Stochastic calibration of HEC-1 model parameters using SUFI ‎algorithm

Rainfall runoff models are used mostly in simulation of flood events. Also, calibration of rainfall runoff model parameters is an important and challenging issue in flood simulation. Due to random characteristic of these parameters, the deterministic optimization is not a suitable approach for calibration of rainfall runoff model. So, in this study, the SUFI (Sequential Uncertainty Fitting) algorithm is used as a stochastic approach and the optimized range of parameters were extracted. The obtain results shown, in calibration step, the correlation coefficient between observed hydrographs (three events) and the best generated hydrographs were more than 0.9 and also, the average difference between observed hydrographs and the best generated hydrographs were less than 5 percent. Furthermore, in validation step, the correlation coefficient between observed hydrograph and the best generated hydrograph was 0.99 and also, the average difference between observed hydrograph and the best generated hydrograph was 11 percent. So, the SUFI algorithm is a suitable approach in stochastic calibration of HEC-1 model.