Global optimization techniques for the calibration of conceptual rainfall-runoff models

In this study we present the results of the comparison of three different algorithms: the Genetic Algorithm coupled with Sequential Quadratic Programming (GA-SQP), the Pattern Search also coupled with SQP (PS-SQP) and the Shuffled Complex Evolution (SCE-UA), The analyses were conducted using a conceptual rainfall-runoff model applied both to a single basin and to a complex basin, For both types of basin, a theoretical case without model and data errors was considered, in which the true values of the parameters are known a priori, and several real-world cases where model and data errors exist. With reference to the single basin, the SCE-UA algorithm was the most reliable while the other two algorithms gave solutions equivalent to those of the SCE-UA in the theoretical case, but in the real-world cases they showed an increasing tendency (particularly the PS-SQP) to be trapped in local minima. With reference to the complex basin, none of the three algorithms identified the exact solution in the theoretical case. However, the SCE-UA was the one which systematically approximated it better than the others, In the real-world case its solutions were stable but characterized by many parameter values set at the boundary of their own range. The other two algorithms produced a very unstable set of parameters.