report on precipitation error modeling and ensemble error …earth2observe.eu/files/public...

This project has received funding from the European Union’s Seventh Programme for research,technological development and demonstration under grant agreement No. 603608

DG Research –FP7-ENV-2013-two-stage

Global Earth Observationfor integrated water resource assessment

Report on precipitation error modeling and ensembleerror propagation using LSM and GHM models fromtier 1 reanalysis

Deliverable No: D.4.2 – Report on precipitation error modelling and ensemble error propagation using LSMand GHM models from tier 1 reanalysis

Ref.: WP4 - Tasks 4.2Date: Aug 2016

WP4 - Task 4.2 – D.4.2Report on precipitation error modelling and ensemble error propagation using

LSM and GHM models from tier 1 reanalysis

Deliverable Title D.4.2 – Report on precipitation error modeling andensemble error propagation using LSM and GHM modelsfrom tier 1 reanalysis

Filename E2O_report_D42_v3.docxAuthors Efthymios Nikolopoulos (ITC SA)

Marios Anagnostou (ITC SA)

Contributors Clement, Albergel (Meteo France)Emanuel, Dutra (ECMWF)Gabriel, Fink (Uni Kassel)Alberto, Martinez de La Torre (CEH)Simon Munier (Meteo France)Jan, Polcher (CNRS)Pere, Quintana-Segui (Observatori de l’Ebre)

Reviewers Emmanouil Anagnostou (University of Connecticut, USA)Date 04/08/2016

Prepared under contract from the European CommissionGrant Agreement No. 603608Directorate-General for Research & Innovation (DG Research), Collaborative project, FP7-ENV-2013-two-stage

Start of the project: 01/01/2014Duration: 48 monthsProject coordinator: Stichting Deltares, NL

Dissemination level

X PU Public

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the CommissionServices)

CO Confidential, only for members of the consortium (including the CommissionServices)

WP4 - Task 4.2 – D.4.2Report on precipitation error modelling and ensemble error propagation using

LSM and GHM models from tier 1 reanalysis

Deliverable status version control

Version Date Author Reviewer Singed-off by

1.0 29/07/2016 Efthymios,Nikolopoulos(ITC, SA)

Emmanouil,Anagnostou(University ofConnecticut, USA)

First, Last Name(Institute)





WP4 - Task 4.2 – D.4.2Report on precipitation error modelling and ensemble error

propagation using LSM and GHM models from tier 1reanalysis

i

Table of Contents1 ExecutiveSummary................................................................................................................................. 42 Introduction............................................................................................................................................... 53 StudyAreaandData................................................................................................................................ 54 ModellingSystems.................................................................................................................................... 8

4.1 HTESSEL-CaMa........................................................................................................................... 104.2 JULES.............................................................................................................................................. 114.3 ORCHIDEE.................................................................................................................................... 134.4 SURFEX-TRIP.............................................................................................................................. 144.5 WaterGAP3................................................................................................................................... 16

5 AnalysisofEnsembleSimulations.................................................................................................... 185.1 Inter-comparisonofmulti-modelmulti-forcingsimulations......................................18

5.1.1 SurfaceRunoff(Qs) 20

5.1.2 SubsurfaceRunoff(Qsb) 22

5.1.3 Evapotranspiration(ET) 24

5.2 Comparisonwithindependentobservations....................................................................266 StochasticErrorModellingofPrecipitation.................................................................................. 29

6.1 Methodology................................................................................................................................ 296.1.1 QuantileRegressionForests(QRF) 29

6.1.2 MetricsofModelPerformanceEvaluation 35

6.2 EvaluationoftheErrorModelandDiscussionofResults.............................................357 ClosingRemarks..................................................................................................................................... 44References...................................................................................................................................................... 45



ii

List of FiguresFigure3.1.MapofIberianPeninsula.Dashrectangularboxdenotestheextentoftheanalysisdomain.......................................................................................................................................... 6Figure4.1:HTESSELsurfaceandsubsurfacediscretizationscheme......................................11Figure4.2:Julesmodelconcept..........................................................................................................13Figure4.3:ORCHIDEEriverroutingscheme.Thehydrologymodulecomputesthepartitioningofprecipitation(P)intoinfiltration(I)andsurfacerunoff(R)andthenintoevapotranspitration(ET)andsubsurfacerunoff(D)foreachgridbox.................................14Figure4.4:SchematicrepresentationoftheISBAsoilhydrologicalandthermalcolumnforfoursoil/vegetationtypes.Thesoiltemperature,T,andmoisture,w,nodesarecollocated.Toextendthetemperatureprofileuptoadepthof12m,thesoilwatercontentisextrapolatedtoeachdeepernodes................................................................................15Figure5.1.Spatialmapofrelativedifferenceinaverage3hprecipitationwithrespecttoSAFRANdataset.......................................................................................................................................20Figure5.2.Q-Qplotof3hprecipitationintensitybetweenSAFRANandthesatellite/reanalysisproductsexamined...........................................................................................20Figure5.3.Relativedifferenceofsimulatedsurfacerunoff(Qs)withrespecttoSAFRANbasedsimulation.Notethatscaleofrelativedifferencevariesfrom40%underestimation(darkblue)to40%overestimation(darkred)..............................................................................23Figure5.4.Q-Qplotforsimulatedsurfacerunoff(Qs)betweenSAFRANandthesatellite/reanalysisprecipitationproductsused.Resultsareshownforallmodelingsystemsinvolved.....................................................................................................................................22Figure5.5.Relativedifferenceofsimulatedsubsurfacerunoff(Qsb)withrespecttoSAFRANbasedsimulation.Notethatscaleofrelativedifferencevariesfrom40%underestimation(darkblue)to40%overestimation(darkred)............................................24Figure5.6.Q-Qplotforsimulatedsubsurfacerunoff(Qsb)betweenSAFRANandthesatellite/reanalysisprecipitationproductsused.Resultsareshownforallmodelingsystemsinvolved.....................................................................................................................................24Figure5.7.Relativedifferenceofsimulatedevapotranspiration(ET)withrespecttoSAFRANbasedsimulation.Notethatscaleofrelativedifferencevariesfrom40%underestimation(darkblue)to40%overestimation(darkred)............................................25Figure5.8.Q-Qplotforsimulatedevapotranspiration(ET)betweenSAFRANandthesatellite/reanalysisprecipitationproductsused.Resultsareshownforallmodelingsystemsinvolved.....................................................................................................................................26Figure5.9.CorrelationcoefficientbetweennormalizeddeviatesofSSMvaluesofHTESSELsimulationsandsatellitesoilmoistureestimates......................................................27Figure5.10.CorrelationcoefficientbetweennormalizeddeviatesofSSMvaluesofJULESsimulationsandsatellitesoilmoistureestimates.........................................................................28Figure5.11.CorrelationcoefficientbetweennormalizeddeviatesofSSMvaluesofSURFEX-TRIPsimulationsandsatellitesoilmoistureestimates.............................................28Figure6.1.TimeseriesofcumulativerainfallofCMORPH,PERSIANN,3B42(V7),re-analysisrainfall,andQRFensemblemembersforwarmseason.............................................32Figure6.2.TimeseriesofcumulativerainfallofCMORPH,PERSIANN,3B42(V7),re-analysisrainfall,andQRFensemblemembersforcoldseason................................................32Figure6.3.QRF-generatedmeanensembleforwarmseason..................................................33Figure6.4.QRF-generatedmeanensembleforcoldseason.....................................................34Figure6.5.CenteredRootMeanSquareerrorforwarmseason.............................................36Figure6.6.CenteredRootMeanSquareerrorforcoldseason................................................37Figure6.7.MeanRelativeErrorforwarmseason.......................................................................39



iii

Figure6.8.MeanRelativeErrorforcoldseason...........................................................................40Figure6.9.Exceedanceprobabilityforwarmseason.................................................................41Figure6.10.Exceedanceprobabilityforcoldseason..................................................................41Figure6.11.Uncertaintyratioforwarmseason...........................................................................43Figure6.12.Uncertaintyratioforcoldseason..............................................................................44

List of TablesTable3.1.Informationonprecipitationproductsused...............................................................7Table4.1.Detailsonmodelingsystems............................................................................................. 8


propagation using LSM and GHM models from tier 1 reanalysis

4

1 Executive Summary

Oneoftheprincipalobjectivesofthisprojectistheintegrationofearthobservationdatasets(e.g.precipitation,soilmoisture,evapotranspiration)with theatmosphericreanalysisandglobalscalemodels(hydrologicandlandsurfacemodels)inordertodevelopanadvancedwater resource reanalysis product. Precipitation is arguably one of the most importantforcingvariables thatdrive terrestrialwatercycleprocesses.Theprocessofprecipitationexhibitssignificantvariability inspaceandtime, isassociatedwithdifferentwaterphases(liquid or solid) and its dynamics depends on several factors (aerosols, orography, etc.),which make its quantification and prediction a particularly challenging task. As such,precipitationinformationfromdifferentsatellitesensorsandassociatedearthobservationproducts is associatedwith uncertainty. Propagation of this uncertainty into hydrologicsimulations canhave a considerable impact on the accuracy of the simulated hydrologicvariables. Therefore, to make hydrologic predictions more usable in water resourceapplications, it is important tostudythe impactofprecipitationuncertainty inhydrologicsimulationstounderstandapplicabilitylimitationsandinvestigatewaystominimizeit.Theprimarygoalof thisreport is todocument theuncertaintyassociatedwithdifferentearthobservation precipitation datasets and the propagation of this uncertainty in hydrologicsimulations for a number of global hydrologic and land surfacemodels. Specifically, thereport presents a comparative analysis of multi-model/multi-forcing simulations for anumberofdifferenthydrologicvariables.Evaluation results are reportedwith respect toreference-based simulations, as well as independent observations from satellite-basedestimates. In addition, the report presents initial results from the development of astochasticprecipitationerrormodel forcharacterizing theprecipitationuncertainty fromthe different satellite products. Through an optimal Bayesian blending framework thestochastic error model produces ensemble precipitation realizations that on averageperform better than the individual earth observation datasets used to force the model,whilethepredictedensembleenvelopsaccuratelyencapsulatetheprecipitationestimationuncertainty.



