Quantifying renewable groundwater stress with GRACE

23 Pages·2015·2.77 MB·English

UC Irvine
UC Irvine Previously Published Works
Title
Quantifying renewable groundwater stress with GRACE.
Permalink
https://escholarship.org/uc/item/2t59q0k8
Journal
Water resources research, 51(7)
ISSN
0043-1397
Authors
Richey, Alexandra S
Thomas, Brian F
Lo, Min-Hui
et al.
Publication Date
2015-07-01
DOI
10.1002/2015wr017349

Peer reviewed
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Water Resources Research
RESEARCH ARTICLE Quantifying renewable groundwater stress with GRACE
10.1002/2015WR017349
AlexandraS.Richey1,BrianF.Thomas2,Min-HuiLo3,JohnT.Reager2,JamesS.Famiglietti1,2,4,
Special Section: KatalynVoss5,SeanSwenson6,andMatthewRodell7
The50thAnniversaryofWater
ResourcesResearch 1DepartmentofCivilandEnvironmentalEngineering,UniversityofCalifornia,Irvine,California,USA,2NASAJetPropulsion
Laboratory,CaliforniaInstituteofTechnology,Pasadena,California,USA,3DepartmentofAtmosphericSciences,National
TaiwanUniversity,Taipei,Taiwan,4DepartmentofEarthSystemScience,UniversityofCalifornia,Irvine,California,USA,
KeyPoints:
5DepartmentofGeography,UniversityofCalifornia,SantaBarbara,California,USA,6ClimateandGlobalDynamics
(cid:2)Renewablegroundwaterstressis
quantifiedintheworld’slargest Division,NationalCenterforAtmosphericResearch,Boulder,Colorado,USA,7HydrologicSciencesLaboratory,NASA
aquifers GoddardSpaceFlightCenter,Greenbelt,Maryland,USA
(cid:2)Characteristicstressregimesare
definedtodeterminetheseverityof
stress Abstract Groundwaterisanincreasinglyimportantwatersupplysourceglobally.Understandingthe
(cid:2)Overstressedaquifersaremainlyin
rangelandbiomeswithsome amountofgroundwaterusedversusthevolumeavailableiscrucialtoevaluatefuturewateravailability.We
croplands presentagroundwaterstressassessmenttoquantifytherelationshipbetweengroundwateruseandavail-
abilityintheworld’s37largestaquifersystems.Wequantifystressaccordingtoaratioofgroundwateruse
Correspondenceto: toavailability,whichwecalltheRenewableGroundwaterStressratio.Theimpactofquantifyingground-
J.S.Famiglietti, waterusebasedonnationallyreportedgroundwaterwithdrawalstatisticsiscomparedtoanovelapproach
[email protected]
toquantifyusebasedonremotesensingobservationsfromtheGravityRecoveryandClimateExperiment
(GRACE)satellitemission.Fourcharacteristicstressregimesaredefined:Overstressed,VariableStress,
Citation:
Human-dominatedStress,andUnstressed.Theregimesareafunctionofthesignofuse(positiveornega-
Richey,A.S.,B.F.Thomas,M.-H.Lo,
J.T.Reager,J.S.Famiglietti,K.Voss, tive)andthesignofgroundwateravailability,definedasmeanannualrecharge.Theabilitytomitigateand
S.Swenson,andM.Rodell(2015), adapttostressedconditions,whereuseexceedssustainablewateravailability,isafunctionofeconomic
Quantifyingrenewablegroundwater
capacityandlandusepatterns.Therefore,wequalitativelyexploretherelationshipbetweenstressand
stresswithGRACE,WaterResour.Res.,
51,5217–5238,doi:10.1002/ anthropogenicbiomes.Wefindthatestimatesofgroundwaterstressbasedonwithdrawalstatisticsare
2015WR017349. unabletocapturetherangeofcharacteristicstressregimes,especiallyinregionsdominatedbysparsely
populatedbiometypeswithlimitedcropland.GRACE-basedestimatesofuseandstresscanholistically
Received7APR2015
quantifytheimpactofgroundwateruseonstress,resultinginbothgreatermagnitudesofstressandmore
Accepted29MAY2015
variabilityofstressbetweenregions.
Acceptedarticleonline16JUN2015
Publishedonline14JUL2015
1.Introduction
Freshwaterisafundamentalresourcefornaturalecosystemsandhumanlivelihoods,andaccesstoitiscon-
sidered a universal human right [United Nations Committee on Economic, Social and Cultural Rights, 2003].
Waterresourcesareunderpressuretomeetfuturedemandsduetopopulationgrowthandclimatechange,
bothofwhichmayalterthespatialandtemporaldistributionoffreshwateravailabilityglobally[Do€ll,2009;
Kundzewicz et al., 2008; Kundzewicz and Do€ll, 2009; Famiglietti, 2014]. As the distribution of freshwater
changes, the global population without access to potable water will likely increase [Alcamo et al., 2007;
Kundzewiczetal.,2008].Itiscriticaltounderstandhowhumanandnaturaldynamicsareimpactingavailable
waterresourcestodeterminelevelsofsustainableuseandtoensureadequateaccesstofreshwater.
