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

Alan M Macdonald, R Murray Lark, Richard G Taylor, Tamiru Abiye, Helen C Fallas, Guillaume Favreau, Ibrahim B Goni, Seifu Kebede, Bridget Scanlon, James P R Sorensen, et al.

► To cite this version:

Alan M Macdonald, R Murray Lark, Richard G Taylor, Tamiru Abiye, Helen C Fallas, et al.. Mapping groundwater recharge in Africa from ground observations and implications for water security. Environmental Research Letters, 2021, 16 (3), pp.034012. 10.1088/1748-9326/abd661 . ird-05268911

HAL Id: ird-05268911

https://ird.hal.science/ird-05268911v1

Submitted on 19 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

LETTER • OPEN ACCESS

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

To cite this article: Alan M MacDonald *et al* 2021 *Environ. Res. Lett.* **16** 034012

View the [article online](#) for updates and enhancements.

You may also like

- Management decision of optimal recharge water in groundwater artificial recharge conditions- A case study in an artificial recharge test site
  H Y He, X F Shi, W Zhu et al.

- Effect of Internal Resistance on Consistency in a set of Batteries at Low Temperature
  Hongwei Wang, Jun Liu, Weizhe Zhao et al.

- Estimation of Groundwater Recharge in Semarang City, Indonesia
  Sanidhya Nika Purnomo and Wei Cheng Lo

UNITED THROUGH SCIENCE & TECHNOLOGY

<figure><img src="image_3.png" /></figure>

The Electrochemical Society
Advancing solid state & electrochemical science & technology

248th
ECS Meeting
Chicago, IL
October 12-16, 2025
Hilton Chicago

<figure><img src="image_4.png" /></figure>

Science +
Technology +
YOU!

Register by
September 22
to save $$

REGISTER NOW
<figure><img src="image_5.png" /></figure>

CrossMark

OPEN ACCESS

RECEIVED

29 September 2020

REVISED

2 December 2020

ACCEPTED FOR PUBLICATION

23 December 2020

PUBLISHED

16 February 2021

Original content from
this work may be used
under the terms of the
Creative Commons
Attribution 4.0 licence.

Any further distribution
of this work must
maintain attribution to
the author(s) and the title
of the work, journal
citation and DOI.

<figure><img src="image_6.png" /></figure>

LETTER

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

Alan M MacDonald¹, R Murray Lark², Richard G Taylor³, Tamiru Abiye¹, Helen C Fallas¹, Guillaume Favreau⁵,¹³, Ibrahim B Goni⁶, Seifu Kebede⁷, Bridget Scanlon⁸, James P R Sorensen⁹, Moshood Tijani¹⁰, Kirsty A Upton¹ and Charles West¹¹,¹²

¹ British Geological Survey, Lyell Centre, Edinburgh EH14 4AP, United Kingdom

² University of Nottingham, Sutton Bonington Campus, LE12 5RD Loughborough, United Kingdom

³ Department of Geography, University College London, WC1E 6BT London, United Kingdom

⁴ School of Geosciences, University of the Witwatersrand, Johannesburg, South Africa

⁵ Université Grenoble Alpes, Institut de Recherche pour le Développement, CNRS, Grenoble INP, IGE, Grenoble, France

⁶ Department of Geology, University of Maiduguri, Borno, Nigeria

⁷ Center for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu Natal, Durban, South Africa

⁸ Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, United States of America

⁹ British Geological Survey, Maclean Building, Wallingford, United Kingdom

¹⁰ Department of Geology, University of Ibadan, Ibadan, Nigeria

¹¹ Department of Civil Engineering, University of Bristol, Bristol, United Kingdom

¹² Cabot Institute, University of Bristol, Bristol, United Kingdom

¹³ Institut de Recherche pour le Développement, 276 avenue de Maradi, BP 11416, Niamey, Niger

E-mail: amm@bgs.ac.uk

**Keywords:** water security, Africa, climate change, groundwater, water supply

Supplementary material for this article is available [online](#)

## 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 km³ (4900–45 000 km³), 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.
# 1. Introduction

Quantifying the rate of groundwater recharge is fundamental to assessing current water security and help forecast future changes (Taylor et al 2013a, Gleeson et al 2020). However, recharge is a hydrological parameter that is difficult to quantify accurately (Scanlon et al 2002, Healy 2010). In Africa, where considerable groundwater resources exist (MacDonald et al 2012), groundwater south of the Sahara has not yet been widely exploited other than for low-yielding drinking-water supplies (WHO and UNICEF 2015). Only 1% of the cultivated land of Africa is estimated to be irrigated by groundwater (Siebert et al 2010); 80% of this occurs in North Africa. Across the continent there is no recent evidence of significant widespread decline in groundwater storage in regional aquifers (Bonsor et al 2018) but localised depletion, particularly in urban areas, has been observed (Adelana et al 2008, Oiro et al 2020).

Rapid population growth and development in many African countries, together with the increasing availability of solar powered pumps (Wu et al 2017) have now focused attention on increased development of groundwater for irrigation and piped drinking-water supplies (Gaye and Tindimugaya 2019, Cobbing 2020). In many other parts of the world, rapid increases in groundwater abstraction have led to unsustainable conditions, characterised by falling water tables and problems with water quality (Scanlon et al 2012, MacDonald et al 2016, Rodell et al 2018). Consequently, reliance alone on maps of groundwater storage and aquifer permeability is insufficient to inform effective governance and investment decisions (Edmunds 2012). Quantifying the scale of groundwater recharge is required to characterise the resilience of groundwater supplies to both increased abstraction and climate change.

Estimating groundwater recharge is challenging with no widely applicable method available that can directly and accurately quantify the volume of rainwater that reaches the water table (Scanlon et al 2002, Healy 2010). Instead, several different methods are relied upon to estimate recharge. The most common are: chloride mass balance (CMB); soil physics methods; environmental and isotopic tracers; groundwater-level fluctuation methods; water balance (WB) methods (including groundwater models (GMs)); and the estimation of baseflow (BF) to rivers. Regional, continental and global estimates of recharge commonly derive from global hydrological models (e.g. Döll and Fiedler 2008) and have proved useful in providing a first estimate of the likely magnitude of groundwater recharge compared to surface water. However, global models have been shown to be unreliable at predicting long-term trends in groundwater storage when compared to variations in terrestrial water storage measured using GRACE (Scanlon et al 2018). There is therefore renewed interest in using

ground-based estimates of groundwater recharge to constrain global models (e.g. Mohan et al 2017, Moeck et al 2020) and to provide an independent estimate of groundwater recharge rates. However, to aggregate and map recharge from a meta-analysis of published, ground-based studies is challenging, as these can feature spatial and temporal biases, duplicate studies, and inappropriate applications of recharge methodologies.

Africa has a long history of groundwater recharge research with three areas particularly well studied. In southern Africa (Xu and Beekman 2003, 2019, Abiye 2017) and particularly the Kalahari desert (the site of several long-term research projects (Verhagen et al 1974, de Vries et al 2000)), work has focussed on the role of vegetation and rainfall intensity on controlling recharge. The North African sedimentary basins have benefited from many research projects in collaboration with the International Atomic Energy Agency primarily examining paleo recharge during wetter climates (Edmunds and Wright 1979, Guendouz et al 2003, Sturchio et al 2004) and localised depletion, for example in Morocco (Bouchaou et al 2008). Finally, research continues in the Sudano-Sahel of West Africa where research focuses on the impact of land clearing and coupling between groundwater and climate (Edmunds and Gaye 1994, Leduc et al 2001, Leblanc et al 2008, Favreau et al 2009). Recent research, focussed on groundwater-level responses to rainfall, has illustrated the complexity and episodic nature of recharge, and its large year-to-year variability particularly in drylands (Taylor et al 2013b, Cuthbert et al 2019a). Characterising the resilience of groundwater systems is therefore more meaningful using recharge volumes evaluated over decades, rather than annual, time periods.

