hydrological modelling - yash

Dr. H. L. Tiwari, Dr. S. Suresh,

Er. R. K. Jaiswal

Maulana Azad National Institute of TechnologyBhopal 462 051, Madhya Pradesh, India

Editors

HYDRAULICS, WATER RESOURCES,COASTAL AND ENVIRONMENTALENGINEERING

HYDRAULICS, WATER RESOURCES, COASTAL AND ENVIRONM

ENTAL ENGINEERING

HYDRAULICS, WATER RESOURCES,

COASTAL AND ENVIRONMENTAL

ENGINEERING

Editors

Dr. H. L. Tiwari

Dr. S. Suresh

Er. R. K. Jaiswal

Maulana Azad National Institute of Technology Bhopal (M. P) India- 462003

First Impression : 2014

© Maulana Azad National Institute of Technology Bhopal

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or

transmitted in any form or by any means, electronic, mechanical, photocopying, recording or

otherwise, without the prior permission of the Editors.

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violation of copyright law and is subject to legal action.

ISBN: 978–93 –84935–04–7

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The authors are solely responsible for the contents of the papers compiled in this volume. The

publishers or editors do not take any responsibility for the same in any manner. Errors, if any, are

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Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) i

Preface

In the process of development, quality and quantity of the resources are generally depleted

day by day unless they are replenished by natural or artificial process. Water resource

which is an important resource to sustain the life on earth is under tremendous pressure all

over the world due to climate change, population growth and socioeconomic development.

Hence effective management of water resources with use of latest available technologies

and scientific research have become very crucial for water resources planners and

engineers. Aiming with this HYDRO 2104 INTERNATIONAL CONFERENCE on Hydraulics,

Water Resources, Coastal and Environmental Engineering jointly organized by MANIT

Bhopal and ISH in association with NIH Roorkee, IIT Bombay, VNIT Nagpur, SVNIT

Surat, People’s University Bhopal during December 18-20,2014. HYDRO conference is

organized every year by ISH in association with Institutions/organizations.

We have received overwhelming response from researchers, academicians, scholars, water

resource managers across the globe and received two hundred ninety papers for the

conference. One hundred twenty papers have selected for the publication of the book. This

book contains one hundred twenty chapters covering in twenty five themes which includes

Advance in Fluid Mechanics, Application of Geospatial Techniques, Costal, Harbour and

Ocean Engineering, Computational Fluid Dynamics, Decision Support System, Drought

Assessment and Mitigation, Effect of Climate Change on Water Resources, Environmental

Hydraulics, Environmental Impact Assessment, Flood Forecasting and Protection

Measures, Fluvial Hydraulics, Ground Water Modelling and Management, Hydel Energy,

Hydrological Modelling and forecasting, Hydraulics of Spillway and Energy Dissipaters,

Hydraulic Structures, Integrated Watershed Management, Rehabilitation of Dam,

Reservoir Operation and Irrigation Management, Reservoir Sedimentation, Risk Reliability

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) ii

Analysis and Design, Soft Computing Techniques, Water and Wastewater Management,

Water Quality Assessment and Modelling, Water Resource and Hydrology.

We wish to take this opportunity to express my sincere appreciation to all contributors,

who have helped on bringing out this for dissemination of knowledge to the society,

organisations, planners, researchers and managers. We are thankful to the Dr. Appukuttan

K. K., Director, MANIT Bhopal for his constant encouragement and guidance to bring out

this book. We place our sincere gratitude to efforts of ISH office bearers who have helped

to complete the book. We are grateful to Dr. A. K. Sharma, Prof. and Head, Civil

Engineering Deptt., MANIT for constant support and help to publish this book. We are

grateful to all the authors who contributed for this book. We are also thankful to all those

who have helped directly or indirectly in this regard.

With warm regards

Dr. H. L. Tiwari

Dr. S. Suresh

Er. R. K. Jaiswal

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) iii

Contents

Preface i

Chapter 1. Numerical Analysis of Centrifugal Pump Performance with Varrying Number

of Blades 1 V. K.Gahlot, H. L. Tiwari , Tarun Kumar Sharma

Chapter 2. Surge Protection Design for Water Conveyance System for the Case of Power

Failure To Pumps in Lift Irrigation Scheme Using SAP2 8 Ruben Nerella , E.Venkata Rathnam , P. Raghuveer Rao

Chapter 3. Hydrodynamic Studies on Liquid - Liquid Two Phase Flow 21

Through A Horizontal Pipe R.B. Katiyar, Shashank Tiwari, Piyush Pratap Singh, Sanjay Singh, Shakti Nath Das, S. Suresh

Chapter 4. Flood forecasting Using Soft Computing: A Case Study 28 A. K. Lohani, A. K. Kar, R. K. Jaiswal, R. D. Singh

Chapter 5. Flood Plain Mapping of Shivnath River By Using Gis and HEC RAS 42

Sanjeev Kumar Bhraria, Ishtiyaq Ahmad , M. K. Verma

Chapter 6. Accessing Carrying Capacity of River Reach Using HEC RAS 52 D. J. Mehta, S. M. Yadav, S. I. Waikhom

Chapter 7. Flood Disasters, River Training & Flood Control Measures in River Ganga 61

and Its Two Tributaries S.K. Mazumder

Chapter 8. Comparative Evaluation of VPMM and Mike 11 Models for Estimating 70

Ungauged River Floods Ratnakar Swain , Bhabagrahi Sahoo

Chapter 9. Experimental and Numerical Studies on Aggradation for Alluvial Stream Bed 83 B. R. Andharia, P. L. Patel, V. L. Manekar , P. D. Porey

Chapter 10. Boundary Shear Stress Distribution Along the Converging Floodplain of A 96

Non Prismatic Compound Channel Flow B. Naik, Kishanjit K. Khatua,Shiba Shankar Satapathy

Chapter 11. Morphological Changes of River Kosi From Chatra to Nirmali 109 Sanjay A. Burele, Nayan Sharma, Z. Ahmad, I. D. Gupta

Chapter 12. Characterization of Turbulence in Mobile Boundary Channels 124 Dhvani Y. Patwa, P. L. Patel , P. V. Timbadiya

Chapter 13. Study of Flow Characteristics for Parshall Flume 139 Jalam Singh, S. K. Mittal, H. L.Tiwari

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) iv

Chapter 14. Study of Sediment Concentration Distribution in Vortex Settling Basin 149

