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

Mark Cuthbert, Richard Taylor, Guillaume Favreau, Martin Todd, Mohammad Shamsudduha, Karen Villholth, Alan Macdonald, Bridget Scanlon, D. Kotchoni, Jean-Michel Vouillamoz, et al.

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## To cite this version:

Mark Cuthbert, Richard Taylor, Guillaume Favreau, Martin Todd, Mohammad Shamsudduha, et al. Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa. Nature, 2019, 572 (7768), pp.230-234. 10.1038/s41586-019-1441-7 ird-04823556

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

Mark O. Cuthbert<fnref n="1" />,²,³,⁴*, Richard G. Taylor<fnref n="1" />, Guillaume Favreau⁵,³⁰, Martin C. Todd⁶, Mohammad Shamsuddha<fnref n="1" />,⁷, Karen G. Villholth⁸, Alan M. MacDonald⁹, Bridget R. Scanlon¹⁰, D. O. Valerie Kotchoni¹¹, Jean-Michel Vouillamoz¹², Fabrice M. A. Lawson¹¹, Philippe Armand Adjomayi¹³, Japhet Kashaigili¹⁴, David Seddon<fnref n="1" />, James P. R. Sorensen¹⁵, Girma Yimer Ebrahim⁸, Michael Owor¹⁶, Philip M. Nyenje¹⁷, Yahaya Nazoumou¹⁸, Ibrahim Goni¹⁹, Boukari Issoufou Ousmane¹⁸, Tenant Sibanda²⁰, Matthew J. Ascott¹⁵, David M. J. Macdonald¹⁵, William Agyekum²¹, Youssouf Koussoubé²², Heike Wanke²³,²⁴, Hyungjun Kim²⁵, Yoshihide Wada²⁶, Min-Hui Lo²⁷, Taikan Oki²⁵,²⁸ & Neno Kukuric²⁹

Groundwater in sub-Saharan Africa supports livelihoods and poverty alleviation<fnref n="1" />,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.

Groundwater is a fundamental component of the global hydroclimatic system³,⁵ and plays a central role in sustaining water supplies and livelihoods in sub-Saharan Africa, owing to its widespread availability⁶, generally high quality, and intrinsic ability to buffer⁷ the impacts of episodic drought and pronounced climate variability that characterize this region<fnref n="1" />. Groundwater in sub-Saharan Africa is poised to

enable increased freshwater withdrawals as demand rises⁸ and climate change increases variability in surface water resources. It is therefore essential to understand the renewability of groundwater under present and future climate. Groundwater levels and fluxes are governed by a dynamic interplay between recharge (replenishment of groundwater) and discharge (loss of groundwater to streams, lakes, oceans or atmosphere), with a variety of controls and types of feedback from climate, soils, geology, landcover and human abstraction⁹. It is notoriously difficult¹⁰ to determine variations in recharge magnitudes over time and space and their relationship to climate, because direct, long-term observations of groundwater levels to inform such understanding in this region are sparse¹¹. Regional water-security assessments have therefore relied heavily on large-scale hydrological models to derive estimates of potential groundwater resources across the continent⁸, but these remain unvalidated by groundwater observations⁵,¹². A robust, data-driven understanding of groundwater recharge—and, critically, its dependence on climate—is required to inform decision-making regarding water resources. Improved understanding of groundwater sensitivity to climate is also integral to understanding important interactions between hydrology, climate, ecology and humans across the region, both in the present day¹³ and in the deeper past¹⁴.

We address this challenge here by exploring relationships between precipitation and recharge across a diverse range of climatic and geological contexts in sub-Saharan Africa, using a unique archive of multidecadal, groundwater-level hydrographs (or time series). By applying a consistent methodology across the archive, we are able to characterize the observed climate-groundwater relations into indicative types, which each lead to implications for understanding climate-change impacts on groundwater systems and sustainable water management.

We contend that long-term (that is, decadal or longer) groundwater-level hydrographs, with little or known interference from human activities, offer the most direct way of assessing variations in groundwater storage and—through inversion using a water-table fluctuation (WTF) technique (see Methods)—of assessing the temporal sensitivity of groundwater recharge to climate variability. We have, therefore, collated new unpublished records and updated previously published records to evaluate recharge and relationships with climate using a

<footnote n="1">¹Department of Geography, University College London, London, UK. ²School of Earth and Ocean Sciences, Cardiff University, Cardiff, UK. ³Connected Waters Initiative Research Centre, University of New South Wales, Sydney, Australia. ⁴Water Research Institute, Cardiff University, Cardiff, UK. ⁵Université Grenoble Alpes, IRD, CNRS, Grenoble INP, IGE, Grenoble, France. ⁶Department of Geography, University of Sussex, Falmer, UK. ⁷Institute for Risk and Disaster Reduction, University College London, London, UK. ⁸International Water Management Institute, Pretoria, South Africa.
⁹British Geological Survey, Lyell Centre, Edinburgh, UK. ¹⁰Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA. ¹¹Université d'Abomey-Calavi, Institut Nationale de l'Eau, Chaire Internationale de Physique Mathématique et Applications, Institut de Recherche pour le Développement, Cotonou, Benin. ¹²Université Grenoble Alpes, Institut de Recherche pour le Développement, Centre Nationale de la Recherche Scientifique, Institut Polytechnique de Grenoble, Institut des Géosciences de l'Environnement, Grenoble, France. ¹³Direction Générale de l'Eau, Cotonou, Benin. ¹⁴Department of Forest Resources Assessment and Management, Sokoine University of Agriculture, Morogoro, Tanzania. ¹⁵British Geological Survey, Maclean Building, Wallingford, UK. ¹⁶Department of Geology and Petroleum Studies, Makerere University, Kampala, Uganda. ¹⁷Department of Civil and Environmental Engineering, Makerere University, Kampala, Uganda. ¹⁸Department of Geology, Université Abdou Moumouni, Niamey, Niger. ¹⁹Department of Geology, University of Maiduguri, Maiduguri, Nigeria. ²⁰Cernex, Rugby, UK. ²¹Water Research Institute, Accra, Ghana. ²²Département des Sciences de la Terre, Université Ouaga I Pr Joseph Ki-Zerbo, Ouagadougou, Burkina Faso. ²³Department of Geology, University of Namibia, Windhoek, Namibia. ²⁴Department of Geography and Environmental Management, University of the West of England, Bristol, UK. ²⁵Institute of Industrial Science, The University of Tokyo, Tokyo, Japan. ²⁶International Institute for Applied Systems Analysis, Laxenburg, Austria. ²⁷Department of Atmospheric Sciences, National Taiwan University, Taipei City, Taiwan. ²⁸Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan. ²⁹International Groundwater Resources Assessment Centre, Delft, The Netherlands. ³⁰Institut de Recherche pour le Développement, Niamey, Niger.
*e-mail: cuthbertm2@cardiff.ac.uk</footnote>
<figure><img src="image_3.png" /><figcaption>**Fig. 1** | Long-term groundwater and precipitation records in the context of varying aridity across sub-Saharan Africa. **a–m**, Collated multidecadal groundwater-level (black or grey traces) and precipitation (grey bars) time series, showing a wide range of hydrograph responses: for example, relatively consistent (**a**, Natitingou, Benin) or highly variable (**d**, Cococodji, Benin) annual fluctuations; highly episodic variations (**j**, Namibia; **e**, Tanzania); interdecadal oscillations (**f**, Ouagadougou, Burkina Faso) or long-term trends (**b**, **c**, Niger). These wide-ranging</figcaption></figure>

