towards decision support models for an ungauged catchment in india, the case of anas catchment

204 Pages·2004·13.77 MB·English

Institut für
Wasserwirtschaft und Kulturtechnik
Universität Karlsruhe (TH)
Towards Decision Support Models for an Ungauged
Catchment in India, the Case of Anas Catchment
Anupam K. Singh
Heft 225
Mitteilungen des Instituts für Wasserwirtschaft und Kulturtechnik
der Universität Karlsruhe (TH)
mit ″Theodor-Rehbock-Wasserbaulaboratorium″
Herausgeber: Prof. Dr.-Ing. Dr. h. c. mult. Franz Nestmann, Ordinarius
2004
Towards Decision Support Models for an Un-gauged Catchment
in India, The case of Anas catchment

Zur Erlangung des akademischen Grades eines

DOKTOR-INGENIEURS

von der Fakultät für
Bauingenieur-, Geo und Umweltwissenschaft
der Universität Fridericiana zu Karlsruhe (TH)

genehmigte

DISSERTATION

von
M. Sc. Anupam K. Singh

aus Kanasi/ Indien

Tag der mündlichen Prüfung: 26. 05. 2004

Hauptreferent: Prof. Dr.-Ing. Dr. h.c. mult. Franz Nestmann
Korreferenten: Prof. Dr.-Ing., Dr. rer. nat. Andras Bardossy

Karlsruhe, 2004

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Acknowledgement

Only the timely support of Prof. Dr.-Ing., Dr. h.c. mult. Franz Nestmann has enabled me to
successfully perform this research work at Institute for Water Resources Management,
Hydraulics and Rural Engineering (IWK). He has co-operated and motivated me during difficult
moments and provided financial support in spite of no external research funding.

My thanks are due to Prof. Dr. rer. nat., Dr.-Ing. Andras Bardossy for his invaluable comments,
in-depth understanding and moral support in improving several loosely written parts of this
research work. His supervision strengthened my confidence and professional abilities.

Over the last 2-years the entire hydrology group at IWK provided a warm and friendly working
atmosphere, be it exchange of professional ideas or introduction to new scientific tools. Dr.-Ing.
Jurgen Ihringer and my colleagues Marcus Casper, Dr. Charlotte Kämpf, Falk Lindenmaier,
Martin Helms, Wolfram Schädel, Rolf Becker, Jutta Szabadics, Oleg Evdakov, Andrea Blatter,
Joachim Liebert, Bruno Buechle, and Patric Preuss for their day-to-day help related to
Geographical Information System (GIS), hydrologic modelling, remote sensing and database
management. Mrs. Kay Dietner took the responsibility of correcting my Indian English and
Resources Engineering office including its current and former staff for their all time help for
daily life. Besides the professional skills, I am grateful to all of them for the common time in
which they introduced me with the fascinating nature and culture of German folks and
particularly of the Baden and Black Forest region. Although this was a wondrous and lengthy
journey, but will be fondly remembered in years to come.

I am very much thankful to Dr.-Ing. Erwin Zehe, Junior Professor at University of Potsdam
(formerly at Institute of Hydraulics Engineering at University of Stuttgart) and Dr. Jens Lange,
Institute of Hydrology, Albert-Ludwigs University of Freiburg im. Brs. for sharing the
responsibility of translating my research vision through their professional support in explaining
the complex models, model parameters and structures during our several untiring meetings in
Stuttgart and Freiburg during 2002 and 2003 without asking any service in return. Also thanks
for Dr. Thea Vogt from University of Strasbourg, France for introducing me the basics of
remote sensing and the art of digital image classification.

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My special thanks to Mr. Salilendra K. Shrivastav and Mr. A.N. Borbankar from State Water
Data Centre, Water Resources Department at Bhopal for their efforts in organising the rainfall
and discharge data and their encouragement for initiating water resources research in this virgin
Anas catchment. Ms. Meera Shahi and Mr. P.S. Sodhi both from Gramin Vikas Trust Jhabua
believed in my concept and established a pilot hydrological measurement station in Kanjawani
mili-watershed since June 2002. Also due thanks to Prof. Dr. Eldho T. Iype from Civil
Engineering Department at Indian Institute of Technology (IIT) Bombay for supporting my
research ideas and exposing them to the larger research community in India. Our research co-
operation has been recognised as we won the best poster award from Stockholm International
Water Institute (SIWI) during 2002 World Water Week in Sweden. Thanks to Mr. Wasim
Akhtar former District Collector of Jhabua for providing much needed logistics during two field
visits.

