National Aeronautics and Space Administration

Global Modeling and Assimilation Office NASA Goddard Space Flight Center

GMAO RESEARCH BRIEF

Using SMAP Soil Moisture Data to Calibrate a Land Surface Model Randal D. Koster, Sarith P. P. Mahanama, and Rolf H. Reichle

BACKGROUND

SUMMARY SoilmoistureretrievalsfromtheSoilMoistureActivePassive(SMAP)mission are used to evaluate and calibrate the treatment of soilmoisturerechargeintheGMAOCatchment landsurfacemodel.Theimprovements lead to better simulations of soil moisture andstreamflow, as demonstrated through comparisons againstindependentinsitudata.

#2017-04, May 2017

SatellitedatahavethepotentialtotransformourunderstandingoftheEarthsystemthroughtheimprovedquantificationofvariationsinthatsystem. Much of the GMAO’s focus is on assimilating satelliteinformationdirectly intoGMAOmodelsinordertoproduceoptimal,quantitativeestimatesforacomprehensivesetofEarthsystemstates.A complementary approach toward improved state estimation,however, is also worth considering – we can use the satelliteinformationtoimprovetheparameterizationsunderlyingthemodelsthemselves.

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

Global Modeling and Assimilation Office NASA Goddard Space Flight Center

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NASA’s Soil Moisture Active-Passive (SMAP) mission provides, among other things,estimates(orretrievals)ofthemoisturecontentinthetopseveralcentimetersofsoilataresolutionofroughly40kmacrosstheglobeandwitharevisittimeoflessthanthreedays.Variousaspectsofmissiondesign(useofL-bandfrequencies,reductionofimpactsfromnon-natural radio-frequency interference, etc.) give SMAPdataanaccuracy, andthus a hydrological utility, not achievable with other satellite-derived soil moisturedatasets(see,e.g.,Kosteretal.2016).Giventhatthedatahaveonlybeenavailabletousers since spring of 2015,muchof thewealth of hydrological information containedwithinthemisstillwaitingtobetapped.

TheGMAOhasastrongconnectiontotheSMAPmission,asithoststheproductionoftheSMAPLevel4products–enhancedproducts(includingrootzonesoilmoistureandcarbonfluxestimates)achievedthroughdataassimilation.Dataassimilation,however,isnottheonlymeansbywhich theSMAPdatacan interactwithGMAOmodeling.SoilmoisturebehaviorasrevealedbytheSMAPretrievalscouldalsoguidetheevaluationandfurtherdevelopment of the GMAO land surfacemodel (LSM) itself. This land surfacemodel,referred to here as the Catchment LSM, features state-of-the-art treatments of landprocesses, including an explicit representation of the effects of subgrid soil moistureheterogeneityonthesurfaceenergyandwaterbalances(Kosteretal.2000).ThisLSM’streatmentofnear-surfacemoistureandhow it relates to the rootzone,however,hasneverbeenproperlycalibratedandthusmaybenefitfromacarefulanalysisoftheSMAPdata.

Theideaexaminedhereissimple:SMAPprovidesforthefirsttimeahighlyaccurateglobalpictureofhowsurfacesoilmoisturevariesintime–howitincreases,forexample,withprecipitation and how quickly a rainfall-induced soil moisture anomaly dissipates.Through careful joint analysis of SMAP data and the structure of the Catchment LSMparameterizations,thelattercanbemodifiedtobehavemorerealistically.

ThemodelbehaviortargetedforimprovementinthepresentstudyisillustratedinFigure1.Figure1ashowsthetimeseries(May–September,2016)ofSMAPretrievalsatagridcell containing the LittleWashitawatershed in southwesternOklahoma (O’Neill et al.2016),andFigure1bshowsthecorrespondingtimeseriesofsoilmoisturesimulatedbytheoperationalversionoftheGMAOCatchmentmodelwhendrivenwithobservations-basedmeteorologicalforcing(rainfall,airtemperatures,windspeeds,etc.)Thetwo-timeseries have a distinctly different character. Consider, for example, the speed of soilmoisturedrydown following the rainfall-induced increaseonday43.According to the

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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SMAPdata,thesoildriesquickly, losingmostoftheaddedmoisturewithinafewdaysandreturningtoitsdrieststateinabouttendays.Inthedefaultlandsurfacemodel,ontheotherhand,thedrydownfollowingtheimpositionofrainfallonday43isclearlymoregradual.

