Hydrogeol J DOI 10.1007/s10040-017-1646-5
PAPER
A quantitative assessment of groundwater resources in the Middle East and North Africa region Khalil Lezzaik 1 & Adam Milewski 1
Received: 16 January 2017 / Accepted: 10 July 2017 # Springer-Verlag GmbH Germany 2017
Abstract The Middle East and North Africa (MENA) region is the world’s most water-stressed region, with its countries constituting 12 of the 15 most water-stressed countries globally. Because of data paucity, comprehensive regional-scale assessments of groundwater resources in the MENA region have been lacking. The presented study addresses this issue by using a distributed ArcGIS model, parametrized with gridded data sets, to estimate groundwater storage reserves in the region based on generated aquifer saturated thickness and effective porosity estimates. Furthermore, monthly gravimetric datasets (GRACE) and land surface parameters (GLDAS) were used to quantify changes in groundwater storage between 2003 and 2014. Total groundwater reserves in the region were estimated at 1.28 × 106 cubic kilometers (km3) with an uncertainty range between 816,000 and 1.93 × 106 km3. Most of the reserves are located within large sedimentary basins in North Africa and the Arabian Peninsula, with Algeria, Libya, Egypt, and Saudi Arabia accounting for approximately 75% of the region’s total freshwater reserves. Alternatively, small groundwater reserves were found in fractured Precambrian basement exposures. As for groundwater changes between 2003 and 2014, all MENA countries except for Morocco exhibited declines in groundwater storage. However, given the region’s large groundwater reserves, groundwater changes between 2003 and 2014 are minimal and represent no immediate short-term threat to the MENA * Khalil Lezzaik
[email protected] * Adam Milewski
[email protected] 1
Department of Geology, University of Georgia, Water Resources & Remote Sensing Group (WRRS), 210 Field Street, 306 Geography-Geology Bldg., Athens, GA 30602, USA
region, with some exceptions. Notwithstanding this, the study recommends the development of sustainable and efficient groundwater management policies to optimally utilize the region’s groundwater resources, especially in the face of climate change, demographic expansion, and socio-economic development. Keywords Geographic information system . Groundwater statistics . Middle East and North Africa . Regional analysis . Remote sensing
Introduction Among the serious challenges facing the Middle East and North Africa (MENA) region, freshwater scarcity ranks highest, given its impact on food security, economic development and poverty reduction, and socio-political stability. Natural water scarcity, as a function of the predominantly (hyper-)arid environment of the MENA region, is exacerbated by increased water demand consistent with population growth, urbanization, and economic growth. So much so that the projected doubling of the region’s population in the next 50 years is expected to decrease per capita water availability by 40% (Terink et al. 2013). The centrality of groundwater resources in the healthy functionality of the MENA region arises from the fact that 76% of freshwater is primarily sourced from groundwater systems of which 65.6% are non-renewable fossil aquifers (Klingbeil and Al-Hamdi 2010). According to a study by the British Geological Survey (BGS), fossil groundwater resources in Africa have an estimated volume of more than a hundred times the estimate of surface freshwater resources, with the largest groundwater aquifers found in large sedimentary lithologies in North African countries such as Egypt,
Hydrogeol J
Libya, and Algeria (MacDonald et al. 2012). This paradigm extends to the Arabian Peninsula and most of the Levant (Israel and Palestinian Territories, Lebanon, Syria, Iraq, and Jordan). Given the region’s dependence on groundwater resources, accurate quantitative assessments of predominantly nonrenewable groundwater systems and subsequent changes to their water table levels, are necessary for planning, developing, and implementing future sustainable water-management practices and policies. However, data paucity limitations and the MENA region’s extensive spatial extent hamper scientific efforts designed to understand the nature, distribution, and accessibility of groundwater resources. In the past 20 years, long-term baseline high-quality water datasets have been reduced due to the continuing disintegration of monitoring networks, with a marked 90% decline in the number of active discharge stations (Vörösmarty et al. 2001). The constraints of data unavailability and inaccessibility in the MENA region are arguably one of the main causes behind the limited number of studies and publications addressing water resources generally and groundwater resources specifically in the region. While the advent of satellite-based remote sensing, geographic information systems (GIS), and improved computing power has given rise to global and regional-scale, high-resolution, and gridded data sets to counter in-situ data paucity (Milewski et al. 2009), these technological advancements have been more effectively used in simulating renewable surface-water processes as opposed to modeling groundwater systems. The study by Droogers et al. (2012) is one good example. In a World Bank study, Droogers et al. (2012) used a physically based, distributed, hydrological model, parameterized with a suite of global and regional data sets constructed primarily from remote sensing data and modeling approaches, to assess renewable water resources in the MENA region. Recently, however, a number of studies attempting to quantify and characterize groundwater systems have been published. Using simple conceptual models, MacDonald et al. (2012) and Richey et al. (2015) estimated continentalscale groundwater storage estimates by combining aquifer saturated thickness and effective porosity across Africa and globally, respectively. Fundamental to their analysis is their reliance on publications, reports, maps, and gray literature, which are quantitatively data-poor and of varying quality, to populate and/or calculate hydrogeological parameters. According to MacDonald et al. (2012), the lack of quantitative national hydrogeological maps in North Africa was replaced by regional (> 62,500 km2) qualitative reports and maps. Similarly, Richey et al. (2015) used regional storage estimates of individual aquifers from scientific literature and archives. If not available, Richey et al. (2015) calculated groundwater storage by following the approaches of Nace (1969) and Korzun et al. (1978) that designated arbitrary values to
hydrogeologic parameters and broad assumptions, such as assuming uniform minimum and maximum saturated thicknesses of 1,000 and 2,000 m respectively. Richey et al. (2015) revised their approaches to constrain the ranges of possible groundwater storage—for example, assuming a suggestion of 200 m as an acceptable average saturated thickness. Despite efforts to accurately assess groundwater systems through comprehensive reviews and analyses of existing hydrogeological knowledge, the outdated, data-poor, and qualitatively focused state of hydrogeological literature, maps, and archives continually limited and undermined these assessments—for example, the majority of previous studies utilize d at ab a s e s th at la c k di s t r i b u t e d a nd q u an t i t a t i v e hydrogeological data, with most sources providing a single to few data points (<10) in large regional aquifer systems. Dependence on few data points to over-extrapolate hydrogeological variables is a large source of errors and inaccuracies. Additional major sources of errors are discrepancies in data quality, collection and processing protocols, size of sample base, and description of field and modeling methodologies that compromise the consistency of and confidence in the results. Consistent with overall data-scarce conditions in developing countries, the unavailability of and/or inaccessibility to long-term and spatially distributed water-table fluctuation data hinders the measurement and understanding of groundwater storage changes and their drivers in the MENA region. The launch of NASA’s Gravity Recovery and Climate Experiment (GRACE) satellite mission in March 2002, however, has offset the historical lack of groundwater monitoring programs and long-term hydrological data sets in data-sparse areas such as the MENA region, by providing spatially and temporally continuous measurements of terrestrial water storage changes. The GRACE satellite mission has enabled experts to quantify groundwater depletion on a global scale through the use of integrated measurements and modeled terrestrial water mass (Wahr et al. 1998), thus giving rise to numerous studies aimed at characterizing groundwater depletion worldwide (Feng et al. 2013; Joodaki et al. 2014). In reference to the Middle East, Voss et al. (2013) used GRACE to estimate groundwater depletion in the Euphrates-Tigris River basin between 2003 and 2009 by isolating the groundwater component within total water storage from variations in land surface-water parameters (e.g. surface water, soil moisture). Similarly, a study by Longuevergne et al. (2013) utilized analogous methods to determine GRACE-derived groundwater change estimates in the lower Nile and the Euphrates-Tigris River basins. GRACE-based groundwater assessments are in the MENA region are limited by aquifer-level and basin-level analysis. Moreover, with the exception of Richey et al. (2015), no studies have attempted to integrate groundwater storage estimates with groundwater storage anomalies to assess current stress conditions of groundwater systems in the MENA region.