5

2 Introduction

Work package 4 (WP4) of the eartH2Observe project involves the validation of earthobservation products (developed in Work Package 3) for use in regional and globalhydrologicandlandsurfacemodelingthatsupportsglobalwaterresourcereanalysis(WorkPackage5)andregionalcasestudies(usedinWorkPackage6).EvaluationofEOproductsisbasedonhigh-resolutionandhigh-qualitygroundreferencedatasetsthatrepresentseveralhydro-meteorological variables (precipitation, soil moisture, evapotranspiration, groundwater)andwaterquality.Theseerrorinvestigationswillfeedbacktomodelingactivitiesbyproviding baseline performance and improvement verifications for the various Earthobservation retrieval algorithms and modelling systems used in the water resourcereanalysis.Ultimately, the errorpropagation research in this studywill :1)quantify theimpactofusingEOdata improvement inensemble-basedhydrologicalpredictionof floodpeaks,streamflows,soilmoisture,andgroundwater;2)provideanoverallunderstandingoftheuncertaintyintheEOproductsandEO-drivenwaterresourcesmodelsthatisneededforoptimaldata-model integration leading to thewater resources reanalysisofWP5;and itsrelationship to basin scale and end-user applications (e.g. floods, droughts, basinwaterbudgets,streamflowsimulations).In the herein report we document the uncertainty associated with the different earthobservation precipitation datasets and the propagation of precipitation uncertainty inhydrologicsimulationsforanumberofglobalhydrologicand landsurfacemodelsused inthe globalwater resources reanalysis. Specifically,we discuss a comparative analysis ofmulti-model/multi-forcing simulations using as reference the reference precipitation-driven simulations, as well as independent observations from satellite-based estimates.Second part of this report presents initial results from the development of a stochasticprecipitation errormodel aimed at characterizing theprecipitation uncertainty from thedifferentsatelliteproducts,andthroughitsoptimalBayesianblendingframework,produceensembleprecipitationrealizations thataresuperior toeach individualearthobservationdatasets,andaccuratelyencapsulatethevariabilityofprecipitationestimationuncertainty.

3 Study Area and Data

Thestudyareaselected for this task is the IberianPeninsula(Figure3.1)withparticularfocus over Spain,which represents one of themost data-rich regions and is part of theMediterraneancasestudyofEasth2Observeproject.Theregion isprimarilycharacterizedbythreemainclimaticzones(Mediterranean,oceanicandsemiarid).Mediterraneanclimatedominates over the Iberian Peninsula apart from the northern part, which is mostlycharacterized by oceanic climate and the southeastern part, which is characterized bysemiaridclimate.Topographyintheareavariesconsiderablyfromlowelevationincoastalareastomountainousterrainat3500melevationinthePyrenees.



6

Figure3.1.MapofIberianPeninsula.Dashrectangularboxdenotestheextentoftheanalysisdomain.

Oneofthemainreasonsthatmadetheareasuitableforinvestigationofprecipitationerrorpropagation was the availability of a high quality precipitation product derived from adense in situ gauge network and was used as our reference precipitation datasetthroughout the analysis. The reference precipitation dataset (referred to hereinafter asSAFRAN) was developed following the SAFRAN methodology over Iberian Peninsula(Quintana-Segui et al. 2016). In addition to the SAFRAN product, a number of otherprecipitationdatasetsincludingthreewidelyusedsatellite-precipitationproducts(3B42v7,CMORPHandPERSIANN)and theECMWF tier2reanalysisproduct(WRR2)wereused toconstruct the ensemble precipitation forcing thatwas used for the ensemble hydrologicsimulationsandcharacterizationofforcinguncertainty.The3B42v7(Huffmanetal.2007)is thegauge-adjustedproductof thenear real-time3B42productof theTropicalRainfallMeasuring Mission Multi-satellite Precipitation Analysis (TMPA). The PERSIANN(Precipitation Estimation from Remotely Sensed Information using Artificial NeuralNetworks)product(Sorooshianetal.2000)isthebiasadjustedversionbasedontheGPCP(GlobalPrecipitationClimatologyProject)product.TheCMORPH(ClimatePredictionCenterMORPHingtechniqueofNationalOceanicandAtmosphericAdministration)product istherecentlyavailablegauge-adjustedversionutilizingnearly30,000gaugesworldwide(Xieetal. 2011). The ECMWF WRR2 reanalysis corresponds to the tier 2 reanalysis baselineproductproducedintheframeworkofEarth2ObserveprojectbyEmanuelDutraofECMWF



7

using a downscaled version of ERA-Interim (Dee et al., 2011) and a gauge adjustmentprocedurebasedontheGPCCv7globalraingaugeprecipitationanalysis.Table3.1belowsummarizesinformationandreferencesoftheseproductsthatcanbeusedtoobtainmoredetailsondataandproductionalgorithms.

Table3.1.Informationonprecipitationproductsused.

PrecipitationProduct

OriginalResolution

(spatial/temporal)

References Details

SAFRAN 5km/1h Quintana-Seguietal.(2016) Contact developer [email protected]

3B42v7 0.25o/3h Huffmanetal.(2007)

Technical Documents:3B42_3B43_doc

Download Link:ftp://disc2.nascom.nasa.gov/ftp/data/s4pa//TRMM_L3/TRMM_3B42/

PERSIANN 0.25o/3h Sorooshianetal.(2000)

Technical Documents:http://fire.eng.uci.edu/PERSIANN/adj_persiann_3hr.html\

Download Link:http://fire.eng.uci.edu/PERSIANN/data/3hrly_adj_cact_tars/

CCRC 0.25o/3h Joyce et al. (2004),Xie et al.(2011)

Technical Documents:EGU_1104_Xie_bias-CMORPH

Download Link:ftp://ftp.cpc.ncep.noaa.gov/precip/CMORPH_V1.0/CRT/0.25deg-3HLY/

WRR2 0.25o/3h

https://publicwiki.deltares.nl/display/INFORMED/Precipitation+corrections+for+WRR2

DownloadLink:

https://wci.earth2observe.eu/thredds/catalog/ecmwf/met_forcing_v1_rgpcc/catalog.html

Twoimportantnotesregardingtheprecipitationdataarethefollowing:

· All data products were processed to match the spatiotemporal resolution of0.25deg/3hourly spatio-temporal scale. This is the nominal resolution of the



8

satelliteproductsused,soessentiallyonlytheSAFRANdatasethadtobeaggregatedinspace-timesinceitwasoriginallyavailableathigherresolution(5km/1h).

· DataweremergedwiththeWRR2rainfallfiles(e.g.“Rainf_EI_RGPCC”files)providedbyEmanuelDutra (ECMWF).Themergingwasperformed taking intoaccount thepresence of snow as indicated in the corresponding “Snowf_EI_RGPCC” files.Specifically, “Rainf” values from the rainfall products were used to replace theoriginalvaluesinthe“Rainf_EI_RGPCC”filesonlywhensnowwasnotpresentwithinagivenday.Thiswasdone inordertoavoidusingsatelliteprecipitationestimatesduring snow occurrence, due to the known limitation of satellite precipitationestimatesofsolidprecipitation.

4 Modelling Systems

Thissectionprovidesanoverviewoftheglobalhydrologicmodels(GHM)andlandsurfacemodels(LSM)participated in this task.Note thatTable4.1 thatsummarizessomeof themodel structural characteristics as well as the model description text provided in thissectionaretakenfromsection4ofreportondeliverable5.1(DutraE.,2015).

Table4.1.Detailsonmodelingsystems.



9

M

odellingsystem

sInterception

EvaporationSnow

Soil

GroundWater

RunoffReservoirLakes

RoutingW

ateruseM

odeltim

estepReferences

H

TESSEL-CaMa

Yes,single

reservoir,potentialevaporation

Penman-

Monteith

Energybalance,1layer

4LayersN

otrepresented

Saturationexcess

Not

represented

CaMa-Flood

Riverchannel,floodplains,localInertialequation

Not

represented1hour

Balsamoetal.

(2011);Yam

azakietal.(2011)

JULES

Yes,single


Penman-

Monteith

1composit

esoil/snowlayer

4layersN

otrepresented

Infiltrationcapacity

andsaturationexcess

No

No

representedN

otrepresented

1hour(Best

etal.

2011);(Clarketal.2011)

O

RCHID

EESinglereservoirstructuralresistance

toevaporation

BulckETP

(Barella-Ortizetal.2013)

1moisture

layer,1-5therm

odynam

iclayers

11Layers

2reservoirsGreen-Am

ptinfiltration+gravitationaldrainage

No

linearcascade

ofreservoirs

atthe

sub-gridlevel

irrigationonly

900senergy

balance,3hoursrouting

(d'Orgevaletal.2008)

SURFEX-TRIP

Yes,single


Penman-

Monteith

Energyandm

assbalance,12layers

14Layers

Onlyadeep-

waterreservoir

thatonlydelaysthedeepflow

contributiontothe

surfaceriver

Horton

andDunnerunoff(infiltrationcapacity

andsaturationexcess)

Not

represented

TRIPw

ithstream

and

deep-water

reservoirat

0.5°

Not

represented900sforISBA3600s

forTRIP

(Decharme

etal.2013)(Decharm

eet

al.2011)

W

aterGAP3Yes,

singlereservoir

Priestley-Taylor

Degree-day,

1layer

1layerRenew

ablegroundw

ater,single-layerlinearreservoir

BetafunctionYes

Manning-

StricklerYes,representedfor

fivesectors:irrigation,livestockfarm

ing,dom

estic,m

anufacturing,

thermal

elec.production

1dayVerzano(2009)

Flörkeetal.(2013)



10

4.1 HTESSEL-CaMa

The land surface model (LSM) HTESSEL (Hydrology Tiled ECMWF Scheme for SurfaceExchangesoverLand)computesthe landsurfaceresponsetoatmosphericconditions,andestimates the surface water and energy fluxes and the temporal evolution of soiltemperature, soil moisture content, vegetation interception and snowpack conditions.Thesearecomputedforeachgrid-pointindependently,i.e.thereisnohorizontalinteractionbetween each surface/soil column. At the interface to the atmosphere each grid box isdividedintofractions(tiles),withuptosixfractionsover land(bareground,lowandhighvegetation, interceptedwaterandshadedandexposedsnow)(seeFigure4.1).Vegetationtypes and cover fractions are derived from an external climate database, based on theGlobal Land Cover Characteristic (Loveland et al. 2000). The grid box surface fluxes arecalculated separately for each tile, leading to a separate solution of the surface energybalanceequationandskintemperature.

