Evaluation of 23 gridded precipitation datasets across West Africa

• Published in: Journal of hydrology (Amsterdam) 2020-02, Vol.581, p.124412, Article 124412
• Main Authors: Satgé, Frédéric, Defrance, Dimitri, Sultan, Benjamin, Bonnet, Marie-Paule, Seyler, Frédérique, Rouché, Nathalie, Pierron, Fabrice, Paturel, Jean-Emmanuel
• Format: Article
• Language: English
• Subjects: Life Sciences; Precipitation datasets; Reliability; Vegetal Biology; West Africa
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2019.124412

•Unprecedented report on 23 precipitation datasets reliability across West Africa.•Large discrepancies are observed between the precipitation datasets reliability.•The datasets reliability differ in space and according to the considered time step.•MSWEP is the most accurate dataset to represent the daily precipitation dynamic.•CHIRPS is the most accurate dataset to represent the monthly precipitation dynamic. This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000–2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling–Gupta Efficiency (KGE) changes at the local scale and the P-dataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v.2.2 and CHIRPS v.2 for daily and monthly time-step, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (i.e day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v.7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i.e. GSMaP-RT v.6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa.