solid (e.g., powder), liquid or gas physically. Chlorine is a critical property relevant to drinking-water disinfection (WHO 2017; Darmawan 2019).

7 Reliability Analysis of Water Distribution Network …

101

7.3 Reliability Analysis of Case Study Network 7.3.1 Case Study For reliability analysis, the Yeka and Bole Sub-City of Addis Ababa, Ethiopia’s water distribution network, is considered as shown in Fig. 7.1. Ethiopia is highly populated and the largest country in the Horn of Africa. It has a total population of 115 million in 2020 and 1.1 million square kilometers of area. Yeka and Bole Sub-City are two subcities found in the Addis Ababa administration. The two sub-cities cover 113 square kilometers of area and a population of 278,488 in 2020. It lies between longitude 8°58′ 30′′ to 9°40′ 30′′ N and latitude 38°47′ 0′′ to 38°55′ 30′′ E as shown in Fig. 7.1. The temperature in the area varies from 11 to 25 °C. The average annual rainfall of the study area is 1165 mm. The network consists of 168 pipes and 123 nodes. It has four sources of supply. The network is simulated for 24 h of supply. Further, the total water demand is 42.9 (MLD), and its details are given in Table 7.1; WDN consists of 5 pressure zones.

Fig. 7.1 Map of Bole and Yeka sub-cities of Addis Ababa, Ethiopia

102

M. L. Kansal et al.

Table 7.1 Zone wise population numbers and their water needs

Site (zone)

Population

Water demand (l/d)

Yeka Abado

75,096

11,564,784

Yeka Ayat

41,096

6,328,784

Bole summit

43,452

6,691,608

Bole Ayat

35,944

5,535,376

Bole Arabsa

82,900

12,766,600

278,488

42,887,152

Total

7.3.2 Modeling and Simulation of WDN 7.3.2.1

Data Collection

Data collection is mandatory and essential for any study. Both primary and secondary data are collected from different sources for this study. The prior date is field observations of the study area. To understand the status of existing water supply components. Further, the secondary data collected includes design documents of WDN, map of WDN, (X, Y, Z) coordinate of each node in the system, the pipe properties including diameter, length, pipe material, pipe age, and the other such as water sources, water demands, and properties of the reservoir, tanks, pumps, etc. Those data are collected from Addis Ababa Water Supply and Sewerage Authority (AWSSA).

7.3.2.2

Model Building

Model Building is the most crucial. For modeling, it is necessary to follow the procedure, which is implemented in the real world, starting from collecting data to checking the result of the model, increasing the level of information progressively. Starting with the full network model with all parts would almost definitely result minimize the problems at the time of the model’s testing. In certain instances, required data must be in a series way that follows the format of the input information for the model; the software can read by the program adjusted for that purpose. ArcGIS is used to model the WDN and prepare all necessary data for hydraulic simulations. WaterGEMS software has been used for the simulation of WDN.

7.3.2.3

Network Analysis

Several parameters involved in network analysis are pipe lengths, pipe diameters, pipe roughness coefficients, source supply pattern, and demand pattern. Various methods are available for analysis of WDNs. The gradient method is the most popular method used in EPANET and WaterGEMS software. After building the model, the network

7 Reliability Analysis of Water Distribution Network …

103

Fig. 7.2 Methodology adopted for reliability analysis

is analyzed for the hydraulic reliability of the WDNs. The general methodology flow chart is shown in Fig. 7.2.

7.3.3 Reliability Assessment From nodal flow analysis, as discussed in Sect. 7.2.2.1, it is possible to obtain the available nodal flow as given in Eq. (7.6) the required amount of water or required flow should be greater than or equal to the available nodal flow in the distribution system. Hydraulic reliability is evaluated as discussed earlier in Sects. 7.2.2.2–7.2.2.5 and Eqs. (7.1)–(7.5) and (7.6)–(7.14).

7.3.4 Water Quality Reliability Water quality reliability (RWQ ) is defined as the probability of receiving the quality of water supply for the community in the required amount. It is determined based on the quantity of water supplied with the desired quality. The RWQ is defined as the ratio of the total volume of the water provided with needed quality at the given time to the total quantity of water of the desired quality required in at the given time. Gupta et al. (2012) adapted for RWQ determination as expressed by Eq. (7.16)

104

M. L. Kansal et al.

Σ Σ

avl s j b js qjs ts RWQ = Σ Σ req s j qjs ts

(7.16) req

where, qavl js represents available flow rate during state s; qjs = required flow rate during state s; b js = 1 or 0, depending upon whether the water quality criteria (residual chlorine concentration at the node is in the recommended limit or not; ts is time for the state; j is subscript represent nodal demand and s is subscript donetes the state, which is a time in which the hydraulic conditions of the WDN remains same (Gupta et al. 2012).

7.4 Results and Discussion 7.4.1 Water Source and Demand The water supply system of the case study is produced from Legedadi well field located near Addis Ababa city. The well field area covers nearly 5000 ha. It has ten deep wells with safe discharge in the wellfield area. The results indicate the total water source has a capacity of 45,792 m3 /d. At the same time, the maximum day demand of the consumer is 58,958.63 m3 /d. Further, Table 7.1 reveals the water demands and total population served in each pressure zones of the study area.

7.4.2 Water Distribution Network Modeling Model is the representation of a network in the two dimensions, which is the actual representation of the real-world object, and the main difference between the model and the real object is the object’s size. Figure 7.3 shows the model of WDN output of WaterGEMS in the case study, Ethiopia, as discussed earlier in the two sub-cities, i.e., Yeka and Bole Sub-city. It consists of five pressure zones, as shown in Table 7.2.

7.4.3 Simulation of Water Distribution Network The water distribution network model is the small representation of the real network. Whereas Simulation is the mathematical representation of the network. The mathematical expression of all systems represents each system component and element of a WDS. Figure 7.4a, b revealed the simulation output of models for pressure at a node for a selected junction and water flow in the pipes for 24 simulation periods. The result indicated that nodal pressure is maximum at low hourly demand (1:00

7 Reliability Analysis of Water Distribution Network …

105

Fig. 7.3 Model of water distribution network

Table 7.2 Five zones of the area

Site (zone)

Area (m2 )

Population

Density (pop/m2 )

Yeka Abado

3,403,488

75,096

0.0221

Yeka Ayat

2,057,200

41,096

0.0120

Bole summit

5,911,406

43,452

0.0126

Bole Ayat

6,675,942

35,944

0.0061

Bole Arabsa

3,436,281

82,900

0.0124

am) and lowest at maximum hourly demand (8:00 pm) and vice versa for water flow in the pipes (Fig. 7.4a, b).

7.4.4 Reliability Assessment 7.4.4.1

Hydraulic Reliability in Normal Working Condition of WDN

Reliable WDNs supply adequate water to the community with sufficient pressure and velocity for all working conditions. Reliability analysis of WDN is the probability of the nodal and system satisfaction for the required amount of flow, pressure, and

106

M. L. Kansal et al.

(a)

(b)

Fig. 7.4 a Variation of the pressure of five junctions. b Variation of flow demand of eight pipes

water quality in different situations like pipe breaks and others. The results revealed that the volume reliability, node reliability, and overall system hydraulic reliability are 0.909, 0.904, and 0.833, respectively in working conditions. It is observed that the system hydraulic reliability is very good (0.833), which implies that the system satisfies 83.3% of nodes in normal working conditions of the WDN. However, water demand is satisfied at many nodes but fails to meet in nodes such as J-1 to J-9, J-12, J-13, J-20, J-22, J-23, J-53, J-54 J-74, J-76 to J-81, J-87, J-107, and J-108. Furthermore, the low-reliability areas in circles are shown in Fig. 7.5, especially in the four selected areas: Yeka Abado, Yeka Ayat, Bole Ayat, and Bole Arabsa. This means that water demands are not fully satisfied in these areas, which has adverse effects during firefighting, pipe breakage, etc.

7.4.4.2

Hydraulic Reliability in Failure Scenario

Hydraulic reliability is the probability of the available flow in the system satisfying all nodes. Here, hydraulic reliability is calculated when one pipe fails. However, the time is categorized under the hydraulic pattern of 24 h. Results showed that hydraulic reliability is low at the time of 8:00 am because of high water usage at that period, and reliability is high from 1:00–5:00 am and 24 h because there is minimum water usage at that time, as shown in Fig. 7.6. The overall average system reliability in the failure scenario is 0.475.

7.4.4.3

Water Quality Reliability

Water quality modeling is carried out based on the hydraulic simulation (i.e., the extended period simulation for 72 h). The RWQ was assessed in normal working conditions of WDN. Results of water quality reliability for critical nodes are shown in Fig. 7.7, and it observed that the residual chlorine concentration of selected nodes is varied with time between 0.00 and 1.20 mg/l. To ensure a recommended residual

7 Reliability Analysis of Water Distribution Network …

Fig. 7.5 Low-reliability areas

Fig. 7.6 Variation of hydraulic system reliability in a 24 h

107

108

M. L. Kansal et al.

Fig. 7.7 Chlorine residual concentration at Junction 39, 77 and 122

chlorine concentration of 0.2–0.5 mg/l at each end-user stage, 1.5 mg/L of the average concentration of pure chlorine is applied to the system (Ketema et al. 2015). The water quality reliability is the probability of the satisfaction in disinfection or residual chlorine concentration content at the nodes in the desired amount (Li et al. 2013). The results indicate that the total system water quality reliability is 0.911, with an average of 24 h duration. The reliability value satisfies the required amount of water quality in the majority of the nodes in the distribution network. The minimum chlorine residual concentration dosage limit should be 0.2 mg/l if it is below 0.2 mg/l, which means it is unreliable. So, it needs booster chlorination in the system to satisfy the minimum dosage.

7.5 Conclusion This study analyses the reliability of the Yeka and Bole sub-city of Addis Ababa, which is considered the case study. The reliability analysis of WDN is carried out considering hydraulic, mechanical, and water quality to evaluate the desired amount and desired quality of water to the consumers of the case study areas. The reliability is carried out by considering two cases (i.e., normal working and pipe failure conditions). The overall system hydraulic reliability was assessed based on the time, nodal, and volume reliability. The system hydraulic and water quality reliability in the pipe working condition is 0.833 and 0.911. Furthermore, the following conclusions are made for this study. • The water supply of a case study does not satisfy the demand until the end of the design period, so it needs an additional water source, in 2034, there is a 7806 m3 /day deficit observed. It needs other wells to satisfy the demand at the final design period. To overcome the problem of water shortage within the design period, also it is recommended to recycle and reuse the water. • More than 8.33% of pipes line have flow velocity less than 0.1 m/s particularly in p-46, p-94, p-149, p-92, p-91, p-147, p-111, p-41, p-27, p-84, p-110, p-112, p-20

7 Reliability Analysis of Water Distribution Network …

109

and p-120 that cause siltation, water accumulation that lead to the stagnation of water (health impact). • Around 5% of nodes have nodal pressure above 75 m, which can cause pipe failures, 7.3% of nodes are critical as they have pressure below 10 m in J-23, J-107, J-78, J-20, J-74, J-22, J-77, J-87, J-108 which can cause the water shortage in this critical node. • Low water quality reliability is observed at some nodes in WDN due to being far from the boasting station and insufficient chlorination. So, it needs proper management of residual chlorine concentration to disinfect the water supply in the critical area. Acknowledgements The authors are thankful to the Indo_Netherland Project titled Water for Change, Integrative and fit-for-Purpose water Sensitive Design Framework for fast Growing Livable Cities” sponsored by DST_NWO through the Dean SRIC, IIT Roorkee (Project No. DST_1429WRC).