Figure4.1:HTESSELsurfaceandsubsurfacediscretizationscheme

Theinterceptionreservoirisathinlayerontopofsoil/vegetation,collectingliquidwaterbytheinterceptionofrainandthecollectionofdew,andevaporatingatthepotentialrate.Themaximumcapacityofthisreservoirisafunctionofthegrid-boxleafareaindex.Thesnowrepresentsanadditional layeron topof theuppersoil layer,with independentprognostic,thermalandmasscontents(Dutraetal.2010).Thesnowpackisrepresentedbya single layer with an evolution of snow temperature, snow mass, snow density, snowalbedo,andadiagnosticformulationforthesnowliquidwatercontent.



11

Belowthesurface,thesoilisdiscretizedinfourlayers(0.07,0.21,0.72and1.89m)forthewaterandenergytransfer.SoilheattransferfollowsaFourierlawofdiffusion,modifiedtotakeintoaccountsoilwaterfreezing/melting(Viterboetal.1999).TheverticalmovementofwaterintheunsaturatedzoneofthesoilmatrixfollowsRichards’sequationandDarcy’slaw. HydraulicconductivityanddiffusivityarederivedusingvanGenuchten formulationand6soiltexturesgloballydistributedareused.Watermovementislimitedinthecaseofpartially frozen soil, by reducing the hydraulic conductivity and diffusivity. The topboundary condition is precipitation minus evaporation minus runoff, and the bottomboundaryconditionassumesfreedrainage.Aresistanceparameterizationisusedtocalculatetheturbulentfluxes(seeFigure4.1),andinparticularforevaporationtheaerodynamicresistanceisaddedtothecanopyresistance.Thecanopyresistanceisafunctionofdownwardshort-waveradiation,leafareaindex,soilmoisture,water vapor deficit and aminimum stomatal resistance. For the soilmoisturedependence,thefractionofrootsineachsoillayerisalsoconsidered.Water leaves thesoilcolumn in thebottom layeras freedrainage,and this isdenotedassub-surfacerunoff.Atthesurface,avariableinfiltrationratethataccountsforthesub-gridvariabilityrelatedtoorographyisusedtocomputethesurfacerunoff(Balsamoetal.2009).Thesurfaceandsub-surfacerunoffgeneratedbyHTESSELarefeedtotheCatchment-basedMacro-scaleFloodplainmodel(CaMa-Flood,Yamazakietal.(2011).CaMa-Floodsimulatesthehydrodynamics incontinental-scale rivers.Theentire rivernetworkof theworld arediscretized to hydrological units named unit-catchments for achieving efficient flowcomputationattheglobalscale(Yamazakietal.2009).Thewaterlevelandfloodedareaarediagnosed from thewaterstorageat eachunit-catchmentusing thesub-grid topographicparametersoftheriverchannelandfloodplains.Theriverdischargeandflowvelocityarecalculatedwiththelocalinertialequationalongtherivernetworkmapwhichprescribestheupstream-downstream relationship of unit-catchments. The time evolution of the waterstorage, the only prognostic variable, is solved by the water balance equation whichconsiders inflow from theupstreamcells,outflow to thedownstreamcelland input fromrunoffforcingateachunit-catchment.

4.2 JULES

JULES(theJointUKLandEnvironmentSimulator) isacommunity landsurfacemodelthathas evolved from the Met Office Surface Exchange Scheme (MOSES). JULES representsdifferent landsurfaceprocesses (surfaceenergybalance,hydrologicalcycle,carboncycle,leafphenology,etc.)andallowsthemtointeractwitheachother(Figure4.2).JULES has a tiled model of sub-grid heterogeneity with separate surface temperatures,short-wave and long-wave radiative fluxes, sensible and latent heat fluxes, ground heatfluxes, canopymoisture contents, snowmasses and snowmelt rates computed for eachsurfacetypeinagrid-box.Ninesurfacetypesareused:fivePlantFunctionalTypes(PFTs);broadleaftrees,needleleaftrees,C3(temperate)grass,C4(tropical)grassandshrubs,andfournon-vegetationtypes:urban, inlandwater,baresoiland land-ice.Fractionsofsurfacetypeswithin each land-surface grid-box are read from an ancillary file, derived from anexternal database (Global Land Cover Characteristics Data Base Version 2.0). Airtemperature, humidity and wind-speed above the surface and soil temperatures andmoisturecontentsbelowthesurfacearetreatedashomogeneousacrossagrid-box.Thesurfaceenergybalanceforeachtileincludesfluxesofsensibleheatandmoisture,andlatentheatofvaporizationforsnow-freetilesorsublimationforsnow-coveredoricetiles.Theheat flux into the ground, combining radiative fluxesbelowvegetation canopies and



12

conductive fluxes for the unvegetated fraction, is parameterized as a function of thethicknessandtemperatureofthesurfacesoil layer.RadiativecanopyfractioniscalculatedseparatelybyaCanopyHeatCapacitymodel.Surface evaporation is drawn from soil, canopy and snowmoisture stores. Evaporationfromsaturatedpartsofthesurface(lakes,wetvegetationcanopiesandsnow)iscalculatedat the potential rate (i.e. subject to an aerodynamic resistance only). Evaporation fromtranspiringvegetationiscontrolledbythecanopyconductance.Theabilityofvegetationtoaccessmoistureateachlevelinthesoilisdeterminedbyrootdensity.Theevaporativefluxextractedfromeachsoillayerisdependentonthesoilmoistureavailabilityfactor.Bare-soilevaporation isextractedfromthesurfacesoil layer.Afractionofthetile isassumedtobesaturatedandhencehasaerodynamicresistanceonly.

Figure4.2:Julesmodelconcept.

Theinitialpartitioningofprecipitationintointerception,throughfall,runoffandinfiltrationisappliedseparatelyoneachtile.Essentially,therainfallrateisassumedtobedistributedexponentiallyacrossthearea.Inaddition,iftherainfallisconvective,thenitisassumedtocoveronly30%ofthearea.AnadditionalsaturationexcessrunoffproductioniscalculatedusingaProbabilisticDistributedModel(PDM;Moore,1985)approach.Belowthesurface,thesoil isdiscretized infour layers(0.10,0.25,0.65and2.0m)forthewaterandenergytransfer.Subsurfacetemperaturesareupdatedusingadiscretizedformoftheheatdiffusionequation,which iscoupledtothesoilhydrologymodule.It includessoilwater phase changes and the associated latent heat, the soil thermal characteristics aredependentonsoilmoisturecontent(liquidwaterandice).Thetemperatureofthenthsoillayerisincrementedbythediffusiveheatfluxesintoandoutofthe layer,andthenetheatfluxadvectedfromthelayerbythemoistureflux.ThesoilhydrologycomponentofJULESisbasedonafinitedifferenceapproximationtotheRichards'equation(Richards,1931).The totalsoilmoisturecontentwithin a soil layer isincrementedbythediffusivewater flux flowing infrom the layerabove, thediffusive fluxowingouttothelayerbelow,andtheevaporationextracteddirectlyfromthelayerbyplantroots and soil evaporationwhich is calculated from the total evaporation, based on theprofilesofsoilmoistureandrootdensity.ThewaterfluxesaregivenbytheDarcyequation



13

whichdependsonthehydraulicconductivityandthesoilwatersuction.Toclosethemodelitisnecessarytoassumeformsforthehydraulicconductivityandthesoilwatersuctionasafunctionofthesoilmoistureconcentration(BrooksandCorey,1964).Soil carbon storage is increased by the total litterfall and reduced by microbial soilrespirationwhichoccursataratedependentonsoilmoisture,temperatureandsoilcarboncontent.Leafphotosynthesis isdependenton anumberof environmentalvariablesaswellas theinternalCO2concentration.Stomatalopeningsarethepathwaysthroughwhichbothwaterandcarbondioxideareexchangedbetweenvegetationandtheatmosphere.Consequently,netleafphotosynthesisandstomatalconductancetowatervapourarelinked.Thefluxesofcarbon andwater vapour are proportional to the gradient ofwater vapour and carbondioxiderespectively.

4.3 ORCHIDEE

TheversionofORCHIDEEusedinthisarticleworksatthreedifferentscales:(i)theenergybalance issolvedon0.5°×0.5°gridboxes,which is thescaleof the forcingused; (ii) thehydrologicalbalanceissolvedseparatelyonthreedifferenttilesthatmakeupeachgridbox-thesizeofthetilesdependonthedistributionofvegetation;and(iii)theriverflowsarecomputedthroughbasinsdefinedata0.5×0.5scale.

Herethehydrologicalmoduleandtheroutingmodulearemorespecificallydescribed.Thefull documentation of the model is available athttp://forge.ipsl.jussieu.fr/orchidee/wiki/Documentation.

The hydrological module used to produce the tier-1 simulations is fully described andtested in (D'Orgeval 2006). It is based on developments by (de Rosnay et al. 2000; deRosnay et al. 2002). Partitioning between surface infiltration and runoff is computedthrough a time-splitting procedure. This allows to solve the surface infiltration ofprecipitationwithafinertimestepthan30minutes.Thespatialheterogeneityofthe localmaximum infiltration rates is approximated by an exponential probability densitydistribution (Decharme et al. 2006; Yu 2000). The vertical diffusion ofwater in the soilcolumnissolvedbytheFokker-PlanckequationwithvanGenuchten(vanGenuchten1980)parameters.Baresoilevaporation isthemaximumupwardhydrologicalfluxpermittedbythediffusion if this flux is inferior topotentialevaporation.Anadapted(MonsiandSaeki1953)lawisusedforbaresoilevaporationundervegetation.Waterextractionfromrootsisdeterminedbyanexponentialrootprofile(deRosnayandPolcher1998)andfreedrainageistheboundaryconditionat2mbelowthesurface.

