Effective and efficient global optimization for conceptual rainfallrunoff models

The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not. i.•possible, to obtain unique optimal values for their parameters using automatic calibration methods. Unless the best set of parameters.associated witha given calibration data set chn be found, it is difficult o determine how sensitive the parameter estimates (and hence the model forecasts) are to factors uch as input and output data error, model error, quantity and quality of data, objective function used, and so on. Results are presented that establish clearly the nature of the multiple optima P.r0b!em for the research CRR model SIXPAR. These results uggest that the CRR model optimization problem is more difficult than had been previously thought and that currently used local search procedures have a very low probability of successfully finding the optimal parameter sets. Next, the performance of three existing lobal search procedures are evaluated on the model SIXPAR. Finally, a powerful new global optimization procedure is presented, entitled the shuffled complex evolution (SCE-UA) method, which was able to consistently ocate the global optimum of the SIXPAR model, and appears to be capable of efficiently and effectively solving the CRR model optimization problem.