Groundwater recharge can occur through diffuse or focused mechanisms (Lerner et al 1990, Healy 2010). Diffuse recharge occurs when precipitation infiltrates through the soil to the water table, and is by definition distributed over large areas. Focussed recharge occurs where water leaks from surface water sources (rivers, lakes, wadis, wetlands) or land surface depressions, and generally becomes more dominant with aridity (Scanlon et al 2006, Cuthbert et al 2019a). Focussed recharge can be widely distributed where ephemeral rivers, depressions or rock fractures are common over a large area and contribute to regional recharge (e.g. parts of the Sahel (Leduc et al 2001)) or discrete, where an individual river or water body provides significant local recharge (e.g. the Nile (Aly et al 1993)). Groundwater recharge can also be significantly enhanced by returns from irrigation (Scanlon et al 2010) or from urban areas (Lapworth et al 2017).

Here we quantify long-term (multi-decadal) distributed natural groundwater recharge volumes for Africa for the period 1970–2019 by mapping existing estimates from ground-based studies. We include both diffuse and focussed recharge where this is
widespread, since many methods cannot distinguish which process dominates; estimates of focussed recharge from an identified large discrete feature such as a river or lake are excluded. A quality assurance procedure was used to provide a robust dataset of 134 long-term estimates that minimised duplication. These estimates were then related to a range of climatic and terrestrial parameters to scale across the African continent. The results are used to discuss water security implications for different African countries.

## 2. Methods

### 2.1. Collating recharge studies

We developed a database of existing recharge studies for Africa. Recharge studies were compiled using published and grey literature identified from online databases, Google Scholar® and the Africa Groundwater Atlas (accessed May 2020) for the period 1970–2019, supplemented from the authors’ personal databases and knowledge. For each study, meta-data including the time period of the recharge estimate, the geographic extent of the study, and an accurate geolocation checked using aerial photography from Google Maps®. Long-term average recharge (LTA recharge) values were recorded for each study area along with the employed methods noting the primary and secondary methods used. Each study was critically reviewed and given a confidence rating based on the number and type of methods applied, the quality of the application of the methods, and the number of sites included in the study (see supplementary data for more information (available online at stacks.iop.org/ERL/16/034012/mmmedia)). A range of uncertainty was also included for each study: for studies with many measurements uncertainty was often the quoted standard deviation, interquartile range (IQR), or range; where studies noted uncertainty in input factors (such as specific yield or rainfall chloride) these were included in calculating the uncertainty. Estimates of groundwater recharge are inherently uncertain due to the assumptions made, therefore particular care was taken to exclude studies where the assumptions were invalid, and uncertainty was reduced when multiple independent methods were applied.

Different recharge-estimation methods included: CMB; water-table fluctuation (WTF) methods; environmental tracers (EnTs); WB; calibrated GMs; BF; and soil physics methods. Studies of groundwater recharge explicitly from river or lake infiltration, irrigation returns or urban leakage were excluded, leaving only studies representing distributed recharge from rainfall, which can include both diffuse and distributed focussed pathways. After the database was completed, studies for individual countries were assessed together to identify and remove duplicates; preference was given to syntheses and studies with

higher confidence ratings. Studies with lower confidence were retained if no other study had been repeated at the location. Records in the final dataset had equal weighting in the analysis.

### 2.2. Sources of additional African data

An analysis in a geographical information system (GIS) was then used to attribute various physical parameters known from previous studies to have an influence on groundwater recharge: long-term (1981–2010) climate data (Harris and Osborn 2020) (LTA rainfall, aridity index, number of wet days in a year, potential evapotranspiration (PET)); aquifer domain (MacDonald et al 2012), land cover (Arino et al 2007); the reference soil group as mapped in the Soil Atlas of Africa (Jones et al 2013), and normalised difference vegetation index (NDVI) (Didan et al 2015). Maps of these data are shown in the supplementary material. Each of these parameters were used in the linear mixed model (LMM) described below.

### 2.3. Statistical analysis

We required a statistical model which used a suitable subset of the measured covariates to predict LTA recharge at unsampled sites so that LTA Recharge could be mapped at continental scale. It was necessary to use an approach that was suitable for data with significant clustering (and therefore not an independent random sample) and that also selected a subset of predictor variables without over-fitting. To achieve this we used an LMM. Technical details are given in supplementary material and by Lark et al (2006). In an LMM a variable is modelled as a combination of fixed effects and random effects. The fixed effects define the expected value of the dependent variable given values of covariates (including categorical variables such as aquifer domain or soil group, and continuous ones such as LTA rainfall). The fixed effects therefore constitute a regression-type model.

Random effects in the LMM comprised a spatially dependent Gaussian random variable, and an independent error term (see supplementary material). The spatial term accounts for variability in LTA recharge observations around the fitted value, given that fixed effects are likely to be spatially dependent such that observations at nearby locations are expected to be more similar than those further apart. The independent error term accounts for factors such as error in the processes of estimating LTA recharge at a site, as well as any fine-scale sources of variation in the variable which are not resolved by the spatial distribution of the sample locations.

To fit the LMM, the parameters of the random effects were first estimated by residual maximum likelihood. The parameters of the fixed effects were then estimated by generalised least squares (Lark et al 2006). To select the final subset from predictors listed above, we considered predictors sequentially, starting with a simple ‘null’ model in which the only fixed
<figure><img src="image_7.png" /><figcaption>**Figure 1.** The distribution of quantitative recharge studies for Africa; (a) map of the location of the studies; (b) the number of studies according to aridity of study location; and (c) the type of methodologies used for calculating recharge.</figcaption></figure>

effect was a constant mean value. A model with the first predictor was then fitted, and the strength of evidence that the predictor should be retained was assessed by a log-likelihood ratio (LLR) test following Welham and Thompson (1997). A continuous predictor would appear in the model as a regression-type predictor with an estimated coefficient. A categorical predictor (such as soil group) would appear in the model as a set of different intercept terms for the prediction. If a categorical predictor was accepted by the LLR test, then its interactions with continuous predictors already in the model would be tested next. Once all predictors had been considered, the evidence for each one included was reassessed against a threshold for the *P*-value determined to control the false discovery rate (FDR) over all tests conducted. The FDR is the probability that a predictor which has no relation to the target variable is incorrectly regarded as significant within a set of multiple hypothesis tests. We followed the approach described by Lark (2017) to maximise power to detect meaningful predictors while controlling FDR by ordering the initial sequence of predictors so that those thought most likely to be predictive were considered first.

The final model was then used to compute the empirical best linear unbiased prediction (E-BLUP) of LTA recharge at unsampled sites on a prediction grid. The E-BLUP combines the predicted value from the fixed effects with an interpolation of the random effect by a kriging-type predictor. The E-BLUP thus minimises the expected squared prediction error

(prediction error variance), and computes this value as a measure of uncertainty. After an exploratory least-squares fit of a model with all predictors, and examination of the distribution of residuals, we transformed LTA recharge to natural logarithms to make the assumption of Gaussian errors plausible; continuous predictors were also transformed to logarithms because of their marked skewness. The prediction was back-transformed to the original scales of measurement by exponentiation, which gives a median-unbiased prediction, appropriate for skewed variables.

## 2.4. Water security analysis

Average decadal groundwater recharge was subsequently calculated for each African country with uncertainty characterised by the prediction error variance of the best linear unbiased predictor. Groundwater recharge was then compared with published estimates of groundwater storage for each country (MacDonald *et al* 2012) to form a basis to discuss water security in terms of a combination of LTA recharge and storage (Foster and MacDonald 2014).

