{"id":68,"date":"2022-12-27T14:50:17","date_gmt":"2022-12-27T14:50:17","guid":{"rendered":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/chapter\/dynamics-of-groundwater-storage\/"},"modified":"2022-12-29T01:32:54","modified_gmt":"2022-12-29T01:32:54","slug":"dynamics-of-groundwater-storage","status":"publish","type":"chapter","link":"https:\/\/books.gw-project.org\/large-aquifer-systems-around-the-world\/chapter\/dynamics-of-groundwater-storage\/","title":{"raw":"3.3 Dynamics of Groundwater Storage","rendered":"3.3 Dynamics of Groundwater Storage"},"content":{"raw":"
\r\n

3.3.1 Observing Groundwater Storage Variation Over Time by In-Situ Monitoring<\/h1>\r\n

Groundwater levels and the volume of groundwater stored in an aquifer vary continuously over time, in response to recharge from different sources (natural recharge, artificial recharge, irrigation return flows and other anthropogenic sources), groundwater abstraction and natural discharge (regulated by system-specific characteristics). In-situ monitoring (i.e., monitoring groundwater levels in wells) forms the most direct and commonly used method to observe these variations. The water level observations are point values that in principle reveal local conditions only, but by interpolating between the data of nearby monitoring wells a spatially continuous picture of the potentiometric surface can be derived for aquifer zones that are adequately covered by monitoring wells. Sets of monitoring records that can provide an aquifer-wide picture of the groundwater storage dynamics are available for many relatively small aquifers around the world, but not for the majority of the mega aquifer systems. The enormous size of those systems makes it very difficult and costly to obtain good coverage with reasonably simultaneous field observations. Nevertheless, long-term groundwater level monitoring records with good spatial resolution and area-wide coverage are available for some of the mega-aquifer systems. This is briefly illustrated below for a few mega aquifer systems.<\/p>\r\n

The first example is the North China Plain Aquifer System<\/em>, 136,000 km2<\/sup> in extent. This is a multilayer aquifer system, with a shallow aquifer consisting of interconnected layers and separated from the deep aquifers by confining layers. The shallow and deep aquifers are hydraulically connected only in the piedmont region in the western and northern part of the plain. Gong and others (2018) derived a time series of groundwater storage anomalies for the entire North China Plain from historical monthly groundwater level data for the period 1971 to 2013. This time series indicates a prolonged declining trend of groundwater storage. On average, the volume lost from the groundwater system is equivalent to a depth of water throughout the plain of 17.8\u00a0mm\/year, which corresponds to an average storage depletion of 2.4\u00a0km3<\/sup>\/year. The anomalies are correlated with groundwater abstraction and precipitation; consequently, the average volumetric decline in equivalent depth of water throughout the plain varies between sub\u2011periods: 6.2\u00a0mm\/year from 1971 to 1980, 20.6 mm\/year from 1981 to 2002, and 18.2\u00a0mm\/year from 2003 to 2015.<\/p>\r\n

Yang and others (2021) created time\u2011series graphs for water levels in typical monitoring wells as shown for shallow and deep aquifers (Figure\u00a015 and Figure\u00a016, respectively) during the period from 1990 to 2020. In addition to seasonal fluctuations and variation between wet and dry years, the graphs related to the deeper aquifers show pronounced declining trends, but this is observed in only one of the selected wells in the shallow aquifer. Figure\u00a017 shows the cumulative changes in groundwater levels over the period 1980 to 2020 for the shallow and the deeper aquifers, derived from in\u2011situ monitoring records (Yang et al., 2021). The groundwater levels in the deep aquifers declined more, especially in the central and eastern regions.<\/p>\r\n

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

Figure\u00a0<\/strong>15<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Groundwater levels in typical monitoring wells in the shallow aquifer, North China Plain (after Yang et al,<\/em> 2021)<\/p>\r\n

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

Figure\u00a0<\/strong>16<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Groundwater levels in typical monitoring wells in the deep aquifers, North China Plain (after Yang et al., 2021)<\/p>\r\n

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

Figure\u00a0<\/strong>17<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Cumulative decline of groundwater levels in: a) shallow; and, b) deep aquifers of the North China Plain during the period from 1980\u00a0to\u00a02020 (after Yang et al., 2021).<\/p>\r\n