Hydrogeol J
However, the groundwater resilience assessment by Richey et al. (2015) is severely limited by the poor state of current hydrogeological data that produce groundwater storage estimates across magnitudes. The scope of this research is two-fold. First, this study introduces an original methodological approach, based on a systematic and innovative integration of easily available, up-todate, globally gridded datasets and models, to accurately characterize and quantify groundwater storage (reserves). The presented approach offers an alternative to current groundwater storage assessments that rely on outdated, data-poor, and qualitatively based studies and maps of questionable quality, with arbitrary estimations of hydrogeologic parameters, and wide ranges of uncertainty. The proposed approach addresses the limitations of analyses based on the collation and review of published and gray literature by standardizing model inputs and datasets, which improves the quality and consistency of groundwater storage simulations, provides finer spatial resolutions than achieved with few a few data points, and reduces errors caused by the small sample base sizes and sampling biases usually associated with regional groundwater reports. Another major advantage offered by the approach reported here is its replicability globally, especially in data-scarce regions, given its dependence on the combined use of a simple conceptual model with global-scale gridded data and models. Second is the assessment and characterization of groundwater resources in the MENA region by integrating groundwater storage estimates—calculated using the highlighted integrated approach—with GRACE-derived groundwater storage anomalies in a distributed, conceptually lumped, hydrogeological model parameterized with a suite of accurate and up-to-date gridded remote sensing data sets and models.
Study area The MENA region is a land mass covering a surface area of approximately 9 × 106 km2, with a geographical extent between 12°N–37°N and 17°W–60°E (Fig. 1). Deserts form the greater part of the surface area of the region, with the Great Sahara Desert (7 × 106 km2) covering most of North Africa. Given both the current conditions of low precipitation and high evapotranspiration on one hand, and evidence of many pluvial periods in the Quaternary on the other, the predominance of fossil groundwater resources is the most salient regional scale hydrogeological phenomenon in the MENA region (Burdon 1982). The mass of groundwater resources in the region are found in post-Cambrian lower Paleozoic sandstones and Quaternary unconsolidated sediments (e.g. sand dunes, alluviums). In the Gulf Cooperation Countries (GCC) and Libya, groundwater is used as the primary source of freshwater. In the GCC, groundwater is mainly used for agricultural irrigation, whereas demand by the domestic sector is met by
desalinated water. In the remaining countries, especially Algeria, Egypt, and Iraq, groundwater is still utilized as a secondary source (Shahin 2007); however, climate change assessments for the MENA region show a 15–20% decrease in average annual precipitation for the majority of the countries (Terink et al. 2013). These changes are bound to stress surface-water resources and increase the reliance on groundwater resources in the region. According to the World Bank (2013), the MENA region is home to 345.4 million people (2% annual growth rate), of which 60% live in urban concentrations. The projected doubling of the population by 2050 is expected to exacerbate water stress.
Data Water-table depth Depth to water table (WTD) was determined using a global water-table pattern map developed by Fan et al. (2013). The map was constructed using global WTD observations at well sites compiled from government sources and published literature. To fill in large data gaps in data-sparse regions, primarily in Asia and Africa, a groundwater model parameterized with the present climate, terrain, and sea level parameters was used to simulate groundwater movement in 1 × 1-km grid cells to determine WTD. Out of the 1,603,781 well sites, only 1,143 sites are in Asia and 431 sites are in Africa—primarily in the sub-Saharan region. With the actual number of well sites in the MENA region being significantly lower (<20 well sites), WTD values in this study area are effectively simulated using the groundwater model. Fan et al.’s (2013) groundwater model simulates vertically integrated lateral movements of groundwater and is forced by current sea level, climate, and terrain. The primary driver of WTD is determined by geographic scale, with sea level, climate, and terrain being the dominant drivers on the global, regional, and local scales respectively. However, Fan et al. (2013) stresses the effect of regional topographic gradients as a fourth driver, particularly at local scales, where terrain signals override climate due to the lateral groundwater convergence from surrounding topographic highs to topographic lows. The fourth WTD forcing explains the shallow WTD simulations, which are common in humid climates, in arid areas in the MENA region such as the Rub’ Al Khali, Oman, and to a smaller extent, some lower areas in central Yemeni highlands. These areas experience lateral groundwater convergence from the Arabian Shield and the Asir mountains along the western flank of the Arabian Peninsula towards Rub’ Al Khali, Oman, and the central Yemeni highlands. These model simulations are supported by other sources. According to Wood and Alsharhan (2003), the unconfined water table in Rub’ Al Khali often occurs close the surface
Hydrogeol J
Fig. 1 Topographic map displaying the spatial extent of the Middle East and North Africa (MENA) region. The black boundaries delineate political boundaries of the MENA countries included in this study. The Levant
constitutes Israel and Palestinian Territories, Lebanon, Syria, Iraq, and Jordan. SRTM digital elevation data were modified from Jarvis et al. (2008)
in large areas of active sabkha; furthermore, the existence of shallow groundwater is indicated in the publications describing several scientific investigations of evaporative pumping— evaporation of shallow water by the capillary upward movement of groundwater—from inland salt pans in the eastern Arabian Peninsula (Schulz et al. 2015; Sultan et al. 2008). Notwithstanding this, Fan et al. (2013) acknowledge that the shallow water tables in arid environments are likely exaggerated and attribute this to use of the WaterGAP Global Hydrology Model (WGHM) which underestimates evapotranspiration rates in water-limited regions and overlooks the role of vegetation in controlling recharge. Other assumptions and uncertainties are presented in the Fan et al. model. Given its global scale and data limitations, the groundwater model does not represent human impacts (e.g. groundwater pumping, irrigation, drainage), instead it focuses on providing global continuous natural WTD patterns. Consequently, WTD simulations in areas with high demographic concentrations and significant human influence would expectedly show higher model-observation differences, and erroneously present comparable WTD simulations between areas with similar natural forcing and different anthropogenic influences. For this study, the map was aggregated to a 0.25 × 0.25° resolution (Fig. 2a).
(2013). Sediment thickness information was compiled from published literature. In most continental areas, including the MENA region, sediment thickness was digitized from a Tectonic Map of the World provided by Exxon Production Research (1985). Sediment thickness was obtained from a compilation of active sources experiments, receiver functions, and published Moho maps. In areas with no seismic and gravity data, sediment thickness was acquired by extrapolating the averages of crustal properties in each crustal type. Potential errors may arise in areas with no seismic or gravity data, where sediment thickness is calculated by crustal properties. Numerous crustal types are needed to accommodate variations in sediment and crustal thickness (>360 crustal types). The manual assignment of these crustal types is cumbersome and does not always lead to sediment thicknesses within 1 km of the true values (Laske et al. 2013). For this study, the sediment thickness map was resampled to a 0.25 × 0.25° resolution (Fig. 2b).
Sediment thickness Sediment thickness estimates were obtained from a 1 × 1° global crustal model (CRUST1.0) developed by Laske et al.
Effective porosity and Lithology Effective porosity values of lithological sedimentary units, which constitute 87% of the MENA region, were derived from McWhorter and Sunada (1977). Effective porosity estimates were indirectly calculated by subtracting the field capacity estimates of soils, derived via physical methods, from total porosity values obtained indirectly from measuring soil densities. Mean, minimum, and maximum effective porosity values were assigned to respective lithologies to account for
Hydrogeol J
Fig. 2 Analysis input datasets: a depth to water table (Fan et al. 2013), b sediment thickness (Laske et al. 2013), c lithological classification (Hartmann and Moosdorf 2012), and d effective porosity
the natural variability inherent in geologic parameters. In addition to the type of the lithological sedimentary unit, sediment grain size was also accounted for, if available, when designating effective porosity value to different lithological units. Effective porosity values are then spatially assigned using a global lithological database, GLiM, developed by Hartmann and Moosdorf (2012). GLiM is the most recently published vector-based global lithological map, developed from 92 regional lithological maps of the highest available resolution. For this study, the lithological map was rasterized to a 0.25 × 0.25° resolution (Fig. 2c,d). To validate effective porosity estimates, the values were compared with those from a global porosity map by Gleeson et al. (2014). The comparison displayed a high degree of agreement in most lithological units. For instance, Gleeson’s porosity estimates for unconsolidated (p = 0.22) and siliciclastic sedimentary (p = 0.19) lithologies correlate well with this study’s estimates of 0.23 and 0.22 respectively. In some lithologies, differences in porosity estimates are found, primarily due to the derivation of porosity estimates from different scientific literature—in carbonate sedimentary units, the study’s porosity estimate (p = 0.14) diverges from Gleeson’s (p = 0.06). GRACE-derived terrestrial water storage (TWS) variations Several versions of the GRACE data are available online in the public domain. The logic behind the existence of different versions has to do with the complex processing of GRACE
level-one observations, which require the inversion of ranging observations between the two GRACE satellites, GPS orbit determinations, and non-gravity related acceleration corrections. The processing is made possible by many parameter choices and different solution strategies, which are tested and adopted by different processing centers and that result in different GRACE solutions. The most widely used GRACE data versions are provided by The Jet Propulsion Laboratory (JPL) in Pasadena, California (USA), The Center for Space Research (CSR) in Austin, Texas (USA), and The GFZ German Research Centre (GFZ) in Potsdam, Germany. Other less popular versions also exist such as those provided by The Research Group of Space Geodesy (GRGS) with the French National Space Center. Terrestrial water storage change (TWS) values between January 2003 and December 2014 were acquired from GRACE satellite observations of the earth’s gravity field. Level-three Release-05 monthly land mass grids (1 × 1°) from the JPL, CSR, and GFZ processing centers were used. GRACE land data were processed by Sean Swenson (Landerer and Swenson 2012; Swenson, 2012), supported by the NASA MEaSUREs Program, and are available at NASA (2012). Level-three TWS grids were multiplied by a scale factor raster, provided by the GRACE Tellus website, to account for the signal attenuation caused by sampling and post-processing of GRACE observations (Swenson and Wahr 2006). To minimize uncertainty in Level-three TWS data, the approach presented by Sakumura et al. (2014) was adopted. Ensemble GRACE data sets produced from the
Hydrogeol J
arithmetic mean of JPL, CSR, and GFZ TWS solutions are most effective in reducing noise and minimizing uncertainty. Consequently, an ensemble solution was used to generate TWS datasets for this study. TWS total errors estimates were computed from measurement and leakage errors using a least squares estimation (Swenson and Wahr 2006). Global land data assimilation system (GLDAS) outputs Hydrological data are essential for isolating groundwater storage change (GWS) from GRACE-based TWS. Given data scarcity in the MENA region, outputs from NASA’s Global Land Data Assimilation System (GLDAS) (Rodell et al. 2004) were used to quantify surface-water components: surface runoff, soil moisture, snow water equivalent, and canopy storage. GLDAS is a system that generates land surface states and fluxes by parametrizing satellite and ground-based observational data into advanced land surface models. For this study, monthly, 1 × 1°, version one land-surface parameters simulated by NOAH 2.7.1 in GLDAS (Hua-lan 2011) were used.
Methodology Estimating groundwater storage capacity The calculation of groundwater storage capacity is dependent on the geometry and the hydrogeologic characteristics of the aquifer system. Groundwater storage capacity has been estimated by combining effective porosity values with the saturated thickness of aquifer systems and their areal extent. The following approach was used: V sc ¼ ðAÞ ðH sat Þ ðφe Þ
ð1Þ
where Vsc denotes groundwater storage capacity (km3), A denotes surface areal extent (km2), Hsat denotes saturated thickness (km), and φ e denotes effective porosity (dimensionless). Saturated thickness (Hsat) was generated on a 0.25 × 0.25° grid by subtracting WTD estimates (Fan et al. 2013) from sediment thickness estimations (Laske et al. 2013). Effective porosity (φe) was also generated on a 0.25 × 0.25° grid by assigning φe estimates to their assigned lithological units as developed and mapped by Hartmann and Moosdorf (2012). Given the difficulty of establishing regional-scale distributed hydrological models, especially in ungauged domains such as the MENA region, the approach above attempts to balance the analytical complexity in estimating groundwater storage capacity with the reality of data paucity at such regional scales. Thus, the methodology allows for the first-order estimation of groundwater storage reserves using limited input data sets while presenting some limitations that need to be
addressed. One limitation requiring mitigation is the proclivity for overestimating saturated thickness by subtracting depth to water table from sediment thickness estimations from a crustal model. The adopted approach assumes that water saturates the entire vertical column of sediments (~ 10 km) all the way down to the Precambrian crystalline basement. If left unaddressed, this assumption will generate hyperbolized storage capacity estimates that are unrealistic and inconsistent with the presented literature. Therefore, to constrain the range of groundwater reserves an upper limit of 2 km was established for the saturated thickness estimates. The limit was selected based on the argument that groundwater resources in zones below sea or ocean levels with a depth of 2,000 m (m) are not abstracted in the MENA region, which withdraws groundwater from zones entirely in the upper earth’s crust, up to depths of 400 m (Shahin 2007). Finally, to better constrain saturated thickness, previously published saturated thickness estimates and isopach maps from existing literature and reports were incorporated into the results. In the Arabian Peninsula, deterministic values for aquifer saturated thickness were obtained from the Economic and Social Commission for Western Asia (2013). Saturated thickness of the North-Western Sahara Aquifer System (NWSAS) were obtained from isopach maps from the Sahara and Sahel Observatory (2004). In the Nubian Sandstone Aquifer, the saturated thickness was obtained from an isopach map in Thorweihe (1990). Finally, saturated thickness of the Kufra and Sirte basins were obtained from isopach maps provided by Wright et al. (1982). Another limitation that was addressed pertains to the assigned porosity values and their relationship to depth. Given that groundwater storage capacity is defined by effective porosity and saturated aquifer depths of up to 2 km, it was necessary to incorporate the effect of depth on effective porosity values, primarily porosity reduction with increasing depth. The porosity reduction calculations are based on porosity-depth relationships in sandstones as discussed by Magara (1980). According to Magara (1980), the porositydepth relationship in sandstones is close to an arithmetic straight line, where the rate of porosity reduction is similar in both shallow and deep depths, assuming the lack of any significant development of secondary porosity. While this porosity-depth relationship applies to siliciclastic aquifers, it also applies to carbonate systems as well. Based on a comparison of porosity-depth relationships in sandstone and carbonate petroleum aquifers, Ehrenberg and Nadeau (2005) noted that both siliciclastic and carbonate lithologies display similarly linear decreases in average porosity with increasing depth. Similar to Magara (1980), Ehrenberg and Nadeau (2005) do not consider complicating factors into the porosity-depth relationship such as the development of secondary porosity, and variations in the geothermal gradient. In line with these analyses, a porosity-depth relationship was integrated into the groundwater storage model. Using a linear
Hydrogeol J
regression, based on the sandstone porosity-depth relationship provided by Magara (1980), effective porosity values were calculated at 250-m intervals, which were then averaged to calculate depth-dependent effective porosity estimates. For example, in a grid representing a saturated thickness of 750 m, the depth-dependent effective porosity estimate used would be the average of effective porosities values at depths of 0, 250, 500, 750 m respectively. Estimating groundwater storage change (GWS) GRACE-derived TWS represent the combined effects of surface water and groundwater storage changes. Separately estimating groundwater storage changes requires isolating it by quantifying surface-water storage parameters (e.g. soil moisture) and removing them from GRACE’s TWS observations (Chen et al. 2014; Joodaki et al. 2014; Konikow 2015). Due to a lack of ground-based measurements in the study area, model estimates from GLDAS were used to account for surfacewater storage components (Rodell et al. 2004). GWS was calculated using the following approach: ΔGWS ¼ ΔTWS–ΔSM–ΔSWE–ΔCW–ΔSR
