Contrasting dynamics of hydrological processes in the Volta River basin under global warming

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of twelve general circulation models (GCM) from CMIP5 that are dynamically downscaled by five regional climate models (RCM) from CORDEX-Africa is used. In total, 43 RCM-GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The corrected simulations are then used as input to the fully distributed mesoscale Hydrologic Model (mHM) for hydrological projections over the twenty-first century (1991–2100). Results reveal contrasting changes in the seasonality of rainfall depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture and terrestrial water storage) is only projected under RCP8.5. High and low flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water-energy-food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socio-economic development.