Surfacewateristheprincipalfreshwatersupplyappropriatedtomeethumanwaterdemandglobally,butthe
importanceofgroundwaterisincreasingassurfacesuppliesbecomelessreliableandpredictable[Kundzewicz
VC2015TheAuthors. andDo€ll,2009]andgroundwaterisincreasinglyrelieduponduringtimesofdroughtasaresilientwatersup-
Thisisanopenaccessarticleunderthe ply source [Famiglietti, 2014]. Groundwater is currently the primary source of freshwater for approximately
termsoftheCreativeCommons
twobillionpeople[Alley,2006;KundzewiczandDo€ll,2009].Despiteitsimportance,knowledgeonthestateof
Attribution-NonCommercial-NoDerivs
large groundwater systems is limited as compared to surface water [Foster and Chilton, 2003; Famiglietti,
License,whichpermitsuseand
distributioninanymedium,provided 2014],largelybecausethecostandcomplexityofmonitoringlargeaquifersystemsisoftenprohibitive.
theoriginalworkisproperlycited,the
The United States government has identified water stress as a potential driver of regional insecurity that
useisnon-commercialandno
cancontributetoregionalunrest[IntelligenceCommunityAssessment(ICA),2012].Waterstressanalysespro-
modificationsoradaptationsare
made. videaframeworktounderstandthedynamicsbetweenhumanandnaturalsystemsbydirectlycomparing
RICHEYETAL. QUANTIFYINGRENEWABLEGROUNDWATERSTRESSWITHGRACE 5217
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water availability to human water use. There are three main approaches to quantify physical water stress
[Rijsberman,2006]:(1)aper-capitawateravailabilityratio[Falkenmark,1989],(2)acomparisonbetweenuse
andavailabilityeitherasthedifferencebetweenthetwo[Wadaetal.,2010,2011;vanBeeketal.,2011]oras
theratio[Alcamoetal.,1997;Vo€ro€smartyetal.,2000;OkiandKanae,2006;Do€ll,2009],and(3)theevaluation
of the socio-economic and physical factors that impact stress [Sullivan et al., 2003]. This study defines
renewable groundwater stress (RGS) following the second approach as the ratio of groundwater use to
groundwateravailabilityinequation(1)[Alcamoetal.,1997].
use
RGS5 (1)
availability
The simplicity of equation (1) provides a proverbial ‘‘two edge sword.’’ On one hand, renewable ground-
waterstresscanbecalculatedwithestimatesoftwovariables.Ontheother,inconsistentassumptionsand
differingestimatesanddefinitionsofuseandavailabilityresultinvariablecalculationsofrenewablestress.
Previousstudiesdefinedwateruseaswaterwithdrawalsandquantifiedusewithnationalwithdrawalstatis-
tics in which a single value represents per-capita water use for an entire country [e.g., Vo€ro€smarty et al.,
2000], thus assuming water is used homogenously within a country. The statistics represent groundwater
withdrawalsbutdonotaccountfortheimpactofwithdrawalsonthestateofthesystem.Additionally,the
definitionofavailabilityhasfocusedontherenewablefluxesofthedynamicwatercycle[UNWorldWater
AssessmentProgram(WWAP),2003],includingriverrunoffandgroundwaterrecharge[Lvovich,1979;Falken-
marketal.,1989;Posteletal.,1996;Shiklomanov,2000;WWAP,2003;ZeksterandEverett,2004].Onlyrecently
havestressstudiesevolvedfromimplicitlyincludinggroundwaterasbaseflowinmodeledrunoff[Alcamo
etal.,1997;Vo€ro€smartyetal.,2000;OkiandKanae,2006],toexplicitlyquantifyingstresswithgroundwater
withdrawalstatistics,modeledrecharge[Do€ll,2009;Wadaetal.,2010],andnonrenewablegroundwateruse
fromcompiledwithdrawalstatistics[Wadaetal.,2011;vanBeeketal.,2011].
Theserecentadvancesingroundwaterstressanalysishaveimprovedourglobalunderstandingofground-
water availability to meet current water demands. However, groundwater withdrawal statistics are often
outdatedandmeasuredbyinconsistentmethodsbetweengeopoliticalboundaries[ShiklomanovandPen-
kova,2003;Alley,2006].Thus,theacquisitionofaccuratewaterusedatarepresentsamajorchallengeand
an impediment to accurate estimates of water stress and associated security threats. Remote sensing has
been shown to greatly improve estimates of groundwater depletion [Colesanti et al., 2003; Schmidt and
Bu€rgmann,2003;Lanarietal.,2004;Rodelletal.,2009;Famigliettietal.,2011;Vossetal.,2013;Castleetal.,
2014], specifically, with the Gravity Recovery and Climate Experiment (GRACE) satellite mission from the
NationalAeronauticsSpaceAdministration(NASA)[Tapleyetal.,2004].