References Abdallah M (2020) Pump scheduling for optimised energy cost and water quality in water distribution networks. Ph.D. thesis. University of Exeter, Centre for Water Systems, USA Aragaw HM, Goel MK, Mishra SK (2021) Hydrological responses to human-induced land use/land cover changes in the Gidabo River basin, Ethiopia. Hydrol Sci J 66(4):640–655 Aragaw HM, Mishra SK (2022) Multi-site multi-objective calibration of SWAT model using a large dataset for improved performance in Ethiopia. Arab J Geosci 15(4):1–18 Bao Y, Mays LW (1990) Model for water distribution system reliability. J Hydraul Eng 116:1119– 1136 Beker BA, Kansal ML (2021) Use of WaterGEMS for hydraulic performance assessment of water distribution network: a case study of Dire Dawa City, Ethiopia. In: Advances in energy and environment. Springer, Singapore, pp 151–162 Beker BA, Kansal ML (2022) Fuzzy logic-based integrated performance evaluation of a water distribution network. AQUA—Water Infrastruct Ecosys Soc 71(3):490–506 Bhave PR (1991) 1991. Analysis of flow in water distribution networks. Technomic Publishing Co., Inc., Lancaster, PA, p 461 Bhave PR (1981) Node flow analysis of water distribution systems. J Transp Eng 107(4):457–467 Darmawan D (2019). Drinking water distribution systems. J Chem Inf Model 53(9) Duan N, Mays LW (1990) Reliability analysis of pumping system. J Hydraul Eng 116(2):230–248 Germonopoulos G, Jowitt PW, Lumbers JP (1986) Assessing the reliability of supply and level of service of water distribution system. Proc Inst Civ Engrs Lond 80(Apr part I):413–428 Gheisi A, Forsyth M, Naser G (2016) Water distribution systems reliability: a review of research literature. J Water Resour Plan Manag 142(11):04016047 Goulter IAN (1995) Analytical and simulation models for reliability analysis in water distrbution systems. 235–266 Gupta R, Bhave PR (1992) Discussion of “Optimal upgrading of hydraulic network reliability.” by L. Ormsbee and A. Kesseler. J Water Resour Plan Manage 118(4):466–467 Gupta R, Bhave PR (1994) Reliability analysis of water-distribution system. J Environ Eng 120(2):447–460

110

M. L. Kansal et al.

Gupta R, Bhave PR (2004) Comments on “Redundancy model for water distribution systems”, by P. Kalungi, and T. T Tanyimboh. Reliab Eng Syst Saf 86:331–333 Gupta R, Dhapade S, Ganguly S, Bhave PR (2012) Water quality-based reliability analysis for water distribution networks. ISH J Hydraulic Eng 18(2):80–89 Kansal ML, Kumar A, Sharma PB (1995) Reliability analysis of water distribution systems under uncertainty. Reliab Eng Syst Saf 50(1):51–59 Kansal ML (2004) Water quality reliability analysis in an urban distribution network. J Ind Water Works Assoc 186–198 Kesseler A, Ormsbee LE, Shamir U (1990) A methodology for least cost design of invulnerable water distribution networks. Civ Eng Syst 7(1):20–28 Ketema AA, Lechner M, Tilahun SA, Langergraber G (2015) Development of cost functions for water supply and sanitation technologies: case study of Bahir Dar and Arba Minch, Ethiopia. J Water Sanitation Hygiene Dev 5(3):502–511. https://doi.org/10.2166/washdev.2015.067 Kitessa BD, Ayalew SM, Gebrie GS, Teferi ST (2021a) Long-term water-energy demand prediction using a regression model: a case study of Addis Ababa city. J Water Clim Change 12(6):2555–2578 Kitessa BD, Ayalew SM, Gebrie GS, Teferi STM (2021b) Assessing the supply for a basic urban service demand with a focus on water-energy management in Addis Ababa city. PloS one 16(9):e0249643 Li X, Sun Y, Han X, Zhao XH (2013) Water quality reliability analysis of water distribution systems based on Monte-Carlo simulation. In: Advanced materials research, vol 777. Trans Tech Publications Ltd., pp 401–406 Mays LW (1985) Methods for reliability analysis of water distribution networks. In: Waldrop WR (ed) Hydraulics and hydrology in small computer age, vol 1, pp 347–351 Mays LW (ed) (1989) Reliability analysis of water distribution systems. ASCE Menapace A, Righetti M, Avesani D (2018) Application of distributed pressure driven modelling in water supply system. In: WDSA/CCWI joint conference proceedings, vol 1 Ostfeld A (2012) Optimal reliable design and operation of water distribution systems through decomposition. Water Resour Res 48(10):1–14 Ostfeld A, Kogan D, Shamir U (2002) Reliability simulation of water distribution systems—Single and multi-quality. Urban Water 4(1):53–61 Rowell WF, Barnes WJ (1982) Optimal layout of water distribution systems. J Hydraul Eng 108(1):137–148 Roy PK, Konar AN, Banerjee GO, Paul SO, Mazumdar AS, Chkraborty RO (2015) Development and hydraulic analysis of a proposed drinking water distribution network using watergems and GIS. Pollution Research Paper. 34(2):371–379 Shamir U, Howard CDD (1981) Water supply reliability theory. J Am Water Works Assoc 73(7):379–384 Su YC, Mays LW, Duan N, Lansey KE (1987) Reliability-based optimization model for water distribution systems. J Hydraul Eng 113(12):1539–1556 Tany1mboh TT, Templeman AB (2000) A quantified assessment of the relationship between the reliability and entropy of water distribution systems. Eng Optim 33(2):179–199 Tanyimboh TT, Tabesh M, Burrows R (2001) Appraisal of source head method for calculating the reliability of water distribution networks. J Water Resour Plan Manage 127(4):206–213 Wagner J, Shamir U, Marks D (1988a) Water distribution system reliability: analytical methods. J Water Resour Plan Manage 114(3):253–275 Wagner J, Shamir U, Marks D (1988b) Water distribution system reliability: simulation methods. J Water Resour Plan Manage 114(3):276–293 WHO (2017) Principles and practices of drinking-water chlorination. Indraprastha Estate, WHO Publications, s0ee http://apps.who.int/bookorders. To submit requests for commercial use and queries on rights and licensing Xu C, Goulter IC (1999) Reliaability-based optimal design of water distribution networks. J Water Resour Plan Manage 125(6):352–362

Chapter 8

HEC-HMS and Geo-HMS Based Flood Hazard Modeling of an Industrial Complex Dhananjay Singh, S. K. Mishra, and Prabeer Kumar Parhi

8.1 Introduction Urbanization is a rapid global trend, where complex systems govern different land use patterns and development densities in proportion to the watershed land cover. The growth process of urban societies all over the world has increased and it is predicted that it will increase up to 60% by the year 2030. In several studies (Pappas et al. 2007; Anderson 1970), the effects of suburban development have been characterized with increased flood frequencies in areas with impervious surfaces. An urban area reflects the effects of rainfall in the form of runoff in much faster way as compared to the equivalent rural area having similar type of soil texture and slope. For a specific amount of rainfall over an area, the various urbanization effects are increased runoff, flow velocity and pollutants loads (Elliot and Trowsdale 2006) etc. Therefore due to urbanization, as more runoff is generated from the area, it is essential to provide proper drainage system with adequate escape channels. Channels are to be designed to convey the runoff from whole area to various outlet points of a catchment. If runoff from an area exceeds the channel carrying capacity, it spills out from the channels and drains through the side of channels creating floods. This increases the importance of modeling of runoff in urban areas so that these problems are handled easily and an easy method to design and plan the urban water conveyance system is developed. Further, in urban areas percentage increase in impervious area leads to the reduced infiltration of water into the soil which is also responsible for creating floods. This

D. Singh · S. K. Mishra (B) Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India e-mail: [email protected] P. K. Parhi Centre for Water Engineering and Management, Central University of Jharkhand, Ranchi 835205, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_8

111

112

D. Singh et al.

necessitates the proper alignment and design of the channels so that water can be discharged to the outlet in lesser time period thus reducing flood. In the above context, the present study attempts to illustrate the relationship between land use change and runoff response to develop a flood inundated map using Remote Sensing and Geological Information system (GIS) techniques. This is due to the fact that GIS provides a broad range of tools for determining the flood affected area (Islam and Sado 2000). It is also experienced that even though the occurrences of flood cannot be prevented, the negative consequences can be minimized by integrated approach to flood management (Dewan et al. 2007). Hence in the present study, to estimate the magnitude of flood and to design the drainage network, Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS Version 4.2 (2016)) has been used. The sensitivity analysis has been carried out to check the change in model output with change in the value of initial parameter/input. This model concerns the spatial distribution of basin characteristics by subdividing the basin into sub-basins that are treated as homogeneous in land use, soil type etc.

8.2 Study Area The study area is located at Pudimadaka Village in the Vishakapatnam District of Andhra Pradesh (India).The latitude, longitude and altitude of the area are 17.4927° N, 83.0028° E and 14–6 m above the mean sea level respectively. It is predominantly plain with gentle slope in west–east direction towards sea coast. Small hillocks are observed in west and north to the site area. There is not much surface drainage in the plains because of the high infiltration and soil permeability. In this area soil is mostly sandy clay, and it contains sufficient amount of nutrients for crop growth. Climatologically, the area experiences tropical sub humid type of climate with moderate summer and good seasonal rainfall. The average annual rainfall of the district is 1116 mm. The catchment area gets most of the rainfall from the northeast monsoon (October–December). Figure 8.1 depicts the study area and index map respectively. Natural drainage pattern indicates that the study area slopes form west to east direction with Doraipalem stream forming eastern boundaries. Ground water occurs under unconfined to semi-confined conditions in soft formations.

8.3 Materials and Method Daily Rainfall data for Vishakapatnam region for duration 2000 to 2011 year and for Kakinada Region for duration 1996 to 2014 year were collected from National Institute of Hydrology, Roorkee, India. The Plant layout map with contour map based on topographic survey were provided by the NTPC. Using AUTOCAD the blue prints for buildings, bridges and drainage systems are created. The layout plan of the drainage system and the topography by using of contour map are also derived.

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling …

113

Fig. 8.1 Index map of the study area (Pudimadaka)

Arc-GIS software is used for preparation of the digital elevation model (DEM). The derived DEM has been used for further processing in the context of stream generation and basin delineation. DEM has been smoothened out for eliminating uneven irregularities (such as pits) using filter size. Corrected DEM has been used as input data to generate pattern of flow directions and for identification of flow accumulation through each pixel of given elevation. AUTOCAD software is also used to know the pipe length, area, layout plan of the drainage system and also the topography by using of contour map. Using Arc-GIS the derived DEM has been used for further processing in the context of stream generation and basin delineation. Corrected DEM has been used as input data to generate pattern of flow directions and for identification of flow accumulation through each pixel of given elevation. Generated information on flow pattern and flow accumulation considers Eight-Point Pour model for the purpose of computing flow directions for use in basin delineation with DEMs directions. With the flow directions assigned for each DEM point, the flow accumulation at each DEM point has been computed. Streams are identified by large values of flow accumulation since the flow paths of many points pass through the stream points. Outlet of a watershed has the highest value of flow accumulation of any of the DEM points since the flow paths of all points in the watershed will eventually pass through the outlet point. Streams have been identified by displaying all DEM points with a flow accumulation value. Then the industrial area was disaggregated into 60-sub-basins and 4-outlets as represented in the model. All the above information was incorporated into basin model of HEC-HMS. The basic step for development and application of a model in establishing the credibility of the model comprises the sensitivity analysis and calibration and validation processes. Sensitivity analyses were applied to highlight the most sensitive parameters during hydrological modeling using HEC-HMS.

114

D. Singh et al.

8.4 Model Inputs Various HEC-HMS model components include basin models, meteorological models, control specifications and input data. The control specifications include the time period and the time step of the simulation run. The basin model represents the physical watershed. In this study, basin model is developed in the HEC-GeoHMS and imported into the HEC-HMS. The meteorological model calculates the precipitation input required by a sub basin element and utilizes both point and gridded precipitation. In this study, specified hyetograph is implemented by using the precipitation data provided by the rain gauges of Pudimadaka region namely Kakinada and Vishakapatnam. Control specifications are given in order to predict the peak flow for different return periods of 25, 50 and 100 years. The time series data from precipitation gages (Vishakapatnam and Kakinada) were entered into the model. The 6 h distributed rainfall data has been used as an input in the HEC-HMS to carry out simulation study. The 25, 50 and 100 year return period rainfall has been linked with all sub catchment of the area. This time series provides uniform rainfall over the area and according to the physical property of individual area runoff has been simulated using HEC-HMS. The details of industrial complex, Pudimadaka in HEC-HMS format has been shown in Fig. 8.2.

Fig. 8.2 Details of industrial complex, Pudimadaka in HEC-HMS software

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling …

115

8.5 Calibration of HEC-HMS Model Parameters The HEC-HMS model is calibrated for 6 h distributed rainfall data as an input to carry out the simulation study. In order to determine the loss and transformation different methods were selected keeping in view the data availability. The SCS Curve Number method was used to calculate losses. HEC-HMS has different options for the method for computation of the hydrological fluxes in the basin (loss, transform and base flow method). The selected methods considered for the Pudimadaka Industrial Complex are presented in Table 8.1. In the present study, the rainfall recorded at the rain gauge stations (Vishakapatnam and Kakinada) for the period from 2000 to 2011 (with missing data for 2003 and 2004) has been considered for study. Thus the actual data of Vishakapatnam available for analysis is for 10 years. Also the daily rainfall data for Kakinada has been obtained for the period from 1996 to 2014 (18 years). In order to check the consistency of the observed rainfall records at Vishakapatnam and Kakinada, the data for concurrent period from 2000 to 2011 was utilized and double mass curve analysis of annual rainfall has been applied. The analysis indicated that the observed rainfall at Vishakapatnam is consistent with that of Kakinada. Therefore, rainfall records of Kakinada can be used to increase data population and to supplement missing data. For calibration, rain gauge weights are assigned using thiession polygon approach to both the rain gauge stations (Vishakapatnam and Kakinada) within the catchment of the basin. The time distribution of storm rainfall of a given duration is the main input in deciding the flood peak and the shape of the flood hydrograph. According to Central Water Commission (CWC) report for this eastern coastal region, storm data for 25, 50 and 100 year return period has been collected. (Flood Estimation Report for Eastern Coast Region (Subzones-4a, 4b, 4c (1987)), Hydrology Directorate CWC, New Delhi). Design storm duration is adopted as 6 h considering the geomorphological characteristics of the area. The outfalls receive water through connecting channels, these channels also receives water according to the rainfall pattern. During simulation period, channels attain its peak value in terms of depth, velocity and flow by using the formula. Q=

2 √ Cd (2g)bh3/2 3

(8.1)

where Q = Discharge, Cd = Coefficient of Discharge, g = Acceleration due to gravity, b = Width of rectangular weir and h = head on the rectangular weir respectively. Table 8.1 HEC-HMS selected methods in basin model Methods

Type

Loss method

SCS curve number

Transform method

SCS unit hydrograph

Base flow method

None

116

D. Singh et al.

Accordingly the depths of flows at different outfalls were estimated for different return periods (25, 50 and 100 year) and the values are 0.57 m, 1.62 m, and 2.4 m respectively.