Another mega aquifer system with widespread long-term groundwater monitoring records is the High Plains Aquifer<\/em>. This aquifer is 175,000\u00a0square miles in extent (around 450,000\u00a0km2<\/sup>) and consists of unconsolidated or partly consolidated clastic sediments of Tertiary and Quaternary age. Groundwater is generally under unconfined conditions and the saturated thickness of the aquifer varies from nearly zero to 1200\u00a0ft (366\u00a0m). Figure\u00a018 shows cumulative changes in the groundwater levels between predevelopment time (around\u00a01950) and 2015. This map, based on water levels from 3164 wells and other published data, shows a highly variable pattern, with zones of largest water-level declines in the southern half of the plains, where groundwater recharge is lower than in the north. Water-level changes, by well, ranged over the indicated period from a rise of 54\u00a0feet (16\u00a0m) to a decline of 234\u00a0feet (71\u00a0m), with an area-weighted average of 15.8\u00a0feet (4.8\u00a0m) decline. The monitoring data \u2013 in combination with specific yield estimates \u2013 allowed estimation of net groundwater storage depletion as 273.2\u00a0million acre-feet (337\u00a0km3<\/sup>) for the period from predevelopment time (around 1950) to 2015, and 10.7\u00a0million acre-feet (13.2\u00a0km3<\/sup>) for 2013 to 2015. In some zones, especially in Texas, the aquifer\u2019s saturated thickness has declined by more than 50% since predevelopment (Scanlon et al., 2012; McGuire, 2017).<\/p>\r\n

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

Figure\u00a0<\/strong>18<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Water-level changes in the High Plains aquifer, predevelopment (about 1950) to 2015 (McGuire, 2017).<\/p>\r\n

The Indus and Ganges<\/em>-<\/em>Brahmaputra Basins<\/em> (the mega aquifer systems 23 and 24 combined, extending over around 920,000\u00a0km2<\/sup>) form another large region where groundwater levels have been monitored for many years and with high spatial resolution. Most groundwater abstraction wells in this region tap from the upper 200\u00a0m of the thick series of alluvial sediments accumulated in the foredeep depression south of the Himalayas. Groundwater levels are predominantly shallow (< 5\u00a0m) and they are monitored monthly or quarterly mainly in shallow tube wells (0-100\u00a0m deep). Based on published national assessments and a subset of 2300 higher-quality monitoring records, MacDonald and others (2015) defined and mapped the long-term trends in groundwater levels (Figure\u00a019). The map shows a significant decline in groundwater levels in the western half of the Ganges Basin and the upper part of the Indus Basin, which is strongly correlated with the areas of most intensive groundwater abstraction. On the other hand, groundwater levels in the lower part of the Indus Basin show a rising trend, driven by leakage from surface water irrigation canals (MacDonald et al., 2015). The estimated net annual groundwater depletion in the Indo-Gangetic basin amounts to some 8 km3<\/sup> (MacDonald et al., 2016).<\/p>\r\n

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

Figure<\/strong>\u00a0<\/strong>19<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Long-term trends of groundwater-level change on the Indus-Ganges-Brahmaputra basin, from high-resolution, 25-year series in situ monitoring data sets (MacDonald et al., 2015; reproduced with permission of BGS \u00a9 UKRI, http:\/\/nora.nerc.ac.uk\/id\/eprint\/511898\/<\/span><\/a>).<\/p>\r\n\r\n

3.3.2 Groundwater Storage Variations Derived from GRACE Observations<\/h1>\r\n

The Gravity Recovery and Climate Experiment (GRACE) satellite mission, operational from March 2002 to October 2017, has produced a new category of data that can be used to estimate changes in groundwater storage. This innovative project monitored changes in gravity (gravity anomalies) around the globe with low spatial resolution. These anomalies can be transformed into low spatial resolution estimates of changes in total water storage (\u0394TWS), from which, in turn, changes in groundwater storage (\u0394GWS) can be obtained. The latter is done by subtracting estimated changes in stored surface water, soil moisture and snow\/ice from the changes in total water storage. Despite many uncertainties related to the data processing and interpretation methods, these estimates provide interesting information on the variations over time of the aggregated groundwater storage in each of the mega aquifer systems. A GRACE Follow-On mission (GFO) was started in May 2018 to continue the observations. A brief overview of the history and scientific-technical principles of GRACE, as well as its application to global ground\u00adwater investigations, is presented by Chen and Rodell (2020). A recent study by Rateb and others (2020) compared GRACE estimates of storage anomalies with those from intensely monitored aquifers in the United States and found generally good agreement.<\/p>\r\n

