{"id":59,"date":"2022-12-27T14:50:11","date_gmt":"2022-12-27T14:50:11","guid":{"rendered":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/chapter\/groundwater-recharge\/"},"modified":"2022-12-28T18:06:14","modified_gmt":"2022-12-28T18:06:14","slug":"groundwater-recharge","status":"publish","type":"chapter","link":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/chapter\/groundwater-recharge\/","title":{"raw":"3.1 Groundwater Recharge","rendered":"3.1 Groundwater Recharge"},"content":{"raw":"
\r\n

3.1.1 Estimates of Current Recharge<\/h1>\r\n

Groundwater recharge<\/em> cannot be measured directly, except in very small experimental settings such as a lysimeter<\/a>2<\/a><\/sup>. 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-\u00a0to 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<\/em> global groundwater recharge<\/em> (excluding Antarctica) vary between approximately 11 and 15\u00a0thousand\u00a0km3<\/sup>\/year (Margat and Van der Gun, 2013). An assumed mean value of 13,000\u00a0km3<\/sup>\/year corresponds to an equivalent mean annual recharge depth of 87\u00a0mm averaged over the global land area.<\/p>\r\n

How do recharge estimates of the mega aquifer systems compare to this global average? Table\u00a07 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 \u2018recharge\u2019. All reported recharge values in Table\u00a07 have been rounded to no more than two significant digits because suggesting a higher degree of precision is not realistic.<\/p>\r\n