Considering Three Dimensional Flow Mujib Ahmad Ansari , Mohd Athar

Chapter 15. Morpho Hydrodynamic Modelling of Kosi River 162 V. Parmar, R. Khosa, R. Maheswaran

Chapter 16. Turbulence Characteristics Over A Fluvial Channel Bed 171 Sudhanshu Dixit, P. L. Patel

Chapter 17. Flow Characteristics in A Vegetated Open Channel 185 S. K. Debbarm, S. K. Biswal

Chapter 18. Experimental Study on incipient Motion of Cohesive Sediment Mixture 196 Umesh K. Singh, Z. Ahmad, Ashish Kumar

Chapter 19. Scour and Deposition Around Submersible Hydraulic Structures, Case Studies 205 M. Athar, M Aamir

Chapter 20. Flow Resistance in Alluvial Channel 217 Shri Ram

Chapter 21. Explicit Equation for Sediment Settling Velocity 226 Manish Kumar, Shri Ram

Chapter 22. Evaluation of Existing Equations for Maximum Scour Depth Near Spur Dikes 236 Manish Pandey, Z. Ahmad, P. K. Sharma

Chapter 23. Assessment of Groundwater Level in Southwest Punjab, India 248 Gopal Krishan, M.S.Rao, A. K. Lohani, C.P. Kumar,

K.S. Takshi, N.K. Tuli, R. S. Loyal, G. S. Gill

Chapter 24. Capture Zone Delineation of An Unconfined Well Field Using Analytic 255

Element Method and Reverse Particle Tracking Technique Partha Majumder, T. I. Eldho

Chapter 25. Estimation of Groundwater Recharge Due To Monsoon Rains in Parts 265

of Narsinghpur (M.P.), India Using Isotopic Technique S. K. Verma

Chapter 26. Experimental Investigation of Solute Transport Through Fractured 274

Porous Rock P. K. Sharma, Suman Pran Sonowal

Chapter 27. Groundwater Storage Analysis in Changing Land Use / Land Cover for Four 283

Districts of Upper Ganga Canal Command (1972 2011) Nitin Mishra, Deepak Khare, S. Kumar, Rituraj Shukla

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) v

Chapter 28. Groundwater Flow Simulation in Confined Aquifers Using Meshfree Radial 295

Point Collocation Method (RPCM) L. Guneshwor S., T. I. Eldho, A. Vinod Kumar

Chapter 29. A Review on the Design Efficient Blade of Hydrokinetic Turbines 305 Dinesh Kumar, Shibayan Sarkar

Chapter 30. Model Studies on Oscillating Water Column Based Simple Wave Energy Buoy 316 Jincy Rose, M. A, B.V. Mudgal, Prasad Dudhgaonkar

Chapter 31. Multiscale Analysis of Winter Temperature Datasets From Southern India 327

Using the Hilbert Huang Transform S. Adarsh, M. Janga Reddy

Chapter 32. On Utility of CFS forecasts for Long Lead Time Stream Flow 337

forecasting in Mahanadi Basin Alok Pandey, V. V. Srinivas, Ravi S. Nanjundiah

Chapter 33. Use of Artificial intelligence for Sediment Rating and Gauge Discharge Curve 347 Sanjay A. Burele, Nayan Sharma, Z. Ahmad, I. D. Gupta

Chapter 34. Event Based Stream Flow Estimation and Validation Using Semi Distributed 362

Hydrological Model in Netravati River Basin, Karnataka State, India B. P. Ganasri, Neeraj Rajasekar, Tasneem Ashraf , Suruchi Sah, Raju A., Dwarakish G. S.,

Chapter 35. Assessing Swat for Discharge and Sediment Yield Estimation From Satluj 373

Basin in Indian Himalayas J. Tyagi, Sanjay K. Jain

Chapter 36. Effect of Presence of Cohesive Wash Load on Flow Resistance 388 Nitin K. Samaiya, N. K. Khullar

Chapter 37. Rain fall Runoff Model Development Under Regulated River Flow Condition 399 R.V. Galkate, R. K. Jasiwal, T. Thomas, T. R. Nayak

Chapter 38. Calibration and Validation of Hydrologic Model for Yerli Sub Catchment 413

(Maharashtra, india) V. D. Loliyana, P. L. Patel

Chapter 39. Numerical Flow Simulation Through a Breastwall Spillway – 430

An Application of CFD Software “Flow 3D” Kulhare, A. Gadge, P.P. Bhajantri, M. R. Bhosekar V.V.

Chapter 40. Experimental investigations for Hydraulic Design of Kotlibhel Dam Spillway 441

Stage 1A, uttarakhand A case study Sangeeta Patnaik, B.S. Sunderlal, B.M. Simpiger,Vaishali Gadhe, V.V. Bhosekar

Chapter 41. Simulation of Simple Cylindrical Flume in Trapezoidal Channel 453 Avinash M.Badar, Valsson Varghese, Aniruddha D. Ghare

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) vi

Chapter 42. Effect of Clear Spacing and Width (Flatness) of Rack on Discharge 463

Characteristics of Trench Weir S. Kumar, Z. Ahmad

Chapter 43. An Approach To Analyze the Flow Characteristics of Sharp – Crested 475

W – Planform Weirs K. K.Gupta, S. Kumar, Z. Ahmad

Chapter 44. Gaps and Scope of Turbulence Study Near Piano Key Weir (PKW) 486 Harinarayan Tiwari, Nayan Sharma

\

Chapter 45. Stilling Basin Models with Square intermediate Sill 493 H.L.Tiwari, A. Goel, V. K. Gehlot, S.Tiwari

Chapter 46. Analytical Approach for the Critical Submergence for Horizontal intakes 502

in Open Channel Flows M. Hashid, A. Hussain, Z. Ahmad

Chapter 47. Experimental investigation of Levee Breach Due To Overtopping 512 Shikha Chourasiy, P. K. Mohapatra, S. Tripathi

Chapter 48. Hydraulics Performance of A Trench Weir Under Supercritical 522

Approach Flow S. Bhave, V. Verma , Z. Ahmad

Chapter 49. The Effect of Parthenium Hysterophorus Weed on 531

Basin Hydrology Soham Adla, Shivam Tripathi

Chapter 50. Flow Runoff and Sediment Yield Modeling of An Agricultural Hilly 542

Watershed Using WEPP Model Saroj Das, Laxmi Narayan Sethi, R. K. Singh

Chapter 51. Stability Assessment of Chang Dam After Rehabilitation 552 R. Singh, D. Roy

