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Assessment of the impacts of climate variability on total water storage across Africa: implications for groundwater resources management

  • Tales Carvalho ResendeEmail author
  • Laurent Longuevergne
  • Jason J. Gurdak
  • Marc Leblanc
  • Guillaume Favreau
  • Nienke Ansems
  • Jac Van der Gun
  • Cheikh B. Gaye
  • Alice Aureli
Paper
Part of the following topical collections:
  1. Determining groundwater sustainability from long-term piezometry in Sub-Saharan Africa

Abstract

The links between climate variability, depicted by time series of oceanic indices, and changes in total water and groundwater storage are investigated across nine large aquifer basins of the African continent. The Gravity Recovery and Climate Experiment (GRACE) mission’s observations represent a remarkable tool that can provide insight into the dynamics of terrestrial hydrology in areas where direct in situ observations are limited. In order to evaluate the impact of interannual and multidecadal climate variability on groundwater resources, this study assesses the relationship between synoptic controls on climate and total water storage estimates from (i) GRACE from 2002 to 2013 and (ii) a two-variable climate-driven model that is able to reconstruct past storage changes from 1982 to 2011. The estimates are then compared to time series of groundwater levels to show the extent to which total water storage covaries with groundwater storage. Results indicate that rainfall patterns associated with the El Niño Southern Oscillation (ENSO) are the main driver of changes in interannual groundwater storage, whereas the Atlantic MultiDecadal Oscillation (AMO) plays a significant role in decadal to multidecadal variability. The combined effect of ENSO and AMO could trigger significant changes in recharge to the aquifers and groundwater storage, in particular in the Sahel. These findings could help decision-makers prepare more effective climate-change adaptation plans at both national and transboundary levels.

Keywords

GRACE Climate change Groundwater management Groundwater storage Sub-Saharan Africa 

Evaluation des impacts de la variabilité climatique sur l’ensemble des réserves en eau en Afrique: conséquences sur la gestion des ressources en eau souterraine

Résumé

Les liens entre la variabilité climatique, représentés par des séries chronologiques d’indices océaniques, et les changements de volumes d’eau total et d’eaux souterraines stockés (réserves d’eau) sont étudiées au niveau de neuf grands bassins aquifères du continent africain. Les observations de la mission Gravity Recovery and Climate Experiment (GRACE) constituent un outil remarquable, susceptible d’approfondir les connaissances sur la dynamique de l’hydrologie superficielle dans les secteurs où les observations de terrain sont limitées. Afin d’évaluer l’impact de la variabilité interannuelle et multidécennale du climat sur les ressources en eau souterraine, cette étude évalue la relation entre le climat et les estimations de volume total des réserves en eau, à partir (i) des données graphiques de GRACE de 2002 à 2013 et (II) des données graphiques des résultats d’un modèle à deux variables assujetti sur le climat susceptible de reconstituer les évolutions passées des réserves entre 1982 et 2011. Les estimations sont ensuite comparées aux chroniques des niveaux piézométriques afin de montrer dans quelle mesure les réserves en eau totales varient avec les réserves en eau souterraine. Les résultats indiquent que le comportement des précipitations associé à l’oscillation australe – El Niño (ENSO) contrôle les modifications interannuelles des réserves en eaux souterraines, alors que l’oscillation multidécennale atlantique (AMO) joue un rôle significatif dans la variabilité décennale à multidécennale. Les effets combinés d’ENSO et d’AMO peuvent provoquer des modifications notables de la recharge des aquifères et des réserves en eau souterraine, en particulier dans le Sahel. Ces résultats pourraient aider les décideurs à préparer des plans d’adaptation au changement climatique plus efficaces, tant au niveau national que transfrontalier.

Evaluación de los impactos de la variabilidad climática en el almacenamiento total de agua en África: Implicancias para la gestión de los recursos de agua subterránea

Resumen

Las relaciones entre la variabilidad climática, representada por series de tiempo de índices oceánicos, y los cambios en el almacenamiento total de agua y agua subterránea se investigan en nueve grandes cuencas de acuíferos del continente africano. Las observaciones de la misión Gravity Recovery and Climate Experiment (GRACE) representan una herramienta notable que puede proporcionar información sobre la dinámica de la hidrología terrestre en áreas donde las observaciones directas in situ son limitadas. Con el fin de evaluar el impacto de la variabilidad climática interanual y multidecadal en los recursos de agua subterránea, este estudio evalúa la relación entre los controles sinópticos sobre el clima y las estimaciones de almacenamiento total de agua de (i) GRACE de 2002 a 2013 y (ii) un modelo de dos variables forzado por el clima que es capaz de reconstruir los cambios de almacenamiento pasados desde 1982 hasta 2011. Las estimaciones se comparan con series temporales de niveles de agua subterránea para mostrar el grado en que el almacenamiento total de agua covaría con el almacenamiento de agua subterránea. Los resultados indican que los patrones de lluvia asociados con la Oscilación Austral de El Niño (ENSO) son el principal impulsor de los cambios interanuales de almacenamiento del agua subterránea, mientras que la Oscilación Multidecadal del Atlántico (AMO) desempeña un papel significativo en la variabilidad decadal a multidecadal. El efecto combinado de ENSO y AMO podría desencadenar cambios significativos en la recarga de los acuíferos y el almacenamiento de agua subterránea, en particular en el Sahel. Estos hallazgos podrían ayudar a los responsables de la toma de decisiones a preparar planes de adaptación al cambio climático más efectivos tanto a nivel nacional como transfronterizo.

气候变化对非洲整个储水量影响评价:地下水资源管理的启示

摘要

调查研究了非洲大陆9个大型含水层盆地由海洋指数时间序列描述的气候变化与整个水储量和地下水储量变化之间的联系。重力恢复和气候试验项目观测结果展示了在直接现场观测受限的地区可以深入了解陆地水文动力学的一种卓越的工具。为了评估年际和数个十年间的气候变化对地下水资源的影响,本研究评价了天气对气候的控制因素和整个出水量估算值之间的相互关系,出水量估算值是2002年到2013年通过重力恢复和气候试验以及1982年到2011年通过能够重建过去储量变化的二变量气候驱使模型得到的。然后估算值与地下水水位的时间序列对比,显示出整个水储量与地下水储量的共变程度。结果表明,与厄尔尼诺南振荡相关的降雨模式是年际地下水储量变化的主要驱动力,而大西洋数十年振荡在十年间到数十年间变化中发挥着重要作用。厄尔尼诺南振荡和大西洋数十年振荡的综合影响可触发含水层补给量和地下水储量发生重大变化,特别是在萨赫勒地带。这些发现有助于决策者在国家和跨界层面上制定更有效的气候变化适应规划。

Avaliação dos impactos da variabilidade climática na distribuição do armazenamento total de agua na África: implicações para a gestão de recursos hídricos subterrâneos

