developing a global assimilation and modeling framework to

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Developing a global assimilation and modeling framework to produce SWOT data products Kostas Andreadis 1 , Dongyue Li 2 , Dennis Lettenmaier 3 , Steve Margulis 2 1 Jet Propulsion Laboratory, California Institute of Technology 2 Department of Civil and Environmental Engineering, University of California, Los Angeles 3 Department of Geopgraphy, University of California, Los Angeles Introduction and Objectives Although observations from SWOT will be tremendously important for hydrologic science, there are certain limitations. One is the discontinuity in space and time of SWOT-derived water surface elevations, discharge and storage change. Due to the orbital characteristics of SWOT, water bodies will be observed between 2 and >10 times per cycle depending on latitude. For example, the number of observations in the Amazon River basin will range from 2-4 times per repeat cycle whereas the Lena River will be observed 4-10 times. This could prove problematic when attempting to derive aggregate (weekly, monthly or seasonal) estimates of river discharge for instance, or lake, reservoir, or wetland storage change. For example, if a given river is sampled only twice per repeat cycle and those observations coincide with peak (low) flows there will be an over (under)-estimation of discharge. A methodology to generate products with spatially and temporally continuous fields of SWOT observables would be highly desirable. Data assimilation is such a methodology; it can merge observations from SWOT with model predictions in order to produce estimates of quantities such as river discharge, storage change, and water heights for locations and times when there is no satellite overpass or layover renders the measurement unusable. Our project aims to develop a modeling and data assimilation framework that can be implemented efficiently for generating a SWOT Level 4 data products consisting of continuous fields of water surface elevation, discharge, and storage change globally. Specifically, the objectives of the proposed research include: 1. Develop a framework for generating a Level 4 SWOT data product that provides continuous fields of water surface elevation, discharge, and storage change. 2. Evaluate data assimilation algorithms for SWOT observations. 3. Quantify errors in estimating water availability from using the Level 3 and 4 products. 4. Evaluate whether SWOT observations can be used to estimate human impacts on the water cycle (e.g. reservoirs, diversions).

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Page 1: Developing a global assimilation and modeling framework to

DevelopingaglobalassimilationandmodelingframeworktoproduceSWOTdataproducts

KostasAndreadis1,DongyueLi2,DennisLettenmaier3,SteveMargulis2

1JetPropulsionLaboratory,CaliforniaInstituteofTechnology2DepartmentofCivilandEnvironmentalEngineering,UniversityofCalifornia,LosAngeles3DepartmentofGeopgraphy,UniversityofCalifornia,LosAngeles

IntroductionandObjectivesAlthoughobservationsfromSWOTwillbetremendouslyimportantforhydrologicscience,therearecertainlimitations.OneisthediscontinuityinspaceandtimeofSWOT-derivedwatersurfaceelevations,dischargeandstoragechange.DuetotheorbitalcharacteristicsofSWOT,waterbodieswillbeobservedbetween2and>10timespercycledependingonlatitude.Forexample,thenumberofobservationsintheAmazonRiverbasinwillrangefrom2-4timesperrepeatcyclewhereastheLenaRiverwillbeobserved4-10times.Thiscouldproveproblematicwhenattemptingtoderiveaggregate(weekly,monthlyorseasonal)estimatesofriverdischargeforinstance,orlake,reservoir,orwetlandstoragechange.Forexample,ifagivenriverissampledonlytwiceperrepeatcycleandthoseobservationscoincidewithpeak(low)flowstherewillbeanover(under)-estimationofdischarge.AmethodologytogenerateproductswithspatiallyandtemporallycontinuousfieldsofSWOTobservableswouldbehighlydesirable.Dataassimilationissuchamethodology;itcanmergeobservationsfromSWOTwithmodelpredictionsinordertoproduceestimatesofquantitiessuchasriverdischarge,storagechange,andwaterheightsforlocationsandtimeswhenthereisnosatelliteoverpassorlayoverrendersthemeasurementunusable.

OurprojectaimstodevelopamodelinganddataassimilationframeworkthatcanbeimplementedefficientlyforgeneratingaSWOTLevel4dataproductsconsistingofcontinuousfieldsofwatersurfaceelevation,discharge,andstoragechangeglobally.Specifically,theobjectivesoftheproposedresearchinclude:1. DevelopaframeworkforgeneratingaLevel4SWOTdataproductthatprovides

continuousfieldsofwatersurfaceelevation,discharge,andstoragechange.2. EvaluatedataassimilationalgorithmsforSWOTobservations.3. QuantifyerrorsinestimatingwateravailabilityfromusingtheLevel3and4

products.4. EvaluatewhetherSWOTobservationscanbeusedtoestimatehumanimpacts

onthewatercycle(e.g.reservoirs,diversions).