Figure\u00a020 shows GRACE results obtained and interpreted for the High Plains Aquifer (Ogallala Aquifer, USA), as presented by Shamsudduha and Taylor (2020). The marked seasonal variation of both \u0394TWS and \u0394GWS is evident, while also significant interannual variation is observed, correlated with annual deviations from the long-term annual average rainfall. As could be expected, \u0394GWS constitutes the lion\u2019s share of \u0394TWS, which causes both time series to be very similar. The \u0394GWS time series suggests long-term groundwater storage depletion, but the time series is too short and too much dominated by seasonal and interannual fluctuations for estimating a linear trend representing the long-term depletion rate reliably and accurately. Similar time series of \u0394GWS for the other 36 mega aquifer systems are presented in Figure\u00a021 and Figure\u00a022.<\/p>\r\n

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

Figure\u00a0<\/strong>20<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Monthly time series of groundwater and total water storage anomalies derived from GRACE for the High Plains Aquifer (USA), August 2002 to July 2016: a)\u00a0Groundwater storage (GWS) anomaly and range of uncertainty from 20 realizations; b)\u00a0Ensemble Total water storage (TWS) fitted with a non-linear trend curve and annual precipitation anomalies; and, c)\u00a0Ensemble GRACE-derived GWS fitted with a non-linear trend curve and annual precipitation anomalies (Adapted from Shamsudduha and Taylor,\u00a02020).<\/p>\r\n

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

Figure\u00a0<\/strong>21<\/strong>\u00a0<\/strong>-<\/strong>\u00a0<\/strong>Monthly time series of groundwater storage anomalies derived from GRACE for the mega aquifers 1 through 19 (except number 16), August 2002 to July 2016. The ensemble GRACE-derived \u0394GWS (in black) is fitted with a non-linear trend curve (the area shaded in semi-transparent gold shows the 95 percent confidence interval) and annual precipitation anomalies (i.e., percentage deviation from the mean precipitation for the period of 1901 to 2016) are shown as bars (Adapted from Shamsudduha and Taylor, 2020).<\/p>\r\n

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

Figure\u00a0<\/strong>22<\/strong>\u00a0<\/strong>-<\/strong>\u00a0Monthly time series of groundwater storage anomalies derived from GRACE for the mega aquifers 20 through 37, August 2002 to July 2016. The ensemble GRACE-derived \u0394GWS (in black) is fitted with a non-linear trend curve (the area shaded in semi-transparent gold shows the 95 percent confidence interval) and annual precipitation anomalies (i.e., percentage deviation from the mean precipitation for the period of 1901 to 2016) are shown as bars (Adapted from Shamsudduha and Taylor, 2020).<\/p>\r\n

