Published February 14, 2020
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Evaluation of General Circulation Models over the Upper Ouémé River Basin in the Republic of Benin

  • 1. International Chair in Mathematical Physics and Applications (ICMPA—UNESCO CHAIR), University of Abomey-Calavi, Cotonou 072 Po Box 50, Benin
  • 2. Laboratory of Applied Hydrology, National Institute of Water, University of Abomey-Calavi, Cotonou 01 Po Box 4521, Benin
  • 3. Aquapole, PeGIRE R&D Unit, FOCUS Unit Research, University of Liege, Campus du Sart Tilman, B53, Quartier Polytech 1, Allee de la découverte, 11, B-4000 Liege, Belgium
  • 4. University of Liège

Description

This study assessed the performance of eight general circulation models (GCMs) implemented in the upper Oueme River basin in Benin Republic (West Africa) during the Fifth Assessment Report on Climate Change. Historical rainfall simulations of the climate model of Rossby Regional Centre (RCA4) driven by eight Coupled Model Intercomparison Project (CMIP5) GCMs over a 55-year period (1951 to 2005) are evaluated using the observational data set. Apart from daily rainfall, other rainfall parameters calculated from observed and simulated rainfall were compared. U-test and other statistical criteria (R2, MBE, MAE, RMSE and standard of standard deviations) were used. According to the results, the simulations correctly reproduce the interannual variability of precipitation in the upper Oueme River basin. However, the models tend to produce drizzle. Especially, the overestimation of April, May and November rains not only explains the overestimation of seasonal and annual cumulative rainfall but also the early onset of the rainy season and its late withdrawal. However, we noted that this overestimation magnitude varies from one model to another. As for extreme rainfall indices, the models reproduced them poorly. The CanESM2, CNRM-CM5 and EC-EARTH models perform well for daily rainfall. A trade-off is formulated to select the common MPI-ESM-LR, GFDL-ESM2M, NorESM1-M and CanESM2 models for different rainfall parameters for the reliable projection of rainfall in the area. However, the MPI-ESM-LR model is a valuable tool for studying future climate change.
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