13differentvegetationtypesaredefinedandthedefaultmapisderivedfromtheIGBPmapwithOlsonclassification(deRosnayandPolcher1998).Vegetationtypesaregroupedinto3ensembles (bare soil, trees, and grass/crop). Transpiration and interception loss arecomputedseparatelyforeachvegetationtype,buttheinducedthroughfallandrootuptakeare aggregated per vegetation ensemble. Therefore, in each grid box, the hydrologicalbalanceiscomputedforthreetilescorrespondingtothe3differentvegetationensembles.3differentsoiltypesaredefinedandthedefaultmapisderivedfromadatasetby(Reynoldsetal.2000).

The dominant soil type over a grid box is used for each tile in the grid box. Newparameterizationshavebeen introduced torepresent three infiltrationprocesses(surfaceinfiltration,deep-soilinfiltration,root-zoneinfiltration)thatareconsideredtobeimportant



14

to accurately represent the West African water cycle. Parameters for this version ofORCHIDEE have been fixed in accordance with validations against Hapex-Sahelobservations (Goutorbe et al. 1994).More details are provided in the description of theparameterizationsbelowandin(D'Orgeval2006).Dependingon theslopeof the landsurface, thesurface runoffmay reinfiltrate,especiallythrough small pond systems that are common in West Africa (Cappelaere et al. 2003;Peugeotetal.2003).InORCHIDEE,reinfiltration isallowed incaseofslopesbelow0.5%.The compactness of the soil increases with depth (z) as the smallest particles tend topercolate towards the bottom of the soil (Beven 1984 ; Beven and Germann 1982). InORCHIDEE, the saturated conductivity is modified in a similar way to what is done in(Decharmeetal.2006).Themaindifferencesarethattheconductivityisconstantfrom0tozl=0.3m, and can be decreased by a factor 5 at most. This factor is chosen to roughlycorrespondtoachangeofsoiltypefromcoarsetomediumormediumtofine.Theroutingmodule isbasedon(HagemannandDümenil1997 )and(Milleretal.1994).Surface,subsurfacerunoff,andriverfluxesareroutedthroughthreedifferentreservoirsineachbasinofeachgridbox(seereservoirsVi,i=1,2,3forbasinsB,B',...inFigure4.3).Eachreservoirhasadifferenttimeconstantwhichonlydependsonthemeanslopeoftheriverand on three constants fixed globally (one per reservoir type). A floodplain module isincludedinthisversionofORCHIDEEtodealwithswampsandfloodplainssuchastheonesobservedintheNigerInnerDeltaorintheCongobasin.

Figure 4.3: ORCHIDEE river routing scheme. The hydrologymodule computes thepartitioningofprecipitation(P) into infiltration(I)andsurfacerunoff(R)andthenintoevapotranspitration(ET)andsubsurfacerunoff(D)foreachgridbox

4.4 SURFEX-TRIP

TheSURFEXmodellingsystem(Massonetal.2013)oftheCNRMusestheISBAlandsurfacemodeltocomputethesoil/snow/vegetationenergyandwaterbudgetsandtheTRIPriverroutingmodeltosimulatetheriverflowattheglobalscale.ISBAcontainsthebasicphysicsof the landsurfaceandrequiresonlya limitednumberofparameters,whichdependonthetypeofsoilandvegetation.Thepresentversionsolvestheone dimensional Fourier law for soil temperature and the mixed form of the Richardsequation forsoilmoisture.Theclosed-formequationsbetween thesoilmoistureand thesoil hydrodynamic parameters, such as the soil matric potential and the hydraulicconductivity, aredetermined according to theBrooks andCoreymodel (Decharme et al.2011).Adiscretizationwith14 layersover12mdepthisused.AsshowninFigure4.4,the



15

hydrologicalsoildepthvariesaccordingtothesoil/vegetationsurfacetypeswhilethesoiltemperature iscomputedthroughoutthe12m.Forexample,hydrologicaldepthsforgrassorcropsarenear1.5mor2m.Thesoiltemperatureandmoisturenodesarecollocatedoverthissuperficialhydrologicalsoilcolumn.Toextendthetemperatureprofileuptoadepthof12m,thesoilwatercontent isextrapolatedtoeachdeepernodes(Decharmeetal.2013).Note that over potential permafrost area the hydrological depth reaches 12m to wellrepresentsoilfreezing/meltingprocessesthroughoutthesoilcolumn.

Figure 4.4: Schematic representation of the ISBA soil hydrological and thermalcolumn for four soil/vegetation types. The soil temperature, T, and moisture, w,nodesarecollocated.Toextend the temperatureprofileup to adepthof12m, thesoilwatercontentisextrapolatedtoeachdeepernodes.

Hydrodynamicandthermalsoilpropertiesaccountfortheamountofclay,sandandorganiccarbon present in the soil and given by the HWSD global database(http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/). Forthesnowpack, adiscretizationusing12 layersallows theexplicit representationofsomesnowkeyprocessesasitsviscosity,itscompactionduetowind,itsageanditsalbedoonthevisibleandnearinfraredspectra.In terms of hydrology, the soil water balance accounts for infiltration, land surfaceevapotranspiration,andtotalrunoff.Theinfiltrationrateisgivenbythedifferencebetweenthethroughfallrateandthesurfacerunoff.Thethroughfallrateisthesumoftherainfallnotinterceptedbythecanopy,thedrippingfromtheinterceptionreservoir,andthesnowmeltfromthesnowpack.Waterforsoilevaporation isdrawnfromthesuperficial layerofathicknessof1cm.Thissoilevaporation isweightedbytherelativehumidityofthissuperficial layer.Thisrelativehumidity evolves non linearly with the superficial water content, allowing that the soilevaporation ismaximumforasuperficialwatercontentgreaterthanthewatercontentat



16

field capacity specified as matric potential at -0.33 bar. The water used for planttranspiration is removed throughout the rootzone inwhich the rootsareasymptoticallydistributed.SurfaceresistanceintheformulationoftranspirationisproportionaltotherootzonewaterstressandthestomatalresistancefromtheAg-svegetationinteractivescheme(Calvet et al.1998).This scheme allows to explicitly simulate theLeafArea Index (LAI).Plant transpiration stops when the root zone water content is below the usual watercontentatwiltingpoint,correspondingtoamatricpotentialof-15bar.Thetotalrunoff iscomposedofthesurfacerunoff,a lateralsubsurfaceflow inthetopsoil,anda freedrainageconditionat thebottomof thehydrologicalsoilcolumn that issimplyequal to the hydraulic conductivity of the last hydrological node.TheDunne runoff andlateral subsurface flow within the topsoil is computed via a subgrid distribution of thetopography using a simple TOPMODEL approach. The Horton runoff is solved using asubgrid exponential distribution of rainfall intensity and the maximum soil infiltrationcapacity. Thismaximum infiltration capacity is computed using a Green-Ampt approachoveradepthcloseto10cmaccountingforfrozensoilconditions.ThepresentTRIPversionusesaglobalriverchannelnetworkat0.5°resolution.Itisbasedonatwoprognosticequationsforthesurfacestreamwaterandthedeep-watermasswithineachgridcellofthehydrologicnetwork.Thestreamflowvelocity isassumedconstantanduniformat0.5m.s-1while thedeep-water feeds thesurfacestream reservoirwith a timedelayfactorof30days(Decharmeetal.2010).

4.5 WaterGAP3

TheglobalwatermodelWaterGAP3(Water–GlobalAssessmentandPrognosis) isagrid-based,integrativeassessmenttooltoexaminethestateofglobalfreshwaterresources.Themodel framework consists of a spatially-distributed rainfall-runoff model, five sectorialwaterusemodels,andalarge-scalewaterqualitymodel.Theglobalhydrologicalmodelsimulatestheterrestrialpartoftheglobalhydrologicalcyclebyasequenceofstorageequationsforthemostrelevantcontinentalstoragecompartments:canopy, snowpack, soil, renewable groundwater, and surface water bodies. The modelrequiresdailyfieldsofprecipitation,near-surfaceairtemperature,downwellingshortwaveand longwaveradiationasexternalmeteorological forcing.PotentialevapotranspirationisestimatedaccordingtothePriestley-Taylorapproachwithsurfacenetradiationcalculatedonthebasisofland-coverdependentalbedoandemissivityvalues.Thecanopystorageisconceptualizedasasinglelayerthatinterceptsprecipitationuntilthemaximum storage capacity is exceeded, and inceptedwater evaporates atpotential rate.MaximumcanopystoragedependsondailyLAIwhichismodelledasafunctionoflandusedependentmaximumLAI,fractionofdeciduousplantsandclimate.Snowaccumulationandmeltaresimulatedona1arcminutesub-gridfollowingadegree-day approach based on land-cover specific melting rates. Surface air temperature isdisaggregatedfrom5to1arcminuteaccordingtotheelevationdifferencebetweeneach5arcminutecellanditscorrespondingsub-gridcells.Thesoilcolumnisrepresentedasasinglelayerwhosestoragecapacityisdeterminedasafunctionofthesoiltexturedependentavailablewatercapacity(derivedfromsoilmaps)andthe land-cover specific rooting depth. Total runoff from land is a function of effectiveprecipitation,soilsaturation,anon-linearityparameterandthefractionofurbanarea.Theremainingpart(effectiveprecipitationminusrunofffrom land) ispassedas infiltrationtothe soil storage. Groundwater recharge is calculated as a functionof slope, soil texture,



