Parameter uncertainty analysis by Monte-Carlo method for flood forecasting using WRF Prediction of Precipitation and Air Temperature in Dez Basin

Parameters uncertainty of rainfall-runoff models are the main sources of uncertainty in real time flood forecasting. In this paper, the Monte Carlo method is used to estimate the uncertainty of the forecasted flood hydrograph due to uncertainty in the calibration parameters of the rainfall-runoff model in Dez Basin in southwestern Iran. Precipitation and air temperature were predicted using Weather Research and Forecasting (WRF) model. The HEC-HMS hydrological model was used to forecast the flood hydrograph corresponding to the predicted precipitation and air temperature. The SCS-CN, Clark Unit Hydrograph, and Muskingum-Cung methods were used to model losses, transform and flood routing, respectively. The results show that the best scheme in WRF model is MYJLG to predict hourly precipitation and air temperature in Dez Basin. Therefore, the MYJ boundary layer scheme, Lin cloud microphysics scheme and GODDARD radiant scheme have the best performance in flood forecasting in Dez basin. In addition, the results of this study show that considering the simultaneous uncertainty in all parameters, the uncertainty in peak discharge of the forecasted flood hydrograph is higher than the uncertainty in the volume of the hydrograph. So that the uncertainty in peak discharge and the volume of forecasted flood hydrograph due to the uncertainty of all parameters are equal to 32.5 and 21.2%, respectively. Thus, with the lack of flood forecasting and warning based on risk, quantifying uncertainty has provided additional information about forecasts that will help decision makers make better decisions.