AdvancesinWaterResources124(2019)41–52
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Advances in Water Resources
journalhomepage:www.elsevier.com/locate/advwatres
The spatio-temporal variability of groundwater storage in the Amazon River Basin
F. Frappart
b,∗, F. Papa
b,c, A. Güntner
d, J. Tomasella
e, J. Pfeffer
f, G. Ramillien
a, T. Emilio
g,l, J. Schietti
h, L. Seoane
a, J. da Silva Carvalho
i, D. Medeiros Moreira
j, M.-P. Bonnet
k, F. Seyler
kaGéosciences Environnement Toulouse (GET), UMR 5563, CNRS/IRD/UPS, Observatoire Midi-Pyrénées (OMP), 14 Avenue Edouard Belin, 31400 Toulouse, France
bLaboratoire d’Etudes en Géophysique et Océanographie Spatiales (LEGOS), UMR 5566, CNRS/IRD/UPS, Observatoire Midi-Pyrénées (OMP), 14 Avenue Edouard Belin, 31400 Toulouse, France
cIFCWS, IRD-IISc Joint International Laboratory, Indian Institute of Science, 560012 Bangalore, India
dDeutsches GeoForschungsZentrum (GFZ), Telegrafenberg, Potsdam, Germany
eCentro Nacional de Monitoramento e Alerta de Desastres Naturais - CEMADEN. Rodovia Presidente Dutra km 39, 12630-000 Cachoeira Paulista, SP, Brazil
fResearch School of Earth Sciences, Australian National University, Canberra, Australian Capital Territory, Australia
gComparative Plant & Fungal Biology, Royal Botanic Gardens, Kew, Richmond, Surrey, UK
hInstituto Nacional de Pesquisas da Amazônia (INPA), Manaus, AM, Brazil
iUniversidade Federal do Manaus (UFAM), Manaus, AM, Brazil
jCPRM/Geological Survey of Brazil, Rio de Janeiro, Brazil
kIRD, UMR Espace-Dev, Maison de la télédétection, 500 rue JF Breton, 34093 Montpellier Cedex 5, France
lDepartment of Plant Biology, Institute of Biology, University of Campinas, CEP 13083-970, Campinas, SP, Brazil
a r t i c le i n f o
Keywords:
Groundwater Amazon Multisatellite Hydrological models
a b s t r a ct
InlargeTropicalRiverbasinssuchastheAmazon,groundwaterplaysamajorroleinthewaterandecological cycleswithlargeinfluencesontherainforestecosystemsandclimatevariability.However,duetothelackof monitoringnetworks,Amazongroundwaterstorageanditsvariabilityremainpoorlyknown.Here,weprovide anunprecedenteddirectestimateofthespatio-temporalvariationsoftheanomalyofgroundwaterstorageover theperiodJanuary2003-September2010intheAmazonBasinbydecomposingthetotalterrestrialwaterstorage measuredbytheGravityRecoveryandClimateExperiment(GRACE)missionintotheindividualcontributions ofotherhydrologicalreservoirs,usingmulti-satellitedataforthesurfacewatersandfloodplainsandmodels outputsforthesoilmoisture.Weshowthattheseasonalvariationsofgroundwaterstoragerepresentbetween20 and35%oftheterrestrialwaterstorageseasonalvolumevariationsoftheAmazon.Largerseasonalamplitudes ofgroundwaterstorage(>450mm)arefoundintheAlterdoChãoandIçaaquifersinthecentralpartofthe AmazonBasin.Anomaliesofgroundwaterstorageexhibitastronginterannualvariability(STDreaching120mm alongthecentralcorridor)duringthestudyperiodinresponsetohydrologicvariabilityandclimaticeventssuch astheextremedroughtthatoccurredin2005.
1. Introduction
Beingthelargest reservoiroffreshwateron Earth,accountingfor morethan30%(i.e.,8,000,000to10,000,000km3)ofglobalfreshwater resources,groundwaterplaysakeyroleintheterrestrialhydrological cycleandinsustainingecosystems(Tayloretal,2013).Itis alsothe majorresourceoffreshwatersupplyfor40%oftheworldpopulation and50%oftheworldfoodproduction(SeilerandGat,2007;Margat andvanderGun,2013),becoming astrategicimportanceforglobal waterandfoodsecurityunderglobalchange.IntheAmazonbasin,the world’slargestriversystem,groundwaterstorageplaysacentralpartin
∗Correspondingauthorat:GéosciencesEnvironnementToulouse(GET),UMR5563,CNRS/IRD/UPS,ObservatoireMidi-Pyrénées(OMP),14AvenueEdouard Belin,31400Toulouse,France.
E-mailaddress:[email protected](F.Frappart).
theecologicalcyclesanddirectlyinfluencestherainforestecosystemsas wellasclimatevariability(Pokhreletal.,2013).Strongmemoryeffects ofAmazongroundwatersystemevenconveyclimateanomaliesoverthe regionforseveralyears(Tomasellaetal.,2008;Pfefferetal.,2014).
However, groundwater storage andits variationsare stillpoorly knownatlargetoglobalscalesduetothelimitedextentandcapability ofcurrentmonitoringnetworksandthedifficultytosurveylargeandre- moteareas.IntheAmazon,moststudiesonhydrogeologyofgroundwa- terreservoirsarecarriedoutonlyatlocal(DoNascimentoetal.,2008;
Tomasellaetal.,2008)toregionalscales(PimentelandHamza,2012;
Pfefferetal.,2014)limitingourunderstandingoftheroleofgroundwa-
https://doi.org/10.1016/j.advwatres.2018.12.005
Received19October2016;Receivedinrevisedform7December2018;Accepted12December2018 Availableonline14December2018
0309-1708/© 2018PublishedbyElsevierLtd.
Fig.1. MapoftheAmazonbasinwithitsmajortributarieswithinSouthAmerica.Thelocationsofthewellsusedforvalidationarerepresentedusingbluedots (a).Contribution(%)fromsurface,soilmoistureandgroundwaterreservoirstoTotalWaterStorageChanges(TWSC)fromGRACE(b).(Forinterpretationofthe referencestocolorinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)
terintheterrestrialwatercycle.Onlyrecentlytheinfluenceofground- wateronterrestrialwaterstorageintheAmazonBasinwascharacter- izedin anintegrativemodel-basedstudyof thegroundwaterforthe entirebasin(Miguez-MachoandFan,2012;Pokhreletal.,2013).