Thisstudyestimatesgroundwaterstress fromequation(1)andassesses thevariabilityinstressthatresults
from different definitions of groundwater use. In this study, groundwater availability is defined as ground-
waterrecharge. Weassess groundwaterusewith groundwater withdrawalstatistics,Q inequation(2), and
thenredefineuseasthetrendinsubsurfacestorageanomaliesusingremotesensingapproaches,dGW/dtin
equation (2). Equation (2) represents the water balance in a system with groundwater withdrawals, Q,
asintroducedbyBredehoeftandYoung[1970].Theequationshowsthatwhenpumpingoccurs,thereisan
increase in recharge (DR ) from its natural state (R ) and/or a decrease in discharge (DD ) from its natural
0 0 0
state(D )[Theis,1940].Lohman[1972]defined(DR -DD )ascapture.Ifequilibriumhasbeenreachedsuch
0 0 0
thatcapturebalancesQthendGW/dt,thechangeingroundwaterstorage,iszero.However,storagelosswill
occur while Q exceeds capture and an increase in storage will occur where capture exceeds Q. The time
scalesrequiredtoreachequilibrium,especiallyforlargeaquifersystems,canbeuptohundredsofyears[Bre-
dehoeftandYoung,1970]andwellbeyondourstudyperiodofJanuary2003toDecember2013.
dGW
ðR 1DR Þ2ðD 1DD Þ2Q5 (2)
0 0 0 0 dt
Groundwater sustainability, defined as the continued development of groundwater resources such that
negativeenvironmental,societal,oreconomicimpactsdonotoccur,requiresabalanceofwithdrawalsand
replenishmentovertime [Alley et al., 1999].Therefore, a stress studyis inherently a sustainabilitystudy to
understandthebalancebetweensupplyanddemand.SimplydefiningQasameasureofuseindependent
fromthe remainingcomponentsof equation(2)cannot fully characterize theimpact ofQ on thestateof
thesystemandtherefore,itssustainability.Instead,weusethetrendinsubsurfacestorageanomaliesover
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Figure1.StudyaquifersbycontinentbasedontheWHYMAPdelineationsoftheworld’sLargeAquiferSystems[WHYMAPandMargat,
2008].Thenumberrepresentstheaquiferidentificationnumberforeachaquifersystem.Theworld’slargestlakesandreservoirsarebased
ontheGlobalLakeandWetlandDatabaseLevel-1lakesandreservoirs[LehnerandDo€ll,2004].
ourstudyperiodtoquantifydGW/dtinequation(2)toholisticallyaccountforwithdrawals,capture,andchanges
inR andD duetonaturalfactorssuchasdrought.Forexample,anegativetrendindGW/dtindicatestherate
0 0
ofwithdrawals,representedasanegativevalueofQ,isgreaterthantherateofcapture,(DR -DD).Anegative
0 0
trend in dGW/dt can also indicate that D exceeds R during the study period due to natural variability (i.e.,
0 0
drought).
By categorizing characteristic stress regimes (Section 2.1) we can holistically assess the impact of ground-
wateruseonthestateofanaquifersystem.Understandingtheimpactofdepletionongroundwaterstor-
age is crucial for quantifying groundwater stress in a way that accounts for an aquifer’s response to
withdrawals and natural climate variability. Our results illustrate that stress will not occur in every region
where withdrawals exceed recharge, as is implied when groundwater withdrawal statistics are used to
defineuse.Instead,wefindthatstressoccursinthesystemswherewithdrawalsexceedcapturesuchthat
storagelossoccurs.
2.DataandMethods
Renewable groundwater stress (RGS) is computed for the 37 largest global aquifer systems in the World-
wideHydrogeologicalMappingandAssessmentProgram(WHYMAP)[WHYMAPandMargat,2008](Figure1
andTable1)forastudyperiodofJanuary2003toDecember2013.WHYMAPwascreatedin2000asajoint
projectbetweentheUnitedNationsEducational,Scientific,andCulturalOrganization(UNESCO),theCom-
mission for the Geological Map of the World (CGMW), the International Association of Hydro-geologists
(IAH),theInternationalAtomicEnergyAgency(IAEA)andtheGermanFederalInstituteforGeosciencesand
NaturalResources(BGR).TheWHYMAPnetworkservesasacentralrepositoryandhubforglobalground-
water data, information, and mapping with a goal of assisting regional, national, and international efforts
towardsustainablegroundwatermanagement.Assuch,theWHYMAPnetworkcontainsthebestavailable
globalaquiferinformation.Wedefineourstudyareaasthe37‘‘LargeAquiferSystemsoftheWorld’’[WHY-
MAP and Margat, 2008]. These systems represent the international consensus on the boundaries of the
world’smostproductivegroundwatersystemsthatcontainthemajorityoftheworld’saccessible ground-
watersupply[Margat,2007;MargatandvanderGun,2013].Additionally,theareaofeachoftheseaquifer
systemsisconsistentwiththespatialresolutionrequiredbyGRACEobservations(Section2.2.2).
