comparison of optimization and uncertainty analysis methods in hydrological modeling

rainfall-runoff modeling is most important component in the water resource management of river basins. the successful application of a conceptual rainfall-runoff model depends on how well it is calibrated. the degree of difficulty in solving the global optimization method is generally dependent on the dimensionality of the model and certain of the characteristics of object 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 the present study an uncertainty analysis of conceptual rainfall - runoff model (hymod) were evaluated and compared using the four different evolutionary optimization methods for a leaf river representative watershed in us. results appealed that particle swarm optimization (pso) and shuffled complex evolution (sce) algorithms had better performances compared to hybrid genetic algorithm & pso (hybrid-ga&pso;) and shuffled frog leaping algorithm (sfla).