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Inverse Modeling to Quantify Irrigation System Characteristics and Operational Management

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  • Hydrologic Sciences
  • Remote Sensing
  • Agricultural Science
  • Earth Science
  • Geography
  • Mathematics


Remotely sensed (RS) data is a major source to obtain spatial data required for hydrological models. The challenge for the future is to obtain besides the more direct observable data (landcover, leaf area index, digital elevation model and evapotranspiration), non-visible data such as soil characteristics, groundwater depth and irrigation practices.In this study we have explore the option of using inverse modeling to obtain these non-RS-visible data. For a command area in Haryana, India, we applied for the 2000–2001 rabi season a RS-GIS-combined inverse modeling approach to derive non-RS-visible data required in the regional application of hydrological models. A Genetic Algorithm loaded stochastic physically based soil-water-atmosphere-plant model (SWAP) was developed for the inverse problem and used in the study. The results showed good agreement with the inventoried data such as soil hydraulic properties, sowing dates, ground water depths, irrigation practices and water quality. The derived data could be used to predict the state of the system at anytime in the cropping season, which can be used to evaluate operational management strategies.

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