17

aquifertype,andtheoccurrenceofpermafrost/glaciers.Renewablegroundwaterresourcesare represented as a linear storagewhose outflow is passed as subsurface runoff to theroutingscheme.Surfaceandsubsurfacerunoffgeneratedineachgridcellandinflowfromupstreamcellsistransportedthroughaseriesoflinearandnonlinearretentionstoragesrepresentinglakes,reservoirs, and wetlands before contributing to streamflow. Flow velocity in the riversegment iscalculatedas a functionof riverbed roughness, riverbedslopeandhydraulicradiusof the channel according to theManning-Strickler equation (Verzano et al.2012).Lateralflow,i.e.betweengridcells,isassumedtooccurasstreamflowonly.WaterGAP3 iscalibrated in abasin-specificmanneragainst long-termmeandischargebyadjusting a runoff-nonlinearity parameter in each basin. In basins where adjusting thisparameterdoesnotprovideanacceptablerunoffestimate,i.e.thedeviationfromobserveddischarge remains larger than ±1%, an additional runoff correction factor is assigned toeachcellwithinthebasin(samevalueforeachcell).Thiscorrectionfactoreffectsallwaterbalance components; if simulated discharge is too low, runoff will be increased andevaporationwill be reduced and vice versa.This procedure is aimed to ensure a closedwaterbalance,hencemassconservation,withintheindividualbasin.WaterGAP3 is explicitly designed to account for human interference on the naturalstreamflow regime through water abstraction and flow regulation by large dams andreservoirs. Spatially explicit time seriesofwaterwithdrawal andwater consumption areprovidedbytheWaterGAP3waterusemodels(ausderBeeketal.2010;Flörkeetal.2013)forfivesectors:Domesticuse(householdsandsmallbusinesses),manufacturingindustries,thermal power plant cooling, irrigated agriculture and livestock farming. Sectorialwaterdemands can be abstracted from surface water (rivers, reservoirs and lakes) andgroundwater resources. Water demands for thermoelectric power plant cooling andlivestock farming are assumed to be exclusively abstracted from surfacewater. For theremaining sectors, water withdrawals are allocated to groundwater and surface waterabstractions according to sector- and cell-specific temporally constant groundwater usefractionsderivedfromnationalandsub-nationalstatistics(Dölletal.2012).The regulating effect of reservoirs on river discharge is simulated by a two-purposeoperationschemedistinguishingirrigationandnon-irrigationreservoirs(Dölletal.2009).Operatingrulesaresetindividuallyforeachreservoirasafunctionofmeanannualinflowand water demand in lower reaches. Non-irrigation reservoirs are operated with theobjective of providing constant release throughout the year. In the case of irrigationreservoirs,monthlyreleaseisadditionallygovernedbydownstreamwaterdemand,i.e.thecumulativemonthlynetabstractioninthenext20downstreamgridcells.



18

5 Analysis of Ensemble Simulations

All modeling systems described in the previous section were forced with the variousprecipitationdatasetsdescribed inTable3. 1 for anearly11yrsperiod (Mar2000 –Dec2010).Modelsimulationswerecarriedout at the0.25deg resolution forHTESSEL-CaMA,ORCHIDEE,JULES,SURFEX-TRIPand5arcminuteforWATERGAP3.Finalmodeloutputwasaggregatedto0.25degforallmodelsandvariablesexceptforthedischarge,whichwaskeptattheoriginalspatialresolutionbecausespatialaggregationisnotmeaningfulforavariablethat is routed through stream network. Temporal resolution of the output was either3hourly(HTESSEL-CaMA,JULES,SURFEX-TRIP)ordaily(ORCHIDEEandWATERGAP3).The following sectionspresent the simulation results for thedifferentmodeling systemsandprecipitation forcingdatasets.Note that apart fromprecipitation, the restof forcingvariableswerekeptconsistentinallsimulationsandweretakenfromtheWRR2reanalysisdataset.

5.1 Inter-comparison of multi-model multi-forcing simulations

In this section the simulation results are analyzed in comparison to the SAFRAN-basedsimulation,which is considered the “reference” simulation throughout this analysis.Themain purpose of this analysis is to examine themagnitude and variability of differencesamong both models and forcing data for a range of hydrological variables. Results areexpressedintermsofrelativeerrors(relativetoSAFRAN-basedsimulations)anddeviationsinstatisticaldistributions(shownasquantile-vs-quantileplots).AsafirststepanoverviewofdifferencesinprecipitationforcingispresentedinFigure5.1,wherethespatialpatternofrelativedifferencesinaverage3hprecipitationisshown.Notethat differences are expressed with respect to SAFRAN dataset or SAFRAN-drivensimulations.



19

Figure5.1.Spatialmapofrelativedifferenceinaverage3hprecipitationwithrespecttoSAFRANdataset.

AfewimportantpointsthatemergefromtheresultsshowninFigure5.1are:

i) Significantdifferencesinaverageprecipitationareapparent.ii) Differencesexhibitconsiderablespatialvariability.iii) Magnitude of differences and direction (i.e. overestimation vs.

underestimation)aswellas the spatialpatternvaryacross thedifferentproducts.

Examination of the differences in the distribution of 3h precipitation intensity amongproducts,depictedasQ-Q(quantile-quantile)plotinFigure5.2,revealsthatwhilethetypeofprecipitationdistribution appears the same (i.e.high linearity inQ-Qplots) there is aconsiderable bias associatedwith different products and thismanifests on the graph asdeviationintheslopefromthe1-1line.



20

Figure5.2.Quantile-vs-Quantileplotof3hprecipitation intensitybetweenSAFRANandthesatellite/reanalysisproductsexamined.

Arguably, thedifferences inprecipitation forcingpresentedaboveareexpected tohave aconsiderableimpactinthehydrologicsimulations.Themagnitudeofthisimpactisexpectedto vary across the different hydrologic models and variables of interest. The followingsections report the results for themajority ofmodels used in the Earth2Observewaterresourcesreanalysisandmulti-forcingsimulationsofdifferenthydrologicvariables.

5.1.1 Surface Runoff (Qs)

MapofrelativedifferenceandQ-Qplotsforthemulti-modelmulti-forcingsimulationsareshownin

Figure5.3and

Figure5.4below.



21

Figure 5. 3. Relative differences of simulated surface runoff (Qs) with respect toSAFRAN based simulation. Note that scale of relative difference varies from 40%underestimation(darkblue)to40%overestimation(darkred).



22

Figure 5. 4. Q-Q plot for simulated surface runoff (Qs) between SAFRAN and thesatellite/reanalysisprecipitationproductsused.Resultsareshown forallmodelingsystemsinvolved.

Results for surface runoff show a high sensitivity of surface runoff generation toprecipitation but also reveal a high dependence of the direction (i.e.overestimation/underestimation) and magnitude of the sensitivity to modeling system,highlighting that the structureof themodelplays an important roleon the sensitivity toprecipitation. For example, looking at the different model simulations for the case ofCMORPH once can see that while for HTESSEL and ORCIDEE results reveal an overalloverestimationovertheentirepeninsula,thispatternissomewhatreverseforothermodels(likeSURFEX-TRIPandWATERGAP3).

5.1.2 Subsurface Runoff (Qsb)

Similarresultsforsubsurfacerunoffareshownin

Figure5.5and

Figure5.6below.



23

Figure5.5.Relativedifferenceofsimulatedsubsurfacerunoff(Qsb)withrespecttoSAFRAN based simulation. Note that scale of relative difference varies from 40%underestimation(darkblue)to40%overestimation(darkred).



24

Figure5.6.Q-Qplotforsimulatedsubsurfacerunoff(Qsb)betweenSAFRANandthesatellite/reanalysisprecipitationproductsused.Resultsareshown forallmodelingsystemsinvolved.

Resultsforsubsurfacerunoffagaindemonstratethesignificanceofprecipitationsensitivitybut in contrast to what was observed for surface runoff simulations, results amongmodelingsystemsappearmoreconsistent.

5.1.3 Evapotranspiration (ET)

Similarresultsforevapotranspiration(ET)areshowninFigure5.7and

Figure5.8below.



25

Figure5.7.Relativedifferenceofsimulatedevapotranspiration(ET)withrespecttoSAFRAN based simulation. Note that scale of relative difference varies from 40%underestimation(darkblue)to40%overestimation(darkred).



26

Figure5.8.Q-Qplotforsimulatedevapotranspiration(ET)betweenSAFRANandthesatellite/reanalysisprecipitationproductsused.Resultsareshown forallmodelingsystemsinvolved.

Forevapotranspiration,againresultsrevealthatprecipitationuncertaintyisimportantandcan change considerably themagnitude andpattern ofET but themodeling uncertaintyappearsfarlessimportantthansurfacerunoff.

5.2 Comparison with independent observations

In this section we provide results from an independent evaluation of the multimodel/precipitation simulations. Simulation results were evaluated against satellite soilmoistureobservations.Specifically,thesurfacesoilmoisturesimulationsfromtheoutputofHTESSEL, JULES and SURFEX-TRIP were evaluated against the ESA CCI Surface SoilMoisturedataset (productversion2.02).TheESACCISMv02.2productconsistsof threesurfacesoilmoisturedatasets:

(1) The “Active product” is the output ofmerging scatterometer-based soilmoisturedata,whichwerederivedfromAMI-WSandASCAT.

(2) ThePassiveproductmergesdata fromSMMR,SSM/I,TMI,AMSR-E,WindSat,andAMSR2.

(3) TheCombinedproductmergestheactiveandthefirsttwoproducts.The homogenized and merged products present surface soil moisture with a global

coverage and a spatial resolutionof0.25°, and the temporal resolution is 1daywith itsreference time at 0:00UTC.The soilmoisture data used in this study is the “Combinedproduct”providedinvolumetricunits[m3m-3].Themodelandsatelliteproductsmergedtomatch thedomainof interest thatspans from -10deg to5degeastand35.5deg to44degnorth and cover the period March 2000 – Dec 2010. In addition, the high temporal



27

resolution (i.e.3h)productswere averaged indaily intervals tomatch thedaily satelliteestimates.Duetodifferencesbetweenunitsexpressed indifferentsimulationoutputsandsatelliteestimates,comparisonwascarriedout intermsofnormalizeddeviatesaccordingtothefollowingformulation:

( , , ) =( , , ) − ( , )

( , )

where SSM(x,y,t) is the surface soil moisture value at location x,y and time t and it isnormalized by subtracting the long-term temporal average ( , )and dividing by thestandarddeviationofthelong-termSSMvalues( ( , ) )atthesamelocation.Thismetricwascalculatedforeachpixel(i.e. location)ofthestudyareaandforeachavailablesurfacesoilmoistureoutputand then thecorrelationbetweensatelliteestimatesand thevariousmodeloutputswasdeterminedandispresentedinfiguresbelow.

