report of the joint workshop between wp3, 4, 5 and 6earth2observe.eu/files/public...

ThisprojecthasreceivedfundingfromtheEuropeanUnion’sSeventhProgrammeforresearch,technologicaldevelopmentanddemonstrationundergrantagreementNo.603608

DGResearch–FP7-ENV-2013-two-stage

GlobalEarthObservationforintegratedwaterresourceassessment

ReportoftheJointWorkshopbetweenWP3,4,5and6

DeliverableNo:D.3.4–WorkshopWP3+4+5+6ReportRef.:WP3-Task1Date:February2016

WP3 - Task 1 – D.3.4 Report of the WP3+4+5+6 Joint Workshop

DeliverableTitle D.3.4–ReportoftheJointWorkshopbetweenWP3,4,5and6

Filename E2O_D3.4_Joint_Workshop_v04.docxAuthor VincenzoLevizzani(CNR-ISAC)

Contributors EmmanouilN.Anagnostou(KKTITC)

GianpaoloBalsamo(ECMWF)EleanorBlyth(NERC)Jean-ChristopheCalvet(MetéoFrance)ElsaCattani(CNR-ISAC)WouterDorigo(TU-Wien)EmanuelDutra(ECMWF)JaapSchellekens(Deltares)GeertSterk(UniversiteitUtrecht)

Date 26/02/2016PreparedundercontractfromtheEuropeanCommissionGrantAgreementNo.603608Directorate-General forResearch& Innovation(DGResearch),Collaborativeproject,FP7-ENV-2013-two-stageStartoftheproject: 01/01/2014Duration: 48monthsProjectcoordinator: StichtingDeltares,NLDisseminationlevel

X PU Public

PP Restrictedtootherprogrammeparticipants(includingtheCommissionServices)

RE Restrictedtoagroupspecifiedbytheconsortium(includingtheCommissionServices)

CO Confidential,onlyformembersoftheconsortium(includingtheCommissionServices)

Deliverablestatusversioncontrol

Version Date Author

0.1 23/02/2016 VincenzoLevizzani(CNR-ISAC)

0.2 24/02/2016 ElsaCattani(CNR-ISAC)review

0.3 25/02/2016 JaapSchellekens(Deltares)review

0.4 26/02/2016 VincenzoLevizzani(CNR-ISAC)


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TableofContents1 Executive Summary ................................................................................................................................. 12 Workshop schedule ................................................................................................................................. 23 Participants .............................................................................................................................................. 24 Overarching goals .................................................................................................................................... 45 Specific points of discussion .................................................................................................................... 4

5.1 WP3 .............................................................................................................................................. 45.2 WP4 .............................................................................................................................................. 55.3 WP5 .............................................................................................................................................. 55.4 WP6 .............................................................................................................................................. 6

6 Results and decisions from the discussion .............................................................................................. 76.1 Precipitation dataset characteristics (lead WP3) .......................................................................... 76.2 Use of precipitation datasets for WRR2 (lead WP5) .................................................................. 106.3 Error characterization activities (lead WP4) ............................................................................... 11

6.3.1 Precipitation error propagation investigations in hydrological modelling 116.3.2 Improvements in hydrologic simulations from V2 EO datasets 126.3.3 Overall performance evaluation of the water resource reanalysis 13

6.4 Case studies activities (lead WP6) ............................................................................................. 136.5 Extending the precipitation datasets for error characterization and case study usage .............. 15

7 Conclusions and actions ........................................................................................................................ 168 Glossary ................................................................................................................................................ 17

ListofTablesTable1Workshopparticipants....................................................................................................................2


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1 ExecutiveSummary

The eartH2Observe project is based on the exploitation of Earth Observation (EO)products from space-based platforms for integrated water resources management.First, this implies that the data are carefully screened and characterized as to theirstructureandphysicalcontent.Secondly,anaccurateerrorstructureanalysisistobeconductedforallowingtheendusertohaveaclearideaofwhattheerrorsareandtowhat kind of process they should be ascribed to. Finally, the application of data ininitializingthereanalysismodelsononesideandintheworkonthecasestudiesonthe other are the natural result of the process. The Joint Workshop of the WorkPackages3,4,5and6washeldduringtheGeneralAssembly2(GA2)oftheprojectinBologna to make sure that there was the right kind of debate between the dataproducers, theerrormodellers, thenumericalmodellersand theendusers fromthecasestudies.Thisbriefreporthighlightsthemajoroutcomesoftheworkshop.