responses reflect the complex interplay of climate, geology, soils and landcover represented across the monitoring locations. **n**, Axis definitions for panels **a–m**. **bgl**, below ground level. **o**, Map showing aridity-index classes, defined according to the Consortium of International Agricultural Research Centers (CGIAR) Consortium for Spatial Information (CSI) global-aridity and global-PET database²⁵. Locations of monitoring stations from **a–m** are shown with plus symbols. The analysed Namibian rain gauge is indicated by a filled black square.

WTF methodology. The 14 multidecadal hydrographs and accompanying precipitation records collated from nine countries in sub-Saharan Africa cover a wide range of climate zones from hyper-arid to humid, including both the unimodal precipitation regimes (local summer wet season) of the Northern and Southern Hemisphere subtropics and the bimodal equatorial regime, as well as a diverse range of geological and landscape settings (Fig. 1 and Extended Data Table 1).

Most of the groundwater hydrographs show seasonal groundwater-level rises of varying magnitude that indicate recharge in excess of net groundwater drainage at some point during most years on record. The exceptions are Tanzania, Namibia and South Africa (Modderfontein), where multiyear continuous groundwater-level declines are observed, punctuated by episodic recharge events (Fig. 1). Long-term rising trends observed in the Niger hydrographs reflect increases in recharge rates since clearance of native vegetation in the 1960s (ref. ¹⁵) that have not yet equilibrated with rates of net groundwater drainage owing to long groundwater response times⁹ in the area. The absence of long-term trends in other areas indicates a relatively stable balance between long-term (multidecadal) rates of groundwater recharge and discharge.

Groundwater recharge is often described as being on a continuum between 'focused' (or 'indirect') recharge, taking place through leakage

of ephemeral streams or ponds, to 'diffuse' (or 'direct') recharge, occurring in a more evenly distributed manner through the direct infiltration of precipitation at the land surface¹⁶,¹⁷. The predominance of focused recharge is thought to increase with aridity¹⁸, although there is no established threshold for when this occurs and diffuse recharge can also be substantial in some semi-arid areas¹⁹. As part of conceptual models derived for each site, we developed a process-based understanding of recharge, resolving specifically whether diffuse or focused recharge is dominant. This was assessed for each location on the basis of additional reports, data, local knowledge and analysis of the form of the groundwater hydrographs themselves (see Methods and Supplementary Information). We found that the transition from focused-dominated to diffuse-dominated recharge occurs around the boundary between semi-arid and sub-humid conditions (as defined by the aridity index, which is the ratio of long-term precipitation to potential evapotranspiration, *P*/PET; Extended Data Table 1).

We have classified hydrographs according to their sensitivity of annual recharge to precipitation, as reflected in annual precipitation–recharge cross-plots (herein ‘P–R’ plots; Fig. 2), and according to an analysis of how the proportion of recharge accumulates when years are ranked by annual precipitation (herein ‘rP–cR’ plots; Fig. 3 and Extended Data Fig. 3). We then used a suite of idealized
<figure><img src="image_4.png" /><figcaption>**Fig. 2** | Observed relationships between precipitation and groundwater recharge on an annual (hydrological years) basis (*P*–*R* plots). *P*–*R* plots for the indicated locations. Error bars represent the total range of uncertainty resulting from the choice of recession parameters within the WTF method. We used a best estimate of specific yield to estimate groundwater recharge values. The percentage errors in recharge resulting from uncertainty in specific yields (as stated on the y axis) will result in</figcaption></figure>

a linear rescaling of values along that axis, but will not alter the form of the relationships. Dashed boxes outline Tukey outlier values of extreme recharge. Long-term average recharge values are given in Extended Data Table 1. Note the variable axis ranges. Sites are colour coded to represent the precipitation–recharge relationship types defined in Figs. 3, 4 (purple, type 1; green, type 2; orange, type 3).

forward recharge modelling experiments to investigate how observed precipitation–recharge relationships relate to the magnitude of precipitation thresholds required to initiate recharge (see Methods and Extended Data Fig. 3). We observe three distinct types of precipitation–recharge sensitivity on the basis of the empirical relationships derived from the data, as described below (see Methods for site-by-site details).

First, we observe consistent recharge rates from year to year across the range of annual precipitation (purple in Figs. 2–4). This regime— exemplified by Natitingou, Benin and Soroti, Uganda—shows little variation in annual recharge across a wide range of precipitation on *P*–*R* plots (Fig. 2) and lies close to the 1/1 line in rP–cR plots (Fig. 3). This type of precipitation–recharge response is observed in sub-humid to humid locations and reflects the impact of local geology and soils in governing diffuse recharge processes.

Second, we observe increasing annual recharge with annual precipitation above a threshold (green in Figs. 2–4). This type of regime shows positive *P*–*R* correlations (Fig. 2) and shifts in the rP–cR relationship, increasingly deviating to the left of the 1/1 line (Fig. 3). This type is found at a majority of sites (*n* = 9), across a wide range of aridity from humid to semi-arid conditions, and in areas dominated by diffuse or focused recharge. Sites with the largest apparent precipitation

thresholds for recharge are located in semi-arid regions (Tanzania, Zimbabwe and Sterkloop, South Africa).

