Evaluation of the performance of remotely sensed rainfall datasets for flood simulation in the transboundary Mono River catchment, Togo and Benin

Study region: This study focused on the Mono River Basin in west Africa. Study focus: The lack of extensive and functional measurement networks for flood monitoring, introduces satellite-based rainfall datasets as an alternative which needs however to be evaluated beforehand. This study investigated the performance of four satellite and gauge-based rainfall products – Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT), the Global Precipitation Climatology Centre full daily data (GPCC) – with grid-to-point and hydrologic modelling approaches at different time scales over the Mono basin. New Hydrological Insights for the Region: With the grid-to-point assessment, results show poor performances at daily and annual scales while the seasonal cycles were well reproduced with Nash-Sutcliffe efficiency (NSE) equal or higher than 0.94, and correlation coefficient above 0.9. All assessed products exhibited high probability of detection (POD) and low false alarm ratio (FAR) at dekadal scale. Based on NSE values of hydrologic modelling, best results were achieved by PERSIANN, followed by GPCC and TAMSAT, but CHIRPS performed worst with negative values. By filling the gaps of gauge data with the satellite-based products, we noticed that filling the missing does not necessarily improve the quality of the data and that may not be needed in the case of the Mono basin if interpolation methods like kriging are applied