I am thankful to Ministry of Science, Research and Culture (MWK) Baden-Würtemburg for
sponsoring my most of the stay in Karlsruhe, German Academic Exchange Service (DAAD) for
sponsoring few field visits to India and Karlsruher Hochschulvereinigung (KHV) for partial
financial assistance for buying digital satellite data of Landsat TM from United States Geological
Survey (USGS).

Last but foremost appreciation to my wife Rachna Gangwar for her constant support, lot of
sympathy and financial backing in completing this research work. Finally, this work is dedicated
to late my father and teacher (1936-2003) who always dreamed best in me for improving the
miserable life of rural folks in India.

Anupam K. Singh Karlsruhe, May 2004

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Contents

Forward

Acknowledgement

Abstract
Page
Chapter 1: Introduction

1.1 Research objectives and model structure 3
1.2 Research outline and end users 6
1.3 Decision support models
1.3.1 Need for DSMs 8
1.3.2 Concept of DSM 9
1.3.3 Classification of decisions support models 10
1.4 Review of decision support models 11

Chapter 2: Research Project Settings

2.1 General characteristics of India
2.1.1 Climate and agro-climatic zones 16
2.1.2 Water resources distribution and drainage basins 17
2.1.3 Water resources use and potential 18
2.1.4 Socio-economic development 20
2.2 Characteristics of Anas catchment
2.2.1 Location of Mahi basin 20
2.2.2 Location of Anas catchment 22
2.3 Topography and drainage profile 23
2.4 Rainfall and climate 24
2.5 Land cover and land-use 27
2.6 Soils and geology 27
2.7 Hydrology and water resources 30
2.8 Socio-economic characteristics 33

Chapter 3: Catchment Modelling and Database Development

3.1 Catchment modelling
3.1.1 Classification of catchment models 35
3.1.2 Structure of catchment model 36
3.1.3 Selection of catchment models 38
3.2 Catchment modelling with GIS and remote sensing
3.2.1 SCS-CN runoff model 40
3.2.2 Catchment water balance model 42
3.3 Generation of thematic maps and GIS database 44
3.4 Surface modelling with TIN
3.4.1 Data source and surface analysis 48
3.4.2 Conversion of TIN into DEM 48
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3.5 Digital Elevation Model (DEM)
3.5.1 Data for DEM modelling 49
3.5.2 Interpolation methodology and results 49
3.5.3 DEM uncertainty analysis 50
3.6 Geo-database management system
3.6.1 Structure of database 53
3.6.2 Data query and analysis 54
3.7 Results and discussion 55

Chapter 4: Remote Sensing for Hydrological Model Parameterisation

4.1 Concept of remote sensing 58
4.2 Digital image processing and pattern recognition 60
4.3.1 Image segmentation and enhancement 61
4.3.2 Feature extraction 61
4.3 Multi-spectral Image classification
4.3.1 Gaussian maximum likelihood classification 64
4.3.2 Fishers Linear-discriminant classification 65
4.3.3 ECHO classification 66
4.3.4 Principal component analysis 66
4.4 Accuracy assessment of classification image 67
4.5 Application for Anas catchment
4.5.1 Selection of satellite remote sensing data 69
4.5.2 Pre-classification process 69
4.5.3 Preparation of false colour composite 70
4.5.4 Design of supervised land-use classification system 70
4.6 Results of image classification
4.6.1 Extraction of training features 73
4.6.2 Classification of training fields 74
4.6.3 Image classification results 75
4.7 Image integration in GIS 76
4.8 Results and discussion 78

Chapter 5: Rainfall Prediction based on Atmospheric Circulation

5.1 Downscaling approaches
5.1.1 Dynamic downscaling 83
5.1.2 Statistical downscaling 83
5.1.3 Statistical-Dynamic downscaling 84
5.2 Atmospheric circulation and statistical downscaling
5.2.1 CP based approach to downscaling 85
5.2.2 State of art on CP-based downscaling 86
5.3 Modelling methodology and structure of downscaling model
5.3.1 Classification of circulation types 87
5.3.2 Downscaling of rainfall 89
5.4 Materials and database
5.4.1 Large scale circulation data 91
5.4.2 Station rainfall data 92
5.4.3 Spatial database 92
5.4.4 Rainfall data characteristics 93
5.5 Sensitivity analysis for rainfall model 93
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5.6 Model simulation results
5.6.1 Conditional rainfall probability and amount 95
5.6.2 CP frequency analysis 98
5.6.3 Objective circulation pattern 100
5.6.4 Observed and simulated rainfall 101
5.7 Prediction of long-term rainfall 105
5.8 Results and discussion 107

