Water Productivity of Irrigated Crops in Sirsa District, India
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Water productivity of irrigated crops in Sirsa district, India Integration of remote sensing, crop and soil models and geographical information systems J.C. van Dam and R.S. Malik (editors) 2003 CHAUDHARY CHARAN SINGH HARYANA AGRICULTURAL UNIVERSITY Overview of scientists who participated in the WATPRO project Chaudhary Charan Singh Haryana Agricultural University Dr. R.S. Malik, chief scientist Prof. R. Kumar, soil and water engineering Prof. J. Singh, soil and water engineering Dr. R.K. Jhorar, soil and water engineering Dr. A.S. Dhindwal, agronomy Dr. H. Singh, agronomy Dr. M.S. Bhatto, agronomy Dr. B.S. Jhorar, soil science CHAUDHARY CHARAN SINGH Dr. D.S. Dabas, soil science HARYANA AGRICULTURAL UNIVERSITY Mr. Devender Singh, MSc., research associate Mr. Udaivur Singh, MSc., research associate Mr. Jhaber Mal, MSc., research associate Mr. Sher Singh, MSc., research associate International Water Management Institute Dr. P. Droogers, water resources Dr. H. Murray-Rust, water resources Wageningen University and Research centre Dr. J.J.E. Bessembinder, plant production systems Dr. P.A. Leffelaar, plant production systems Mr. T. Ponsioen, MSc., plant production systems Mr. J. Wolf, MSc., plant production systems Mr. J.G. Kroes, MSc., regional water analysis Mr. H.W. ter Maat, MSc., regional water analysis Mr. W. Immerzeel, MSc., geographical information systems Prof. R.A. Feddes, water resources Dr. J.C. van Dam, water resources Mr. R.S. Khatri, MSc., water resources Mr. A. Roelevink, BSc., water resources WaterWatch Dr. W.G.M. Bastiaanssen, remote sensing Dr. H. Pelgrum, remote sensing Mr. S.J. Zwart, MSc., remote sensing Contents 1 Introduction 11 1.1 Refocusing irrigation water management 11 1.2 General background of water productivity 14 1.3 Summary of earlier work in Northwest India 15 1.4 The toolbox 18 1.5 WATPRO objectives 19 2 Water management and crop production in Sirsa Irrigation Circle 21 2.1 Introduction 21 2.2 Climate and rainfall 21 2.3 Topography and soil types 22 2.4 Canal irrigation 22 2.4.1 Canal irrigation system 22 2.4.2 Canal irrigation system performance 23 2.5 Groundwater 25 2.5.1 Groundwater quality 25 2.5.2 Groundwater depths 25 2.5.3 Groundwater use 26 2.6 Crop production 26 2.6.1 Cropping pattern 26 2.6.2 Crop yields 27 2.7 Major issues related to water management and crop production 27 3 Measurement program and description database 29 3.1 Introduction 29 3.2 Farmer fields 29 3.2.1 Soil measurements 30 3.2.2 Irrigation water measurements 32 3.2.3 Crop measurements 32 3.2.4 Management practices 34 3.3 Field experiments at research stations 34 3.4 Regional data 36 3.4.1 Meteorological data 36 3.4.2 Land use 38 3.4.3 Soil information 38 3.4.4 Canal irrigation water 39 4 Water and salt balances at farmer fields 41 4.1 Introduction 41 4.2 SWAP model description 42 4.2.1 Water and salt balance 43 4.2.2 Soil water flow 43 4.2.3 Top boundary condition 44 4.2.4 Bottom boundary condition 46 4.2.5 Solute transport 46 4.3 Materials and methods 47 4.3.1 Monitoring of farmer fields 47 4.3.2 Input parameters of SWAP 48 4.3.3 Inverse modeling of soil hydraulic functions 49 4.3.4 Water management response indicators 50 4.4 Results and discussion 51 4.4.1 Soil hydraulic functions 51 4.4.2 Water and salt balances 54 4.5 Soil hydraulic parameters for regional scale 57 4.6 Conclusions 58 5 Analysis of crop growth 59 5.1 Introduction 59 5.1.1 Water productivity and simulation models 59 5.1.2 Research objectives 60 5.2 Calibration of SWAP/WOFOST for wheat, rice and cotton 60 5.2.1 The detailed crop module in SWAP 60 5.2.2 Methodology 61 5.2.3 Calibration for wheat 62 5.2.4 Calibration for rice 65 5.2.5 Calibration for cotton 66 5.3 Comparison of actual and simulated crop production and evapotranspiration 67 5.3.1 Methodology 67 5.3.2 Comparison for wheat 68 5.3.3 Comparison for rice 72 5.3.4 Comparison for cotton 73 5.4 Management options and water productivity 74 5.4.1 Definitions of water productivity 74 5.4.2 Levels of water productivity 74 5.4.3 Deficit irrigation 77 5.4.4 Variation between years 78 5.4.5 Sowing date 79 5.4.6 Soil type 80 5.4.7 Irrigation water quality 81 5.5 Discussion and conclusions 81 5.5.1 Methodology and recommendations for further research 81 5.5.2 Management options, water productivity and yield level 82 6 Remote sensing analysis 85 6.1 Introduction 85 6.2 Satellite images used 85 6.3 Land cover classification 87 6.4 The Surface Energy Balance Algorithm for Land (SEBAL) 90 6.5 SEBAL results Rabi season 2001-2002 