3.1 Groundwater Recharge

3.1.1 Estimates of Current Recharge

Groundwater recharge cannot be measured directly, except in very small experimental settings such as a lysimeter2. Consequently, in practice, numerical values of recharge are indirectly established and are subject to a large margin of uncertainty. Groundwater recharge varies over time, both in the short- to intermediate-term (day-to-day, seasonal, interannual, etc.) and in the very long term (on a millennial to geological time scale). Recent estimates of the mean value of the current global groundwater recharge (excluding Antarctica) vary between approximately 11 and 15 thousand km3/year (Margat and Van der Gun, 2013). An assumed mean value of 13,000 km3/year corresponds to an equivalent mean annual recharge depth of 87 mm averaged over the global land area.

How do recharge estimates of the mega aquifer systems compare to this global average? Table 7 lists estimates of mean recharge as found in the literature or produced based on relevant sources. Since the reported values have different origins, they are based on different estimation methods, but often the method is not specified in the publications. Furthermore, some of the reported values include natural recharge (direct and indirect) plus anthropogenic inflows from used waters, induced recharge or artificial recharge, while other estimates refer only to natural recharge, or even to only direct natural recharge, which is recharge produced by locally infiltrating precipitation. Unfortunately, the publications do not always specify what is included in ‘recharge’. All reported recharge values in Table 7 have been rounded to no more than two significant digits because suggesting a higher degree of precision is not realistic.

Table 7  Estimated current mean recharge and abstraction rates (all reported values have been rounded to no more than two significant digits).

# Aquifer System Estimated Mean Recharge Estimates of Abstraction (circa 2010)
mm/yr Source km3/yr mm/yr Source
1 Nubian Aquifer System 1.2 Voss & Soliman, 2014 6.3 2.9 Voss & Soliman, 2014
2 North-Western Sahara Aquifer System 2.1 Gonçalves et al., 2013 2.8 2.7 Gonçalves et al., 2013
0.98 OSS, 2020
3 Murzuk-Djado Basin 0.33 OSS, 2020 1.7 3.8 Seguin, 2016
4 Taoudeni-Tanezrouft Basin 5.5 OSS, 2020 0.06 0.03 Seguin, 2016
5 Senegalo-Mauritanian Basin 0.43 OSS, 2020 0.26 0.87 Seguin, 2016
0.871 2.91 NTALT2
6 Iullemeden-Irhazer Basin 13 OSS, 2020 0.28 0.43 Seguin, 2016
7 Lake Chad Basin 3.6 OSS, 2020 0.25 0.13 Seguin, 2016
1.9 UNEP, 2008 0.51 0.31 IBDR, 2020
8 Sudd Basin 0.93 Salama, 1976 0.03 0.08 RSS, 2015 (Y & A, 2010)
1.4 RSS, 2015 (Omar, 2009) 0.014 0.04 RSS, 2015 (Omar, 2009)
9 Ogaden-Juba Basin 51 WHYMAP, 2008 0.381 0.381 NTALT2
10 Congo Basin > 4001 WHYMAP, 2008; Margat & Van der Gun, 2013 0.951 0.651 NTALT2
11 Cuvelai-Upper Zambezi Basin (Upper Kalahari) 15 UNEP, 2008 0.191 0.211 NTALT2
12 Stampriet-Kalahari Basin (Lower Kalahari) 6.0 UNEP, 2008 0.041 0.121 NTALT2
13 Karoo Basin 35 UNEP, 2008 1.021 1.701 NTALT2
14 Northern Great Plains 10?1 Reitz et al., 2017; WHYMAP, 2008; Rivera, 2017 0.501 0.661 NTALT2; Maupin & Barber, 2005; Lovelace et al., 2020
15 Cambrian-Ordovician 1501 Reitz et al., 2017; WHYMAP, 2008 1.31 5.11 Maupin & Barber, 2005; Lovelace et al., 2020
16 Central Valley 320 Meixner et al., 2016 151 2801 Maupin & Barber, 2005; Lovelace et al., 2020
14 260 USGS/Maven, 2020
18 350 Meixner et al., 2016
17 High Plains Aquifer 42 Meixner et al., 2016 191 431 Maupin & Barber, 2005; Lovelace et al., 2020
26 58 McGuire, 2017
24 54 Meixner et al., 2016
18 Atlantic & Gulf Coastal Aquifer System 1801 Reitz et al., 2017; WHYMAP, 2008 301 261 Maupin & Barber, 2005; Lovelace et al., 2020
19 Amazon Basin > 4001 WHYMAP, 2008; Margat & Van der Gun, 2013 0.591 0.291 Feitosa et al., 2016; NTALT2
20 Maranhão Basin 501 Antunes et al., 2005; WHYMAP, 2008 0.591 0.851 Feitosa et al., 2016; NTALT2
21 Guarani Basin 250 Antunes et al., 2005; WHYMAP, 2008 2.61 2.41 Feitosa et al., 2016; NTALT2
21a Guarani Aquifer System3 0.50 Gonçalves et al., 2020 1.00 0.84 Munier et al., 2012
22 Arabian Aquifer System 1.8 Odhiambo, 2016 161 111 UN-ESCWA & BGR, 2013
23 Indus Basin 160 CGWB, 2019; Margat & Van der Gun, 2013 961 3001 MacDonald et al., 2015; CGWB, 2014
24 Ganges-Brahmaputra Basin 280 CGWB, 2019; Margat & Van der Gun, 2013 1101 1801 MacDonald et al., 2015; CGWB, 2014
25 West-Siberian Basin ? 1.31 0.391 NTALT2; Pykhtin et al., 2019
26 Tungus Basin ? 0.121 0.121 NTALT2; Pykhtin et al., 2019
27 Angara-Lena Basin ? 0.221 0.371 NTALT2; Pykhtin et al., 2019
28 Yakut Basin ? < 0.11 < 0.21 NTALT2; Pykhtin et al., 2019
29 Greater North China Plain 2001 Chen et al., 2012 371 1201 Chen et al., 2012
29a North China Plain (Hai Plain only)4 130 Liu et al., 2011 22 160 Gong et al., 2018
200 Cao et al., 2013 22 160 Liu et al, 2011
30 Song-Liao Plain 75 Chen et al., 2012 131 431 Chen et al., 2012
31 Tarim Basin 32 Chen et al., 2012 3.1 6.01 Chen et al., 2012
21 Huang & Pang, 2013 2.5 4.81 NTALT2
32 Paris Basin 1301 Bodelle & Margat, 1980 2.71 141 NTALT2
33 Russian Platform Basins 1201 WHYMAP, 2008 8.51 2.71 NTALT2; Pykhtin et al., 2019
34 North Caucasus Basin 251 WHYMAP, 2008 1.31 5.51 NTALT2; Pykhtin et al., 2019
35 Pechora Basin ? < 0.11 <0.41 NTALT2; Pykhtin et al., 2019
36 Great Artesian Basin 0.59 Hillier & Foster, 2002 0.55 0.32 Habermehl, 2006;2020
37 Canning Basin 2–101 WHYMAP, 2008; Munier et al., 2012 < 0.1 < 0.23 Munier et al., 2012