First,weintroducecharacteristicstressregimesthatdefinefourtypesofstressthatcanoccurbasedonthe
sign of water use and availability (Section 2.1). Two methods to quantify use, the numerator presented in
equation(1),areintroducedbasedonspatiallydistributedwithdrawalstatistics(Section2.2.1)andthetrend
inGRACE-basedsubsurfacestorageanomalies(Section2.2.2).Modeledgroundwaterrechargeisintroduced
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as the definition of groundwater availability
Table1.StudyAquifersWiththeAquiferIdentificationNumber
(Section 2.3), the denominator in equation (1).
AquiferID AquiferName
The RGS ratio is computed based on equation
1 NubianAquiferSystem(NAS)
(1) (Section 2.4). Finally, anthropogenic biomes
2 NorthwesternSaharaAquiferSystem(NWSAS)
3 Murzuk-DjadoBasin areintroduced(Section2.5)toanalyzetheland-
4 Taoudeni-TanezrouftBasin use patterns that influence different stress
5 Senegalo-MauritanianBasin
regimesandseveritylevels.Simplificationsand
6 Iullemeden-IrhazerAquiferSystem
7 LakeChadBasin assumptions are made in our approach that
8 SuddBasin(UmmRuwabaAquifer) allow for a consistent method of assessment
9 Ogaden-JubaBasin
across 37 diverse aquifer systems. We utilize
10 CongoBasin
11 UpperKalahari-Cuvelai-UpperZambeziBasin remote sensing observations, described in Sec-
12 LowerKalahari-StamprietBasin tion2.2.2,andmodeloutputsincethequantity
13 KarooBasin
and quality of available in situ observations in
14 NorthernGreatPlainsAquifer
15 Cambro-OrdovicianAquiferSystem thestudyaquifersishighlyvariable.
16 CalifornianCentralValleyAquiferSystem
17 OgallalaAquifer(HighPlains) 2.1.CharacteristicStressRegimes
18 AtlanticandGulfCoastalPlainsAquifer
The Renewable Groundwater Stress (RGS) ratio
19 AmazonBasin
20 MaranhaoBasin ofgroundwaterusetogroundwateravailability
21 GuaraniAquiferSystem isusedtodefinegroundwaterstress,according
22 ArabianAquiferSystem
to equation (1) [Alcamo et al., 1997]. Water
23 IndusBasin
24 Ganges-BrahmaputraBasin stressindicatorsfollowingtheU.N.waterstress
25 WestSiberianBasin scale (Table 2) [UN/WMO/SEI, 1997] are based
26 TungussBasin
on traditional approaches where use in equa-
27 Angara-LenaBasin
28 YakutBasin tion(1)isnegativeandavailabilityestimatesas
29 NorthChinaAquiferSystem annual recharge in equation (1) are positive.
30 Song-LiaoBasin
Stress regimes, however, can theoretically
31 TarimBasin
32 ParisBasin exhibit four end-member behaviors similar to
33 RussianPlatformBasins those of Weiskel et al. [2007] (Figure 2):
34 NorthCaucasusBasin
Unstressed, Variable Stress, Human-dominated
35 PechoraBasin
36 GreatArtesianBasin Variable Stress and Overstressed. These end
37 CanningBasin members encompass the spectrum of out-
comes given positive (gaining) or negative
(depleting) estimates of use and positive
(recharging)ornegative(discharging)estimatesofannualrecharge.Thus,quitesimply,theratioinequation
(1)representsthepercentofrechargethatisusedtomeetwaterdemands.
IntheOverstressedcase,theRGSratioispositivesincebothrechargeandusearenegative.Thiscase,result-
ingfromacombinationoflargewithdrawalsandnegativerecharge,impliesgroundwaterminingoractive
depletion.Inshallowaquifers,negativeornegligiblerechargeislargelydrivenbygroundwatersupported
evapotranspiration,especiallyinsummermonthsandduringdryperiods[YehandEltahir,2005a,b;Yehand
Famiglietti,2009;Szilagyietal.,2013;Koiralaetal.,2014].Scanlonetal.[2003]foundthatinsemiaridtoarid
regions,thevadosezoneisonlyinfluencedbysurfaceclimateforcingstoadepthofabout3m.Capillary
rise, which we term negative recharge, beneath this depth is the dominant subsurface moisture flux
[Coudrain-Ribstein et al., 1998; Walvoord et al., 2002; De Vries and Simmers, 2002; Scanlon et al., 2003;
Walvoord and Scanlon, 2004]. Aquifer systems undergoing Overstressed conditions may trigger or exacer-
batelandsubsidence[GallowayandRiley,1999;Bawdenetal.,2001;KonikowandKendy,2005],ecosystem
habitat destruction [Stromberg et al., 1996;
Gleeson et al., 2012] and aquifer compaction
Table2.UnitedNationsRenewableStressScalea [Gallowayetal.,1998;KonikowandKendy,2005]
StressRatio StressLevel that limit future aquifer productivity and
0–0.1 Low rechargepotential.
