using smap soil moisture data to calibrate a land surface ......figure 1. time series of smap...

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

Global Modeling and Assimilation Office NASA Goddard Space Flight Center

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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

Global Modeling and Assimilation Office NASA Goddard Space Flight Center

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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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