TheCatchmentLSM’sformulationofsurfacesoilmoisturedynamicsincludesatreatmentof the replenishmentofdryingsoilvia recharge frombelow.Aparameter (referred tohere as α)was recently incorporated into the code to control this replenishment – αreduces the ability of soilmoisture to flow upward against gravity in non-equilibriumsituations, to account for the fact that near-surface soils in nature are moreheterogeneousthanthosetestedinlaboratories.Thevalueofαturnsouttohaveafirstorderimpactonthecharacterofthesimulatedsoilmoisture,asillustratedinFigure1c.Atthisgridcell,replacingα’sdefaultvalueof1withavalueof0.01producesabettermatch(intermsoftemporalvariability,speedofdrydown,etc.)withtheSMAPdata.Inacalibrationexercise,anumberofsimulations,eachutilizingadifferentvalueforα,generatedsoilmoisturetimeseriesfor2015-2016acrossthecontinentalUSandportionsof Canada and Mexico. At each 36km × 36km grid cell, by comparing the differentsimulated time series to the local time series of SMAP retrievals, we were able todetermine the single α value that produces the closest reproduction of the SMAP

Figure1.TimeseriesofSMAPretrievals(a)andmodel-simulatednear-surfacemoisture(b,c)atagridcellcenteredontheLittleWashitawatershedinsouthwesternOklahoma.Thefirstmodelsimulatedtimeseriesusesavalueof1forthecalibratedparameterα(thedefaultvalueinGEOS-5systems),andthesecondusesanαvalueof0.01.

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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retrievals.(ClosenesshereismeasuredbythetemporalcorrelationcoefficientbetweentheSMAPretrievalsandthemodelestimates.)Figure2showsamapoftheseoptimizedαvalues.Noticethatthedefaultvalueof1(again,standardinGEOS-5operations)worksbestatonlyahandfuloflocations.

Wenowaddressanimportantquestion:whenasimulationutilizingthedefaultαvalueof1everywhere(the“control”simulation,runovertheyears2001-2016)iscomparedtoonethatusestheoptimizedαvaluesinFigure2(the“experiment”simulation,runoverthe same years), is the latter’s skill in reproducing independent (i.e., non-SMAP)hydrologicalmeasurementshigher?Theanswerisyes:

a) InsitumeasurementsofsoilmoistureatSMAPcorevalidationsites.TheSMAPmission is working with local partners who provide in situ soil moisturemeasurementssuitableforvalidatingSMAPdataproducts.Thesemeasurementsarecomprehensiveenoughspatiallytoprovidereliableestimatesoflarge-scale,areally-averagedsoilmoisture.FourofthesesitesprovidedataforseveralyearspriortothelaunchofSMAP.Figure3showsthetemporalcorrelationsbetweentheCatchmentLSMresultsandtheinsitumeasurementsateachofthesefoursites,withresultsforthecontrolsimulationinredandthosefortheexperimentsimulationinblue.Inallcases,andespeciallyforReynoldsCreek,WalnutGulch,andLittleRiver(forwhichthedifferencesinthecorrelationsshownaresignificantat the99.9%confidence level,evenunder theassumptionthat thenumberofindependent data values is 1/10 the total numberof values), theoptimizedα

Figure2.Optimalvaluesofstudiedmodelparameter,asdeterminedbyoptimizationagainstSMAPretrievaltimeseries.

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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valueshaveledtoanimprovedagreementwiththeinsitumeasurementsforthe~10 year subset of the simulation period over which measurements wereavailable.The temporal correlation is themeasure of simulation skill that we aremostinterestedinimprovinghere,giventhattheinformationwewanttoreproducemostwiththeCatchmentLSMislargelycontainedwithinthetimevariabilityoftheobservedsoilmoisture.An“error”intheabsolutemagnitudeofasimulatedsoilmoisturevariable(fromanyLSM)isinfactlargelyareflectionofthemodel-dependent nature of that variable; differences in observational and LSM-generated soil moistures are expected, do not necessarily affect modelperformance,andarelargelycorrectableasneededafterthefact(Kosteretal.2009).Thissaid,itisstillofinteresttoexaminetheimpactsofourcalibratedαvaluesontheabsolutemagnitudesofthesimulatedsoilmoistures,asmeasuredby the rootmeansquareerror (RMSE) relative to theobservations.TheRMSEdecreasedbycloseto30%forReynoldsCreek,WalnutGulch,andLittleRiver,anditincreasedbyabout10%forLittleWashita(notshown).Thislargelyreflectsareductionintheoverallmodelbias.Resultsfortheunbiasedrootmeansquareerror(ubRMSE)weremixed,beingbetterforonly2outofthe4sites(notshown).The reader is referred to Entekhabi et al. (2010) for a discussion of theconnectionsbetweenthetemporalcorrelation,theRMSE,andtheubRMSEskillmetrics.

b) Insitumeasurementsofsoilmoistureatsparsenetworksites.Anumberofinsitusoil moisture measurement sites are encompassed by the USDA NaturalResourcesConservationServiceSoilClimateAnalysisNetwork(SCAN;Schaeferetal.2007)andtheUSClimateReferenceNetwork(USCRN;Diamondetal.2013,Belletal.2013).Whilethesesites,unliketheabovecoresites,arenotdesignedtoprovidethelarge-areaestimatesproducedbybothSMAPandtheLSM,bothnetworkshavetheadvantageofencompassingthecontinentalUS.andtherebycoveringabroadrangeofsoil texturesandbackgroundclimates(Reichleetal.2016).

