جستجوی مقالات مرتبط با کلیدواژه « wrf model » در نشریات گروه « مهندسی آب »

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.

This study presents verification of the Weather Research and Forecasting (WRF) model for the 24-h accumulated precipitation forecasts in Iran for the period of November 2008 to June 2009 for 24, 48, and 72.houre-ranges. Verification is done separately for three precipitation ranges and nine different regions in the country via 2×2 contingency table and quantities attributed to it. Though precipitation is the most difficult parameter to forecast, results show the good skill of WRF Forecasts.However regarding the bias, the model has over-forecasting. In northern parts of Iran with the highest rainfall rate, the value of TS is more than 0.50 and shows the highest accuracy of precipitation forecasts in different regions. In the regions with the lowest rainfall rates (east and southeast of Iran) TS is less than 0.40 and the model results show the least accuracy. Economic value of the forecasts reaches 60% for the cost to loss ratios of less than 0.40. This recommends the operational use of this model.

Despite the fact that the quality of forecasts from numerical weather prediction (NWP) models has increased in recent years, yet exact forecast of precipitation is a difficult and challenging task. In order to obtain more accurate precipitation forecasts, efforts have been made to improve the models, formulations, and the accuracy of the initial conditions. One important alternative is to improve the model output via postprocessing. In this paper, the WRF model was applied for a six month period from 1 November 2008 to 30 April 2009 with two nests using 45 and 15 Km grid. The model outputs were then postprocessed for 24-hour precipitation forecasts for 205 synoptic stations over Iran using two methods of the moving average (MA) and the best easy systematic estimator (BES). Data for the first three months were used for training and the rest of data were used for the test and comparison. Statistical scores including degree of mass balance (DMB), mean absolute error (MAE) and its corresponding skill score were calculated for both direct and postprocessed outputs. Results showed that both methods improve the direct outputs of the model. The MA method decreased MAE for different stations from 5 to 50 percent. The mean of MAE decrease for all stations was about %25. In the BES method the average value of MAE for all stations is around 13 percent.