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2 Workshopschedule

Thursday28January13.30–15.00 Plenary15..00–15.30 Coffeebreak15.30–18.00 SplitsessionsWP3-WP5andWP4-WP6-WP3Friday29January9.00–10.30 SplitsessionsWP3-WP6andWP4-WP510.30–11.00 Coffeebreak11.00–12.30 Plenarysession:conclusionfromtheJointWorkshop

3 Participants

Table1Workshopparticipants

Lastname Name Affiliation Nationality

Aires Filipe Estellus FR

Anagnostou EmmanouilN. KKTITC GR

Balsamo Gianpaolo ECMWF UK

Beck Hylke JRC EC

Blyth Eleanor NERC UK

Burke Sophia AMBIOTEK UK

Calton Ben PML UK

Calvet Jean-Christophe METEO-FRANCE FR

Cattani Elsa CNR IT

Claud Chantal CNRS FR

Detry Geoffroy I-MAGEConsult BE

Dorigo Wouter TU-WIEN AT

Drobinski Philippe CNRS FR

Dutra Emanuel ECMWF UK

Eisner Stephanie UNIKASSEL DE

Fink Gabriel UNIKASSEL DE

Flörke Martina UNIKASSEL DE

Gevaert Anouk VUA NL

Groom Steve PML UK

Gruber Alexander TUWIEN AT


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Lastname Name Affiliation Nationality

Hailu Dereje AAiT ET

Jindrova Marketa GISAT CZ

Kallos George KKTITC GR

Kossida Maggie SEVEN GR

Kutser Tiit UT EE

Lambotte Michel I-MAGE BE

Laviola Sante CNR IT

Levizzani Vincenzo CNR IT

LinesDiaz Clara UNESCO-IHE NL

LopezLopez Patricia UNIVERSITEITUTRECHT NL

Marra AnnaCinzia CNR IT

MartinezdelaTorre Alberto CEH UK

Marzano FrankS. UNIROMA IT

Miguez-Macho Gonzalo USC ES

MonowarHossain Mohammad IWM BD

Montopoli Mario CNR&UNIROMA IT

Mori Saverio UNIROMA IT

Nikolopoulos Efthymios KKTITC GR

Panegrossi Giulia CNR IT

Polcher Jan CNRS FR

QuintanaSegui Pere OBSERVATORIODELEBRO ES

Rodriguez Erasmo UNAL CO

Schellekens Jaap DELTARES NL

SalehKhan Abu IWM BD

SohelMasud Mohammad IWM BD

Soomets Tuuli UT EE

SpernaWeiland Frederiek DELTARES NL

Sterk Geert UNIVERSITEITUTRECHT NL

Stroheimer Stefan ICARDA LB

Tekidou Anastasia SEVEN GR

Toming Kaire UT EE

Veldkamp Ted VUA NL

Ward Phil VUA NL

Werner Micha UNESCO-IHE NL

WenhajiNdomeni Claudine CNR IT

Westerhoff Rogier DELTARES NL


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4 Overarchinggoals

The workshop is organized at the end of the first half of the project, which wascharacterized by the production of the datasets (both space-based and model-derived), therefinementof tools forerrorcharacterizationandthesetupof thecasestudiesaround theworld.Thedriving ideaof theworkshop is thus togatherall theparticipantstoWorkPackages3,4,5and6todiscussasetofoverarchinggoals:

1. AfterthepreparationoftheEOproductstheprojectneedstomakesurethatthe EO datasets are ready for an effective usage in WP4+5+6. In fact, theproject is fundedunder theEOumbrellaof theFP7programand its successlargely depends on how the participants will make an effective use of suchdatasets. Producingdatasets andorganizing a repository is by allmeansnotenough.

2. TheprojectneedstocomeupwithaWP4+5strategy(supportedbyWP3and6) that uses the good work on the evaluation at different scales that iscurrently going on and see how much of the detected discrepancies comesfromtheforcing(i.e.,theWATCHForcingDataERAInterim,WFDEI).

5 Specificpointsofdiscussion

5.1 WP3

ThefollowingpointsofdiscussionwereidentifiedfromaWP3perspective:

1. TheEOdatasetsneed tobeready for theWaterResourcesReanalysisTier-2(WRR2)andfortheapplication inthecasestudiesandmostof themalreadyare. A thorough check needs to be made and possible extensions beconsidered.

2. A first group of datasets is represented by the “global” ones. In a recentWP3+4+5teleconferencedecisionswereadoptedtoincreasetheprecipitationdataset number and their duration. This applies mostly to the use ofprecipitation(P)datasetsintheWRRsexercises.

3. A second group consists of “local/research” datasets. They consist of high-resolutiondatasetswithsomewhat limitedtimespan,butuseful forcheckingerrorstructure,orographicinfluence,applicationtocatchmenthydrologyandingeneralexplorationofnewtechniquesandtheirpotential.

4. The second part of the project should see the “data providers” from WP3collaboratemorecloselywiththeinternalusers.Someoftheactivitiesalreadystarted with WP4 and WP5 and more should be foreseen with WP6. TheworkshopcouldbeaperfectoccasiontomakecontactsandbemoreeffectiveinEOdatausage.