0.1–0.2 Moderate
0.2–0.4 High The Variable Stress case follows the criticality
>0.4 Extreme ratio of previous stress studies [Alcamo et al.,
aThestressratiorepresentsthedimensionlessRenewable 1997;Vo€ro€smartyetal.,2000],whereuseisneg-
GroundwaterStressRatiousedinthisstudy. ative(withdrawals)andrechargeisenteringthe
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Figure2.Characteristicstressregimesthatencompassthepossiblebehaviorofstressgivenpositive(gaining)ornegative(extracting/depleting)usebehaviorandpositive(recharging)
ornegative(capillaryfluxes)groundwateravailability.Theschematicsrepresentintegratedbehavioracrossanaquifersystem.
system, resulting in a negative RGS ratio. There are four levels of Variable Stress according to the United
Nations(Table2).Consideraratiolessthanone.Therateofuseislessthanthenaturalrechargerate;how-
ever, small perturbations to the system can result in negative environmental impacts, for example, by
decreasing base flow and ultimately drying streams, marshes, and springs [Sophocleous, 1997; Bredehoeft,
1997;Faunt,2009].Aratiowithanabsolutevaluegreaterthanonerepresentsuseratesthatexceednatural
recharge rates and increases the rate of capture. This condition can create the potential for water quality
impactsifrechargeisinducedfromcontaminatedsources[Theis,1940].
Both the statistics-based method and the GRACE-based method to estimate use can result in the Over-
stressed and Variable Stress cases. Only the GRACE-based estimate can quantify the remaining Human-
dominatedVariableStressandUnstressedcases.Inthesecases,thestudyaquifershavepositivetrendsin
subsurface storageanomaliesandaretherefore‘‘gaining.’’WeconsidertheHuman-Dominatedcaseto be
theresultofapositivetrendfromGRACEandnegativerecharge.Naturalbehaviorofthesesystemswould
be a loss of groundwater through capillary flux to the root zone [Coudrain-Ribstein et al., 1998; Walvoord
etal.,2002;DeVriesandSimmers,2002;Scanlonetal.,2003;WalvoordandScanlon,2004;Loetal.,2008]or
RICHEYETAL. QUANTIFYINGRENEWABLEGROUNDWATERSTRESSWITHGRACE 5221
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bydirectevapotranspiration[YehandEltahir,2005a,b;YehandFamiglietti,2009;Szilagyietal.,2013;Koirala
et al., 2014]. A combination of induced capture and human practices may be contributing to the gaining
trend in groundwater storage, for example, from artificial recharge using surface water diversions in irri-
gated areas. The Unstressed case has a positive trend in groundwater storage anomalies and positive
recharge.Thiscaseisonlyconsideredunstressedfromawaterquantityperspective.Inducedcapturemay
drawadditionalrechargefromsourcesthatcouldnegativelyimpactwaterquality.
2.2.WaterUse
2.2.1.CompiledWithdrawalStatistics
WefollowmethodssimilartoVo€ro€smartyetal.[2000]andWadaetal.[2010]tospatiallydistributeavailable
groundwater withdrawal statistics into the study aquifers, representing Q (equation (2)). First, we compile
national groundwater withdrawal statistics from multiple sources in cubic kilometers per year [FAO, 2003;
IGRAC,2004;MargatandvanderGun,2013].Thestatisticsrepresentgroundwaterwithdrawalsacrossallsec-
tors of water use (agriculture, domestic, industrial) and provide percentages of groundwater use for each
sector.Weusethesepercentagestodeterminetherateofagricultural,domestic,andindustrialwithdrawals
asafunctionofthenationalwithdrawalrate.Themajorityofthesepercentagesarebasedsolelyonsectoral
withdrawals as a function of total groundwater withdrawals, although percentages based on total with-
drawalsareusedwhengroundwaterpercentagesareunavailable.
Nationallevelagriculturalstatisticsaredistributedspatiallybasedonthe0.5830.58gridded‘‘WaterWith-
drawals for Irrigation’’ data set [GWSP Digital Water Atlas, 2008a], which provides the theoretical water
demand for irrigated crops as a function of climate. The single national agricultural statistic is distributed
basedonthepercentofnationalirrigationdemandineachgridcellbyassuminggroundwaterwithdrawals
occursincloseproximityto whereitis neededto meetdemand[Wadaetal.,2010].Similarly, thesumof
nationaldomesticandindustrialwithdrawalstatisticsisdistributedbygriddedpopulationdensity,following
Vo€ro€smarty et al. [2000], based on the 0.58 x 0.58 gridded ‘‘Population (Total)’’ [GWSP Digital Water Atlas,
2008b]dataset.Theresultingspatiallydistributedagricultural,domestic,andindustrialwithdrawalratesare
summed within each grid cell and scaled up to 18 x 18 spatial resolution, to match the resolution of the
remote sensing observations. Basin-averaged groundwater withdrawals are computed for the 37 study
aquifersasthestatistics-basedestimateofuse.