Resumo

Os elos entre variabilidade climática, ilustradas por series temporais de índices oceânicos, e alterações no armazenamento de agua total e subterrânea são estudados em nove bacias aquíferas do continente Africano. As observações da missão GRACE (Gravity Recovery and Climate Experiment) representam uma ferramenta notável que permite compreender as dinâmicas da hidrologia terrestre em áreas onde observações diretas são limitadas. De forma a avaliar o impacto da variabilidade climática interanual e multidecadal nos recursos hídricos subterrâneos, este estudo avalia a relação entre controles sinóticos sobre o clima e estimativas do armazenamento total de agua a partir de (i) GRACE de 2002 a 2013 e (ii) um modelo de duas variáveis derivadas do clima capaz de reconstruir as alterações de armazenamento de 1982 a 2011. As estimativas são subsequentemente comparadas com as series temporais de níveis de aguas subterrâneas de forma a demonstrar o quanto o armazenamento total de agua covaria com o armazenamento de águas subterrâneas. Resultados indicam que os padrões de precipitação associados com El Nino Oscilação Sul (ENOS) são os impulsionadores principais da variação interanual de armazenamento das águas subterrâneas, enquanto que a Oscilação Multidecadal do Atlântico (OMA) tem um papel importante na variabilidade multidecadal. O efeito conjunto do ENOS e OMA poderá desencadear mudanças significativas na recarga dos aquíferos e no armazenamento de águas subterrâneas, em particular no Sahel. Estes resultados podem ajudar decisores a preparar planos para adaptação as mudanças climáticas a nível nacional e transfronteiriço mais efetivos.

Introduction

Africa faces major water resources management challenges, largely because water is unevenly distributed over the continent and over time. About 64% of the population rely on limited and highly variable quantities of water, and 25% of the population experience difficulties in water use due to accessibility or mobilization issues (e.g., water infrastructure, flow controls, costs) (Vorosmarty et al. 2005). As a result of rapid population growth and increased industrial activity, water demand in Africa is projected to more than double by the end of the 21st century (Wada and Bierkens 2014), which may compromise the future livelihoods and living standards of millions of people. Global climate change and variability is expected to exacerbate this issue, as it will bring more extreme climate conditions, such as droughts (Prudhomme et al. 2014; Trenberth et al. 2014; Malherbe et al. 2016). Groundwater plays an important role in society’s adaptation to climate change and variability, in particular because it is more resilient to the effects of climate change than surface water (Green et al. 2011; Treidel et al. 2012; Van der Gun 2012; Taylor et al. 2013a). Groundwater’s unique buffer capacity provides a major strength to reduce the risk of temporary water shortage and to create conditions for survival in areas where climate change is expected to cause water stress (Falkenmark 2013; Cuthbert et al. 2017).

An estimated 75% or more of Africans use groundwater as their main source of drinking water (UNEP, 2010), particularly in rural areas that rely on low-cost dug wells and boreholes. There is a paucity of reliable and comprehensive statistics on groundwater use in Africa, but previous assessments indicate an underutilized potential to support irrigated agriculture, as most farming in Africa is currently rainfed (Wani et al. 2009). Groundwater is over-exploited for irrigation in many parts of the world (Famiglietti 2014; Konikow 2015), including Asia, where 14% of cultivated land is irrigated with groundwater (Siebert et al. 2010), but in Africa, only about 1% of the cultivated land (about 2×106 ha) is irrigated with groundwater (Altchenko and Villholth 2015). In contrast to other regions of the world, including Western Mexico, the High Plains in the central United States, the Middle East, North-East Pakistan, North-West India, and North-East China, most of Sub-Saharan Africa has not yet experienced the “groundwater crisis” (Famiglietti 2014) caused by the widespread over-abstraction of groundwater to support large-scale agriculture. Moreover, some major African aquifers tend to coincide with areas of relatively lower population density and water demand (e.g., the Sahel aquifer basins, the Congo Basin in Central Africa, and the Kalahari basins in Southern Africa) (Foster et al. 2006; Wada et al. 2010; Gleeson et al. 2012; MacDonald et al. 2012). Many African countries and/or joint bodies in charge of implementing transboundary water agreements thus have an opportunity to anticipate future groundwater use and management challenges through planning, sustainable utilization, and effective protection of groundwater resources (Tuinhof et al. 2011; Gorelick and Zheng 2015).

However, the development of long-term effective and reliable groundwater management strategies for coping with the threat of water scarcity and the effects of climate variability and change in Africa is undermined by the lack of adequate data for decision-making (Bates et al. 2008). In recent years, there has been a substantial decline in hydrometeorological data collection and management (Houghton-Carr and Fry 2006; Robins et al. 2006). Decades ago, Africa had a relatively dense network of stations to measure rainfall, temperature, and other weather data, but some weather centers have aged badly because of reductions in budgets for field maintenance and inspection, and many of these stations are no longer operating (Giles 2005). The current density of hydrometeorological stations in Africa is eight times lower than the minimum recommended by the World Meteorological Organization (WMO 1996). Many governments have a limited ability to collect the data needed for long-term water resources management, and current efforts have focused mainly on rainfall and river flow data collection. As a result, most African countries lack groundwater-monitoring stations, and this limits the understanding of the response of groundwater to human and natural conditions (Gaye and Tindimugaya 2012).

Over the last decade, significant advances in remote sensing techniques have led to a more complete view of the water cycle at the global scale. Launched in March 2002, the Gravity Recovery and Climate Experiment (GRACE) is the first satellite mission able to provide global observations of changes in total water storage (ΔTWS) (Richey et al. 2015; Chen et al. 2016). Given that the dynamics of groundwater are affected by interannual to multidecadal climate variability (Gurdak et al. 2007; Kuss and Gurdak 2014), longer observations than GRACE’s current 15-year record (2002 to 2017) are desirable in order to better evaluate the past and current evolution of groundwater resources, as well as to provide insights for future management.

To overcome GRACE’s time frame limitation and the lack of adequate long-term piezometry data, this study used an approach that involved the reconstruction of past water storage variations in major aquifers across Africa through a climate-driven model using precipitation and actual evapotranspiration data from global data sets over the period from 1982 to 2011. Validation of the results was carried out by comparing the results of modeled total water storage changes (ΔTWSMODEL) with GRACE-based total water storage changes (ΔTWSGRACE) estimated from 2002 to 2013. The model and GRACE-based estimates were then compared to long-term piezometry measurements to show the extent to which total water storage changes covaried with observed groundwater storage changes (ΔGWSOBSERVED). This approach also allows one to quantify teleconnections between total water and groundwater storage changes with global-scale climatic oscillations such as the El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO).

Acronyms and abbreviations are given in the Appendix.

