Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light

Water is a crucial resource in West Africa, where large parts of the population rely on rainfed agriculture. Therefore, accurate knowledge of the water resources is of the utmost importance. Due to the declining number of rain gauging stations, the use of satellite and reanalysis precipitation datasets in hydrological modelling is steadily rising. However, accurate information on the benefits and deficits of these datasets is often lacking, especially in the West African subcontinent. For validation purposes, these products are commonly compared to freely available rain gauge data, which has in some cases already been used to bias correct the products in the first place. We therefore explored the possibility of a hydrological evaluation, where a model is calibrated for each dataset using streamflow as the observed variable. In this study, ten freely available satellite and reanalysis datasets (CFSR, CHIRPS, CMORPHv1.0 CRT, CMORPHv1.0 RAW, PERSIANN CDR, RFE 2.0, TAMSAT, TMPA 3B42v7, TMPA 3B42 RTv7 and GPCC FDDvl) were thus evaluated for six differently sized and located basins in West Africa. Results show that while performances differ, most datasets manage to somewhat accurately predict the observed stream flow in a given basin. Best results were achieved by datasets which use a multitude of input data, namely infrared and microwave satellite data, as well as observations from rain gauges (usually GPCC) for bias correction. If considering only the Nash Sutcliffe Efficiency averaged for all six basins during the calibration phase, best results were achieved by CMORPH CRT and PERSIANN CDR (both 0.66), followed by TAMSAT, CHIRPS and TMPA 3B42 (all three 0.64). Average results were achieved by RFE 2.0 (0.63), GPCC (0.61) and TMPA 3B42 RT (0.54). CMORPH RAW and CFSR performed worst (0.36 and -0.34 on average). (C) 2017 Elsevier B.V. All rights reserved.