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, th e shuffled complex evolution (SCE-UA) global optimization method was u sed to calibrate the NWSRFS-SMA conceptual rainfall-runoff flood forec asting model of the US National Weather Service, using a 40-year recor d of historical data. Based on 344 calibration runs using different le ngths of data from different sections of the historical record, we con clude that approximately 8 years of data are required to obtain calibr ations that are relatively insensitive to the period selected. Further , the reduction in parameter uncertainty is maximal when the wettest d ata periods on record are used. A residual analysis is used to compare the performance of the daily root mean square (DRMS) and heteroscedas tic maximum likelihood error (HMLE) objective functions. The results s uggest that the factor currently limiting model performance is the una vailability of strategies that explicitly account for model error duri ng calibration.