<\/a>Table\u00a0<\/strong>7<\/strong> -<\/strong>\u00a0<\/strong>Estimated current mean recharge and abstraction rates (all reported values<\/em> have been<\/em> rounded to no more than two significant digits)<\/em>.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n
#<\/strong><\/td>\r\nAquifer System<\/strong><\/td>\r\nEstimated Mean Recharge<\/strong><\/td>\r\nEstimates of Abstraction (circa 2010)<\/strong><\/td>\r\n<\/tr>\r\n
mm\/yr<\/strong><\/td>\r\nSource<\/strong><\/td>\r\nkm<\/strong>3<\/strong><\/sup>\/yr<\/strong><\/td>\r\nmm\/yr<\/strong><\/td>\r\nSource<\/strong><\/td>\r\n<\/tr>\r\n
1<\/td>\r\nNubian Aquifer System<\/td>\r\n1.2<\/td>\r\nVoss & Soliman, 2014<\/td>\r\n6.3<\/td>\r\n2.9<\/td>\r\nVoss & Soliman, 2014<\/td>\r\n<\/tr>\r\n
2<\/td>\r\nNorth-Western Sahara Aquifer System<\/td>\r\n2.1<\/td>\r\nGon\u00e7alves et al., 2013<\/td>\r\n2.8<\/td>\r\n2.7<\/td>\r\nGon\u00e7alves et al., 2013<\/td>\r\n<\/tr>\r\n
<\/td>\r\n0.98<\/td>\r\nOSS, 2020<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
3<\/td>\r\nMurzuk-Djado Basin<\/td>\r\n0.33<\/td>\r\nOSS, 2020<\/td>\r\n1.7<\/td>\r\n3.8<\/td>\r\nSeguin, 2016<\/td>\r\n<\/tr>\r\n
4<\/td>\r\nTaoudeni-Tanezrouft Basin<\/td>\r\n5.5<\/td>\r\nOSS, 2020<\/td>\r\n0.06<\/td>\r\n0.03<\/td>\r\nSeguin, 2016<\/td>\r\n<\/tr>\r\n
5<\/td>\r\nSenegalo-Mauritanian Basin<\/td>\r\n0.43<\/td>\r\nOSS, 2020<\/td>\r\n0.26<\/td>\r\n0.87<\/td>\r\nSeguin, 2016<\/td>\r\n<\/tr>\r\n
<\/td>\r\n<\/td>\r\n<\/td>\r\n0.871<\/sup><\/td>\r\n2.91<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
6<\/td>\r\nIullemeden-Irhazer Basin<\/td>\r\n13<\/td>\r\nOSS, 2020<\/td>\r\n0.28<\/td>\r\n0.43<\/td>\r\nSeguin, 2016<\/td>\r\n<\/tr>\r\n
7<\/td>\r\nLake Chad Basin<\/td>\r\n3.6<\/td>\r\nOSS, 2020<\/td>\r\n0.25<\/td>\r\n0.13<\/td>\r\nSeguin, 2016<\/td>\r\n<\/tr>\r\n
<\/td>\r\n1.9<\/td>\r\nUNEP, 2008<\/td>\r\n0.51<\/sup><\/td>\r\n0.31<\/sup><\/td>\r\nIBDR, 2020<\/td>\r\n<\/tr>\r\n
8<\/td>\r\nSudd Basin<\/td>\r\n0.93<\/td>\r\nSalama, 1976<\/td>\r\n0.03<\/td>\r\n0.08<\/td>\r\nRSS, 2015 (Y & A, 2010)<\/td>\r\n<\/tr>\r\n
<\/td>\r\n1.4<\/td>\r\nRSS, 2015 (Omar, 2009)<\/td>\r\n0.014<\/td>\r\n0.04<\/td>\r\nRSS, 2015 (Omar, 2009)<\/td>\r\n<\/tr>\r\n
9<\/td>\r\nOgaden-Juba Basin<\/td>\r\n51<\/sup><\/td>\r\nWHYMAP, 2008<\/td>\r\n0.381<\/sup><\/td>\r\n0.381<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
10<\/td>\r\nCongo Basin<\/td>\r\n>\u00a04001<\/sup><\/td>\r\nWHYMAP, 2008; Margat & Van der Gun, 2013<\/td>\r\n0.951<\/sup><\/td>\r\n0.651<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
11<\/td>\r\nCuvelai-Upper Zambezi Basin (Upper Kalahari)<\/td>\r\n15<\/td>\r\nUNEP, 2008<\/td>\r\n0.191<\/sup><\/td>\r\n0.211<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
12<\/td>\r\nStampriet-Kalahari Basin (Lower Kalahari)<\/td>\r\n6.0<\/td>\r\nUNEP, 2008<\/td>\r\n0.041<\/sup><\/td>\r\n0.121<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
13<\/td>\r\nKaroo Basin<\/td>\r\n35<\/td>\r\nUNEP, 2008<\/td>\r\n1.021<\/sup><\/td>\r\n1.701<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
14<\/td>\r\nNorthern Great Plains<\/td>\r\n10?1<\/sup><\/td>\r\nReitz et al., 2017; WHYMAP, 2008; Rivera, 2017<\/td>\r\n0.501<\/sup><\/td>\r\n0.661<\/sup><\/td>\r\nNTALT2<\/sup>; Maupin & Barber, 2005; Lovelace et al., 2020<\/td>\r\n<\/tr>\r\n
15<\/td>\r\nCambrian-Ordovician<\/td>\r\n1501<\/sup><\/td>\r\nReitz et al., 2017; WHYMAP, 2008<\/td>\r\n1.31<\/sup><\/td>\r\n5.11<\/sup><\/td>\r\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\r\n<\/tr>\r\n
16<\/td>\r\nCentral Valley<\/td>\r\n320<\/td>\r\nMeixner et al., 2016<\/td>\r\n151<\/sup><\/td>\r\n2801<\/sup><\/td>\r\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\r\n<\/tr>\r\n
<\/td>\r\n<\/td>\r\n<\/td>\r\n14<\/td>\r\n260<\/td>\r\nUSGS\/Maven, 2020<\/td>\r\n<\/tr>\r\n
<\/td>\r\n<\/td>\r\n<\/td>\r\n18<\/td>\r\n350<\/td>\r\nMeixner et al., 2016<\/td>\r\n<\/tr>\r\n
17<\/td>\r\nHigh Plains Aquifer<\/td>\r\n42<\/td>\r\nMeixner et al., 2016<\/td>\r\n191<\/sup><\/td>\r\n431<\/sup><\/td>\r\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\r\n<\/tr>\r\n
<\/td>\r\n<\/td>\r\n<\/td>\r\n26<\/td>\r\n58<\/td>\r\nMcGuire, 2017<\/td>\r\n<\/tr>\r\n
