The Ouémé watersheds at Bétérou and Bonou has been recently facing increased sensitivity to extreme hydroclimatic phenomena that occurred by flooding or drought events. In the same time, the population growth and the related socio-economic activities increased the pressure state on water resources. In this context, hydrological modeling is an important issue and this study aims at analyzing the calibration of the hydrological model GR4J based on PET Penman-Monteith and Oudin methods. Daily rainfall, Penman-Monteith and Oudin evapotranspiration and daily data flow from the Bétérou and Bonou hydrometric stations on the Ouémé Basin have been implemented in the GR4J model over the period 1971 to 2010. Oudin PET values are slightly higher than the Penman-Monteith PET ones. However, the difference between the two PET methods have only few impacts on the optimization and performance criteria of the GR4J model. The Nash values ranges from 0.83 to 0.91 in Bonou, and 0.52 to 0.70 in Bétérou for the calibration in dry period, while in validation, they are 0.59 to 0.78 in Bétérou, and 0.56 to 0.88 in Bonou in wet season. In view of these results, with the two PET methods used which do not result from the same climatic variables, it should be said that the formulation of PET has only few impacts on the results of GR4J for these tropical basins.

This study investigated drought and flooding changes in West Africa between 1983–2012 and projected near future (2025–2054) periods. The datasets used are the CHIRTS and CHIRPS-2 for observed reanalysis and five (05) models of ISIMIP2b for Shared Socio-economic Pathways (SSP1.2-6 and SSP5-8.5). Extremely and very wet days total precipitation (R95pTOT; R99pTOT) and Standardized Precipitation Evapotranspiration Index (SPEI) were employed to investigate floods and drought spatial distribution using Sen Slope trend analysis method. The results showed that there is a variability in the spatial distribution of extreme indices with an upward and downward trend of dry and wet rainfall periods in West Africa in both historical and projected periods. This observation suggests that the study area is faced with rainfall variability marked by extreme events. A further examination on the spatial and temporal distribution of flood occurrence showed that more flood events were observed in the Gulf of Guinea and Savannah countries, followed by an increase in uniform spatial distribution and moderate wet days both under SSP1.2.6, and SSP 5.8.5. In addition, result showed that an upward trend in wet periods can cause the occurrence of extreme events associated with floods in the context of global warming. However, with these scenarios negative changes are not excluded in the East, the Sahel and some western part of the Gulf of Guinea in the study area for the SSP5.8.5 scenario. Thus, the results revealed that the spatio- temporal variability of extreme rainfall can have repercussions on the hydrological functioning of watersheds, water availability and water-dependent activities.

Global climate change, characterised mainly by an upsurge in extreme rainfall events, is affecting the precarious economies of most vulnerable countries. The aim of this study is to analyse extreme rainfall events in the Oti watershed, which is characterised by high climatic variability. Daily observed and simulated rainfall and temperature data from two models (CCLM and REMO) in the Cordex programme were used. The data is subjected to statistical processing methods. Climatic indices were calculated using RclimDex software. The results obtained show a spatio-temporal variability in observed and simulated rainfall that follows an increasing north-south gradient. Both models overestimate observed rainfall, with deviations of 1.95 for the CCLM and 8.53 for the REMO. This shows that the CCLM is more realistic in reproducing observed rainfall, is therefore validated for the rainfall projection. The rainfall indices show a non-statistically significant increase in PRCPTOT, R99p, RX1day, RX5 days and CWD on the one hand, and a decrease in the SDII, R10 mm, R20 mm and R95p indices on the other for the observed rainfall. For simulated rainfall, the RCP 8.5 and RCP 4.5 scenarios predict an upward trend in some indices and a downward trend in others from 2025 to 2069.