Document Type : Research Article
Ph.D. in Water Resources Engineering, Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran
Professor, Department of Hydrology and Water Resources, Faculty of Water Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran.
Head of the Office of Water and Environment Models of Khuzestan Water and Power Organization, Ahwaz
Assistant Professor of Faculty of Civil, Water and Environmental Sciences, Shahid Beheshti University, Tehran, Iran
Precipitation is a vital variable in hydrological studies which its applications and disciplines can be seen widely in water resources management. This parameter differs significantly over location and time; the lack of suitable data for precipitation results in difficulties in hydrological predictions. Therefore, the availability of this parameter in high spatial and temporal resolution is of great importance. Satellite precipitation estimation systems can provide information in areas where precipitation data is not available. So, studying the accuracy of this type of data is very crucial. In this study, precipitation data from three satellite data sets ,TRMM, ERA5 and PERSIANN-CCS, for the Idenak region, located at south western part of Iran, (with four stations including Dehno, Ghale-Raeesi, Idenak, Margoon) from 2003 to 2014 was used and evaluated on a daily, monthly and annual basis. The results of this study indicate that the estimation of annual and monthly precipitation data obtained with the ERA5 model and TRMM have a better fit with the observational data in terms of precipitation values and spatial distribution respectively. On a daily basis, the evaluation results show that at all stations, other than Margoon, the ERA5 model has been more appropriate with respect to RMSE and CC values and provides better results. Moreovre, according to the CSI values, in the detection of rainy and non-rainy days, the best detection is associated with the ERA5 model at all stations except the Ghale-Raeesi station while PERSIANN-CCS model has higher ability at this station.