<\/td>\r\n<\/td>\r\n<\/td>\r\n24<\/td>\r\n54<\/td>\r\nMeixner et al., 2016<\/td>\r\n<\/tr>\r\n
18<\/td>\r\nAtlantic & Gulf Coastal Aquifer System<\/td>\r\n1801<\/sup><\/td>\r\nReitz et al., 2017; WHYMAP, 2008<\/td>\r\n301<\/sup><\/td>\r\n261<\/sup><\/td>\r\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\r\n<\/tr>\r\n
19<\/td>\r\nAmazon Basin<\/td>\r\n>\u00a04001<\/sup><\/td>\r\nWHYMAP, 2008; Margat & Van der Gun, 2013<\/td>\r\n0.591<\/sup><\/td>\r\n0.291<\/sup><\/td>\r\nFeitosa et al., 2016; NTALT2<\/sup><\/td>\r\n<\/tr>\r\n
20<\/td>\r\nMaranh\u00e3o Basin<\/td>\r\n501<\/sup><\/td>\r\nAntunes et al., 2005; WHYMAP, 2008<\/td>\r\n0.591<\/sup><\/td>\r\n0.851<\/sup><\/td>\r\nFeitosa et al., 2016; NTALT2<\/sup><\/td>\r\n<\/tr>\r\n
21<\/td>\r\nGuarani Basin<\/td>\r\n250<\/td>\r\nAntunes et al., 2005; WHYMAP, 2008<\/td>\r\n2.61<\/sup><\/td>\r\n2.41<\/sup><\/td>\r\nFeitosa et al., 2016; NTALT2<\/sup><\/td>\r\n<\/tr>\r\n
21a<\/td>\r\nGuarani Aquifer System3<\/sup><\/td>\r\n0.50<\/td>\r\nGon\u00e7alves et al., 2020<\/td>\r\n1.00<\/td>\r\n0.84<\/td>\r\nMunier et al., 2012<\/td>\r\n<\/tr>\r\n
22<\/td>\r\nArabian Aquifer System<\/td>\r\n1.8<\/td>\r\nOdhiambo, 2016<\/td>\r\n161<\/sup><\/td>\r\n111<\/sup><\/td>\r\nUN-ESCWA & BGR, 2013<\/td>\r\n<\/tr>\r\n
23<\/td>\r\nIndus Basin<\/td>\r\n160<\/td>\r\nCGWB, 2019; Margat & Van der Gun, 2013<\/td>\r\n961<\/sup><\/td>\r\n3001<\/sup><\/td>\r\nMacDonald et al., 2015; CGWB, 2014<\/td>\r\n<\/tr>\r\n
24<\/td>\r\nGanges-Brahmaputra Basin<\/td>\r\n280<\/td>\r\nCGWB, 2019; Margat & Van der Gun, 2013<\/td>\r\n1101<\/sup><\/td>\r\n1801<\/sup><\/td>\r\nMacDonald et al., 2015; CGWB, 2014<\/td>\r\n<\/tr>\r\n
25<\/td>\r\nWest-Siberian Basin<\/td>\r\n?<\/td>\r\n<\/td>\r\n1.31<\/sup><\/td>\r\n0.391<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
26<\/td>\r\nTungus Basin<\/td>\r\n?<\/td>\r\n<\/td>\r\n0.121<\/sup><\/td>\r\n0.121<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
27<\/td>\r\nAngara-Lena Basin<\/td>\r\n?<\/td>\r\n<\/td>\r\n0.221<\/sup><\/td>\r\n0.371<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
28<\/td>\r\nYakut Basin<\/td>\r\n?<\/td>\r\n<\/td>\r\n<\u00a00.11<\/sup><\/td>\r\n<\u00a00.21<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
29<\/td>\r\nGreater North China Plain<\/td>\r\n2001<\/sup><\/td>\r\nChen et al., 2012<\/td>\r\n371<\/sup><\/td>\r\n1201<\/sup><\/td>\r\nChen et al., 2012<\/td>\r\n<\/tr>\r\n
29a<\/td>\r\nNorth China Plain (Hai Plain only)4<\/sup><\/td>\r\n130<\/td>\r\nLiu et al., 2011<\/td>\r\n22<\/td>\r\n160<\/td>\r\nGong et al., 2018<\/td>\r\n<\/tr>\r\n
<\/td>\r\n200<\/td>\r\nCao et al., 2013<\/td>\r\n22<\/td>\r\n160<\/td>\r\nLiu et al, 2011<\/td>\r\n<\/tr>\r\n
30<\/td>\r\nSong-Liao Plain<\/td>\r\n75<\/td>\r\nChen et al., 2012<\/td>\r\n131<\/sup><\/td>\r\n431<\/sup><\/td>\r\nChen et al., 2012<\/td>\r\n<\/tr>\r\n
31<\/td>\r\nTarim Basin<\/td>\r\n32<\/td>\r\nChen et al., 2012<\/td>\r\n3.1<\/td>\r\n6.01<\/sup><\/td>\r\nChen et al., 2012<\/td>\r\n<\/tr>\r\n
<\/td>\r\n<\/td>\r\n21<\/sup><\/td>\r\nHuang & Pang, 2013<\/td>\r\n2.5<\/td>\r\n4.81<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
32<\/td>\r\nParis Basin<\/td>\r\n1301<\/sup><\/td>\r\nBodelle & Margat, 1980<\/td>\r\n2.71<\/sup><\/td>\r\n141<\/sup><\/td>\r\nNTALT2<\/sup><\/td>\r\n<\/tr>\r\n
33<\/td>\r\nRussian Platform Basins<\/td>\r\n1201<\/sup><\/td>\r\nWHYMAP, 2008<\/td>\r\n8.51<\/sup><\/td>\r\n2.71<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
34<\/td>\r\nNorth Caucasus Basin<\/td>\r\n251<\/sup><\/td>\r\nWHYMAP, 2008<\/td>\r\n1.31<\/sup><\/td>\r\n5.51<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
35<\/td>\r\nPechora Basin<\/td>\r\n?<\/td>\r\n<\/td>\r\n<\u00a00.11<\/sup><\/td>\r\n<0.41<\/sup><\/td>\r\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\r\n<\/tr>\r\n
36<\/td>\r\nGreat Artesian Basin<\/td>\r\n0.59<\/td>\r\nHillier & Foster, 2002<\/td>\r\n0.55<\/td>\r\n0.32<\/td>\r\nHabermehl, 2006;2020<\/td>\r\n<\/tr>\r\n
37<\/td>\r\nCanning Basin<\/td>\r\n2\u2013101<\/sup><\/td>\r\nWHYMAP, 2008; Munier et al., 2012<\/td>\r\n<\u00a00.1<\/td>\r\n<\u00a00.23<\/td>\r\nMunier et al., 2012<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

