Optimal use of the SCE-UA global optimization method for calibrating watershed models

Abstract The difficulties involved in calibrating conceptual watershed models have, in the past, been partly attributable to the lack of robust optimization tools. Recently, a global optimization method known as the SCE-UA (shuffled complex evolution method developed at The University of Arizona) has shown promise as an effective and efficient optimization technique for calibrating watershed models. Experience with the method has indicated that the effectiveness and efficiency of the algorithm are influenced by the choice of the algorithmic parameters. This paper first reviews the essential concepts of the SCE-UA method and then presents the results of several experimental studies in which the National Weather Service river forecast system-soil moisture accounting (NWSRFS-SMA) model, used by the National Weather Service for river and flood forecasting, was calibrated using different algorithmic parameter setups. On the basis of these results, the recommended values for the algorithmic parameters are given. These values should also help to provide guidelines for other users of the SCE-UA method.