Autocalibration in hydrologic modeling: Using SWAT2005 in small-scale watersheds

  • Authors:
  • C. H. Green;A. van Griensven

  • Affiliations:
  • USDA ARS, Grassland Soil and Water Research Laboratory, 808 E. Blackland Road, Temple, TX 76502, USA;UNESCO-IHE Water Education Institute, Department of Hydroinformatics and Knowledge Management, The Netherlands

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2008

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Abstract

SWAT is a physically based model that can simulate water quality and quantity at the watershed scale. Due to many of the processes involved in the manual- or autocalibration of model parameters and the knowledge of realistic input values, calibration can become difficult. An autocalibration-sensitivity analysis procedure was embedded in SWAT version 2005 (SWAT2005) to optimize parameter processing. This embedded procedure is applied to six small-scale watersheds (subwatersheds) in the central Texas Blackland Prairie. The objective of this study is to evaluate the effectiveness of the autocalibration-sensitivity analysis procedures at small-scale watersheds (4.0-8.4ha). Model simulations are completed using two data scenarios: (1) 1 year used for parameter calibration; (2) 5 years used for parameter calibration. The impact of manual parameter calibration versus autocalibration with manual adjustment on model simulation results is tested. The combination of autocalibration tool parameter values and manually adjusted parameters for the 2000-2004 simulation period resulted in the highest E"N"S and R^2 values for discharge; however, the same 5-year period yielded better overall E"N"S, R^2 and P-values for the simulation values that were manually adjusted. The disparity is most likely due to the limited number of parameters that are included in this version of the autocalibration tool (i.e. Nperco, Pperco, and nitrate). Overall, SWAT2005 simulated the hydrology and the water quality constituents at the subwatershed-scale more adequately when all of the available observed data were used for model simulation as evidenced by statistical measure when both the autocalibration and manually adjusted parameters were used in the simulation.