Automatic calibration of a distributed catchment model

Parameters of hydrologic models often are not exactly known and therefore have to be determined by calibration. A manual calibration depends on the subjective assessment of the modeler and can be very time-consuming though. Methods of automatic calibration can improve these shortcomings. Yet, the high number of parameters in distributed models makes special demands on the optimization. In this paper a strategy of imposing constraints on the parameters to limit the number of independently calibrated values is outlined. Subsequently, an automatic calibration of the version SWAT-G of the model SWAT (Soil and Water Assessment Tool) with a stochastic global optimization algorithm, the Shuffled Complex Evolution algorithm, is presented for a mesoscale catchment.