Background

Study areas

Nine large aquifer systems in Africa were selected for this study on the basis of hydrogeological, climate, and governance conditions, as presented in Table 1 and Fig. 1.
Table 1

Basic hydrogeological, socioeconomic, and governance overview of the studied aquifers

Aquifer no. (Fig. 1)

Aquifer

Zone

Population (approx. no. of inhabitants)

Approx. area (km2)

Rainfall (mm/year)

Aquifer type

Institutional arrangement

1

North-Western Sahara Aquifer System (NWSAS)

Northern Africa

4,000,000

1,300,000

10–300

Sand, sandstone, sandy clay, calcareous, dolomite

Observatory of the Sahara and the Sahel (OSS)

2

Nubian Sandstone Aquifer System (NSAS)

Northern Africa

67,000,000

2,800,000

1–550

Nubian and Post-Nubian

Joint Authority

3

Senegalo-Mauritanian Basin

Sahel

12,000,000

330,000

20–1850

Quaternary–Maastrichtian

Senegal River Basin Development Authority (potential)

4

Irhazer-Iullemmeden Basin

Sahara-Sahel

13,000,000

580,000

80–900

Sedimentary deposit including Terminal Continental and Intercalary Continental (Cretaceous–Tertiary)

Consultation mechanism to be operationalized

5

Lake Chad Basin

Sahara-Sahel

22,000,000

2,300,000

40–1400

Sedimentary: Upper Quaternary, lower Pliocene, and Continental Terminal (Tertiary)

Lake Chad Basin Commission (potential)

6

Volta Basin

Tropical/Equatorial Africa

14,000,000

145,000

500–1100

Sedimentary rocks

Volta Basin Authority (potential)

7

Karoo-Carbonate

Equatorial Africa

10,000,000

600,000

1000–1800

Limestone/sandstone

International Commission of the Congo-Oubangui-Sangha Basin (potential)

8

Stampriet Transboundary Aquifer System

Southern Africa

50,000

90,000

200–350

Kalahari group aquifers and Karoo supergroup aquifers

Orange-Senqu River Commission

9

Karoo Sedimentary

Southern Africa

6,000,000

170,000

350–1200

Consolidated sedimentary rocks

Orange-Senqu River Commission (potential)

Fig. 1

Location of the studied aquifers in Africa

The distribution of aquifers in Africa is now reasonably mapped following long-term programs launched in the 1960s by national and international agencies and supported variously by the United Nations Educational, Scientific and Cultural Organization (UNESCO), International Atomic Energy Agency (IAEA), and British, French, German, and Dutch technical assistance. These efforts were subsequently integrated by the International Association of Hydrogeologists (IAH)/UNESCO/BGR WHYMAP [World-wide Hydrogeological Mapping and Assessment Programme] Africa Groundwater Resources Map in 2008, which was the baseline for the first quantitative maps of groundwater resources in Africa (MacDonald et al. 2012). Approximately 45% of the African land surface is underlain by large sedimentary basins hosting relatively homogeneous aquifers that may offer good conditions for groundwater abstraction. Approximately 11% of the land has a geologically complex structure, highly productive aquifers in heterogeneous folded or faulted regions in close vicinity to non-aquifers. Almost half of the territory (44%) consists of regions with only limited groundwater resources, generally in local and shallow aquifers in weathered crystalline bedrock or alluvial deposits that may be locally productive (BGR and UNESCO 2008; Maurice et al. 2018).

With regard to storage, a considerable proportion of Africa’s groundwater resources are located in the large sedimentary basins in (semi)arid zones (e.g., Northern Africa, Sahel, and the Kalahari and Karoo basins in Southern Africa) and tropical zones (e.g., the Congo Basin in Central Africa) (MacDonald et al. 2012). These basins usually contain multilayered aquifer systems with major alluvial formations forming shallow unconsolidated aquifers underlain by consolidated sedimentary rocks forming deeper aquifers. Even in semi-arid parts of Africa, the shallow unconsolidated aquifers can be recharged in response to episodic storm events, climate oscillations, and land-use change (Taylor et al. 2009, 2013b). In the Sahara/Sahel aquifers, isotope and hydrochemical investigations have revealed that recharge occurs mainly by direct infiltration of rainwater or by river/surface-water interaction, as in the case of the Senegal and Niger Rivers (Diaw et al. 2012; Nazoumou et al. 2015; Abdou Babaye et al. 2018). Stable and radioactive isotope contents in shallow aquifers confirm the presence of modern infiltration water (Lapworth et al. 2013; Zouari 2015). Recharge rates over these areas are usually low, ranging from 0.1 to 5% of annual precipitation (Scanlon et al. 2006). The recharge mechanisms and dynamics of the deeper aquifers is still uncertain. Data from the Sahara/Sahel tend to exhibit piston-flow behavior (i.e., new water “pushing” old water downward). Depleted stable isotope contents observed in some aquifers (Taoudeni and Iullemmeden basins) suggest the presence of paleoclimatic water or old–recent mixed groundwater (Fontes et al. 1991; Zouari 2015). However, the deeper aquifers can be considered as not being actively recharged, as most of the recharge dates back more than 5000 years (Edmunds 2008).

Most of these aquifers located in large sedimentary basins are transboundary. More than 75 transboundary aquifers have been identified in Africa, but as more information and knowledge becomes available, this number is likely to increase (IGRAC-UNESCO 2015). The identified transboundary aquifers represent approximately 42% of African continental land area and 30% of the population. Arrangements for the management of these transboundary aquifers remain insufficiently developed, as groundwater has traditionally been considered a national matter. Only three transboundary aquifers are under an operational agreement, namely the Nubian Sandstone Aquifer System (NSAS), the North-Western Sahara Aquifer System (NWSAS), and the Stampriet Transboundary Aquifer System (STAS). A memorandum of understanding was signed in 2014 for the establishment of a consultation mechanism for the Iullemmeden–Taoudeni/Tanezrouft Aquifer System (ITTAS), but it has not yet entered into force. Africa is, however, the continent with the highest proportion of transboundary surface-water catchments under an operational arrangement (Meyer 2016; UNESCO and UNECE 2017). Given that aquifers and river/lake basins do not necessarily coincide, the most appropriate body to oversee the management of a transboundary aquifer may not necessarily be a river or lake basin organization (if existent). However, these arrangements and institutions could play a crucial role in promoting cooperation over transboundary aquifers through action programs.

Climate variability modes in Africa

Exchanges between the Earth’s atmosphere, oceans, cryosphere, and continental hydrology give rise to natural climate fluctuations of various periodicities. Some of the most important global-scale climate oscillations on interannual to multidecadal timescales that influence local water resources are the El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). These natural climate oscillations are monitored using scalar-valued indices, which characterize positive, negative, and neutral phases of a climate variability mode to identify the strength of these phases.

ENSO is considered the most important pattern of natural interannual climate variability on Earth (Palmer and Anderson 1994). It is a coupled ocean-atmospheric phenomenon that has interannual variability with irregular 2- to 7-year cycles between the warm (El Niño) and cold (La Niña) phases that have been occurring for at least the past 700 years (Li et al. 2013). El Niño is characterized by stronger than average sea surface temperatures in the central and eastern equatorial Pacific Ocean, reduced strength of the easterly trade winds in the tropical Pacific, and an eastward shift in the region of intense tropical rainfall. La Niña is characterized by the opposite—cooler than average sea surface temperatures, stronger than normal easterly trade winds, and a westward shift in the region of intense tropical rainfall. Three very strong El Niño events have occurred since the early 1980s, namely 1982–1983, 1997–1998, and more recently 2015–2016. Although ENSO is centered in the tropics, the changes associated with El Niño and La Niña events affect climate around the world. ENSO’s influence on annual rainfall levels has been reported all across Africa, particularly in Southern Africa (Manatsa et al. 2011). Droughts generally occur during the warm phase of ENSO (Masih et al. 2014). On the other hand, the trend towards more La Niña-like conditions since 2000 is likely a contributing factor driving the increase in Southern Africa rainfall (Maidment et al. 2015). Compared with the other African regions, the climate of Central Africa and its variability has been the subject of very few studies. The few existing studies suggest that there is not a significant relationship between ENSO and precipitation in Central Africa (Philippon et al. 2012; Taylor et al. 2013b). ENSO has also been linked to the devastating droughts of the 1970s and 1980s in the Sahel (e.g., Giannini et al. 2003). Drier (wetter) decades in the Sahel are usually correlated with El Niño (La Niña) events (e.g., Nicholson and Selato 2000; Janicot et al. 2011). Several different ENSO indices have been developed over the years, but the Multivariate ENSO Index (MEI) is favored over other indices because it combines the significant features of all observed surface fields in the tropical Pacific. MEI monthly values were obtained from the NOAA Earth System Research Laboratory (NOAA 2017).

The PDO is often described as a long-lived El Niño-like pattern of Pacific climate variability (Zhang et al. 1997). The PDO index is based on patterns of variation in the sea surface temperature of the North Pacific Ocean with warm and cold phases that can persist for 20–30 years. Unlike ENSO, the PDO is not a single physical mode of ocean variability, but rather the sum of several processes with different dynamic origins. The PDO monthly values were obtained from the NOAA Earth System Research Laboratory database (NOAA 2017).

The NAO represents a north–south oscillation atmospheric mass between the Icelandic low-pressure system and the Azores high-pressure system. The positive phase of the NAO reflects below-normal surface pressure over the Icelandic to Arctic regions and above-normal surface pressure over the subtropical Atlantic. The negative phase reflects the opposite. The NAO exhibits considerable interseasonal, interannual, and multidecadal variability, with irregular 1- to 24-year cycles (Hurrell 1995, Chelliah and Bell 2004), but has a dominant quasi-periodic oscillation of 3 to 6 years and a less significant 8- to 10-year mode (Hurrell et al. 2003). The NAO is among the known modes of natural variability influencing Northern Africa precipitation on a variety of timescales, especially in winter and early spring. Drier (wetter) decades in Northern Africa largely correspond to the positive (negative) NAO phase (López-Moreno et al. 2011). Correlations are considerably stronger, however, for negative NAO phases (Donat et al. 2014). For example, the negative phase of the NAO from the mid-1950s to the late 1970s indicates relatively wet conditions, with a gradual shift towards drier conditions in the early 1970s. Recent years have also been considered wetter. NAO monthly values were obtained from the NOAA Earth System Research Laboratory database (NOAA 2017).

The AMO is an index of sea surface temperature over the North Atlantic Ocean (quasi-periodic cycles of roughly 50 to 70 years), with negative and positive phases that may each last 20–40 years and lead to differences of about 15 °C between extremes. Paleoclimatologic studies have confirmed that these changes have been occurring over the past 8000 years (Knudsen et al. 2011). The AMO was in positive phases from 1860 to 1880 and 1930 to 1960, and in negative phases from 1905 to 1925 and 1970 to 1990. The AMO flipped to a positive phase in the mid-1990s and is believed to be gradually moving to a negative phase (McCarthy et al. 2015). Higher rainfall over the Sahel is associated with positive phases of the AMO (Diatta and Fink 2014), while the opposite occurs in the Gulf of Guinea (Mohino et al. 2011). The AMO monthly values were obtained from the NOAA Earth System Research Laboratory database (NOAA 2017).

The interaction of climate variability modes can enhance or diminish certain climate forcings on local hydrologic processes (Hanson et al. 2004). When El Niño (La Niña) occurs with the warm (cold) PDO phase, rainfall tends to increase over the Sahara to the Gulf of Guinea and Southern Africa, while the opposite occurs in the Horn of Africa (Wang et al. 2014). Several studies suggest that the AMO modulates ENSO and NAO variability (Dong et al. 2006; Dong and Sutton 2007; Timmermann et al. 2007; Zhang et al. 2012; García-García and Ummenhofer 2015). Strengthened (weakened) La Niña effects coincide with a positive (negative) phase of the AMO (Geng et al. 2016). In the Sahel (Lake Chad), the severe impact of droughts of the 1970s and 1980s is tied to the combined effects of the negative phase of the AMO and El Niño events (Okonkwo et al. 2015). Finally, ENSO and AMO are well-known climate teleconnections that have been associated with extreme rainfall variability in Western/Central Africa (Ndehedehe et al. 2017). An inverse relationship exists between the AMO and the NAO decadal tendencies. When the AMO is negative, NAO tends more often to the positive state. Statistical analyses over the 20th century suggest that the AMO precedes the NAO by 10–15 years (Peings and Magnusdottir 2014). These findings provide an interesting possibility for decadal forecasting.

Methods

GRACE observations

There has been great interest in the use of GRACE satellites to monitor changes in water storage, especially in regions with limited ground-based data such as Africa (Henry et al. 2011; Ramilien et al. 2014; Richey et al. 2015; Chen et al. 2016; Hassan and Jin 2016; Rateb et al. 2017). GRACE satellites provide a spatially filtered image of real TWS that needs to be processed to produce information on changes in TWS. There are generally three approaches for processing GRACE total water storage change signals: the scaling factor approach, the additive correction approach, and the multiplicative correction approach. The validation of GRACE-based estimates is challenging, because results can differ by up to 100% depending on which processing approach is used, particularly over (i) (semi-)arid areas, (ii) areas with intensive irrigation, and (iii) relatively small basins (i.e., ≤200,000 km2), for which the additive correction approach may be more appropriate (Long et al. 2015). It is thus imperative that GRACE-based estimates are compared with ground-based data to assess their validity.

Considering that most of the studied aquifer systems are located in arid and semi-arid areas, the additive correction approach was used, as previously presented by Longuevergne et al. (2010), to provide total water storage estimates per month from 2002 to 2013. This is based on monthly spherical harmonics (SH) gravity field solutions R05 monthly data from CSR (Center for Space Research, University of Texas at Austin, USA), truncated at degree and order 60 (Bettadpur 2007), including a destriping filter (Swenson and Wahr 2006) and additional 300 km Gaussian smoothing. Alternative methods for leakage corrections include the mascon-type approach. The basic difference between SH and mascons is that SH solutions are global, whereas mascons can be applied at regional to global scales. A recent study has shown that although long-term trends for SH are lower than those for mascons, they remain highly correlated (Scanlon et al. 2016). The method used for detrending total water storage time series consists in fitting and removing a trend model consisting of a long-term linear and seasonal cycles by using linear regression (Sun et al. 2017). Results are compared with groundwater changes from piezometry (ΔGWSOBSERVED) and modeled changes in total water storage (ΔTWSMODEL) in order to identify any long-term covariance. Particular attention is given to aquifer systems whose area is below the limit of the GRACE footprint (Karoo Sedimentary, Stampriet Transboundary Aquifer System, and Volta Basin Aquifer).

A modeling approach to extend the GRACE time frame

Trends during the GRACE era (2002–2017) are dominated by internal climate variability (i.e., arising from interactions and chaotic variability within the climate system, particularly in rainfall) rather than by the forced response (i.e., driven primarily by human-induced changes in atmospheric composition) (Fasullo et al. 2016). Key to understanding reported changes during the GRACE record is quantifying the character of internal climate modes. The GRACE era covers a very limited number of climatic oscillation cycles, as modes such as the AMO, PDO, and NAO have oscillation periods longer than the GRACE observation record. Several studies indicate that low-frequency cycles like the AMO and PDO are particularly influential in modulating high-frequency cycles such as ENSO. To bridge this gap, there is a need to extend the GRACE time frame to the “past” using a model that is able to reconstruct the interannual to decadal climate-driven changes in water storage. A climate-driven model for estimating long-term water storage dynamics, independent from GRACE data, was developed and applied at the aquifer scale.

According to MacDonald et al. (2012), groundwater development stress is relatively low in most of the large African aquifers with renewable groundwater resources. This suggests that changes in groundwater storage in such aquifers tend to be dominated by climatic variations. For that reason, human influences (such as abstraction and land-use changes) are neglected in the simple simulation model. This model is based on elaboration of the general water balance equation:
$$ \frac{d\mathrm{TWS}}{dt}=P-E-R $$
(1)
where P is precipitation, E is actual evapotranspiration, R is runoff (or discharge at the basin outlet), and TWS is total water storage (sum of water stored in vegetation, ice, snow, lakes and streams, soil moisture, and groundwater). All components are given in millimeters per month. Mean precipitation and actual evapotranspiration were computed by averaging data available from global data sets with spatial resolution of 0.5° × 0.5° at the aquifer scale from 1982 to 2011 (Global Precipitation Climatology Centre data set for precipitation (Becker et al. 2013) and Max Planck Institute data set for actual evapotranspiration) (Jung et al. 2010). These data sets were selected because of their ability to capture the regionally averaged seasonal cycles (Mueller et al. 2011; Sun et al. 2018).
Integration of Eq. 1 gives:
$$ \Delta \mathrm{TWS}=\int Pdt-\int Edt-\int Rdt $$
(2)
ΔTWS is the change in total water storage over the time interval of integration; thus it includes the changes in water stored in vegetation, snow, ice, lakes and streams, soil moisture, and groundwater. Integration over time of P, E, and R will generate a long-term trend in ΔTWS, which is attributed to integration of systematic errors in these variables. Unbiased data sets are therefore required. Considering that P is the dominant factor controlling long-term variations in E and R, it is assumed that runoff is constant over time, i.e., contribution to storage as a linear trend (Bouwer et al. 2006; Liu et al. 2013). By doing so, when estimating storage changes by integration of R, a long-term trend related to runoff is removed. Thus, Eq. 2 becomes:
$$ {\Delta \mathrm{TWS}}_{\mathrm{MODEL}}=\int Pdt-\int Edt-\int Rdt\approx \mathrm{detrend}\left(\int Pdt-\int Edt\right) $$
(3)

In the absence of detailed field data on all these storage components, it is difficult to isolate the change in groundwater storage (ΔGWS), but under certain circumstances—to be judged by the modeler—it is plausible that ΔGWS is nearly equal to ΔTWSMODEL. Such circumstances may, for instance, apply to relatively long integration intervals (several years) in arid and semi-arid regions where the assumed combined non-groundwater storage capacity is small compared to the simulated change in total water storage (ΔTWSMODEL).

Ground-based measurements

Due to the general lack of continuous long-term groundwater-level data, the selection of records for the validation of GRACE-based estimates was carried out based on the limited data available in the literature and their representativeness. The selected records are typically located no farther than 20 km from surface-water bodies (e.g., river, lake, oued) and tap shallow unconfined aquifers. The records provide groundwater levels on a monthly basis for different periods ranging from 5 to 20 years (Fig. 2 and Table 2). In order to compare piezometry with total water storage, long-term groundwater-level data were detrended using the MATLAB function detrend that subtracts the mean or a best-fit line (in the least-square sense) from data. If the data do have a trend, detrending forces the mean to zero and reduces overall variation.
Fig. 2

Groundwater-level fluctuation in the studied aquifers

Table 2

Ground-based measurements in the studied aquifers

Aquifer no.

Aquifer

Groundwater-level time frame

Well/borehole depth

Source

1

North-Western Sahara Aquifer System (NWSAS)

1982–2011

Shallow piezometer

(located near the aquifer boundaries)

Massuel and Riaux 2017

2

Nubian Sandstone Aquifer System (NSAS)

1998–2004

Shallow piezometer (vicinity of Lake Nasser)

El Shazli 2018

3

Senegalo-Mauritanian Basin

1997–2002

Shallow piezometer (vicinity of Senegal River)

Gning 2015

4

Irhazer-Iullemmeden Basin

1991–2015

<75 m (<75 km from the Niger River)

Updated from Favreau et al. 2009

5

Lake Chad Basin

2006–2011

85 m (Maiduguri – vicinity of Lake Chad)

Vasollo  S. (personal communication) 2017

6

Volta Basin

2006–2011

Shallow piezometer

Lutz et al. 2015

7

Karoo-Carbonate

N/A

N/A

N/A

8

Stampriet Transboundary Aquifer System

1986–2008

Shallow piezometer (<50 m)

UNESCO 2016

9

Karoo Sedimentary

1994–1999

Shallow piezometer (<10 m)