Third and finally, we find complex relationships between annual precipitation and recharge amount (orange in Figs. 2–4). This type shows greater scatter on the *P*–*R* plots (Fig. 2) and large ‘steps’ in rP–cR plots (Fig. 3), as shown by Swartbank and Rooibank, Namibia and Modderfontein, South Africa. A key feature of the annual precipitation– recharge relationship is that some of the greatest recharge can occur during years of relatively low total precipitation as a consequence of intense precipitation occurring over a range of timescales depending on the local conditions. This type is found in semi-arid to hyper-arid locations that are dominated by focused recharge.

Key insights regarding the relationships among aridity, recharge frequency, dominant recharge process and rP–cR relationships across the records are synthesized in Fig. 4. This analysis indicates the complex reality of controls on groundwater recharge and a lack of one-to-one correspondence with any individual factor. For example, although there is some correspondence between the rP–cR relationships and the degree of aridity (Fig. 3 and Extended Data Fig. 2d, e), variation in local conditions (principally in soils/geology and precipitation intensity) results in distinctive characteristics in each location’s recharge response
<figure><img src="image_5.png" /><figcaption>**Fig. 3** | Cumulative contribution of annual recharge (by hydrological year) to total recharge for ranked annual precipitation (largest to smallest) (rP-cR plots). Values on both axes have been normalized by the total number of years in the record to provide fractional contributions for comparative purposes. Categorization as either predominantly diffuse</figcaption></figure>

or predominantly focused recharge is made on the basis of the derived site conceptual models described in Supplementary Information. Colour coding is consistent with Figs. 2, 4 (purple, type 1; green, type 2; orange, type 3).

to precipitation (see Methods). Hence, as aridity increases, although there are transitions from seasonal to episodic recharge frequency and from diffuse to focused recharge, there is also a substantial spread of rP-cR types across different climates. We also recognize (although not informed directly by our data) that groundwater in some currently hyper-arid regions was recharged when a wetter climatic regime prevailed in the past¹ (referred to as having ‘palaeo’ recharge frequency in Fig. 4).

Where larger precipitation thresholds for recharge are inferred, a smaller proportion of precipitation years yields the majority of long-term recharge, and most of the variance in this relationship can be explained by increased aridity or by coefficients of precipitation variability (Extended Data Fig. 2d–g), where wetter years contribute disproportionately to recharge. Furthermore, values of extreme annual recharge identified as Tukey outliers (see Methods) were found only in more arid locations (with aridity indices of less than 0.5; Extended Data Table 1). By considering the wider regional precipitation distribution and the associated climate drivers during those years, we find that most years of substantial recharge are associated with widespread regional and seasonal-scale precipitation anomalies, themselves associated with major known modes of global and/or regional climate variability (see Methods, Extended Data Table 2 and Extended Data Fig. 4). As such, substantial variability in groundwater recharge reflects the local impact of large-scale climate processes.

The different precipitation–recharge sensitivities observed here have clear implications for understanding potential changes to groundwater levels and fluxes under climate change, and therefore for developing sustainable strategies for groundwater provision for water supply or improving food security in sub-Saharan Africa. Type 1 relationships imply that climate-change impacts on precipitation may have little impact on recharge (other factors being equal). However, decreased groundwater levels resulting from pumping in such environments could provide more ‘room’ for recharge to occur through capture²⁰ of evapotranspiration or runoff. Increasing the distribution of groundwater monitoring in sub-Saharan Africa would help to identify type 1 locations in which groundwater abstraction can induce additional recharge. In these cases, and also for type 2 sites with small precipitation

thresholds, the sensitivity of recharge to changes in PET may also be low, because recharge is not sensitive either to precipitation (type 1) or to factors other than precipitation (type 2) such as soil-moisture status. For type 2 locations in which thresholds are more highly influenced by antecedent dryness, recharge may be more sensitive to climate-change impacts on both precipitation and PET, and land-use change could also be important if soil structure is altered and affects runoff and infiltration processes²¹.

The episodic nature of recharge in more arid locations and the prevalence of large groundwater response times⁹ in such areas together indicate the importance of long-timescale planning horizons. In this context, the observed dependence of recharge on large-scale patterns of climate variability within types 2 and 3 suggests the potential for a degree of predictability with seasonal lead times. It also suggests that future changes in variability are likely to be of greater importance than mean precipitation. There is therefore a need to understand potential changes to such climate processes in longer, multidecadal climate-change projections—a major challenge at present for climate models²².

In contrast with the rather uncertain recharge projections, modelled projections of increased flood hazards are more consistent for tropical Africa⁴, and our results here show that focused recharge is likely to be widespread during such events. Hence, an important recommendation of strategy for climate-change adaptation is for more widespread consideration of schemes to harness and enhance focused recharge during flood flow, storing water in the subsurface by managed aquifer recharge²³. Thus the increased flood risk under climate change may have a silver lining in this respect (issues of water quality notwithstanding), and schemes to store flood water more effectively also have the potential to mitigate flood risk downstream. For type 3 situations, a key insight provided by our results in dry subtropical areas is that the intensification of precipitation—on the particular temporal and spatial scales determined by local conditions—may actually increase recharge, and thus available renewable water resources, despite an overall drying trend in annual precipitation totals²⁴.

Our data-driven results suggest that, from a groundwater perspective, there is a greater resilience to climate change than previously
<figure><img src="image_6.png" /><figcaption>**Fig. 4** | Synthesis of controls on recharge variations and processes in time and space in sub-Saharan Africa. Solid lines imply dominance of a recharge frequency or process, whereas dashed lines indicate lack of dominance. **a**, As aridity increases (moving leftwards in the figure), groundwater recharge tends to become increasingly heterogeneous in both space and time. ‘Episodic’ refers to infrequent recharge occurring during a few seasons or years in a decade; ‘interannual’ means recharge occurring more than once a year; ‘seasonal’ denotes recharge associated with wet or dry seasons; and ‘palaeo’ refers to recharge that occurred in some currently hyper-arid regions at times in the past when a wetter climatic regime prevailed¹. **b**, Where recharge occurs via focused pathways, recharge may become ‘increasingly indirect’ as aridity increases (in the direction of the grey arrow), meaning that the distance between the location of rainfall and the location of recharge increases. **c**, Colours for the observed rP–cR sensitivity types correspond to those in Figs. 2, 3 (purple, type 1; green, type 2; orange, type 3).</figcaption></figure>

supposed in many locations, and thus call into question, for example, the model-driven Intergovernmental Panel on Climate Change (IPCC) consensus that “Climate change is projected to reduce renewable surface water and groundwater resources significantly in most dry subtropical regions (robust evidence, high agreement)”⁴. More observation-driven research is needed to clarify this issue, and to address the balance of change between groundwater and surface-water resources. Our results also pose a challenge to the reliance on standard large-scale model assessments for inferring climate–groundwater dependencies, until climate models can simulate with greater credibility both the large-scale and the local-scale drivers of precipitation variability in the region, and until hydrological models include the necessary recharge processes and the influence of geological variability. Greatly increased spatial coverage of long-term groundwater monitoring is needed to address the challenge of model validation in this context.