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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Figure4showsthetemporalcorrelation,averagedoverthecomponentsitesofeach network, between the simulated and observed time series of surfacemoistureforboththecontrolandtheexperimentsimulations.Themodel’sabilityto reproduce theobservedsurfacemoisture is improvedwith theoptimizedαvalues.ThisisparticularlytruefortheSCANnetwork;theimprovementforthisnetworkextendswellbeyondtheindicateduncertaintiesintheaverages,whicharequantifiedhereas inReichleetal. (2016). (Bytheseuncertaintymeasures,theimprovementfortheUSCRNnetwork,whilepositive,maynotbesignificant.)Resultsforothersoilmoisturemetrics(notshown)aresomewhatmixedbutstillindicate improved overall performance through the use of the optimized αvalues.Whileforbothnetworkstheoptimizedvaluesleadtoreducedtemporalcorrelationsbetweensimulatedrootzonemoisturecontentsandcorrespondingmeasurements, the reductions are not significant, lying well within themuchlargeruncertaintylevelsfortherootzonemetric–soilmoisturemeasurementsin the root zone are fewer in number and in someways aremore difficult tointerpret.Theabsolutebias forsurfacemoisture is significantly improvedwiththe optimized α values for both networks, as is the ubRMSE for the USCRNnetwork.

Figure3.TheLSM’sabilitytoreproduceobservedsurfacesoilmoisture(asmeasuredbythetemporalcorrelationcoefficient)atthefourSMAPcorevalidationsiteswithmultipleyearsofmeasurements.Resultsareshownforthecontrolsimulation(usingthedefaultmodel,redbars)andtheexperimentsimulation(usingtheoptimizedmodel,bluebars).

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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c) Observed runoff ratios. Mahanama et al. (2012) evaluated the streamflows

producedbytheGMAO’sCatchment landsurfacemodelagainsttimeseriesofnaturalizedstreamflows(derivedfromstreamgaugeobservations)inanumberofhydrologicalbasinsinthecontinentalUS.Thesenaturalizedobservationsareused in Figure 5 to evaluate the relative performance of the control andexperimentsimulationsinreproducingobservedrunoffratios(long-termaveragestreamflowdividedbythelong-termaveragerainfallintheareaupstreamofthestreamgaugesite).Whiletheexperimentsimulationstillproduceslargeerrorsinrunoff ratio in three of the basins (an error that can be treated using otherapproaches–seeKosterandMahanama2012forfurtherdiscussionofthiserror),ineverycasetheexperimentsimulationimprovesoverthecontrolsimulation–the SMAP-based tuning of the recharge parameter has translated into animprovementinstreamflowsimulation.

Figure4.Thelandmodel’sabilitytoreproduceobservedsurfacesoilmoistureattwonetworksofsitescoveringthecontinentalUS,asmeasuredbytheaveragevalueofthetemporalcorrelationcoefficientoverthesites.Resultsareshownforthecontrolsimulation(usingthedefaultmodel,redbars)andtheexperimentsimulation(usingtheoptimizedmodel,bluebars).Errorbarsindicate95%confidenceintervals.

Figure5.AbsoluteerrorsinrunoffratioforthetenlargestbasinsexaminedbyMahanamaetal.(2012).(Resultsforthesmallerbasinsaresimilar.)Redbarsareforthecontrolsimulationandbluebarsarefortheexperimentsimulation.

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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Insummary,utilizingSMAPdatatocalibrateaparameterinthesurfacerechargemoduleoftheGMAO’sCatchmentlandsurfacemodelleadstoimprovedmodelperformance,asdemonstratedbycomparisonsofsimulatedhydrologicalstatesandfluxestoseveralsetsofSMAP-independentobservations(aswellastoothersnotshownhere).Figure6putsthisresultinperspective,highlightingthecomplementarynatureofthiscalibrationeffortandthedataassimilationworkalsounderwayintheGMAO.Figure6ashowsthestandardland modeling approach to producing soil moisture states; a land model driven withobservations-basedmeteorologicalforcing(rainfall,etc.)produces,asamatterofcourse,estimatesforhydrologicalstates(e.g.,soilmoisture)andfluxes(e.g.,evapotranspirationandrunoff).IntheGMAO,SMAPdataareassimilatedintothesystem(Figure6b),guidingthestatestowardmorerealisticvalues.Thisisquitedistinctfromthecalibrationeffortdescribedabove(Figure6c)whereintheSMAPdataareusedtochangethemodelitself.Wecaninfactspeculatethatacombinationofthetwoefforts(or,moreambitiously,theinclusion of parameter estimation along with soil moisture state estimation in anenhancedGMAOdataassimilationeffort)wouldleadtostateandfluxestimatesofevenhigherquality(Figure6d).

Figure6.SchematicshowingmeansbywhichSMAPdatacanimprovemodel-basedestimatesofhydrologicalstatesandfluxes

GMAO RESEARCH BRIEF Using SMAP Soil Moisture Data to Calibrate a Land Surface Model

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References

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