8.6 Result and Discussion The results of the hydrological model were direct runoff hydrograph for each scenario. Figures 8.3, 8.4, 8.5 and 8.6 show the different return period simulated runoff peak and volume for each defined scenarios for this industrial complex at outlet 1–4 respectively. 4

T-25 year

Discharge(m3/s)

3.5

T-50 year

3

T-100 year

2.5 2 1.5 1 0.5 0

0

50

100

150

Time(hrs)

Fig. 8.3 Flood hydrograph for different return period at outlet-1 7

T-25 year T-50 Year T-100 Year

Discharge(m3/s)

6 5 4 3 2 1 0

0

50

100 Time(hrs)

Fig. 8.4 Flood hydrograph for different return period at outlet-2

150

Discharge(m3/ s)

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling …

117

16 14 12 10 8 6 4 2 0

T-25 Year T-50 Year T-100 Year

0

50

100

150

Time(hrs)

Fig. 8.5 Flood hydrograph for different return period at outlet-3 12

T-25 Year T-50 Year T-100 Year

Discharge(m3/s)

10 8 6 4 2 0 0

50

100

150

Time(hrs)

Fig. 8.6 Flood hydrograph for different return period at outlet-4

8.7 Sensitivity Analysis of the Calibrated Parameters Sensitivity analysis has been carried out to know the effects of change in input variables on HEC-HMS output results. To provide an insight into the relative contributions of three parameters (SCS Curve Number, imperviousness and Lag time) of HEC-HMS on magnitude of flood peaks (Qp ) and time to achieve flood peaks (Tp ) during a particular event at different outlets were simulated in the industrial complex. In the present study the magnitude of variation in result (magnitude of flood peaks and time to achieve flood peaks) are studied by changing one parameter by certain amount without changing the other parameters. The effect from the sensitivity analysis it is observed that the imperviousness and Curve Number are the most sensitive parameters which affect the output flood hydrograph.

118

D. Singh et al.

8.8 Sensitivity Analysis of Lag Time In the present study the sensitivity analysis of lag time has been studied for various lag time (80, 85, 90, 95 min) for all the outlets for 25, 50 and 100 years return periods and are shown in Figs. 8.7, 8.8, 8.9, 8.10, 8.11, 8.12, 8.13 and 8.14. From the graphs it is visible that the in almost all the cases the magnitude of flood peak and time to peak decreases with increase in the lag time except at outlet-2 for 25 years return period, outlet-3 for 100 year return period and outlet-4 for 100 year return periods. At outlet 3 and 4 for 100 years return period flood the peak flood is maximum for lag time of 85 min where as at outlet-2 for 25 years return period flood the peak flood is minimum for lag time of 85 min. 3

Lag Time=80

25 yrs return period

Lag Time=85

Discharge(m3/sec)

2.5

Lag Time=90

2

Lag Time=95

1.5 1 0.5 0 00:00

02:24

Discharge(m3/sec)

3.5

04:48 07:12 Time(hr:min)

09:36

Lag Time=80

50 yrs return period

3

12:00

Lag Time=85

2.5

Lag Time=90

2

Lag Time=95

1.5 1 0.5 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.7 Event model sensitivity on different lag times at outlet-1 for 25 and 50 year return period

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling …

Discharge(m3/sec)

5

119

100 yrs return period Lag Time=80

4

Lag Time=85 Lag Time=90

3

Lag Time=95

2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.8 Event model sensitivity on different lag times at outlet-1 for 100 year return period

Discharge(m3/sec)

6

Lag Time=85

4

Lag Time=90

3

Lag Time=95

2 1 0 00:00

02:24

6 Discharge(m3/sec)

Lag Time=80

25 yr return period

5

04:48 07:12 Time(hr:min)

09:36

12:00 Lag Time=80

50 yr return period

5

Lag Time=85

4

Lag Time=90

3

Lag Time=95

2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.9 Event model sensitivity on different lag times at outlet-2 for 25 and 50 year return period

8.9 Sensitivity Analysis of Imperviousness On analyzing the sensitivity of imperviousness (0, 5 and 10%) on the magnitude of flood peak and time to peak at all the outlets for 25, 50 and 100 years return periods, it is observed that the magnitude of flood peak and time to peak increases with increase in the imperviousness of the catchment except the outlet 4 where there is not any significant effect of imperviousness either on the magnitude of flood peak and

120

D. Singh et al. 8

100 yr return period

Discharge(m3/sec)

7

Lag Time=80

6

Lag Time=85

5

Lag Time=90

4

Lag Time=95

3 2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.10 Event model sensitivity on different lag times at outlet-2 for 100 year return period

Discharge(m3/sec)

14

Lag Time=85

10

Lag Time=90

8

Lag Time=95

6 4 2 0 00:00

02:24

12

Discharge(m3/sec)

Lag Time=80

25 yr return period

12

04:48 07:12 Time(hr:min)

09:36

50 yr return period

12:00

Lag Time=80

10

Lag Time=85

8

Lag Time=90

6

Lag Time=95

4 2 0 00:00

02:24

04:48

07:12

09:36

12:00

Time(hr:min)

Fig. 8.11 Event model sensitivity on different lag times at outlet-3 for 25 and 50 year return period

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling …

Discharge(m3/sec)

12

121

100 yr return period

10

Lag Time=80 Lag Time=85 Lag Time=90 Lag Time=95

8 6 4 2 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.12 Event model sensitivity on different lag times at outlet-3 for 100 year return period 16

25 yr return period

Discharge(m3/sec)

14

Lag Time=80 Lag Time=85

12 10

Lag Time=90

8

Lag Time=95

6 4 2 0 00:00

02:24

7

09:36

12:00

50 yr return period

6 Discharge(m3/sec)

04:48 07:12 Time(hr:min)

Lag Time=80 Lag Time=85

5

Lag Time=90

4

Lag Time=95

3 2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.13 Event model sensitivity on different lag Times at outlet-4 for 25 and 50 year return period

122

D. Singh et al. 8

100 yr return period

Discharge(m3/sec)

7

Lag Time=80 Lag Time=85 Lag Time=90 Lag Time=95

6 5 4 3 2 1 0 00:00

02:24

04:48

07:12

09:36

12:00

Time(hr:min)

Fig. 8.14 Event model sensitivity on different lag times at outlet-4 for 100 year return period

time to peak. As the increased imperviousness decreases the infiltration losses and therefore increases both surface runoff and flow velocities. According to Plan layout of NTPC Plant, boiler unit, pump house crushed coal stock pile unit, desalination plant is located at outlet-3. That location is highly sensitive to impervious than the other outlet. Figures 8.15, 8.16, 8.17, 8.18, 8.19, 8.20, 8.21 and 8.22 shows the hydrograph of different types of imperviousness.

8.10 Sensitivity Analysis of Curve Number On analyzing the sensitivity of curve number (60, 65, 70,75 and 80) on the magnitude of flood peak and time to peak at all the outlets for 25, 50 and 100 years return periods, it is observed that the magnitude of flood peak and time to peak increases with increase in the curve number of the catchment at all the outlets except outlet 4 where curve number 65 and 70 yields almost equal results. The result further shows that for 25 years return period flood at outlet 1, the increase in the CN value from 60 to 80 increases the discharge from 1.6 to 3.6 m3 /s, which is very significant. Figures 8.23, 8.24, 8.25, 8.26, 8.27, 8.28, 8.29 and 8.30 shows the hydrographs using different curve numbers (Table 8.2).

8.11 Conclusion The rainfall-runoff simulation process of the industrial complex of Pudimadaka (study area) using HEC-HMS hydrological model shows that all the drainage channels already designed to carry a flood discharge of 10 years return period appears not

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling …

Discharge(m3/sec)

3

25 yr return period

Impervious=0%

2.5 Impervious=5%

2

Impervious=10%

1.5 1 0.5 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

50 yr return period

3.5 Discharge(m3/sec)

123

Impervious=0%

3

Impervious=5%

2.5

Impervious=10%

2 1.5 1 0.5 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.15 Event model sensitivity on the imperviousness at outlet-1 for 25 and 50 year return period

5 Discharge(m3/sec)

Fig. 8.16 Event model sensitivity on the imperviousness at outlet-1for 100 year return period

100 yr return period

4 Impervious=0%

3

Impervious=5% Impervious=10%

2 1 0 00:00

02:24

04:48

07:12

09:36

12:00

Time(hr:min)

adequate to carry the designed flood. Hence the rainfall-runoff process in the area is simulated for 25, 50 and 100 year return period floods and corresponding flood hydrographs are derived. The industrial complex authorities are suggested to design the suitable drainage channels depending on the availability of funds and degree of safety needed. Further, the, the sensitivity analysis of the curve number, impervious factor and lag time was carried out which show that the curve number is the most

124

D. Singh et al. 3.5

Discharge(m3/s)

3

25 yr return period

Outflow for impervious 0% Outflow for impervious 5%

2.5 2

Outflow for impervious 10%

1.5 1 0.5 0 00:00 4

02:24

04:48 07:12 Time(hr:min)

12:00

50 yr return period Outflow for impervious 0%

3.5 Discharge(m3/sec)

09:36

3

Outflow for impervious 5%

2.5 2

Outflow for impervious at 10%

1.5 1 0.5 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.17 Event model sensitivity on the imperviousness at outlet-2 for 25 and 50 year return period

Discharge(m3/s)

5

100 yr return period

4

Outflow for impervious 0% Outflow for impervious 5%

3

Outflow for impervious at 10%

2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.18 Event model sensitivity on the imperviousness at outlet-2 for 100 year return period

sensitive input parameter which significantly affects the flood magnitude and also the time to achieve peak flood.

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling … 1.4 Discharge(m3/sec)

1.2

125

25 yr return period Outflow for impervious 0%

1 Outflow for impervious 5%

0.8 0.6

Outflow for impervious 10%

0.4 0.2 0 00:00

04:48 09:36 Time(hr:min)

1.6

14:24

50 yr return period

Outflow for impervious 0%

Discharge(m3/sec)

1.4 1.2

Outflow for impervious 5%

1 0.8

Outflow for impervious 10%

0.6 0.4 0.2 0 00:00

02:24

04:48 Time(hr:min)

07:12

09:36

12:00

Fig. 8.19 Event model sensitivity on the imperviousness at outlet-3 for 25 and 50 year return period 100 yr return period

2 Discharge(m3/sec)

Fig. 8.20 Event model sensitivity on theimperviousness at outlet-3 for 100 year return period

1.5

Impervious for 0% Impervious for 5% Impervious for 10%

1 0.5 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

126 25 yr return period

7 6 Discharge(m3/sec)

Fig. 8.21 Event model sensitivity on the imperviousness at outlet-4 for 25 and 50 year return period

D. Singh et al.

Impervious for 0%

5

Impervious for 5%

4

Impervious for 10%

3 2 1

Discharge(m3/sec)

0 00:00

04:48 07:12 Time(Hr:min)

09:36

12:00

50 yr return period Impervious for 0% Impervious for 5% Impervious for 10%

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

100 yr return period

3 2.5

Discharge(m3/sec)

Fig. 8.22 Event model sensitivity on the imperviousness at outlet-4 for 100 year return period

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 00:00

02:24

Impervious for 0%

2

Impervious for 5%

1.5

Impervious for 10%

1 0.5 0 00:00

02:24

04:48

07:12

Time(hr:min)

09:36

12:00

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling … 50 yr return period

5

Discharge(m3/s)

4

Curve Number=60 Curve Number =65 Curve Number=70

3

Curve Number=75 Curve Number=80

2 1 0 00:00

04:48 09:36 Time(hr:min) 25 yr return period

4 3.5 Discharge(m3/s)

127

14:24

Curve Number=60 Curve Number =65

3

Curve Number=70

2.5

Curve Number=75

2

Curve Number=80

1.5 1 0.5 0 00:00

04:48 09:36 Time(hr:min)

14:24

Fig. 8.23 Event model sensitivity on the curve number at outlet-1 for 25 and 50 year return period 100 yr return period

Discharge(m3/s)

6

Curve Number=60

5

Curve Number =65

4

Curve Number=70

3

Curve Number=75 Curve Number=80

2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.24 Event model sensitivity on the curve number at outlet-1 for 100 year return period

128

D. Singh et al.

Discharge(m3/s)

7

25 yr return period

6

Curve Number=60

5

Curve Number=65

4

Curve Number=70

3

Curve Number=75

2

Curve Number=80

1 0 00:00

02:24

09:36

Curve Number=60 Curve Number=65 Curve Number=70 Curve Number=75 Curve Number=80

6 4 2 0 00:00

12:00

50 yr return period

8 Discharge(m3/s)

04:48 07:12 Time(hr:min)

