Evaluation of precipitation and actual evaporation products over the Nile Basin

An improved representation of the spatio-temporal patterns of climatological variables is crucial for ecological, agricultural, and hydrological applications and can improve the decision-making process. Traditionally, precipitation (P) and actual evaporation (ETa) are estimated using ground-based measurements from meteorological stations. However, the estimation of spatial patterns derived solely from point-based measurements is subject to large uncertainties, particularly in data-scarce regions as the Nile Basin, which has an area of about 3 million km². This study evaluates six state-of-the-art P products (CHIRPSv2, CMORPHv1, CRU TS4.02, MSWEPv2.2, PERSIANN-CDR and GPCCv2018) and five ETa products (SSEBop, MOD16-ET, WaPOR, GLEAM and GLDAS) over the Nile Basin to identify the best-performing products. The P products were evaluated at monthly and annual temporal scales (from 1983 onwards) through a point-to-pixel approach using the modified Kling-Gupta Efficiency and its components (linear correlation, bias, and variability ratio) as continuous performance indices. The ETa products were evaluated through the water balance approach (due to the lack of ground-based ETa measurements) for 2009-2018 at the multiannual scale. Because streamflow data were not available for this period, an empirical model based on the Random Forest machine learning technique was used to estimate streamflow at 21 catchments at the monthly scale. For this purpose, we used streamflow data from 1983 to 2005 as the dependent variable, while CHIRPSv2 precipitation and ERA5 potential evaporation and temperature data were used as predictors. For the catchments where the model performed well over the validation period, streamflow estimates were generated and used for the water balance analysis. Our results show that CHIRPSv2 was the best performing P product at monthly and annual scale when compared with ground-based measurements, while WaPOR was the best-performing ETa product in the water balance evaluation. This study demonstrates how remote sensing data can be evaluated over extremely data-scarce scenarios to estimate the magnitude of key meteorological variables, yet also highlights the importance of improving data availability so that the characterisation of these variables can be further evaluated and improved.