The set of \u0394GWS graphs for all 37 mega aquifer systems shows a diversity of groundwater storage regimes. In the first place, different intensities of seasonal variation<\/em> led to large differences in the annual range of groundwater storage variation. Seasonal variation is nearly non-existent in nine aquifer systems: six in Northern and Eastern Africa (numbers 1, 2, 3, 4, 6, 9), one in Southern Africa (number\u00a012), one on the Arabian Peninsula (number\u00a022), and one in Central Asia (number\u00a031). All these are located in zones with hyper-arid or arid climates. The absence of significant seasonal variation in groundwater storage in these aquifer systems is consistent with their low ranking on the recharge intensity scale (Table\u00a07<\/a>) and with their groundwater resources being classified as non- or weakly renewable. More clearly visible, but still very modest (mean range less than 100\u00a0mm) is the seasonal storage fluctuation in sixteen other mega aquifer systems: four in arid and semi-arid Africa (numbers 5, 7, 12, 13), three in North America (numbers 14, 17 and 18; in semi-arid, sub-humid and humid climates, respectively), one in South America (number 21), six in Asia (numbers 23, 26, 27, 28, 29 and 30), and two in Australia (numbers 36 and 37). The remaining twelve mega aquifer systems show a mean annual groundwater storage fluctuation range greater than 100\u00a0mm. The largest mean annual ranges are observed in the Amazon Basin (~300\u00a0mm), Ganges-Brahmaputra Basin (~220\u00a0mm), Pechora Basin (~180\u00a0mm), West-Siberian Basin (~180\u00a0mm) and California\u2019s Central Valley (~175\u00a0mm). The seasonal variation looks regular in some aquifer systems (e.g., numbers 19, 24, 25 and 33), but irregular in others (e.g., numbers 10, 11, 15, 20 and 23).<\/p>\r\n

The fitted non-linear trend curves reflect interannual meteorological oscillations<\/em>: alternations of relatively dry and wet years within the period of observation. In addition, several mega aquifer systems reveal a significant overall linear trend<\/em>: some of them show storage depletion (for instance, California\u2019s Central Valley, the Ganges-Brahmaputra Basin, the North China Plain Aquifer, the High Plains Aquifer, the Arabian Aquifer System and the Canning Basin), which is in contrast with increases in groundwater storage in several other aquifer systems (in particular the Upper Kalahari, Amazon Basin, Tungus Basin, Yakut Basin and Pechora Basin). These longer-term trends can only be defined, interpreted, confirmed and explained reliably after studying in more detail the specific conditions of the aquifer systems. Analyzing information on renewable groundwater development stress (as presented in the Section<\/a> \u2018Interpreting and Comparing the Estimates\u2019 and in particular Figure\u00a014<\/a>) can serve as a first step toward predicting and confirming a long-term trend.<\/p>\r\n\r\n

3.3.3 Groundwater Storage Depletion<\/h1>\r\n

Several researchers have identified or studied groundwater storage depletion trends in one or more mega aquifer systems. Table\u00a08 summarizes their estimates of the rate of depletion, averaged over the specified periods. The estimates are expressed both in cubic kilometers per year and in mm per year, to facilitate comparisons.<\/p>\r\n