IGRAC 2017

N/A: not applicable

Wavelet analysis

Wavelet transforms were used to analyze teleconnections between groundwater level, GRACE-based estimates, climate-driven model, and climate indices time series in both amplitude and frequency. A MATLAB script developed by Grinsted et al. (2004) was applied that enables the performance of continuous wavelet transform (CWT), cross-wavelet transform (XWT), and wavelet coherence (WTC) plots. CWT expands the time series into time–frequency space, XWT finds regions in time–frequency space where the time series show high common power, and WTC finds regions in time–frequency space where the two time series covary (but does not necessarily have high power) (Torrence and Compo 1998; Labat et al. 2000; Grinsted et al. 2004; Labat 2005, 2008; Holman et al. 2011). Although these three methodological steps were necessarily followed, the presentation of the results and discussion focuses on the WTC plots. High correlation between time series is indicated by light yellow zones. The arrows → and ← in zones of the WTC figures indicate the positive (in-phase) and negative (anti-phase) relationships between two time series, respectively, while the arrows ↓ and ↑ show that time series 1 lags time series 2 by 90°. The interpretation of lags in these zones can be challenging, however, and should be done carefully, as a lead of 90° can also be interpreted as a lag of 270° or a lag of 90° relative to the anti-phase (opposite sign). A good indication that there is a connection and link between time series is that the phase arrows generally point in only one direction for a given wavelength.

Results and discussion

Evaluation of GRACE-based and climate-driven model estimates

The GRACE-based and climate-driven model total water storage changes (ΔTWSGRACE and ΔTWSMODEL, respectively) were compared to groundwater levels in the studied aquifers to assess to what extent they covaried with ΔGWSOBSERVED. All data were detrended in order to focus the analysis on the fluctuations in the data. However, it is worth mentioning that total water storage time series are nonlinear and nonstationary, and tend to vary at multiple temporal scales, making filtering and detrending of total water storage a nontrivial task (Sun et al. 2017). The data were further normalized based on the mean and standard deviation of each data set in order to provide a benchmark and comparison basis. Results suggest that changes in total water storage estimates generally describe groundwater-level dynamics well (Fig. 3). These results are further verified by wavelet transform analysis indicating a high correlation between GRACE-based and climate-driven model water storage estimates and groundwater levels at both intra- and interannual scale (Fig. 4a and b). Given that the comparison is performed with groundwater-level records from shallow boreholes that are located in the vicinity of surface-water bodies (usually <20 km), which are likely to have a strong surface-water/groundwater interaction, it is fair to conclude that ΔTWSGRACE, ΔTWSMODEL and ΔGWSOBSERVED representative of groundwater fluctuations in shallow unconfined aquifers are strongly correlated. This could also suggest that storage changes in deep aquifers are limited, thus supporting the assumptions that they are not being actively recharged and that they are still exploited largely at low rates. The near-synchronous signals of groundwater levels and the climate-driven model water storage estimates (Fig. 3, Fig. 4a and b) reveal that shallow aquifers are highly responsive to rainfall temporal patterns, and reinforce the concept that natural climate variability, in particular changes in precipitation, contributes substantially to groundwater storage changes.
Fig. 3

Normalized observed groundwater ΔGWSOBSERVED (red), GRACE-based ΔTWSGRACE (blue), and model-based total water storage variability ΔTWSMODEL (green) in the studied aquifers

Fig. 4

Wavelet coherence (WTC) plots in the studied aquifers: a Northern Africa and Sahara-Sahel, and b Tropical/Equatorial Africa and Southern Africa (Note: x-axis is date (year) and y-axis is period in years. Correlation coefficients vary from 0 (dark blue) to 1 (light yellow))

The results of the model enabled the identification of four types of groundwater storage dynamics that are largely correlated with African climate zones. The model indicates that the North-Western Sahara Aquifer System (NWSAS) located in Northern Africa had a decrease in storage from the early 1980s to late 1980s, an increase in the early 1990s followed by a decrease from the mid-1990s to mid-2000s, an increase in the mid-2000s, and a decrease since then (Fig. 5). This is in relatively good agreement with rainfall patterns and shallow groundwater fluctuations in the vicinity of oueds in the Ouargla Plain in Algeria/Tunisia (Bellaoueur 2008) and central Tunisia (Massuel and Riaux 2017) (Fig. 2). The model’s water storage increase in the mid-2000s also supports the assumption that the shallow aquifers (including outcrops of the deep aquifers) of the NWSAS are receiving a fraction of modern water as recharge from infiltration of rainfall coming from the Sahara Atlas Mountains in Algeria and the Dahar and Nafusa Mountains in Tunisia and Lybia (Baba-Sy 2005; Al-Gamal 2011). It should be noted, however, that groundwater abstraction in the NWSAS has risen steeply over the past few decades because of tapping of confined aquifers by drilling deep boreholes in the 1980s for water supply and irrigation schemes (OSS 2003). These boreholes have had very little maintenance since they were drilled, and recently observed water-table rises in shallow aquifers could be locally influenced by upward leakage through corroded borehole casings (Messekher et al. 2012).
Fig. 5

Groundwater storage variability and its association with climate teleconnections in Northern Africa (1982–2011): a Northern Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) indices, b simulated changes in total water storage (ΔTWSMODEL), and c ΔTWSMODEL-NAO wavelet coherence plot (see text for description of boxes 1, 2, and 3)

(Note: x-axis is date (year) and y-axis in c is period in years)

The aquifers located in the Sahel (Nubian Sandstone Aquifer System, Lake Chad Basin, Irhazer-Iullemmeden Basin, and Senegalo-Mauritanian Basin) have similar multidecadal behavior, which is characterized by a significant decrease in groundwater storage from the early 1980s to mid-1990s, followed by a partial recovery (Fig. 6). As rainfall in the Sahel has generally increased since the mid-1990s, this result proves to be in good agreement with the observations from piezometry indicating that the water table has risen since the mid-1980s in large parts of the central Sahel (Favreau et al. 2009, 2012). It also supports the assumption that rainfall infiltration is a primary source of recharge, though recharge from surface-water bodies such as the Niger and the Nile Rivers is non-negligible and limited to the vicinities of the rivers (perennial and seasonal) and endoreic ponds (Nazoumou et al. 2015; Ngounou-Ngatcha et al. 2015). Aquifers located in the tropics (Volta Basin and Karoo Carbonate) show opposite behavior, although changes are not as pronounced as in the aquifers in the Sahel (Fig. 7). This is in line with recent observations that indicate a drying trend in central Equatorial Africa (west of Albertine Rift) (Diem et al. 2014) and over Guinea regions, such as Benin and Nigeria (Bamba et al. 2015).
Fig. 6

Groundwater storage variability and its association with climate teleconnections in the Sahel (1982–2011): a Atlantic Multidecadal Oscillation (AMO) index and b simulated changes in total water storage (ΔTWSMODEL)

