This study analyzed the trends of extreme daily rainfall indices over the Ouémé basin using the observed data from 1950 to 2014 and the projected rainfall of regional climate model REMO (REgional MOdel) for the period 2015-2050. For future trends analysis, two Intergovernmental Panel on Climate Change (IPCC) new scenarios are considered, namely RCP4.5 and RCP8.5. The indices considered are number of heavy rainfall days, number of very heavy rainfall days, consecutive dry days, consecutive wet days, daily maximum rainfall, five-day maximum rainfall, annual wet-day total rainfall, simple daily intensity index, very wet days, and extremely wet days. These indices were calculated at annual and seasonal scales. The Mann-Kendall non-parametric test and the parametric linear regression approach were used for trends detection. As result, significant declining in the number of heavy and very heavy rainfall days, heavy and extremely heavy rainfall, consecutive wet days and annual wet-day rainfall total were detected in most stations for the historical period as well as the future period following the scenario RCP8.5. Furthermore, few stations presented significant trends for the scenario RCP4.5 and the high proportion of stations with the inconsistence trends invites the planners to get ready for an uncertain future climate following this scenario.

This study analyzed trends in extreme precipitation based on daily rainfall data provided by Bénin Méteo Agency for the Upper Ouémé valley in Benin over the period 1951-2014. Eleven indices divided into two groups were considered. The first group consists of frequency indices: number of heavy rainfall days, very heavy rainfall days and extremely heavy rainfall days; and maximum number of Consecutive dry days and wet days. The second group concerns intensity: daily maximum rainfall (RX1day), maximum five-day rainfall (RX5day), annual total wet-day rainfall (PRCPTOT), simple daily intensity index (SDII), very wet day (R95P) and extremely wet day rainfall (R99P). The non-parametric Mann-Kendall test was used to assess trends in those indices. The results show that only 30% of the stations experienced decreasing trends for the number of heavy rainfall days (R10mm) and daily maximum rainfall (RX1day). For the annual total wet-day rainfall (PRCPTOT), the simple daily intensity index (SDII) and the very wet day rainfall (R95P), 20% of stations faced significant negative trends. In addition, the decreasing trends are observed for 10% stations considering the number of very heavy rainfall days (R20mm), the maximum five-day rainfall (RX5day) and the extremely wet day rainfall (R99P). About the increasing trend, 10% stations are identified for the number of consecutive dry days (CDD), very heavy rainfall days (R20mm), the daily maximum rainfall (RX1day), the simple daily intensity index, and the extremely wet day rainfall (R99P). These results show the absence of clear trend of climate indices evolution in almost all stations. Consequently, uncertainties in the evolution of rainfall indices must be taken into account in the definition of adaptation strategies for flood or drought risks. Similarly, these results show a slight drop in the dry sequences of the 1970s and 1980s revealed in the region by previous studies.

This work focuses on impacts of climate change on Ouémé River discharge at Bonou outlet based on four global climate models (GCM) over Ouémé catchment from 1971 to 2050. Empirical quantile mapping method is used for bias correction of GCM. Furthermore, twenty-five rain gauges were selected among which are three synoptic stations. The semi-distributed model HEC-HMS (Hydrologic Modeling System from Hydrologic Engineering Center) is used to simulate runoff. As results, HEC-HMS showed ability to simulate runoff while taking into account land use and cover change. In fact, Kling–Gupta Efficiency (KGE) coefficient was 0.94 and 0.91 respectively in calibration and validation. Moreover, Ouémé River discharge is projected to decrease about 6.58 m3/s under Representative Concentration Pathways (RCP 4.5) while an insignificant increasing trend is found under RCP 8.5. Therefore, water resource management infrastructure, especially dam construction, has to be developed for water shortage prevention. In addition, it is essential to account for uncertainties when designing such sensitive infrastructure for flood management.

This study assessed the performance of eight general circulation models (GCMs) implemented in the upper Ouémé 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 (R 2 , 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 Ouémé 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.Hydrology 2020, 7, 11 2 of 21 not allow taking into account fine-scale physical processes (e.g., local convection that determines point precipitation), which are necessary for a good representation of the local climate. To overcome this major drawback, researchers have developed regional climate models (RCMs). These models are applied in a limited area domain with lateral boundary conditions (LBCs) provided by a global climate model or reanalysis. The high-resolution RCM therefore simulates small-scale features from lower-resolution boundary information [6]. This allows the addition of small-scale information related to climate change projections [7]. However, the results of these climate models remain dependent on fairly large uncertainties [8,9]. In Africa, climate models are relatively satisfactory in predicting temperature changes. On the other hand, uncertainties remain on the results of rainfall projections. Giannini [10] has shown that there is a significant disagreement between GCMs on the future evolution of rainfall in West Africa [11,12]. In the IPCC report, no conclusions are drawn regarding the rainfall regime in West Africa. Climate projections of rainfall are therefore still uncertain for West Africa. However, West Africa is one of the most vulnerable regions of the continent, often subject to the adverse effects of climate change [13,14]. It is therefore essential to evaluate climate simulation tools in West Africa, especially considering that rainfall forec...