91 6.6 SEBAL results Kharif season 2002 96 7 A regional approach to model water productivity 101 7.1 Introduction 101 7.2 Available regional datasets 102 7.2.1 Soils 102 7.2.2 Land use 102 7.2.3 Climate 103 7.2.4 Irrigation: groundwater 103 7.2.5 Irrigation: canal water 106 7.3 Methodology 107 7.3.1 Stratification 107 7.3.2 Parameterisation 109 7.3.3 Regional modeling 113 7.4 Results 114 7.4.1 Reference situation 114 7.4.2 Comparison with remote sensing 116 7.5 Conclusions and recommendations 118 8 Integration of remote sensing and simulation of crop growth, soil water and solute transport at regional scale 121 8.1 Defining water productivity 121 8.2 An appraisal of the water productivity definitions 122 8.3 Calculation, aggregation and validation of water productivity 123 8.4 Application at investigated farmer fields 126 8.5 Proposed measures in Sirsa district 129 8.6 Concluding remarks 133 9 Future water management in Sirsa: options to improve water productivity 135 9.1 Introduction 135 9.2 Scale issues in water productivity 135 9.3 Water productivity under current conditions: the remote sensing approach 136 9.3.1 Linking remote sensing and models 136 9.3.2 Components of water productivity 137 9.3.3 Water balance 142 9.3.4 Water productivity of entire Sirsa Irrigation Circle 143 9.4 Options to increase water productivity: the modelling approach 146 9.4.1 Field scale scenarios 148 9.4.2 Regional scale scenarios 150 9.5 Overall conclusions and recommendations 153 9.5.1 Conclusions from the remote sensing analysis 153 9.5.2 Conclusions from the modelling analysis 155 9.5.3 Recommendations 155 10 References 157 Appendix A. Description of CD-ROM 165 Appendix B. SEBAL evapotranspiration 169 Dam, J.C. van, and R.S. Malik (Eds.), 2003. Water productivity of irrigated crops in Sirsa district, India. Integration of remote sensing, crop and soil models and geographical information systems. WATPRO final report, including CD-ROM. ISBN 90-6464-864-6. 173 pp. Abstract Major issues with respect to water management in Sirsa district are waterlogging and salinization in areas with saline groundwater and over-exploitation of groundwater in areas with fresh groundwater. The present crop yield increase of the major crops in Sirsa district is marginal. Recent studies show that water is the main limiting factor to increase the crop yields. In order to identify the main water losses, an extensive WAter PROductivity study (WATPRO) has been performed in Sirsa district. The WATPRO project focussed on (1) the integration and application of advanced research tools (remote sensing, detailed crop and soil models, and GIS), (2) the upscaling from local field scale to regional scale, (3) the application of recent concepts on water productivity at various scales, and (4) a survey of the most viable scenario’s that improve water productivity in Sirsa district. During the agricultural year 2001/02 extensive measurements were collected for the major crops wheat, cotton and rice at both experimental fields and at farmer fields with various soil, irrigation and management conditions. The water flow and salt transport model SWAP and the detailed crop growth model WOFOST were calibrated in order to reproduce the measurements at the experimental and farmer fields. The calibrated models have been used to analyse viable water management options at field level to improve WP. High and low resolution satellite images have been used to derive the cropping pattern, and in conjunction with the remote sensing algorithm SEBAL to estimate evapotranspiration, biomass production and water productivity as distributed over Sirsa district. The main conclusion from the remote sensing analysis is that WP is good and rather uniform for wheat, and moderate for rice and cotton. The wider range in WP for rice suggests that by narrowing the variability and increasing the WP for rice, productivity if water resources in Sirsa can be improved substantially. Available data sets of Sirsa district on meteorology, crops, soils, groundwater, canal and tubewell water and cultivable command areas, have been integrated into a GIS. The data were downscaled to a level of 30x30 m to allow comparison with remote sensing data. The followed stratification resulted in a final overlay with 2404 calculation units. For each unit the tuned SWAP/WOFOST combination was used to simulate crop growth and water and salt balances. The results of the current regional analysis are such that it can be used to compare regional scenario studies qualitatively. Improvement of both both instrument and data sets may enable a more quantitative approach.