This paper assessed the current and mid-century trends in rainfall and temperature over the Mono River watershed. It considered observation data for the period 1981–2010 and projection data from the regional climate model (RCM), REMO, for the period 2018–2050 under emission scenarios RCP4.5 and RCP8.5. Rainfall data were interpolated using ordinary kriging. Mann-Kendall, Pettitt and Standardized Normal Homogeneity (SNH) tests were used for trends and break-points detection. Rainfall interannual variability analysis was based on standardized precipitation index (SPI), whereas anomalies indices were considered for temperature. Results revealed that on an annual scale and all over the watershed, temperature and rainfall showed an increasing trend during the observation period. By 2050, both scenarios projected an increase in temperature compared to the baseline period 1981–2010, whereas annual rainfall will be characterized by high variabilities. Rainfall seasonal cycle is expected to change in the watershed: In the south, the second rainfall peak, which usually occurs in September, will be extended to October with a higher value. In the central and northern parts, rainfall regime is projected to be characterized by late onsets, a peak in September and lower precipitation until June and higher thereafter. The highest increase and decrease in monthly precipitation are expected in the northern part of the watershed. Therefore, identifying relevant adaptation strategies is recommended.

Climate change has severe impacts on natural resources, food production and consequently on food security especially in developing countries. Likely accentuated by climate change, flooding is one of the disasters that affects people and destroies agricultural land and products. At different governance levels and scales, appropriate responses are needed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. A methodology for detecting change was applied to heavy daily rainfall of 34 stations across the Ouémé basin, Benin, in order to assess potential change in its characteristics. The spatial variability of the detected changes in return periods was analyzed using the kriging interpolation method. For this analysis, up to 92 years (1921–2012) of rainfall data were used. Three homogeneous regions were found by the cluster analysis. For all studied return periods, 82% of the stations showed statistically significant change in daily precipitation, among which 57% exhibited a positive change and 43% negative change. A positive change is associated with an increase in heavy rainfall over the area of concern. An analysis of the interpolated change in heavy rainfall of different return periods revealed an east-west gradient from negative to positive along the lower Ouémé basin (Region 2). From the middle to the upper Ouémé (Region 1 and 3), a decreasing tendency of heavy rainfall is dominant mainly for the non-homogeneous period. This result of the complex pattern of changes could be veritable information for decision makers and consequently for development of appropriate adaptation measures.

West Africa experienced severe drought during the 1970s and 1980s, posing a threat to water resources. A wetter climate more recently suggests recovery from the drought. The Mann-Kendall trend and Theil-Sen’s slope estimator were applied to detect probable trends in weather elements in four sub-basins of the Niger River Basin between 1970 and 2010. The cross-entropy method was used to detect breakpoints in rainfall and runoff, Spearman’s rank test for correlation between the two, and cross-correlation analysis for possible lags. Results showed an overall increase in rainfall and runoff and a decrease in sunshine duration. Spearman’s coefficients suggest significant (5%) moderate to strong rainfall–runoff correlation for three sub-basins. A significant lower runoff was observed around 1979, with a rainfall break around 1992, indicating possible cessation of the drought. Temperatures increased significantly, at 0.02–0.05°C year^-1, with a negative wind speed trend for most stations. Half of the stations exhibited an increase in potential evapotranspiration.

With climate change and intensification of the hydrological cycle, the stationarity of hydrological variables is becoming questionable, requiring appropriate flood assessment models. Frequency analysis is widely used for flood forecasting. This study aims to determine the most suitable models (stationary and non-stationary) for estimating the maximum flows observed at some stations spread across West Africa. A statistical analysis of the annual maximum flows in terms of homogeneity, stationarity, and independence was carried out through the Pettitt, modified Mann–Kendall, and Wald–Wolfowitz tests, respectively, to identify the stations whose flows are non-stationary. After that, the best-correlated climate covariates with the annual maximum flows of the non-stationary stations were determined. The covariates explored are the climatic indices of sea surface temperatures (SST). Finally, different non-stationary GEV models were derived by varying the scale and position parameters of the best-correlated index for each station. The results indicate that 56% of the annual maximum flow series are non-stationary. As per the Bayes information criterion (BIC) values, the performance of the non-stationary models (GEV, generalized extreme values) is largely greater than that of the stationary models. These good performances of non-stationary models using climatic indices open perspectives for the prediction of extreme flows in the study area.