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Understanding climate change impacts helps adaptation efforts in the energy sector. This study evaluates the impact of future climate change on the hydropower potential (HPP) of the Bamboi catchment in West Africa combining the HBV-light rainfall-runoff model and a hydropower generation model. Two regional climate simulation datasets MPI-ESM-REMO and WASCAL under RCP 4.5 were applied to the validated HBV light to simulate the catchment discharge. Based on reference and future simulated discharges, a 1.3 MW run-of-river hydropower plant was designed to evaluate the HPP of the catchment. Hydrological and HPP changes were expressed as the difference between two future periods (2020–2049 and 2070–2099) and a reference period (1983–2005). The climate datasets projected a mean annual precipitation increase by 8.8% and 7.3% and discharge increases by 11.4% and 9.735% for the 2020–2049 and 2070–2099 periods, respectively. However, an overall decrease of hydropower generation by − 9.1% and − 8.4% for the 2020–2049 and 2070–2099 periods, respectively, was projected. These results stem from an increase in discharge for the rainy season not convertible into hydropower, combined with a decrease in discharge during the dry months that leads to important HPP losses.

We analysed in the work how change in land use/land cover influences on flood characteristics (frequency and magnitude) using a model inter-comparison approach, statistical methods and two land use scenarios (land use scenario A and land use scenario B) for three time horizons. The derived land use maps from these scenarios were considered as forcing inputs to two physically based hydrological models (SWAT and WaSiM). The generalized Pareto distribution combined with the Poisson distribution was used to compute flood frequency and magnitude. Under land use scenario A, croplands increase at the annual rate of 0.7% while under land use scenario B, it increases by 1.13% between 2003 and 2029. The expansion of croplands indubitably enhances flood risks. Although there was a general agreement about the sense of the variation, the magnitude of change in flood characteristics was highly influenced by the model type. The rate of increase in flood quantiles simulated from SWAT (0.36–1.3% for 10-year flood) was smaller than the corresponding magnitude of changes simulated from WaSiM (2.6–7.0% for 10-year flood) whatever the scenarios. The expansion of agricultural and pasture lands at the yearly rate of 0.7% under land use scenario A (respectively, 1.13% under land use scenario B) leads to an increase of 3.6% (respectively, 5.4%) in 10-year flood by considering WaSiM. This study is among the first of its kind to establish a strong statistical relation between flood severity/frequency and agricultural land expansion and natural vegetation reduction. The results of this study are relevant and useful to the scientific research community as well as the decision makers for framing appropriate policy decisions towards the management of extreme events and the land use planning/management in future in the region.

High rainfall events and flash flooding are becoming more frequent, leading to severe damage to crop production and water infrastructure in Burkina Faso, Western Africa. Special attention must therefore be given to the design of water control structures to ensure their flexibility and sustainability in discharging floods, while avoiding overdrainage during dry spells. This study assesses the hydroclimatic risks and implications of floodplain climate-smart rice production in southwestern Burkina Faso in order to make informed decisions regarding floodplain development. Statistical methods (Mann-Kendall test, Sen’s slope estimator, and frequency analysis) combined with rainfall-–runoff modeling (HBV model) were used to analyze the hydroclimatic conditions of the study area. Moreover, the spatial and temporal water availability for crop growth was assessed for an innovative and participatory water management technique. From 1970 to 2013, an increasing delay in the onset of the rainy season (with a decreasing pre-humid season duration) occurred, causing difficulties in predicting the onset due to the high temporal variability of rainfall in the studied region. As a result, a warming trend was observed for the past 40 years, raising questions about its negative impact on very intensive rice cultivation packages. Farmers have both positive and negative consensual perceptions of climatic hazards. The analysis of the hydrological condition of the basin through the successfully calibrated and validated hydrological HBV model indicated no significant increase in water discharge. The sowing of rice from the 10th to 30th June has been identified as optimal in order to benefit from higher surface water flows, which can be used to irrigate and meet crop water requirements during the critical flowering and grain filling phases of rice growth. Furthermore, the installation of cofferdams to increase water levels would be potentially beneficial, subject to them not hindering channel drainage during peak flow.

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 stati...

A statistical model to predict the probability and magnitude of floods in non-stationary conditions is presented. The model uses a time-dependent and/or covariate-dependent generalized extreme value (GEV) distribution to fit the annual maximal (AM) discharge, and it is applied to five gauging stations in the Ouémé River Basin in Benin Republic, West Africa. Different combinations of the model parameters, which vary with respect to time and/or climate covariates, were explored with the stationary model based on three criteria of goodness of fit. The non-stationary model more adequately explains a substantial amount of variation in the data. The GEV-1 model, which incorporates a linear trend in its location parameter, surpasses the other models. Non-stationary return levels for different return periods have been proposed for the study area. This case study tested the hypothesis of stationarity in estimating flood events in the basin and it demonstrated the strong need to account for changes over time when performing flood frequency analyses.

Global change has raised concerns among hydrologists about the use of the stationary assumption (independent and identically distributed flood series) in infrastructure-designed methods. This confirms the necessity of evaluating the stationary or non-stationary behaviour of hydrological variables before deriving flood plans for infrastructure projects and flood mitigation. Trends were evaluated in the annual maximal streamflow of 49 stations in Peninsular Malaysia, using the Mann Kendall and Spearman Rho trend tests. Three models, a stationary model (GEV0) and two non-stationary models, with: 1) location parameter; 2) location and log-transformed scale parameters as a linear function of time (GEV2), were considered for stations with significant trend. It was found that a quarter of the analysed stations show statistically significant trends in their annual maximal streamflow. These results indicate the importance of taking into consideration the non-stationary behaviour of the flood series in order to improve the quality of flood estimation.

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.