Auto Calibration of a Rainfall-Runoff Model Based on SCE Method

The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Generally, CRR model deals with many parameters that should be estimated through robust optimization tools. The degree of difficulty in solving a global optimization method is generally dependent on the dimensionality of the CRR model and certain characteristics of objective function. The purpose of optimization is to finalize the best set of parameters associated with a given calibration data set that optimize the evaluation criteria. In this study, a global optimization method known as the SCE (Shuffled Complex Evolution) has been developed for autocalibration of CRR parameters. This method is developed based on the nature of optimization problems in CRR models, combination of probabilistic and deterministic approaches, and clustering and competitive evolution. SCE method has shown promise as an effective and efficient optimization technique. Model verification and validation results indicated that automatic calibration was superior to other existing algorithms. Also, the proposed SCE algorithm was programmed via innovative ways to reduce the memory allocation and improve the speed of computations. In addition, a new method of storing very large matrices with small number of non-zero members was implemented. Thus, personal computers can also be used for automatic calibration of up to 35 parameters. In this study, the developed SCE technique has been applied for auto calibration of storage CRR model, namely NAM, in Gamasiab watershed within the greate Karkhe basin. The calibration and validation results and evaluating criteria shows the effectiveness and efficiency of this method for autocalibrating of CRR parameters.