1 <\/sup>Value not explicitly mentioned in cited sources but derived from the information they present.\r\n2 <\/sup>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).\r\n3 <\/sup>The data shown for Guarani Aquifer System (21a) do not include overlying post-GAS units such as the Serra Geral basalts and Bauru-Caiu\u00e1 sandstone.\r\n4 <\/sup>Data refer to the Hai Plain (136,000 km2<\/sup>), which covers only 42.5 percent of the Greater North China Plain.<\/p>\r\n

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 D\u00f6ll 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).<\/p>\r\n\r\n

3.1.2 Interpreting and Comparing the Estimates<\/h1>\r\n

As shown in Table\u00a07, 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:<\/p>\r\n\r\n

    \r\n \t
  1. Aquifer systems receiving significant to abundant recharge<\/em> (mean recharge rates >\u00a0100\u00a0mm\/year)<\/em>. <\/em>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 Maranh\u00e3o 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.<\/li>\r\n \t
  2. Aquifer systems receiving insignificant modern recharge (mean annual rates < 5 mm\/year)<\/em>. <\/em>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.<\/li>\r\n \t
  3. Poorly recharged aquifer systems (mean annual rates between 5 and 100 mm\/year)<\/em>. <\/em>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.<\/li>\r\n<\/ol>\r\n