Chapter 52. Rehabilitation and Improvement of Sher Tank Project 557 Vishnu Arya

Chapter 53. Optimal Reservoir Operation Policy in Fuzzy Environment 561 Balve P. N., Patel J. N.,

Chapter 54. Water Balance Assessment of Krishna River Basin Through System Simulation 569 N.S.R. Krishna Reddy, S.K. Jain

Chapter 55. Performance Evaluation of A Multi Purpose Reservoir Using Simulation 581

Models for Different Scenarios Priyank J. Sharma , P. L. Patel , V. Jothiprakash

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) vii

Chapter 56. Development of Agro Climatic Gram Yield Model for Surat 598

District of Gujarat State P.G. Zore, N. N. Bharadiya, V. L. Manekar

Chapter 57. Stochastic Dynamic Programming Model with A Fuzzy State Variable for 604

Reservoir Operation Sangeeta Kumari

Chapter 58. Fuzzy Logic Based Modelling of Reservoir Operation: 612

A Case Study of Ukai Dam, Tapi Basin, India Utkarsh Nigam, S. M. Yadav

Chapter 59. Vulnerability Assessment of Karnataka Coast 622 A. Vittal Hegde, B. J. Akshaya

Chapter 60 Estimation of Runoff Potential Using Scs Cn Method with Remote 637

Sensing and GIS Ishtiyaq Ahmad1, Vivek Verma , M. K. Verma

Chapter 61. Sugarcane Crop Mapping of Sangli District Maharashtra Using Remote 646

Sensing and GIS Technique Prakash Bhamare, Ravindra Shrigiriwar, Deepak kumar Meshram, Sanjay Pande, Anita Morkar

Chapter 62. Evaluation of Remote Sensing Based Newly Developed Rain Detection 653

index Over Indian Region Shruti Upadhyaya , RAAJ Ramsankaran

Chapter 63. Hydrological Modelling of Upper and Middle Narmada River Basin, 663

India Using geospatial tools A.Gupta, P.K. Thakur, B. R. Nikam, A. Chouksey

Chapter 64. Comparison of Sediment Deposition/Erosion in Lower Siang Reservoir Using 676

Selected Transport Function in HEC RAS Kaoustubh Tiwari , S.M Yadav, Neena Isaac, P. D. Porey

Chapter 65. Sediment Trap Efficiency of Porcupine Systems for Riverbank Protection 688 Mohd. Aamir, Nayan Sharma

Chapter 66. Contrast in Sediment Yield Patterns of Subcatchments of Upper Tapi Basin 699 Prabhat Chandra, P. L. Patel , P. D. Porey

Chapter 67. Assessment of Revised Capacity in A Reservoir of Chhattisgarh State of India 711

Using Digital Image Processing Technique of Remote Sensing Data S. K. Awadhiya, D. K. Sonkusale, R. K. Jaiswal, R. V. Galkate

Chapter 68. Reliability Analysis of Spillway Against Scour 723 Mohammad Muzzammil, Javed Alam

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) viii

Chapter 69. Agricultural Risk Analysis for Mahanadi Basin Underclimate 737

Change Scenarios S.S. Satpute, Raaj Ramsankaran, D. Raje , T. I. Eldho

Chapter 70. Wave Prediction at Karwar Using Neural Networks and Particle 747

Swarm Optimization Deepthi. I. Gopinath, P. Sriram Kumar , G. S. Dwarakish

Chapter 71. Neural Network Assessment for Scour Depth Around Bridge Piers 759 Arun Goel

Chapter 72. The ANN Based Scour Pridiction At Bridge Pier in Clayey Sand 768 Javed Alamand, Mohd. Muzzammil

Chapter 73. Support Vector Machines for Predicting Sequent Depth Ratio 777 Rishabh Bansal, Sudeep, Mahesh Pal

Chapter 74. Neuro Fuzzy Assessments for Sediment Removal Efficiency of 787

Vortex Settling Basins Mujib Ahmad Ansari

Chapter 75. Performance of Different Daubechies Wavelets in Wave forecasting 800 P. R. Dixit, S. N. Londhe

Chapter 76. Water Use Prioritization Using Fuzzy – C – Means (FCM) Clustering 810 Subash P. Rai, Nayan Sharma , A. K. Lohani

Chapter 77. River Sedimentation Prediction Using Wavelet ANN and LS SVM 820 Raj Mohan Singh, Shilpi

Chapter 78. Estimation of Runoff and Flood Risk in the Narmada River Basin Using 829

Hydrological Time Series Data Mining Satanand Mishra, H. L. Tiwari, J. P. Shukla, Rakesh Purvia

Chapter 79. Rejuvenation of River Ganga: Technical and Societal Issues 841 C. S. P. Ojha, Himanshu Arora, Pragya Ojha, Anoop Kr. Shukla

Chapter 80. Exigency of Managing Coal Mining in Meghalaya for Sustainability of Water 857

Resources in the Area Anu Radha Bhatia, Sangita. P. Bhattacharjee, Vekhosa Kezo

Chapter 81. Development of An indigenous Effluent Treatment System for Chemical 868

Processing of Textiles in Cottage Sector Prabir Kumar Choudhuri

Chapter 82. Microbial Approaches for Treatment of Textile Dyes in Waste Water 875 Viraj Krishna Mishra, Jyotirmay Dubey

Chapter 83. A Comparative Study on Water Quality Assessment of A River 880

Using AHP and Promethee Techniques Ajit Pratap Singh, Parnika Shrivastava

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) ix

Chapter 84. Spatial Uncertainty Modelling in Water Quality Networks Using Entropy 891 P.G. Jairaj, P. Athulya

Chapter 85. Development of Optimization Model for Booster Chlorination Stations for 901

Drinking Water Distribution System Roopali V. Goyal , H. M. Patel

Chapter 86. A Comparative Study of Arsenic Removal Techniques for Rural Areas 911 S. Lata , S. R. Samadder

Chapter 87. Comparison of Surface Drainage Schemes in Different Parts of West Bengal 923 G. N. Raju, Y. Abinay Kumar, Rajesh P. R. , V. R. Desai

Chapter 88. Development of IDF Curve: A Study for Dholera Region of Gujarat, India 940 Ankit P. Patel, P. V. Timbadiya and P. L. Patel

Chapter 89. Derivation and Analysis of Dimensionless Unit Hydrograph and S Curve 950

for Cumulative Watershed Area Brunda G. S. , Shivakumar J. Nyamathi

Chapter 90. On Potential of Geomorphological Attributes in 964

Regionalization of Watersheds S.R. Chavan, V.V. Srinivas

Chapter 91. Regionalization of Watersheds Using Dimensionality Reduction Technique 975 Ganvir, Kanishka, Raje Deepashree , Eldho T. I.