Fig. 7

Groundwater storage variability and its association with climate teleconnections in Equatorial Africa (1982–2011): a Atlantic Multidecadal Oscillation (AMO) index and b simulated changes in total water storage (ΔTWSMODEL)

In contrast to the aquifers in the Sahel, the aquifers in Southern Africa do not show a particular multidecadal pattern, but rather a strong interannual pattern. The model indicates a decrease in storage from the early 1980s to late 1980s, an increase in the late 1980s/early 1990s, a decrease from the early 1990s to mid-1990s/late 1990s, an increase in the late 1990s/early 2000s, a decrease from the early 2000s to mid-2000s, and an increase since the mid-2000s (Fig. 8). This result is consistent with studies revealing that long-term rainfall trends in Southern Africa are weak but have exhibited increased variability since 1970 (Richard et al. 2001).
Fig. 8

Groundwater storage variability and its association with climate teleconnections in Southern Africa (1982–2011): a El Niño Southern Oscillation (ENSO) index, b simulated changes in total water storage (ΔTWSMODEL); and ΔTWSMODEL-ENSO wavelet coherence plots for the c Karoo Sedimentary Aquifer and d Stampriet Transboundary Aquifer System (Note: x-axis is date (year) and y-axis in c and d is period in years)

Groundwater storage variability and its association with climate teleconnections

Groundwater storage variability and its association with climate teleconnections is studied by applying wavelet transforms between simulated changes in total water storage (ΔTWSMODEL) and climate indices (NAO, ENSO, and AMO). In Northern Africa, groundwater storage appears to correlate with NAO (Fig. 5c). WTC reveals three regions with high coherence (good correlation), i.e., a 6–8-year band from 1990 to 1995 (Box 1 in Fig. 5c), a 1–2-year band from the mid-1990s to mid-2000s (Box 2 in Fig. 5c), and 2–3-year band from the mid-2000s onward (Box 3 in Fig. 5c), which indicates that NAO exerts an influence on changes in groundwater storage. A positive (negative) NAO phase largely corresponds to decreasing (increasing) groundwater storage. The results confirm that interannual correlations tend to be stronger for negative NAO phases (Box 2 and Box 3 in Fig. 5c). This has been particularly true since the AMO shift back to a positive phase in the mid-1990s, thus suggesting that the AMO exerts a low-frequency modulating influence on changes in groundwater storage.

The influence of the AMO on groundwater storage appears to be much more direct in the Sahel. The AMO appears to exert a multidecadal influence, as a positive (negative) phase largely corresponds to increasing (decreasing) aquifer storage (Fig. 6). For instance, all aquifers in the Sahel show a significant modeled decrease in aquifer storage during a negative phase of the AMO from the early 1980s to mid-1990s, followed by an increase in groundwater storage during a positive phase of the AMO since the mid-1990s. Mega-droughts in the Sahel are considered to be linked to the combined effect of the negative phase of the AMO and El Niño events (Shanahan et al. 2009; Masih et al. 2014). Such combination also appears to have a substantial adverse effect on groundwater storage, as it can potentially result, depending on the recharge processes in play, in both reduced recharge to the aquifers and decreased water levels as a result of to climate-induced pumping (Gurdak 2017; Russo and Lall 2017). The AMO exerts an opposite influence in the aquifers located in the tropics (Volta Basin and Karoo Carbonate), as a positive (negative) phase largely corresponds to (increasing) decreasing groundwater storage (Fig. 7). Interannual variability in total water storage—and consequently in groundwater storage—in both the Sahel and Equatorial Africa is likely to be affected by ENSO, as WTC plots for the Nubian Sandstone Aquifer System and the Senegalo-Mauritanian Basin (Fig. 9) and for the Volta Basin and Karoo Carbonate Aquifers (Fig. 10) reveal several regions with high coherence (good correlation). These regions largely coincide with El Niño events (1982–1983, 1986–1988, 1991–1992, and 1997–1998) and La Niña events (1998–2000). A recent study by Siam and Eltahir (2017) revealed a strong correlation between ENSO, rainfall, and flow in the Nile basin. El Niño years usually lead to drought conditions, whereas La Niña years are more flood-prone. Considering that recharge of the shallow aquifers occurs mainly through rainfall infiltration and river/surface-water interaction, it could be assumed that El Niño (La Niña) years could lead to decreased (increased) groundwater storage. This assumption could be extended to the Sahel and/or Equatorial Africa, as similar observations have been noted for Lake Chad (Okonkwo et al. 2015), the Senegal and Niger River basins (Diaw et al. 2012; Nazoumou et al. 2015), and Lake Volta (Owusu et al. 2008). Due to the relative shortage of long-term climate data, any assumption on cause–effect relationship in the correlation results of the model and climate indices in Central Africa is significantly more complex, as there have been only a very limited number of studies on the climate of this region (Philippon et al. 2012). Central Africa has the lowest gauge density in Sub-Saharan Africa (Washington et al. 2013) and has seen a dramatic decline in the number of rain gauges, especially after the 1980s (Asefi-Najafabady and Saatchi 2013; Zhou et al. 2014). Studies diverge in their conclusions in this region. According to Gao et al. (2016), drier conditions are associated with El Niño events, while Taylor et al. (2013a) suggest that the influence of ENSO varies spatially, and studies have indicated an opposite pattern, as El Niño years are associated with increased recharge generated by heavy rainfall (Taylor et al. 2013b).
Fig. 9

Groundwater storage variability and its association with climate teleconnections in the Sahel (1982–2011): a El Niño Southern Oscillation (ENSO) index, b simulated changes in total water storage (ΔTWSMODEL); and ΔTWSMODEL-ENSO wavelet coherence plots for the c Nubian Sandstone Aquifer System and d Senegalo-Mauritanian Basin Aquifer (Note: x-axis is date (year) and y-axis in c and d is period in years)

Fig. 10

Groundwater storage variability and its association with climate teleconnections in Equatorial Africa (1982–2011): a El Niño Southern Oscillation (ENSO) index, b simulated changes in total water storage (ΔTWSMODEL); and ΔTWSMODEL-ENSO wavelet coherence plots for the c Volta Basin Aquifer and d Karoo Carbonate Aquifer (Note: x-axis is date (year) and y-axis in c and d is period in years)