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.25 Event model sensitivity on the curve number at outlet-2 for 25 and 50 year return period

Discharge(m3/s)

10

100 yr return period

Curve Number=60

8

Curve Number=65

6

Curve Number=70 Curve Number=75

4

Curve Number=80

2 0 00:00

02:24

04:48

07:12

09:36

12:00

Time(hr:min)

Fig. 8.26 Event model sensitivity on the curve number at outlet-2 for 100 year return period

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling … 14

25 yr return period

Curve Number=60

12 Discharge(m3/s)

129

Curve Number=65

10

Curve Number=70

8

Curve Number=75

6

Curve Number=80

4 2 0 00:00

Discharge(m3/s)

14

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

50 yr return period

Curve Number=60

12

Curve Number=65

10

Curve Number=70

8

Curve Number=75

6

Curve Number=80

4 2 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.27 Event model sensitivity on the curve number at outlet-3 for 25 and 50 year return period 18

Discharge(m3/s)

16

100 yr return period

Curve Number=60

14

Curve Number=65

12

Curve Number=70

10

Curve Number=75

8

Curve Number=80

6 4 2 0 00:00

02:24

04:48

07:12

09:36

12:00

Time(hr:min)

Fig. 8.28 Event model sensitivity on the curve number at outlet-3 for 100 year return period

130

D. Singh et al. 9

25 yr return period Curve Number=60

Discharge(m3/sec)

8 7

Curve Number=65

6

Curve Number=70

5

Curve Number=75

4

Curve Number=80

3 2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

50 yr retun period

9

Curve Number=60

8 Discharge(m3/sec)

12:00

Curve Number=65

7

Curve Number=70

6 5

Curve Number=75

4

Curve Number=80

3 2 1 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.29 Event model sensitivity on the curve number at outlet-4 for 25 and 50 year return period 14

100 yr return period

Discharge(m3/sec)

12

Curve Number=60 Curve Number=65 Curve Number=70

10

Curve Number=75

8

Curve Number=80

6 4 2 0 00:00

02:24

04:48 07:12 Time(hr:min)

09:36

12:00

Fig. 8.30 Event model sensitivity on the curve number at outlet-4 for 100 year return period

8 HEC-HMS and Geo-HMS Based Flood Hazard Modeling … Table 8.2 Parameters used for different return period in HEC-HMS model

Method

Parameters

131 Return period (years)

Impervious (%)

5

25, 50 and 100

Curve number

70

25, 50 and 100

Lag time (min)

85

25, 50 and 100

References Anderson JJ (1970) Real-time computer control of urban runoff. J Hydraul Div 96 Dewan AM, Islam MM, Kumamoto T, Nishigaki M (2007) Evaluating flood hazard for land-use planning in greater Dhaka of Bangladesh using remote sensing and GIS techniques. Water Resour Manage 21(9):1601 Elliot AH, Trowsdale SA (2006) A review of models for low impact urban stormwater drainage. Environ Model Softw 22(3):394–405 Islam MM, Sado K (2000) Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS. Hydrol Sci J 45(3):337–355 Pappas EA et al (2007) Impervious surface impacts to runoff and sediment discharge under laboratory rainfall simulation. Catena, Elsevier, pp 146–152

Chapter 9

A Stochastic Model-Based Monthly Rainfall Prediction Over a Large River Basin Sabyasachi Swain, S. K. Mishra, Ashish Pandey, and Deen Dayal

9.1 Introduction The evolution of life and the stages of human development are directly linked to the availability and utilization of water. The regularly increasing influence of human activities has largely affected the water resources, resulting in an increased water demands across all the sectors (Aadhar et al. 2019; Bahita et al. 2021a, b; Dhal and Swain 2022; Guptha et al. 2021, 2022; Sahoo et al. 2021; Sharma et al. 2020; Swain 2017). While rainfall is a key meteorological variable that largely governs the hydrological cycle, its behaviour has become anomalous due to climate change, leading to higher frequency of hydroclimatc extremes (Kalura et al. 2021; Nandi and Swain 2022; Patel et al. 2022; Swain et al. 2017a, b, 2021a, b, c, 2022a). Therefore, sustainable management of water resources has become a challenging task, especially over the arid and semi-arid regions. The concept of ‘sustainability’ refers to a principle to be followed on a finite planet, i.e., utilizing the resources to meet the needs of the present while ensuring their preservation for the future generation. From the point of view of water resources engineering, sustainable management aims to uphold the quantity and quality of water by different measures, viz., water conservation, land use management, wastewater treatment, conjunctive use, etc. (Himanshu et al. 2019; Pandey and Palmate 2019; Swain et al. 2022b, c, d, e). Further, it is also crucial to conserve the water available from nature and utilize it judiciously to fulfil the water demands of different sectors. S. Swain (B) · S. K. Mishra · A. Pandey · D. Dayal Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India e-mail: [email protected]; [email protected] A. Pandey e-mail: [email protected] D. Dayal e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_9

133

134

S. Swain et al.

In Indian context, the water resources management is largely dependent on rainfall due to its diverse spatiotemporal patterns (Hengade and Eldho 2016; Palmate et al. 2017; Swain et al. 2018a, b, 2019). It is well known that majority of the population of the country, especially in Central region, are having agriculture as their primary occupation. Further, rainfall over the region is highly seasonal, i.e., mostly concentrated to the southwest monsoon season (June to September) (Pandey and Khare 2017, 2018). The irrigation infrastructures and facilities are also not adequate to meet the huge agricultural water demands (Swain et al. 2020a, b). Considering all the above concerns, it becomes essential to quantify or predict the rainfall accurately, which can be very useful for timely decision making and sustainable water management. The use of stochastic or data-driven models for hydrological forecasting is common, as evident from the previous studies (Dayal et al. 2019; Himanshu et al. 2017a, b; Kumar et al. 2019, 2021; Narayanan et al. 2013, 2016; Swain et al. 2017a, 2018a; Valipour 2015, 2016; Yadav et al. 2016, 2020). The autoregressive integrated moving average (ARIMA) is amongst the most widely used stochastic models for hydro-climatic prediction. ARIMA is a combination of three operators, viz., autoregressive (AR) function, moving average (MA) function, and an integration (I) part to reduce the difference between them or to remove the non-stationarity (Dayal et al. 2019; Rahman et al. 2017). Some of the previous studies on the ARIMA model applications for forecasting purposes are discussed as follows. Kaushik and Singh (2008) predicted monthly rainfall and temperature over Uttar Pradesh (India) for 5 years using ARIMA model. The predictions were adjudged to be reliable. Chattopadhyay and Chattopadhyay (2010) carried out univariate modeling of monsoonal rainfall over India as a whole using ARIMA considering the available data for 1871–1999 and obtained the best-fit model for the rainfall simulation. Similarly, Narayanan et al. (2013) forecasted pre-monsoon (March to May) rainfall lumped over the entire Indian region using ARIMA and obtained satisfactory results. Valipour et al. (2013) forecasted monthly streamflow over Dez Dam reservoir using ARIMA and autoregressive moving average (ARMA) models. The performance of ARIMA was found to be superior. Dastorani et al. (2016) applied the ARIMA model for monthly rainfall forecasting in the semi-arid North Khorasan province of Iran and obtained encouraging results. They also recommended using the Akaike information criterion (AIC) to select the best model and correlation coefficient for assessing the performance of models. Kumar and Jain (2010) adopted ARIMA to forecast daily mean concentration of ambient air pollutants (carbon monoxide, nitric oxide, nitrous oxide, and ozone) at an urban traffic site of Delhi, India. Rahman et al. (2017) utilized the ARIMA model with seasonal components for modeling the country-mean rainfall over Bangladesh. The results concluded the model to be useful for future applications. Islam et al. (2021) used the ARIMA model to predict the hydro-climatic variables at annual scales over Northeast Bangladesh. The research recommended the use of long-term time-series data for trend appraisal and stochastic model-based forecasting. Dimri et al. (2020) applied the seasonal ARIMA model to predict monthly rainfall and temperature over the Bhagirathi river basin, India. They used the time series of 100 years for model training and 20 years for validation purposes. The modeled values fitted well with

9 A Stochastic Model-Based Monthly Rainfall Prediction …

135

the observed time series patterns. The seasonal ARIMA is preferred over the regions with high rainfall seasonality, e.g., monsoon-dominated regions. All these studies have justified the applicability of the ARIMA model for forecasting purposes. Therefore, this study aims to evaluate the capability of ARIMA model for accurate prediction of monthly rainfall over a large basin in Central India. The seasonal ARIMA model is employed for monthly rainfall forecasting, as Central India is exposed to highly seasonal rainfall patterns. This will help in developing a model pertinent to the regional climatic conditions. In this regard, the details of the materials and methods, study area, results and discussion, and the conclusions derived from this study are presented in the subsequent sections.

9.2 Study Area and Data The present study is performed over the Narmada River basin, which is located in the Central India. The location of the basin and the constituent districts are presented in Fig. 9.1. It is a large basin with an area of about 100,000 km2 . Majority of the basin lies within the state of Madhya Pradesh. Rainfall over the basin is mostly concentrated to the southwest monsoon season (Swain et al. 2021a). The daily rainfall data over the basin is collected from the high-resolution (0.25° × 0.25°) gridded observation dataset (Pai et al. 2014) provided by the India Meteorological Departemnt (IMD). This dataset is prepared considering the rainfall measurements from rain gauges spread all over India. The gridded dataset was found to represent trends of daily rainfall, climatology, orographic rainfall, and precipitation variability more realistically, when compared to the previous coarser resolution gridded products of IMD. This dataset is updated regularly and is widely used for hydrometeorological applications (Kumar et al. 2019; Pai et al. 2015; Prakash et al. 2016, 2018; Swain et al. 2021b). It is also recommended suitable for large catchment/basin studies (Kumar et al. 2019, 2021). The total duration considered for this study was 68 years, i.e., from June 1951 to May 2019. The time series of the annual rainfall over the basin for the aforementioned duration is presented in Fig. 9.2. It can be observed that the annual rainfall exhibits remarkable year-to-year fluctuations. The maximum and minimum value of annual rainfall during 1951–2018 is found to be 1618 mm and 681 mm, respectively. The mean annual rainfall from 68 years of rainfall data over the basin is obtained to be 1087 mm, which is lesser than the all India mean annual rainfall of 1194 mm. Considering the year-to-year fluctuations, it is important to develop a robust rainfall forecasting method, that will be useful for sustainable water resources management under such fluctuating conditions. However, annual timescale is a too coarse temporal resolution and a prediction of annual rainfall may not be very useful. Moreover, the month-to-month variation is also crucial, which is not considered for forecasting at annual scales. Given the fact that the Indian monsoon is highly seasonal and they are non-uniformly distributed among different months, the development of a stochastic model for monthly rainfall prediction may be useful.

136

S. Swain et al.

Fig. 9.1 Location map of the study area

Annual Rainfall over Narmada River Basin Annual Rainfall (mm)

Fig. 9.2 Annual rainfall variation over Narmada River Nasin from 1951 to 2018

1800 1600 1400 1200 1000 800 600 400 200 0 1950

1960

1970

1980

1990

Year

2000

2010

2020

9 A Stochastic Model-Based Monthly Rainfall Prediction …

137

Table 9.1 Statistical properties of rainfall at different months ove the entire Narmada River Basin Coefficient of variation

Skewness

January

9.8

11.8

1.20

1.52

2.14

February

8.8

11.8

1.34

2.53

8.12

March

7.9

11.9

1.50

2.60

7.08

April

3.4

3.6

1.07

2.16

6.60

Month

Mean (mm)

Standard deviation

Kurtosis

May

7.3

9.4

1.29

3.24

13.19

June

139.4

66.3

0.48

0.92

0.37

July

335.1

92.3

0.28

0.01

0.23

August

339.5

97.3

0.29

0.36

0.15

September

184.6

107.7

0.58

1.12

1.32

October

33.5

32.5

0.97

1.21

1.19

November

10.4

19.1

1.83

2.35

4.96

December

7.3

17.1

2.35

3.96

16.91

The statistical properties (mean, standard deviation, coefficient of variance, coefficient of skewness and coefficient of kurtosis) of rainfall at different months over the entire Narmada River Basin is provided in Table 9.1. It can be observed that the maximum rainfall occurs during the months of August and July, with magnitudes of 339.5 mm and 335.1 mm, respectively, followed by September (mean rainfall = 184.6 mm) and June (mean rainfall = 139.4 mm). On the other hand, the minimum rainfall occurs in the month of April with a magnitude of 3.4 mm. Overall, the nonmonsoon months (October to May) receives a aggregate rainfall amount of 88 mm, which is only about 8% of the annual rainfall, i.e., 1087 mm. Hence, on an average, 92% of the annual rainfall over the basin occurs in four consecutive months. The statistical properties viz., coefficient of variance, coefficient of skewness and coefficient of kurtosis are higher for the non-monsoon months, and relatively lower for the monsson months. This can be attributed to the lower magnitude of rainfall in the non-monsoon months. A little fluctuation in these months may appear to be a higher percentage change with respect to their mean values.