Inthis context, remote sensing techniquesoffer a unique oppor- tunityforhydrologicinvestigations(Alsdorfetal., 2007; Rastet al., 2014) and recentadvances have demonstrated that waterresources can be monitoredreliably fromspace. Inparticular, theGravityRe- coveryAndClimateExperiment(GRACE)mission,launchedin 2002, detectstinychangesintheEarth’sgravityfield,whichcanberelated tospatio-temporal variationsof terrestrial water storage at monthly orsub-monthly time-scales(Tapleyetal.,2004). AnalysisofGRACE datashowedthatthefrequent extremeclimatic eventsthatoccurred intheAmazonBasinsincemid-2000(e.g.,droughtsof2005and2010 droughts,floodof2009)hadastrongimpactonterrestrialwaterstor- age(Chenetal.,2009;2010;Frappartetal.,2013a,b;Espinozaetal., 2013).Iftheirsignaturesonsurfacewaterstoragewerewidelydescribed (e.g.,Marengoetal.,2008,2011;Tomasellaetal.,2011;Frappartetal., 2012),theirconsequencesongroundwaterremainpoorlyknown.Vari- ationsingroundwaterstoragecanbeseparatedfromtheterrestrialwa- terstorageanomalies measuredbyGRACE(seeFrappart andRamil- lien,2018forareviewontheuseofGRACEforgroundwatermonitor- ing)usingexternalinformationontheotherhydrologicalstores(snow andice,soilmoistureandsurfacewater)frominsitu(Yehetal.,2006) andsatellite(Tianet al.,2017) observations,modeloutputs(Roddel etal.,2009;JinandFeng,2013),orboth(Leblancetal.,2009).Very fewstudies(e.g.,Frappartetal.,2011;Shamsudduhaetal.,2012)have beenconductedyetinlargeriverbasinscharacterizedbyextensivewet- landsandfloodplains,duetothelackofreliableandtimelyinformation onthespatio-temporaldistributionoftheextentandstorageofsurface waters.Inthesebasins,surfacewaterstoredinfloodplainsrepresentsa largepartoftheterrestrialwaterstorage(e.g.,∼50%ofthewaterstored and15–20%ofthewaterthatflowedoutoftheAmazonBasinbetween 2003–2007wasstoredintheAmazonfloodplains(Frappartetal.,2012;
Papaetal.,2013).Inspiteofrecentadvances,thecapabilityofglobal hydrologicalmodelstosimulatewaterstorageinlargefloodplainsare stilllimitedduetothelackofavailabilityandaccuracyofdataasthe digitalelevationmodelandcomputationalexpense(LehnerandGrill, 2013;Sampsonetal.,2015).Toovercomethislimitation,ourapproach takesintoaccountthewaterstoredinthesurfacereservoirusingwater levelmapsobtainedbycombiningsatellitealtimetryandimagery.Us- ingmulti-satelliteobservationsalongwithhydrologicalmodeling,we estimatethecontributionofeachhydrologicalreservoiroftheAmazon basin(surfacewater,soilmoistureandgroundwater)toterrestrialwater
storagevariationsandmap,forthefirsttime,theseasonalchangesof freshwaterstoredintheAmazonaquifersovertheperiodJanuary2003 – September2010.Weshowthattheexceptionaldroughtof2005had alargeimpactongroundwaterstorage.
2. Datasets
Toestimategroundwater(GW)storageanomalies,thecontribution ofsurfaceandsoilmoisturestoragesareremovedfromtheterrestrial waterstorage(seeSection3).Thedatausedforthispurposearemul- tisatellitesurfacewater(SW)levelsmaps,soilmoisture(SM)fromWa- terGAPGlobalHydrologyModel,terrestrialwaterstorage(TWS)from GRACElandwatersolutionsprocessedwitharegionalapproach.Ancil- larydata(i.e.,insitugroundwaterlevelsandgroundwaterstoragefrom twoglobalhydrologicalmodels)werealsousedforcomparisonandval- idationoftheresults.
2.1. Themultisatellitesurfacewaterlevelandstoragemaps
MapsofwaterlevelsoverthefloodplainsoftheAmazonBasinwere obtained bycombiningobservationsfroma multisatelliteinundation datasetandaltimetry-basedwaterlevelsatmonthlytime-scalefromJan- uary2003toSeptember2010.Theapproachusedisthesameastheone presented in(Frappart etal.,2012)butusingamonthlyclimatology forthesurfacewaterextentfromtheGlobalInundationExtentMulti- Satellite(GIEMS)asthisdatasetisavailableonlyfromJanuary1993 toDecember2007(Papaetal.,2010;Prigentetal.,2007,2012).Wa- terlevelsfrom984ENVISATRA-2altimetrystationsmadeavailableby Hydroweb(http://hydroweb.theia-land.fr)(SantosdaSilvaetal.,2010;
Normandinetal.,2018)wereinterpolatedwithrespecttotheinverse of thedistancetothegridpointoverinundated surfacesfromGIEMS monthlyclimatology.Eachmonthlymapofsurfacewaterlevelshasa spatialresolutionof0.25° andisreferencedtoEGM2008geoid.Time variationsofSWvolumeovertheentireAmazonBasinfor1)theesti- matesfromFrappartetal.(2012)usingmonthlyGIEMSinundationex- tentover2003–2007and534altimetry-basedwaterlevels(calledC1), 2)theestimatesobtainedusingmonthlyGIEMSinundationextentover 2003–2007and984altimetry-basedwaterlevelsfromHydroweb(C2) and3)theestimatesobtainedusingmonthlyGIEMSclimatologyofin- undationextentand984altimetry-basedwaterlevelsfromHydroweb (C3)arepresentedinFig.S1.Theyshow verysimilarbehavior,with biasesof−55and−32km3,RMSEof23and31km3,(tobecompared withanannualamplitudeof>1000km3),andcorrelationcoefficients
F. Frappart, F. Papa and A. Güntner et al. Advances in Water Resources 124 (2019) 41–52
Fig.2. TimevariationsofTotalWaterStorage(black)fromGRACEregionalsolutions(Ramillienetal.,2012),SurfaceWater(blue)frommultisatelliteobservations (Frappartetal.,2012),SoilMoisture(green)fromWGHM,Groundwater(red)whenthecontributionofSWandSMareremovedfromTWSfortheAmazon(a) andsomeofitsmajortributaries:downstreamMadeira(b),Negro(c)andTapajos(d).Over2003–2010(leftpanel)andintermsofannualcycle(rightpanel).(For interpretationofthereferencestocolorinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)
of0.997and0.998foundbetweenC2andC1andC2andC3respec- tively.TheseresultsconfirmthattheuseofGIEMSmonthlyclimatology inorder toretrievetime-seriesofSW volumeover thewholeperiod ofavailabilityoftheENVISATdataprovidesacceptableestimates.The errorontheseestimatesislowerthan17%(seesupplementaryinfor- mationfordetailson theerror estimates).Amapof minimumwater levelswasestimatedfortheentireobservationperiodusingahypso- metricapproachtotakeintoaccountthedifferenceofaltitudebetween theriverandthefloodplain(seeFrappartetal.,2012formoredetails).
ThisdatasetismadeavailablebyCentredeTopographiedesOcéanset del’Hydrosphère(CTOH):http://ctoh.legos.obs-mip.fr.
2.2. WGHMandISBA-TRIPhydrologicalmodeloutputs
TheWaterGAPGlobalHydrologyModel(WGHM)isoneofthemost commonlyusedwaterbalancemodelfortheassessmentofwaterre- sourcesandwaterbalancesinlargeriverbasins.WGHMaccountsforall maincontinentalwaterstoragecomponentsincludinggroundwater,soil moisture,snow,canopystorage,andsurfacewatersinlakes,rivers,wet- lands,andreservoirs.Inthisstudy,WGHMintheversionSTANDARD withinWaterGAP2.2asdescribedinMüllerSchmiedetal.(2014)was applied.Formodelcalibrationagainstobservedriverdischargetimese- ries,1319gaugingstationsworldwide,madeavailablebyGlobalRunoff Data Centre(GRDC) in Koblenz, Germany (http://grdc.bafg.de), are
Fig3. Anomalyofgroundwaterstoragefromwellmeasurements(green),the decompositionmethod(blue),WGHM(red)andISBA-TRIP(black)modelout- putsatAsucatchment(NegroRiver).(Forinterpretationofthereferencesto colorinthisfigurelegend,thereaderisreferredtothewebversionofthisarti- cle.)
usedin WGHM.Simulated waterstorageof SMandGWis provided with0.5° ofspatialresolutionatdailyormonthlytime-scales.Inthis study,weusethemonthlySMandGWoutputsfromWGHMover2003–
2010forestimatingtheGWcomponentandcomparisonpurposerespec- tively.Forevaluationpurposes,wealsousedthesimulatedstorageof theSMandGWreservoirsfromtheISBA-TRIPmodel(Alkamaetal., 2010;Decharmeetal.,2010).
