Abstract
This study highlights the advantage of satellite-derived rainfall products for hydrological modeling in regions of insufficient ground observations such as West African basins. Rainfall is the main input for hydrological models; however, gauge data are scarce or difficult to obtain. Fortunately, several precipitation products are available. In this study, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was analyzed. Daily discharges of three rivers of the Upper Senegal basin and one of the Upper Niger basin, as well as water levels of Manantali reservoir were simulated using PERSIANN-CDR as input to the CEQUEAU model. First, CEQUEAU was calibrated and validated using raw PERSIANN-CDR, and second, rainfalls were bias-corrected and the model was recalibrated. In both cases, ERA-Interim temperatures were used. Model performance was evaluated using Nash–Sutcliffe efficiency (NSE), mean percent bias (MPBIAS), and coefficient of determination (R2). With raw PERSIANN-CDR, most years show good performance with values of NSE > 0.8, R2 > 0.90, and MPBIAS < 10%. However, bias-corrected PERSIANN-CDR did not improve the simulations. The findings of this study can be used to improve the design of dam projects such as the ongoing dam constructions on the three rivers of the Upper Senegal Basin.
Highlights
In hydrological modeling, rainfall data are the main source of information [1,2]
As a contribution to the use of satellite precipitation in hydrological modeling, this study aims to assess the performance of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) to simulate daily discharges and water level in West African basins
This validation was done through point-to-pixel analysis by computing the coefficient of determination (R2) and the root mean square error (RMSE) between the monthly rainfalls
Summary
Satellite precipitation has become an important source, especially in areas where the rainfall measurements are nonexistent, scarce, or difficult to access. Meteorological and hydrological studies show that rain estimation and runoff modeling turn out to be very acceptable with this type of data [2,4,5,6,7,8,9,10]. Some of these studies emphasize that the use of satellite information must be verified for local applications. There is a considerable number of studies on this subject, but in the western part of Africa, these studies are limited, e.g., References [5,7,8,11]
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