9.3 Methodology The flowchart of the overall methodology of the study is presented in Fig. 9.3. The daily rainfall at 0.25° × 0.25° resolution provided by IMD was extracted for the grids enclosing the Narmada River Basin. The total number of grids over the basin was found to be 191, which were then used to estimate the average rainfall over the entire basin. The daily rainfall time series for the whole catchment was then converted to monthly scale. The monthly time series was splitted into two parts, i.e., 54 years (from June 1951 to May 2005) as the training/calibration period, and the

138

S. Swain et al.

remaining 14 years (from June 2005 to May 2019) as the testing/validation period. The best trained model is selected, which is then evaluated for performance during the validation period. The results regarding the rainfall forecasting skill of the model is obtained and analyzed. In this study, ARIMA technique is considered for framing models for rainfall prediction. As the rainfall over the study area is highly seasonal (i.e., monsoondominated), the seasonal ARIMA can be preferred. A seasonal ARIMA model is represented as ARIMA (p, d, q) (P, D, Q) m, where p, d and q are the non-seasonal AR, I and MA components, whereas P, D and Q are the respective seasonal components with m denoting number of observations in a year (Valipour 2015). In the present study, m = 12 since the model is developed for monthly rainfall forecasting. For empirical or stochastic model development, typically ~80% of the data is used for Fig. 9.3 Overall methodology of the study

9 A Stochastic Model-Based Monthly Rainfall Prediction …

139

model training and the remaining ~20% data is used for model testing or validation purpose (Guyon 1997; Alqahtani and Whyte 2016). As previously mentioned, the training period and validation period is taken as 54 years (June 1951 to May 2005) and 14 years (June 2005 to May 2019), respectively. Various combinations of autoregressive, integration and moving average operators are checked during model training for the monthly rainfall from June 1951 to May 2005. The range of AR, MA, and I are taken as 0 to 6, 0 to 6 and 0 to 5, respectively, for both seasonal and non-seasonal components. Hence, 86,436 (= 7 × 7 × 6 × 7 × 7 × 6) possible models are tested for determining the most parsimonious combination. The best-fit seasonal ARIMA model among these 86,436 models is obtained on the basis of Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. Further details of the ARIMA modeling procedure can be referred from the literature (Dayal et al. 2019; Dimri et al. 2020; Kumar and Jain 2010; Swain et al. 2018a; Valipour et al. 2013). The best-fit model is employed to predict monthly rainfall for 14 years, i.e., from June 2005 to May 2019, which is validated with respect to the observed rainfall. The model efficiency during the validation/testing period is evaluated using the statistical measures, viz., coefficient of correlation (r) and Nash–Sutcliffe Efficiency (NSE). The value of r ranges from −1 to 1, whereas the NSE ranges from −∞ to 1 (Dayal et al. 2019; Narayanan et al. 2016; Swain et al. 2017a, 2018a). These previous studies may be referred for further details of these statistical measures. The ideal value is unity for both the measures. Hence, a model exhibiting values of NSE and r close to unity can be regatrded an excellent model. The formulae for computing these statistical measures are provided in the following equations. Σn

(Oi − Si )2 N S E = 1 − Σ i=1( )2 n i=1 Oi − O )( ) Σn ( i=1 Oi − O Si − S r = /Σ Σn n 2 2 i=1 (Si − S) i=1 (Oi − O)

(9.1)

(9.2)

where, Oi is the reference/observed value,Si is the simulated/modelled value, O is the mean of the observed and simulated values, S is the mean of the simulated values, and n is the total number of observations.

9.4 Results and Discussion For developing a robust ARIMA model, the rainfall data over the entire basin for 54 years i.e., June 1951 to May 2005 was used for model training purposes. Based on the minimum AIC and BIC, the best-fitted model is selected out of the 86,436 combinations. In the present study, (2, 0, 0) (4, 1, 4)12 is determined to be the best-fit

140

S. Swain et al.

model, which is parsimonious with respect to the observed rainfall data. The (2, 0, 0) refers to the auto-regressive, differencing, and moving average of the model’s non-seasonal part. On the other hand, (4, 1, 4)12 refers to the components of the seasonal part of the model with a periodicity of 12, i.e., there are 12 observations per year as the model is based on monthly rainfall. In the context of climatology over the Indian monsoon region, rainfall is extremely seasonal. Hence, the seasonal ARIMA model is more efficient over the study area for rainfall forecasting. It can also be observed from the auto-regressive, differencing, and moving average components of the seasonal part of the best-fit model. The model testing is carried out for 14 years i.e., from June 2005 to May 2019. The plot of observed versus forecasted rainfall for the testing period is presented in Fig. 9.4, while the scatterplot is presented in Fig. 9.5. The model performed exquisitely, especially in terms of reproducing the seasonality pattern. The statistical measures viz., r and NSE are found to be 0.925 and 0.855, respectively. It is interesting to note that the model underestimates the peak

Fig. 9.4 Performance of ARIMA model with respect to the observed monthly rainfall over Narmada Basin from June 2005 to May 2019

Fig. 9.5 Scatterplot of observed versus forecasted monthly rainfall over the basin

9 A Stochastic Model-Based Monthly Rainfall Prediction …

141

rainfall in some years, which may be due to the presence of extreme rainfall events. The results are consistent with the findings of the previous studies (Dayal et al. 2019; Dimri et al. 2020; Khashei and Bijari 2011; Swain et al. 2018a). It can be observed that while the best-fit model is able to simulate the seasonality pattern, some underestimations/overestimations are present in the monthly rainfall predictions. ARIMA models are adept at modelling the overall trend of a time series along with seasonal patterns; however, they may not be efficient to capture the extreme weather conditions (Dayal et al. 2019). Further, the I operator of ARIMA reduces the difference between AR and MA such that the time series becomes stationary. Hence, ARIMA is not very effective for predicting time series possessing a higher degree of non-stationarity (Swain et al. 2018a). In such cases, the application of machine learning techniques may be useful. Nevertheless, the parsimony with the observed series of 14 years and the high efficiency measures obtained by the developed model clearly justifies its applicability for future rainfall forecasting over the basin.

9.5 Conclusion An accurate large-scale prediction of rainfall is vital to fulfil the sustainability goals in water resources sector. The rainfall over the entire Narmada River Basin from June 1951 to May 2005 is used to develop an ARIMA model for monthly rainfall forecasting, which is validated for fourteen years (i.e., June 2005 to May 2019). The seasonal ARIMA (2, 0, 0) (4, 1, 4)12 is found to be the best-fit model with respect to the observed series during the validation period. The high values of the efficiency measures (r = 0.925 and NSE = 0.855) justifies its applicability for future rainfall forecasting over the basin. Acknowledgements The authors are thankful to the organizers of the International e-Conference on Water Source Sustainability (ICWSS21) for providing an opportunity to present the research and write the chapter.

References Aadhar S, Swain S, Rath DR (2019) Application and performance assessment of SWAT hydrological model over Kharun river basin, Chhattisgarh, India. In: World environmental and water resources congress 2019: watershed management, irrigation and drainage, and water resources planning and management. ASCE, pp 272–280 Alqahtani A, Whyte A (2016) Estimation of life-cycle costs of buildings: regression versus artificial neural network. Built Environment Project and Asset Management 6(1):30–43 Bahita TA, Swain S, Pandey P, Pandey A (2021b) Assessment of heavy metal contamination in livestock drinking water of Upper Ganga Canal (Roorkee City, India). Arab J Geosci 14(24):2861

142

S. Swain et al.

Bahita TA, Swain S, Dayal D, Jha PK, Pandey A (2021a) Water quality assessment of Upper Ganga Canal for human drinking. In: Climate impacts on water resources in India. Springer, Cham pp 371–392 Chattopadhyay S, Chattopadhyay G (2010) Univariate modelling of summer-monsoon rainfall time series: comparison between ARIMA and ARNN. CR Geosci 342(2):100–107 Dastorani M, Mirzavand M, Dastorani MT, Sadatinejad SJ (2016) Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition. Nat Hazards 81(3):1811–1827 Dayal D, Swain S, Gautam AK, Palmate SS, Pandey A, Mishra SK (2019) Development of ARIMA model for monthly rainfall forecasting over an Indian River Basin. In: World environmental and water resources congress 2019: watershed management, irrigation and drainage, and water resources planning and management. ASCE, pp 264–271 Dhal L, Swain S (2022) Understanding and modeling the process of seawater intrusion: a review. In: Advances in remediation techniques for polluted soils and groundwater, pp 269–290 Dimri T, Ahmad S, Sharif M (2020) Time series analysis of climate variables using seasonal ARIMA approach. J Earth Syst Sci 129(1):1–16 Guptha GC, Swain S, Al-Ansari N, Taloor AK, Dayal D (2021) Evaluation of an urban drainage system and its resilience using remote sensing and GIS. Remote Sens Appl: Soc Environ 23:100601 Guptha GC, Swain S, Al-Ansari N, Taloor AK, Dayal D (2022) Assessing the role of SuDS in resilience enhancement of urban drainage system: a case study of Gurugram City, India. Urban Clim 41:101075 Guyon I (1997) A scaling law for the validation-set training-set size ratio. AT&T Bell Laboratories, Berkeley, California, USA Hengade N, Eldho TI (2016) Assessment of LULC and climate change on the hydrology of Ashti Catchment, India using VIC model. J Earth Syst Sci 125(8):1623–1634 Himanshu SK, Pandey A, Yadav B (2017a) Assessing the applicability of TMPA-3B42V7 precipitation dataset in wavelet-support vector machine approach for suspended sediment load prediction. J Hydrol 550:103–117 Himanshu SK, Pandey A, Yadav B (2017b) Ensemble wavelet-support vector machine approach for prediction of suspended sediment load using hydrometeorological data. J Hydrol Eng 22(7):05017006 Himanshu SK, Pandey A, Yadav B, Gupta A (2019) Evaluation of best management practices for sediment and nutrient loss control using SWAT model. Soil Tillage Res 192:42–58 Islam ARMT, Karim MR, Mondol MAH (2021) Appraising trends and forecasting of hydroclimatic variables in the north and northeast regions of Bangladesh. Theor Appl Climatol 143(1):33–50 Kalura P, Pandey A, Chowdary VM, Raju PV (2021) Assessment of Hydrological Drought Vulnerability using Geospatial Techniques in the Tons River Basin, India. J Indian Soc Remote Sens 49(11):2623–2637 Kaushik I, Singh SM (2008) Seasonal ARIMA model for forecasting of monthly rainfall and temperature. J Environ Res Dev 3(2):506–514 Khashei M, Bijari M (2011) A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl Soft Comput 11(2):2664–2675 Kumar U, Jain VK (2010) ARIMA forecasting of ambient air pollutants (O3 , NO, NO2 and CO). Stoch Env Res Risk Assess 24(5):751–760 Kumar A, Ramsankaran R, Brocca L, Munoz-Arriola F (2019) A machine learning approach for improving near-real-time satellite-based rainfall estimates by integrating soil moisture. Remote Sens 11(19):2221 Kumar A, Ramsankaran R, Brocca L, Muñoz-Arriola F (2021) A simple machine learning approach to model real-time streamflow using satellite inputs: demonstration in a data scarce catchment. J Hydrol 595:126046

9 A Stochastic Model-Based Monthly Rainfall Prediction …

143

Nandi S, Swain S (2022) Analysis of heatwave characteristics under climate change over three highly populated cities of South India: a CMIP6-based assessment. Environ Sci Pollut Res. https://doi. org/10.1007/s11356-022-22398-x Narayanan P, Basistha A, Sarkar S, Kamna S (2013) Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India. CR Geosci 345(1):22–27 Narayanan P, Sarkar S, Basistha A, Sachdeva K (2016) Trend analysis and forecast of pre-monsoon rainfall over India. Weather 71(4):94–99 Pai DS, Rajeevan M, Sreejith OP, Mukhopadhyay B, Satbha NS (2014) Development of a new high spatial resolution (0.25 × 0.25) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam 65(1):1–18 Pai DS, Sridhar L, Badwaik MR, Rajeevan M (2015) Analysis of the daily rainfall events over India using a new long period (1901–2010) high resolution (0.25° × 0.25°) gridded rainfall data set. Clim Dyn 45(3):755–776 Palmate SS, Pandey A, Kumar D, Pandey RP, Mishra SK (2017) Climate change impact on forest cover and vegetation in Betwa Basin, India. Appl Water Sci 7(1):103–114 Pandey BK, Khare D (2017) Assessment of reference evapotranspiration in the context of climate change for Central India (Madhya Pradesh). In: Development of water resources in India. Springer, Cham, pp 245–253 Pandey BK, Khare D (2018) Identification of trend in long term precipitation and reference evapotranspiration over Narmada river basin (India). Global Planet Change 161:172–182 Pandey A, Palmate SS (2019) Assessing future water–sediment interaction and critical area prioritization at sub-watershed level for sustainable management. Paddy Water Environ 17(3):373–382 Patel P, Thakur PK, Aggarwal SP, Garg V, Dhote PR, Nikam BR, Swain S, Al-Ansari N (2022) Revisiting 2013 Uttarakhand flash floods through hydrological evaluation of precipitation data sources and morphometric prioritization. Geomat Nat Haz Risk 13(1):646–666 Prakash S, Mitra AK, AghaKouchak A, Liu Z, Norouzi H, Pai DS (2018) A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region. J Hydrol 556:865–876 Prakash S, Mitra AK, Rajagopal EN, Pai DS (2016) Assessment of TRMM-based TMPA-3B42 and GSMaP precipitation products over India for the peak southwest monsoon season. Int J Climatol 36(4):1614–1631 Rahman MA, Yunsheng L, Sultana N (2017) Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman’s rho tests and ARIMA model. Meteorol Atmos Phys 129(4):409–424 Sahoo S, Swain S, Goswami A, Sharma R, Pateriya B (2021) Assessment of trends and multidecadal changes in groundwater level in parts of the Malwa region, Punjab, India. Groundwater Sustain Dev 14:100644 Sharma I, Mishra SK, Pandey A, Kumre SK, Swain S (2020) Determination and verification of antecedent soil moisture using soil conservation service curve number method under various land uses by employing the data of small indian experimental farms. In: Watershed management 2020. ASCE, pp 141–150 Swain S, Dayal D, Pandey A, Mishra SK (2019) Trend analysis of precipitation and temperature for Bilaspur District, Chhattisgarh, India. World environmental and water resources congress 2019: groundwater, sustainability, hydro-climate/climate change, and environmental engineering. American Society of Civil Engineers, Reston, VA, pp 193–204 Swain S, Mishra SK, Pandey A (2021a) A detailed assessment of meteorological drought characteristics using simplified rainfall index over Narmada River Basin, India. Environ Earth Sci 80:221 Swain S, Mishra SK, Pandey A (2021b) Assessing contributions of intensity-based rainfall classes to annual rainfall and wet days over Tehri Catchment, India. In: Advances in water resources and transportation engineering. Springer, Singapore pp 113–121