2.2.2.GRACEObservations
RemotesensingobservationsfromtheGravityRecoveryandClimateExperiment(GRACE)satellitemission
[Tapleyetal.,2004]areusedtoquantifyanovelestimateofgroundwateruse,dGW/dtinequation(2).The
GRACEsatellites,ajointmissionbetweentheNationalAeronauticsandSpaceAdministration(NASA)inthe
United States and the Deutsche Forschungsanstalt fu€r Luft und Raumfahrt (DLR) in Germany, measure
monthlychangesintotalterrestrialwaterstoragebyconvertingobservedgravityanomaliesintochanges
ofequivalentwaterheight[RodellandFamiglietti,1999;Syedetal.,2008;Ramillienetal.,2008].
TheCenterforSpaceResearchattheUniversityofTexasatAustinprovidedthe132monthsofGRACEgrav-
itycoefficientsfromRelease-05datausedinthisstudy.Gravityanomaliesforthistimeperiod(January2003
to December 2013) underwent processing to obtain an estimate of the average terrestrial water storage
anomaliesforeachofthe37studyaquifers[SwensonandWahr,2002;Wahretal.,2006;SwensonandWahr,
2006]. Aquifer-specific scaling factors were used to account for the lost signal power from truncating the
gravity coefficients (at degree and order 60) and filtering for unbiased estimates of mass change in each
aquifersystem[VelicognaandWahr,2006].
DS 5DðSW1SWE1SM1GWÞ (3)
N1A N1A
DSUB 5DS 2DðSW1SWEÞ (4)
N1A N1A N
Thetotalwaterstoragechangescanbepartitionedintocomponentsresultingfromnaturalchange(N)or
anthropogenicchange(A)accordingtoequation(3)whereSisthetotalterrestrialwaterstorageanomalies
fromGRACE,SWissurfacewater,SWEissnowwaterequivalent,SMissoilmoisture,andGWisgroundwater.
IndividualstoragecomponentscanbeisolatedfromthetotalGRACEsignalwithsupplementaldatasetsto
represent the remaining storage terms [Rodell and Famiglietti, 2002; Swenson et al., 2006; Yeh et al., 2006;
Strassberg et al., 2007, 2009; Rodell et al., 2004b, 2007, 2009; Swenson et al., 2008; Famiglietti et al., 2011;
RICHEYETAL. QUANTIFYINGRENEWABLEGROUNDWATERSTRESSWITHGRACE 5222
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Scanlonetal.,2012;Castleetal.,2014].Weisolatesubsurfaceanomalies(SUB)ascombinedanomaliesinsoil
moisture(SM)andgroundwater(GW)inequation(4).
Modeloutputorinsituobservationsarerequiredtoisolatechangesinastoragecomponentfromthetotal
GRACE terrestrial water storage anomalies. We use monthly output from three models within the NASA
Global Land Data Assimilation System (GLDAS) modeling system including Noah [Chen et al., 1996; Koren
etal.,1999],VariableInfiltrationCapacity(VIC)[Liangetal.,1994],andCommunityLandModel2.0(CLM2.0)
[Daietal.,2003]tocomputemonthlymeangriddedoutputat18x18spatialresolutionforcanopysurface
water (CAN) and SWE. Surface water storage in lakes, reservoirs, and river channels is not included in the
GLDAS modeling system [Rodell et al., 2004a]. We estimate SW as the sum of CAN from the three-model
GLDASensembleandroutedsurfacewaterdischarges(RIV)fromofflineoutputfromCLM4.0[Olesonetal.,
2010].TheCLM4.0modelrunisdescribedinSection2.3.Themodel-basedstorageanomaliesofSWEand
SW are subtracted from the GRACE storage anomalies to estimate monthly GRACE-derived subsurface
anomaliesforeachaquifer.
Errorinthesubsurfaceanomaliesiscomputedaccordingtoequation(5)foreachmonth(i),assuminginde-
pendencebetweencomponenterrors.Aquiferspecificsatellitemeasurementandleakageerrorfromproc-
essingthegravityanomaliesiscomputedfollowingWahretal.[2006]toestimateerrorinthetotalGRACE
signal.VarianceofSWEandCANwasdeterminedusingthethree-modelensemble,whichweassumerepre-
sentstheuncertaintyinducedbytheestimateerrorandmodelstructuralerror.TheU.S.GeologicalSurvey
errorsforhydrologicmeasurementsrangefromexcellent(5%error)tofair(15%error)[U.S.GeologicalGeo-
logicalSurvey(USGS),2014];therefore,forourevaluation,weassumemeasurementerrorof50%inrouted
discharge to represent a conservative uncertainty in GRACE subsurface variability. It is the assumed the
errors in equation (5) are independent. Area-weighted basin averages of SWE and SW are computed for
each of the study aquifers to account for latitudinal differences in gridded area. The temporal mean is
removedfromthebasinaveragestocomputeanomaliesinSWEandSW.