Chapter 92. Land Use/Land Cover Mapping for Nandyal Taluk of Erdas – 985

Kurnool District Using A Remote Sensing and GIS Application Thammineni Kapilesh, Gopu Sreenivasulu

Chapter 93. Fractal Analysis of Kosi, Gandak and Baghmati River 996 V. Parmar, R. Khosa, R. Maheswaran

Chapter 94. Application of Gis for Estimation of Water Potential of A Basin 1006 Shobha Maliwal, Vivek Verma, M. K. Verma

Chapter 95. Planning of Hydrological Data Monitoring Network for integrated 1015

Water Resources Management of Bina River Pilot Basin Surjeet Singh, N. C. Ghosh, R. K. Jasiwal, T. Thomas, T. R. Nayak , R.V. Galkate

Chapter 96. Modernization of Kakrapar Right Bank Main Canal 1030 B. J. Batliwala , J. N. Patel, P. D.Porey

Chapter 97. Investigation in Observational Rainfall Characterstics in 1037

Gangotri Glacier Basin Manohar Arora, Rakesh Kumar, R. D. Singh, Jatin Malhotra , Naresh Kumar

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) x

Chapter 98. Analysis of Maximum Hourly Rainfall fordesign of Storm 1044

Network of Surat City Gaurav Ninama , Pingul Jignesh, Gulshan Yadav, Rahul S. Yadav, S. M. Yadav

Chapter 99. Flow Duration Curves for Estimating Environmental Flows in Mahnadi 1053

River System, India Ramakar Jha, Somesh Jena

Chapter 100. Investigating Regional Trends for the KBK Region of Odisha, India 1069 P. K. Mishra, Sharad K. Jain, Sanjay K. Jain, M. K. Nema

Chapter 101. Sediment Gradation and Its Spacial Distribution in Harbour Basins 1080 M. A. Mohamed Ansari

Chapter 102. Optimum Layout of Approach Channel To A Port 1091 R. K.Chaudhari, A. M.Vaidya , S. Kulkarni, M. D. Kudale

Chapter 103. Simulation of Hydrodynamics and Siltation in A Typical Harbour

in East Coast of India 1097 J. Sinha, Anil Bagwan, M. D. Kudale

Chapter 104. A Study on the Prerequisties and Methodologies of Creating Repeatable

Signals for Wave Maker 1107 X.L. Li, S.Y. Zhuang

Chapter 105. Experimental investigation on Performance of inclined Perforated Plate

As Wave Absorbers 1116 P. Krishnendu, R. Balaji

Chapter 106. Optimization of Layout for the Proposed Mega Container Terminal

off Tekra, Kandla Port 1124 Prabhat Chand, S.S. Chavan, M.D. Sawant , T. Nagendra

Chapter 107. Assessment of Wave Tranquility in the Proposed Harbour with 1136

Ro Ro Jetty K. H. Barve, L. R. Ranganath, M. Karthikeyan, M. D. Kudale

Chapter 108. Numerical Model Studies for Hydrodynamic Aspects of 1143

A Multi Cargo Port L. R. Ranganath, B. Krishna, M. D. Kudale

Chapter 109. Shoreline Change Analysis of Dakshina Kannada Coast Along 1153

West Coast of India Using Remotely Sensed Data Raju Aedla, Ganasri B P, Vijayalakshmi, Dwarakish G S, Jayappa K. S.

Chapter 110. Sea Water intrusion in the Coastal Area of Navsari District and Its Control 1165

By Direct Surface Method Vijendra kumar, B. K. Samtani , S. M. Yadav

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) xi

Chapter 111. Integrated Flexible Marine Structures for Hybrid Green Energy Source 1176 Aswani U. Asha L. S. P.Duttagupta , T. I. Eldho

Chapter 112. A Study on Berm Breakwater with Concrete Cubes As Armour Unit 1188

Sainath Vaidya, Geetha Kuntoji, Prashanth, J. Subba Rao

Chapter 113. Analysis of Boundary Shear Stress in A Two Stage 1195

Converging Compound Channel A.Mohanta, K. C.Patral, K. K. Khatua

Chapter 114. Verification of Effects of Turbulence Penentration on Valve Leakage 1206

in Nuclear Reactor Coolant System Rajesh Gupta, Sagar Paudel, Utkarsh Sharma, Amit Kumar Singh

Chapter 115. Numerical Modelling for Orifice Spillway 1217 Prajakta P. Gadge , C. V. Jothiprakash, V. V. Bhosekar

Chapter 116. Decision Support System (DSS) for integrated Water Resources 1226

Management in Madhya Pradesh Sanjiv Das, R. V. Galkate, Sanjay Gupta , H. L. Tiwari

Chapter 117. A New index for Agricultural Droughts Based on Crop Needs and 1243

Available Soil Moisture Meenu Ramadas, Rao S. Govindaraju

Chapter 118. Study of Spatio Temporal Variation of Groundwater Drought in 1254

Bearma Basin

Dinesh Kumar, T. Thomas, R. M. Singh

Chapter 119. A Standardized Precipitation index Based Draught Analysis in Upper 1266

Seonath Sub Basin Using GIS. Preeti Rajput, VivekVerma, M. K. Verma

Chapter 120. Effective Drought index Based Evaluation of Meteorological Drought 1278

Characterisitcs in Bundelkhand Region of Central India T. Thomas R. K. Jaiswal, R. V. Galkate, N. C. Ghosh

Chapter 121. Groundwater Drought Scenario in Bundelkhand Region of Central India 1292

A Case Study for Sonar Basin in Madhya Pradesh Vivek Kumar Bhatt, T. Thomas

Chapter 122. Selection of Step Change and Temporal Trend Detection Tests and 1306

Data Processing Approaches Ganesh D. Kale (MISH), D. Nagesh Kumar (FISH)

Chapter 123. Neural Networks To Predict Sea Surface Temperature 1317 Kalpesh Pati, M. C. Deo, M. Ravichandran

Chapter 124. Analysis of Extreme Precipitation Events in Climate Change Perception 1327

Paresha M. Baria, S. M. Yadav

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) xii

Chapter 125. Comparative Study of Climate Change Crop Yield 1340

Models for Surat District, Gujarat P.G. Zore, N. N. Bharadiya, V. L. Manekar

Chapter 126. Climate Change Impact on Nagpur’s Water Supply 1350 Shravan kumar, S. Masalvad, A.D.Vasudeo

Chapter 127. Optimal Water Allocation for Wheat Production Under Climate Change 1359

Using Cropwat Model Nivedita Singh, K. K. Singh

Chapter 128. Estimation of Soil Erosion Using Modified Universal Soil Loss Equation 1367

(Musle) in A GIS Environment Chandramohan T., M. K. Jose, Purandara B. K.