## Online content

Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-019-1441-7.

Received: 28 January 2019; Accepted: 6 June 2019; Published online 7 August 2019.

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## METHODS

**Groundwater hydrograph and precipitation data.** We compiled multidecadal time series of groundwater levels and precipitation from records of observation wells initiated and maintained by government departments and research institutions in nine countries in sub-Saharan Africa (Extended Data Table 1 and Fig. 1). The pan-African collation of these hydrographs was initiated at the 41st Congress of the International Association of Hydrogeologists (IAH) in Marrakech, Morocco on 14 September 2014. We subjected all records to a rigorous review, during which the integrity, continuity, duration and interpretability of records were evaluated. This process included dedicated workshops in Benin, Tanzania and Uganda, and records failing these tests were discarded from the analysis. Procedures included taking the first time derivative in order to identify anomalous spikes that are commonly associated with data-entry errors. Where multiple records in same geographic and climate zone were available (for example, for Benin and South Africa), we prioritized records that were remote from potential areas of intensive abstraction. Statistical clustering of records was also used in the Limpopo Basin of South Africa in order to identify the representativity of employed records at Modderfontein and Sterkloop. We carried out hierarchical clustering using hydrographs converted into a Standardized Groundwater Index²⁶, and identified three clusters through a k-means approach; one of these clusters is an intermediary type between two end-members of the hydrograph represented by Modderfontein and Sterkloop.

Recognizing that the substantial spatial variability in precipitation in sub-Saharan Africa may affect the relationships observed between precipitation and recharge, we used precipitation records that are representative of the recharge generation process (that is, diffuse or focused). As a result, either rain gauges are co-located (for example, less than 5 m away) with groundwater monitoring sites, or we used the most proximate rain gauge, which is typically less than 10 km away (see Supplementary Information). In the case of the Namibian data, the relevant rain gauge was based more than 200 km away from the groundwater monitoring locations, in order to be representative of the runoff generation area for the River Kuiseb, which acts as the source for focused recharge in these locations.

Each groundwater record thus has an accompanying daily (for 9 of the 14 records) or monthly (for 5 of the 14 records) precipitation record covering the same period. Infilling of occasional gaps of less than a week in daily groundwater-level records was achieved by linear interpolation. All locations show seasonal, mostly unimodal, precipitation distributions, with the exception of those in Uganda, southern Benin (Cococodji) and Ghana, which have a more complex bimodal pattern (Extended Data Fig. 1).

**Relationships between climatic variables, processes and recharge.** We calculated coefficients of monthly (or annual) precipitation variability as the standard deviation of monthly (or annual) precipitation of the whole record, divided by the mean precipitation of the whole record, and multiplied by 100%. For analysis of wider climatic anomalies during major recharge events, we use the following gridded data: for monthly precipitation, data from the Global Precipitation Climatology Centre (GPCC) product v8²⁷ at 1.0° resolution; for daily precipitation, data from the Climate Hazards InfraRed Precipitation with Station²⁸ at 0.1° resolution; and data from the Extended Reconstructed Sea Surface Temperature (ERSST) version 4 from the National Oceanographic and Atmospheric Administration (NOAA)²⁹ on a monthly 2° grid.

Linear regression analysis indicates a strong correlation ($R^2 = 0.90$) between precipitation and the adidity index (P/PET; Extended Data Fig. 2a), because rates of potential evapotranspiration (PET) have a relatively small range across these tropical latitudes in comparison with rates of annual average rainfall. PET correlates neither with precipitation ($R^2 = 0.00$) nor with P/PET ($R^2 = 0.00$). The adidity index correlates strongly with the coefficient of monthly precipitation variability ($R^2 = 0.77$), but less so with the coefficient of annual precipitation variability ($R^2 = 0.38$; Extended Data Fig. 2b, c), indicating that adidity is a strong control on the degree of rainfall seasonality.

Long-term average recharge rates correlate poorly with rainfall or adidity (Extended Data Fig. 2h). In humid regions this is expected owing to geological variations that cause large differences in absolute recharge rates; in Benin, for example, recharge in Cococodji is nearly an order of magnitude greater than that in Natitingou, despite similar rainfall and adidity (Fig. 2). In more arid regions, increasing spatial heterogeneity in recharge rates is expected owing to the increasing predominance of focused recharge (Fig. 4). Thus the Namibian records, for example, show high rates of recharge that reflect the 'footprint' of the observation well located near an ephemeral stream; such values, which are often higher than the local precipitation, would nevertheless be expected to be larger than average recharge rates for the wider hyper-arid region. Thus, it could be misleading to directly compare recharge rates between sites if these potentially confounding factors are not considered.

We show that most of the extreme recharge events—which we identify as recharge outliers (Fig. 2)—are associated with relatively widespread regional and

seasonal scale precipitation anomalies (see the example in Extended Data Fig. 4). These precipitation anomalies themselves can be associated with large-scale structures of climate variability that are known to affect the different regions of Africa (Extended Data Table 2). While recognizing that the observed precipitation variability typically results from a complex set of drivers occurring simultaneously over various spatial and temporal scales, we note the following associations of large-scale precipitation anomalies during the outlier extreme local recharge years and climate drivers.

Across our sites south of the equator, we note that the major recharge years are associated with El Niño events concurrent with the positive phase of the Indian Ocean Zonal Mode³⁰ (IOZM) in the east African (Tanzania) site, and La Niña events in southern Africa (South Africa and Zimbabwe; see Extended Data Fig. 3 as an example). This is consistent with the well established north-south dipole response of precipitation to El Niño-Southern Oscillation events, which typically, but neither exclusively nor consistently, bring wet (or dry) rainfall anomalies across eastern (or southern) Africa during El Niño events, and the reverse during La Niña³¹,³².