# 3. Results

## 3.1. Summary of groundwater recharge data

A total of 134 separate studies with robust quantitative LTA recharge data were identified for Africa. Figure 1 shows their location and the primary method employed in their analysis of recharge (MacDonald *et al* 2020). In arid, semi-arid and dry
<figure><img src="image_8.png" /><figcaption>**Table 1.** Sequence of linear mixed models for LTA recharge with log-likelihood ratio (LLR) test and probability (*p*-value) for the addition of each new predictor to a null model.</figcaption></figure>

<table><thead><tr><th></th><th>Model</th><th>Null model</th><th>LLR</th><th><i>P</i>-value</th></tr></thead><tbody><tr><td>1</td><td>log<sub>e</sub> LTA rainfall</td><td>μ</td><td>48.20</td><td>&lt;&lt;0.0001</td></tr><tr><td>2</td><td>log<sub>e</sub> LTA rainfall + log<sub>e</sub> LTA PET</td><td>log<sub>e</sub> LTA rainfall</td><td>1.47</td><td>0.225</td></tr><tr><td>3</td><td>log<sub>e</sub> LTA rainfall + soil group</td><td>log<sub>e</sub> LTA rainfall</td><td>10.04</td><td>0.968</td></tr><tr><td>4</td><td>log<sub>e</sub> LTA rainfall + aquifer domain</td><td>log<sub>e</sub> LTA rainfall</td><td>1.05</td><td>0.593</td></tr><tr><td>5</td><td>log<sub>e</sub> LTA rainfall + log<sub>e</sub> NDVI</td><td>log<sub>e</sub> LTA rainfall</td><td>3.28</td><td>0.070</td></tr><tr><td>6</td><td>log<sub>e</sub> LTA rainfall + log<sub>e</sub> LTA wet days</td><td>log<sub>e</sub> LTA rainfall</td><td>0.90</td><td>0.344</td></tr><tr><td>7</td><td>log LTA rainfall + log<sub>e</sub> LTA aridity</td><td>log<sub>e</sub> LTA rainfall</td><td>1.48</td><td>0.224</td></tr></tbody></table>

sub-humid areas, CMB was the most commonly applied method whereas EnT (such as tritium) and WTF methods were the most common methods in arid and humid areas respectively. However, 75% of the studies used two or more methods to estimate recharge, with CMB and EnT representing the most common combination. Studies are generally well distributed across Africa, though there is a relative dearth of studies in humid areas, such as the Congo basin, coastal West Africa, and southeastern Africa including Madagascar. Most arid and semi-arid areas are well represented with the exception of the Horn of Africa (figure 1).

Measurable modern groundwater recharge was found in studies across much of Africa and rates decrease with aridity (figure 2). Although recharge is not detected in the majority of studies in hyper-arid areas, 40% of studies indicated some measurable recharge, and was generally associated with episodic rainfall and wadi infiltration (Edmunds et al 1992, Gonçalvès et al 2013). In arid and semi-arid climates, some component of modern groundwater

recharge was detected in all studies, with median values of 60 mm (IQR 30–140 mm) and 200 mm (IQR 90–430 mm) respectively decade⁻¹, although recharge rarely occurred every year but was episodic (e.g. Taylor et al 2013b). In dry sub humid and humid areas groundwater recharge is significantly higher and recharge is recorded to occur during most years (figure 2): dry sub humid 920 mm (IQR 480–1600 mm) decade⁻¹; humid 1300 mm (IQR 760–2200 mm) decade⁻¹.

## 3.2. Mapping groundwater recharge across Africa

Table 1 shows the results for successive addition of predictors in the fixed effects of the model. In row one of the table, a model with log<sub>e</sub> LTA rainfall as the only predictor is tested against the null model with the only fixed effect a constant mean (μ). The LLR is a measure of the strength of evidence against the null model, with larger values reflecting stronger evidence. The *P*-value (≪0.0001) indicates the low probability that there is evidence to support the preference of the null model, therefore log<sub>e</sub> LTA rainfall is retained.
<table><caption>**Table 2.** Parameters for the selected model.</caption><thead><tr><th colspan="3">Fixed effects parameters</th></tr><tr><th>Parameter</th><th>Estimate</th><th>Standard error</th></tr></thead><tbody><tr><td>β<sub>0</sub></td><td>-5.00</td><td>0.574</td></tr><tr><td>β<sub>1</sub></td><td>1.338</td><td>0.096</td></tr></tbody><thead><tr><th colspan="6">Random effects parameters</th></tr><tr><th>Fixed effects</th><th>κ</th><th>φ</th><th>c<sub>0</sub></th><th>c<sub>1</sub></th><th>R<sup>2</sup><sub>adj</sub></th></tr></thead><tbody><tr><td>~μ</td><td>0.5</td><td>2001.1</td><td>0.551</td><td>6.203</td><td>—</td></tr><tr><td>~log<sub>e</sub> [LTA rainfall]</td><td>0.5</td><td>288.0</td><td>0.759</td><td>0.489</td><td>0.82</td></tr></tbody></table>

<figure><img src="image_9.png" /><figcaption>**Figure 3.** LTA groundwater recharge for Africa; (a) map of best linear unbiased prediction of LTA recharge from observations and the fitted linear mixed model, with study values for comparison and (b) relationship between LTA groundwater recharge and LTA rainfall with data from Cuthbert *et al* (2019a) plotted for comparison, the only independent predictor was LTA rainfall.</figcaption></figure>

In the next row a model in which $log_e$ PET is added as a second predictor is tested against the alternative model with LTA rainfall only. In this, and for all subsequent predictors considered, the evidence to retain the new predictor is weak. Because none of the categorical predictors were included, their interaction with LTA rainfall was not considered. Applying the FDA control, only LTA rainfall was retained as a predictor in the model. The final fixed effect model therefore takes the form of equation (1):

$$ \log_e [\text{LTA recharge}] = \beta_0 + \beta_1 \log_e [\text{LTA rainfall}] \quad (1) $$

where $β_0$ is -5 and $β_1$ is 1.388 (table 2). Random effects parameter estimates are also shown for the overall null model where a constant mean is the only fixed effect and for the selected model. Spatially correlated random effects showspatial dependence up to distances approximately three times the distance

parameter, $φ$ (see supplementary material). Using a constant mean as the only fixed effect there is spatial dependence in the LTA recharge data at distances up to approximately 6000 km (reflecting the spatial dependence of long-term climatic trends). The spatial dependence of the random variation after including LTA rainfall as a predictor is restricted to a shorter range of 900 km. The term $c_1$ is the variance of this spatially correlated random effect. Note that $c_1$ is much reduced on inclusion of rainfall as a fixed effect. The term $c_0$ is the variance of the uncorrelated random error. The adjusted $R^2$ for the model, computed from the reduction in random variance on inclusion of the fixed effect, is 0.82. This is the proportion of the variance in $log_e$ LTA recharge accounted for by including LTA rainfall in the model. The non-linearity in the relationship between LTA rainfall and LTA recharge represented by the model is consistent with ground-based studies highlighting
<figure><img src="image_10.png" /></figure>

the disproportionate contribution of heavy rainfall to groundwater recharge in Africa (e.g. Taylor *et al* 2013b, Cuthbert *et al* 2019a, Banks *et al* 2020).

The LMM was used to produce the map of LTA recharge (figure 3) along with observed data for comparison. The mapped value at any location is obtained as a combination of the predicted value obtained by applying the model in equation (1) to the known LTA rainfall values, and a kriging-type prediction from the residuals for this fit to minimise the expected prediction error variance. Figure 3(b) plots the relationship between LTA recharge and LTA rainfall along with the modelled relationship and data from Cuthbert et al. (2019a) for comparison. Recharge estimates are more uncertain in the wetter areas of Congo basin, coastal West Africa and Madagascar as there are no recharge observations where LTA rainfall exceeds 2000 mm a⁻¹. Using this method the LTA decadal groundwater recharge for Africa is estimated to be 15 000 km³ (4900–45 000 km³). The range is the prediction error variance of E-BLUP.

