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Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa


An Author Correction to this article was published on 03 December 2020

This article has been updated


Groundwater in sub-Saharan Africa supports livelihoods and poverty alleviation1,2, maintains vital ecosystems, and strongly influences terrestrial water and energy budgets3. Yet the hydrological processes that govern groundwater recharge and sustainability—and their sensitivity to climatic variability—are poorly constrained4,5. 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 region4 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 resources4 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.

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Fig. 1: Long-term groundwater and precipitation records in the context of varying aridity across sub-Saharan Africa.
Fig. 2: Observed relationships between precipitation and groundwater recharge on an annual (hydrological years) basis (PR plots).
Fig. 3: Cumulative contribution of annual recharge (by hydrological year) to total recharge for ranked annual precipitation (largest to smallest) (rP–cR plots).
Fig. 4: Synthesis of controls on recharge variations and processes in time and space in sub-Saharan Africa.

Data availability

Data license agreements do not allow us to upload the raw precipitation and groundwater-level time series. However, the agencies from whom these data can be requested are listed in the Supplementary Information, and the authors are able to provide guidance on doing so. Digital data sets of calculated annual recharge values and precipitation anomalies are freely available to download online from and time series of groundwater-level deviations from the mean are available from and

Code availability

A Python script for generating the forward models used to produce Extended Data Fig. 3 and a spreadsheet tool used for conducting WTF analyses are freely available online from

Change history

  • 03 December 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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M.O.C. acknowledges support under an Independent Research Fellowship from the UK Natural Environment Research Council (NERC; NE/P017819/1) and fellowship funding from Cardiff University’s Water Research Institute. R.G.T. acknowledges support from The Royal Society (UK)–Leverhulme Trust Senior Fellowship (ref. LT170004) and The Royal Society–Department for International Development (DFID) Africa Capacity Building Initiative (ref. AQ140023). R.G.T., J.K. and M.O.C. acknowledge support from the UK Engineering and Physical Sciences Research Council (EPSRC) Global Challenges Research Fund administered by University College London (ref. 172313). R.G.T., G.F., M.C.T., M.S., K.G.V., A.M.M., D.O.V.K., J.-M.V., F.M.A.L., J.K., D.S., J.P.R.S., G.Y.E., M.O., P.M.N., Y.N., B.I.O., M.J.A., D.M.J.M. and Y.K. acknowledge support from the NERC–ESRC–DFID UPGro programme under the GroFutures (NE/M008932/1, NE/M008576/1, NE/M008207/1, NE/M008266/1, NE/M008622/1 and NE/M008592/1), Hidden Crisis (NE/L001969/1), BRAVE (NE/M008827/1) and T-Group (NE/M008045/1) consortia. T.O. and H.K. acknowledge support from Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (KAKENHI; 16H06291). K.G.V. and G.Y.E. acknowledge support from the CGIAR Research Program on Water, Land and Ecosystems (WLE), and the Groundwater Recharge in the Limpopo River Basin (GRECHLIM) project, funded by the United States Agency for International Development and National Research Foundation (NRF), South Africa (USAID–Southern Africa, AIDOAA-A-11-00012). Data for this project were obtained from the Namibian Ministry of Agriculture, Water and Forestry, Geohydrology Division, GROWAS2 and E. Freyer from Claratal, Namibia; C. Tindimugaya and the Ministry of Water and Environment, Uganda; the Ministry of Water and Irrigation, Tanzania; the Direction Générale de l’Eau and M. Boukari from Université Abomey Calavi, Benin; the Water Research Institute, Ghana; the Department of Water and Sanitation, South Africa; the Zimbabwe National Water Authority; Ministère de l’Hydraulique et de l’Environnement, Niger; and Direction Générale des Ressources en Eau, Agence Nationale de la Météorologie and Université Ouaga I, Burkina Faso. A.M.M., M.J.A., D.M.J.M. and J.P.R.S. publish with the permission of the Executive Director of the British Geological Survey, NERC. We thank P. Döll and H. Müller-Schmied at Goethe-University Frankfurt, Germany for comments on early drafts of this manuscript.