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
r 5 r2 1r2 1r2 1r2 (5)
DSUB;i S;i SWE;i CAN;i RIV;i
Wearguethattheanthropogenicimpactsontotalwaterstorageanomaliesinthestudyaquifersaredomi-
natedbysubsurfacevariations,particularlyfromgroundwater,astheseaquiferscontainthemajorityofpro-
ductive and available supply for groundwater use [Margat and van der Gun, 2013]. Therefore,
anthropogenicchangesinsurfacewaterandsnowwaterarenegligibleatthestudy’sspatialscale.Natural
waterstocksorbuiltinfrastructurearenecessarytocapturewatersuppliesforhumanuse[Vo€ro€smartyetal.,
2000], for example, lakes or reservoirs, particularly for surface water and snow meltwater. However, only
0.5% of the study aquifers’ land area is overlain with lakes and reservoirs larger than 50 km2 [Richey and
Famiglietti, 2012], which is significantly smaller than the 18 spatial resolution of this study. We therefore
assumenegligibleanthropogenicinfluencesofsurfacewaterandsnowinthestudyaquifersascompared
togroundwater.
1
Y5b 1b wx1E where w5 (6)
i 0 1 i i i i r2
DSUB;i
Themajorityofsoilwaterstoragetrendsarenotsignificantglobally[SheffieldandWood,2008;Dorigoetal.,
2012].Therefore,weuseaconservativeestimateofgroundwatertrendsbyattributingobservedsubsurface
trendssolelytogroundwaterstorage.Weconsiderthegroundwatertrendtoberepresentativeofthenet
fluxofwaterstorageresultingfromgroundwateruse(DGW ),includingtheaquiferresponsetopumping
N1A
aspredictedbyTheis[1940],andnaturalclimaticvariability.Annualtrendmagnitudes,DGW ,wereesti-
trend
matedusingtheweightedregressioninequation(6)toquantifythechangeingroundwaterstoragefrom
equation(2).Theweights,w,areafunctionofthevarianceinthemonthlyestimatesofsubsurfacestorage
i
anomalies. Aquifers with a negative coefficient were considered to be depleting in aquifer storage while
positive coefficients were considered to be recharging systems. Here we evaluate only the magnitude of
trendswithoutregardtotrendsignificance.
2.3.WaterAvailability:GroundwaterRecharge
Renewablegroundwateravailabilityisdefinedasmeanannualgroundwaterrecharge,followingDo€ll[2009]
andWadaetal.[2010].Themajorityoflandsurfaceparameterizationsdonothaveanexplicitrepresentation
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10.1002/2015WR017349
of groundwater and are therefore unable to capture both positive and negative recharge fluxes [Yeh and
Famiglietti,2009].Instead,groundwaterrechargeisfrequentlyestimatedasmodeldrainagefromthebottom
ofasoilcolumn[e.g.,Rodelletal.,2004a]orastheresidualofprecipitationandevapotranspiration[e.g.,Weis-
keletal.,2007].Theseapproachesassumethefluxisalwayspositive(downward)andthataveragerechargeis
approximately equal to base flow. These assumptions are not always true, particularly in semiarid and arid
regions where capillary fluxes can be the dominant subsurface flux as opposed to downward recharge
[DeVriesandSimmers,2002].Assumingrechargeisalwayspositivemayfalselyrepresentthelevelofstressin
aregion.
DirectmodeloutputfromtheCommunityLandModelversion4.0[Olesonetal.,2010]isusedtoestimate
naturalrecharge, R ,in equation (2). CLM4.0 is thelandsurface model usedwithin theCommunity Earth
0
SystemModel(CESM)[Olesonetal.,2010].CLM4.0isoneofthefewlandsurfacemodelsthatincludesan
unconfinedaquiferlayercoupledtothebottomsoillayerandisthereforeabletocapturebothpositiveand
negative recharge. Recharge is computed as the vertical flux between the aquifer and bottom soil layer,
such that positive recharge flows downward as gravity drainage and negative recharge flows upward by
capillaryfluxes[Olesonetal.,2008;Loetal.,2008].
CLM 4.0 was run in an offline simulation driven by atmospheric forcing data including precipitation, near
surfaceairtemperature,solarradiation,specifichumidity,windspeed,andairpressure.Threehourlyforcing
datafromGLDASVersion-1[Rodelletal.,2004a]wereusedtodrivethemodelata1htimestep,whichis
theninterpolatedtomonthlymodeloutput.Themodelwasrunat0.98x1.258spatialresolutionandlinearly
interpolated to 18 x 18. Basin averaged recharge is computed for each study aquifer as an area-weighted
averageacrossallgridcells.Themeanannualrechargeiscomputedfromthemonthlyvaluesforeachstudy
aquiferforourstudyperiodofJanuary2003toDecember2013.ThespatialdistributionofmodeledCLM4.0
recharge results are comparable to previous modeled recharge estimates using the PCR-GLOBWB global
hydrologicalmodel[Do€ll,2009;Wadaetal.,2010].
2.4.GroundwaterStress
2.4.1.RenewableStress:CriticalityRatio
Following the traditional water stress approach [Alcamo et al., 1997; UN/WMO/SEI, 1997; Vo€ro€smarty et al.,
2000; Oki and Kanae, 2006; Do€ll, 2009], we define Renewable Groundwater Stress (RGS) as the ratio of
groundwaterusetorenewablegroundwateravailabilityinequation(1).Thisdimensionlessratiorepresents
thepercentofrenewablewaterbeingusedtomeethumanwaterdemand.