The countries with the lowest rates of distrib-
uted groundwater recharge flux are Western Sahara,
Egypt, Libya, Algeria and Mauritania with less than
50 mm decade⁻¹, and the highest groundwater

recharge fluxes (>1500 mm decade⁻¹) are found
in Liberia, Equatorial Guinea, Sierra Leone, Gabon
and the Republic of Congo. Figure 4 shows the
LTA recharge volume for individual countries and
table 3 reports the total recharge volume for each
country along with the average decadal recharge
depth.

3.3. Water security

Combining estimates of LTA recharge with available
groundwater storage provides a platform to discuss
water security (Foster and MacDonald 2014) and the
resilience of aquifer systems to drought (Calow *et al*
2010). This analysis can be undertaken at different
scales, from individual aquifers to regions or coun-
tries. Figure 4(a) shows a map of combined ground-
water recharge fluxes and groundwater storage for
Africa and highlights high storage but low recharge
in many of the North African sedimentary aquifers,
and low storage but high recharge across many crys-
talline basement aquifers. Figures 4(b) and (c) com-
pare groundwater storage and recharge for individual
countries—with a combination of high groundwa-
ter storage and recharge giving increased water secur-
ity. Countries in this situation are rare with only five
countries, Guinea Bissau, the Republic of Congo, the
<table><caption>**Table 3.** Distributed groundwater recharge estimates for each African country, the range is the calculated prediction error variance. Groundwater storage from MacDonald *et al* (2012) is shown for comparison. Average annual recharge and storage per capita is also calculated.</caption><thead><tr><th rowspan="2">Country</th><th colspan="3">Recharge</th><th colspan="2">Storage</th><th colspan="2">Groundwater per capita</th></tr><tr><th>Volume (km<sup>3</sup> decade<sup>-1</sup>)</th><th>Depth (mm decade<sup>-1</sup>)</th><th>Volume (km<sup>3</sup>)</th><th>Volume (km<sup>3</sup>)</th><th>Annual recharge (m<sup>3</sup>)</th><th>Storage (×1000 m<sup>3</sup>)</th></tr></thead><tbody><tr><td>Algeria</td><td>75</td><td>24–220</td><td>33</td><td>92 000</td><td>56 000–243 000</td><td>170</td><td>2100</td></tr><tr><td>Angola</td><td>850</td><td>270–2600</td><td>680</td><td>17 000</td><td>7800–46 500</td><td>2600</td><td>520</td></tr><tr><td>Benin</td><td>99</td><td>37–280</td><td>880</td><td>720</td><td>320–2000</td><td>820</td><td>59</td></tr><tr><td>Botswana</td><td>49</td><td>17–140</td><td>84</td><td>18 000</td><td>9560–58 300</td><td>2100</td><td>7700</td></tr><tr><td>Burkina Faso</td><td>190</td><td>67–540</td><td>710</td><td>980</td><td>319–3330</td><td>900</td><td>47</td></tr><tr><td>Burundi</td><td>35</td><td>12–100</td><td>1100</td><td>47</td><td>8–183</td><td>290</td><td>3.9</td></tr><tr><td>Cameroon</td><td>650</td><td>220–1900</td><td>1400</td><td>1600</td><td>667–4810</td><td>2500</td><td>59</td></tr><tr><td>Cent African Rep</td><td>660</td><td>210–2100</td><td>1100</td><td>4200</td><td>1900–13 100</td><td>14 000</td><td>880</td></tr><tr><td>Chad</td><td>250</td><td>82–770</td><td>200</td><td>46 000</td><td>26 600–112 000</td><td>1500</td><td>2800</td></tr><tr><td>Côte d'Ivoire</td><td>410</td><td>140–1300</td><td>1200</td><td>240</td><td>42–1020</td><td>1600</td><td>9.1</td></tr><tr><td>Djibouti</td><td>2.1</td><td>0.7–5.9</td><td>120</td><td>170</td><td>35–546</td><td>210</td><td>170</td></tr><tr><td>DR Congo</td><td>3000</td><td>970–9200</td><td>1300</td><td>38 000</td><td>18 600–103 000</td><td>3300</td><td>430</td></tr><tr><td>Egypt</td><td>8.8</td><td>2.6–24</td><td>8.9</td><td>55 000</td><td>36 000–130 000</td><td>8.6</td><td>540</td></tr><tr><td>Equatorial Guinea</td><td>47</td><td>15–140</td><td>1700</td><td>48</td><td>20–147</td><td>3300</td><td>34</td></tr><tr><td>Eritrea</td><td>18</td><td>6.3–56</td><td>160</td><td>330</td><td>94–1120</td><td>510</td><td>95</td></tr><tr><td>eSwatini</td><td>7.7</td><td>2.8–23</td><td>560</td><td>24</td><td>6–104</td><td>660</td><td>21</td></tr><tr><td>Ethiopia</td><td>770</td><td>260–2200</td><td>670</td><td>13 000</td><td>4340–39 300</td><td>670</td><td>110</td></tr><tr><td>Gabon</td><td>420</td><td>140–1300</td><td>1600</td><td>1200</td><td>499–4190</td><td>19 000</td><td>550</td></tr><tr><td>Gambia</td><td>6</td><td>2.1–19</td><td>510</td><td>750</td><td>498–1750</td><td>250</td><td>310</td></tr><tr><td>Ghana</td><td>230</td><td>80–660</td><td>970</td><td>1400</td><td>369–4418</td><td>730</td><td>45</td></tr><tr><td>Guinea</td><td>340</td><td>110–1000</td><td>1400</td><td>540</td><td>133–1935</td><td>2600</td><td>41</td></tr><tr><td>Guinea-Bissau</td><td>36</td><td>12–110</td><td>1100</td><td>1200</td><td>742–2824</td><td>1800</td><td>600</td></tr><tr><td>Kenya</td><td>230</td><td>78–710</td><td>400</td><td>8800</td><td>4090–23 300</td><td>430</td><td>160</td></tr><tr><td>Lesotho</td><td>13</td><td>4.3–37</td><td>400</td><td>290</td><td>78–936</td><td>620</td><td>140</td></tr><tr><td>Liberia</td><td>170</td><td>55–530</td><td>1800</td><td>86</td><td>25–333</td><td>3300</td><td>17</td></tr><tr><td>Libya</td><td>20</td><td>6.3–58</td><td>13</td><td>100 000</td><td>64 600–234 000</td><td>300</td><td>15 000</td></tr><tr><td>Madagascar</td><td>640</td><td>200–2000</td><td>1100</td><td>1100</td><td>207–4160</td><td>2300</td><td>38</td></tr><tr><td>Malawi</td><td>98</td><td>33–300</td><td>840</td><td>270</td><td>91–885</td><td>510</td><td>14</td></tr><tr><td>Mali</td><td>250</td><td>86–750</td><td>200</td><td>27 000</td><td>10 600–87 000</td><td>1200</td><td>1300</td></tr><tr><td>Mauritania</td><td>36</td><td>12–110</td><td>34</td><td>12–110</td><td>10 500–67 200</td><td>770</td><td>5100</td></tr><tr><td>Morocco</td><td>74</td><td>24–220</td><td>170</td><td>7400</td><td>3970–20 700</td><td>200</td><td>200</td></tr><tr><td>Mozambique</td><td>560</td><td>180–1700</td><td>720</td><td>6300</td><td>2684–20 300</td><td>1800</td><td>200</td></tr><tr><td>Namibia</td><td>79</td><td>27–220</td><td>97</td><td>7700</td><td>3520–24 600</td><td>3100</td><td>3100</td></tr></tbody></table>

