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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...
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