    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.<\/p>\r\n\r\n

    3.1.3 Groundwater Recharge during Previous Geological Epochs and in the Near Future<\/h1>\r\n

    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 \u2018large hydraulic memory\u2019 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\u00a013 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).<\/a><\/p>\r\n

    \"Graph<\/p>\r\n

    Figure\u00a0<\/strong>13<\/strong>\u00a0-<\/strong>\u00a0Modeled decay of the natural Nubian Aquifer System discharge, assuming recharge stopped 10 thousand years ago under full-aquifer conditions (Voss and Soliman, 2014).<\/p>\r\n

    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.<\/p>\r\n\r\n\r\n

    \r\n

    <\/a>2<\/a><\/sup>A 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<\/a>.<\/p>\r\n\r\n<\/div>","rendered":"

    \n

    3.1.1 Estimates of Current Recharge<\/h1>\n

    Groundwater recharge<\/em> cannot be measured directly, except in very small experimental settings such as a lysimeter<\/a>2<\/a><\/sup>. 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-\u00a0to 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<\/em> global groundwater recharge<\/em> (excluding Antarctica) vary between approximately 11 and 15\u00a0thousand\u00a0km3<\/sup>\/year (Margat and Van der Gun, 2013). An assumed mean value of 13,000\u00a0km3<\/sup>\/year corresponds to an equivalent mean annual recharge depth of 87\u00a0mm averaged over the global land area.<\/p>\n

    How do recharge estimates of the mega aquifer systems compare to this global average? Table\u00a07 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 \u2018recharge\u2019. All reported recharge values in Table\u00a07 have been rounded to no more than two significant digits because suggesting a higher degree of precision is not realistic.<\/p>\n