Mean annual recharge, R , from Section 2.3 is used to calculate renewable groundwater availability. It has
0
been repeatedly cited that recharge cannot be used to define renewable available groundwater and that
onlyapercentofrecharge(lessthanorequaltotherateofcapture)canbeconsideredavailableforsustain-
ableuse[Bredehoeft,1997;Sophocleous,1997;Bredehoeft,2002;Zhou,2009].Thus,thisstudyusessimulated
recharge to represent the maximum available natural renewable groundwater and is therefore the most
optimisticestimateofavailablesuppliesandresultingstress.Additionally,systemswithnegativemodeled
meanannualrechargeareconsideredtolackrenewablesupplies.Inthiscase,thereisnorechargeavailable
toreplenishthesystemandthelevelofstressisdeterminedbythemagnitudeofusealone.
Groundwater use is quantified by groundwater withdrawal statistics, Q , in equation (7), as described in
stat
Section2.2.1andthetrendinGRACE-derivedsubsurfaceanomaliesinequation(8),DGW ,asdescribed
trend
inSection2.2.2,toassessthedifferenceinstressbetweentheestimationschemes.
Q
RGS 5 stat (7)
stat R
0
DGW
RGS 5 trend (8)
GRACE R
0
2.5.ApproximatingAnthropogenicInfluences
Weintroduceanadditionaldatasettobetterunderstandthedrivingfactorsbehinddifferinglevelsofuse
andstress.Theworldmapofanthropogenicbiomes[EllisandRamankutty,2008],isusedtodeterminethe
dominantlandusetypebyaccountingforbothlanduse/landcovertypesandthedegreetowhicharegion
is inhabited. There are six broad characteristic biome types including Dense Settlements, Villages,
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Figure3.Anthropogenicbiometypeswithinthestudyaquifers.Biometypesaregriddedat0.08338spatialresolutionfromEllisand
Ramankutty[2008].
Croplands,Rangeland,Forested,andWildlands,withatotalof21subcategorieswithinthesetypes.Thesub-
categoriesbreakdowntheanthropogenicbiometypesintodifferentlevelsofremoteandpopulatedareas
that are dominated by rain or irrigated area (Figure 3). The six most dominant anthropogenic biome
typesareassignedforeachstudyaquiferbasedonthepercentofaquiferareacoveredbyeachbiometype
(TableA1inAppendixA).
3.Results
3.1.GroundwaterUse:StatisticsandGRACE
AcomparisonbetweenGRACE-depletionmethods(Figure4)andstatistics-basedmethods(Figure5)showhow
theGRACE-basedapproachincorporatestemporalvariationsinuseoverthestudyperiodwhereasthestatistics
approach quantifies use as a static value in time. Figure 4 illustrates the GRACE-depletion method that uses
model output to isolate groundwater storage changes from the GRACE observations of total terrestrial water
storage anomalies. The figure presents the time series components of the water budget for the Ganges-
Brahmaputra Basin (Aquifer #24, ‘‘Ganges’’). By comparing the modeled storage anomalies to the GRACE-
derived groundwater anomalies, it is clear that changes in groundwater storage are dominating the GRACE
observationsofdecliningterrestrialwaterstorage.Figure5presentsthestatistics-basedmethodtoestimateuse
asgroundwaterwithdrawalstatisticsthatarespatiallydistributedbypopulationdensityandtheoreticalwater
withdrawals for irrigation. The influence of geopolitical boundaries on the method is clear as national level
groundwater withdrawals can differ between neighboring countries. In the United States, the national with-
drawalrateis111.7cubickilometersperyear(km3/yr)versusCanada’swithdrawalrateof1.87km3/yr[Margat
andvanderGun,2013].Table3summarizestheratesofusebasedonGRACEandthestatistics.
Figure6illustratesthebasin-averagesofgroundwater useas determinedbythegroundwaterwithdrawal
statistics (Figure 6a) and the GRACE-derived trend in groundwater storage anomalies (Figure 6b) within
eachstudyaquifer.ThedifferencesbetweenFigure6aandFigure6bresultsolelyfromthedefinitionofuse
inequation(1).InFigure6a,usestatisticsareconsistentlynegativeandthusdonotrepresentthefullvari-
ability in stress regimes as illustrated in Figure 2. The GRACE-derived trend captures the dynamics of
groundwaterusebyintegratingthehumanandnaturalimpactsofuseongroundwaterstorage,including
changesinrechargeanddischargeregimesandwatermanagementpractices.Asaresultoftheintegrated
storagechanges,aquiferscanhaveeitherapositiveornegativetrendingroundwaterstorageanomaliesas
observed from GRACE. There are 16 study aquifers that have positive subsurface trends from GRACE-
deriveduseand21thatarenegative.
There are five aquifers with negative rates of use where the statistics-based withdrawal rate exceeds the
GRACE-based estimates. These include the Ganges, the Indus Basin (Aquifer #23, ‘‘Indus’’), the Californian
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