Satellite-based streamflow simulation using CHIRPS satellite precipitation product in Shah Bahram Basin, Iran

Precipitation is the most important climate variable in hydrological practices, so accurate estimation of its intensity and volume is very crucial for hydrological applications. Remote sensing precipitation estimations have recently been widely employed in water resources management due to the lack of observed precipitation measurements in remote areas. However, remote sensing precipitation estimations are not free from systematic errors. This study aims to bias-correct the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) satellite precipitation estimations using the Gaussian-Copula approach and illustrates how it improves the simulated flow characteristics in the Shah Bahram basin in Kohgiluyeh and Boyer-Ahmad Province, southwestern Iran. The Nash-Sutcliff Efficiency (NSE) calculated between the original CHIRPS precipitation estimation and observation in the Shah Bahram basin equals − 0.14; however, when bias-corrected CHIRPS data was compared to observation, the NSE increased to 0.23, suggesting about 158% improvement in the CHIRPS precipitation estimation when bias-corrected with the Gaussian-Copula approach. Next, the bias-corrected precipitation time series were utilized as the hydrologic modeling system inputs to simulate flow specifications such as discharge and peak value. Then, the simulation of the flow parameters was carried out with both original and bias-corrected CHIRPS satellite precipitation estimations and the ground-based precipitation. Though the NSE statistic of the simulation for the testing period has not changed significantly, the Pbias statistic has considerably improved. The result of the study indicates the good performance of the proposed bias correction approach in reducing the CHIRPS satellite estimations errors, concluding that it is a suitable approach for bias correction of the other satellite precipitation estimations in areas that suffer from the lack of ground-based observations necessary for flood forecasting and other hydrological practices.