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5. Deriveadatasetofrunofffieldsanddemonstrateitsvaluebycalibratingahydrologymodelagainstit.

ApproachOuroverallapproachtodevelopingamodelingandassimilationframeworkforgeneratinghigh-levelSWOTdataproductsconsistsofthecouplingofahydrologicandhydrodynamicmodelandtheirmodificationsothatSWOTobservationscanbeassimilated.Theexperimentaldesign(identicaltwinsyntheticexperiment)startswiththecoupledmodelgenerating"true"fieldsofsurfacewatervariables(e.g.waterheight,discharge,storagechange,andrunoff)usingabaselineconfiguration.The"true"fieldsarethenusedtogeneratesyntheticSWOTobservationsoverthestudyareaswiththeproperorbitandaccuracycharacteristics.ThelatteraredefinedusingtheSWOTInstrumentSimulator.A"first-guess"(oropen-loop)simulationwasalsoperformedwiththecoupledmodelusingaconfigurationthatcontainserrorsrepresentingtheimperfectknowledgeofparametersandinputdata.SubsequentlythesyntheticSWOTobservationswereassimilatedintotheopen-loopmodeltoestimatedischargeandstoragechange.Finally,theoutputoftheassimilationmodeliscomparedwiththedesignated“true”fieldsinordertovalidatetheapproach.

ThecoupledmodelingframeworkconsistsoftheVariableInfiltrationCapacity(VIC)hydrologymodelandtheLISFLOOD-FPhydrodynamicmodel.VICsolvesthelandsurfaceenergyandwaterbalancesoveragriddeddomainusingasoil-vegetation-atmosphereschemethatmodelshowmoistureandenergyfluxesbetweenlandandatmospherearecontrolledbyvegetationandsoil.Oneofthemodel’sadvantagesisitsrepresentationofsub-gridvariabilityinsoils,vegetationandtopographyviathepartitioningofeachgridcellintotilesofuniformphysiography.AlthoughVICincludesasimpleflowroutingmodelthattransportsthegeneratedrunoffandbaseflowofeachgridcellthroughtherivernetwork,ithascertainlimitationsthatprecludeitfrombeingusedinconjunctionwithSWOTobservations.LISFLOOD-FPovercomestheselimitationsandsimulateswaterflowthrougheachmodelgridcellbysolvingtheinertialmomentumequationthroughasingleexplicitfinitedifferencescheme.Theresultingmodelissimpleyetcontainsenoughphysicstodescribefloodandriverflowprocessesadequatelywhilerequiringanorderofmagnitudefewercomputationaloperationsthanafullshallowwatermodel.

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Figure1.MapoftheUpperMississippiRiverbasinanditstopography,alongwiththesimulatedrivers.

ThestudydomainforthefirstexperimentistheUpperMississippiRiverbasin(Figure1),andthecoupledmodelwasusedtosimulatehydrodynamicvariablesat1-kmspatialresolution.The"truth"modelusestheNationalElevationDataset(NED)DEMtoderivetherivernetwork(thresholdedat10,000km2drainage),riverchannelwidthsanddepthfromtheHydroSHEDSdatabase,andinflowssimulatedbyVICusingmeterologicaldata(precipitation,airtemperature,andwindspeed)at1/16o.Theopen-loopsimulationusesaDEMderivedfromtheShuttleRadarTopographyMission(SRTM),andaddserrorstothebankfullwidthsanddepthsaswellastheinflowscreatinganensembleof20modeltrajectories.The"truth"modelsimulationwasabletoreproduceriverdischargewithreasonableaccuracyovera3-yeartimeperiod(selectedtomatchthedesignlifecycleofSWOT).Figure2showsacomparisonofthe"truth"-simulateddischargewithactualmeasurementsfromaUSGSgauge.

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Figure2.Validationofthe"truth"-simulated(redline)riverdischargeagainstin-situmeasurements(blueline).

The"truth"watersurfaceelevations(WSE)wereusedasinputstotheSWOTInstrumentSimulatortoproducethesyntheticobservations.Inordertocorrectlyrepresenterrorsfromtopographiclayover(amongothererrors),the1-kmWSEfieldsweredeemedinadequateandweresubsequentlydownscaledtoa30-mspatialresolution.ThesyntheticSWOTobservationsaretheassimilatedintotheopen-loopmodelbyusinganumberofalgorithmsthatarevariantsoftheEnsembleKalmanFilter(EnKF).TheEnKF,andtheKalmanFilteringeneral,solvetheoptimalestimationproblembyupdatingthemodelstatebasedontheerrorsofboththemodelpredictionsandtheobservations.TheuncertaintyinthemodelandtheobservationsisrepresentedthroughanensembleusingaMonteCarloapproachanda-prioriassumptionsaboutthestatisticsoftheseerrors.ThealgorithmstestedincludetheEnKF,thesquare-rootEnKF(SQRTENKF),andtheLocalEnsembleTransformKalmanFilter(LETKF).