Further west in the hyper-arid Namibia sites, the drivers of the outlier recharge events are more complex, as expected given the complex 'type 3' relationship between precipitation and highly episodic recharge (Fig. 2), dependent on the triggering of ephemeral surface river flow. Of the five outlier recharge events, two can be linked to regional/seasonal-scale rainfall anomalies associated with an anomalous warming of the cold Benguela current off the west coast of Africa. Such 'Benguela Niño' events³³ are known to trigger convection and rainfall across much of northern Namibia and southern Angola³⁴,³⁵. The remaining three events appear to be linked to spatially extensive but shorter-duration heavy rainfall anomalies resulting from subseasonal variability. These include: in February 2000, the notable, anomalous westward propagation of tropical cyclone Eline from the Indian Ocean basin to Namibia, causing widespread precipitation extremes across much of southeastern Africa and compounding existing La Niña-related rainfall; and synoptic-scale tropical low pressure systems in 2009.

The west African sites show a smaller number of outlier recharge events. The 2012 event in Burkina Faso appears to be part of wider, regional and seasonal scale precipitation anomalies, in which the Sahel region as a whole experienced the strongest monsoon season since 1953, probably resulting from the combination of seasonal tropical Atlantic temperature anomalies³⁶ and subseasonal variability from active phases of the Madden-Julian oscillation³⁷. The 1998 recharge event in Niger coincided with far less spatially coherent seasonal anomalies and probably resulted from intensive subseasonal precipitation events.

**Site conceptual models.** For each hydrograph location, we formulated a conceptual hydrogeological model on the basis of available data, literature and site visits, as necessary (see Supplementary Information). This formulation included an assessment of the main hydrogeological boundaries, such as groundwater divides and perennial or ephemeral drainage features; the local context for factors that might influence recharge, such as geology, soils, climate variables, groundwater abstraction and the thickness of the unsaturated zone; and estimations of aquifer storage and transmissivity. A particular focus was to develop an appreciation, based on the local context, of how 'diffuse' the recharge is likely to be spatially, or whether 'focused' recharge is likely to be more important in causing local variations in the magnitude of water-table fluctuations. Of most importance for determining the predominance of diffuse versus focused recharge are: the presence or absence of perennial versus ephemeral streams and ponds; coincident timing of ephemeral or seasonal stream flows with water-table responses; and the form of groundwater hydrographs with respect to the presence or absence of groundwater mounding as indicative of focused recharge (see further details in the subsection 'Groundwater recessions' below). The development of our conceptual hydrogeological model also enabled us to ensure that the observed changes in groundwater level are likely to be representative of water-table fluctuations in an unconfined aquifer (that is, vertical flow in the aquifer is insubstantial and poro-elastic or other 'confined' responses are negligible).

**Recharge estimation using the WTF method.** *Approach and equations.* We used inverse WTF models to infer the recharge timing and magnitude at the location of each hydrograph. The WTF technique is the most direct method available for estimating transient groundwater recharge, and has very few embodied assumptions by comparison with other methods such as geochemical tracers or modelling approaches¹⁶,³⁸. In a review¹⁰ of recharge estimation methods, the WTF technique was strongly recommended for application in humid and semi-arid African regions, and it is also applicable for both diffuse³⁹,⁴⁰ and focused⁴¹ recharge situations.

We assume that the groundwater level (or hydraulic head, *h* [L]) at an observation point is naturally controlled by the combined influence of the rate of net groundwater recharge ($R$ [LT⁻¹]), balanced by the rate of 'net groundwater drainage' ($D$ [LT⁻¹]) acting at that point in space and time. (L and T are units of length and time, respectively.) Further variations in WTF may be superimposed owing to
the rate of ‘net drawdown’ (s [LT⁻¹]) caused by changes in groundwater abstraction occurring at some distance from the observation point.

We used the following water-balance equation to approximate a time series (with time step Δt) of the ratio of recharge (R_t) to specific yield (S_y [-]):

$$ \frac{R_t}{S_y} = \frac{(h_t - h_{(t-\Delta t)})}{\Delta t} + \text{GWL}_t + s_t $$

where GWL_t (= D_t / S_y) is the rate of groundwater level recession⁴² [LT⁻¹] and R_t, h_t and s_t are R, h and s at time t, respectively. We then also calculated absolute values of recharge by multiplying by the applicable specific yield at the position of the water table.

To enable exact accounting periods for comparison with precipitation records, and between hydrographs, where observations were less frequent than daily, we used linear interpolation between groundwater-level observations. We carried out calculations on a daily time step and calculated sums for accounting periods between one dry season and the next, referred to herein as ‘hydrological years’. We used the same hydrological years for both recharge and precipitation, and these are given for each site in the Supplementary Information. If observations were missing across either end of the hydrological year in the first or last years of record, those years were removed from further analysis. Within the annual recharge time series generated, we identified Tukey outliers as any years with values greater than 1.5 times the interquartile range above the third quartile.

**Groundwater recessions.** We estimated GWL_t on the basis of the observed form and magnitude of the groundwater hydrograph during long dry periods, by setting either a constant rate, or an exponential decay controlled by the following equation:

$$ \text{GWL}_t = (h_{(t-\Delta t)} - h_b)C $$

where h_b is the elevation of the assumed lateral groundwater drainage boundary [L], and C is a decay constant [T⁻¹].

Most of the hydrographs have very distinctive seasonal precipitation patterns, with long dry seasons during which the true form of groundwater recession (that is, a groundwater-level decline in the absence of any recharge) can be directly observed, assuming no human interferences⁴². This enables the choice of recession model (constant rate or exponential) to be made confidently, and constant rates or decay constants to be easily determined. This stands in contrast with more humid parts of the world with limited dry periods, where the WTF is harder to apply robustly³⁹,⁴⁰. For hydrographs in Ghana, Burkina Faso and Modderfontein, South Africa, we used an exponential recession model owing to the presence of a shallow water table, inferred high permeability fracture flow, and close proximity of the groundwater drainage boundary, respectively (see Supplementary Information). For Soroti, Uganda, the absence of long dry seasons and the observed form of groundwater-level declines did not make the choice between exponential and straight-line recessions obvious, and so we applied both to represent the uncertainty. For all other locations, we used the observed variation of dry-season groundwater-level recessions to define maximum and minimum constant-rate end members in order to constrain the uncertainty in recharge estimates due to this parameter. This is consistent with theoretical expectations of linear recessions for these locations with drainage boundaries (where known) being sufficiently distant given the aquifer properties⁴².