144

S. Swain et al.

Swain S, Mishra SK, Pandey A, Dayal D (2021c) Identification of meteorological extreme years over Central Division of Odisha using an index-based approach. In: Hydrological extremes. Springer, Cham, pp 161–174 Swain S, Mishra SK, Pandey A, Dayal D (2022a) Spatiotemporal assessment of precipitation variability, seasonality, and extreme characteristics over a Himalayan catchment. Theor Appl Climatol 147(1):817–833 Swain S, Mishra SK, Pandey A, Kalura P (2022b) Inclusion of groundwater and socio-economic factors for assessing comprehensive drought vulnerability over Narmada River Basin, India: a geospatial approach. Appl Water Sci 12(2):14 Swain S, Sahoo S, Taloor AK (2022c) Groundwater quality assessment using geospatial and statistical approaches over Faridabad and Gurgaon districts of National Capital Region India. Appl Water Sci 12(4):75 Swain S, Sahoo S, Taloor AK, Mishra SK, Pandey A (2022d) Exploring recent groundwater level changes using Innovative Trend Analysis (ITA) technique over three districts of Jharkhand, India. Groundwater Sustain Dev 18:100783. https://doi.org/10.1016/j.gsd.2022.100783 Swain S, Taloor AK, Dhal L, Sahoo S, Al-Ansari N (2022e) Impact of climate change on groundwater hydrology: a comprehensive review and current status of the Indian hydrogeology. Appl Water Sci 12(6):120. https://doi.org/10.1007/s13201-022-01652-0 Swain S, Patel P, Nandi S (2017a) A multiple linear regression model for precipitation forecasting over Cuttack district, Odisha, India. In: 2017a 2nd International conference for convergence in technology (I2CT). IEEE pp 355–357 Swain S, Patel P Nandi S (2017b) Application of SPI, EDI and PNPI using MSWEP precipitation data over Marathwada, India. In 2017b IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 5505–5507 Swain S, Nandi S, Patel P (2018a) Development of an ARIMA model for monthly rainfall forecasting over Khordha district, Odisha, India. In: Recent findings in intelligent computing techniques. Springer, Singapore, pp 325–331 Swain S, Verma MK, Verma MK (2018b) Streamflow estimation using SWAT model over Seonath river basin, Chhattisgarh, India. In: Hydrologic modeling. Springer, Singapore, pp 659–665 Swain S, Mishra SK, Pandey A (2020a) Assessment of meteorological droughts over Hoshangabad district, India. In: IOP conference series: earth and environmental science, vol 491, 012012. IOP Publishing Swain S, Sharma I, Mishra SK, Pandey A, Amrit K, Nikam V (2020b) A Framework for managing irrigation water requirements under climatic uncertainties over Beed District, Maharashtra, India. In: World environmental and water resources congress 2020b: water resources planning and management and irrigation and drainage. ASCE, pp 1–8 Swain S (2017) Hydrological modeling through soil and water assessment toolin a climate change perspective a brief review. In: 2017 2nd International conference for convergence in technology (I2CT). IEEE, pp 358–361 Valipour M (2015) Long-term runoff study using SARIMA and ARIMA models in the United States. Meteorol Appl 22(3):592–598 Valipour M (2016) How much meteorological information is necessary to achieve reliable accuracy for rainfall estimations? Agriculture 6(4):53 Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441 Yadav B, Ch S, Mathur S, Adamowski J (2016) Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, Germany. Measurement 92:433–445 Yadav B, Gupta PK, Patidar N, Himanshu SK (2020) Ensemble modelling framework for groundwater level prediction in urban areas of India. Sci Total Environ 712:135539

Chapter 10

Study of Meteorological Drought Using Standardized Precipitation Index in Chaliyar River Basin, Southwest India Mohd Izharuddin Ansari, L. N. Thakural, Quamrul Hassan, and Mehtab Alam

10.1 Introduction Drought is weather related natural calamity. The popular explanation considers drought as considerable decline in water availability over a vast area for a long duration of time. In fact, drought is one of the most complicated and less explored amongst all natural disasters, having its negative influence over a large section of Human Population (Wilhite 2000). Because of slow development of a drought, people often are not aware of the emergence of droughts in time. Hence, more insights in the development of drought can help the people to be aware of drought at initial stages. Drought usually initiates with precipitation deficit, but affects ground water, soil moisture, streamflow, ecosystem and even human beings also, in further stages resulting in identification of numerous types such as Hydrological, meteorological, agricultural, socio-economic and physiological droughts etc. Each of these types reflects perspectives of various sectors on water shortages (Wilhite and Glantz 1985). Among all these kinds of droughts, only Meteorological drought has importance for our study, which is primarily induced by deficit in precipitation and may have large scale influence over various components of basin ecosystem.

M. I. Ansari (B) · Q. Hassan · M. Alam Jamia Millia Islamia, New Delhi 110025, India e-mail: [email protected] L. N. Thakural National Institute of Hydrology, Roorkee 247667, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_10

145

146

M. I. Ansari et al.

10.2 Study Area The Study Area is Chaliyar river basin, Kerala, India, that falls between 11° 30′ N and 11° 10′ N latitudes and 75° 50′ E and 76° 30′ E longitudes. Chaliyar is the 3rd largest river in Kerala. It originates from Elambalari Hills of Nilgiri District in Tamil Nadu, having an average elevation of about 2066 m above mean sea level (MSL). This river is an all season perennial stream, which covers a distance of around 169 km before joining Lakshadweep Sea at Beypore near Kozhikode. The approximate area drained by the river is 2918 km2 , out of which 2530 km2 lies in Kerala and the remaining area lies in the state of Tamil Nadu. The drainage Map for our study area is shown by Fig. 10.1. (Ansari et al. 2020).

10.3 Materials and Methods 10.3.1 Description of Data Used The data was collected from CWRDM, Kerala for ten rain gauge stations situated within the basin (Table 10.1). This information was provided by various agencies to the CWRDM. The duration of data provided is very small for few stations and

Fig. 10.1 Study area map (India River Week-Kerala 2016)

10 Study of Meteorological Drought Using Standardized Precipitation Index …

147

Table 10.1 Geographical description for rain gauge stations of Chaliyar basin S. No

Rain gauge station

Latitude

Longitude

Agency

1

Ambalavayal

11.61° N

76.23° E

IMD

2

Manjeri

11.12°N

76.13° E

IMD

3

Nilambur

11.28° N

76.23° E

IMD

4

Kalladi

11.43°N

76.15° E

WRD

5

Edakkara

11.36°N

76.32° E

WRD

6

Kalikavu

11.13°N

76.33° E

WRD

7

Nilambur High School

11.28°N

76.23° E

WRD

8

Pudupady

11.49°N

76.01° E

WRD

9

Repon Estate

11.53°N

76.18° E

WRD

10

Thamarassery

11.48°N

75.94° E

WRD

Source CWRDM: Information obtained from CWRDM, Kerala, India

infact large data series length is missing for many other stations. Hence, Monthly Rainfall Data for only 4 stations of Ambalavayal, Kalladi, Manjeri, and Nilambur for the duration of 1993–2012 was used.

10.3.2 Drought Characterization For characterization and statistical analysis of Drought, drought indices are commonly used. These indices transforms present situation in historical context and provide temporal-spatial representations for drought history in a region (Akhtar et al. 2021). Analysis of Drought with respect to stochastic view point provides information necessary for subsequent risk analysis i.e. probabilities of drought occurrence and drought impacts. To carry out measurement of meteorological dryness, many indices are used. Among them, popular indices are ‘simple rainfall deviation from historical norms’, ‘palmer drought severity index’ and ‘standardized precipitation index’ etc. (Kumar et al. 2011). Out of the above, SPI has become popular in recent years, as it is easy, versatile and efficient technique for analysing drought climatology (Hughes and Saunders 2002).

10.3.3 Standardized Precipitation Index (SPI) Standard Precipitation Index chiefly designed as a technique for Drought analysis, monitoring and description. This was introduced by T.B. Mckee, N.J. Doesken and J. Kleist, Colorado State University, 1993.

148

M. I. Ansari et al.

Table 10.2 SPI Significance on various timescales S. No SPI

Significance

1

1-month SPI

Indicates comparatively short-duration situations Applications are often connected to moisture in soil on short-term, particularly during growing season

2

3-months SPI

Indicates moisture conditions on small and medium term It provides seasonal estimate for precipitation and it is more relevant for defining the moisture content

3

6-months SPI

Signifies trends in precipitation on medium-term and could be much prominent in computing the precipitation trend over distinct seasons. Infact, information obtained through it, additionally began to be related with reservoir levels and stream flows

4

9-months SPI

Signifies Precipitation patterns on medium-term scale

5

12-months SPI Suggests patterns in precipitation on long term and this could also be co-related with reservoir and ground-water levels and stream flows etc., at longer time scales

Source National Drought Mitigation Centre, University of Nebraska-Lincoln: Explanation available on website for SPI Map Interpretation

This index shows much potential, as it is flexible, versatile, powerful and easy to estimate (McKee et al. 1993), infact only precipitation needed as input parameter. The probability of observed precipitation is converted to this index, known as SPI. The basis for developing this parameter is to enumerate deficit in precipitation on numerous timescales (Chahal et al. 2021). Using the rainfall records for long term, computation of this index for desired location and desired period can be carried out. For this analysis, the initial step is to fit long duration rainfall records to probability distribution and thereafter transformation to normal distribution takes place, which gives average SPI value for period and location as zero (Edwards and McKee 1997). + ve SPI values designate > median precipitation whereas − ve values designate < median precipitation. Generally, SPI computation is carried out on 3, 6, 12, 24 and 48 months, timescale. These timescales indicates Drought’s impact on various components of Hydrological cycle (Sappa et al. 2019).The significance of SPI on multiple time scale is given in Table 10.2.