Chapter 6: Runoff Modelling for Prediction in Un-gauged Catchments

6.1 Modelling with ZIN-model
6.1.1 Development of catchment database 115
6.1.2 Rainfall parameter 116
6.1.3 Runoff generation parameters 119
6.1.4 Runoff concentration parameters 122
6.1.5 Channel routing parameters 124
6.2 Sensitivity analysis for distributed model 128
6.3 Model simulation results
6.3.1 Model run for 07/09/1994 130
6.3.2 Model run for 06/09/1994 131
6.3.3 Analysis of volume check 132
6.4 Model validation, up-scaling discharge 133
6.4 Effect of input parameter variability
6.4.1 Effect of cloud velocity 135
6.4.2 Effect of alluvium infiltration rate 136
6.4.3 Effect of reservoir storage 138
6.5 Results and discussion 139

Chapter 7: Conclusions and Perspectives

7.1 Catchment decision support models 143
7.2 Hydrological modelling and database development 143
7.3 Remote sensing for hydrological model parameterisation 144
7.4 Rainfall modelling based on GCM 145
7.5 Runoff modelling for prediction in un-gauged catchments 146
7.6 Application of decision support models 148

References 149

Appendix

A.1
Annex 1.1 In depth overview of decision support model reviewed 167
A.2
Annex 2.1 Major agro-ecological zones in India 168
Annex 2.2 Average monthly flow of selected major rivers in India 170
Annex 2.3 Scenario narratives and definition of term 170
A.3
Annex 3.1 Algorithms 171
Annex 3.2 Accuracy assessment of DEM 171
Annex 3.3 Comparing TIN, grid and lattice 172
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A.4
Annex 4.1 Histogram and density function for April 6 2000 image 173
Annex 4.2 Weighted inter-class distance for training classes 173
Annex 4.3 Seperability index between training classes for October 15 174
2000 image
Annex 4.4 Training class performance for October 15 2000 image 174
Annex 4.5 Class distribution for image area for October 15 2000 image 174
Annex 4.6 Seperability index between training classes for April 6 2000 175
image
Annex 4.7 Class distribution for image area for April 6 2000 image 175
Annex 4.8 Definition of terms 175
Annex 4.9 The land-use classes distribution using GML and ECHO 176
classifiers for April 6 2000 and October 15 images
A.5
Annex 5.1 Comparison of main strengths and weaknesses of dynamic 177
and statistical downscaling
Annex 5.2 Predictor variables and techniques on downscaling daily 177
precipitation
Annex 5.3 Topographic parameters of rain-gauge stations in Anas 177
catchment India
Annex 5.4 Yearly frequencies of CP occurrence during monsoon season 178
for 1985-94 period
Annex 5.5 Spatial distribution of 500hPa geo-potential heights anomalies 178
for wet and dry CP’s
Annex 5.6 Conditional rainfall probability and conditional rainfall 178
amount for various CP-types at rainfall station
Annex 5.7 Mean monthly rainfall totals for observed and simulated series 179
during 1985-94
A.6
Annex 6.1 Land-use classes, curve number and runoff coefficients 180
derived from October 15 2000 image
Annex 6.2 Manning’s roughness coefficients for various channel types 180
Annex 6.3 Modelling the effect of cloud velocity for size rainfall event on 180
02/08/1994 at Anterbeliya and Mod stations
Annex 6.4 Modelling the effect of infiltration rate for small rainfall event 180
on 02/08/1994 at Anterbeliya and Mod stations

