Mapping groundwater recharge in Africa from ground observations and implications for water security

Abstract

Abstract Groundwater forms the basis of water supplies across much of Africa and its development is rising as demand for secure water increases. Recharge rates are a key component for assessing groundwater development potential, but have not been mapped across Africa, other than from global models. Here we quantify long-term average (LTA) distributed groundwater recharge rates across Africa for the period 1970–2019 from 134 ground-based estimates and upscaled statistically. Natural diffuse and local focussed recharge, where this mechanism is widespread, are included but discrete leakage from large rivers, lakes or from irrigation are excluded. We find that measurable LTA recharge is found in most environments with average decadal recharge depths in arid and semi-arid areas of 60 mm (30–140 mm) and 200 mm (90–430 mm) respectively. A linear mixed model shows that at the scale of the African continent only LTA rainfall is related to LTA recharge—the inclusion of other climate and terrestrial factors do not improve the model. Kriging methods indicate spatial dependency to 900 km suggesting that factors other than LTA rainfall are important at local scales. We estimate that average decadal recharge in Africa is 15 000 km 3 (4900–45 000 km 3 ), approximately 2% of estimated groundwater storage across the continent, but is characterised by stark variability between high-storage/low-recharge sedimentary aquifers in North Africa, and low-storage/high-recharge weathered crystalline-rock aquifers across much of tropical Africa. African water security is greatly enhanced by this distribution, as many countries with low recharge possess substantial groundwater storage, whereas countries with low storage experience high, regular recharge. The dataset provides a first, ground-based approximation of the renewability of groundwater storage in Africa and can be used to refine and validate global and continental hydrological models while also providing a baseline against future change.