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Assessment of a SWAT model for soil and water management in India

Author

Listed:
  • Kaur, Ravinder
  • Srinivasan, Raghavan
  • Mishra, Kamal
  • Dutta, D.
  • Prasad, Durga
  • Bansal, Gagan

Abstract

The potential of a Spatial Decision Support System (SDSS) for estimating water and sediment yields was assessed in a large (92.46 km2) experimental catchment in the Damodar-Barakar basin. The SDSS is based on a SWAT model and was operated under prevailing resource management conditions. Application of the proposed SDSS predicted average water and sediment yields of 383.37 mm yr-1 and 21.28 t ha-1 yr-1 were predicted as against actual observations of 390.69 mm yr-1 and 25.35 t ha-1 yr-1 for the validation periods 1981-1983; 1985-1989 and 1991. Simulations of the annual dynamics of total water and sediment yields showed good to moderately good correlation coefficients of 0.83 and 0.65; model efficiency coefficients of 0.54 and 0.70; mean relative errors of -4.28% and -17.97% and root mean square prediction errors of 71.8 mm and 9.63 t ha-1, respectively. The study demonstrated that the proposed SDSS could also be used to identify priority arrears having high water and soil losses within the test catchment. The presence of large areas under long duration paddy rice and maize crops and/or low forest cover appeared to be the major reasons for the high water and soil losses from some experimental areas.

Suggested Citation

  • Kaur, Ravinder & Srinivasan, Raghavan & Mishra, Kamal & Dutta, D. & Prasad, Durga & Bansal, Gagan, 2003. "Assessment of a SWAT model for soil and water management in India," Land Use and Water Resources Research, University of Newcastle upon Tyne, Centre for Land Use and Water Resources Research, vol. 3, pages 1-7.
    • Handle: RePEc:ags:luawrr:47873
    DOI: 10.22004/ag.econ.47873
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    Cited by:

    1. Sarangi, A. & Bhattacharya, A.K., 2005. "Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India," Agricultural Water Management, Elsevier, vol. 78(3), pages 195-208, December.

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    Keywords

    Resource /Energy Economics and Policy;

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