Abstract

Conceptual urban runoff (CUR) models, such as the U.S. Environmental Protection Agency Storm Water Management Model (Huber and Dickinson, 1988), or SWMM, are commonly used for planning and design of urban drainage systems. These models require usually a large number of variables and parameters in order to describe adequately the complex relationships between rainfall, runoff and watershed characteristics. This requirement has frequently become a barrier to the use of these models because of the difficulties involved in the estimation of all the model parameters. More specifically, the successful application of conceptual runoff models depends on how accurate the model is calibrated. However, the calibration of these models has been recognized as a complex and difficult task because of the presence of multiple optimal solutions encountered in the calibration. The main objective of the present study is to propose an automatic calibration scheme for CUR models using an appropriate optimization technique.

Two optimization methods were selected: the Downhill Simplex (DHS) method, and the Shuffled Complex Evolution (SCE). The proposed automatic calibration procedures were applied to the SWMM model. Two different scenarios were considered using "error-free" synthetic data, and using observed data available on the Upper Bukit Timah catchment in Singapore. Results of this study have indicated that, for the SWMM model, there are many local optima within a given feasible parameter range, and hence the use of the DHS local optimization technique would not be appropriate. In such cases, the calibration problem should be treated as a global optimization one. More specifically, it has been shown that the proposed SCE-based calibration scheme was able to provide consistent parameter estimates for the SWMM model.

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