Comparing Techniques of Rosenbrock, GA, URS and SCE-UA Optimization to Determine the Parameters of SIMHYD Model to Simulate Discharge Flow

The optimization of conceptual rainfall- runoff model parameters is important in calibration whose goal is determining the values of the model parameters providing the best fit between observed and estimated flows data. In this research, the performance of probabilistic optimization techniques was studied for calibrating the SIMHYD model in Khorramabad watershed with the area of 2467 square kilometers. These techniques for calibration are consist of genetic algorithms (GA), Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA), Uniform Random Sampling (URS) and Rosenbrock algorithm. The results showed that changing the optimization algorithms has an important effect on the performance of conceptual model. So that, the values of Nash-Sutcliff (NS) coefficient for used algorithms were derived as 0.73, 0.72, 0.70 and 0.75, respectively. Compared with the others, the Rosenbrock algorithm had more performance, thus this algorithm was selected to simulate SIMHYD model for calibration during the period, from 2004 to 2008 and for validation during the period from 2009 to 2010. The values of coefficients NS, RMSE and R in calibration period were derived 0.73, 0.66 and 0.86, respectively and in validation period, they were 0.68, 0.80 and 0.83, respectively. The results showed that optimization algorithms in simulating SIMHYD model had high accuracy to identify the parameter values and they are suitable for the study area. Therefore using hydrological models and selecting the appropriate optimization techniques, we could simulate the watersheds flows with highly accuracy.