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Sensitivity analysis, calibration, and testing of a distributed hydrological model using error-based weighting and one objective function
Civil and Environmental Engineering, University of California, Davis, California, USA
U.S. Geological Survey, Boulder, Colorado, USA
Institute for Hydromechanics and Water Resources Management, ETH Zurich, Zurich, Switzerland
Institute for Hydromechanics and Water Resources Management, ETH Zurich, Zurich, Switzerland
We evaluate the utility of three interrelated means of using data to calibrate the fully distributed rainfall-runoff model TOPKAPI as applied to the Maggia Valley drainage area in Switzerland. The use of error-based weighting of observation and prior information data, local sensitivity analysis, and single-objective function nonlinear regression provides quantitative evaluation of sensitivity of the 35 model parameters to the data, identification of data types most important to the calibration, and identification of correlations among parameters that contribute to nonuniqueness. Sensitivity analysis required only 71 model runs, and regression required about 50 model runs. The approach presented appears to be ideal for evaluation of models with long run times or as a preliminary step to more computationally demanding methods. The statistics used include composite scaled sensitivities, parameter correlation coefficients, leverage, Cook's D, and DFBETAS. Tests suggest predictive ability of the calibrated model typical of hydrologic models.
Received 1 July 2008; accepted 26 March 2009; published 24 June 2009.
Citation: (2009), Sensitivity analysis, calibration, and testing of a distributed hydrological model using error-based weighting and one objective function, Water Resour. Res., 45, W06427, doi:10.1029/2008WR007255.
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