ð2Þ
Where GWS denotes water volume in the groundwater compartment, TWS denotes total water volume in the hydrosphere, SM denotes soil moisture, SWE denotes snow water equivalent, CW denotes canopy water storage, and SR denotes surface runoff. All units are expressed as a vertical water column in centimeters (cm). Based on GWS calculations from GRACE and GLDAS datasets, associated errors in GWS were estimated using a least squares approach (Voss et al. 2013): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi σGWS ¼ ðσTWS Þ2 þ ðσSM Þ2 þ ðσSWE Þ2 þ ðσCW Þ2 þ ðσSR Þ2 ð3Þ where σTWS denotes the error of GRACE-derived total water storage change; and σSM, σSWE, σCW, σSR denote the errors of GLDAS-computed soil moisture, snow water equivalent, canopy storage, and surface runoff respectively. Calculating σTWS required decorrelating nearby pixels for both leakage and measurement errors before computing σTWS using a least squares estimation (Swenson and Wahr 2006). Errors for soil moisture, snow water equivalent, and canopy storage are the mean monthly standard deviations of each respective surface-water parameter. Integrating groundwater reserves with groundwater storage anomalies To account for changes in groundwater storage reserves, GRACE-derived groundwater storage anomalies were
quantitatively incorporated into those estimations. A simple arithmetic approach was adopted where aggregate groundwater storage anomalies between January 2003 and December 2014 were added to storage capacity estimations. The calculations were performed using raster analysis in ArcGIS to provide a spatial representation of storage capacity changes within the MENA region. For instance, a grid with a groundwater reserve of 100 km3 experiencing a total groundwater depletion of 1 km3 would be shown to exhibit a 1% decrease in storage with a resultant groundwater reserve of 99 km3 in 2014.
Results and discussion Groundwater storage reserves (capacity) First-order estimates of groundwater reserves in the MENA region were generated by integrating saturated thickness and effective porosity values (Fig. 3). Total groundwater reserves in the MENA region were estimated at approximately 1.28 × 106 km3, with an uncertainty range between 816,000 and 1.93 × 106 km3 (Fig. 3, Table 1). The bulk of the region’s groundwater was found in large sedimentary basins of the Sahara Desert and the Arabian Peninsula. Consequently, countries with vast areal extent overlying primarily sedimentary basins accounted for the largest share of the region’s groundwater storage—for instance, Algeria, Libya, Saudi Arabia, and Egypt alone accounted for 74% of the region’s total groundwater reserves at 360, 250, 249, and 93 (103 km3) respectively (Table 1). Alternatively, areas with the least storage, equivalent to 0–5 m in water depth, were located predominantly in Precambrian crystalline basement extrusions, typically exhibiting low effective porosity values and thin sedimentary columns. Three main examples are the Arabian Shield in Saudi Arabia, the Hoggar region in southern Algeria, and the Atlas Mountains in Morocco. Aquifer capacity within fissured crystalline rocks is almost nil and their potential are expected to be very weak (Shahin 2007). Countries with the lowest reserves are Lebanon, Qatar, and Kuwait. It is worth noting that reserve estimates in those countries are less a function of their sedimentary lithology and more a result of their limited surficial area. Establishing the validity and reliability of the results through comparative analysis is difficult, given the overall lack of data and publications on groundwater reserves in the MENA region, with the exception of the study by MacDonald et al. (2012) and Richey et al. (2015). A country-level comparison of groundwater reserve estimates in North African countries shows that the results are consistently larger than those by MacDonald et al. (2012). Discrepancies in groundwater reserve estimates are primarily attributable to the different approaches
Hydrogeol J
Fig. 3 Groundwater reserve estimates for the MENA region based on effective porosity and saturated aquifer thickness: a bar graph showing minimum, mean, and maximum estimates of volumetric groundwater storage in each country. Conservative and liberal estimates of storage
capacity are calculated by using a range of effective porosity values to account for margins of error. b Map of groundwater storage expressed with vertical water depth in meters
followed in establishing saturated thickness estimates. While the MacDonald et al. study estimated saturated thickness using available hydrogeological reports and data, this study adopted an approach dependent upon spatially and temporally consistent gridded data sets with an inherent proclivity to inflate estimates, as discussed in the methodology section. To elaborate, groundwater reserve estimates for Libya, Egypt, Tunisia, and Morocco were 2.5, 1.6, 2.5, and 8 times higher respectively than those by MacDonald et al. (2012). Lower differences in groundwater reserve estimates in the former three countries are explainable because they overlie the Nubian, North Western Sahara, and Kufra and Sirte aquifer systems that had been constrained with isopach maps generated from in situ data. However, in Morocco’s case, the absence of isopach maps justifiably results in larger discrepancies in groundwater storage estimates. While groundwater reserves were estimated using the same lumped conceptual model adopted
by MacDonald et al. (2012), this study diverges in its data sets selection, by relying on a suite of remote sensing and modeled data sets that attempt to address the limitations of previous assessments, primarily the reliance on data-poor and qualitatively based hydrogeological literature. Richey et al. (2015) quantifies groundwater reserves in major groundwater aquifers, including the three largest systems in the MENA region: the Arabian Aquifer System (AAS), Nubian Sandstone Aquifer System (NSAS), and North-Western Sahara Aquifer System (NWSAS). An aquifer-level comparison of storage estimates against those of Richey et al. (2015) and other scientific literature indicate a close convergence of groundwater reserve estimates in the AAS, NSAS, and NWSAS. Given the state of limited hydrogeological knowledge and data paucity that result in uncertainty ranges across orders of magnitude in the MENA region—for example, groundwater storage assessments for the NSAS range between 15,000 and 373,000 km 3 —
Hydrogeol J Table 1
Country-level estimates of groundwater reserves, groundwater storage changes, and changes in groundwater capacity
Country
Groundwater storage change (km3)
Groundwater reserves Average estimate (km3)
Rangea (km3)
Water depth, normalized by country area, (km)
Annual change
Δ Groundwater capacity (%)b
Total change (2003–2014)
Algeria
361,327
231,204–522,763
0.15
−64 ± 23.5
−762
−0.21
Egypt
92,863
58,304–144,483
0.09
−106 ± 15.6
−1,268
−1.37
Iraq IPTc
50,963
19,391–92,946
0.12
−111 ± 5.2
−1,326
−2.60
7,134
3,646–12,417
0.26
−7 ± 0.3
−82
−1.15
Jordan Kuwait
11,618 1,543
5,680–20,216 588–2,698
0.13 0.09
−6 ± 0.2 −3 ± 0.2
−71 −36
−0.62 −2.35
Lebanon
2,929
985–5,590
0.28
−5 ± 0.1
−56
−1.92
Libya Morocco
249,469 64,279
154,076–375,209 32,199–102,154
0.14 0.14
−46 ± 62.2 29 ± 13.4
−555 352
−0.22 0.55
Oman Qatar Saudi Arabia Syria Tunisia UAEc Yemen a
42,167 2,038 248,159 38,872 18,944 30,826 64,383
21,709–67,816
0.14
−1 ± 1.5
−11
−0.03
605–3,828 178,822–348,777
0.18 0.12
−0.2 ± 0.2 −43 ± 6.2
−2 −515
−0.10 −0.21
13,246–72,848 7930–31,140 25,322–39,000 62,319–94,642
0.21 0.12 0.37 0.12
−49 ± 3 −18 ± 3.4 −1 ± 0.8 −12 ± 10.6
−584 −215 −8 −149
−1.50 −1.14 −0.03 −0.23
Range was determined by recalculating groundwater storage using a minimum–maximum spectrum of effective porosity values
b
Percent change to groundwater storage capacity between is January 2003 and December 2014 is generated by comparing GRACE-derived groundwater storage change to initial estimates of storage capacity
c
IPT Israel and Palestinian Territories; UAE United Arab Emirates
country-level and aquifer-level comparisons support the groundwater storage estimates and the underlying adopted methodology and data sets that produced them. While the study comparison in Table 2 displays that groundwater reserves in large sedimentary basins in North Africa and the Arabian Peninsula (78% of the MENA region) are consistent and reliable, groundwater reserve estimates in remaining areas with no constraining data such as the Levant, Oman, Yemen, United Arab Emirates and Morocco should be cautiously utilized while taking into consideration methodology limitations. It is worth noting that groundwater storage reserves do not equate to abstractable or utilizable water. BAbstractable volume^ is a concept that extends beyond physical determinants such as transmissivity and water quality to technical, financial, legal, and political variables that are unique to different communities. Notwithstanding the aforementioned uncertainties, estimated groundwater reserves (1.28 × 106 km3) represent a water resource of growing importance in a region where precipitation is projected to decrease by 15–20%, and evapotranspiration is forecasted to increase in the majority of MENA countries (Terink et al. 2013). Given that internal and external renewable water resources between 2000 and 2009 in the MENA region constituted only 250 km3, with a projected
annual decline of 0.6 km3 by 2050 (Droogers et al. 2012), these reserves serve as an important buffer against climate change that need to be properly characterized to better guide their management and use in a manner that is socioeconomically and environmentally optimal.