<\/a>Table\u00a0<\/strong>8<\/strong>\u00a0<\/strong>-<\/strong>\u00a0Estimates of groundwater storage depletion trends in selected mega aquifer systems.<\/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\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
References<\/strong><\/td>\r\nEstimates for 21st century periods<\/strong><\/td>\r\nEstimates for periods before 2000<\/strong><\/td>\r\n<\/tr>\r\n
Period<\/strong><\/td>\r\nMethods used<\/strong>1<\/strong><\/sup><\/td>\r\nStorage depletion<\/strong>2<\/strong><\/sup><\/td>\r\nPeriod<\/strong><\/td>\r\nMethods used<\/strong>1<\/strong><\/sup><\/td>\r\nStorage depletion<\/strong>2<\/strong><\/sup><\/td>\r\n<\/tr>\r\n
km<\/strong>3<\/strong><\/sup>\/yr<\/strong><\/td>\r\nmm\/yr<\/strong><\/td>\r\nkm<\/strong>3<\/strong><\/sup>\/yr<\/strong><\/td>\r\nmm\/yr<\/strong><\/td>\r\n<\/tr>\r\n
1 Nubian Aquifer System (NAS)<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011<\/td>\r\n2000-2008<\/td>\r\n3,5,7<\/td>\r\n2.36<\/td>\r\n1.07<\/em><\/td>\r\n1900-2000<\/td>\r\n3,5,7<\/td>\r\n0.80<\/td>\r\n0.36<\/em><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n6.40<\/em><\/td>\r\n2.91<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Mohamed et al., 2016<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n0.69<\/em><\/td>\r\n0.32<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n4.40<\/em><\/td>\r\n2.00<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
2 North-Western Sahara Aquifer System (NWSAS)<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011<\/td>\r\n2000-2008<\/td>\r\n5<\/td>\r\n2.20<\/td>\r\n2.16<\/em><\/td>\r\n1900-2000<\/td>\r\n5<\/td>\r\n0.53<\/td>\r\n0.52<\/em><\/td>\r\n<\/tr>\r\n
Gon\u00e7alves et al., 2013<\/td>\r\n2003-2010<\/td>\r\n2<\/td>\r\n0.55<\/em><\/td>\r\n0.54<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n2.86<\/em><\/td>\r\n2.81<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n2.04<\/em><\/td>\r\n2.00<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
16 California\u2019s Central Valley Aquifer System<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011<\/td>\r\n2000-2008<\/td>\r\n2,3<\/td>\r\n3.93<\/td>\r\n75.<\/em>5<\/em><\/td>\r\n1900-2000<\/td>\r\n3<\/td>\r\n1.13<\/td>\r\n21.8<\/em><\/td>\r\n<\/tr>\r\n
Scanlon et al., 2012<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n1860s-1961<\/td>\r\n3<\/td>\r\n1.46<\/td>\r\n28.1<\/em><\/td>\r\n<\/tr>\r\n
Scanlon et al., 2012<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n1962-2003<\/td>\r\n3<\/td>\r\n1.95<\/td>\r\n37.5<\/em><\/td>\r\n<\/tr>\r\n
Konikow, 2013<\/td>\r\n2000-2008<\/td>\r\n2,3<\/td>\r\n3.93<\/td>\r\n75.<\/em>5<\/em><\/td>\r\n1900-2000<\/td>\r\n3<\/td>\r\n1.13<\/td>\r\n21.8<\/em><\/td>\r\n<\/tr>\r\n
Faunt et al., 2015<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n1962-2014<\/td>\r\n9<\/td>\r\n1.85<\/td>\r\n35.<\/em>6<\/em><\/td>\r\n<\/tr>\r\n
Hanak et al., 2015<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n1921-2009<\/td>\r\n9<\/td>\r\n2.15<\/td>\r\n41.3<\/em><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n0.46<\/em><\/td>\r\n8.89<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n0.64<\/em><\/td>\r\n12.3<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Rateb et al, 2020<\/td>\r\n2003-2017<\/td>\r\n1,2,3<\/td>\r\n1.7<\/td>\r\n33.2<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
17 High Plains Aquifer (Ogallala)<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011<\/td>\r\n2000-2008<\/td>\r\n1<\/td>\r\n11.8<\/td>\r\n26.3<\/em><\/td>\r\n1900-2000<\/td>\r\n1<\/td>\r\n2.59<\/td>\r\n5.75<\/em><\/td>\r\n<\/tr>\r\n
Scanlon et al., 2012<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n1950-2007<\/td>\r\n1<\/td>\r\n5.79<\/td>\r\n12.<\/em>9<\/em><\/td>\r\n<\/tr>\r\n
Konikow, 2013<\/td>\r\n2000-2008<\/td>\r\n1<\/td>\r\n10.2<\/td>\r\n22.7<\/em><\/td>\r\n1900-2000<\/td>\r\n1<\/td>\r\n2.59<\/td>\r\n5.76<\/em><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n-0.14<\/td>\r\n-0.31<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n3.00<\/em><\/td>\r\n6.67<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Rateb et al, 2020<\/td>\r\n2003-2017<\/td>\r\n1,2,3<\/td>\r\n1.2<\/td>\r\n2.67<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