2.3. GRACE-derivedlandwatermasssolutions
TheGravity RecoveryAndClimateExperiment(GRACE)mission, launchedinMarch2002,providesmeasurementsofthespatio-temporal changes inEarth’s gravity field(Tapleyetal., 2004).Severalrecent studieshaveshownthatGRACEdataoverthecontinentscanbeused toderive the monthly changes of thetotal land water storage with anaccuracyof∼1.5cmofequivalentwaterthicknesswhenaveraged oversurfacesofafewhundredsquare-kilometres(seeFrappartetal., 2016forarecentreview).Inthisstudy,weusedmapsofequivalentwa- terheightderivedfromGRACEmeasurementsusingaregionalapproach (Ramillienetal.,2011,2012).Thisnewstrategyisbasedontheoptimal localizationinspace,insteadofthebestlocalizationinspatialfrequency, andleadstheoreticallytobetterspatiallocalizationandresolution(see FreedenandSchreiner,2008).Ramillienetal.(2012)demonstratedthat thisregionalmethodoffersareductionofnorth-southstripingdueto boththedistributionofGRACEsatellitetracksandthetemporalaliasing ofcorrectingmodelsthatarepresentintheglobalGRACEsolutionsover SouthAmerica(Frappartetal.,2013a),Australia(Seoaneetal.,2013), andAfrica(Ramillienetal.,2014).Accordingtothesethreelatterstud- ies,regionalmapspresentmorerealisticspatialandtemporalpatterns thantheglobalsolutions whencomparedtoindependentdatasetsof rainfall,waterlevelsanddischarges,wetlandslocations.
2.4. Masksofthesub-basin
Masksat0.25° ofspatialresolutionofthelargestsub-basinsofthe Amazon werebuiltusing thewatershed delineation forthe Amazon sub-basinsystemsbasedonGTOPO30DEMandadrainagenetworkex- tractedfromJERSSARimagesfromSeyleretal.,(2009).TheAmazon drainagesystemwasdecomposed into8largedrainage areas(larger than400,000km2tobecompatiblewiththelowspatialresolutionof theGRACEdata,seeFrappartetal.,2013b):upstreamSolimõesencom- passingUcayaliandMarañonflowingfromthesouth,andtheJapura andtheIçaflowingfromnorthwest, downstreamSolimões,upstream Madeira,downstreamMadeira,Negro,downstreamAmazon,Tapajos, andXingubasins.
2.5. Insitugroundwaterlevels
Groundwaterlevelsaremonitoredinsituusingwells.Intheregion northofManaus,PVC15cmdiameterdipwellswereinstalledatdepth varyingfrom30to50m,asdescribedbyTomasellaetal.,(2008)and Carvalho(2012).AlongthePurus–Madeira transect,waterlevelwere monitored usingPVC tubes (3cmdiameter)installedat amaximum depthof7m.Allwellsweremonitoredmanually.Groundwaterlevels wererecordedinsitufromJanuary2004toNovember2005andfrom August2010toMay2011atAsucatchment,andfromSeptember2011 toDecember2012attheothermeasurementsites.Theconversionfrom welldepthstoGWvariationsisperformedusingspecificyieldvalues (Sy)of0.17intheAsucatchment(Tomasellaetal.,2008)andof0.1 elsewhere.Theirlocations,altitudeandtheSyaregiveninTableS1.
2.6. Altimetry-basedgroundwatertablemaps
Theannualfluctuationsofthegroundwaterbase-level(GWBL)were derivedfromtheanalysisofENVISAT-satelliteradaraltimetrymeasure- mentsacquiredfromlate2002tomid-2009acrossthecentralAmazon basin(Pfefferetal.,2014).TheGWBListheminimumheightreached eachyearbytheGWtableduringlowwaterperiods,whentheGWta- bleisconnectedtothewaterlevelsinrivers,lakesandfloodplains(Fig.
1fromPfefferetal.,2014).TheGWBLisobtainedthroughtheinter- polationof theannualminimaidentifiedintheENVISATtime-series extracted from491 VSlocated alongrivers,lakesandfloodplainsof thecentralAmazon [Pfefferetal., 2014].TheGWBL mapsacquired from 2003to2008werevalidatedagainstinsitu observationsof the groundwater table depth (Fan et al., 2013) and hydrological model outputs(Pfefferetal.,2014).TheGWBLdataareavailableonlineat:
https://julia-pfeffer.weebly.com/data.html. In thefollowing, we will useGWBLanomalies,definedasthedifferencebetweenheGWBLfora givenyearandtheaverageGWBLcomputedoverthe2003–2008time- period.ThemeanGWBLanomalyisindeedmostlyrepresentativeofthe spatialvariationsoftheGWBL,formingacontinuousdrainagestruc- turestronglydrivenbysurfacetopography,whiletheGWBLanomaly highlightsthefluctuationsoftheGWBLintime(Pfefferetal.,2014).
2.7. TRMMmonthlyrainfall
Tropical RainfallMeasuring Mission(TRMM)3B43 v7product is a combination of satellite information from the passive microwave imager(TMI)andprecipitationradar (PR)onboardTRMM,aJapan- United States satellite launched in November1997, the Visible and Infrared Scanner (VIRS) onboard the SpecialSensor Microwave Im- ager(SSM/I)andraingaugeobservations.TheTRMM3B43v7 prod- uct mergesthe TRMM3B42-adjustedinfrared precipitationwith the monthly-accumulatedClimateAssessmentMonitoringSystemorGlobal PrecipitationClimatologyCenterRainGaugeanalyses(Huffmannetal., 1995, 2007). It has a spatial resolution of 0.25° and a temporal resolution of one month. It is available on the Goddard Earth Sci- ences Data and Information Services Center (GES DISC) website:
http://disc.sci.gsfc.nasa.gov/. In this study,data from 2003to2010 wereused.
3. Methods
3.1. Groundwaterstorageestimatesusingadecompositionmethod
TWSisthesumofthecontributionsofthedifferentstoragecompart- ments:
Δ𝑇𝑊𝑆=Δ𝑆𝑊+Δ𝑆𝑀+Δ𝐺𝑊 (1) whereSW representsthetotalsurfacewaterstorageincludinglakes, reservoirs,in-channelandfloodplainswater; SMisthesoil moisture, GWis thegroundwaterstoragein theaquifers.Morespecifically,SM
F. Frappart, F. Papa and A. Güntner et al. Advances in Water Resources 124 (2019) 41–52
Fig4. Anomalyofgroundwaterstoragefromwellmeasurements(green),thedecompositionmethod(blue),WGHM(red)andISBA-TRIP(black)modeloutputs.
ThewellmeasurementsarelocatedatAsucatchmentintheNegroBasin(a),atM01,M02,M10intheMadeiraBasin(btod).(Forinterpretationofthereferences tocolorinthisfigurelegend,thereaderisreferredtothewebversionofthisarticle.)
inourstudyisdefinedasthewatercontentintherootzoneasrepre- sentedbytheWaterGAPGlobalHydrologyModel.Rootdepthin this modelisdependentonthelandcovertypewithamaximumrooting depthof 4m(MüllerSchmied etal.,2014).Thus,whensolvingEq.