10.3.4 SPI Computation Algorithm The development of algorithm for SPI computation is based on following rationale. The initial step for Computation of SPI is to find out the probability density function optimally describing precipitation-data distribution on various scales of time (Guttman 1998). This pattern is separately applied for each month. Using the Lmoments ratios diagrams, suitable distribution function was chosen (Hosking and

10 Study of Meteorological Drought Using Standardized Precipitation Index …

149

Wallis 1997). Thereafter, gamma probability density function with 2 parameters was used and the maximum likelihood approach was applied to quantify the pertinent parameters. Thereafter, variable scales of time i.e. 1 month, 3 months, 6 months, 12 months and 24 months are selected for index calculation, representing random scales of time for deficits in precipitation related to SPI application. Every Information set is applied to gamma probability density function having scale parameter β and shape parameter α, so as to define relation between precipitation and probability. Gamma-cumulative-distribution-function get transformed in to standardize normalcumulative-distribution-function having equal-probability transformation, accompanying a mean and standard deviation of zero and unity respectively. The above standardization offers the benefit of possessing persistent values in space and time for cycle of extremely dry and wet event (Karavitis et al. 2011). To be very explicit, the computation of SPI can be performed by fitting probability density function in to precipitation frequency distribution summed for desirable scale of time which carried out exclusively for different months and locations of space. Thereafter, transformation of every probability density function to standardized normal distribution takes place i.e. for calculation of SPI, on every time scale, precipitation totals variability, initially defined as gamma distribution, later on transformed in to normal distribution (Abeysingha nd Rajapaksha 2020). The gamma distribution defined by its probability density function is as follows (Mishra and Desai 2005): g(x) =

1 β α Γ(α)

x α−1 e−x/β

(10.1)

where, α(shape parameter) > zero, β (scale parameter) > zero, x(precipitation amount) > zero. Γ (α) (gamma function)is denoted as: ∞ Γ(α) =

y α−1 e−y dy

(10.2)

0

For fitting, distribution to data, α and β should be known. A method was recommended by Edwards and McKee (1997) for estimation of above parameters using the theory provided by Thom (1958) regarding optimum likelihood: ( ) / 4A 1 α= 1+ 1+ 4A 3 β= A = ln(x) −

x a Σ

(10.3)

(10.4) ln(x) n

(10.5)

150

M. I. Ansari et al.

n number of observation Resultant parameters will be utilized for computing cumulative-probability for an observed precipitation event of given month or some alternative scale of time (Mishra and Desai 2005). x G(x) =

g(x)d x

(10.6)

0

Since gamma function is not defined for x = zero and precipitation distribution could consist of zeros, cumulative probability H(x) is calculated from the given equation (Abeysingha and Rajapaksha 2020): H(x) = q + (1 − q) G(x)

(10.7)

Here q = probability of zero precipitation and G(x) = cumulative probability of the incomplete gamma function. H(x), cumulative probability, thereafter, transformed to standard normal random variable Z (accompanying mean as 0 and variance as 1), that finally will give the value of SPI (Mishra and Desai 2005). Above mentioned algorithm, although simple, is not practically suitable for SPI computation in case of large number of Data points. Hence following Edwards and McKee (1997), Hughes and Saunders (2002), other methods for approximate conversions are in practice. In this paper, we will use a program for the calculation of SPI on different time scales and the methodology followed for carrying out research work is shown in Fig. 10.2 with the help of a flowchart. In the present paper, monthly precipitation data for 4 rain gauge stations located within Chaliyar basin were utilized to compute SPI values at timescales of 3, 6 and 12 months for the period 1993–2012.

10.4 Results and Discussion The standardized precipitation index exhibits a statistical Z-score or number of standard deviations (Edwards and McKee 1997). It is used in our analysis to estimate monthly precipitation deficit anomalies over different scales of time. In present paper, its estimation on numerous scales of time i.e. 3, 6 and 12 months have been carried out. Agricultural and Meteorological droughts impacting precipitation and soil moisture respectively, are generally related to short term time scales of 3 and 6 months SPIs whereas long term time scale i.e. 12 month SPI or more are related with hydrological droughts impacting stream flow and reservoir levels (Khan et al. 2018). If we plot variation of time series of the years against SPI for any station, it provides fair reflection of drought history for that station. Accordingly time series

10 Study of Meteorological Drought Using Standardized Precipitation Index … Fig. 10.2 Methodology of drought assessment

151

Convert Precipitation as Monthly Values

Calculation of SPI Values on various time scales for various stations

Results of SPI

Time series plots of SPIs

Analysis & Assessment of Drought from SPI time series Plots

plots of SPI on time scale of 3, 6 and 12 months for Ambalavayal, Kalladi, Manjeri and Nilambur are given as Figs. 10.3, 10.4, 10.5 and 10.6 respectively. Estimated SPI values (Z-score) distribute precipitation events for specified time duration amongst excess (Heavy precipitation), normal and low (deficits precipitation). Z score value i.e. Z > 2.0 shows extremely wet events/weather over the particular scale of time. The SPI value between 1.5 and 1.99 indicate the very wet event and the moderately wet event is represented by values between 1.0 and 1.49. SPI value lying between 0.99 > z > -0.99 denotes normal precipitation event. Moreover, − 1 > z > − 1.49 indicate moderately drought events. If Z score value falls between − 1.99 < z < − 1.5, it indicates severe drought condition and when Z score goes below − 2, it indicates extreme drought condition. After plotting the above SPIs time series curves on multiple timescales for different stations within the basin, we perform the Drought Characterization in Microsoft Excel for the same stations based on the SPI values interval. Accordingly, multiple Moderate, Severe and Extreme Drought events are reported for all the four stations on different timescales. The intensity of Drought events decreases from Moderate to Extreme Drought i.e. higher numbers of Moderate Drought events are reported as compare to severe and extreme. Although, the intensity of Extreme Drought events is least, but still those can’t be neglected, as those are the events of serious concern. Noticeable Severe Drought events are also reported.

M. I. Ansari et al.

4 3 2 1 0 -1 -2 -3

3 MONTHS

1993 1993 1994 1995 1996 1997 1998 1998 1999 2000 2001 2002 2003 2003 2004 2005 2006 2007 2008 2008 2009 2010 2011 2012

SPI

152

YEARS

3 2 1 0 -1 -2 -3

6 MONTHS

1993 1993 1994 1995 1996 1997 1998 1998 1999 2000 2001 2002 2003 2003 2004 2005 2006 2007 2008 2008 2009 2010 2011 2012

SPI

(a) 3 Months Timescale

YEARS

(b) 6 Months Timescale

3 2 1 0 -1 -2 -3 1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011

SPI

12 MONTHS

YEARS

12 Months Timescale Fig. 10.3 SPIs time series on timescale of 3, 6 and 12 months for Manjeri

The Drought Characterization for Nilambur, Kalladi, Manjeri and Ambalavayal for the time duration of 20 years (1993–2012) is shown below with the help of Tables 10.3, 10.4, 10.5 and 10.6 respectively.

153

3 MONTHS

4 3 2 1 0 -1 -2 -3

1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011 2012

SPI

10 Study of Meteorological Drought Using Standardized Precipitation Index …

YEARS

3 Months Timescale 6 MONTHS

4 SPI

2 0 -2 1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011 2012

-4

YEARS

6 Months Timescale 12 MONTHS

3 2 SPI

1 0 -1 -2 1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011

-3

YEARS

12 Months Timescale

Fig. 10.4 SPIs time series on timescale of 3, 6 and 12 months for Nilambur

10.5 Conclusion Drought analysis was performed using Standardized Precipitation Index (SPI) on both long-term as well as short-term time scales at all the four rain gauge stations (Ambalavayal, Kalladi, Manjeri and Nilambur) of Chaliyar river basin. Analysis of time series plots for all stations on different time scales shows fluctuations representing wide range of weather events ranging from extremely drought to extremely wet events. Drought Characterization at all the four stations reveals history of Moderate, Severe as well as Extreme Drought Events. Periodical re-occurrence of drought and wet periods was also observed.

154

M. I. Ansari et al.

1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011

SPI

3 MONTHS 4 3 2 1 0 -1 -2 -3

YEARS

3 Months Timescale

1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011

SPI

6 MONTHS 3 2 1 0 -1 -2 -3

YEARS

3 2 1 0 -1 -2 -3

12 MONTHS

1993 1993 1994 1995 1995 1996 1997 1997 1998 1999 1999 2000 2001 2001 2002 2003 2003 2004 2005 2005 2006 2007 2007 2008 2009 2009 2010 2011

SPI

6 Months Timescale

YEARS

12 Months Timescale Fig. 10.5 SPIs time series on timescale of 3, 6 and 12 months for Ambalavayal

In today’s world of Climate change, these repeated Drought events are of grave concern. Sustainable Development Goal 15 of the 2030 Agenda talks about Desertification, Land Degradation and Drought, however, recent COVID 19 Pandemic has aggravated the situation; especially poor’s are worse affected. Although, more scope of research is still there, but it can be concluded that multi scope Standardized Precipitation Index has efficiently characterized the Drought in the Chaliyar basin as all the four rain gauge stations were found to affected by meteorological drought in near history, when analyzed using SPI. This study may also be utilized for preparation of drought mitigation plan by water resources development planners, scientists and engineers ensuring sustainable water resource planning and drought management for the basin.

10 Study of Meteorological Drought Using Standardized Precipitation Index …

155

3 MONTHS

4 SPI

2 0 -2 1993 1993 1994 1995 1996 1996 1997 1998 1999 1999 2000 2001 2002 2002 2003 2004 2005 2005 2006 2007 2008 2008 2009 2010 2011 2011

-4

YEARS

(a) 3 Months Timescale 6 MONTHS

4 SPI

2 0 -2 1993 1994 1994 1995 1996 1996 1997 1998 1998 1999 2000 2000 2001 2002 2002 2003 2004 2004 2005 2006 2006 2007 2008 2008 2009 2010 2010 2011

-4

YEARS

(b) 6 Months Timescale

1993 1994 1995 1995 1996 1996 1997 1998 1998 1999 1999 2000 2000 2001 2002 2002 2003 2003 2004 2005 2005 2006 2006 2007 2007 2008 2009 2009 2010 2010 2011

SPI

12 MONTHS 3 2 1 0 -1 -2 -3

YEARS

(c) 12 Months Timescale Fig. 10.6 SPIs time series on timescale of 3, 6 and 12 months for Kalladi Table 10.3 Drought characterization at Nilambur (1993–2012) S. No.

Timescale

Moderate drought events

Severe drought events

Extreme drought events

1

3 month

20

7

3

2

6 month

13

6

7

3

12 month

10

9

11

156

M. I. Ansari et al.

Table 10.4 Drought characterization at Kalladi (1993–2011) S. No Timescale Moderate drought events Severe drought events Extreme drought events 1

3 month

22

8

4

2

6 month

31

10

5

3

12 month

17

21

1

Table 10.5 Drought characterization at Manjeri (1993–2012) S. No Timescale Moderate drought events Severe drought events Extreme drought events 1

3 month

20

5

5

2

6 month

18

11

3

3

12 month

23

8

0

Table 10.6 Drought characterization at Ambalavayal (1993–2011) S. No Timescale Moderate drought events Severe drought events Extreme drought events 1

3 month

32

6

2

2

6 month

22

13

0

3

12 month

26

7

1

Last but not the least, it is suggested that, this Natural Hazard needs to be explored furthermore. More awareness amongst the society is still needed and more participation of stakeholders should be promoted to address this grave issue.

References Abeysingha NS, Rajapaksha URLN (2020) SPI-based spatiotemporal drought over Sri Lanka. Adv Meteorol 2020:10 Akhtar MP, Faroque FA, Roy LB, Rizwanullah M, Didwania M (2021) Computational analysis for rainfall characterization and drought vulnerability in Peninsular India. Math Probl Eng 2021:1–27 Ansari, MI, Thakural LN, AnbuKumar S (2020) Streamflow modelling for a peninsular Basin in India. In Ahmed S et al (eds) Smart cities-opportunities and challenges. Lecture notes in civil engineering, vol 58, pp 645–659 Chahal M, Singh O, Bhardwaj P, Ganapuram S (2021) Exploring spatial and temporal drought over the semi-arid Sahibi river basin in Rajasthan, India. Environ Monit Assess 193:743 CWRDM: Centre for Water Resources Development and Management, Kozhikode, Kerala, India Edwards DC, Mckee TB (1997) Characteristics of 20th century drought in the United States at multiple time scales. Atmos Sci Pap 634:1–30 Guttman NB (1998) Comparing the palmer drought index and standardized precipitation index. J Am Water Resour Assoc 34(1):113–121 Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-moments. Cambridge University Press, Cambridge, UK Hughes BL, Saunders MA (2002) A drought climatology for Europe. Int J Climatol 22:1571–1592

10 Study of Meteorological Drought Using Standardized Precipitation Index …

157

Karavitis CA, Alexandris S, Tsesmelis DE, Athanasopoulos G (2011) Application of the standardized precipitation index (SPI) in Greece. Water 3:787–805 Khan MMH, Muhammad NS, Shafie AE (2018) A review of fundamental drought concepts, impacts and analyses of indices in Asian continent. J Urban Environ Eng 12:106–119 Kumar MN, Murthy CS, Seshasai MVR, Roy PS (2011) Spatiotemporal analysis of meteorological drought variability in the Indian region using standardized precipitation index. Meteorol Appl 19:256–264 Latha A, Vasudevan M. (2016) Kerala report: State of India’s Rivers for India River Week-Kerala Mckee TB, Doesken NJ, Kliest J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, 17–22 January, Anaheim, CA. American Meteorological Society, Boston, MA, USA, pp 179–184 Mishra AK, Desai VR (2005) Spatial and temporal drought analysis in the Kansabati river basin, India. Int J River Basin Manag 3(1):31–41 Sappa G, Filippi FMD, Lacurto S, Grelle G (2019) Evaluation of minimum Karst spring discharge using a simple rainfall-input model: the case study of Capodacqua di Spigno Spring (Central Italy). Water 11:807 SPI Map Interpretation, National Drought Mitigation Center, University of Nebraska-Lincoln. Available from https://drought.unl.edu/droughtmonitoring/SPI/MapInterpretation.aspx Thom HCS (1958) A note on the gamma distribution. Mon Weather Rev 86:117–122 Wilhite DA (2000) Chapter 1: drought as a natural hazard: concepts and definitions. In: Wilhite DA (ed) Drought: a global assessment. Routledge, pp 3–18 Wilhite DA, Glantz MH (1985) Understanding the drought phenomenon: the role of definitions. WaterInternational 10:111–120

Chapter 11

Estimation of the Function of a Paddy Field for Reduction of Flood Risk Nobumasa Hatcho, Keigo Yamasaki, Okumura Hirofumi, Masaomi Kimura, and Yutaka Matsuno

