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Parameter calibration is fundamental for the implementation and operation of a hydrological model. Automatic calibration techniques have been widely studied. However, even the most modern optimization schemes cannot always help us to obtain an optimal parameter set due to high dimensionality of the parameter space and complex interactions between parameters. The main purpose of this study was to test our strategy for automatic parameter calibration: lowering the dimensionality. Our modified Xinanjiang model was selected for study. It consists of 15 parameters controlling data adjustment and representing hydrological processes. Morris’ global sensitivity analysis technique was used to get better understanding about the structure of the parameter space. Parameters were found to have significantly different sensitivities at yearly, monthly and daily temporal scales. Also strong interactions between the parameters were observed at all three scales. A multi-step optimization scheme was designed and tested based on these observations. In this scheme, the 15 parameters are divided into three groups and optimized group by group at the time scale they are most sensitive to by using the SCEM-UA algorithm, a global optimization algorithm. The newly developed scheme is shown to be very efficient and robust.

Strategy to automatically calibrate parameters of a hydrological model: a multi-step optimization scheme and its application to the Xinanjiang model