AnalysisandAnticipatedResultsTheSWOTInstrumentSimulatorisrelativelyexpensivecomputationally,andgiventhesizeofthestudydomainwearetestinganapproachtoapproximatetheerrorsatthe1-kmscale.Theapproachinvolvestheselectionofrepresentativesubdomains,whichmakerunningtheInstrumentSimulatormoretractable,andthe

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derivationofprobabilitydistributionfunctions(PDF)oftheerrors(afterbeingaggregatedto1-km)conditionedtophysiography(topography,riverwidth,landcover).TheerrorPDFsthenareusedtosampleerrorsfortheentiredomain,matchingtheorbitalcharacteristics(i.e.spatialcoverage)ofSWOT.Figure3showsanexampleofthegeneratedsyntheticSWOTobservationsforanumberofpassesoverthestudydomain.

Figure3.ExampleelevationmapsoverUpperMississippiRiverbasinfordifferentsatelliteoverpasses.

Animportantaspectofthedataassimilationalgorithmsisthedefinitionoftheobservationoperator(i.e.themappingfunctionalbetweenthepredictedvariablesandobservations).Inthecaseofhydrodynamicmodeling,thisbecomescomplicatedduetotheerrorsinrivertopologybetweenthe"truth"andopen-loopsimulations.Inaddition,theexperimentaldesignintroducederrorsinmultipleparameters(inflows,channelwidthanddepth,roughness)makingtheassessmentofSWOTdataassimilationmorerealisticthanpreviousandcurrentwork.Inordertoaccountforthedifferingrivertopology,weperformedtheassimilationin"reprojected"coordinatesexpressingthevariablesintermsofflowdistance.

Figure4showsacomparisonofthethreedataassimilationalgorithmswiththeopen-loopand"truth"simulationsintermsofthedownstreamprofileofWSEofariverreachoftheMisouriRiver.TheLETKFappearstooutperformtheothertwoKalmanfiltervariantsbybetterreproducingthepools,whichcouldbeattributedtothelocalizationinherentinthealgorithm.Nonetheless,allassimilationalgorithmsimprovetheestimationofWSEovertheentirelengthoftheriverreach,whencomparedwiththepriorestimate.

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Figure4.ComparisonofalgorithmsassimilatingsyntheticSWOTobservationswithrespecttowatersurfaceelevationspatialprofilesoverareachoftheMissouriRiveronspecificdate.

WeexpecttotestandestablisharobustmodelingandassimilationframeworkforSWOTobservations,anddemonstrateitsfeasibilityforoperationalimplementationovercontinental-scaleriverbasinsaswellasglobally.Theaddedvalueofthehigher-levelversustheinstantaneous(Level2)dataproductwillbeassessedbycalculatingandthencomparingtemporally-aggregateddischargeandstoragechangeforweekly,monthly,andseasonalperiods.Theevaluationoftheproductwillalsobeperformedseparatelyforrivers,lakes,wetlandsandreservoirsandtheerrorswillbelinkedtocharacteristicssuchasbasinphysiography,riverchannelwidth/slopeetc.providingsomeinsightintotheexpectederrorsinareasotherthanourtestcases.

TheframeworkwearedevelopingwillproducetemporallycontinuousestimatesofdischargethatcanalsobeusedtocalibratetheVIChydrologymodel.Streamflowistheresponseoftheintegrationofrunoffinspaceandtime(mathematicallyrepresentedbytheroutingmodel).Hence,runofffieldscanalsobeusedtocalibratehydrologicmodels(onagridcellbygridcellbasisforspatiallydistributedmodels).ThederivationofrunofffieldsfromSWOTobservationscouldgreatlyfacilitatehydrologicmodelcalibration,whichisinmanyrespectstheAchillesHeelofhydrologicmodeling.Runoff,thekeyquantityproducedbyspatiallydistributedmodels,isnotdirectlyobserved,andinsteadstreamflowmeasurementsaretypicallyused,whichhasmanydrawbacks(includingpoorlyposedmodelidentification,whichcanresult,forinstance,in“cliffs”atbasinboundaries.Streamflowisanintegratedmeasureofthehydrologicprocessesoftheriverbasin;hencethehydrologicsignalattheoutlet(orstreamflowmeasurementlocation)losesanyspatialandtemporalinformationupstreamatsmallerorshorterscales.Theworkofthisprojectcouldalleviatetheselimitationsbyfacilitatingandallowing

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theestimationofspatiallydistributedmodelparametersaswellasatungaugedbasins.