Assessment of Hydrologic Data Estimates From ERA5 Reanalyses in Benin, West Africa

Abstract

ABSTRACT In West Africa, the validation of distributed models is limited by the quality and availability of point station data measured in situ. ERA5 is a climate reanalysis product produced by the European Centre for Medium‐range Weather Forecasts (ECMWF) and is suggested to overcome this constraint. This study assessed and compared the quality of ERA5 and its variant ERA5‐Land (namely, LAND) over Benin at spatial and monthly time scales. ERA5 relies on a single‐level version with a 0.25° × 0.25° resolution, while LAND is a land surface version with a 0.1° × 0.1° resolution. Four variables were collected, namely, surface runoff (SRO), evapotranspiration (PET), water table depth (WTD) and soil water content (SWC). Single nearest pixel (SNP) and inverse distance weighting (IDW) selection methods were used to compare the reanalyse data to point station data based on the correlation (c), mean absolute error (MAE) and relative mean absolute error (RMAE). With the SNP method, both reanalyses showed a best peak simulation in mean SRO. Their performance in terms of correlation ranged from 0.26 to 0.65 for ERA5 vs. 0.34 to 0.60 for LAND. The reanalyses showed high correlations (generally > 0.80) for SWC and for the PET (sometime greater than 0.90). The correlations were below 0.5 in both reanalyses for the WTD, with slight overestimations (4.73 m for ERA5 vs. 3.13 m for LAND). Similar results were reported with the IDW selection method. One or the other of the two reanalyses can be recommended for model calibration/validation, but care must be taken in the choice because the one chosen may be better in terms of correlation even though it has significant biases and vice versa. Correcting the variables of these reanalysis datasets could also improve their performance.

Keywords

• evapotranspiration
• single nearest pixel selection method
• surface runoff
• water table depth