(1)forGW,thislattercomponentmaycomprisewaterstorageinthe unsaturatedzonebelowtherootingdepthinthecaseofdeeperground- waterbodies.Becauseoftheirsmallcontributions,weneglectherethe variationsofcanopystorageandsnowstorage.Thestoragetermsare generallyexpressedinvolume(km3)ormmofequivalentwaterheight.
GWmonthly storageanomaliesat 1° ofspatialresolutionwerecom- putedusingEq.(1)over2003–2010,theperiodofoverlapofallthe datasets.TWSmonthlyanomaliesat2° ofspatialresolutioncomefrom regionalsolutionscomputedoverSouthAmerica(Ramillienetal.,2012, seeSection2.3),SWfrommonthlySWlevelmapsat0.25° ofspatialreso- lutionobtainedasthecombinationofGIEMSandaltimetry-basedwater levelsfromENVISATRA-2(see2.1),SMfromWGHMmonthlyoutputs at0.5° ofspatialresolution(seeSection2.2).Thisdirectresolutionof Eq.1willbereferredinthefollowingsasthedecompositionmethod.
UsingEq.(1),theerroronGWstoragevariations(𝜎GW)canbeexpressed asafunctionoftheerroronTWS(𝜎TWS),SW(𝜎SW)andSM(𝜎SM)(see FrappartandRamillien,2018):
𝜎𝐺𝑊 =
√( 𝜎𝑇 𝑊𝑆)2
+( 𝜎𝑆𝑊)2
+( 𝜎𝑆𝑀)2
(2) 3.2. Timeseriesofwatervolumevariations
Atbasinscale,thetime-variationsofsurfacewatervolumeissimply computedasinFrappartetal.(2012,2015):
𝑉𝑆𝑊(𝑡)=𝑅2𝑒∑
𝑗∈𝑆𝑃( 𝜆𝑗,𝜑𝑗,𝑡)(
ℎ( 𝜆𝑗,𝜑𝑗,𝑡)
−ℎmin
(𝜆𝑗,𝜑𝑗,𝑃(
𝜆𝑗,𝜑𝑗,𝑡))) cos(
𝜑𝑗)
Δ𝜆Δ𝜑 (3)
whereVSWisthevolumeofsurfacewater,RetheradiusoftheEarth equals6378km, P(𝜆j,𝜑j,t), h(𝜆j,𝜑j,t), hmin(𝜆j,𝜑j) arerespectively the percentage of inundation, and the water level at time t, the mini- mum of water level of thepixel of coordinates (𝜆j,𝜑j), Δ𝜆 andΔ𝜑
are respectively the grid steps in longitude and latitude. The mini- mumwaterlevelwasdeterminedusingahypsometricapproachrelat- ingthe percentageof inundationof a pixeltoits elevation(see the supplementary informationfrom Frappart et al. (2012) available at stacks.iop.org/ERL/7/044010/mmediaformoredetails).
Accordingly, the time variations of volume of TWS and SM (VTWS/SM(t))anomaliesarecomputedfollowingRamillienetal.(2005):
Δ𝑉𝑇 𝑊𝑆∕𝑆𝑀(𝑡)=𝑅2𝑒∑
𝑗∈𝑆
Δℎ𝑇 𝑊𝑆∕𝑆𝑀( 𝜆𝑗,𝜑𝑗,𝑡)
cos( 𝜑𝑗)
Δ𝜆Δ𝜑 (4)
wherehTWS/SM(𝜆j,𝜑j,t)istheanomalyofTWSorSMattimetofthepixel ofcoordinates(𝜆j,𝜑j).
3.3. ThecontributionsofhydrologicalreservoirstoTWS
Thecontributionofeachhydrologicalcomponent(CTWSHR)toTWS (i.e.,SW,SM,andGW)iscomputedforeachsub-basinastheratiobe- tweenthechangeineachhydrologicalreservoirandthechangeinTWS: 𝐶𝑇𝑊𝑆𝐻𝑅= 𝑚𝑎𝑥(
𝑆𝐻𝑅)
−𝑚𝑖𝑛( 𝑆𝐻𝑅)
𝑚𝑎𝑥(𝑇𝑊𝑆)−𝑚𝑖𝑛(𝑇𝑊𝑆) (5) Thechangesineachhydrologicalreservoir(SHR)andinTWSareob- tainedasthedifferencebetweentheseasonalextremavaluesaveraged overtheobservationperiod(2003–2007).Tosmoothoutpossibleout- liers,theseasonalextremaareaveragedovertherange85to100%of seasonalchange.
4. Results
Acontinuousmappingofgroundwaterstorageat1° ofspatialres- olutionintheAmazonbasinandforeachofitsmajorsub-basins(see Fig.1afortheirlocations)ispresentedatmonthlytime-scaleoverthe January2003–September2010time-periodinFig.S2.Fig.1bpresents thecontributionofeachhydrologicalreservoirtoTWSforeightmajor sub-basinsoftheAmazon(seeHydrologicalreservoirscontributionto TWS insectionMethods).ForthewholeAmazon basin,SW,SMand
Fig5. a)Minimumgroundwaterstock(GW)andgroundwaterbase-level(GWBL)anomaliesfrom2003to2008.
GWrepresentaround49%,27%,and24%oftheseasonalTWSchange of1800km3respectively.Ourestimatesagreewellwiththeprevious studiesbasedon modeloutputs(Güntner etal., 2007).Comparisons betweenGRACEdataandmodeloutputsshowedthatTWSisequally partitionedbetweensurfaceandsub-surfacereservoirs,andsoilwater asshowedbyHanetal.(2009)usingGLDAS/NOAH,orthatSWandsoil water(SM+GW)represents56%and44%ofTWSrespectivelybasedon
thelarge-scalehydrologicandhydrodynamicmodelMGBsimulations (Paivaetal.,2013).UsingLEAFHydroFlood,Pokhreletal.(2013)found alargercontributionfromthesubsurfacestorage(71%)thanfromSW (29%)toTWS.UsingEq.(2),weestimatedtheerrorontheGWanoma- liesdeterminedusingthedecompositionmethod.Usingclassicalerrors onTWS(15%)andSM(20%)andthemaximumerroronSWfoundin Frappartetal.(2012)(17%),weobtainedanerrorof18%onGW.
F. Frappart, F. Papa and A. Güntner et al. Advances in Water Resources 124 (2019) 41–52
Fig.6. Meanannualchangesinthesub-surface– rootzoneandgroundwater-(a)andgroundwater(c)reservoirsandassociatedstandarddeviations(bandd)over 2003–2010usingthedecompositionmethod.
Atsub-basinscale,wefoundthatthecontributionofGWtoTWS variesfrom20%(downstreamSolimões)to70%(Xingu)consistently withwhatiscurrentlyknownonthedistributionofSW(Frappartetal., 2012).Theseresultsarealsoingoodagreementwiththeestimatesfrom Pokhreletal.(2013)andPaivaetal.(2013)forsubsurface(GW+SM) storage.