    <\/a>Table\u00a0<\/strong>7<\/strong> –<\/strong>\u00a0<\/strong>Estimated current mean recharge and abstraction rates (all reported values<\/em> have been<\/em> rounded to no more than two significant digits)<\/em>.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    #<\/strong><\/td>\nAquifer System<\/strong><\/td>\nEstimated Mean Recharge<\/strong><\/td>\nEstimates of Abstraction (circa 2010)<\/strong><\/td>\n<\/tr>\n
    mm\/yr<\/strong><\/td>\nSource<\/strong><\/td>\nkm<\/strong>3<\/strong><\/sup>\/yr<\/strong><\/td>\nmm\/yr<\/strong><\/td>\nSource<\/strong><\/td>\n<\/tr>\n
    1<\/td>\nNubian Aquifer System<\/td>\n1.2<\/td>\nVoss & Soliman, 2014<\/td>\n6.3<\/td>\n2.9<\/td>\nVoss & Soliman, 2014<\/td>\n<\/tr>\n
    2<\/td>\nNorth-Western Sahara Aquifer System<\/td>\n2.1<\/td>\nGon\u00e7alves et al., 2013<\/td>\n2.8<\/td>\n2.7<\/td>\nGon\u00e7alves et al., 2013<\/td>\n<\/tr>\n
    <\/td>\n0.98<\/td>\nOSS, 2020<\/td>\n<\/td>\n<\/td>\n<\/td>\n<\/tr>\n
    3<\/td>\nMurzuk-Djado Basin<\/td>\n0.33<\/td>\nOSS, 2020<\/td>\n1.7<\/td>\n3.8<\/td>\nSeguin, 2016<\/td>\n<\/tr>\n
    4<\/td>\nTaoudeni-Tanezrouft Basin<\/td>\n5.5<\/td>\nOSS, 2020<\/td>\n0.06<\/td>\n0.03<\/td>\nSeguin, 2016<\/td>\n<\/tr>\n
    5<\/td>\nSenegalo-Mauritanian Basin<\/td>\n0.43<\/td>\nOSS, 2020<\/td>\n0.26<\/td>\n0.87<\/td>\nSeguin, 2016<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n<\/td>\n0.871<\/sup><\/td>\n2.91<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    6<\/td>\nIullemeden-Irhazer Basin<\/td>\n13<\/td>\nOSS, 2020<\/td>\n0.28<\/td>\n0.43<\/td>\nSeguin, 2016<\/td>\n<\/tr>\n
    7<\/td>\nLake Chad Basin<\/td>\n3.6<\/td>\nOSS, 2020<\/td>\n0.25<\/td>\n0.13<\/td>\nSeguin, 2016<\/td>\n<\/tr>\n
    <\/td>\n1.9<\/td>\nUNEP, 2008<\/td>\n0.51<\/sup><\/td>\n0.31<\/sup><\/td>\nIBDR, 2020<\/td>\n<\/tr>\n
    8<\/td>\nSudd Basin<\/td>\n0.93<\/td>\nSalama, 1976<\/td>\n0.03<\/td>\n0.08<\/td>\nRSS, 2015 (Y & A, 2010)<\/td>\n<\/tr>\n
    <\/td>\n1.4<\/td>\nRSS, 2015 (Omar, 2009)<\/td>\n0.014<\/td>\n0.04<\/td>\nRSS, 2015 (Omar, 2009)<\/td>\n<\/tr>\n
    9<\/td>\nOgaden-Juba Basin<\/td>\n51<\/sup><\/td>\nWHYMAP, 2008<\/td>\n0.381<\/sup><\/td>\n0.381<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    10<\/td>\nCongo Basin<\/td>\n>\u00a04001<\/sup><\/td>\nWHYMAP, 2008; Margat & Van der Gun, 2013<\/td>\n0.951<\/sup><\/td>\n0.651<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    11<\/td>\nCuvelai-Upper Zambezi Basin (Upper Kalahari)<\/td>\n15<\/td>\nUNEP, 2008<\/td>\n0.191<\/sup><\/td>\n0.211<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    12<\/td>\nStampriet-Kalahari Basin (Lower Kalahari)<\/td>\n6.0<\/td>\nUNEP, 2008<\/td>\n0.041<\/sup><\/td>\n0.121<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    13<\/td>\nKaroo Basin<\/td>\n35<\/td>\nUNEP, 2008<\/td>\n1.021<\/sup><\/td>\n1.701<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    14<\/td>\nNorthern Great Plains<\/td>\n10?1<\/sup><\/td>\nReitz et al., 2017; WHYMAP, 2008; Rivera, 2017<\/td>\n0.501<\/sup><\/td>\n0.661<\/sup><\/td>\nNTALT2<\/sup>; Maupin & Barber, 2005; Lovelace et al., 2020<\/td>\n<\/tr>\n
    15<\/td>\nCambrian-Ordovician<\/td>\n1501<\/sup><\/td>\nReitz et al., 2017; WHYMAP, 2008<\/td>\n1.31<\/sup><\/td>\n5.11<\/sup><\/td>\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\n<\/tr>\n
    16<\/td>\nCentral Valley<\/td>\n320<\/td>\nMeixner et al., 2016<\/td>\n151<\/sup><\/td>\n2801<\/sup><\/td>\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n<\/td>\n14<\/td>\n260<\/td>\nUSGS\/Maven, 2020<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n<\/td>\n18<\/td>\n350<\/td>\nMeixner et al., 2016<\/td>\n<\/tr>\n
    17<\/td>\nHigh Plains Aquifer<\/td>\n42<\/td>\nMeixner et al., 2016<\/td>\n191<\/sup><\/td>\n431<\/sup><\/td>\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n<\/td>\n26<\/td>\n58<\/td>\nMcGuire, 2017<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n<\/td>\n24<\/td>\n54<\/td>\nMeixner et al., 2016<\/td>\n<\/tr>\n
    18<\/td>\nAtlantic & Gulf Coastal Aquifer System<\/td>\n1801<\/sup><\/td>\nReitz et al., 2017; WHYMAP, 2008<\/td>\n301<\/sup><\/td>\n261<\/sup><\/td>\nMaupin & Barber, 2005; Lovelace et al., 2020<\/td>\n<\/tr>\n