Groundwater storage change (2003–2014) GRACE-derived datasets integrated with GLDAS land surface parameters were processed to generate spatially distributed groundwater storage anomalies between January 2003 and December 2014 (Fig. 4). Also, groundwater storage anomalies were compared to first-order groundwater storage capacity estimates to calculate the percent change in groundwater as a function of GRACE-derived changes in groundwater (Table 1; Fig. 5). Figure 4a displays groundwater storage changes in the MENA region and highlights spatial distributions within the study area. Results display high groundwater depletion clusters and concentrations along coastal areas on the North African coast stretching from Algeria to Egypt, within or near the Nile River basin in Egypt, and in the Levant. Given the regional scale of the analysis, results are difficult to interpret; however, the authors argue that groundwater-storage-change patterns indicate two central
Hydrogeol J Table 2 Study comparison of groundwater storage capacity in the Arabian Aquifer System, Nubian Aquifer System, and Northwestern Sahara Aquifer system
Aquifer
Arabian Aquifer System
Source
This Study
Richey et al. (2015) Nubian Sandstone System
Al-Ibrahim (1991) This Study
Groundwater storage/reserves (km3) Value
Period
Min: 198,948 Mean: 278,906
-
Max: 407,537 500,000 500,000 Min: 50,421 Mean: 73,497
2015 1985 -
Max: 106,004
North-Western Sahara Aquifer System
Richey et al. (2015) Gossel et al. (2004)
87,000 135,000
2015 2004
Bakhbakhi (2006) Sefelnasr et al. (2015)
373,000 212,000
2006 2015
This study
Min: 59,368 Mean: 99,088
-
Richey et al. (2015)
Max: 145,206 43,000
2015
CEDARE (2008)
60,000
2014
Fig. 4 a Total groundwater-storage change in the MENA region between January 2003 and December 2014 and b Population density map (CIESIN 2005)
Hydrogeol J
Fig. 5 Percent change in estimated groundwater storage capacity (Fig. 3) as a function of GRACE-derived total groundwater storage anomalies between January 2003 and December 2014
drivers: urbanization and demographic pressures on one hand and climatic forcing on the other. Figure 4a,b clearly displays the juxtaposition between areas of high population densities (> 500 persons/km2) and areas exhibiting high declines in groundwater storage—for instance, Riyadh in Saudi Arabia, Cairo in Egypt, Damascus in Syria, and Tripoli in Libya all overlie areas exhibiting 3– 4 m declines in groundwater. The impact of human population growth and urbanization on groundwater depletion is well documented (Ferguson and Gleeson; 2012, Jat et al. 2008). The increasing size and population of cities and urban concentrations is a major driver of groundwater depletion given the benefits and uses of the groundwater in urban processes. For instance, groundwater is a freshwater supply source that is often of better quality than surface water, requires low capital costs, and where staged development is possible (Foster 1990). Moreover, urbanization also changes recharge regimes, with land surface impermeability reducing direct recharge. The exception to this observation is the Moroccan Coast, where recharge rates between 80 and 90 cm over a span of 12 years can be explained by the relatively high precipitation rates induced by the orographic effect of the Atlas Mountains on approaching saturated air masses. Naturally higher precipitation rates would account for more aquifer recharge and more constrained rates of groundwater decline, especially in Morocco where groundwater use is secondary (25% of total water use; Bzioui 2004). The expansive spatial distribution of groundwater-storage declines, however, indicates that climatic factors are another major, if not dominant factor, contributing to observed groundwater patterns in the MENA region. According to many studies, climate change is projected to decrease precipitation and increase the frequency of droughts (Lelieveld et al. 2012). Consequently, reduced precipitation and recharge and
the subsequent overreliance on already stressed groundwater reserves in a framework of worsening freshwater scarcity would exacerbate groundwater depletion (Sultan et al. 2013). Severe and widespread groundwater declines in the Levant (Fig. 4a) are associated with climate change-driven drying trends. According to a recent study by Cook et al. (2016), which conducted a 900-year analysis of drought variability in the Mediterranean using paleoclimate reconstruction, the Levant has recently been the center of persistent multiyear droughts, with the recent 15 years (1998–2012) drought being the driest on record. Kelley et al. (2015) also highlights the severity of the 2007–2010 drought on Syria and its neighbors on freshwater availability. The effect of multiyear droughts on the Levant is clearly displayed by the downward trends in groundwater storage, especially after 2006, in Israel and Palestinian Territories, Iraq, Jordan, Lebanon, and Syria (Fig. 6). It is also highly probable that groundwater declines in North Africa are related to climate change-driven droughts. While the twentieth century was also the driest for North Africa in 900 years, climate change is projected to exacerbate water stress in the twenty-first century with more intense and frequent droughts, driven by a 4–27% decrease in rainfall and an increase in evapotranspiration (Radhouane 2013). This will undoubtedly shift the pressure of meeting water demands to increasingly depleted groundwater systems. Despite clear evidence on the effects of climatic factors on groundwater declines, the impact of anthropogenic effects on groundwater depletion cannot be discounted, especially given that agriculture and food production consumes 85% of water supplies in the MENA region. According to Aw-Hassan et al. (2014), groundwater-irrigated agriculture comprises 53% of total irrigated land in Syria. Moreover, 30% of Syrian wheat is irrigated using groundwater (Rodriguez et al. 2000). Furthermore, agricultural and groundwater
Hydrogeol J
Fig. 6 Time series of GRACE-derived groundwater storage change in the 16 MENA countries. Gray shaded bands indicate ranges of uncertainty
policies that focus on short-term gains and agricultural subsidies, such as diesel subsidies and crop-price support, have driven unsustainable groundwater use, resulting in an average water-table decline rate of 1.5 m/year (Aw-Hassan et al. 2014). In Iraq, agriculture consumed 40 km3 of groundwater in 2000 (Al-Ansari 2013). Similarly in the Azraq Basin, Jordan, groundwater abstraction approximately tripled from ~21 million cubic meters (MCM) to ~60 MCM between 1983 and 2003 respectively (El-Naqa et al. 2007). A more recent study by Al-Zyoud et al. (2015) estimated groundwater depletion at ~70 MCM by 2013, with an average rate of water decline of 1.64 m/year. Notwithstanding differing local and national contexts, an overarching trend of increased groundwater abstraction, use, and depletion is visible in the Levant due to increased water demand, primarily for agricultural production. While empirical data and investigations indicate the role of both climatic and anthropogenic factors in driving groundwater depletion in the Levant, the adopted GRACE/GLDAS approach does not allow for the determination of precise causal mechanisms and of dynamic factors causing groundwater depletion. Based on the method and datasets used in this study,