Figure 5. 9. Correlation coefficient between normalized deviates of SSM values ofHTESSELsimulationsandsatellitesoilmoistureestimates.



28

Figure5.10.Correlation coefficientbetweennormalizeddeviatesofSSMvaluesofJULESsimulationsandsatellitesoilmoistureestimates.

Figure5.11.Correlation coefficientbetweennormalizeddeviatesofSSMvaluesofSURFEX-TRIPsimulationsandsatellitesoilmoistureestimates.

ComparisonwithindependentsatellitesoilmoistureestimatesshownaboveinFigure5.9toFigure5.11revealthattemporaldynamicsofsurfacesoilmoisturearecapturedequallywellfromthedifferentmodel/forcingscenarios.Thespatialpatternoftheresultsappearsalsothesamerevealingconsistentlyareaswherecorrelationofnormalizeddeviatesishigh(southwest)andlow(northeast)inallsimulationscenarios.



29

6 Stochastic Error Modelling of Precipitation

Results in the previous section highlight clearly that uncertainty in precipitationforcinghasagreatimpactonthesubsequentsimulationofhydrologicvariables.Thereforethere is a clearneed tobe able to account for thisuncertainty and furthermore identifyways tominimize it. In this sectionwe present initial results from developing an errormodel of precipitation uncertainty and moreover construct a Bayesian framework thatblendsinformationfromvariousprecipitationdatasourcesandauxiliaryvariableswiththeaimtoproduceanimprovedensemblebasedrealizationofprecipitationfields.

6.1 Methodology

A non-parametric tree-based regression error model, i.e. Quantile RegressionForests(QRF)model(Meinhausen,2006),isappliedtoproducerainfallensemblesbasedonmultiple precipitation forcing datasets. The error model uses as response variable thereferenceprecipitationpergridcell.Thethreesatelliteproducts(CMORPH,PERSIANNand3B42v7), theWRR2 re-analysis (from tier 1product)precipitation, surface soilmoisture(fromESACCIproductdiscussedintheprevioussection),grid-averageelevation,elevationrange (difference between the maximum and minimum elevation) per grid cell and airtemperature(fromreanalysistier1product)areusedasinputvariableforthemodel.Theoutputofthemodelis20rainfallensemblesoverthestudyarea.

Inthefollowingwedescribethemodelandtheperformancemetrics.

6.1.1 Quantile Regression Forests (QRF)

QuantileRegressionForests(QRF) inferringconditionalquantiles,aregeneralizedforms of Random Forests. The core method applied in QRF is called Classification andRegressionTrees(CART)(Breimanetal.1984).QRFgivenon-parametricandprecisewayto evaluate conditional quantiles for high-dimensional predictor of variables. TheconditionaldistributionfunctionofYisdefinedby:

( | = ) = ( ≤ ⎹ = ) = 1{ }⎹ = (1)Where,Y isobservationsof the responsevariable. (1{ }⎹ = ), isapproachedby theweightedmeanovertheobservationof1{ }.Thenequation(1)canbeexpressedas:

^( | = ) = ( )1{ } (2)

Where, ( ) = ∑ ( , )using random forests; k indicates number of singletrees;eachtreesbuiltwithani.i.d.,vector , = 1, … . . , .So, it is seen that, QRF utilize weighted average of all trees to compute the empiricaldistributionfunction.QRFkeepnotonlymeanbutalsoallobservationvaluesinnodesandbuilt on this information QRF calculate the conditional distribution. In this method,consistencyoftheempiricalquantitiesis inducedbasedona largenumberof instances interminalnodes.UsingR,QRFpackage isavailableassoftware(RdevelopmentCoreTeam,2005).LiawandMeinshausenalsoprovidedRpackagecalled“quantregForest”(LiawandWiener,2002).



30

Tobuildanerrormodel(QRF),theavailabledatasetisdividedbasedon3cases:(1)allthreesatelliteproductsandre-analysisrainfall>0,(2)amongallthreesatelliteproductsandre-analysisrainfallatleastoneisgreaterthan0,and,(3)allthreesatelliteproductsandre-analysis rainfall are 0.Each casewas categorized based on itsmonths of occurrence:‘‘warmseason’’ includedvariables fromMay throughOctoberand ‘‘coldseason’’ includedvariablesfromNovemberthroughApril.Eachseasonalcategorywasclassifiedintotwosub-categories(gridareaswithlowelevationandgridareaswithhighelevation)basedonmeanelevations,whichareusedformodelvalidation.Whenmeanelevationislessthan1000m,itwasconsideredlowelevation,otherwisehighelevation.Intheerrormodel,arandomforestof20treesforeachterminalnodewasusedandtheempiricaldistributionwascreatedforeach grid cell.For each grid cell10% and90%predictionquantileswere calculated andresampled from theempiricaldistribution function for20 timespercell. In theend,QRFgenerated20rainfallensemblesforthestudyarea.After calibration, QRF is used to generate rainfall ensembles for all cases at eachseasonalcategorybasedonelevation(Highelevationandlowelevation).Afterthatresultsfromallcaseswerecombinedandclassifiedintoseasoncategory(Warmandcoldseason).Finallyeachseasoncategorywasdividedintolowandhighelevationrespectively.Figure6.1showsthetimeseriesofcumulativerainfallpredictedbyQRFforhighandlowelevationinwarmseason.Thepredictedensemblescorrectwelltheerrorsfromthevariousproductsand encapsulate well the actual rainfall time series with better convergence of QRFensemblemembers.Similarly,Figure6.2alsodisplayedanoverallsatisfyingagreementbytheensemblepredictionsofprecipitationencapsulatingwelltheactualrainfallforthecoldseason.

Figure6.3andFigure6.4showstheresultsofestimationofmeanensemblerainfallfor the two scenarios (warm and cold season). In terms of elevation, the spatialdistributionsofmeanensemblerainfallaresimilarforbothscenarios,butrainfallestimatesoflowelevationhavemorepixelscomparedtohighelevation.Ingeneral,thenorthwesternpartwhich is near the ocean,more precipitationwas observed for both seasons at lowelevation.Specifically, inthecaseof lowelevation, thedifferencebetweenthetworainfallfieldswere exhibited that indicate higher precipitation occurred in cold season. In coldseasonhigherprecipitationwaspredictedbymodelnotonlyinNorthwesternpartbutalsosoutheasternpart.



31

Figure6.1.TimeseriesofcumulativerainfallofCMORPH,PERSIANN,3B42(V7),re-analysisrainfall,andQRFensemblemembersforwarmseason.



32

Figure6.2.TimeseriesofcumulativerainfallofCMORPH,PERSIANN,3B42(V7),re-analysisrainfall,andQRFensemblemembersforcoldseason.



33

Figure6.3.SpatialmapofQRF-generatedmeanensembleforwarmseason.



34

Figure6.4.SpatialmapofQRF-generatedmeanensembleforcoldseason.



35

6.1.2 Metrics of Model Performance Evaluation

To illustrate the discrepancies between different rainfall estimations, we utilize thecentered root mean square error (CRMSE) as our error metric. CRMSE represents therandomcomponentoferrorinbetweenthetworainfalldata.Theyaredefinedas:

=1

^ − −1

(^ − ) (3)

CRMSEvalueequalsto0meansindicatesnorandomerror,whileCRMSEvalue1indicateslargerandomerrors.TheMeanrelativeerror(MRE)indicatesthemeanoftherelativepercentageerrorandEq.(1)isusedtocalculatethe(MRE):

=1 ^ −

(4)

ForaperfectmodeltheMREwouldbezero.To assess the ability of QRF-generated ensembles in encapsulating the reference

rainfall, theExceedanceProbability (EP) isusedwhich indicates theprobability that thereferencevalueexceedsthepredictioninterval:

{ . } = 1−1

1 (5)

EPwouldbezero foraperfectmodel thatmeansaperfectencapsulationofthereferencewithinpredictioninterval.

ToevaluatetheaccuracyoftheQRF-generatedensembles,theUncertaintyRatio(UR)isusedwhichmeasures uncertainty from the prediction interval (Qlower, Qupper), as used in(Ozkaynaketal.2009):

=∑ −

∑ (6)

To achieve accurate and successful predictions comparatively smaller predictionintervalsareexpected.

6.2 Evaluation of the Error Model

Toevaluatetheimprovementoftheerrormodel,themeanrelativeerror(MRE)andthe centered rootmean square error (CRMSE) areused to represent the systematic andrandomcomponentoferrorinbetweenthemodelpredictionsandreferenceprecipitation.We also performed ensemble verification methods (exceedance probability (EP),uncertaintyratio(UR)),fortheconfirmationoftheQRF-generatedrainfallensembles.Theensemble-based errormodeling results are presented here for different elevations fromdifferentseasons.

The centered rootmean square error (CRMSE)wasused tomeasure the randomerrorofthepredictions(Figure6.5andError!Referencesourcenotfound.Figure6.6).CRMSEwas calculated for three rainfall thresholds: low (25-90th percentiles),moderate(90-99thpercentiles),andhigh(>99thpercentile)rainrates.TheresultsexhibitedthatQRFwereabletosignificantlyreducetherandomerror in lowtohighrainfallrate.It isshownthat themodels predicted best for all three thresholds by considerably reducingCRMSEvalue inwarm season forhigh elevation. Similarly, for theother cases results show thatQRF-basedpredictionisclosertothereferenceprecipitationandreducesrandomerror.



36

Figure6.5.CenteredRootMeanSquareerrorforwarmseason.



37

Figure6.6.CenteredRootMeanSquareerrorforcoldseason.

Toevaluatethemodelperformance,themeanrelativeerror(MRE)wasalsoassessedforbothseason.In

thisstudy,MREwasdeterminedtoshowtheseasonalvariationforthethreesatelliteproducts(CMORPH,PERSIANNand3B42(V7)),re-analysisrainfall,andtheblendedproduct(



38

Figure6.7and

Figure6.8).InQRFpredictions,themodelslightlyimprovedbasedonMREvalues.Forthemoderatetohigherrainrate(>90thpercentile),themodelexhibits lowerMRE(inmagnitude)incomparisontolowerrainrate(25-90thpercentiles).

Figure6.7.MeanRelativeErrorforwarmseason.