    19<\/td>\nAmazon Basin<\/td>\n>\u00a04001<\/sup><\/td>\nWHYMAP, 2008; Margat & Van der Gun, 2013<\/td>\n0.591<\/sup><\/td>\n0.291<\/sup><\/td>\nFeitosa et al., 2016; NTALT2<\/sup><\/td>\n<\/tr>\n
    20<\/td>\nMaranh\u00e3o Basin<\/td>\n501<\/sup><\/td>\nAntunes et al., 2005; WHYMAP, 2008<\/td>\n0.591<\/sup><\/td>\n0.851<\/sup><\/td>\nFeitosa et al., 2016; NTALT2<\/sup><\/td>\n<\/tr>\n
    21<\/td>\nGuarani Basin<\/td>\n250<\/td>\nAntunes et al., 2005; WHYMAP, 2008<\/td>\n2.61<\/sup><\/td>\n2.41<\/sup><\/td>\nFeitosa et al., 2016; NTALT2<\/sup><\/td>\n<\/tr>\n
    21a<\/td>\nGuarani Aquifer System3<\/sup><\/td>\n0.50<\/td>\nGon\u00e7alves et al., 2020<\/td>\n1.00<\/td>\n0.84<\/td>\nMunier et al., 2012<\/td>\n<\/tr>\n
    22<\/td>\nArabian Aquifer System<\/td>\n1.8<\/td>\nOdhiambo, 2016<\/td>\n161<\/sup><\/td>\n111<\/sup><\/td>\nUN-ESCWA & BGR, 2013<\/td>\n<\/tr>\n
    23<\/td>\nIndus Basin<\/td>\n160<\/td>\nCGWB, 2019; Margat & Van der Gun, 2013<\/td>\n961<\/sup><\/td>\n3001<\/sup><\/td>\nMacDonald et al., 2015; CGWB, 2014<\/td>\n<\/tr>\n
    24<\/td>\nGanges-Brahmaputra Basin<\/td>\n280<\/td>\nCGWB, 2019; Margat & Van der Gun, 2013<\/td>\n1101<\/sup><\/td>\n1801<\/sup><\/td>\nMacDonald et al., 2015; CGWB, 2014<\/td>\n<\/tr>\n
    25<\/td>\nWest-Siberian Basin<\/td>\n?<\/td>\n<\/td>\n1.31<\/sup><\/td>\n0.391<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    26<\/td>\nTungus Basin<\/td>\n?<\/td>\n<\/td>\n0.121<\/sup><\/td>\n0.121<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    27<\/td>\nAngara-Lena Basin<\/td>\n?<\/td>\n<\/td>\n0.221<\/sup><\/td>\n0.371<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    28<\/td>\nYakut Basin<\/td>\n?<\/td>\n<\/td>\n<\u00a00.11<\/sup><\/td>\n<\u00a00.21<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    29<\/td>\nGreater North China Plain<\/td>\n2001<\/sup><\/td>\nChen et al., 2012<\/td>\n371<\/sup><\/td>\n1201<\/sup><\/td>\nChen et al., 2012<\/td>\n<\/tr>\n
    29a<\/td>\nNorth China Plain (Hai Plain only)4<\/sup><\/td>\n130<\/td>\nLiu et al., 2011<\/td>\n22<\/td>\n160<\/td>\nGong et al., 2018<\/td>\n<\/tr>\n
    <\/td>\n200<\/td>\nCao et al., 2013<\/td>\n22<\/td>\n160<\/td>\nLiu et al, 2011<\/td>\n<\/tr>\n
    30<\/td>\nSong-Liao Plain<\/td>\n75<\/td>\nChen et al., 2012<\/td>\n131<\/sup><\/td>\n431<\/sup><\/td>\nChen et al., 2012<\/td>\n<\/tr>\n
    31<\/td>\nTarim Basin<\/td>\n32<\/td>\nChen et al., 2012<\/td>\n3.1<\/td>\n6.01<\/sup><\/td>\nChen et al., 2012<\/td>\n<\/tr>\n
    <\/td>\n<\/td>\n21<\/sup><\/td>\nHuang & Pang, 2013<\/td>\n2.5<\/td>\n4.81<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    32<\/td>\nParis Basin<\/td>\n1301<\/sup><\/td>\nBodelle & Margat, 1980<\/td>\n2.71<\/sup><\/td>\n141<\/sup><\/td>\nNTALT2<\/sup><\/td>\n<\/tr>\n
    33<\/td>\nRussian Platform Basins<\/td>\n1201<\/sup><\/td>\nWHYMAP, 2008<\/td>\n8.51<\/sup><\/td>\n2.71<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    34<\/td>\nNorth Caucasus Basin<\/td>\n251<\/sup><\/td>\nWHYMAP, 2008<\/td>\n1.31<\/sup><\/td>\n5.51<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    35<\/td>\nPechora Basin<\/td>\n?<\/td>\n<\/td>\n<\u00a00.11<\/sup><\/td>\n<0.41<\/sup><\/td>\nNTALT2<\/sup>; Pykhtin et al., 2019<\/td>\n<\/tr>\n
    36<\/td>\nGreat Artesian Basin<\/td>\n0.59<\/td>\nHillier & Foster, 2002<\/td>\n0.55<\/td>\n0.32<\/td>\nHabermehl, 2006;2020<\/td>\n<\/tr>\n
    37<\/td>\nCanning Basin<\/td>\n2\u2013101<\/sup><\/td>\nWHYMAP, 2008; Munier et al., 2012<\/td>\n<\u00a00.1<\/td>\n<\u00a00.23<\/td>\nMunier et al., 2012<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    1 <\/sup>Value not explicitly mentioned in cited sources but derived from the information they present.
    \n2 <\/sup>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).
    \n3 <\/sup>The data shown for Guarani Aquifer System (21a) do not include overlying post-GAS units such as the Serra Geral basalts and Bauru-Caiu\u00e1 sandstone.
    \n4 <\/sup>Data refer to the Hai Plain (136,000 km2<\/sup>), which covers only 42.5 percent of the Greater North China Plain.<\/p>\n