groundwater depletion is detected in some areas of the MENA region; however no determination can be made on the driving and overriding factors responsible for the trends of groundwater decline. While the findings confirm the effects of demographic concentrations and climate change on groundwater depletion, they also present results that challenge some of the assumptions on groundwater processes and interactions in the MENA region. Given the region is in a predominantly hyper-arid region with negligible precipitation and very limited surface water, it was assumed that the region would exhibit significant groundwater depletion across the spatial domain. However, Fig. 4a presents a more nuanced image of groundwater storage changes that indicates possible groundwater recharge, including within aquifer systems that are traditionally defined as Bnon-renewable^ or Bfossil^ aquifer systems. Reflecting on these results, the authors highlight possible hypotheses that could explain these results either separately or concurrently. Diffuse recharge occurring in continental inland settings and regional groundwater flows can help explain and account for increases in groundwater storage occurring in southern
Hydrogeol J
Egypt and Libya, and in the Arabian shield and the Rub’ AlKhali desert in Saudi Arabia (Sultan et al. 2013). Numerous scientific studies have attempted to quantify groundwater recharge in arid environments such as the MENA region and reexamine the categorization of Bfossil^ groundwater systems. A study by Mohamed et al. (2014), that used GRACE and GLDAS datasets to infer groundwater recharge in the Nubian Sandstone Aquifer System (NSAS), concluded that average annual recharge rates were estimated at 3.34 mm between 2003 and 2014. Another study in the North Western Sahara Aquifer System (NWSAS) that used environmental isotopes and GRACE satellite datasets determined annual recharge values between 2003 and 2010 as ranging between zero to 6.75 mm (Gonçalvès et al. 2013). These results are consistent with a global scale diffuse groundwater recharge model tuned to local in-situ data in semi-arid and arid environments by Döll and Flörke (2005). Per their model, annual groundwater recharge in North Africa and the Arabian Peninsula ranges between 0 and 1 cm, which is equivalent to the dominant observed increases in groundwater storage ranging anywhere between 0 and 10 cm over a span of twelve years across the region (Fig. 4, light blue). However, some limited low-signal-to-noise areas such as the Kufra District in Libya and the Najran province in Saudi Arabia, display positive groundwater anomalies (in some cases up 100–150 cm over 12 years) that exceed the modeled recharge rates cited above. Instead of reflecting actual groundwater recharge, positive groundwater storage anomalies could be a reflection of uncertainties in the adopted GRACE/ GLDAS approach. This is specifically applicable in hyperarid environments where infinitesimal GRACE TWS signals are highly influenced by errors in simulated land surface parameters, more specifically in modeled soil moisture (snow water depth and canopy cover being negligible). The lack of global-scale TWS observations necessitates a model-based approach that is prone to errors. Inter-comparisons between different soil moisture models reflect the uncertainties in soil moisture simulations, especially in arid environments such as the MENA region. A recent study by Cheng et al. (2017), which compared soil moisture outputs of Coupled Model Intercomparison Project Phase 5 (CMIP5), Community Earth System Model (CESM), and GLDAS, determined that while there is a good agreement between spatial and temporal soil moisture patterns, soil moisture variations and anomalies differed significantly, with CMIP5 and CESM displaying smaller soil moisture anomaly magnitudes than GLDAS data. Cheng et al. (2017) concluded that relative standard deviations increase with decreases in mean soil moisture, with high uncertainties concentrated in the Sahara, Arabian Peninsula, the Iranian Plateau, and other regions with relatively low soil moisture values. Soil moisture outputs from GLDAS and remote sensing satellites also display low correlation in arid environments. According to Li et al. (2015), a
statistical comparison of Aquarius remote-send soil moisture and GLDAS simulations displayed a weak correlation (< 0.2), with the lowest RMSD values obtained in arid low soil moisture environments. Given that the GRACE/GLDAS approach subtracts simulated land surface parameters from GRACE TWS signals to determine groundwater anomalies, high soil moisture uncertainties—driven by model structures, initial conditions and coarse resolutions—could result in groundwater anomalies that are more reflective of soil moisture uncertainties than actual groundwater change. Another factor that could potentially explain the lack of pervasive groundwater declines expected in Bfossil^ aquifer systems pertains to the local scale of water-table cones of depression and the low resolution of the GRACE satellite used to monitor those changes. Given the large extent of aquifer systems in inland deserts such as the Sahara, and low population densities in those areas (0–15 persons/km2), groundwater abstraction activities tend to concentrate in spatially restricted areas around towns and agricultural projects in close proximity to oases, which result in local-scale declines in groundwater levels. According to numerical groundwater model simulations of oases in the NSAS, current and projected groundwater extraction rates are predicted to lower groundwater levels significantly. Sefelnasr et al. (2014) predicts a drawdown of 75 m for the next 90 years (0.83 m/year) in the Dakhla Oasis in western Egypt, assuming an extraction rate of 1.2 MCM/day. Similarly, in the Farafra Oasis, groundwater extraction is also expected to drive a water head decline rate of 1 m/year for the next 20 years (Elsheikh 2015). Another study by Ebraheem et al. (2002) simulated potentiometric declines of 0.45 and 1.7 m/year in the Dakhla Oasis and East Oweinat area respectively. GRACE datasets used to monitor groundwater changes, however, are limited by a coarse spatial resolution of 300–400 km (Ramillien et al. 2008) that potentially prevent them from detecting spatially limited changes in groundwater storage and in developed cones of depression. Changes in groundwater storage capacity as a function of GRACE-derived changes in groundwater (Table 1; Fig. 5) show that all MENA countries, except for Morocco, experienced declines in groundwater storage. The three countries with the highest annual rates of groundwater depletion were Algeria (−64 ± 23.5 km3, −0.21%), Egypt (−106 ± 15.6 km3, −1.37%), and Iraq (−111 km3, −2.6%). Groundwater-storage time series in Fig. 6 clearly display significant negative trends in groundwater storage in the Levant, and to a lesser extent in North African countries. In the Arabian Peninsula, negative annual groundwater storage trends are also consistent. In the case of the small Gulf countries—e.g. the United Arab Emirates (UAE), Qatar—along the Persian Gulf, the coarse resolution of the GRACE satellite could account for the inability to detect changes in groundwater within very limited spatial areas.
Hydrogeol J
Overall, with a regional average decline of 0.82% in groundwater reserves, the impact of current levels of groundwater depletion on groundwater reserves is modest and does not pose an immediate threat. While most MENA countries experienced negligible declines in groundwater reserves (less than 1% in 12 years), some countries such as Syria (−1.5%), Iraq (−2.6%), Lebanon (−1.92), and Kuwait (−2.35) display more pronounced groundwater declines that require mitigation before they evolve into more serious threats in the future.