In warm and cold season, all three satellite products (CMORPH, PERSIANN and

3B42(V7)),andre-analysisrainfallwereunderestimatedforthemoderateandhighrainfall.



39

Intermsofelevation,themagnitudeoftheMREwasfoundsignificantlylowerforthehighelevationinwarmseasonincomparisontoothers.Themodelusedtheelevationascontrolparameter,soQRFwereabletoreducesignificantlythesystematicerrorforhighelevationinwarmseason.Duringthewarmseasontheeffectofsoilwetnessinformationontheerrormodels is alsodemonstratedby reducingMRE inmodelpredictions.For thehigher rainrate, reducedMREvalue inwarmseason iscloser toestimationsofcoldseasonover thestudyarea.

Figure6.8.MeanRelativeErrorforcoldseason.



40

In order to verify the skill of ensemble predictions to encapsulate the referenceprecipitation,ExceedanceProbability(EP)wascalculatedoverthestudyarea(Figure6.9and Error!Reference sourcenot found.Figure 6. 10). Inmodel predictions, EP valueswere found lower formoderate tohighrainrate, than lowrainrateduringwarmseason.Specifically, model-generated ensembles showed better performance by significantlyreducingEPvaluesduringwarmseasonforhighelevation.Ontheotherhand,EPvaluesforlowrainrateweresignificantlylessthanmoderateandhighrainrateduringcoldseasonathighelevation.It indicatesabetterencapsulationoftheobservationswithintheensembleenvelope inQRF-based errormodel for low rain rate. In the caseofmoderate rain rates(>90th-99th percentile), QRF also showed better performance in reducing EP valuescomparedtolowandhighrainrateforcoldseasonlowelevation.

Figure6.9.Exceedanceprobabilityforwarmseason.



41

Figure6.10.Exceedanceprobabilityforcoldseason.

TheURvalueswereusednext toexamine theprediction intervalsprovidedbyQRF-ensemblesand isshownthatQRFproducedcomparativelywiderpredictionintervalsinwarmseasonthanincoldseason,indicatingvaryinguncertaintythroughouttheyear(

Figure6.11andError!Referencesourcenotfound.

Figure6.12).Wepresent lowuncertaintyratio,which iscloseto1,forhigherrainrate (>99th percentile), signifying a proof of its superior performance than otherpercentiles.ColdweatherrainfallexhibitedlowerURvaluesthanwarmweatherforthelowtomoderaterainrates(25th-99thpercentile).



42

Figure6.11.Uncertaintyratioforwarmseason.



43

Figure6.12.Uncertaintyratioforcoldseason.



44

7 Closing Remarks

Inthehereinreportwedocumentedtheuncertaintyassociatedwiththedifferentearthobservationprecipitationdatasetsandthepropagationofprecipitationuncertaintyinhydrologicsimulationsforanumberofglobalhydrologicandlandsurfacemodelsusedintheglobalwaterresourcesreanalysis.Specifically,ourcomparativeanalysisofmulti-model/multi-forcingsimulationsusedasreferencetheSAFRANprecipitation-drivensimulations,andsatellitederivedsoilmoistureestimates.Themainpointsfromthiserrorpropagationanalysisaresummarizedasfollowing:• Precipitationfromdifferentsatelliteandreanalysisdataexaminedexhibit

considerabledifferencesinpatternandmagnitudeofprecipitationvalues.• Differencesinprecipitationforcingcausesignificantdifferencesinhydrologic

simulations.Thesensitivityofhydrologicsimulationstodifferentprecipitationforcingdependsonthehydrologicvariableunderconsideration.Forexamplesurfacerunoff(Qs)appearstobehighlysensitivetoprecipitationdifferenceswhileevapotranspirationfluxesarenotsosensitive.

• Oneimportantfindinghighlightedfromthisstudywasthefactthatmodellinguncertainty,i.e.uncertaintyassociatedtomodelstructure,isaveryimportantsourceofuncertaintyandincasesappearsequallyimportanttoprecipitationuncertainty.

• Verificationofsurfacesoilmoisturesimulationswithindependentsatellite-basedobservationsshowedthatdespitethepotentialdifferencesinmagnitude,examinationofthecorrelationofnormalizeddeviatesrevealsthatthetemporaldynamicsofsurfacesoilmoisturearecapturedquitewellinallmodel/precipscenarios.

Thesecondpartofthisreportpresentedinitialresultsfromthedevelopmentofastochasticprecipitationerrormodelaimedatcharacterizingtheprecipitationuncertaintyfromthedifferentsatelliteproducts,andthroughanoptimalBayesianblendingframework,produceensembleprecipitationrealizationsthataresuperiortoeachindividualearthobservationdatasets,andaccuratelyencapsulatethevariabilityofprecipitationestimationuncertainty.Theensemblepredictionswereevaluatedagainstreferenceprecipitationdatausingtwoerrormetrics(MeanRelativeErrorandRelativeRootMeanSquareError)andensembleverificationstatistics(UncertaintyRatioandExceedanceProbability).TheensembleerrormodelpredictionsexhibitedimprovedMREandCRMSEerrormetricsagainsteachprecipitationproductusedasinput.TheEPandURresultsindicatedanaccurateensemblebasedcharacterizationofprecipitationuncertaintythroughtheblendedproducts.Incorporationofsoilmoisture,temperatureandelevationsignificantlyreducedthemagnitudedependenterrorandtheencapsulationofrainfallerrorparticularlyofhighrainrates.



45

References

ausderBeek,T.,Flörke,M.,Lapola,D.M.,Schaldach,R.,Voß,F.,andTeichert,E.:Modellinghistoricalandcurrentirrigationwaterdemandonthecontinentalscale:Europe,Adv.Geosci.,27,79-85,doi:10.5194/adgeo-27-79-2010,2010

Balsamo,G.,Viterbo,P.,Beljaars,A.,VandenHurk,B.,Betts,A.K.,andScipal,K.:Arevisedhydrology for the ECMWF model: Verification from field site to terrestrial waterstorageand impact inthe IntegratedForecastSystem, J.Hydrometeor.,10,623-643doi:610.1175/2008JHM1068.1171,2009

Balsamo,G.,Albergel, C.,Beljaars,A.,Boussetta, S.,Brun,E., Cloke,H.,Dee,D.,Dutra,E.,Muñoz-Sabater, J.,Pappenberger,F.,deRosnay,P.,Stockdale,T.,andVitart,F.:ERA-Interim/Land: a global land surface reanalysisdata set,Hydrol.Earth Syst.Sci.,19,389-407,doi:10.5194/hess-19-389-2015,2015

Beven,K.,andGermann,P.:Macroporesandwaterflowinsoils,WaterResourcesResearch,18,1311-1325,doi:10.1029/WR018i005p01311,1982

Beven, K.: Infiltration into a class of vertically non-uniform soils, Hydrological SciencesJournal,29,425-434,doi:10.1080/02626668409490960,1984

Best,M.J.,Pryor,M.,Clark,D.B.,Rooney,G.G.,Essery,R.L.H.,Ménard,C.B.,Edwards,J.M.,Hendry,M.A.,Porson,A.,Gedney,N.,Mercado,L.M.,Sitch,S.,Blyth,E.,Boucher,O.,Cox, P.M.,Grimmond, C. S.B., andHarding,R. J.:The JointUK LandEnvironmentSimulator(JULES),modeldescription–Part1:Energyandwaterfluxes,Geosci.ModelDev.,4,677-699,doi:10.5194/gmd-4-677-2011,2011

Brooks,R.H.,andCorey,A.T.:Hydraulicpropertiesofporousmedia,HydrologicalPapers,3,ColoradoStateUniv.,FortCollins,1964

Calvet, J.-C., Noilhan, J., Roujean, J.-L., Bessemoulin, P., Cabelguenne, M., Olioso, A., andWigneron, J.-P.:An interactive vegetation SVATmodel tested against data from sixcontrasting sites, Agricultural and Forest Meteorology, 92, 73-95, doi:http://dx.doi.org/10.1016/S0168-1923(98)00091-4,1998

Cappelaere, B., Vieux, B. E., Peugeot, C., Maia, A., and Séguis, L.: Hydrologic processsimulation of a semiarid, endoreic catchment in Sahelian West Niger. 2. Modelcalibration and uncertainty characterization, J Hydrol, 279, 244-261, doi:http://dx.doi.org/10.1016/S0022-1694(03)00182-3,2003

Clark,D.B.,Mercado,L.M.,Sitch,S.,Jones,C.D.,Gedney,N.,Best,M.J.,Pryor,M.,Rooney,G.G.,Essery,R.L.H.,Blyth,E.,Boucher,O.,Harding,R.J.,Huntingford,C.,andCox,P.M.:The Joint UK Land Environment Simulator (JULES), model description – Part 2:Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701-722, doi:10.5194/gmd-4-701-2011,2011

Decharme, B., Boone, A., Delire, C., and Noilhan, J.: Local evaluation of the Interactionbetween Soil Biosphere Atmosphere soil multilayer diffusion scheme using fourpedotransferfunctions,JournalofGeophysicalResearch:Atmospheres,116,D20126,doi:10.1029/2011jd016002,2011

Decharme,B.,Douville,H.,Boone,A.,Habets,F.,andNoilhan, J.: ImpactofanExponentialProfileofSaturatedHydraulicConductivitywithintheISBALSM:SimulationsovertheRhôneBasin,J.Hydrometeor.,7,61-80,doi:10.1175/jhm469.1,2006

Decharme,B.,Alkama,R.,Douville,H.,Becker,M.,andCazenave,A.:GlobalEvaluationoftheISBA-TRIPContinentalHydrological System. Part II:Uncertainties inRiverRouting



46

SimulationRelated toFlowVelocityandGroundwaterStorage, J.Hydrometeor.,11,601-617,doi:10.1175/2010jhm1212.1,2010