Chapter 129. A Study of Effect of Sand Mining on Riverine Environment 1378 Mathew K. Jose, Shantanu K.Y., B. Venkatesh

Chapter 130. An integrated Approach for Arecanut Crop Health Monitoring 1387 Bhojaraja B. E., Amba Shetty, Nagaraj M. K.

Chapter 131. Environmental Impact Assessment for Improving Soil Health and Crop 1397

Productivity of Tea Plantation in North Eastern India Nanda Kumar Singh, Haorongbam Jayashree Rout, Laxmi Narayan Sethi

Chapter 132. Development of an Indigenous Effluent Treatment System for Chemical 1408

Processing of Textiles in Cottage Sector Prabir Kumar Choudhuri

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014)

978–93 –84935–04–7 663

Chapter - 63

Hydrological Modelling of Upper and Middle Narmada

River Basin, India Using Geospatial Tools

A.Gupta1, P.K. Thakur

2 , B.R. Nikam

3, A. Chouksey

4

1Amity University Uttar Pradesh, Sector – 125, Noida-201301, India

2, 3, 4 Indian Institute of Remote Sensing (ISRO), 4, Kalidas Road,

Dehradun-248001, India

Abstract : The need for assessment of water resources availability in large and ungauged river basin is

frequent topic of discussion. It is now becoming increasingly important for water resources evaluation in

India. Water resources development activities have focused attention on development and application of

physically based hydrological models, which was used to simulate the impact of land and water use on

water resources. The main objective of this study was to test the performance and feasibility of the SWAT

(Soil and Water Assessment Tool) model for water balance study and prediction of stream flowin the

Upper and Middle Narmada River Basin of India, which can be used for understanding the effects of

future development and management actions. To simulate these impacts, long-term daily meteorological

data was used.The Sequential Uncertainty domain parameter Fitting algorithm (SUFI-2) of SWAT CUP

(Calibration and Uncertainty Program) with multiple sets of parametervalues is used for calibration and

validation, over the entire basin.This calibration and validation was done based on the observed daily

discharge data from India-WRIS (India – Water Resources Information System).The goal was to bracket

most of the estimated data within the 95% prediction uncertainty (95PPU), by getting a significant

coefficient of determination (R2) and coefficient of efficiency (NS) between observed and estimated data.

The results after post calibration and validation indicates decrease in average annual water yield from

44.83 mm to 36.67 mm and R2 calculated before calibration 0.86 and increases to 0.88 after calibration.

The simulation results indicate that relatively small parts of the total basin area have a high impact on the

water balance in the catchment. It also indicates considerable reduction in surface runoff from 346.42 mm

to 320.91 mm during 1979 to 1987.The results of present work also indicates that the parameter

uncertainty is not the sole source of uncertainty; the model structure uncertainty is also important. These

processes are mainly associated with the existing large reservoirs regulating the runoff of the River

Narmada.

Keywords:Water Balance in Narmada River Basin, Hydrological Modelling, Soil and Water Assessment

Tool (SWAT).

1. INTRODUCTION

Presently high inhabitant’s expansion, fast urbanization and climate change along with the irregular

frequency and intensity of rainfall cause difficulty in appropriate water management and storage

plans. Therefore, there is an urgent need of evaluation of water resources at various scales, as it

plays a primary role in the sustainability of livelihood and regional economics throughout the

world. It is the primary safeguard against drought and plays a central role in food security at local

Hydrological Modelling Of Upper And Middle Narmada River Basin, India Using Geospatial Tools

Hydraulics, Water Resources, Coastal and Environmental Engineering (HYDRO 2014) 664

and national as well as global levels. Modern researches using satellite based data and GIS

techniques have created a very promising research tool for hydrological investigation and

interpretation of landscape. Surface hydrological indications are one of the promising scientific

tools for assessment and management of water resources.

SWAT, is acontinuous-time, semi-distributed, process based river basin model, developed

toevaluate the effects of alternative managementdecisions on water resources and nonpoint-source

pollutionin large river basins (Arnold et al., 2012). Arnold and Fohrer (2005) described the

expanding global use of SWAT as well asseveral subsequent releases of the model. Gassman et al.

(2007) provided further description of SWAT, including SWAT version 2005,and also presented

an in-depth overview of over 250SWAT-related applications that were performed worldwide. It

was developed to predict impact ofland management practices on water, sediment yield,

andagricultural chemical yield such as nitrogen, phosphorus andbiological oxygen demand,

chemical oxygen demand, runoffmodeling, water balances modeling of large basin. For

thecalibration analysis of this model Sequential Uncertainty Fitting (SUFI-2)program, linked with

ArcGIS i.e. SWAT CUP is being usednow a days (Manaswi et al., 2014).

Many other methods were usedin past to simulate hydrology and soils, land use and management,

also several models were developed to simulate single storm events using a square grid

representation of spatial variability (Young et al., 1987; Beasley et al., 1980). However, many of

these models did not consider subsurface flow, ET or plant growth. Continuous models were also

developed (Johansen et al., 1984; Arnold et al., 1990) but generally lacked sufficient spatial detail.

Narmada River, generally known as ‘Life line of Madhya Pradesh’ is a fifth longest river in the

Indian subcontinent and it is the third longest river that flows entirely within India. It also longest

westwardflowing river that drains in toArabian Sea at 30 km west of Bharuch, Gujarat after

running for 1,312 km thorough Madhya Pradesh, Maharashtra, Gujarat. Narmada Basin. The

Narmada Basin, extends over an area of 98,796 km2most of which lies in Madhya Pradesh (86%)

Gujarat (14%) and a comparatively smaller area (2%) in Maharashtra. The Narmada River is

traditionally considered to be originated from Amarkantak in Anuppur district of Madhya Pradesh,

at an elevation of 1037 m above mean sea level. In Basin Atlas, CWC 2014, theNarmada Basin is

divided into 3 Sub-basins viz. Narmada Upper, Narmada Middle and Narmada Lower Sub-basin.

