جستجوی مقالات مرتبط با کلیدواژه « مدل سازی بارش-رواناب » در نشریات گروه « مهندسی آب »

Flood is one of the most important natural disaster that cause human and financial damages and its evaluation needs peak discharge. This study evaluates Beshar River runoff peak discharge due to precipitation with different return periods. The geological and land use maps were provided in ArcGIS and ENVI, based on which the CN map was prepared. Respectively, 53.46 % and 30.75 % of the study area are in A and D hydrologic groups. The best probability distribution for each station was determined using Chi-square test, thereafter, precipitation with different return periods was calculated. After dividing the area into 9 sub-basins and calculating the required parameters for each, the model was created. Three and two flood events were respectively used for calibration and validation and the average values of RMSE and NS indexes were obtained as 0.53 and 0.67 for calibration and 0.55 and 0.68 for validation. The low values of errors indicated the reliability of the simulated model. The peak discharge of each return period was calculated employing type IA of SCS model. Comparing the peak discharges obtained from HEC-HMS and statistical method at the basin outlet showed that the least difference between these two methods was related to the 50-year return period and for the return periods below and above 50 years, the HEC-HMS discharges were correspondingly less and greater than the statistically calculated peak discharges. Consequently, the HEC-HMS model can accurately calculate the flood peak discharge for Beshar basin.

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To achieve this aim, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modeling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast, for the desired historical period, up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models is more accurate for the forecast horizon of the next month than two and three months. So that the forecast horizon of the next month has the highest Nash-Sutcliffe coefficient, in calibration 0.77 and in validation 0.48. The highest correlation coefficient in calibration 0.87 and validation 0.69, the lowest root mean square error in calibration 6.8 and validation 6.3 million cubic meters and also has the best relative bias value for calibration 0.96 and validation 1.1. Also the results, based on the POD and FAR probabilistic indices, showed that the developed predictive model has a high ability to detect different states of stream flow events, especially for extreme flows event.