(Continued)
<table><caption>Table 3. (Continued).</caption><thead><tr><th rowspan="2">Country</th><th colspan="2">Recharge</th><th colspan="2">Storage</th><th colspan="2">Groundwater per capita</th></tr><tr><th>Volume (km<sup>3</sup> decade<sup>-1</sup>)</th><th>Depth (mm decade<sup>-1</sup>)</th><th>Volume (km<sup>3</sup>)</th><th>Annual recharge (m<sup>3</sup>)</th><th>Storage (×1000 m<sup>3</sup>)</th><th>Storage (×1000 m<sup>3</sup>)</th></tr></thead><tbody><tr><td>Niger</td><td>93</td><td>33-300</td><td>79</td><td>28-260</td><td>36 000</td><td>19 000-94 700</td><td>1500</td></tr><tr><td>Nigeria</td><td>1000</td><td>350-3100</td><td>1100</td><td>380-3400</td><td>12 000</td><td>5710-33 600</td><td>57</td></tr><tr><td>Rep of Congo</td><td>510</td><td>170-1600</td><td>1500</td><td>500-4700</td><td>6700</td><td>3 350-18 300</td><td>1200</td></tr><tr><td>Rwanda</td><td>32</td><td>11-92</td><td>1000</td><td>370-3000</td><td>49</td><td>6-198</td><td>3.8</td></tr><tr><td>Senegal</td><td>77</td><td>26-230</td><td>390</td><td>130-1200</td><td>13 000</td><td>8280-29 100</td><td>750</td></tr><tr><td>Sierra Leone</td><td>130</td><td>42-420</td><td>1700</td><td>550-5500</td><td>330</td><td>160-850</td><td>41</td></tr><tr><td>Somalia</td><td>81</td><td>27-250</td><td>130</td><td>43-400</td><td>12 000</td><td>5210-34 500</td><td>770</td></tr><tr><td>South Africa</td><td>290</td><td>100-820</td><td>230</td><td>81-660</td><td>17 000</td><td>6400-56 100</td><td>290</td></tr><tr><td>South Sudan</td><td>420</td><td>140-1300</td><td>670</td><td>220-2100</td><td>13 000</td><td>8800-36 200</td><td>1100</td></tr><tr><td>Sudan</td><td>210</td><td>70-650</td><td>110</td><td>38-350</td><td>50 000</td><td>35 100-143 000</td><td>1100</td></tr><tr><td>Tanzania</td><td>630</td><td>210-1900</td><td>680</td><td>230-2000</td><td>5300</td><td>2040-17 900</td><td>88</td></tr><tr><td>Togo</td><td>54</td><td>20-150</td><td>990</td><td>360-2800</td><td>300</td><td>102-879</td><td>36</td></tr><tr><td>Tunisia</td><td>11</td><td>308-31</td><td>74</td><td>26-210</td><td>7600</td><td>4 910-18 100</td><td>640</td></tr><tr><td>Uganda</td><td>240</td><td>84-690</td><td>960</td><td>330-2800</td><td>340</td><td>73-1270</td><td>7.4</td></tr><tr><td>Western Sahara</td><td>2.2</td><td>0.7-6.6</td><td>8.5</td><td>2.6-26</td><td>6800</td><td>3770-21 400</td><td>11 000</td></tr><tr><td>Zambia</td><td>490</td><td>160-1500</td><td>650</td><td>210-2000</td><td>4000</td><td>1430-12 300</td><td>210</td></tr><tr><td>Zimbabwe</td><td>140</td><td>50-410</td><td>360</td><td>130-1000</td><td>2000</td><td>906-7230</td><td>130</td></tr><tr><td>Africa</td><td>14 750 (4900-45 000)</td><td></td><td>490 (160-1500)</td><td></td><td>0.66 × 10<sup>6</sup> (0.36-1.8 × 10<sup>6</sup>) km<sup>3</sup></td><td>1100</td><td>490</td></tr></tbody></table>
Democratic Republic of Congo, Nigeria and Angola having both recharge and storage above the median for Africa. Having both low groundwater storage and recharge considerably reduces water security; again countries in this condition are rare with only five, eSwatini, Zambia, Lesotho, Zimbabwe and Eritrea having both recharge and storage below the African median.

The majority of African countries have either high storage, or high groundwater recharge. Where storage is high, but LTA recharge low, groundwater is resilient to short-term change in climate, but prone to long-term depletion. In Africa, six of the countries with least recharge have highest storage: Egypt, Algeria, Libya, Tunisia, Niger and Botswana and represent some of the highest groundwater abstractors in Africa (Wada et al 2012). In areas where groundwater storage is low but recharge is high, groundwater supplies are more vulnerable to drought but resilient to long-term irreversible depletion (figure 4). Five of the countries with highest recharge have the least groundwater storage: Liberia, Equatorial Guinea, Guinea, Cote D'Ivoire and Burundi. A similar picture of water security is given by considering the groundwater recharge and storage available per capita (table 3).

# 4. Discussion

## 4.1. Recharge and long-term average rainfall

The LMM shows that at the scale of the African continent only LTA rainfall is related to LTA recharge. The inclusion of other factors, PET, NDVI, number of wet days, soil group, aquifer domain and land cover does not improve the model. This outcome contrasts with that of some other large-scale studies (e.g. Mohan et al 2017), which required several different climate and land-cover parameters to improve model fitting. The good fit of the model presented here using only LTA rainfall may be due to the extra care taken to geolocate studies and only include studies that passed stringent quality assurance tests to give data that estimate long-term recharge. However, the random effect in the LMM still show spatial dependence (up to 900 km), and local prediction of recharge can be improved by using the BLUP, with its kriging-type component, relative to a prediction from rainfall alone. This spatial dependence reflects the importance of local soil and land use conditions in mediating the relationship between recharge and rainfall as demonstrated by many studies (e.g. Sami and Hughes 1996, Scanlon et al 2005, Ibrahim et al 2014). However, in modelling LTA recharge across Africa using continental-scale datasets, these parameters do not have a consistent effect, and LTA rainfall dominates the relationship. The scale of these local effects is represented by the variance observed in the relationship between LTA rainfall and recharge (figure 3 and table 3). Therefore, the approach taken

here has most value for estimating recharge at a regional scale.

There is little evidence from the data to support a threshold value of LTA rainfall below which recharge cannot occur. Measurable distributed modern groundwater recharge is found to occur where average annual rainfall is considerably less than 250 mm (figure 2) and even in some hyper-arid areas. This deduction is most likely explained by the increased probability of high rainfall events occurring when observing over several decades and the dominance of convective precipitation in arid areas. For example in the dune covered Stampriet Basin in Namibia where average rainfall is less than 250 mm a⁻¹, rainfall can be as high as 600 mm in some years—leading to significant diffuse recharge events (Stone and Edmunds 2012). Lack of vegetation in some arid and hyper-arid areas can also contribute to diffuse groundwater recharge, as heavy rainfall can infiltrate beneath the shallow zero flux plane (Dincer et al 1974, Gates et al 2008, Scanlon et al 2010). Focussed groundwater recharge is also of increased importance in arid areas (Scanlon et al 2006, Cuthbert et al 2019a) and can contribute to regional distributed groundwater recharge. In Niger for example, research has demonstrated highly localised redistribution of runoff by vegetation and geomorphology (Bromley et al 1997) and more widely distributed recharge through temporary ponds and ephemeral streams (Leduc et al 1997). The presence of karst feature can also increase focused recharge in arid regions (Schulz et al 2016). This current study examining long-term relationships complements recent findings examining patterns in annual recharge (Taylor et al 2013b, Kolusu et al 2019. Cuthbert et al 2019a), which demonstrate increasing annual variability in recharge with aridity and many years with no measurable observed recharge. These results strongly support measuring and reporting recharge values over decades rather than annually.