The drainage network of Narmada River consists of 19 major tributaries. The Upper and Middle

Narmada River Basin has an elongated shape with a maximum length of 844.86 km. from east to

west and a maximum width of 234 km from north to south. The hilly regions are in the upper part

of the basin, and lower middle reaches are broad and fertile areas well suited for cultivation. It has

been noted from the elevation map of the basin (Figure 2)that the maximum area of the basin falls

in the 300-500m elevation range. Maximum elevation is observed in the uppermost region of the

basin. The highest elevation in the entire basin is around 1,328 m.

There is a need for hydrological research of the Narmada Basin to support improved catchment

management programs that safeguard the degradation of soil and water resources in various

governing states. The lack of decision support tools and limitation of data concerning weather,



hydrological, topographic, soil and land use are factors that significantly hinder research and

development in the area.The main objective of this study was to test the performance and

feasibility of the SWAT (Soil and Water Assessment Tool) model for water balance study and

prediction of stream flowin the Upper and Middle Narmada River Basin of India, which can be

used for understanding the effects of future development and management actions.

1.1 Use of GIS & Remote Sensing in Hydrological Modeling:

Hydrological modeling is the mathematical representation of the major components of hydrological

cycle in which components are derived with the help of various empirical and complex physical

based mathematical formulae. Hydrological models in spatial domain are mainly of two types:

Lumped models and Distributed models. In lumped model, spatial heterogeneity is not

considered i.e. it considers watershed as single entity with single rainfall input as a whole. It

assumes that whole grid is homogenous and physical property such as soil, land cover, climate, etc.

are same everywhere. These models do not use physical formulas to derive water balance

components. Also variations in meteorological, hydrological and geological parameters are

considered as one aggregated value. Whereas in distributed model, grid heterogeneity is considered

by dividing whole area into number of homogenous units and all the properties lying in the area are

given equal weightage(Krysanova et al., 1999; Singh and Frevert, 2006).

The synoptic and temporal coverage of an area or phenomenon by satellite based remote sensing

sensors has a potential advantage in distributed hydrological modelling of various scales.

Parameters such as runoff cannot be directly measured from remote sensing but can be estimated

with the help of hydrological modelling in which remote sensing data goes as major dynamic input.

Remote sensing has emerged as a powerful tool for cost effective data acquisition in shorter time at

periodic intervals (temporal), at different wavelength bands (spectral) and covering large area

(spatial).The availability of GIS tools and more powerful computing facilities makes it possible to

overcome many difficulties and limitations and to develop distributed continuous time models,

based on available regional information(Sahoo, 2013). Geographic Information System (GIS) helps

in generating various hydrological properties from Digital Elevation Model (DEM) such as

drainage network, flow direction map, flow accumulation map, aspect map, stream order, etc. It

also helps in satellite data storing, processing, interpreting and analysing.

1.2 Soil and Water Assessment Tool:

SWAT (Soil and Water Assessment Tool) is used for analysing the impact of land management

practices on water, sediment, and agricultural chemical yields in large complex watersheds(Setegn

et al., 2008). SWAT uses a modified formulation of the soil conservation service (SCS) curve

number (CN) technique to calculate surface runoff. The CN technique relates runoff to soil type,

land use and management practices and is computationally efficient (Arnold et al., 1995a). The

computational components of SWAT can be placed into eight major divisions: hydrology, weather,

sedimentation, soil temperature, crop growth, nutrients, pesticides, and agricultural

management(Sahoo, 2013). Although the model operates on a daily time step and is efficient to run



for many years, it is intended as a long term yield model and is not capable of detailed, single-event

flood routing (Arnold et al., 1998). ArcSWAT represents both pre-processor and user interface to

SWAT model (SWAT User's guide, 2012).

In this study we focus on calibration, evaluation and application of SWAT 2012 model for

simulation of the hydrology of Upper and Middle Narmada River Basin. The main objective of this

study was to test the performance and feasibility of the SWAT 2012 model for prediction of stream

flow in Narmada Basin.

1.3 Calibration and Parameter Uncertainty Analysis:

Model calibration is the modification or adjustment of model parameters, within recommended

ranges, to optimize the model output so that it matches with the observed set of data and Sensitivity

analysis is the determination of the most influential independent parameter of the model in

predicting the flow (Khare et al., 2014).Vandenberghe et al. (2002) have proposed that a sensitivity

analysis should beperformed before model calibration to identify the most sensitive

parameters.Uncertainty Analysis of distributed model is based on generalized likelihood measures.

For which Sequential Uncertainty domain parameter Fitting algorithm (SUFI-2) of SWAT CUP

has been used, with some limitation in model structure (Singh, 2013).

In the new version of SWAT-CUP a more powerful SWAT edit program is available where all

SWAT parameter arehandled, including different soil layers and managementrotation-operation,

precipitation data etc.(Manaswi et al., 2014). Performance of the SUFI-2 techniques was evaluated

using five objective functions, namely P-factor, R-factor, coefficient of determination R2, Nash–

Sutcliffe (NS) and coefficient of determination divided by coefficient of regression bR2 calculated

on daily and monthly time-steps(Singh, 2013).

2. STUDY AREA

Catchment area of the Upper and Middle Narmada basin, with outlet at Garudeshwar in Gujrat

state, extends over an area of 87,581.33 km2 and bounded on the north by the Vindhyas, on the east

by the Maikal range, on the south by the Sapura’s and on the west by the Coastal Alluvial Plain.

Lying in the northern extremity of the Deccan plateau, the basin covers large areas in the States of

Madhya Pradesh, Gujarat and a comparatively smaller area in Maharashtra and Chhattisgarh. Study

area lies between the geographical extent of north 21.25° to 23.875° latitude and east 73.625° to

81.8125° longitude, as shown in Figure 1.

The Tropic of Cancer crosses the Narmada basin in the upper plains area and a major part of the

basin lies just below this line. The climate of the basin is humid and tropical, although at places

extremes of heat and cold are often encountered.Rainfall is heavy in the upper hilly and upper

plains areas of the basin. It gradually decreases towards the lower plains and the lower hilly areas

and again increases towards the coast and south-western portions of the basin. In the upper hilly

areas, the annual rainfall, in general, is more than 1400 mm but it goes up to 1650 mm in some



parts.The average annual water potential of the basin is 45.64 BCM. The utilizable surface water in

the basin accounts to 34.50 BCM(Basin Atlas, CWC 2014; WRIS 2012).