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List of Figures

Fig. 1.1 Monthly rainfall anomalies for west-central India derived using KNMI 3
climate explorer
Fig. 1.2 Scheme of decision support models for Anas catchment in Mahi basin in 4
western India
Fig. 2.1 Topographic features of India 15
Fig. 2.2 Major climatic regions and major river basins in India 16
Fig. 2.3 Long-term average monthly flow of selected Indian rivers 17
Fig. 2.4 Location of Mahi basin in India 21
Fig. 2.5 Anas catchment and its river network 22
Fig. 2.6 Yearly rainfall variability for five stations in Anas catchment during 1965-99 23
Fig. 2.7 Probability plotting of monsoon rainfall totals for station Jhabua derived 24
from observed 40 years rainfall
Fig. 2.8 The rainfall recurrence interval and monsoon season rainfall for Thandla 24
and Ranapur stations
Fig. 2.9 Mean monthly minimum and maximum temperature at meteorological 25
station Alirajpur
Fig. 2.10 Properties of soil type in Anas catchment 26
Fig. 2.11 Geology in Anas catchment 28
Fig. 2.12 Stage-discharge curve for discharge station Anterbeliya for various years 29
(1992-95) during monsoon season
Fig. 2.13 Stage-discharge relationship for two discharge stations Anterbeliya and Mod 30
catchment for 1994 monsoon season
Fig. 2.14 Stage-discharge curve Anterbeliya station at different water levels 30
Fig. 3.1 A flow chart and sketch of integrated watershed model 35
Fig. 3.2 The structure of coupled GIS and hydrological model 39
Fig. 3.3 A manual procedure adopted for digitisation of topographic maps in 42
Arc/Info platform
Fig. 3.4 Getting data into attribute data table and related data tables 43
Fig. 3.5 A TIN generated from input contour lines, geodetic points and drainage 45
network
Fig. 3.6 DEM generated using TIN model and Arc-view 3D Analyst 47
Fig. 3.7 A 3D-digital elevation model for Anas catchment 48
Fig. 3.8 A plot of DEM uncertainty between two datasets 49
Fig. 3.9 The structure of relational database in catchment context 50
Fig. 3.10 Performing query and analysis from simple geo-database system for model 51
parameter identification to distributed hydrologic model
Fig. 4.1 Electro-magnetic spectrum and percentage reflectance for grasses and soils 55
in Landsat TM
Fig. 4.2 The process of pattern recognition for multi-spectral satellite image 57
Fig. 4.3 Multi-spectral image classification process 60
Fig. 4.4 False colour composite of Landsat ETM+ of Anas catchment 66
Fig. 4.5 Histogram and density function for April 6 200 image describing various 68
training classes
Fig. 4.6 A comparison of feature extraction algorithms for April 6 2000 image 69
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Fig. 4.7 Results obtained from ECHO and aximum-likelihood classifier for 6th April 71
2000 image
Fig. 4.8 Land-use classes derived from 6th April 2000 image 72
Fig. 4.9 Distribution of land-use classes in each watershed for 6th April 200 image 73
Fig. 5.1 A typical representation of statistical rainfall downscaling model 79
Fig. 5.2 Location of Anas catchment in atmospheric circulation window at 5°x5° 85
grid over Indian ocean
Fig. 5.3 Model sensitivity analysis for the effect of shifting atmospheric circulation 87
window on the rainfall probability and rainfall amount
Fig. 5.4 Conditional rainfall probability and rainfall amount for all stations using 89
12CP and 10CP types during 1985-94
Fig. 5.5 Analysis on conditional rainfall probability and conditional rainfall amount 91
for individual stations in Anas catchment
Fig. 5.6 Mean frequency of CP-type occurrence and wetness fraction for 12CP and 92
10CP types during monsoon for a period 1985-94
Fig. 5.7 Spatial distribution of mean 500hPa geo-potential height anomalies over 94
Indian ocean for dry and wet CP-types respectively
Fig. 5.8 Mean monthly observed and simulated rainfall for selected stations in Anas 96
catchment
Fig. 5.9 A comparison between observed and simulated number of rain-days during 97
monsoon season of 1985-94
Fig. 5.10 Observed and simulated number of rain-days during monsoon season of 97
1985-94
Fig. 5.11 Observed and simulated monthly rainfall time series for Jhabua and Thandla 98
stations for a period of 1961-94 during monsoon season
Fig. 5.12 Observed and simulated monsoon seasonal rainfall totals for Jhabua and 99
Thandla stations for a period 1961-94
Fig. 5.13 Mass curve for observed and simulated rainfall at station Jhabua 99
Fig. 6.1 Rainfall runoff in the L’Avic and La-Teula catchments in Spain 104
Fig. 6.2 The schematic representation of ZIN model, spatial sub-divisions and 105
hydrological process
Fig. 6.3 Spatial locations of reaches with deep river alluvium 107
Fig. 6.4 Spatial distribution of rainfall volume for 02.08.1994 event 108
Fig. 6.5 Hovmoller diagram derived from INSAT satellite images for 1994 monsoon 109
season
Fig. 6.6 Clouds movement derived from INSAT cloud images 110
Fig. 6.7 Response of catchment to hill-slope flow process, lag times and peak runoff 111
rates with respect to catchment size
Fig. 6.8 A sketch of runoff generation process and bank-full storage in main channel 111
with deep groundwater level
Fig. 6.9 Discretisation scheme for application of distributed model 113
Fig. 6.10 Spatial sub-division of Anas catchment and hydrological time-lag response 113
function
Fig. 6.11 Illustration of distributed flow routing model using Muskingum-Cunge 115
method
Fig. 6.12 Simplified representation of cross-section geometry 116
Fig. 6.13 Determination of channel parameters from Landsat-TM remote sensing 117
image
Fig. 6.14 Results obtained in the simulation for 06-07/09/1994 events for both 121
Anterbeliya and Mod stations
x