For the cases in which focused recharge is substantial owing to local infiltration from ephemeral streams and ponds, the expected theoretical form⁴¹ is for groundwater hydrographs to show steep recessions following a rise in the water table, then trend to a relatively constant lower ‘background’ recession rate. This is observed for example in Zimbabwe, Tanzania, Niger, Namibia and Sterkloop, South Africa, and is explained by localized groundwater ‘mounding’ near the location of focused recharge dissipating on a much quicker timescale than the regional background recession, which operates on much longer spatial scales. In these cases, with the exception of Namibia, the local mounding dissipates before the end of the hydrological year, enabling a seasonal WTF accounting period to be used following the method of ref. ⁴¹. In Namibia, the recession of the recharge mounds occurred over timescales greater than a single season, and therefore had to be extrapolated, leading to much greater uncertainty in the output recharge values (as evidenced by the larger error bars in Fig. 2). As discussed in ref. ⁴¹, the application of this method thus enables recharge rates to be derived that are representative of integrated processes across larger areas of the catchment or aquifer (whichever define the hydraulic boundaries), rather than simply reflecting the local conditions near the stream. However, the spatial representativity of recharge estimated at each location is variable and, as such, direct comparisons of absolute recharge rates (Extended Data Table 1) from site to site should only be made where this can be accounted for.

**Groundwater abstractions.** Once at steady state, groundwater abstractions should have no effect on water-table fluctuations. However, transient abstractions cause

time-varying drawdown at the groundwater-monitoring location. If not accounted for, they will therefore cause recharge underestimations when drawdown is increasing, and overestimations when drawdowns are decreasing (for example, if abstraction temporally reduces (or increases), causing recovery (or decline) in groundwater levels). In most cases, the observation wells are located far from the influence of major changes in groundwater abstraction, as documented in our metadata (Supplementary Information), and s is assumed to be zero for all t. In one location (Makutapora, Tanzania), the monitoring wells are located within a major well-field where abstraction rates have been highly variable during the monitoring period. We therefore made corrections for this site by using a three-dimensional groundwater model to estimate a time series of net drawdown, in order to account for changes in recession due to variations in pumping rate (that is, accounting for drawdown due to increases in pumping, and recovery during decreases in pumping; see Supplementary Information).

**Specific yield.** We estimated ranges of specific yield on the basis of local information and literature for each groundwater-level record (see Supplementary Information), and assumed these specific yields to be constant in time and across the range of water-table fluctuations at a given location. The uncertainty in specific yield can be considerable and represents the main uncertainty in the derived absolute values of recharge. As well as being notoriously hard to estimate⁴³, specific yields can vary in time owing to vertical heterogeneity in lithology, to shallow water tables or to the presence of swelling clays³⁹,⁴⁴. The variation in the chosen value for specific yield has no impact on the form of the relationship that recharge has with precipitation, or on the ranking of recharge events used in Figs. 2, 3 (and in Extended Data Fig. 3). However, we report the likely range of uncertainty in specific yield for each location (see the y axes of Fig. 2 and Extended Data Table 1), as this does affect the absolute magnitude of the recharge estimates, and is one reason why intersite comparisons of long-term average recharge by this method can be problematic.

**Model experiments and interpretation of rP-cR relationships.** P-R cross plots (Fig. 2), showing annual recharge against annual precipitation, allow an initial characterization of precipitation-recharge relationships to be developed. For comparative purposes across all records, we then normalized annual recharge by a cumulative sum as a fraction of the total recharge for all years in a given record, and plotted this against the fractional precipitation ranking for each record (generating rP-cR plots; Fig. 3). To inform process-based inferences from these plots, we ran a suite of numerical recharge model experiments, using models with different structures, for two chosen time series from contrasting climates in sub-Saharan Africa: Dodoma (semi-arid Tanzania) and Cococodji (humid Benin). The purpose was not to calibrate models for each of the locations across Africa, but rather to understand the generic features of rP-cR plots in order to aid interpretation of the relationships we observe in the data.

We explored three model structures of increasing complexity. In the first structure, recharge was assumed to be constant for precipitation events above a daily or annual threshold. Note that, because the values were normalized against the total recharge in all years, the actual recharge value is irrelevant to the result. The second model structure, in the manner of ref. ⁴⁵, assumes that a constant proportion of precipitation becomes recharge above a specified daily precipitation threshold. We applied thresholds at a daily time step and then aggregated results for yearly comparisons. Because the values were normalized against the total recharge in all years, the chosen proportion of rainfall that becomes recharge is irrelevant to the result. The third model was a dynamic, single-layer soil-moisture balance model (SMBM), in the manner of refs ⁴⁶,⁴⁷, also run at a daily time step and then aggregated to annual values. It was assumed in all SMBM runs that the readily available water (RAW) was 50% of the total available water (TAW); that the crop coefficient was equal to one (for example, for grass land cover); that the ratio of actual to potential evapotranspiration rates (AET/PET, a proxy for plant stress) decreased linearly from one to zero as soil moisture-deficit values increased from RAW to TAW; and that runoff was zero.

For Dodoma, we derived daily PET values using the Hargreaves and Samani equation⁴⁸ from temperature data from the Dodoma Meteorological Station. Where data were missing, we used the average value from the month; when, early in the record, entire months were without data, we substituted the average temperature values for the corresponding month from the entire record. The calculated values were calibrated on pan evaporation data from the same location. For Cococodji, daily PET values derive from pan evaporation data collected from the meteorological station at the International Institute of Tropical Agriculture (IITA) office in Cotonou.