Decharme,B.,Martin,E., andFaroux, S.:Reconciling soil thermal andhydrological lowerboundary conditions in land surface models, Journal of Geophysical Research:Atmospheres,118,7819-7834,doi:10.1002/jgrd.50631,2013

deRosnay,P.,andPolcher,J.:ModellingrootwateruptakeinacomplexlandsurfaceschemecoupledtoaGCM,Hydrol.EarthSyst.Sci.,2,239-255,doi:10.5194/hess-2-239-1998,1998

deRosnay,P.,Bruen,M.,andPolcher,J.:Sensitivityofsurfacefluxestothenumberoflayersin the soil model used in GCMs, Geophys. Res. Lett., 27, 3329-3332, doi:10.1029/2000gl011574,2000

deRosnay,P.,Polcher, J.,Bruen,M.,andLaval,K.: Impactofaphysicallybasedsoilwaterflowandsoil-plant interaction representation formodeling large-scale landsurfaceprocesses,JournalofGeophysicalResearch:Atmospheres,107,ACL3-1-ACL3-19,doi:10.1029/2001jd000634,2002

Döll,P.,Fiedler,K.,andZhang,J.:Global-scaleanalysisofriverflowalterationsduetowaterwithdrawals and reservoirs, Hydrol. Earth Syst. Sci., 13, 2413-2432, doi:10.5194/hess-13-2413-2009,2009

Döll,P.,Hoffmann-Dobrev,H.,Portmann,F.T.,Siebert,S.,Eicker,A.,Rodell,M.,Strassberg,G., and Scanlon,B.R.: Impactofwaterwithdrawals from groundwater and surfacewateroncontinentalwaterstoragevariations, JournalofGeodynamics,59–60,143-156,doi:http://dx.doi.org/10.1016/j.jog.2011.05.001,2012

d'Orgeval:ImpactDuChangementClimatiqueSurLeCycledeL’eauEnAfriquede l’Ouest:Mod´elisationetIncertitudes,PhDThesisofUniversitéPierre,MarieCurie.,2006

d'Orgeval,T.,Polcher,J.,anddeRosnay,P.:SensitivityoftheWestAfricanhydrologicalcycleinORCHIDEE to infiltrationprocesses,Hydrol.Earth Syst. Sci., 12, 1387-1401, doi:10.5194/hess-12-1387-2008,2008

Dutra,E.,Balsamo,G.,Viterbo,P.,Miranda,P.M.A.,Beljaars,A.,Schar,C.,andElder,K.:AnImprovedSnowSchemefortheECMWFLandSurfaceModel:DescriptionandOfflineValidation,J.Hydrometeor.,11,899-916,doi:10.1175/2010jhm1249.1,2010

Dutra, E.: Report on the current state-of-the-artWater Resources Reanalysis, Tech. Rep.D.5.1, EartH2Observe, http://earth2observe.eu/files/PublicDeliverables/D5.1_ReportontheWRR1tier1.pdf,2015.

Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F., and Alcamo, J.: Domestic andindustrialwaterusesofthepast60yearsasamirrorofsocio-economicdevelopment:A global simulation study, Global Environmental Change, 23, 144-156, doi:http://dx.doi.org/10.1016/j.gloenvcha.2012.10.018,2013

Goutorbe,J.P.,Lebel,T.,Tinga,A.,Bessemoulin,P.,Brouwer,J.,Dolman,A.J.,Engman,E.T.,Gash, J.H. C.,Hoepffner,M., Kabat, P., Kerr, Y.H.,Monteny, B., Prince, S., Said, F.,Sellers, P., andWallace, J. S.:HAPEX-Sahel: a large-scale study of land-atmosphereinteractionsinthesemi-aridtropics,Ann.Geophys.,12,53-64,doi:10.1007/s00585-994-0053-0,1994

Hagemann, S., andDümenil,L.: Aparametrizationof the lateralwaterflow for the globalscale,ClimateDyn.,14,17-31,doi:10.1007/s003820050205,1997

Huffman,G. J.etal.,2007.TheTRMMMultisatellitePrecipitationAnalysis(TMPA):Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. J.Hydrometeor.,8(1),pp.38-55.



47

Joyce,R. J., Janowiak, J.E.,Arkin,P.A.&Xie,P.,2004.CMORPH: AMethod thatProducesGlobal Precipitation Estimates from PassiveMicrowave and InfraredData atHighSpatialandTemporalResolution.J.Hydrometeor.,5(3),pp.487-503.

Leo Breiman, Jerome Friedman, Charles J. Stone, and R.A. Olshen. Classification andRegressionTrees.Chapman&Hall/CRC,1984.

Loveland,T.R.,Reed,B.C.,Brown, J.F.,Ohlen,D.O.,Zhu,Z.,Yang,L.,andMerchant, J.W.:DevelopmentofagloballandcovercharacteristicsdatabaseandIGBPDISCoverfrom1kmAVHRRdata,Int.J.RemoteSens.,21,1303-1330,2000

Masson,V.,LeMoigne,P.,Martin,E.,Faroux,S.,Alias,A.,Alkama,R.,Belamari,S.,Barbu,A.,Boone,A.,Bouyssel,F.,Brousseau,P.,Brun,E.,Calvet, J.C.,Carrer,D.,Decharme,B.,Delire,C.,Donier, S.,Essaouini,K.,Gibelin,A.L.,Giordani,H.,Habets,F., Jidane,M.,Kerdraon,G.,Kourzeneva,E.,Lafaysse,M.,Lafont,S.,LebeaupinBrossier,C.,Lemonsu,A.,Mahfouf,J.F.,Marguinaud,P.,Mokhtari,M.,Morin,S.,Pigeon,G.,Salgado,R.,Seity,Y.,Taillefer,F.,Tanguy,G.,Tulet,P.,Vincendon,B.,Vionnet,V.,andVoldoire,A.:TheSURFEXv7.2 land and ocean surface platform for coupled or offline simulation ofearthsurfacevariablesandfluxes,Geosci.ModelDev.,6,929-960,doi:10.5194/gmd-6-929-2013,2013

N.Meinhausen, “Quantile regression forests,”The Journal ofMachineLearningResearch,vol.7,pp.983-999,Jun.2006.

Miller, J.R.,Russell,G.L.,andCaliri,G.:Continental-ScaleRiverFlow inClimateModels, J.Climate,7,914-928,doi:10.1175/1520-0442(1994)007<0914:csrfic>2.0.co;2,1994

Monsi, M., and Saeki, T.: Uber den Lichtfaktor in den Pflanzengesellschaften und seineBedeutung fur die Stoffproduktion, Japanese Journal of Botany, 14, 22-52, doi:citeulike-article-id:1687877,1953

Moore,R.J.:Theprobability-distributedprincipleandrunoffproductionatpointandbasinscalesHydrologicalSciencesJournal,30,273-297,1985

H.Ozkaynak,H.Frey, J.Burke,andR.Pinder, “Analysisofcoupledmodeluncertainties insource-to-dose modeling of human exposures to ambient air pollution,” Atmos.Environ,vol.43,no.9,1641–1649,Mar.2009.

Peugeot, C., Cappelaere, B., Vieux, B. E., Séguis, L., and Maia, A.: Hydrologic processsimulationofasemiarid,endoreiccatchment inSahelianWestNiger.1.Model-aideddata analysis and screening, J Hydrol, 279, 224-243, doi:http://dx.doi.org/10.1016/S0022-1694(03)00181-1,2003

P.Quintana-Seguí,C.Peral,M.Turco,M.C.Llasat,E.Martin.Meteorologicalanalysissystemsin north-east Spain. Validation of SAFRAN and SPAN, Journal of EnvironmentalInformatics,27(2)116-130,2016.

Reynolds,C.A., Jackson,T. J.,andRawls,W. J.:Estimatingsoilwater-holdingcapacitiesbylinking the Food and Agriculture Organization Soil map of the world with globalpedondatabasesandcontinuouspedotransferfunctions,WaterResourcesResearch,36,3653-3662,doi:10.1029/2000wr900130,2000

Richards,L.A.:Capillaryconductionofliquidsthroughporousmediums.JournalofAppliedPhysics,1(5),318-333,1931.

Sorooshian, S. et al., 2000. Evaluation of PERSIANN System Satellite–Based Estimates ofTropicalRainfall.Bull.Amer.Meteor.Soc.,81(9),pp.2035-2046.

vanGenuchten,M.T.:AClosedFormEquationforPredictingtheHydraulicConductivityofUnsaturatedSoils,SoilSciSocAmJ,44,892-898,1980

Verzano,K.,Bärlund, I.,Flörke,M.,Lehner,B.,Kynast,E.,Voß,F.,andAlcamo, J.:Modelingvariableriverflowvelocityoncontinentalscale:Currentsituationandclimatechange



48

impacts in Europe, J Hydrol, 424–425, 238-251, doi:http://dx.doi.org/10.1016/j.jhydrol.2012.01.005,2012

Xie,P.,Yoo,S.-H.,Joyce,R.&Yarosh,Y.,2011.Bias-CorrectedCMORPH:A13-YearAnalysisofHigh-ResolutionGlobalPrecipitation.s.l.,EGU.

Yamazaki,D.,Oki,T.,andKanae,S.:Deriving aglobal rivernetworkmapand itssub-gridtopographiccharacteristics froma fine-resolution flowdirectionmap,Hydrol.EarthSyst.Sci.,13,2241-2251,doi:10.5194/hess-13-2241-2009,2009

Yamazaki,D.,Kanae, S.,Kim,H., andOki,T.: Aphysicallybaseddescriptionof floodplaininundationdynamicsinaglobalriverroutingmodel,WaterResour.Res.,47,W04501,doi:10.1029/2010wr009726,2011

Yu,Z.:Assessingtheresponseofsubgridhydrologicprocessestoatmosphericforcingwithahydrologic model system, Global Planet. Change, 25, 1-17, doi:http://dx.doi.org/10.1016/S0921-8181(00)00018-7,2000

Verzano, K.: Climate change impacts on flood related hydrological processes: Furtherdevelopmentandapplicationofaglobalscalehydrologicalmodel,ReportsonEarthSystemScience,71,MaxPlanckInstituteforMeteorology,Hamburg,Germany,2009

Viterbo,P.,Beljaars,A.,Mahfouf, J.F.,andTeixeira, J.:Therepresentationofsoilmoisturefreezinganditsimpactonthestableboundarylayer,Quart.J.Roy.Meteor.Soc.,125,2401-2426,doi:10.1002/qj.49712555904,1999