## 4.2. Water security

Most African countries with little groundwater storage have high annual rainfall and therefore regular recharge. Conversely, many African countries with low rainfall, usually considered as water insecure, have considerable groundwater storage which was mostly recharged millennia ago (figure 4). Generally, assessments of water stress and water security ignore groundwater storage (Taylor 2009), but concentrate on annual ratios between renewable water and water use by people and the environment (Vörösmarty et al 2010). Even where groundwater is considered, early thinking was dominated by the concept of safe yield and ratios between abstraction and recharge (Zhou 2009). However over the last 2 decades this conceptualisation has evolved beyond simple ratios to broader issues of sustainability
(Morris et al 2003, Alley and Leake 2004); and include both the response time of groundwater systems to change (Cuthbert et al 2019b) and dynamic groundwater storage (Damkjaer and Taylor 2017, Bierkens and Wada 2019). Sustainability is increasingly being considered as context specific (Gleeson et al 2020) with an acknowledgement that countries have different priorities depending on other pressures and aims, such as the Sustainable Development Goals. With increasing calls to draw from groundwater storage in order to stimulate economic growth and improve food security in Africa (Wijnen et al 2018, Cobbing 2020) a more nuanced approach to water security is necessary.

Several countries, particularly, (but not exclusively) in North Africa (figure 4(c)) have considerable water security when groundwater storage is taken into consideration. This storage provides a significant buffer before abstraction will impact the regional groundwater system. Since there is little dependency on modern recharge, the supply is secure against current climate variability. Examples from elsewhere, including the High Plains and Californian Central Valley aquifers in the US (Scanlon et al 2012), as well as the Indo-Gangetic Basin in northern India and Pakistan (MacDonald et al 2016) demonstrate that such abstraction can be continued in large aquifers but not without degradation. Similar degradation occurs in smaller heavily exploited aquifers North Africa, such as the Souss in Morocco (Bouchaou et al 2008). In Libya, where groundwater recharge is negligible, groundwater storage was developed as part of the Great Man Made River Project without convincing evidence of its sustainability (Edmunds and Wright 1979, Ahmad 1983). Unsustainable groundwater abstraction increased current water supply considerably but at the expense of water security for future generations. Eventually limits can be reached as abstraction increases and becomes more widespread. As groundwater-levels fall, and groundwater discharge reduces in response to abstraction, ecological consequences can occur and include the destruction of wetlands, reduction of baseflow to rivers, land subsidence and increases in salinity. Equally important is the impact on other groundwater users as shallow wells and boreholes dry up, leaving only those with access to the deepest and most expensive boreholes able to access groundwater.

Countries with low groundwater storage but high groundwater recharge are common in Africa, mainly because of the wide prevalence of crystalline-rock and volcanic aquifers. When abstraction is low (e.g. supporting widespread community hands pumps) the water supply is secure, even through short-term droughts (MacDonald et al 2019). Where aquifers are more permeable however, the increase in abstraction (enabled by the geology) for irrigation or urban supply, can quickly deplete storage in dry years—as

shown in the crystalline basement aquifers in Peninsular India (Collins et al 2020).

Both these examples above highlight the importance of ongoing dedicated groundwater monitoring to act as an early warning of rapid changes in storage or water quality in response to pumping. Although the use of GRACE has proved useful at scales of >100 000 km², it cannot substitute for *in situ* monitoring to capture changes in groundwater levels (Shamsudduha and Taylor 2020), water quality (Lapworth et al 2020) and abstraction (Oiro et al 2020) to identify threats to water security for individual aquifers, regions or countries.

### 4.3. Estimating recharge

The three most common methods used to estimate LTA recharge in Africa are CMB, WTF methods and the use of EnTs. The majority of studies used several techniques to assess recharge, and the most reliable studies (those in the Sahel and the Kalahari) have developed understanding and data over decades, with different methods and new techniques used to challenge and refine previous results. There is no evidence of bias in the dataset from the method used (see supplementary material). This observation likely arises, however, from the quality assurance procedure, which eliminated studies where techniques had been applied incorrectly and gave a lower priority to studies where only one technique was applied.

Where correctly applied (e.g. accounting for surface runoff, and constraining rainfall and atmospheric inputs) CMB offers useful insight for integrating recharge over the long-term. Studies where unsaturated zone profiles of chloride concentrations are combined with measurements at the water -table are particularly helpful (e.g. de Vries et al 2000, Edmunds et al 2002), as are the additional use of EnTs in more arid areas to indicate if recharge has occurred within recent decades (Lapworth et al 2013). The WTF method is the only method which directly measures groundwater storage responses and is well suited for examining temporal responses to recharge. However to estimate LTA recharge in more arid areas a sufficiently long record is required to capture important episodic recharge events (Taylor et al 2013b, Cuthbert et al 2019a) and sufficient local knowledge is needed of the aquifer properties and the groundwater discharge regime (Cuthbert 2010). In humid areas however, where annual recharge is less variable (Cuthbert et al 2019a), shorter hydrographs can also provide valuable data particularly when in combination with other methods. In arid and hyper-arid areas, combinations of EnTs are used to estimate the residence times of groundwater, and in particular identify any component of modern recharge.

There are large parts of Africa including 19 countries lacking a reliable recharge study. In humid areas of Central Africa, West Africa and Madagascar, there is a particular dearth of studies, probably
due to high rainfall and limited reliance on ground-
water. The limited understanding of recharge pro-
cess and volumes in these areas has consequences
when modelling continental and global processes and
constraining terrestrial WBs. In more arid areas, the
Horn of Africa is lacking in studies—an area where
groundwater is fundamental for buffering drought
(MacAllister et al 2020).

5. Conclusions

The development of a quantitative map and data-
base of long-term distributed groundwater recharge
for Africa from ground-based observations is a use-
ful addition to current knowledge of groundwa-
ter resources in Africa. Estimated average decadal
recharge is 15 000 km³ (4900–45 000 km³) and the
statistical model and 134 individual studies can be
used to help constrain global and continental mod-
els (e.g. Döll and Fiedler 2008). Measurable long-
term distributed recharge is found in most environ-
ments; average decadal recharge fluxes in arid and
semi-arid areas are 60 mm (IQR 30–140 mm) and
200 mm (IQR 90–430 mm), respectively. Recharge
occurs episodically in dryland environments so that
integrative methods such as CMB, EnTs or multi-
decadal hydrographs are required to estimate long-
term recharge. As a result, recharge is best reported as
decadal rather than annual averages. Average decadal
recharge is approximately 2% of estimated ground-
water storage across the continent as a whole but
with stark variability between the high-storage/low-
recharge sedimentary aquifers in North Africa and
low-storage/high-recharge crystalline-rock aquifers
across much of tropical Africa. African water security
is greatly enhance by the distribution of groundwa-
ter storage and recharge; many countries that feature
low recharge, possess substantial groundwater stor-
age, whereas countries with low storage typically have
high, regular recharge.

Data availability statement

The data that support the findings of this study
are openly available at the following URL/DOI:
https://doi.org/10.5285/45d2b71c-d413-44d4-8b4b-
6190527912ff.

Acknowledgments

This paper was written in memory of Prof Mike
Edmunds of Oxford University. Mike dedicated much
of his career to understanding groundwater recharge
in drylands and was an integral part of this project
team until his sudden death in 2015. The research was
funded by the UPGro research programme (Grant
Number NE/L001926/1) co-funded by the Natural
Environment Research Council (NERC), UK Foreign,
Commonwealth & Development Office (FCDO), and

the Economic and Social Research Council (ESRC).
AMM and RGT also acknowledge support from
NE/M008606/1 and NE/M008932/1 for additional
analysis since 2016. George Darling helped with initial
gathering of recharge studies. The paper is published
by permission of the Director of the British Geo-
logical Survey. 10.5285/45d2b71c-d413-44d4-8b4b-
6190527912ff

ORCID iDs

Alan M MacDonald https://orcid.org/0000-0001-6636-1499

Kirsty A Upton https://orcid.org/0000-0002-2392-4893

References

Abiye T 2017 Synthesis on groundwater recharge in Southern Africa: a supporting tool for groundwater users *Groundwater Sustain.* **1** 182–9

Adelana S M A, Abiye T A, Nkhuwa D C W, Tindimugaya C and Oga M S 2008 Urban groundwater management and protection in Sub-Saharan Africa *Applied Groundwater Studies in Africa* ed S M A Adelana and A M MacDonald (London: Taylor and Francis) pp 231–60