The generic style of each type of rP-cR plot (Fig. 3) is well captured by the models, for either of the two contrasting precipitation time series (Extended Data Fig. 3). Both show three distinct types of relationship, and it is clear that different models (and thus processes) can lead to a similar sensitivity; that is, a critical point is that each type of observed P-R sensitivity does not necessarily correspond to a particular recharge process. The first type (purple in Extended Data Fig. 3) plots close to the 1/1 line, indicating very consistent recharge values each year despite
wide variations in precipitation. The second type (green in Extended Data Fig. 3) deviates from the 1/1 line increasingly as the size of the potential thresholds in the SMBM (governed by TAW) or of the actual thresholds in the linear models increases. The third type (orange in Extended Data Fig. 3) shows pronounced steps in the curve generated by the largest thresholds in the linear model. Clearly, P-R responses in reality fall on a continuum, but we propose that classifying into three types highlights the end-member responses. This classification can be further tested and refined as more data become available for sub-Saharan Africa (and other parts of the world).

More details of the observed P-R and rP-cR plots (Figs. 2, 3) summarized in the main text are as described below.

*Type 1.* Natitingou (Benin) is characterized by low-storage fractured bedrock (S<sub>y</sub> = 0.4%)<sup>45</sup>; water-table variations are around 10 m annually, and each year the subsurface fills to a shallow level. In combination with straight recessions, this hydrogeological context leads to a temporally small variation in recharge each year (coefficient of variation = 5%), despite large variations in annual precipitation (the observed range is 850–1,592 mm yr<sup>-1</sup>). At Soroti, Uganda, the water table is always deeper than 5 m below ground level within weathered basement rock, but nevertheless exhibits rapid responses to precipitation events, indicative of preferential flow processes<sup>49</sup>. The observed consistency in recharge from year to year may be controlled by a finite near-surface water store to which the water table responds<sup>46</sup>, although further site investigations are needed to confirm precise controls.

*Type 2.* Where diffuse recharge is predominant, this type of sensitivity is expected if precipitation thresholds are governed by prevailing soil moisture deficits (or other near-surface storage/losses). We could expect increased deviation to the left of the 1/1 line on the rP-cR plots to increase with aridity and the build-up of larger soil moisture deficits. However, we could also expect exceptions to this to occur in cases where preferential flow processes<sup>49</sup> are prevalent and recharge can 'bypasts' the soil matrix and be less affected by soil moisture deficits, so that precipitation thresholds may be lower than anticipated under uniform flow assumptions. Where focused recharge is predominant, thresholds for its occurrence are expected to be governed by hydrological processes that dominate in drylands, such as the generation of infiltration-excess runoff producing ephemeral channel flow<sup>50</sup>. These processes can be locally variable and have complex dependencies on, for example, land cover, drainage network density, soil structure and antecedent moisture conditions. In the observed responses of this type in the humid to sub-humid environments (that is, Cococodji, Benin, Apac, Uganda and Accra, Ghana), thresholds appear to be relatively small. This is consistent with detailed analysis available for Cococodji and Apac that suggests values of 5 mm per day and less than 10 mm per day respectively for these sites; there are no existing studies to corroborate this for Ghana. For semi-arid sites in Tanzania and Zimbabwe, we observe that larger precipitation thresholds may need to be overcome for recharge to occur (darker green in Figs. 3, 4). Again, this is consistent with detailed analysis carried out for Tanzania, which indicates that recharge occurs only after persistent rainfall exceeding 70 mm over a 9-day period<sup>51</sup>. For the two Nigerien sites, despite also having greater aridity, thresholds are apparently much lower, but this is explained by the daily precipitation thresholds of 10–20 mm per day known to be required to generate stream flow<sup>52</sup>, and thus focused recharge, in this area. In Burkina Faso, focused recharge from a nearby managed reservoir ('barrage') moderates the impact of interannual precipitation variability on recharge variability, moving the rP-cR line closer to 1/1 (Fig. 3) than might be the case without a reservoir.

*Type 3.* The two Namibian sites are in a hyper-arid environment that is dependent on runoff generation from a large upstream catchment to supply focused recharge during streamflow events. Conditions for runoff generation are governed by intense monthly precipitation occurring not necessarily within years of relatively high total precipitation. By contrast, at Modderfontein, South Africa, focused recharge is much more local, but the limestone bedrock in this location is typified by highly nonlinear hydrological processes that generate complex P-R relationships (see Supplementary Information).

In summary, the controls on the observed P-R and rP-cR sensitivities are a complex interaction between the prevailing climate and local controls on recharge generation.

## 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 https://doi.org/10.6084/m9.fishshare.5103796 and time series of groundwater-level deviations from the mean are available from https://doi.org/10.5285/a6d78c2e-3420-4346-9182-4fd437672412 and https://www.un-igrac.org/ggmn/chronicles.

## 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 https://doi.org/10.6084/m9.fishshare.5103796.

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**Acknowledgements** 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'Éau 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.

**Author contributions** 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.

**Competing interests** The authors declare no competing interests.

**Additional information**

**Supplementary information** is available for this paper at https://doi.org/10.1038/s41586-019-1441-7.

**Correspondence and requests for materials** should be addressed to M.O.C.

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

**Reprints and permissions information** is available at http://www.nature.com/reprints.
Groundwater Level (m bgl)

<figure><img src="image_7.png" /></figure>

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 a–g and h–n 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.

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<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

**Extended Data Fig. 2** | Correlations between combinations of climate parameters and groundwater recharge statistics. Calculations in a–c 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.
<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

**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**, Cocodji (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.
<figure><img src="image_19.png" /><figcaption>**Extended Data Fig. 4** | Precipitation anomalies across southern Africa during the local wet season in December 1999 to February 2000.</figcaption></figure>