WTC plots for the Stampriet Transboundary Aquifer System and the Karoo Sedimentary Aquifer in Southern Africa also reveal an important correlation between groundwater storage and ENSO events, with El Niño (La Niña) events typically leading to drier (wetter) conditions and decreasing (increasing) water levels (Fig. 8). The dynamics of these aquifers are not similar, thus indicating that other climate modes might also be exerting an influence in Southern Africa. Previous studies present clear evidence of the importance of the Indian Ocean Dipole (IOD) index in modulating rainfall variability in Eastern Africa (Taylor et al. 2013b). The IOD has traditionally been linked to ENSO (Marchant et al. 2006; Fan and Liu 2017). Major ENSO warm (El Niño) events combined with a positive phase of the IOD have led to wet extremes and significant recharge in the Karoo Sedimentary Aquifer (Fig. 11). Conclusions about the correlation between the IOD and groundwater storage changes for the Nubian Aquifer Sandstone System and the Karoo Carbonate Aquifer are more challenging because the influence of the IOD varies across the aquifer basin (Awange et al. 2014; Onyutha and Willems 2017) and because of the lack of data, respectively.
Fig. 11

Groundwater storage variability and its association with the Indian Ocean Dipole (IOD) index in Eastern Africa (1982–2011): a Indian Ocean Dipole index, b simulated changes in total water storage (ΔTWSMODEL); and ΔTWSMODEL-IOD wavelet coherence plots for the c Nubian Sandstone Aquifer System, d Karoo Carbonate Aquifer, and e Karoo Sedimentary Aquifer (Note: x-axis is date (year) and y-axis in c, d, and e is period in years)

Conclusions

A two-variable climate-driven model using precipitation and actual evapotranspiration data from global data sets was developed to reconstruct total water storage changes in Africa from 1982 to 2011. Although important human influences such as abstraction, land-use changes, and dam management are not considered in the model, it nonetheless offers a robust approach for assessing the monthly dynamics of groundwater storage at very little computational cost, as model-based total water storage changes ΔTWSMODEL and observed groundwater storage changes ΔGWSOBSERVED (representative of shallow groundwater fluctuations) are strongly correlated. GRACE-based ΔTWSGRACE estimates are also highly correlated with model-based ΔTWSMODEL and ΔGWSOBSERVED. As the GRACE Follow-On (GRACE-FO) mission, successor to the original GRACE mission was launched in May 2018, ΔTWSGRACE estimates will continue to be a well-founded tool for providing a general overview of basin-scale groundwater storage changes that are associated with shallow groundwater fluctuations and are likely to have a strong interaction with surface water. The near-synchronous signals of groundwater levels, GRACE, and the climate-driven model estimates reveal that shallow aquifers are highly responsive to temporal rainfall patterns.

The results indicate that recharge from rainfall patterns associated with NAO and ENSO are the main drivers of interannual groundwater storage changes in Northern Africa and Sub-Saharan Africa, respectively. The AMO plays a significant role in decadal to multidecadal variability, particularly in the Sahel, as the positive (negative) AMO phase largely corresponds with increasing (decreasing) groundwater storage. The AMO has been in a positive phase since the mid-1990s, and this has contributed to a water-table rise in large parts of the Sahel. A change of phase could have an immense impact on surface-water and groundwater resources, as mega-droughts in the early 1980s in the Sahel are tied to the combined effect of a negative phase of the AMO and a positive phase of the ENSO. These devastating droughts could trigger significant groundwater storage changes, resulting in reduced recharge to the shallow aquifers and a decline in water levels as a result of climate-induced pumping from dug wells and shallow boreholes.

The findings of this study could be beneficial to decision-makers and could help ensure adequate preparation of effective climate variability and change adaptation plans at both the national and transboundary level. National groundwater governance frameworks in Africa usually need either review and upgrading of existing water laws and policies or completing them with new regulations (FAO 2015). Integrating climate variability aspects into water laws and policies (e.g., drought and flood management plans, provisions for Managed Aquifer Recharge (MAR) schemes), strengthening national meteorological, hydrological, and groundwater-monitoring networks, and in particular, strengthening links between water decision-makers and meteorological institutions, are crucial measures for improving groundwater governance with reference specifically to climate change. Integrated Water Resources Management (IWRM) is now widely accepted by water decision-makers as the way forward for efficient, equitable, and sustainable development and management of the world’s limited water resources and for coping with conflicting demands (UNESCO 2009). IWRM structures in Africa are rolled out across the continent with the present focus on the establishment of river basin/catchment organizations at national and transboundary levels. However, groundwater is still poorly integrated into these organizations’ IWRM and climate adaptation plans. MAR is a promising adaptation approach to reduce vulnerability to climate variability and aquifer overexploitation. The findings from this study illustrate that MAR operations might take advantage of temporal patterns in precipitation to enhance recharge during the corresponding wet phases of ENSO, NAO, and AMO. Institutions in charge of the management of groundwater resources at the national and transboundary level, as well as river basin/catchment organizations, should strengthen their support of MAR programs and initiatives to incentivize local water managers to store excess renewable water in aquifers during wet periods, which can be used to offset limited surface-water supplies during dry periods. The findings suggest that the preferred periods for artificial recharge are the negative phases of the NAO in Northern Africa, positive phases of the AMO in the Sahel, and La Niña years in Southern Africa. Finally, it is worth emphasizing that developing effective and reliable long-term strategies for coping with water scarcity threats and climate variability and change will also have to overcome the fact that there are still large uncertainties and limited data for decision-making. In this regard, it is also important to undertake joint actions in data collection interpretation and reporting as a means to promote inter-basin/inter-aquifer collaboration, to harmonize strategies, and to promote the exchange of experiences.

Notes

Acknowledgements

This study is a contribution to the UNESCO International Hydrological Programme (IHP) Groundwater Resources Assessment under the Pressures of Humanity and Climate Change (GRAPHIC) project. The authors are grateful for the support provided by Richard Taylor (UCL), Aurélien Dumont, and Marina Rubio (UNESCO-IHP – Groundwater Systems and Settlements Section) on the review of this paper.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Tales Carvalho Resende
    • 1
    Email author
  • Laurent Longuevergne
    • 2
  • Jason J. Gurdak
    • 3
  • Marc Leblanc
    • 4
  • Guillaume Favreau
    • 5
    • 6
  • Nienke Ansems
    • 7
  • Jac Van der Gun
    • 8
  • Cheikh B. Gaye
    • 9
  • Alice Aureli
    • 1
  1. 1.UNESCO International Hydrological Programme (IHP)ParisFrance
  2. 2.University of Rennes, Géosciences Rennes - UMR 6118RennesFrance
  3. 3.Department of Earth & Climate SciencesSan Francisco State UniversitySan FranciscoUSA
  4. 4.University of Avignon-INRA, Hydrogeology Laboratory, UMR EMMAHAvignonFrance
  5. 5.IRD, UMR HydroSciences, University of MontpellierMontpellierFrance
  6. 6.IRD, University of Grenoble-Alpes, CNRS, Environmental Geosciences InstituteGrenobleFrance
  7. 7.International Groundwater Resources Assessment Centre (IGRAC)DelftThe Netherlands
  8. 8.Van der Gun Hydro-ConsultingSchalkhaarThe Netherlands
  9. 9.Department of GeologyUniversity Cheikh Anta DiopDakarSenegal

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