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Authors and Affiliations



The paper was conceived by R.G.T., G.F. and M.O.C., and written by M.O.C. and R.G.T. with input from all authors. R.G.T. and G.F. led the collation of data. M.O.C. designed the applied WTF methodology and forward modelling approach. M.C.T. conducted climate data analyses. K.G.V., M.S., A.M.M., B.R.S. and N.K. contributed to the collation of data and design of the study. Detailed analyses and interpretations of observational records were overseen by M.O.C. and R.G.T. and also conducted by: D.O.V.K., J.-M.V., F.M.A.L. and P.A.A. for Benin; J.K. and D.S. for Tanzania; J.P.R.S. and G.Y.E. for South Africa; M.O. and P.M.N. for Uganda; Y.N., I.G. and B.I.O. for Niger; T.S. for Zimbabwe; M.J.A., D.M.J.M. and W.A. for Ghana; Y.K. for Burkina Faso; and H.W. for Namibia. H.K., Y.W., M.-H.L. and T.O. provided model-based interpretation of results.

Corresponding author

Correspondence to Mark O. Cuthbert.

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Peer review information Nature thanks Diana Allen and Richard Healy for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Long-term groundwater-level records for sub-Saharan Africa, alongside monthly precipitation.

Blue crosses show groundwater depth, and grey bars show monthly precipitation. The timescales plotted in ag and hn are on different, but consistent, relative scales for comparison purposes. MAP, mean annual precipitation, in units of millimetres annually. For Namibia, MAP is reported for the rain gauge at Claratal, as this is representative of the climate of the runoff generation area from which focused recharge is derived at the analysed hyper-arid Namibian groundwater-level monitoring locations (see Supplementary Information for more details). mm/a, mm per year.

Extended Data Fig. 2 Correlations between combinations of climate parameters and groundwater recharge statistics.

Calculations in ac use the aridity index from the location of the Claratal rain gauge for the Namibian data points. Results in d, e use the aridity index from the groundwater monitoring locations for the Namibian data points. f and g indicate that contrasting metrics for extreme recharge give similar strength correlations to annual precipitation variability. Relationships in h include error bars for the combined uncertainty in both recession and specific yield, but note that the estimates of diffuse and focused recharge will be applicable to different spatial scales. In particular, focused recharge estimates will be valid only in regions closest to the recharging stream of interest. Comparisons of long-term average recharge rates must take this into account.

Extended Data Fig. 3 Cumulative contribution of annual recharge (by hydrological year) to total recharge for ranked annual precipitation (largest to smallest) for a suite of forward models (rP–cR plots).

The figure illustrates generic rP–cR plot types derived from running a range of forward recharge model structures, using two different climate time series from: a, Dodoma (semi-arid Tanzania); and b, Cococodji (humid Benin). Types are defined as: purple, type 1, consistent recharge rates from year to year across the range of annual rainfalls; green, type 2, increasing annual recharge with annual rainfall above a threshold (light green to dark green as thresholds increase); and orange, type 3, complex relationships between annual rainfall and recharge amount.

Extended Data Fig. 4 Precipitation anomalies across southern Africa during the local wet season in December 1999 to February 2000.

Locations of six of the groundwater-level records in four countries are indicated. This example year of extreme recharge at the sites in Zimbabwe, South Africa and Namibia (Extended Data Table 2) illustrates the large-scale structure of precipitation anomalies associated with local recharge extremes, in this case associated with La Niña conditions in the tropical Pacific. The north–south dipole of precipitation anomalies around an axis at around 15° S is characteristic of El Niño–Southern Oscillation events, and the major recharge years at the Tanzania site are associated with a reversal of this dipole during El Niño events (Extended Data Table 2).

Extended Data Table 1 Summary details of the groundwater-level and precipitation time series
Extended Data Table 2 Observed extreme recharge events and drivers of climate variability

Supplementary information

Supplementary Table 1

Table showing detailed additional information, including site location metadata, data analysis notes and conceptual model descriptions for 14 long-term groundwater-level time series across sub-Saharan Africa.

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Cuthbert, M.O., Taylor, R.G., Favreau, G. et al. Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa. Nature 572, 230–234 (2019).

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