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Floods are among the most devastating natural disasters and are expected to become more severe with changing climate and population growth. Flood forecasting is one of the key components of flood risk reduction. The potential for seasonal flood forecasting through climate indexes has not been studied for West Africa so far. This work investigates how climate indicators can be used to predict in advance, one to several months ahead of the flood season, above or below normal flood discharge in West Africa. Six global and regional climate indexes were screened for their potential to predict flood discharge of 56 river gauging stations across West Africa. Forecasting models are developed, based on simple and multiple linear regressions between climate indexes and annual maximum discharge, and evaluated using the relative operating characteristics and the relative operating levels scores. The western dipole mode index is the most skillful individual climate index for above normal flood prediction. Combining climate indexes via multiple linear regressions outperforms individual climate indexes for both above and below normal flood prediction. The models show forecasting skills for up to 4 months prior to the flood season. Hence, this study opens promising possibilities for seasonal flood forecasting in West Africa. This may help alert disaster reduction agencies of entering a period of an increased chance of flooding and may trigger adequate mitigation measures.

Climate change has had strong impacts on water resources over the past decades in Côte d’Ivoire, but these impacts on hydrological extremes remain largely unknown in most watersheds. Thus, this work aimed to evaluate the trends and breakpoints in extreme discharge characteristics of five watersheds in Côte d’Ivoire over the period 1970 to 2017. Seven indexes were selected, namely the 5-day maximum flow (QX5-days), peak discharge (Qmaxan), maximum monthly discharge (VCX30), annual minimum discharge (Qminan), average monthly discharge (QMNA), discharge day rate (VCN7), and characteristic of low discharge (WFD). The analysis was done using the modified Mann–Kendall (MMK) test and the standard normal homogeneity test at a 5% significance level for heterogeneous and homogeneous periods of data. The results for the heterogeneous and homogeneous periods were similar, with a predominance of non-significant trends for high discharge, except for the VCX30 index, which showed a significant upward trend at Kahin station. A decreasing trend for QX5-days was found at Loboville station. The variables Qminan, QMNA, VCN7, and WDF show significant upward trends of 33%, 16%, 50%, and 33% for the heterogeneous stations, respectively. A significant breakpoint in almost all variables was obtained, with a strong decrease after 2008. Some differences between the results from the heterogeneous and homogeneous periods of data were found and discussed. This study can help in understanding the behaviour of past hydrological extremes in the study area and in planning for further studies in the future.

Flood risk management is crucial for climate change resilience. The Hadejia River basin is known for severe and frequent floods, which have destroyed houses and farmlands and claimed many lives. This study developed a GIS-based flood risk and vulnerability mapping assessment using the Analytical Hierarchical Process (AHP) to outline scenarios that reduce risk and vulnerability associated with floods in the Hadejia River basin. The risk mapping of the basin integrated seven hydro-geomorphological indicators influencing extreme events (elevation, mean annual rainfall, slope, distance from rivers, soil type, and drainage density) and six socio-economic vulnerability indicators (population density, female population density, literacy rate, land use, employment rate, and road network) using a multi-criterion analysis. The average annual rainfall data of 36 years (1982–2018) were used for flood plain mapping in this study. Combining the flood hazard and socio-economic vulnerability indices of the basin revealed high-to-very high flood risk in the downstream and central upstream portions of the basin, which cover about 43.4% of the basin area. The local areas of Auyo, Guri, Hadejia, Ringim, Kafin Hausa, and Jahun were identified as zones at a very high flood risk. The study also revealed that flood hazard and vulnerability indicators have different influences on flood risk. The validated results resonate with the records of previous flood distribution studies of the basin. This research study is significantly important for developing strategic measures and policy revision through which the government and relief agencies may reduce the negative impact of floods in the Hadejia River basin.

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.

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.