Fig.2andFig.S3presentthetimevariationsofTWS,SW,SMandGW over2003–2010fortheAmazonanditsmajorsub-basins.Inthesub- basinscoveredwithextensivefloodplains(i.e.,downstreamSolimões, upstreamMadeira,Negro,downstreamAmazon,seeFig.S4forthespa- tialdistributionofthesurfacereservoirintheAmazonBasin),important time-lagscanbe observedbetweensurface,shallowsubsurfacereser- voirsandthegroundwater,reachinganinverseofphaseduringsome years.IntheNegrobasin,GWreachedtheirmaxima(minima)between September(February)andNovember(March)respectively(Fig.2c).A time-lagofthreetofourmonthsisobservedbetweenthepeaksofSW andGWintheNegro(Fig.2c),upstreamMadeira(Fig.S3c)andDown- streamAmazon(Fig.S3e)basins,inagreementwithsmaller-scaleob- servations(LesackandMelack,1995;Bonnetetal.,2008).Accordingto thesestudies,extensivefloodplainsmaydelaywaterforseveralmonths, throughstorageandpercolationthroughthevadosezonetoreachthe groundwaterreservoir.InthecaseoftheupstreamMadeira,adecrease ofGWstoragecanbeobservedduringthefloodin2006and2007,the twoyearspresenting the largestSW (and SM) storages of thestudy period,suggesting thatGWstoragecontributestotheflood.Thisen- dogenousinundationprocesswasreportedinseveralstudies(Ronchail etal.,2005; Bourreletal.,2009).Aftertheflood,GWstorageis in-
creasinguptosimilarorslightlyabovelevelsthanthepreviousyears.
Duringtheyearswithlargefloodevents,GWstorageexhibitslargeram- plitudeofvariations.Ontheotherhand,smallerseasonalcycleisob- servedfortheGWstorageintheAmazon(Fig.2a)andthedownstream Madeira(Fig.2b)basinscontrarytowhatcanbeobservedintheother hydrologicalreservoirs.AspreviouslymentionedbyMiguez-Machoand Fan(2012), theseasonality maybesmoothed out(e.g.,theupstream MadeiraandthedownstreamMadeirainthecaseofMadeira,allthe tributariesoftheAmazon)whenspatiallyaveragingoveralargeriver basin.Inthesub-basins(i.e.,upstreamSolimões,downstreamMadeira, Tapajos,Xingu),thetime-variationsinallthehydrological reservoirs arealmostinphase.Soilwaterstorageisfirstreplenishedafterrainfall, followedbydeeperdrainagethatrechargestheGWstorage.InFig.2d, themaximumofSMoccursinMarchwhereasthemaximumofGWis observedinAprilfortheTapajos.ThemaximumofSWisreachedinMay correspondingtothetimenecessaryforwatertocrossthewholesub- basin.IntheTapajosbasin,thecontributionofSWislowasthereisno extensivefloodplainalongitsstream.Intheheadwaterbasins(i.e.,up- streamSolimõesBasin,TapajosandXingu),theseasonalityoftheTWS isverysimilartotheoneoftheGW(Figs.S3a,3d,andS3f).
WeperformedanevaluationoftheGWestimatesfromthedecom- positionmethodusingsparsemeasurementsofGWlevelsavailableat severallocationsintheAmazonBasin(seeSupplementaryinformation, TableS1andFig.1afortheirlocations).ExceptfortheAsucatchment, whereinsituobservationsandourestimatesareoverlappingintimebe- tween2004and2005(Fig.3),itmustbekeptinmindthatmostinsitu observationsareavailablefrom2011,sothatthecomparisonswithour
Fig.7.SpatialandtemporalcomponentsofthetwofirstPCAmodesoftheGWestimatedusingthedecompositionmethod.Thefirstmodehasanexplainedvariance of0.56(a),andthesecondone0.28(b).
estimatesarepresentedintermsofseasonalcycleswiththeirassociated standarddeviation(Fig.4andFig.S5).
Firstly,thetimevariationsofGWbetweenJanuary2004andNovem- ber2005fromthedecompositionmethodexhibitsanoverallgoodagree- mentwiththeinsitumeasurementsacquiredattheAsucatchmentin spiteofthedifferenceofspatialscaleamongthem(Fig.3).Lower/larger annualamplitudeofvariationsisobservedin2004/2005respectively, withashorterpeakdurationobservedinbothdatasetsfor2005.In2005, aftertheannualpeak,theGWfromthedecompositionmethoddecreases muchfasterthanintheobserveddataset.
Secondly, the seasonal GW variations from the decomposition method,averaged over 2003–2010, presentan amplitudeof around 300mm, with a maximum in June and a minimum in December- January,forthegridpointsencompassingtheAsucatchment(60.17°W, 3.13°S),northofManausintheNegroBasin(Tomasellaetal.,2008), and theM01 site (59.837°W, 3.354°S), in the downstream Madeira Basin,directlyaffectedbythewaterlevelfluctuationsoftheAmazon River(Fig.4aandb). Overall,theycomparewell,bothinamplitude andphase,totheGWvariationsobservedatwellsfromtheAsucatch- ment,recordedfromAugust2010toMay2011,andfromtheM01site, recordedbetween September2011 andDecember2012. Other com- parisonswereperformedforsitesthataremorerepresentativeofthe MadeiraBasin,whereagooddrainageoccurs.Forthesesites,thereisa noticeabletime-shift–insitugroundwaterlevelsareonetotwomonths aheadofGWvariationsfromthedecompositionmethod-,buttheampli- tudesareofthesameorderofmagnitude.Smallerdifferencesinphase
areobservedforM07(61.948°W,5.256°S)sitethanforM02(60.314°W, 3.682°S),M03(60.726°W,4.147°S)andM10(62.92°W,6.571°S)sites.
ThelatterwellsareaffectedbythepresenceofJanauariLake(60.032°W, 3.210°S)withanareaof0.79km2,andlargefloodingeventsduringthe wetseasonthatbothdecreasethedepthofgroundwaterbelowtheter- rainsurfaceandincreasethestoragevariationscomparedwiththesur- roundingenvironment.Besides,ithastobekeptinmindthatthespa- tialscalebetweenthedecompositionmethodestimatesandtheinsitu measurementsareverydifferent,andthatthedecompositionmethodis limitedbytheverycoarseresolutionofbothremotesensingproducts (0.25° and2° forbothGIEMS-altimetryandtheGRACEregionalsolu- tionsrespectively)andtheWGHMoutputs(0.5°),sothatlocaleffects monitoredatGWwellsareunlikelytobedetectedinourdataset.Nev- ertheless,quantitativecomparisonsaremadebetweenGWstoragefrom in-situmeasurementsandestimatesfromthedecompositionmethodav- eragedover2003–2010andmodeloutputsfromWGHMandISBA-TRIP.
Asalreadymentioned,onlyoneinsitu site(Asucatchment)provides datafromJanuary2004toNovember2005whichoverlaps bothour estimatesandthehydrologicalmodeloutputsFortheothersites,asthe timeperiodsdonotoverlap,slightchangesintemporalitybetweenthe differentsourcescanoccur.Therefore,cross-correlationswerefirstes- timated.Usingthetime-delaycorrespondingtothemaximumofcorre- lation,RMSE(mm)andRRMSE(%)werethencomputed.Theampli- tuderatiobetweenthedecompositionorthemodeloutputsandthein- situmeasurements,computedastheratiobetweenthemaximumampli- tudesofeitherthedecompositionmethodorthemodeloutputsaveraged
F. Frappart, F. Papa and A. Güntner et al. Advances in Water Resources 124 (2019) 41–52
Table1
Statisticalparametersresultingfromthecomparisonbetweenin-situgroundwatermeasurements andestimatesfromthedecompositionmethod(GRACE),WGHMandISBA-TRIPmodeloutputs.
RMSEandRRMSEwerecomputedforthetime-delay(Δt)correspondingtothemaximumof thecross-correlationfunction.
