English

Abstract

Satellite rainfall estimates (SRE) with high spatial and temporal resolution and large areal coverage provide a potential alternative source to force hydrological models within regions where ground-based measurements are not readily available. The Gambia Basin in West Africa provides a good example of a case where the use of satellite precipitation estimates could be beneficial. This study aims to compare three SRE over a 12-year periods (1998-2010), before and after their integration into the GR4J hydrological model over the Gambia Basin. The inter -compared products are Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and TRMM 3B42v7 (Tropical Rainfall Measuring Mission). The calibration and validation of the GR4J model over the Gambia basin using a reference rainfall product (RRP) pointed out a very good performance. The correlation coefficient between simulated and observed daily discharge is higher than 0.8 both for calibration and validation. The inter-comparison of SRE against RRP and using them as forcing data into the calibrated GR4J hydrological model presented some coherence in the product performance. PERSIANN-CDR performs better both when comparing against RRP and when used in GR4J. The low performance of CHIRPS is surprising because it is supposed to be a product that includes ground-base station. This result may also indicate that in areas without ground stations, the CHIRPS is less accurate than other rainfall products that are based only on satellite images. Finally, a bias correction is applied to the SRE using the RRP. The bias correction had significantly improved the product performance. On average, the bias fell from 100 to 1.5% compared to the RRP, but the impact on the error is less significant. When using the corrected SRE in the hydrological model, the impact is very significant both on the bias and error. The overall performance of the different biases that corrected SRE is comparable. Key words: Gambia, precipitation, satellite, evaluation, modeling, bias correction, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission (TRMM).