1 Value not explicitly mentioned in cited sources but derived from the information they present.
2 NTALT (national-to-aquifer-level transfer): approach to estimating groundwater abstraction from an aquifer by using demographic and irrigated land statistics of its composing sub-national zones, assuming that groundwater abstraction for irrigation is proportional to the area of groundwater-irrigation and that domestic and industrial groundwater abstraction is proportional to population. Unless indicated otherwise, use is made of national groundwater abstraction estimates for 2010 presented by Margat and Van der Gun (2013), demographic data from a census as close to 2010 as possible, and data on areas equipped for groundwater irrigation as presented by Siebert and others (2010).
3 The data shown for Guarani Aquifer System (21a) do not include overlying post-GAS units such as the Serra Geral basalts and Bauru-Caiuá sandstone.
4 Data refer to the Hai Plain (136,000 km2), which covers only 42.5 percent of the Greater North China Plain.

Mean annual recharge estimates have not been found in the literature for almost half of the mega aquifer systems. For most of these systems, provisional estimates have been made based on information presented in relevant papers or have been adopted from summarizing publications such as Margat and Van der Gun (2013) and WHYMAP (2008). The latter, in turn, relies on diffuse recharge modeling by Döll and Fiedler (2008). This was not attempted for the five northernmost Russian mega aquifer systems, located in zones of boreal and polar climates, because permafrost and semi-permafrost conditions present an extremely complicating factor, which precludes groundwater recharge from being estimated reliably without more detailed area-specific information. The presented recharge values form a heterogeneous set, and they are far from accurate. They are nevertheless shown here to give an impression of the order of magnitude of the mean recharge rates of the different aquifer systems and to help understand where and to what extent recharge may be or become a constraint to sustainable groundwater development. To facilitate easy interpretation and comparison, all recharge values are expressed as mean water heights per annum (i.e., total annual recharge volume divided by the horizontal area of the aquifer system).

3.1.2 Interpreting and Comparing the Estimates

As shown in Table 7, the estimates of mean groundwater recharge for the individual mega aquifer systems cover a wide range of values, both above and below the mean global value, which is to a great extent due to differences in climate. Three categories can be distinguished:

