Calibration of watershed conceptual models using local and global ptimizattion

الملخص EN

Many issues related to water resources require the solution of optimization problems.

Optimization problems can be quite difficult such as the calibration of many conceptual watershed models.

Conceptual watershed "models are formulated using empirical relationships between hydrological variables observed in nature or field experiments .The success of automatic calibration depends mainly on the choice of optimization method.

Most early attempts to calibrate watershed models have been based on local-search optimization methods.

Local optimization methods are not designed to handle the presence of multiple regions of attraction, multi-local optima, insensitivities and parameter interdependences, and other problems encountered in the calibration of watersheds models.

It is therefore imperative that global optimization procedures that are capable of dealing with these various difficulties be employed.

In this study, one local search optimization method and one global search method are used to calibrate the parameters a simple ten-parameter rainfall-runoff model.

The local search method is the well known Rosen rock's direct search method ; the global search method is the newly developed Shuffled Complex Evolution (SCE) method .

The results revealed that Shuffle Complex Evolution optimization method is more superior to Roseboro's direct search method.

The results confirmed the finding of many watershed modelers about the dependency of Rosen rock's method on the choice of initial search points.

It further adds that Rosen rock's method is only effective when the initial search points are taken within 5 % or less from the true optimum parameter set.

The results indicate that a proper choice of optimization methods can enhance the possibility of obtaining unique and conceptually realistic parameter estimate.