Africa Groundwater Atlas 2020 (available at: www2.bgs.ac.uk/africagroundwateratlas/) (Accessed May 2020)

Ahmad M U 1983 A quantitative model to predict a safe yield for well fields in Kufra and Sarir Basin, *Libya Ground Water* **21** 58–67

Alley W M and Leake S A 2004 The journey from safe yield to sustainability *Ground Water* **42** 12–16

Aly A I M, Froehlich K, Nada A, Awad M, Hamza M and Salem W M 1993 Study of environmental isotope distribution in the Aswan High Dam Lake (Egypt) for estimation of evaporation lake water and its recharge to adjacent groundwater *Environ. Geochem. Health* **15** 37–50

Arino O *et al* 2007 GlobCover: ESA service for global land cover from MERIS *IEEE Int. Geoscience and Remote Sensing Symp.* (23 July 2007) pp 2412–5

Banks E W *et al* 2020 Environmental tracers to evaluate groundwater residence times and water quality risk in shallow unconfined aquifers in sub Saharan Africa *J. Hydrol.* **125753** (https://doi.org/10.1016/j.jhydrol.2020.125753)

Bierkens M F and Wada Y 2019 Non-renewable groundwater use and groundwater depletion: a review *Environ. Res. Lett.* **14** 063002

Bonsor H C, Shamsudduha M, Marchant B P, MacDonald A M and Taylor R G 2018 Seasonal and decadal groundwater changes in African sedimentary aquifers estimated using GRACE products and LSMs *Remote Sens. 10* 904

Bouchaou L, Michelot J L, Vengosh A, Hsissou Y, Qurtobi M, Gaye C B, Bullen T D and Zuppi G M 2008 Application of multiple isotopic and geochemical tracers for investigation of recharge, salinization, and residence time of water in the Souss–Massa aquifer, southwest of Morocco *J. Hydrol.* **352** 267–87

Bromley J, Edmunds W M, Fellman E, Brouwer J, Gaze S R, Sudlow J and Taupin J D 1997 Estimation of rainfall inputs and direct recharge to the deep unsaturated zone of southern Niger using the chloride profile method *J. Hydrol.* **188–9** 139–54

Calow R C, MacDonald A M, Nicol A L and Robins N S 2010 Ground water security and drought in Africa: linking availability, access, and demand *Ground Water* **48** 246–56

Cobbing J 2020 Groundwater and the discourse of shortage in Sub-Saharan Africa *Hydrogeol. J.* **28** 1143–54
Collins S L et al 2020 Groundwater connectivity of a sheared gneiss aquifer in the Cauvery River basin, India *Hydrogeol. J.* **28** 1371–88

Cuthbert M O 2010 An improved time series approach for estimating groundwater recharge from groundwater level fluctuations *Water Resour. Res.* **46** W09515

Cuthbert M O et al 2019a Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa *Nature* **572** 230–4

Cuthbert M O, Gleeson T, Moosdorf N, Befus K M, Schneider A, Hartmann J and Lehner B 2019b Global patterns and dynamics of climate–groundwater interactions *Nat. Clim. Change* **9** 137–41

Damkjaer S and Taylor R G 2017 The measurement of water scarcity: defining a meaningful indicator *Ambio* **46** 513–31

de Vries J J, Selaolo E T and Beekman H E 2000 Groundwater recharge in the Kalahari, with reference to paleo-hydrologic conditions *J. Hydrol.* **238** 110–23

Didan K, Munoz A B, Solano R and Huete A 2015 *MODIS Vegetation Index User's Guide (MOD13 Series)* (Tucson, AZ: Vegetation Index and Phenology Lab, University of Arizona)

Dincer T, Al-Mugrin A L and Zimmerman U 1974 Study of the infiltration and recharge through the sand dunes in arid zones with special reference to the stable isotopes and thermonuclear tritium *J. Hydrol.* **23** 79–109

Döll P and Fiedler K 2008 Global-scale modeling of groundwater recharge *Hydrol. Earth Syst. Sci.* **12** 863–85

Edmunds W M E 2012 Limits to the availability of groundwater in Africa *Environ. Res. Lett.* **7** 021003

Edmunds W M, Darling W G, Kinniburgh D G, Kotoub S and Mahgoub S 1992 Sources of recharge at Abu Delaig, Sudan *J. Hydrol.* **131** 1–24

Edmunds W M and Gaye C B 1994 Estimating the spatial variability of groundwater recharge in the Sahel using chloride *J. Hydrol.* **156** 47–59

Edmunds W M and Wright E P 1979 Groundwater recharge and palaeoclimatic in the Sirte and Kufra basins, Libya *J. Hydrol.* **40** 215–41

Edmunds W, Fellman E, Goni I B and Prudhomme C 2002 Spatial and temporal distribution of groundwater recharge in northern Nigeria *Hydrogeol. J.* **10** 205–15

Favreau G, Cappelaere B, Massuel S, Leblanc M, Boucher M, Boulain N and Leduc C 2009 Land clearing, climate variability, and water resources increase in semiarid southwest Niger: a review *Water Resour. Res.* **45** W00A16

Foster S and MacDonald A M 2014 The ‘water security’ dialogue: why it needs to be better informed about groundwater *Hydrogeol. J.* **22** 1489–92

Gates J B, Edmunds W M, Ma J and Scanlon B R 2008 Estimating groundwater recharge in a cold desert environment in northern China using chloride *Hydrogeol. J.* **16** 893–910

Gaye C B and Tindimugaya C 2019 Challenges and opportunities for sustainable groundwater management in Africa *Hydrogeol. J.* **27** 1099–110

Gleeson T, Cuthbert M, Ferguson G and Perrone D 2020 Global groundwater sustainability, resources, and systems in the Anthropocene *Annu. Rev. Earth Planet. Sci.* **48** 431–63

Gonçalvès J, Petersen J, Deschamps P, Hamelin B and Baba-Sy O 2013 Quantifying the modern recharge of the ‘fossil’ Sahara aquifers *Geophys. Res. Lett.* **40** 2673–8

Guendouz A, Moulla A S, Edmunds W M, Zouari K, Shand P and Mamou A 2003 Hydrogeochemical and isotopic evolution of water in the Complexe Terminal aquifer in the Algerian Sahara *Hydrogeol. J.* **11** 483–95

Harris I, Osborn T J, Jones P and Lister D 2020 Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset *Sci. Data* **7** 109

Healy R W 2010 *Estimating Groundwater Recharge* (Cambridge: Cambridge University Press)

Ibrahim M, Favreau G, Scanlon B R, Seidel J L, Lecoz M, Demarty J and Cappelaere B 2014 Long-term increase in diffuse groundwater recharge following expansion of rainfed

cultivation in the Sahel, West Africa *Hydrogeol. J.* **22** 1293–305

Jones A et al (ed) 2013 *Soil Atlas of Africa. Publications Office of the European Union* (Luxembourg: European Commission)

Kolusu S R et al 2019 The El Niño event of 2015–16: climate anomalies and their impact on groundwater resources in East and Southern Africa *Hydrol. Earth Syst. Sci.* **23** 1751–62

Lapworth D J et al 2020 Drinking water quality from rural handpump-boreholes in Africa *Environ. Res. Lett.* **15** 064020

Lapworth D J, MacDonald A M, Tijani M N, Darling W G, Gooddy D C, Bonsor H C and Araguás-Araguás L J 2013 Residence times of shallow groundwater in West Africa: implications for hydrogeology and resilience to future changes in climate *Hydrogeol. J.* **21** 673–86

Lapworth D J, Nkhuwa D C, Okotto-Okotto J, Pedley S, Stuart M E, Tijani M N and Wright J J 2017 Urban groundwater quality in sub-Saharan Africa: current status and implications for water security and public health *Hydrogeol. J.* **25** 1093–116