  1. Aquifer systems receiving significant to abundant recharge (mean recharge rates > 100 mm/year). This category includes the Congo and Amazon basins which enjoy by far the most abundant recharge rates, followed (in order of decreasing rates) by the Central Valley, the Ganges-Brahmaputra Basin, the Guarani Basin, the Maranhão Basin, the Atlantic and Gulf Coastal Plains, the Indus Basin, the Cambrian-Ordovician Aquifer System, the Paris Basin, the Russian Platform Basins and the North China Plain. Almost all these aquifer systems are located in humid climates, which explains their significant to abundant recharge rates. Exceptions are the Central Valley, the Indus Basin and the North China Plain, located in semi-arid regions (at least partly); more than half of their recharge consists of return flows from irrigation.
  2. Aquifer systems receiving insignificant modern recharge (mean annual rates < 5 mm/year). This category includes the Nubian and North-Western Sahara aquifer systems and the Murzuk-Djado, Senegalo-Mauritanian, Lake Chad and Sudd basins in Africa; the Arabian Aquifer System and the Tarim Basin in Asia; and the Canning and Great Artesian Basins in Australia. The very low rates of recharge are in most cases mainly explained by dry climatological conditions. Confining layers rejecting potential recharge may also play a role in the Senegalo-Mauritanian and Sudd basins.
  3. Poorly recharged aquifer systems (mean annual rates between 5 and 100 mm/year). This category includes the Taoudeni-Tanezrouft, Iullemeden-Irhazer, Ogaden-Juba, Upper Kalahari-Cuvelai-Upper Zambezi, Stampriet-Lower Kalahari and Karoo basins in Africa; the Northern Great Plains and High Plains in North-America; the North Caucasus aquifer system in Europe and the Song-Liao plain in Asia. The majority of these systems are located in semi-arid to arid climates, which is the main reason for their very modest recharge rates.

Highly simplifying, the groundwater resources of these three categories may be classified as renewable, non-renewable and weakly-renewable, respectively. In practice, estimating mean aquifer recharge rates is usually very difficult, which results in a high degree of uncertainty in most of the estimates.

3.1.3 Groundwater Recharge during Previous Geological Epochs and in the Near Future

As mentioned earlier, the greater part of groundwater stored in the mega aquifer systems is many thousands of years old and thus is either connate water or entered the aquifer system as recharge during previous geological epochs (paleo-recharge). Since climate has varied significantly throughout geological history, the rates of recharge of each aquifer system have varied over time. This has repercussions not only for the total volume of groundwater presently stored and groundwater quality but also for groundwater flow and the groundwater budgets of the aquifer systems. Mainly due to their large size, the present-day groundwater flow regimes and groundwater budgets of the mega aquifer systems may remain markedly influenced by recharge events that took place in the very remote past. This ‘large hydraulic memory’ of mega aquifer systems can be illustrated by an example from Northern Africa, as presented by Voss and others (2014). In this region, the alternating glacial and interglacial periods during the Quaternary had pluvial-humid and arid climates, respectively. It is assumed that the latest pluvial period took place from 10,000 to 5,000 years ago (Gossel et al., 2004; Voss and Soliman, 2014), or ended approximately 8,000 years ago (Thorweihe and Heinl, 2002). Model simulations by each of the three cited teams of investigators showed that the natural groundwater flow regime and discharge in the Nubian Aquifer System had not yet reached a new equilibrium in 1960 which is considered the beginning of groundwater development in the area, but were still in transient conditions, in response to groundwater recharge during the latest pluvial period. Figure 13 shows the simulated natural discharge during the Holocene past and its predicted continuation for 10,000 years into the future, as presented by Voss and Soliman (2014).

Graph showing modeled decay of the natural Nubian Aquifer System discharge

Figure 13  Modeled decay of the natural Nubian Aquifer System discharge, assuming recharge stopped 10 thousand years ago under full-aquifer conditions (Voss and Soliman, 2014).

Looking towards the near future, say the next 50 years, recharge of the groundwater resources of most of the mega aquifer systems is expected to change over time for several reasons. In the first place, groundwater and surface water use are likely to increase in most areas, which may lead to more intensive irrigation return flows and other anthropogenic inflows (such as wastewater) into the groundwater systems. Next, Managed Aquifer Recharge (MAR) has proven to be an effective tool for enhancing groundwater recharge in many areas and there is ample scope for expanding the approach to other parts of the world (Dillon et al., 2018). Furthermore, there are also human activities that may reduce groundwater recharge, such as enhancing irrigation water use efficiencies, and other anthropogenic factors (e.g., changes in land use or land-use practices) that may affect groundwater recharge either positively or negatively. Finally, climate change is currently perceived as a prominent game changer. It will certainly have a significant impact on the recharge of the individual mega aquifer systems, but predicting for each of them whether recharge will increase or decrease and to what degree remains very difficult.


2A lysimeter is a device (usually a tank or container no more than a few meters high) that allows the components of the soil water balance to be monitored, in particular evaporation/evapotranspiration, downward percolation (source of groundwater recharge) and changes in stored soil moisture. It is set up outside in the open air (to be exposed to the local weather) and it is filled with soil of composition and vegetation cover comparable to that of the soils in the surroundings for which the lysimeter is considered to be representative https://en.wikipedia.org/wiki/Lysimeter.

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Large Aquifer Systems Around the World Copyright © 2022 by Jac van der Gun. All Rights Reserved.