18 Atlantic & Gulf Coastal Aquifer System<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011&2013<\/td>\r\n2000-2008<\/td>\r\n1,3,4,7,8<\/td>\r\n8.78<\/td>\r\n7.63<\/em><\/td>\r\n1900-2000<\/td>\r\n1,3,4,7,8<\/td>\r\n2.13<\/td>\r\n1.85<\/em><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n6.82<\/em><\/td>\r\n5.93<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n-<\/em>4.93<\/em><\/td>\r\n-4.29<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
22 Arabian Aquifer System<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011<\/td>\r\n2000-2008<\/td>\r\n3,5,7<\/td>\r\n13.6<\/td>\r\n9.18<\/em><\/td>\r\n1900-2000<\/td>\r\n3,5,7<\/td>\r\n3.59<\/td>\r\n2.41<\/em><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n13.6<\/em><\/td>\r\n9.13<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n4.90<\/em><\/td>\r\n3.30<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
23 Indus Basin<\/strong><\/td>\r\n<\/tr>\r\n
Richey et al., 2015<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n1.36<\/em><\/td>\r\n4.26<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n1.60<\/em><\/td>\r\n5.00<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Sattar & Khalid, 2020<\/td>\r\n2005-2015<\/td>\r\n2<\/td>\r\n0.64<\/em><\/td>\r\n2.00<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
24 Ganges-Brahmaputra Basin<\/strong><\/td>\r\n<\/tr>\r\n
Richey et al., 2015<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n11.7<\/em><\/td>\r\n19. 6<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n10.0<\/em><\/td>\r\n16.7<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
29 Greater North China Plain Aquifer System (Huang-Huai-Hai Plain)<\/strong><\/td>\r\n<\/tr>\r\n
Konikow, 2011<\/td>\r\n2000-2008<\/td>\r\n1,3,7<\/td>\r\n5.00<\/td>\r\n15.6<\/em><\/td>\r\n1900-2000<\/td>\r\n1,3,7<\/td>\r\n1.30<\/td>\r\n4.07<\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n2.40<\/em><\/td>\r\n7.50<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n4.05<\/em><\/td>\r\n12.7<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
29a North China Plain Aquifer System (Hai Plain only)<\/strong><\/td>\r\n<\/tr>\r\n
Gong et al., 2018<\/td>\r\n2003-2015<\/td>\r\n1,2<\/td>\r\n2.53<\/td>\r\n18.6<\/td>\r\n1971-2015<\/td>\r\n1,2<\/td>\r\n2.42<\/td>\r\n17.8<\/td>\r\n<\/tr>\r\n
Kinzelbach et al., 2021<\/td>\r\n2004-2020<\/td>\r\n2<\/td>\r\n3.32<\/em><\/td>\r\n24.4<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
31 Tarim Basin<\/strong><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n0.12<\/em><\/td>\r\n0.23<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n0.00<\/em><\/td>\r\n0.00<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Hu et al., 2019<\/td>\r\n2003-2016<\/td>\r\n2<\/td>\r\n1.52<\/em><\/td>\r\n2.93<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
34 North Caucasus Basin<\/strong><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n3.70<\/em><\/td>\r\n16.1<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n2.61<\/em><\/td>\r\n11.3<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
36 Great Artesian Basin<\/strong><\/td>\r\n<\/tr>\r\n
Welsh et al., 2012<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n1965-1999<\/td>\r\n3<\/td>\r\n0.31<\/td>\r\n0.18<\/em><\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n-<\/em>18.0<\/em><\/td>\r\n-10.6<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n0.00<\/td>\r\n0.00<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
37 Canning Basin<\/strong><\/td>\r\n<\/tr>\r\n
Munier et al., 2012<\/td>\r\n2003-2009<\/td>\r\n2<\/td>\r\n11.00<\/em><\/td>\r\n25.6<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Richey et al., 2015b<\/td>\r\n2003-2013<\/td>\r\n2<\/td>\r\n4.05<\/em><\/td>\r\n9.41<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n
Shamsudduha & Taylor, 2020<\/td>\r\n2002-2016<\/td>\r\n2<\/td>\r\n5.16<\/em><\/td>\r\n12.0<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

1<\/sup> Methods adopted from Konikow (2011):\r\n1 = water level changes times storativity, integrated over area;\r\n2 = gravity changes over time (GRACE);\r\n3 = calibrated flow model;\r\n4 = confining unit analysis;\r\n5 = pumpage data combined with water budget analysis;\r\n6 = extrapolating fraction of pumpage derived from storage to other areas;\r\n7 = partial record extrapolation;\r\n8 = from land subsidence volume;\r\n9 = unknown.<\/p>\r\n

2<\/sup> Values as reported by authors are in roman font; values derived by conversion or interpreted from graphs are in italics<\/em>. Negative values indicate an increase in storage.<\/p>\r\n

The information presented in Table\u00a08 gives rise to the following comments and conclusions.<\/p>\r\n\r\n