2004–2005 Average
Asu Asu M01 M02 M03 M07 M10
GRACE R 0.77 0.97 0.94 0.88 0.95 0.97 0.95
Δt (months) 0 0 0 2 2 2 1
RMSE (mm) 58 69 39 171 109 41 49
RRMSE (%) 22 15 18 21 17 11 13
Amplitude ratio 1.21 0.58 1.27 0.53 0.71 1.30 1.14
WGHM R 0.79 0.94 0.97 0.91 0.99 0.96 0.92
Δt (months) − 1 − 1 − 1 − 1 1 1 0
RMSE (mm) 53 118 36 265 205 115 107
RRMSE (%) 23 26 17 32 32 30 28
Amplitude ratio 0.46 0.25 0.54 0.16 0.2 0.35 0.38
ISBA-TRIP R 0.67 0.98 0.96 0.89 0.95 0.93 0.94
Δt (months) − 2 − 2 − 2 0 0 1 0
RMSE (mm) 87 102 22 267 197 106 99
RRMSE (%) 34 26 11 32 31 28 26
Amplitude ratio 0.86 0.35 0.77 0.16 0.24 0.40 0.43
over2004–2005(onlyforAsucatcment)and2003–2010(allthesites).
Themaximumamplitudeofin-situdata,wasalsoestimated.Allthere- sultsarepresentedinTable1.DirectcomparisonoverAsucatchment for2004–2005showsagoodagreementbetweenourestimates from thedecompositionmethodandthisverylocalmeasurement(R=0.77 andRRMSE=22%)withnotime-lag.Similarresultsareobtainedwith WGHMoutputs(R=0.79andRRMSE=23%)with atime-lag of one month.LoweragreementisfoundwithISBA-TRIPoutputs.Veryhigh correlationvalues,generallyhigherthan0.9,areobtainedforboththe decompositionmethodandthemodeloutputs.Allthesourcesexhibit uptotwomonthscomparedwithin-situdata(between0and2months forthedecompositionmethod,−1and1forWGHM,−2and0forISBA).
RRMSEaregenerallylowerthan20%usingthedecompositionmethod andhigherthan25%forWGHMandISBA-TRIP(exceptforM02well withRRMSEof17and11%respectively).Ingeneral,amplituderatios arealsocloserto1withthedecompositionmethodthanwiththemodel outputs.
TobecomparedwithGWBLanomalies,wecomputedtheminimum GWanomalyinasimilarmanner(minimumGWforagivenyearmi- nustheaverage GWminima over2003–2008).Apositive (negative) anomalymeanstherefore,thattheGWminimumishigher(lower)this yearthanaverageoverthe2003–2008timeperiod.AsshowninFig.5, similarpatternsarefoundintheminimumGWandGWBLanomalies atannualandinterannualtimescales,despitethedifferenceinspatial resolutionbetweenthetwoproducts:2° resampledat1° fortheGRACE regionalsolutionsandhencefortheGWanomaliesand0.25° forthe groundwaterbase level. The dipolebetween drierconditions in the northandwetterinthesouthclearlyappearsonbothdatasetsin2003 andmoreclearlyin2004.Thesignatureofthe2005extremedroughtis clearlyvisibleinthesouthwestofthestudyarea,withextremelylow minimumGWandGWBLanomalies,followedbyaprogressiveincrease ofbothquantitiesduringthethreefollowingyears.
The minimum GW and GWBL anomalies also agree in phase (Table2).Theisonaveragenobiasesbetweenbothanomalies,exceptin 2005,whentheminimumGWanomalyoccurredonemonthlaterthan theGWBL. Largerphasedifferencesareobservedlocally,withRMSE valuesofabout1.5month.Suchdifferencesshouldbeexpectedgiven thetemporalresolution(onemonth)ofGRACEdata,andthereforeof theminimumGWanomaly,andtherevisittimeoftheENVISATsatel- lite(35days).Besides,theGWBLanomalyismorepronetodetectlocal effects,atthescaleoftheriver,orfloodplaincross-section(from1to 10km),thantheminimumGWanomalydisplayingpatternswithatyp- icallengthscaleofa100kmormore.The2005biascouldhoweverbe explainedbytheextremeconditionsencounteredduringthedrought.
Table2
ComparisonofthephaseoftheminimumGWS totheGWBL.
Bias (months) RMSE (months) ALL YEARS − 0.01 1.55
2003 0.13 1.71
2004 − 0.12 1.49
2005 1.02 1.1
2006 − 0.47 1.47
2007 − 0.57 1.71
2008 − 0.05 1.32
Indeed,duringlow-waterperiods,theGWcontributestoalimentrivers, lakesandfloodplains,andthereforetomaintainthewater-levelinsur- facewaterbodies,whiletheGWstockscontinuestodrop(Lesackand Melack,1995).Thefirstmapsofseasonalamplitudeofsubsurface(SM andGW)andGWstoragesfromOctober2003toSeptember2010,and theirassociateddeviationsareshowninFig.6.Theyexhibitquitesimi- larspatialdistributionswithlowerseasonalamplitudefortheGWcom- ponent(Fig.6aandb)butalargerdeviation(Figs.6candd).Maps ofannualamplitudeandassociatedstandarddeviationarepresentedin Fig.S6forSWandSMstorages.Thespatialpatternsofseasonalground- waterchanges,averagedoverOctober2003-September2010,revealed thatthelargeststoragevariations(greaterthan450mm)arelocatedin centralAmazonia,between65°Wand55°W,and0° and7.5°S,in the downstreampartsoftheSolimões,NegroandMadeirasub-basins,and theupstreampartoftheAmazonwhereextensivefloodplainsarelocated (Fig.4candFig.S3forthespatialdistributionofthefloodplainsinthe AmazonBasin).ItcorrespondstotheAmazonsedimentarybasin,where theAlter doChãoandIçaaquifersarelocated (seeFig.S6fortheir locations).Thelargestinter-annualvariabilityisalsoobservedinthe Amazoncentralcorridoralongthemainstem,fromTabatinga(69.9°W, 4.25°S)toObidos(55.68°W,1.92°S)(Fig.6d).Itcanreach120mmin the southof theAmazonmainstem andcorresponds totheAlter do ChãoandIçaaquifersystems.Importantseasonal variations(greater than350mm)arealsopresentinthesouth(TapajosandXingu)and north(Negroupstream)easternpartsofthebasin.Forthelatterone, theycan berelatedtotheBoaVistaaquifer(seeFig.S7foritsloca- tion).Onthecontrary,thewesternpartofthebasinischaracterizedby lowermeanseasonalchanges(lessthan300mm)evenintheupstream MadeiraBasin(Fig.4c).Intermsofinter-annualvariability,asecondary maximum,centeredin62°Wand15°S,alsoappearsandcorrespondsto theParecisaquifer(Fig.6bandseeFig.S7foritslocation),averyporous andhighlyproductiveaquifer.Ourresultsexhibitsimilarpatternswhen
Fig.8. SpatialandtemporalcomponentsofthetwofirstPCAmodesoftherainfallestimatedfromTRMM3B43product.Thefirstmodehasanexplainedvariance of0.52(a),andthesecondone0.19(b).
comparedtothemapofthegroundwaterresourcesofCentralandSouth America,thatprovidesgroundwaterrechargeestimates,andprovided bytheWorldwideHydrologicalMappingandAssessmentProgramme (WHYMAP,http://www.whymap.org)fromtheGeosciencesandNatu- ralResources(BundesanstaltfürGeowissenschaftenundRohstoffe— BGR).Besides, theregions of highestvariabilitycorrespondtoareas wheretheporosity ofthesoilfromGLHYMPS(Gleesonetal., 2013) isgreaterthan25%.Wecan noticethattheporositydecreasesalong theAmazonmainstemasdoestheseasonalamplitudeofGW(Fig.S8a) anditsinter-annualvariability(Fig.S8b).