Conclusion To better characterize and quantify groundwater resources in the MENA region, first-order estimates of groundwater reserves were calculated using a distributed ArcGIS model parametrized with a suite of remote sensing and modeled gridded data sets. Moreover, GRACE-derived datasets were used to calculate groundwater storage anomalies between January 2003 and December 2014 to construct an image on the recent conditions of groundwater resources in the MENA region. Consistent with the available literature, groundwater storage reserves were highest in the deep sedimentary basins of Northern Africa and the Arabian Peninsula that were recharged during previous fluvial periods. Alternatively, the lowest estimates were found in the Precambrian basement exposure. Results on groundwater storage change, however, offer more complex observations than the general assessment of overall groundwater depletion in the MENA region. The association between areas of groundwater depletion and urban concentrations with high population densities ascertains the effects of anthropogenic demand on groundwater depletion. Moreover, climatic conditions were also identified as an essential driver of groundwater changes in the region, especially in the Levant, where countries are currently experiencing the worst drought in 900 years. On the other hand, positive groundwater storage anomalies were observed, particularly in the southern part of the Sahara and Arabian Desert. Several probable hypotheses explaining this phenomenon were presented, including the possibility of groundwater recharge and groundwater anomalies driven by soil moisture uncertainties; however, given the limitations of the adopted methodology and the scope of this study, no definitive causal explanation could be provided to explain these positive storage anomalies. Further research aimed at understanding the source of these groundwater signals is recommended. Given the large groundwater reserves underlying the region, groundwater changes between 2003 and 2014 represent only a small fraction of available water resources and pose no immediate threats to the water supply. However, the impact of climate change, demographic expansion, and socio-economic development necessitates the development of sustainable and
efficient water management policies to address future challenges in one of the driest regions in the world. Acknowledgements The authors thank the anonymous reviewers and associate editor for their careful reading of this manuscript and their many insightful comments and suggestions.
References Al-Ansari NA (2013) Management of water resources in Iraq: perspectives and prognoses. Engineering5(8), 35541, 18 pp Al-Ibrahim AA (1991) Excessive use of groundwater resources in Saudi Arabia: impacts and policy options. Ambio 34–37 Al-Zyoud S, Rühaak W, Forootan E, Sass I (2015) Over exploitation of groundwater in the Centre of Amman Zarqa Basin, Jordan: evaluation of well data and GRACE satellite observations. Resources 4: 819–830 Aw-Hassan A, Rida F, Telleria R, Bruggeman A (2014) The impact of food and agricultural policies on groundwater use in Syria. J Hydrol 513:204–215. doi:10.1016/j.jhydrol.2014.03.043 Bakhbakhi M (2006) Nubian sandstone aquifer system. In Foster S, Loucks P (eds) Non-renewable groundwater resources: a guidebook on socially sustainable management for water-policy makers. HP-VI Series on Groundwater 10, UNESCO, Paris, pp 75–81 Burdon D (1982) Hydrogeological conditions in the Middle East. Q J Eng Geol Hydrogeol 15:71–82 Bzioui M (2004) Rapport national 2004 sur les ressources en eau au Maroc [National report on water resources in Morocco]. UN Water-Africa, Abuja, Nigeria, 94 pp CEDARE (2008) The North-Western Sahara Aquifer System (Algeria, Tunisia, Libya). Sahara and Sahel Observatory, Tunis, Tunisia Chen J, Li J, Zhang Z, Ni S (2014) Long-term groundwater variations in northwest India from satellite gravity measurements. Glob Planet Chang 116:130–138 Cheng S, Huang J, Ji F, Lin L (2017) Uncertainties of soil moisture in historical simulations and future projections. J Geophys Res Atmos 122:2239–2253 CIESIN (2005) Gridded population of the world version 3 (GPWV3): population density grids. Socioeconomic Data and Applications Center, Columbia University, Palisades, NY Cook BI, Anchukaitis KJ, Touchan R, Meko DM, Cook ER (2016) Spatiotemporal drought variability in the Mediterranean over the last 900 years. J Geophys Res Atmos 121(5):2060–2074 Döll P, Flörke M (2005) Global-scale estimation of diffuse groundwater recharge: model tuning to local data for semi-arid and arid regions and assessment of climate change impact. FAO, Rome Droogers P, Immerzeel W, Terink W, Hoogeveen J, Bierkens M, Van Beek L, Debele B (2012) Water resources trends in Middle East and North Africa towards 2050. Hydrol Earth Syst Sci 16:3101– 3114 Ebraheem A, Riad S, Wycisk P, Seif El-Nasr A (2002) Simulation of impact of present and future groundwater extraction from the nonreplenished Nubian sandstone aquifer in southwest Egypt. Environ Geol 43:188–196 Economic and Social Commission for Western Asia (2013) Inventory of shared water resources in western Asia. United Nations, New York Ehrenberg S, Nadeau P (2005) Sandstone vs. carbonate petroleum reservoirs: a global perspective on porosity-depth and porositypermeability relationships. AAPG Bull 89:435–445 El-Naqa A, Al-Momani M, Kilani S, Hammouri N (2007) Groundwater deterioration of shallow groundwater aquifers due to overexploitation in northeast Jordan. CLEAN Soil Air Water 35:156–166
Hydrogeol J Elsheikh AE (2015) Mitigation of groundwater level deterioration of the Nubian sandstone aquifer in Farafra Oasis, Western Desert, Egypt. Environ Earth Sci 74:2351–2367. doi:10.1007/s12665-015-4236-7 Exxon Production Research, AAPG (1985) Tectonic map of the world. Exxon and AAPG, Tulsa, OK Fan Y, Li H, Miguez-Macho G (2013) Global patterns of groundwater table depth. Science 339:940–943 Feng W, Zhong M, Lemoine JM, Biancale R, Hsu HT, Xia J (2013) Evaluation of groundwater depletion in North China using the gravity recovery and climate experiment (GRACE) data and groundbased measurements. Water Resour Res 49:2110–2118 Ferguson G, Gleeson T (2012) Vulnerability of coastal aquifers to groundwater use and climate change. Nat Clim Chang 2:342–345 Foster SS (1990) Impacts of urbanization on groundwater. Hydrological processes and water management in urban areas Wallingford, UK. Publ. no. 198, IAHS, Wallingford, UK Gleeson T, Moosdorf N, Hartmann J, Beek L (2014) A glimpse beneath earth’s surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity. Geophys Res Lett 41:3891–3898 Gonçalvès J, Petersen J, Deschamps P, Hamelin B, Baba-Sy O (2013) Quantifying the modern recharge of the Bfossil^ Sahara aquifers. Geophys Res Lett 40:2673–2678 Gossel W, Ebraheem A, Wycisk P (2004) A very large scale GIS-based groundwater flow model for the Nubian sandstone aquifer in eastern Sahara (Egypt, northern Sudan and eastern Libya). Hydrogeol J 12: 698–713 Hartmann J, Moosdorf N (2012) The new global lithological map database GLiM: a representation of rock properties at the Earth surface. Geochem Geophys, Geosyst 13, Q12004 Hua-lan R (2011) README document for Global Land Data Assimilation System Version 1 (GLDAS-1) products. GES DISC, Greenbelt, MD Jarvis A, Reuter H, Nelson A, Guevara E (2008) Hole-filled SRTM for the globe, version 4. CGIAR-CSI SRTM 90m database. International Center for Tropical Agriculture, Cali, Columbia. http://srtm.csi.cgiar.org/. Accessed July 2017 Jat MK, Khare D, Garg PK (2008) Urbanization and its impact on groundwater: a remote sensing and GIS-based assessment approach. Environmentalist 29:17. doi:10.1007/s10669-008-9176-2 Joodaki G, Wahr J, Swenson S (2014) Estimating the human contribution to groundwater depletion in the Middle East, from GRACE data, land surface models, and well observations. Water Resour Res 50: 2679–2692 Kelley CP, Mohtadi S, Cane MA, Seager R, Kushnir Y (2015) Climate change in the fertile crescent and implications of the recent Syrian drought. Proc Natl Acad Sci 112:3241–3246 Klingbeil R, Al-Hamdi M (2010) Transboundary water and transboundary aquifers in the Middle East: opportunities for sharing a precious resource. World Water Week, Stockholm, 20 August 2009 Konikow LF (2015) Long-term groundwater depletion in the United States. Groundwater 53:2–9. doi:10.1111/gwat.12306 Korzun V, Sokolov A, Budyko M, Voskresensky K, Kalinin G, Konoplyantsev A, Korotkevich E, L’vovitch M (1978) Atlas of world water balance (Engl. translation). USSR National Committee for the International Hydrological Decade, UNESCO, Paris Landerer F, Swenson S (2012) Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour Res 48 Laske G, Masters G, Ma Z, Pasyanos M (2013) Update on CRUST1. 0-A 1° global model of Earth’s crust. Geophys Res Abst, 15, Abstract EGU2013-2658 Lelieveld J, Hadjinicolaou P, Kostopoulou E, Chenoweth J, El Maayar M, Giannakopoulos C, Hannides C, Lange M, Tanarhte M, Tyrlis E (2012) Climate change and impacts in the eastern Mediterranean and the Middle East. Clim Chang 114:667–687
Li D, Zhao T, Shi J, Bindlish R, Jackson TJ, Peng B, An M, Han B (2015) First evaluation of Aquarius soil moisture products using in situ observations and GLDAS model simulations. IEEE J STARS 8: 5511–5525 Longuevergne L, Wilson C, Scanlon B, Crétaux J (2013) GRACE water storage estimates for the Middle East and other regions with significant reservoir and lake storage. Hydrol Earth Syst Sci 17:4817– 4830 MacDonald A, Bonsor H, Dochartaigh BÉÓ, Taylor R (2012) Quantitative maps of groundwater resources in Africa. Environ Res Lett 7:024009 Magara K (1980) Comparison of porosity-depth relationships of shale and sandstone. J Pet Geol 3:175–185 McWhorter DB, Sunada DK (1977) Ground-water hydrology and hydraulics. Water Rources Publ. Fort Collins, CO Milewski A, Sultan M, Yan E, Becker R, Abdeldayem A, Soliman F, Gelil KA (2009) A remote sensing solution for estimating runoff and recharge in arid environments. J Hydrol 373:1–14. doi:10. 1016/j.jhydrol.2009.04.002 Mohamed A, Sultan M, Ahmed M, Yan E (2014) Quantifying modern recharge to the Nubian Sandstone Aquifer System: inferences from GRACE and land surface Models. AGU Fall Meeting Abstracts no. G23A-0465, AAGU, Washington, DC Nace RL (1969) World water inventory and control. Methuen, London NASA (2012) GRACE Tellus Gravity Recovery and Climate Experiment. Available at http://grace.jpl.nasa.gov/. Accessed 14 Mar 2015 Radhouane L (2013) Climate change impacts on North African countries and on some Tunisian economic sectors. JAEID 107:101–113 Ramillien G, Famiglietti JS, Wahr J (2008) Detection of continental hydrology and glaciology signals from GRACE: a review. Surv Geophys 29:361–374. doi:10.1007/s10712-008-9048-9 Richey AS, Thomas BF, Lo MH, Famiglietti JS, Swenson S, Rodell M (2015) Uncertainty in global groundwater storage estimates in a total groundwater stress framework. Water Resour Res 51:5198–5216 Rodell M, Houser P, Uea J, Gottschalck J, Mitchell K, Meng C, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M (2004) The Global Land Data Assimilation System. Bull Am Meteorol Soc 85:381–394 Rodriguez A, Salahieh H, Badwan R, Khawam H (2000) Groundwater use and supplemental irrigation in Atareb, northwest Syria. ICARDA, Beirut, Lebanon Sahara and Sahel Observatory (2004) The North-Western Sahara Aquifer System basin awareness, 1st edn. OSS, Tunis, Tunisia, 322 pp Sakumura C, Bettadpur S, Bruinsma S (2014) Ensemble prediction and intercomparison analysis of GRACE time-variable gravity field models. Geophys Res Lett 41:1389–1397 Schulz S, Horovitz M, Rausch R, Michelsen N, Mallast U, Köhne M, Siebert C, Schüth C, Al-Saud M, Merz R (2015) Groundwater evaporation from salt pans: examples from the eastern Arabian peninsula. J Hydrol 531(part 3):792–801. doi:10.1016/j.jhydrol.2015.10.048 Sefelnasr A, Gossel W, Wycisk P (2014) Three-dimensional groundwater flow modeling approach for the groundwater management options for the Dakhla Oasis, Western Desert, Egypt. Environ Earth Sci 72: 1227–1241. doi:10.1007/s12665-013-3041-4 Sefelnasr A, Gossel W, Wycisk P (2015) Groundwater management options in an arid environment: the Nubian Sandstone Aquifer System, eastern Sahara. J Arid Environ 122:46–58 Shahin M (2007) Water resources and hydrometeorology of the Arab region. Springer, Heidelberg, Germany Sultan M, Ahmed M, Sturchio N, Yan Y, Milewski A, Becker R, Wahr J, Becker D, Chouinard K (2013) Assessment of the vulnerabilities of the Nubian Sandstone Fossil Aquifer, North Africa. In: Climate vulnerability: understanding and addressing threats to essential resources. Elsevier, Amsterdam Sultan M, Sturchio N, Al Sefry S, Milewski A, Becker R, Nasr I, Sagintayev Z (2008) Geochemical, isotopic, and remote sensing
Hydrogeol J constraints on the origin and evolution of the Rub Al Khali aquifer system, Arabian peninsula. J Hydrol 356:70–83 Swenson S (2012) GRACE monthly land water mass grids NETCDF RELEASE 5.0. TELND-NC005, Jet Propulsion Lab, Caltech, Pasadena, CA. doi:10.5067/TELND-NC005 Swenson S, Wahr J (2006) Post-processing removal of correlated errors in GRACE data. Geophys Res Lett 33, L08402 Terink W, Immerzeel WW, Droogers P (2013) Climate change projections of precipitation and reference evapotranspiration for the Middle East and northern Africa until 2050. Int J Climatol 33: 3055–3072 Thorweihe U (1990) Nubian aquifer system. In: Said R (ed) The geology of Egypt. Balkema, Lisse, The Netherlands, pp 601–614 Vörösmarty C, Askew A, Grabs W, Barry R, Birkett C, Döll P, Goodison B, Hall A, Jenne R, Kitaev L (2001) Global water data: a newly endangered species. EOS Trans Am Geophys Union 82:54–58
Voss KA, Famiglietti JS, Lo M, Linage C, Rodell M, Swenson SC (2013) Groundwater depletion in the Middle East from GRACE with implications for transboundary water management in the TigrisEuphrates-Western Iran region. Water Resour Res 49:904–914 Wahr J, Molenaar M, Bryan F (1998) Time variability of the Earth’s gravity field: hydrological and oceanic effects and their possible detection using GRACE. J Geophy Res Solid Earth (1978–2012) 103:30205–30229 Wood W, Alsharhan A (2003) Water resources perspectives: evaluation, management and policy. Elsevier, Amsterdam World Bank (2013) World development indicators database. World Bank, Washington DC Wright EP, Benfield AC, Edmunds WM, Kitching R (1982) Hydrogeology of the Kufra and Sirte basins, eastern Libya. Q J Eng Geol Hydrogeol 15:83–103. doi:10.1144/gsl.qjeg.1982.015. 02.02