Assessing observed and projected flood vulnerability under climate change using multi-modeling statistical approaches in the Ouémé River Basin, Benin (West Africa)

• Published in: Regional environmental change 2022-12, Vol.22 (4), p.112, Article 112
• Main Authors: Hounkpè, Jean, Badou, Djigbo F., Ahouansou, D. M. Maurice, Totin, Edmond, Sintondji, Luc O. C.
• Format: Article
• Language: English
• Subjects: Adaptation; Agglomeration; Analysis; Classification; Climate Change; Climate Change Adaptation; Climate Change/Climate Change Impacts; Climate models; Drainage systems; Early warning systems; Earth and Environmental Science; Environment; Environmental impact; Extreme weather; Flooding; Floods; Geography; Global temperature changes; Intervals; Land cover; Mathematical models; Municipalities; Nature Conservation; Oceanography; Original Article; Rain; Rain and rainfall; Rain water; Rain-water (Water-supply); Rainfall; Regional planning; Regional/Spatial Science; Resilience and Mitigation Challenges in African Agriculture; River basins; Rivers; Warning systems; Wastewater; Water control
• ISSN: 1436-3798; 1436-378X
• DOI: 10.1007/s10113-022-01957-5

Climate change has severe impacts on the livelihoods of West-African communities with the floods of the late 2000s and early 2010s serving as factual evidence. Focusing on the assessment of observed and future vulnerability to extreme rainfall in the tropical Ouémé River Basin, this study aims to provide scientific evidence to inform national adaptation plans. Observed climate variables, historical and future outputs from regional climate models, topographic, land cover, and socioeconomic data were used in the vulnerability assessment. This assessment was based on four indicator normalization methods (min–max, z-scores, distance to target, and ranking), two aggregation techniques (linear and geometric), four classification methods (quantile, standard deviation, equal intervals, natural breaks), and three robustness evaluation approaches (spearman correlation, Akaike Information Criterion (AIC), and average shift in ranks). Based on the AIC, it was found that “equal intervals” is the overall best classification method and the min–max normalization with linear aggregation (MM.LA) outperformed other methods. The median scenario indicates that the population of the Ouémé Basin is vulnerable to the adverse impacts of climate change for the historical (1970–2015) and future periods (2020–2050) as a result of low adaptive capacity. By 2050, the southern part of the Ouémé Basin will be highly vulnerable to pluvial flooding under RCP 4.5. Vulnerable municipalities will continue to suffer from flooding if adequate adaptation measures including water control infrastructure (development and expansion of rainwater and wastewater drainage systems) and appropriate early warning systems to strengthen community members’ resilience are not taken.