Aprincipalcomponentanalysis(PCA)wasfinallyperformedonthe temporalanomalyofAmazonGWstorageobtainedusingthedecom- positionmethodfromOctober2003toSeptember2010.Thetwofirst modesexplainedaround85%ofthetotal variance.Theirspatialand temporalcomponentsarepresentedinFig.7.Thefirstmode,thatac- countsfor56%oftheexplainedvariance,presentsadecreaseoftheGW storageoverthewholeperiodinthesouthernpartofthebasin,thatis especiallylargeinthesouthwesternpartoftheAlterdoChãoaquifer (Fig.7a).Anexceptionallylowwaterstoragecanbeseeninsouthern hemispherespring2005resulting fromtheextremedroughtof 2005 (Tomasellaetal.,2011),followedbyalargemaximumin2006dueto thelargerthanusualfloodthatoccurredthisyear.Similarobservations weremadeontheGWBLderivedfromradaraltimetryalongthecen- tralAmazon(Pfefferetal.,2014)thatshowedalargenegativeanomaly groundwaterbaselevelin2005followedbyaslowrisefromnorthto southstartingin2006.
Ontheonehand,positiveandnegativepeaksarealsoobservedin 2009and2010correspondingtotheexceptionalfloodanddroughtthat
occurredthosetwoyearsrespectively.Asthemaximumofthe2010was recordedinOctober2010,itsimpactonGWcannotcompletelybeob- servedinourdatasetthatstopsinSeptember2010.Ontheotherhand, thesecondmode(𝜎2=0.28)exhibitsanincreaseingroundwaterstor- ageinthenortheasternpartoftheAlterdoChãoaquifer(Fig.5b),with alargepeakduringthe2006flood.Thisisalsoingoodagreementwith theresultsfromPfefferetal.(2014)thatshowsanincreaseofGWBL fromnorthtosouth.Inparallel,PCAisalsoperformedonthetemporal anomalyofrainfallintheAmazonBasin.Thefirsttwomodes,which spatialandtemporalcomponentsarepresentedinFig.8,accountfor 56%and19%oftheexplainedvariancerespectively.Verysimilarpat- ternscanbeobservedbetweenthespatialmodesoftheGWandrainfall anomalies,withdifferences thatmostly occurredin thewestern part ofthebasinexplainedbyinaccuraciesofTRMMrainfallproductover mountainousareas,asalreadyreportedinseveralstudies(e.g.,Mourre etal.,2016;Satgé etal.,2016;Erazoetal.,2018).
5. Discussionandconclusion
Differenceswerefoundinthepartitionofsub-surfacestorageinSM andGWcomponentsbetweentheresultspresented inthisstudyand earlierresults.Comparedwithotherstudies,ourresultsshowedalower contributionoftheSMtothesubsurfacestorage.TheWGHMmodelis oneoftheonlyglobalhydrologicalmodelsthattakesintoaccountall thedifferentwaterstoragereservoirsonthecontinents(excepticeand glaciers),withitssurfacereservoircontainingafloodplainsub-reservoir.
Asaconsequence,lesswaterisattributedtoSMcomponentthaninother hydrologicalmodels(seeRRMSE(%)betweenISBA-TRIPandWGHM
F. Frappart, F. Papa and A. Güntner et al. Advances in Water Resources 124 (2019) 41–52
outputsusingWGHMasthereferenceinFig.S9).Verylargedifferences canbeseenovertheAndeanpartofthebasinaswellas,generally,along theriverswherearelocatedthefloodplainsdue toverylarge values ofSMforISBA-TRIP.This isdue toi) theabsenceofuse ofstations forcalibrationof theISBA-TRIPmodeloutof Brazilandii)thelack ofafloodplainreservoirintheISBA-TRIPmodel.This reinforcesthe choiceof theWGHMmodelforsimulatingtheSMreservoir.Onthe otherlocations,RRMSEisgenerallylowerthan40%.Thedepthofthe rootzoneinWGHMhastypicalvaluesaround1or2mandmayreach 4mfortropicalevergreenbroadleafforest(MüllerSchmiedetal.,2014).
But,intheAmazonBasin,therootzonecanextendtodepthsgreater than4minthelowlandtropicalforests(Nepstadetal.,2004;Markewitz etal.,2010).ThiscouldexplainapossibleunderestimationoftheSM componentcausinganoverestimationoftheGWvariations(seeTableS1 presentingthedepthofthewatertableforthewellsusedforcomparison withourestimates).IntheNegrosub-basin,rootzonedepthsupto5m wereestimatedduringtheextremedroughtof2010.Nevertheless,inthe regionswhereGWstoragevariationsarethelargest,agoodagreement wasfoundbetweenourestimatesandinsitumeasurements(Fig.3aand b).
ThemonthlymapsofanomalyofGWprovideanewandunprece- dentedinformationonthedynamicsofwaterstorageintheaquiferof theAmazonBasin.Theysuggestthatthedroughtof2005inducedan inter-annualanomalyinGWstoragevisibleuptotheendoftheobserv- ingperiodinthesouthwestoftheAlterdoChãoaquifer(Fig.5a).This supportsthemechanismofpersistenceofrainfalldeficitintheGWstor- age(BierkensandvanddenHurk,2007).Theyalsohelpforabetterun- derstandingoftherelationshipbetweenSWandGWreservoirs.Accord- ingtothewaterbalanceequation,changesinTWSrepresent,atbasin- scale,thedifferencebetweentherainfall,theevapotranspirationandthe runoff (differencebetweenoutflowandinflow).Inmostofheadwater catchments(i.e.,upstreamSolimõesBasin,Tapajos,Xingu)changesin TWSagainsttimearedominatedbytheGWreservoir.TWSandoutflow minusinflowarewellcorrelated(Frappartetal.,2013b).Thissuggests thatinheadwatersub-basinswithnoextensivefloodplains,groundwa- teristhemajorcontributortodischarge,confirmingresultsfromhy- drologicalmodeling(Miguez-MachoandFan,2012).Intheothersub- basins,thecontributionofGWtoTWSislower.SWstorageisinphase withTWSwhereasaphaseshiftofseveralmonthsisobservedwithGW (Fig.2andFig.S2).Thetime-lagsobservedbetweenSWandGWneed tobefurtherinvestigatedandcouldprovideimportantinformationon theexchanges(infiltrationandseep)betweenGWandSW.
Acknowledgments
ThisworkwassupportedbytheCNESTOSCAgrant“Variabilityof terrestrialfreshwaterstorageintheTropicsfrommultisatelliteobser- vations– HyVarMultiObs” andtheCNESOSTSTgrant“new Perspec- tivesforhigherResolutIonAltimetry-aMulti-disciplinaryapproach- PRIAM)”.WeacknowledgeBertrandDecharmeforprovidingtheISBA- TRIPmodeloutput.WethankCatherinePrigentandtwoanonymous reviewersfortheirhelpfulcomments.Resultsincorporatedinthispub- licationhavereceivedfundingfromtheEuropeanUnion’sHorizon2020 researchandinnovationprogrammeundertheMarieSklodowska-Curie grant agreementNo 706011. Datacollectionon IPM sites was sup- portedbyPRONEX-FAPEAM(1600/2006), HidrovegUniversalCNPq (473308/2009-6),FAPESP/FAPEM(465/2010),PPBioManaus(CNPq 558318/2009-6),ProjetoCenariosFINEP/CNPq(52.0103/2009-2)and INCTCENBAM.
