This article evaluates the current gaps and describes opportunities for improving flood risk management (FRM) in Ghana, West Africa. A mixed-method participatory approach comprising questionnaires, workshops, interviews with key stakeholders, and a systematic literature review were employed. Existing problems, discourses, FRM practices, and opportunities to enhance flood resilience were identified. They provided the basis for outlining potential research directions into ways of tracking these challenges. The results show how different actors perceive FRM in Ghana. The stakeholders interviewed have different, and even contradictory perceptions of the effectiveness of FRM, which are embedded in their diverse storylines. The findings show that Ghana's FRM is still reactive rather than preventive and that research in the field of quantitative hazard and risk assessment is still rudimentary. FRM policies and flood early warning systems (FEWS) are in place, but efforts should be directed towards their implementation and monitoring, investigation of social and technical capacity aspects, and enhancement of institutions' mandates, and coordination. Moreover, the findings illustrate that FRM is moving toward a more constructive engagement of citizens and stakeholders. However, policies and action plans need to consider more inclusive community participation in planning and management to effectively improve their resilience and develop sustainable solutions.

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.

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Abstract: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 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.

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.