    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 D\u00f6ll 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).<\/p>\n

    3.1.2 Interpreting and Comparing the Estimates<\/h1>\n

    As shown in Table\u00a07, 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:<\/p>\n

      \n
    1. Aquifer systems receiving significant to abundant recharge<\/em> (mean recharge rates >\u00a0100\u00a0mm\/year)<\/em>. <\/em>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 Maranh\u00e3o 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.<\/li>\n
    2. Aquifer systems receiving insignificant modern recharge (mean annual rates < 5 mm\/year)<\/em>. <\/em>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.<\/li>\n
    3. Poorly recharged aquifer systems (mean annual rates between 5 and 100 mm\/year)<\/em>. <\/em>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.<\/li>\n<\/ol>\n

      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.<\/p>\n

      3.1.3 Groundwater Recharge during Previous Geological Epochs and in the Near Future<\/h1>\n

      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 \u2018large hydraulic memory\u2019 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\u00a013 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).<\/a><\/p>\n

      \"Graph<\/p>\n

      Figure\u00a0<\/strong>13<\/strong>\u00a0–<\/strong>\u00a0Modeled decay of the natural Nubian Aquifer System discharge, assuming recharge stopped 10 thousand years ago under full-aquifer conditions (Voss and Soliman, 2014).<\/p>\n

      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.<\/p>\n

      \n

      <\/a>2<\/a><\/sup>A 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<\/a>.<\/p>\n<\/div>\n","protected":false},"author":1,"menu_order":12,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-59","chapter","type-chapter","status-publish","hentry"],"part":105,"_links":{"self":[{"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/chapters\/59","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":10,"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/chapters\/59\/revisions"}],"predecessor-version":[{"id":263,"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/chapters\/59\/revisions\/263"}],"part":[{"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/parts\/105"}],"metadata":[{"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/chapters\/59\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/wp\/v2\/media?parent=59"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/pressbooks\/v2\/chapter-type?post=59"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/wp\/v2\/contributor?post=59"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/wp-json\/wp\/v2\/license?post=59"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}