Figure 1. Location Map of the Study Area

3. MATERIAL AND METHODS

3.1 Material/Data used

In the present study, multispectral satellite data of Landsat 8 (Figure 3) from

http://earthexplorer.usgs.gov has been used to prepare land use land cover (LULC) map of the

study area, asshown in Figure 4. The digital elevation model (DEM) of Shuttle Radar Topography

Mission (SRTM) fromhttp://srtm.csi.cgiar.org,shown in Figure 2, has been utilized for generation

of topographic database and extraction of various parameters.Soil data from National Bureau of

Soil Survey and Land Use Planning (NBSS & LUP), shown in Figure 5and daily weather data by

Climate Forecast System Reanalysis from http://globalweather.tamu.eduhas been used in this

study. Various software’s like ArcGIS 10.1, ERDAS Imagine 2013,Google Earth and SWAT CUP

5.1 (SUFI 2) has been used with other useful extension likeATCOR 2013 of Erdas Imagine for

atmospheric correction and ArcSWAT 2012. For calibration and validation of hydrological model,

observed daily discharge data from India-WRIS (India – Water Resources Information System) has

been used.

3.2 Methodology

The present study concerns the application of SWAT 2012 in the Upper and Middle Narmada

River Basin to examine the influence of topographic, landuse, soil and climatic condition. The

applicationof the model involved calibration, sensitivity and uncertaintyanalysis (SWAT-CUP).

The physical characteristics of watershed are defined and simulated in watershed view of the



Model. These characteristics include division of watershed into subwatershed, location of main

outlet, creating HRUs from Land Use Land Cover and Soil map. Next step for simulation in

modeling is defining climate of the catchment. This has been done by reading or generating

weather data of daily precipitation, maximum/minimum temperature, wind speed, solar radiation,

relative humidity and long term weather data (Aggarwal et al., 2007).

Automatic watershed delineation has been done by using SRTM DEM. The drainage map of the

basin isshown in Figure 6. The impact of hydrologic response units (HRU) definition on stream

flow has also been studiedin which the subwatershed obtained from a watershed is further

subdivided into landuse and soil characteristics. For this, SWAT requires the landuse, soil, weather

and terrain data sets for assessment of water yield at the desired outlet of the basin. The SWAT

Simulation menu allows to finalize the setup of all inputs for the SWAT model and perform

sensitivity analysis and auto simulation.

Figure 2. Elevation Map Figure 3. Landsat 8 Standard FCC Image

Figure 4. LULC Map Figure 5. Soil Map



Calibration of watershed model is a challenging task because of input data uncertainties, model

structure and algorithms, parameterization and output. This can be accomplished manually or using

auto calibration tools in SWAT-CUP (Abbaspour et al., 2007). Using SUFI-2 provides advance

option in hydrological modelling and create control environment between large amounts of data

sets during parameter sensitivity analysis. The long time-series data of discharge is available for the

Garudeshwar gauging station and this was utilized to simulate the model parameters and calibrate

stream-flow correlation between simulated and observed data.A converged solution has been

reached when the objective functions such as coefficient of determination (R2) and coefficient of

efficiency (NS), reaches constant values.

4. RESULTS AND ANALYSIS

The Upper and Middle Narmada catchment has been divided into 51 sub-basins and 106

Hydrological Response Unit’s (HRUs). The HRUs of this catchment have been categorized into

different classes mainly on the basis of landuse, soil and slope. It has been clearly observed from

LULC map that the Upper and Middle Narmada River Basin is a deciduous forest dominated area

followed by an agricultural land which contribute to the significant economic importance of the

area. The slope of the catchment has been divided into five classes, viz. 1º–3º (Very Gentle), 3º–5º

(Gentle), 5º–10º (Moderate), 10º–35º (Moderate to Steep) and > 35º (Very Steep),as shown in

Figure 7. It has been found that most of the catchment area has general smooth slope and it covers

about 60–70% of the total catchment area but the rest of region especially near the origin of river

the area falls under steep slope category. This high-altitude area contributes to a significant amount

of soil erosion as well as high run-off, especially during monsoon periods may be partly due to

inadequate management practices. Clay and Loamy are the most dominating soil categories found

in this catchment, shown in Figure 5.

The result of initial simulations have clearly shown that the hydrology of the basin has not been

well represented in this setup of model, making calibration inevitable. Based on the calibration

results, the hydrology of the selected subbasins and the entire Upper and Middle Narmada River

Basin has been validated. After simulation, the default values of parameter sets has been used for

calibration for the year 1979 – 1983 asshown in Figure 8, which results in new values of

calibrating parameter, which has been used to validate for the year 1984 – 1987 as shown in

Figure 9.

The goal of this parameter fitting procedure was to bracket most of the estimated data within the

95% prediction uncertainty (95PPU). If upon reaching this goal a significant R2 and NS exits

between the observed and measured runoff data then the model can be referred to as calibrated.

However, Schuol and Abbaspour(2005) have suggested the practically it is sufficient to bracket 80

percent of measured data within the 95PPU. The 95PPU represents also the parameter uncertainty

resulting from the non-uniqueness of effective model parameters.

Five parameters were included in the calibration procedure: CN2 value i.e. SCS Runoff Curve

Number [unit less] and some groundwater parameters like ALPHA_BF i.e. Base Flow Factor

[days], GW_DELAY i.e. Groundwater Delay [days], GWQMN i.e. Threshold depth of water in the



shallow aquifer required for return flow to occur [m] and GW_REVAP i.e. Groundwater "revap"

coefficient [unit less].

Figure 6. Drainage Map Figure 7. Slope Map

Figure 8. Calibration with default Figure 9. Validation with simulated

simulated parameters calibration parameters

The fitted values of each parameter shown in Table 1,has been used in first iteration of calibration

for the sensitivity analysis and varied by replacing the values,obtained at the end of each iteration,

within their recommended range.In each iteration, previous parameter ranges were updated by

calculatingthe sensitivity matrix and the equivalent of a Hessianmatrix (Magnus and Neudecker,

1988),followed by the calculation matrix. Parameters were then updated in such a way that new

ranges were always smaller than previous ranges and were centered on the best

simulation(Abbaspouret al., 2007).Table 2 & 3 shows the fitted values, obtained after ninth

iterations of calibration and values obtained after validation, respectively.