11.1 Introduction Japan is now facing a decline in agricultural land due to the economic development of other sectors and the aging of farmers. To deal with these issues, the Japanese government is promoting the enlargement of paddy plot areas in order to use larger machinery to increase the efficiency of farming to make agriculture more cost effective and profitable. The government encourages farmers to practice smart agriculture since there are some advanced technologies currently available for application in Japan (Yamamoto et al. 2022). The other issue is that due to climate change, there is an increased flooding risk and, as a result, possible economic losses. To deal with this risk, constructing flood control dams and river infrastructural improvements have been considered. It is, however, financially difficult and the growing interest in environmental issues prevent carrying out such measures. Therefore, we are looking for alternatives that would be both cost effective and environmentally friendly. Rice farming is the major farming practice in Japan and paddy field rice is cultivated with ponded water by bunding the fields. As such, paddy fields are known to have a function of temporarily storing rainwater and can be a countermeasure for regulating flood damage. Matsuno et al. (2006) and Kim et al. (2006) reviewed the multifunctionality of paddy fields, including flood control, and Masumoto et al. (2006) proposed an index for evaluating the flood prevention function of paddy fields. Studies are being conducted to analyze the effectiveness of utilizing paddy fields for

N. Hatcho · M. Kimura · Y. Matsuno (B) Faculty of Agriculture, Kindai University, 3327-204, Nakamachi, Nara 631-8505, Japan e-mail: [email protected] K. Yamasaki · O. Hirofumi Rural Promotion Division, Food and Agricultural Promotion Department, Nara Prefecture, 30 Noboriojicho, Nara 630-8501, Japan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 B. Yadav et al. (eds.), Sustainability of Water Resources, Water Science and Technology Library 116, https://doi.org/10.1007/978-3-031-13467-8_11

159

160

N. Hatcho et al.

flood regulating purposes and to identify measures to enhance such functions (JIID 2008; MAFF Japan 2014; Kobayashi et al. 2021). For optimizing the water regulating function of paddy fields, the outflow from a paddy field and its outflow can be regulated by an orifice outlet plate or outlet regulating device, referred to as Paddy Field Dams (PFD) as shown in Fig. 11.1. PFD can cut the peak discharge and prolong the duration of the discharge as shown. The idea is to install two plates in a box type drainage chamber. The plate in front is shorter than the other plate behind and maintains the water ponding depth for the normal operation of rice cultivation. The plate placed in the back, which is taller, has a 5 cm diameter orifice at the bottom, where the overflowing water goes through the orifice and then through the culvert to the drainage canal (Figs. 11.2 and 11.3). The advantage of this system is that farmers can save management time for operation and maintenance of PFD because the flow is automatically regulated once it is installed. Several hydrological studies on analyzing the runoff from paddy fields and agricultural areas have been conducted and many of them have used a tank model or its modified version (Hayase 1994; Masumoto et al. 2003; Chen and Yang 2011; Segawa et al. 2016). Chinh et al. (2013) used an HEC-HMS and GIS for simulating runoff and pollutant load in a large basin in Japan. The functions of PFD have been analyzed by

Fig. 11.1 Illustration of a Paddy Field Dam (PFD) and its structure

Without Orifice

With Orifice

Bund

Bund

Orifice Drainage chamber

Drainage chamber

Drainage canal

Fig. 11.2 Illustration of drainage from paddy field

Drainage canal

11 Estimation of the Function of a Paddy Field for Reduction …

161

Fig. 11.3 Outflow from paddy fields to drainage canal

Yoshikawa (2009), Yoshikawa et al. (2010) in paddy field areas in Niigata Prefecture, utilizing a Kinematic Wave model for mountain and urban areas, and a water balance simulation for paddy fields for runoff analysis, and confirmed the effectiveness of PFD for regulating peak discharge. However, the application of HEC-HMS in a basin level with mixed land uses has been limited. To understand the overall impacts of PFD on regulating flood to downstream areas, more cases of basin scale analysis under mixed land use patterns are required. This study was carried out in Nara Prefecture, Japan, which is considered a suburb or Osaka, the second biggest city next to Tokyo. Nara has a greater risk of flooding owing to its large residential and commercial areas and because its rivers are relatively short and narrow. The objectives of this study were to identify outflow characteristics of PFD at a field level and to analyze the flood regulation effects of PFD at a small basin scale in Nara Prefecture and examine the flood regulating effects of PDF.

11.2 Method and Materials 11.2.1 Field Scale Study The study site was set in the northwestern part of Nara (Fig. 11.4), which has a mosaic landscape of semi urban area with scattered paddy fields, forest, commercial, and residential areas. The paddy field for the study is in the Twaramoto City of Nara Prefecture and its water level and rainfall data were monitored during the irrigation season of 2017. The paddy water balance was estimated in cases of with and without PDF device. The water balance of the paddy plot was estimated as a component of irrigation water and rainfall as the input to the paddy plot, and percolation, overflow from the ridge and outflow from PDF as the output variables. Outflows from the paddy plot were estimated in both cases of when the orifice was placed in the box chamber and when the orifice was not present.

162

N. Hatcho et al.

Fig. 11.4 Study location in Japan

For the case of no orifice placement, the square weir overflow formula used for the estimation was: Q dH =− +R+I −P−O dt A

(11.1)

where H is ponding depth (m), t is time(s), Q is outflow from paddy plot (m3 s−1 ), A is paddy plot area (m2 ), R is rainfall (m), I is inflow to the paddy plot (m3 s−1 ), P is percolation (m), and O is overflow from the paddy bund (m). Evapotranspiration was not accounted by assuming that it was negligible due to short durations of rainfall events. The water balance of a paddy field is illustrated in Fig. 11.5. H: ponding depth (m), t: time(s), Q: outflow from paddy plot (m3 s−1 ), R: rainfall (m), I: inflow to the paddy plot (m3 s−1 ), P: percolation (m), and O: overflow from the paddy ridge (m). The outflow (Q) in a case without an orifice was estimated by the flowing Francis’s formula: 3

Q = EbH 2

(11.2)

where Q is the outflow (m2 /s), E is coefficient for square weir, b is width of weir (m), and H is ponding depth (m). When the orifice is placed in the drainage chamber, the flow through the orifice is given by the following equations, which was solved by the Simpson’s rule, a numerical method of approximations for definite integrals. When the orifice is under water, the outflow (Q) can be estimated by:

11 Estimation of the Function of a Paddy Field for Reduction …

163

Fig. 11.5 Schematic of water balance in a paddy field

√ Q = Ca 2g H

(11.3)

where C is coefficient, a is orifice cross sectional area (m2 ), and H is height of water in the drainage chamber (m). When the orifice is partially under water, the outflow can be estimated by:  Q=C

√ b 2g(H − y)dy

(11.4)

where b, y, and dy are given by: b = 2r sin θ

(11.5)

y = r − r cos θ

(11.6)

dy = r sin θ dθ

(11.7)

where y, b, r, and θ are dimensions of featuring the orifice given in Fig. 11.6. The Simpson’s rule is expressed by: b

b f (x)d x ≈

a

P(x)d x =

[ ( ) ] a+b b−a f (a) + 4 f + f (b) 6 2

(11.8)

a

where the subdivision of the integration range is given by the length of points between a and b.Using these equations, drainage outflow volumes were estimated in the cases of with and without paddy field dams.

164

N. Hatcho et al.

Fig. 11.6 Dimension of the orifice (Yoshikawa et al. 2010) and drainage chamber

11.2.2 Basin Scale Modeling For the basin scale study, the study area selected was Tawaramoto-cho, Nara Prefecture. It is a small basin as part of the Yamato River Basin where inundation damage has been reported in several occasions. The basin has an area of 4836 ha, of which the eastern part is covered with forest, and the western part is mainly used for paddy fields and commercial buildings. Land use of the study area and the share of each land use are shown in Fig. 11.7. The forest area occupies 45% of the basin area followed by 26% of paddy area, 14% of residential area and 14% of upland fields. Small tributaries of the Yamato River run through the study area, including Nagataki, Fujii, Furu rivers, etc., which merge with the Yamato River at the Oji water level gauging station, located at 135 47′ 43′′ E longitude and 34 35′ 12′′ N latitude. The basin area has an average annual rainfall of 1316 mm, of which 48% occurs during June to September. Extreme rainfall events often occur during the typhoon season between July and October.

11.2.3 Data Obtained for Simulation The following data were used in this study: (a) digital land use map covering the study area obtained from the National Land Information of the Ministry of Land Infrastructure, and Transportation of Japan (MLIT); (b) hourly rainfall of the five gauging stations (Tenri, Hatasho, Obu, Kashihara, and Kasa Cities) for typhoon rainfalls of August 2014 and October 2017, which were obtained from the Nara prefectural government; (c) hourly discharge data at Oji gauging and Toyoda stations for target dates, from Nara prefectural government, (d) Digital Elevation Model (DEM:10 m mesh by Geospatial Authority, MLIT); and (e) river map (National Land Information) by MLIT.

11 Estimation of the Function of a Paddy Field for Reduction …

165

Fig. 11.7 River systems (a) and land use (b) of study area (National Land Information, Ministry of Land Infrastructure, and Transportation of Japan: MLIT)

Simulation models, such as SWAT, KIMSTORM, MIKE, or HEC-HMS are readily available and with the combination of a GIS system they have been widely applied for basin level analyses in different countries under different soil and climatic conditions (EPA 2011, Jung et al. 2011, Majidi and Shahedi 2012, Choudhari et al. 2014, Nishio and Mori 2015). In particular, Verma et al. (2000) applied HEC-HMS

166

N. Hatcho et al.

GIS Mapping Data preparation for HEC-HMS Simulation Model Calibration and Validation Run Simulation with/without PFD Analyze Effect of PFD Fig. 11.8 Flow of basin level analysis

for simulating a watershed in India and concluded that HEC-HMS could simulate daily streamflow to an acceptable level and recommended the use of the HEC-HMS model for hydrological modeling. HEC-HMS provides various options for calculating runoff, including Clark’s UH, Kinematic wave, SCS UH or user specified UH, etc. To incorporate the effect of PFD, the user specified UH method was used. The Thiessen method was applied to calculate the rainfall over the study area from the rainfall data from five gauging stations located in the region. Total average rainfalls of five stations for two events in 2014 and 2017 were 217.4 mm and 249.2 mm for 9–10 August 2014 and 21–22 October 2017, respectively. Basin database of the study area is established by using “ArcGIS (Arc Map 10.2.2)”. Based on the basin database, flow analysis was conducted by using terrain processing and basing processing modules of HECGeoHMS10.2 and HEC-HMS4.2 of the U.S. Army Corps of Engineers. The schematic flow of the model processing is shown in Fig. 11.8. In the basin model of this study, “Initial loss and constant loss method” was used to calculate direct surface runoff, “Clark Unit Hydrograph Transformation” for runoff volume conversion, and “Recession method” for base flow separation. “Kinematic-wave method” was used for river model and “Constant method” for river loss model. Loss parameters (Table 11.1) used in the model to convert the rainfall into runoff were calibrated taking reference from similar studies in the previous two years from a different basin in Nara (Hatcho and Matsuno 2016). Flow discharge point of the basin is set at Oji water level gauging station of MLIT. Based on the DEM of the target area, the target basin is subdivided into sub-basins so that each sub-basin can represent specific land uses as shown in Fig. 11.9. Sub-basins with paddy fields as the major land use (more than 60% of the land use) were used as the sites for PFD and calculation of the effects of PFD on runoff from the target basin. Total number of paddy field sub-basin was 73 with a total area of 1070 ha. Model calibration/validation and performance evaluation. HEC-HMS basin model was calibrated using hourly rainfalls of the typhoon and river discharge in August 2014 to make a good match of simulated volumes, peak

11 Estimation of the Function of a Paddy Field for Reduction …

167

Fig. 11.9 Target area with sub-basins

Table 11.1 Loss parameters of the model Laud use

Impervious (%)

Initial loss (mm)

Constant rate (mm/h)

Paddy field

10

15

3

O. Agric. field

10

20

20

Forest

20

30

10

Waste land

10

15

5

Building/house

80

2

3

Road/railroad

100

2

0

Vacant land

10

10

30

Water bodies

100

10

0

10

30

30

Golf field

discharge and timing of hydrographs with the observed ones. Several simulation runs were conducted to obtain the best match between the simulated and observed runoffs. Nash–Sutcliffe efficiency (NSE), percent deviation (Dv) and percent bias (PBIAS) indices were used to evaluate the performance of the constructed model (D. N. Moriasi et al. 2007; ASCE 1993). The criteria for NSE and PBIAS are shown in Table 11.2. NSE (Nash–Sutcliffe Efficiency) is given by: )2 Σn ( obs − Q isim i=1 Q i ) NSE = 1 − Σn ( obs − Q imean i=1 Q i PBIAS (Percent Bias) is given by:

(11.9)

168

N. Hatcho et al.

Table 11.2 Evaluation criteria for Nash-Sutcliffe efficiency (NSE) and percent bias (PBIAS) (D. N. Moriasi et al. 2007; ASCE 1993) NSE

PBIAS

Very good

NSE^0.75

PBIAS