TY - JOUR
T1 - Automatic calibration of conceptual rainfall-runoff models
T2 - Sensitivity to calibration data
AU - Yapo, Patrice O.
AU - Gupta, Hoshin Vijai
AU - Sorooshian, Soroosh
N1 - Funding Information:
The authors wish to acknowledge the technical support provided by the Center for Computing Information and Technology (CCIT) at The University of Arizona. Partial financial support for this research was provided by the National Science Foundation (grants #EAR-9415437 and #BCS-9307411) and the Hydrologic Research Laboratory of the National Weather Service (NAgOAA-H-HY505).
PY - 1996
Y1 - 1996
N2 - 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.
AB - 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.
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U2 - 10.1016/0022-1694(95)02918-4
DO - 10.1016/0022-1694(95)02918-4
M3 - Article
AN - SCOPUS:0030162090
SN - 0022-1694
VL - 181
SP - 23
EP - 48
JO - Journal of Hydrology
JF - Journal of Hydrology
IS - 1-4
ER -