Lark R M 2017 Controlling the marginal false discovery rate in inferences from a soil dataset with α-investment *Eur. J. Soil Sci.* **68** 221–34

Lark R M, Cullis B R and Welham S J 2006 On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E-BLUP) with REML *Eur. J. Soil Sci.* **57** 787–99

Leblanc M J, Favreau G, Massuel S, Tweed S O, Loireau M and Cappelaere B 2008 Land clearance and hydrological change in the Sahel: SW Niger *Glob. Planet. Change* **61** 135–50

Leduc C, Bromley J and Schroeter P 1997 Water table fluctuation and recharge in semi-arid climate: some results of the HAPEX-Sahel hydrodynamic survey (Niger) *J. Hydrol.* **188** 123–38

Leduc C, Favreau G and Schroeter P 2001 Long-term rise in a Sahelian water-table: the continental terminal in south-west Niger *J. Hydrol.* **243** 43–54

Lerner D N, Issar A S and Simmers I 1990 *Groundwater Recharge: A Guide to Understanding and Estimating Natural Recharge* (Hannover: Heise)

MacAllister D J, MacDonald A M, Kebede S, Godfrey S and Calow R 2020 Comparative performance of rural water supplies during drought *Nat. Commun.* **11** 1099

MacDonald A M et al 2016 Groundwater quality and depletion in the Indo-Gangetic Basin mapped from *in situ* observations *Nat. Geosci.* **9** 762–6

MacDonald A M et al 2019 Groundwater and resilience to drought in the Ethiopian Highlands *Environ. Res. Lett.* **14** 095003

MacDonald A M et al 2020 Groundwater recharge in Africa from ground based measurements. British Geological Survey (Dataset) (https://doi.org/10.5285/45d2b71c-d413-44d4-8b4b-6190527912ff)

MacDonald A M, Bonsor H C, Dochartaigh B É Ó and Taylor R G 2012 Quantitative maps of groundwater resources in Africa *Environ. Res. Lett.* **7** 024009

Moeck C, Grech-Cumbo N, Podgorski J, Bretzler A, Gurdak J J, Berg M and Schirmer M 2020 A global-scale dataset of direct natural groundwater recharge rates: a review of variables, processes and relationships *Sci. Total Environ.* **717** 137042

Mohan C, Western A W, Wei Y and Saft M 2017 Predicting groundwater recharge for varying land cover and climate conditions—a global meta-study *Hydrol. Earth Syst. Sci.* **22** 2689–703

Morris B L, Lawrence A R, Chilton P J, Adams B, Calow R C and Klinck B A 2003 *Groundwater and Its Susceptibility to Degradation: A Global Assessment of the Problem and Options for Management* (Nairobi: United Nations Environment Programme)

Oiro S, Comte J C, Soulsby C, MacDonald A and Mwakamba C 2020 Depletion of groundwater resources under rapid urbanisation in Africa: recent and future trends in the Nairobi Aquifer System, *Kenya Hydrogeol. J.* **28** 2635–56
Rodell M, Famiglietti J S, Wiese D N, Reager J T, Beaudoing H K, Landerer F W and Lo M H 2018 Emerging trends in global freshwater availability *Nature* **557** 651–9

Sami K and Hughes D A 1996 A comparison of recharge estimates to a fractured sedimentary aquifer in South Africa from a chloride mass balance and an integrated surface-subsurface model *J. Hydrol.* **179** 111–36

Scanlon B R *et al* 2006 Global synthesis of groundwater recharge in semiarid and arid regions *Hydrol. Process.* **20** 3335–70

Scanlon B R *et al* 2018 Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data *Proc. Natl Acad. Sci. USA* **115** E1080–9

Scanlon B R, Faunt C C, Longuevergne L, Reedy C C, Alley W M, Mcguire V L and Mcmahon P B 2012 Groundwater depletion and sustainability of irrigation in the US high plains and central valley *Proc. Natl Acad. Sci. USA* **112** 9320–5

Scanlon B R, Healy R W and Cook P G 2002 Choosing appropriate techniques for quantifying groundwater recharge *Hydrogeol. J.* **10** 18–39

Scanlon B R, Reedy R C and Gates J B 2010 Effects of irrigated agroecosystems: 1. Quantity of soil water and groundwater in the southern High Plains, Texas *Water Resour. Res.* **46** WR008427

Scanlon B R, Reedy R C, Stonestrom D A, Prudic D E and Dennehy K F 2005 Impact of land use and land cover change on groundwater recharge and quality in the southwestern US *Glob. Change Biol.* **11** 1577–93

Schulz S, de Rooij G H, Michelsen N, Rausch R, Siebert C, Schüth C, Al-Saud M and Merz R 2016 Estimating groundwater recharge for an arid karst system using a combined approach of time-lapse camera monitoring and water balance modelling *Hydrol. Process.* **30** 771–82

Shamsudduha M and Taylor R G 2020 Groundwater storage dynamics in the world's large aquifer systems from GRACE: uncertainty and role of extreme precipitation *Earth Syst. Dyn.* **11** 755–74

Siebert S, Burke J, Faures J M, Frenken K, Hoogeveen J, Döll P and Portmann F T 2010 Groundwater use for irrigation—a global inventory *Hydrol. Earth Syst. Sci.* **14** 1863–80

Stone A E and Edmunds W M 2012 Sand, salt and water in the Stampriet Basin, Namibia: calculating unsaturated zone

(Kalahari dunefield) recharge using the chloride mass balance approach *Water SA* **38** 367–78

Sturchio N C *et al* 2004 One million year old groundwater in the Sahara revealed by krypton-81 and chlorine-36 *Geophys. Res. Lett.* **31** GL019234

Taylor R G 2009 Rethinking water scarcity: the role of storage *Eos Trans. AGU* **90** 237–8

Taylor R G *et al* 2013a Ground water and climate change *Nat. Clim. Change* **3** 322–9

Taylor R G, Todd M C, Kongola L, Maurice L, Nahozya E, Sanga H and MacDonald A M 2013b Evidence of the dependence of groundwater resources on extreme rainfall in East Africa *Nat. Clim. Change* **3** 374–8

Verhagen B T, Mazor E and Sellschop J P 1974 Radiocarbon and tritium evidence for direct rain recharge to ground waters in the northern Kalahari *Nature* **249** 643–4

Vörösmarty C J *et al* 2010 Global threats to human water security and river biodiversity *Nature* **467** 555–61

Wada Y, van Beek L P, Sperna Weiland F C, Chao B F, Wu Y H and Bierkens M F 2012 Past and future contribution of global groundwater depletion to sea-level rise *Geophys. Res. Lett.* **39** GL051230

Welham S J and Thompson R 1997 Likelihood ratio tests for fixed model terms using residual maximum likelihood *J. R. Stat. Soc. B* **59** 701–14

WHO and UNICEF 2015 *Progress on Sanitation and Drinking Water: 2015 Update and MDG Assessment* (New York: UNICEF) p 80

Wijnen M, Barghouti S, Cobbing J, Hiller B and Torquebiau R 2018 *Assessment of Groundwater Challenges and Opportunities in Support of Sustainable Development in Sub-Saharan Africa* (Washington DC: World Bank)

Wu G C, Deshmukh R, Ndhlukula K, Radojicic T, Reilly-moman J, Phadke A, Kammen D M and Callaway D S 2017 Strategic siting and regional grid interconnections key to low-carbon futures in African countries *Proc. Natl Acad. Sci. USA* **114** E3004–12

Xu Y and Beekman H E 2003 *Groundwater Recharge Estimation in Southern Africa* (Paris: UNESCO)

Xu Y and Beekman H E 2019 Groundwater recharge estimation in arid and semi-arid southern Africa *Hydrogeol. J.* **27** 929–43

Zhou Y 2009 A critical review of groundwater budget myth, safe yield and sustainability *J. Hydrol.* **370** 207–13