Table 1. Parameter for sensitivity analysis before calibration

Parameter Fitted value Minimum value Maximum value

r__CN2.mgt 78.54 0.0 100.0

v__ALPHA_BF.gw 0.550000 0.0 1.0

v__GW_DELAY.gw 135.000000 30.0 450.0

v__GWQMN.gw 1.700000 0.0 2.0

v__GW_REVAP.gw 0.137000 0.02 0.2

Table 2. Parameter for sensitivity analysis after calibration


r__CN2.mgt 69.92 35.0 98.0

v__ALPHA_BF.gw 0.163513 0.065973 0.243319

v__GW_DELAY.gw 178.5368642 25.2 420.0

v__GWQMN.gw 2.720288 2.604801 3.066749

v__GW_REVAP.gw 0.075097 0.063544 0.096552

Table 3. Parameter for sensitivity analysis after validation


r__CN2.mgt 66.47 35.00 98.00

v__ALPHA_BF.gw 0.145779 0.065973 0.243319

v__GW_DELAY.gw 147.1221125 25.2 420.0

v__GWQMN.gw 2.674093 2.604801 3.066749

v__GW_REVAP.gw 0.075097 0.063544 0.096552

For the evaluation of the calibration (and validation) performance of the model, two statistical

parameters, namely R², the squared correlation coefficient between the observed and simulated

output, which in SWAT is usually the stream flow, and NS, Nash-Sutcliffe efficiency parameter,

have been evaluated. Values of R² > 0.6 and NS > 0.5 for the calibration of the daily and monthly

simulated stream flow are usually considered as adequate for an acceptable calibration (Santhi et

al., 2001).According to Norusis (1999) when the R2value is equal to 1, the model is considered to



be good meanwhile if the R2 is lower than 0.5 (near to zero), the model would be considered as not

suitable.

Abbaspour et al. (2004, 2007)suggest to use two more measures i.e. p-factor and r-factor, in case

the comparison of R2 and NS is not adequate. The p-factor is the percentage of the measured data

bracketed by the 95PPU, which value should ideally be 1 and on the other hand, the r-factor, is a

measure of the quality of the calibration and indicates the thickness of the95PPU, which value

should ideally be near zero (Arnold et al., 2012) Table 4 shows the valuesof accuracy parameters

before calibration, after calibration (obtained after ninth iterations) and values obtained after

validation.

Table 4. Accuracy parameters for sensitivity analysis

VARIABLE BEFORE

CALIBRATION

AFTER

CALIBRATION

AFTER

VALIDATION

p_factor 0.55 0.13 0.06

r-factor 0.61 0.07 0.05

R2 0.86 0.88 0.93

NS 0.63 0.26 0.74

bR2 0.7009 0.5638 0.7054

At first glance, the results appears to be diverse, however a closer look reveals the emergence of

clear patterns. The information gained from ninth iteration of calibration for the year 1979 – 1983,

results in new values of parameter sets, which has been used to validate the hydrological processes

over the entire basin for the year 1984 – 1987, as shown in Figure 10&11 respectively.

Figure 10. Calibration with new Figure 11. Validation with new

fitted parameters fitted calibration parameters

The mismatch in observed and best estimate of model in calibration and validation phase (i.e.

Figure 10&11) may have occurred due to error in measured input data e.g., rainfall and

temperature, error in measured data used in calibration e.g., river discharge or error in model



parameters e.g., hydrologic processes. Thus it reveals that calibration must always be accompanied

by an assessment of the goodness of the calibration,taking intoaccount of all modeling errors. Apart

from error in parameters, it seems that not all processes were included in the model, especially

some that are important in case of large river basins. These processes are mainly associated with

the existing large reservoirs regulating the runoff of the River Narmada. It would be ideal to

include reservoirs and water use in the model, but readily available, detailed information on the

management of the reservoirs and on stored water in the wetlands are almost non-existent. Thus it

emphasizes that the parameter uncertainity is not the sole source of uncertainty, the model structure

uncertainity is also important.

Figure 12. Average Water Balance Components Figure 13. Rainfall and Runoff (1979 – 1983)

Figure 14. Rainfall and Runoff (1984 – 1987)

Figure 12 illustrates the water balance components for the values where as Figure 13 & 14 gives

relation between rainfall and runoff for different years for the values After performing the

calibration from 1979 to 1983 and validation from 1984 to 1987, the results shows decrease in

average annual water yield from 44.83 mm to 36.67 mm and surface runoff from 346.42 mm to

320.91 mm.



5. SUMMARY AND CONCLUSIONS

In present study an attempt has been done to simulate the impact of land and water use in Upper

and Middle Narmada River Basin, using SWAT (Soil and Water Assessment Tool).

For analyzing the influence of topographic, landuse, soil and climatic condition; digital elevation

model of SRTM, multispectral satellite data of Landsat 8, soil data from NBSS & LUP and long

term meteorological data was used, respectively. Model calibration and validation is done by using

SUFI-2 of SWAT CUP to optimize the output so that it matches the observed discharge, available

at Garudeshwar gauging station. To check the performance of the model, five parameters were

used. The final solution has been reached when the two statistical parameters such as coefficient of

determination (R2) and coefficient of efficiency (NS), reaches constant values.

In present study the R2 values were around 0.86 for before calibration and it improves to 0.88after

calibration. The r-factor, whose value should ideally be near zero,was calculated to be 0.61 before

calibration and improved up to 0.07 after calibration. After validation process, the value of

variables viz., r-factor, R2& NS improved up to 0.05, 0.93 & 0.74 respectively, which shows a

close relationship between the observed and simulated discharge.

The results after validation indicates decrease in average annual water yield from 44.83 mm to

36.67 mm. It also indicates considerable reduction in surface runoff from 346.42 mm to 320.91 mm

during 1979 to 1987, after validating all fitted parameters, at the outlet marked in Garudeshwar,

Gujrat. We can conclude from this study that freely available geo-spatial data can be used in

estimation of hydrological variables.

SWAT is a powerful tool for evaluating water flows and productivity of different land uses in such

a big catchment. The simulation results indicate that relatively small parts of the total basin area

have a high impact on the water balance in the catchment, although the uncertainty of result is high.

An improved calibration is realistic but due to the non-uniqueness of effective parameters there

will never be one best fit. The work presented here is only a humble first step; further review of

studies, analysis of data, expert knowledge and experimental work is needed in this field.

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