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Groundwater in sub-Saharan Africa supports livelihoods and poverty alleviation(1,2), maintains vital ecosystems, and strongly influences terrestrial water and energy budgets(3). Yet the hydrological processes that govern groundwater recharge and sustainability-and their sensitivity to climatic variability-are poorly constrained(4,5). Given the absence of firm observational constraints, it remains to be seen whether model-based projections of decreased water resources in dry parts of the region(4) are justified. Here we show, through analysis of multidecadal groundwater hydrographs across sub-Saharan Africa, that levels of aridity dictate the predominant recharge processes, whereas local hydrogeology influences the type and sensitivity of precipitation-recharge relationships. Recharge in some humid locations varies by as little as five per cent (by coefficient of variation) across a wide range of annual precipitation values. Other regions, by contrast, show roughly linear precipitation-recharge relationships, with precipitation thresholds (of roughly ten millimetres or less per day) governing the initiation of recharge. These thresholds tend to rise as aridity increases, and recharge in drylands is more episodic and increasingly dominated by focused recharge through losses from ephemeral overland flows. Extreme annual recharge is commonly associated with intense rainfall and flooding events, themselves often driven by large-scale climate controls. Intense precipitation, even during years of lower overall precipitation, produces some of the largest years of recharge in some dry subtropical locations. Our results therefore challenge the 'high certainty' consensus regarding decreasing water resources(4) in such regions of sub-Saharan Africa. The potential resilience of groundwater to climate variability in many areas that is revealed by these precipitation-recharge relationships is essential for informing reliable predictions of climate-change impacts and adaptation strategies.

This paper explores the agricultural groundwater management system of Mogwadi (Dendron), Limpopo, South Africa – an area associated with intensive use of hard rock aquifers for irrigation – and the potential contribution of seasonal forecasts. These relatively shallow aquifers are often perceived as ‘self-regulating’, yet climate variability and infrequent recharge episodes raise the question of whether seasonal forecasting could contribute to more sustainable groundwater use. Hydro-meteorological observations, interviews and repeat focus groups with commercial farmers were used to examine this question for the 2014–15 rainfall season, with follow-up interviews during the 2015–16 El Niño season. Two long-term borehole series showed effects of episodic recharge events and management interventions. Comparison of formal and informal management practices highlighted important contrasts: a perceived lack of formal coordination within governing bodies, contrary to high levels of informal coordination between farmers despite a persistent ‘tragedy of the commons’ problem. Seasonal forecast use was limited due to lack of awareness and understanding of their relevance, low credibility and trust of forecasts, and poor dissemination. Farmers expressed increased interest in such information after the 2015–16 drought, if tailored to their needs. Increased uptake is, however, contingent on complementary groundwater monitoring network improvements and enhanced cooperation between stakeholder groups.

We investigate the uncertainty associated with downscaling techniques in climate impact studies, using the Upper Beles River Basin (Upper Blue Nile) in Ethiopia as an example. The main aim of the study is to estimate the two sources of uncertainty in downscaling models: (1) epistemic uncertainty and (2) stochastic uncertainty due to inherent variability. The first aim was achieved by driving a Hydrologic Engineering Centre-Hydrological Modelling System (HEC-HMS) model with downscaled daily precipitation and temperature using three downscaling models: Statistical Downscaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) and an artificial neural network (ANN). The second objective was achieved by driving the hydrological model with individual downscaled daily precipitation and temperature ensemble members, generated by using the stochastic component of the SDSM. Results of the study showed that the downscaled precipitation and temperature time series are sensitive to the downscaling techniques. More specifically, the percentage change in mean annual flow ranges from 5% reduction to 18% increase. By analyzing the uncertainty of the SDSM model ensembles, it was found that the percentage change in mean annual flow ranges from 6% increase to 8% decrease. This study demonstrates the need for extreme caution in interpreting and using the output of a single downscaling model.

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