Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data

The identification of hydrologic models requires that appropriate data be selected for model calibration. In the research presented here, the shuffled complex evolution (SCE-UA) global optimization method was used to calibrate the NWSRFS-SMA conceptual rainfall-runoff flood forecasting model of the US National Weather Service, using a 40-year record of historical data. Based on 344 calibration runs using different lengths of data from different sections of the historical record, we conclude that approximately 8 years of data are required to obtain calibrations that are relatively insensitive to the period selected. Further, the reduction in parameter uncertainty is maximal when the wettest data periods on record are used. A residual analysis is used to compare the performance of the daily root mean square (DRMS) and heteroscedastic maximum likelihood error (HMLE) objective functions. The results suggest that the factor currently limiting model performance is the unavailability of strategies that explicitly account for model error during calibration.