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

<table><thead><tr><th>Country</th><th>Location</th><th>Aridity<br>(Aridity Index)</th><th>Period</th><th>Aquifer lithology</th><th>S<sub>v</sub><br>(%)</th><th>Rainfall<br>modality</th><th>Recharge<br>frequency<br>category</th><th>Dominant<br>recharge<br>process</th><th>Long term<br>average<br>recharge<br>(mm/dec)</th><th>Precipitation-<br>Recharge<br>Sensitivity Type</th></tr></thead><tbody><tr><td>Benin</td><td>Cococodji</td><td>humid (0.81)</td><td>1991-2017</td><td>unconsolidated sands</td><td>15-17</td><td>Unimodal</td><td>Seasonal</td><td>Diffuse</td><td>4600-6430</td><td>Type 2</td></tr><tr><td>Benin</td><td>Natitingou</td><td>sub-humid (0.64)</td><td>1997-2014</td><td>fissured quartzite</td><td>0.3-0.5</td><td>Bimodal</td><td>Seasonal</td><td>Diffuse</td><td>430-760</td><td>Type 1</td></tr><tr><td>Burkina Faso</td><td>Ouagadougou</td><td>semi-arid (0.39)</td><td>1978-2016</td><td>weathered granite</td><td>8-12</td><td>Unimodal</td><td>Seasonal</td><td>Focussed</td><td>1400-2500</td><td>Type 2</td></tr><tr><td>Ghana</td><td>Accra</td><td>sub-humid (0.57)</td><td>1976-1994</td><td>weathered phyllite</td><td>2-6</td><td>Bimodal</td><td>Seasonal</td><td>Diffuse</td><td>470-1100</td><td>Type 2</td></tr><tr><td>Namibia</td><td>Rooibank</td><td>hyper-arid (0.01)</td><td>1998-2017</td><td>alluvium</td><td>20-40</td><td>Unimodal</td><td>Episodic</td><td>Focussed</td><td>930-2100</td><td>Type 3</td></tr><tr><td>Namibia</td><td>Swartbank</td><td>hyper-arid (0.01)</td><td>1992-2016</td><td>alluvium</td><td>20-40</td><td>Unimodal</td><td>Episodic</td><td>Focussed</td><td>1200-3500</td><td>Type 3</td></tr><tr><td>Niger</td><td>Banizoumbou</td><td>semi-arid (0.21)</td><td>1996-2016</td><td>sandstone</td><td>3.2-4.3</td><td>Unimodal</td><td>Seasonal</td><td>Focussed</td><td>220-340</td><td>Type 2</td></tr><tr><td>Niger</td><td>Berkiawel</td><td>semi-arid (0.22)</td><td>1994-2016</td><td>sandstone</td><td>1.4-2.7</td><td>Unimodal</td><td>Seasonal</td><td>Focussed</td><td>200-530</td><td>Type 2</td></tr><tr><td>South Africa</td><td>Modderfontein</td><td>semi-arid (0.35)</td><td>1976-2015</td><td>dolomite</td><td>2-4</td><td>Unimodal</td><td>Episodic</td><td>Focussed</td><td>880-2000</td><td>Type 3</td></tr><tr><td>South Africa</td><td>Sterkloop</td><td>semi-arid (0.38)</td><td>1973-2016</td><td>weathered gneiss</td><td>1-5</td><td>Unimodal</td><td>Seasonal</td><td>Focussed</td><td>100-690</td><td>Type 2</td></tr><tr><td>Tanzania</td><td>Makutapora</td><td>semi-arid (0.35)</td><td>1955-2016</td><td>weathered granite</td><td>4-7</td><td>Unimodal</td><td>Episodic</td><td>Focussed</td><td>520-1100</td><td>Type 2</td></tr><tr><td>Uganda</td><td>Apac</td><td>humid (0.72)</td><td>1999-2015</td><td>weathered gneiss</td><td>2-6</td><td>Bimodal</td><td>Seasonal</td><td>Diffuse</td><td>400-1400</td><td>Type 2</td></tr><tr><td>Uganda</td><td>Soroti</td><td>humid (0.74)</td><td>1999-2017</td><td>weathered gneiss</td><td>2-6</td><td>Bimodal</td><td>Seasonal</td><td>Diffuse</td><td>990-3000</td><td>Type 1</td></tr><tr><td>Zimbabwe</td><td>Khami</td><td>semi-arid (0.34)</td><td>1989-2015</td><td>basalt</td><td>2-8</td><td>Unimodal</td><td>Seasonal</td><td>Focussed</td><td>170-730</td><td>Type 2</td></tr></tbody></table>

Long-term average recharge values reflect combined uncertainties in specific yield and the applied rates of groundwater recession in the WTF analysis; the unconstrained spatial representativity of each value is such that direct comparison of these rates between locations may be misleading. We report long-term average recharge values in millimetres per decade (mm/dec) owing to the episodic nature of recharge observed in many of the records, which renders presumptions of an annual flux potentially misleading.
<table><caption>Extended Data Table 2 | Observed extreme recharge events and drivers of climate variability</caption><thead><tr><th>Location</th><th>Extreme recharge years</th><th>Approximate scale of related rainfall anomalies</th><th>Likely large scale drivers</th></tr></thead><tbody><tr><td rowspan="5">Rooibank Swartbank (Namibia)</td><td>2010-2011</td><td>seasonal/regional</td><td>Benguela Niño</td></tr><tr><td>1999-2000</td><td>sub-seasonal/regional</td><td>N/A</td></tr><tr><td>2008-2009</td><td>sub-seasonal/local</td><td>N/A</td></tr><tr><td>1996-1997</td><td>sub-seasonal/local</td><td>N/A</td></tr><tr><td>2005-2006</td><td>seasonal/regional</td><td>Benguela Niño</td></tr><tr><td rowspan="2">Sterkloop (South Africa)</td><td>1999-2000</td><td>seasonal/regional</td><td>La Niña</td></tr><tr><td>1995-1996</td><td>seasonal/regional</td><td>La Niña</td></tr><tr><td rowspan="2">Khami (Zimbabwe)</td><td>1999-2000</td><td>seasonal/regional</td><td>La Niña</td></tr><tr><td>1995-1996</td><td>seasonal/regional</td><td>La Niña</td></tr><tr><td rowspan="4">Makutapora (Tanzania)</td><td>1997-98</td><td>seasonal/regional</td><td>El Niño/Indian Ocean Zonal Mode positive phase</td></tr><tr><td>2006-07</td><td>seasonal/regional</td><td>Indian Ocean Zonal Mode positive phase</td></tr><tr><td>2015-16</td><td>seasonal/regional</td><td>El Niño/weekly positive Indian Ocean Zonal Mode</td></tr><tr><td>1989-90</td><td>sub-seasonal/regional</td><td>N/A</td></tr><tr><td>Berkiawel (Niger)</td><td>1998</td><td>sub-seasonal/local-meso-scale</td><td>N/A</td></tr><tr><td>Ouagadougou (Burkina Faso)</td><td>2012</td><td>seasonal/regional</td><td>Tropical Atlantic dipole and Active Madden-Julian Oscillation</td></tr></tbody></table>

Extreme recharge events for each site are listed in order of magnitude; N/A denotes not applicable.