When More Data Is Not Better: Evaluating Satellite Rainfall Products in a Data-Scarce River Basin

The lack of extensive and functional ground observation networks introduces satellite-based rainfall products as an alternative. However, these datasets require prior evaluation. This study investigates the performance of four satellite- and gauge-based rainfall products: the Climate Hazards Group Infrared Precipitation with Station data version v2.0 (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); and the Global Precipitation Climatology Centre full daily data (GPCC).

The assessment was conducted using grid-to-point comparisons at different time scales, and hydrological modelling over the Mono River Basin, located in the Republics of Benin and Togo. To assess the suitability of the four products for flood purposes, a two-step approach was applied: (1) a satellite-only approach in which each product was used as input to the HBV-light hydrological model for runoff simulation, and (2) an observation-satellite approach in which gaps in observation data were filled using each product prior to the hydrological modelling. In all simulations, areal precipitation was derived with kriging before being input into HBV-light. On the one hand, the simulation with CHIRPS-only showed poor performance (NSE = -0.08 during calibration and -0.22 during validation), while the simulations with PERSIANN-only, TAMSAT-only, and GPCC-only yielded moderate performance, with NSE values ranging from 0.5 to 0.67. On the other hand, simulations with the observation-satellite combinations also showed moderate performances, with NSE values between 0.55 and 0.69, including for the observation-CHIRPS case.

The poor performance of the CHIRPS-only simulation, combined with the similar performance of all observation-satellite combinations, indicates that the quality of the satellite product used for gap filling plays a limited role. Moreover, the absence of significant improvement when using observation-satellite combinations compared to their satellite-only counterparts (except for CHIRPS) suggests that gap filling with satellite products does not necessarily enhance data quality. These results indicate that, in the Mono River Basin, gap filling may not be necessary when spatial interpolation methods such as kriging are applied.