Supplementarymaterials
Supplementarymaterialassociatedwiththisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.advwatres.2018.12.005.
References
Alkama, R. , Decharme, B. , Douville, H. , Becker, M. , Cazenave, A. , Sheffield, J. , Voldoire, A. , Tyteca, S. , Le Moigne, P. , 2010. Global evaluation of the ISBA-TRIP con- tinental hydrological system. Part I: comparison to GRACE terrestrial water storage estimates and in situ river discharges. J. Hydrometeor. 11 (3), 583–600 .
Alsdorf, D.E. , Rodríguez, E. , Lettenmaier, D.P , 2007. Measuring surface water from space.
Rev. Geophys. 45, RG2002 .
Bierkens, M.F.P. , van den Hurk, B.J.J.M , 2007. Groundwater convergence as a possible mechanism for multi-year persistence in rainfall. Geophys. Res. Lett. 34, L02402 . Bonnet, M-P. , Barroux, G. , Martinez, J.M. , Seyler, F. , Moreira-Turcq, P.F. , Cochonneau, G. ,
Melack, J.M. , Boaventura, G.R. , Maurice-Bourgoin, L. , León Hernández, J.G. , Roux, E. , Calmant, S. , Kosuth, P. , Guyot, J.L. , Seyler, P. , 2008. Floodplain hydrology in an Ama- zon floodplain lake (Lago Grande de Curuaí). J. Hydrol. 349 (1), 18–30 .
Bourrel, L. , Phillips, L. , Moreau, S , 2009. The dynamics of floods in the Bolivian Amazon Basin. Hydrol. Process. 23, 3161–3167 .
Carvalho, JS , 2012. Caracterização hidrogeológica da região norte da cidade de Manaus, com base em informações geofísicas (resistividade elétrica), geológicas e geomorfológ- icas. INPA-UEA, p. 87 .
Chen, J.L. , Wilson, C.R. , Tapley, B.D. , Yang, Z.L. , Niu, G.Y , 2009. The 2005 drought event in the Amazon River Basin as measured by GRACE and climate models. J. Geophys.
Res. 114, B05404 .
Chen, J.L. , Wilson, C.R. , Tapley, B.D. , 2010. The 2009 exceptional Amazon flood and interannual terrestrial water storage change observed by GRACE. Water Resour. Res.
46, W12526 .
Decharme, B. , Alkama, R. , Douville, H. , Becker, M. , Cazenave, A , 2010. Global evaluation of the ISBA-TRIP continental hydrological system. Part II: uncertainties in river rout- ing simulation related to flow velocity and groundwater storage. J. Hydrometeorol.
11, 601–617 .
Do Nascimento, N.R. , Fritsch, E. , Bueno, G.T. , Bardy, M. , Grimaldi, C. , Melfi, A.J. , 2008.
Podzolization as a deferraltization process: dynamics and chemistry of ground and surface waters in an Acrisol – Podzol sequence of the upper Amazon basin. Eur. J.
Soil Sci. 59, 911–924 .
Erazo, B. , Bourrel, L. , Frappart, F. , Chimborazo, O. , Labat, D. , Dominguez-Granda, L. , Matamoros, D. , Mejia, R , 2018. Validation of satellite estimates (Tropical Rainfall Measuring Mission, TRMM) for rainfall variability over the Pacific slope and Coast of Ecuador. Water 10 (2), 213 .
Espinoza, J.C. , Ronchail, J. , Frappart, F. , Lavado, W. , Santini, W. , Guyot, J-L. , 2013. The major floods in the Amazonas River and tributaries (Western Amazon basin) dur- ing the 1970–2012 period: a focus on the 2012 flood. J. Hydrometeorol. 14 (3), 1000–1008 .
Fan, Y. , Li, H. , Miguez-Macho, G , 2013. Global patterns of groundwater table depth. Sci- ence 339 (6122), 940–943 .
Frappart, F. , Papa, F. , Güntner, A. , Werth, S. , Santos da Silva, J. , Tomasella, J. , Seyler, F. , Prigent, C. , Rossow, W.B. , Calmant, S. , Bonnet, M.-P. , 2011. Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains.
Remote Sens. Environ. 115 (6), 1588–1594 .
Frappart, F. , Papa, F. , Santos da Silva, J. , Ramillien, G. , Prigent, C. , Seyler, F. , Calmant, S , 2012. Surface freshwater storage in the Amazon basin during the 2005 exceptional drought. Environ. Res. Lett. 7 (4), 044010 .
Frappart, F. , Seoane, L. , Ramillien, G , 2013a. Validation of GRACE-derived water mass storage using a regional approach over South America. Remote Sens. Environ. 137, 69–83 .
Frappart, F. , Ramillien, G. , Ronchail, J , 2013b. Terrestrial water storage variations over 2003–2010 using GRACE and TRMM observations. Int. J. Climatol. 33 (14), 3029–3046 .
Frappart, F. , Papa, F. , Malbéteau, Y. , León, J.G. , Ramillien, G. , Prigent, C. , Seoane, L. , Seyler, F. , Calmant, S , 2015. Surface freshwater storage variations in the Orinoco floodplains using multi-satellite observations. Remote Sens. 7 (1), 89–110 . Frappart, F., Ramillien, G., Seoane, L., 2016. Monitoring water mass redistributions
on land and polar ice sheets using the GRACE gravimetry from space mission. In:
Baghdadi, N., Zribi, M. (Eds.), Land Surface Remote Sensing in Continental Hy- drology. Elsevier, Amsterdam, Nederland, pp. 255–279. https://doi.org/10.1016/
B978-1-78548-104-8.50008-5 .
Frappart, F. , Ramillien, G , 2018. Monitoring groundwater storage changes using the Grav- ity Recovery and Climate Experiment (GRACE) satellite mission: a review. Remote Sens. 10 (6), 829 .
Freeden, W. , Schreiner, M. , 2008. Spherical Functions of Mathematical Geosciences: A Scalar, Vectorial and Tensorial Setup. Springer, p. 602. pp. .
Gleeson, T. , Moosdorf, N. , Hartmann, J. , van Beek, L.P.H , 2013. A glimpse beneath earth’s surface: GLobal HYdrogeological MaPS (GLHYMPS) of permeability and porosity.
Geophys. Res. Lett. 41, 3891–3898 .
Güntner, A. , Stuck, J. , Werth, S. , Döll, P. , Verzano, K. , Merz, K , 2007. A global analysis of temporal and spatial variations in continental water storage. Water Resour. Res. 43 (5), W05416 .
Han, S.-C. , Kim, H. , Yeo, I.-Y. , Yeh, P. , Oki, T. , Seo, K.-W. , Alsdorf, D. , Luthcke, S.B. , 2009. Dynamics of surface water storage in the Amazon inferred from measurements of inter-satellite distance change. Geophys. Res. Lett. 36, L09403 .
Huffmann, G.J. , Adler, R.F. , Rudolf, B. , Schneider, U. , Keehn, P.R , 1995. Global precip- itation estimates based on a technique for combining satellite-based estimates rain gauge analysis and NWP model precipitation information. J. Clim. 8, 1284–1295 . Huffmann, G.J. , Adler, R.F. , Bolvin, D.T. , Gu, G. , Nelkin, E.J. , Bowman, K.P. , Hong, Y. ,
Stocker, E.F. , Wolf, D.B , 2007. The TRMM multi-satellite precipitation analysis (TMPA): quasi-global. multi-year combined-sensor precipitation estimates at fine scale. J. Hydrometeorol. 8, 38–55 .