5. Thewholelistofdatasetsneedstobecarefullycheckedbecausemanytypesofvariablesarepresentandtheusermayloosecontactwiththeirrealusage.Inotherwords,precipitationiscertainlyimportantasitissoilmoisture(SM),but


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howabouttheothervariables?Aretheyusedoratleastconsideredforuseintherestoftheproject?

6. ThereisstillroomtoexploitinmoredepththesynergybetweenthedifferentEO datasets, in particular with respect to error characterisation (WP4). Forexample, are the different datasets consistent with each other: different Pproducts, or Pwith SM and evapotranspiration (ET)? Or canwe use similarevaluationstrategiesforsimilarproducts?

7. Are the error metrics that we compute for the EO datasets useful for themodellers? The recent small workshop in Paris was a nice example of adiscussiononwhatcharacteristicsofSMdatasetscanbemeaningfulformodelevaluation.

5.2 WP4


1. Workonstochasticerrormodellingandgenerationofprecipitationensembleswillsoonbecompleted(bytheendofFebruary)fortheIberianPeninsulaandsoonafterfortheBlueNile(Ethiopia)casestudyareas.

2. Progressing on precipitation error propagation investigations, are currentlyfocusingontheIberianPeninsula.Hydrologicsimulationsfordifferentsatelliteprecipitation datasets, WRR1 forcing and SAFRAN (Système d’AnalyseFournissantdesRenseignementsAtmosphériquesàlaNeige)datasethasbeenintegratedwiththeOrganisingCarbonandHydrologyInDynamicEcosystems(ORCHIDEE) landsurfacemodel (LSM)andwillbecompleted for themodelsWaterGAP3(Water-aGlobalAssessmentandPrognosis)andJULES(JointUKLandEnvironmentSimulator).

3. Stochasticerrormodelbasedprecipitationfieldswillbeusedalsotoevaluatethe improvements in hydrologic simulations and to characterize predictionuncertainty.

4. Investigation on error propagation in hydrologic simulations is currentlycarriedoutoverSpainandtheBlueNile.Doweneedmorecasestudiesand/ormoremodelstobeinvolved?

5. Threeworkshopsonhow touse theEOSMdataand theEOand flux-netETdatawereheldin2015.Theconclusionsindicatethatallthemodellerscanusethedatatoevaluateandpotentiallycalibratetheirmodels.OnlysomemodelscanusetheSMdatadirectlyintheirsimulations.

6. Evaporation evaluation methods and first results using the Global LandEvaporationAmsterdamModel(GLEAM)areready.

5.3 WP5


1. WRR2andhowEOdataisused:

a. Modeldevelopment&dataassimilation.

b. Forcing:


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i. WRR2–baseline: 1979-20140.25×0.25basedonERA-Interim+precipitationadjustments:proposed:Multi-SourceWeighted-EnsemblePrecipitation(MSWEP,cf.HylkeBeck);

ii. WRR2-ensemble: use of different precipitation datasets(shorter period 2000-2010)TRMM/ CMORPH/ PERSIANN/GSMaP.

2. Activity ison itsway formakingsure that theappropriateprecipitationdatasets are available in due time. Technical discussion with details probablyneeded.Forexample:lightrain,snowfallandmergingissues.

3. FortheerrorpropagationinvestigationworkwasdoneinWP4onthemergingof satellite precipitation datasets with WRR1 and a code developed thataccountsforsnowinthemerging.PerhapsthiscanbeusefulfortheactivitiestobecarriedoutinWP5.ThesearetechnicalissuesthatcanbediscussedwithWP4duringthemeeting.

4. Hopefully, the stochastic errormodel corrected ensemble precipitation fieldwillbetestedintheWRR2simulationstoevaluatethefeedbackofWP4erroranalysisintheuseofWP3datasetsinwaterresourcesanalysis.

5. Thispartisatthe"heart"oftheproject.However,isthisgoingtobeavailableover specific regions? A global implementation of error corrected ensembleprecipitationisnotfeasible...Rather,agoodnumberofcasestudiesshouldbeinvolved.Thoughts?

6. Should we compute and provide ensemble mean data in the portal? Userswant/needsinglemodeldataand/orensemblemeanonly(WP6inputonthesubject)?

7.

5.4 WP6


1. How can we stimulate the use and testing of EO and WRR datasets in thedifferentcasestudies?

2. So far, therehasnotbeenenoughuseof theprojectdata in the case studies(thereareafewexceptionsthough),andthisissomethingweneedtoworkonmoreinthesecondphaseoftheproject.Thereasonswhytherehasnotbeendoneenoughyetbasicallyare:

a. mostcasestudieshavebeenverybusycollectingin-situdataandstartthelocalwatershedmodelling,and

b. thereisgenerallyalackofawarenessoftheavailabledatasets.

3. Thus, ifwecandiscusstheuseofEOdatasetsthisduringtheworkshopwiththe case study coordinators, and show them the potential use of differentdatasets,thiscouldindeedstimulatetheuseofthosedatainthedifferentcasestudies.

4. WP6teamstoevaluateEOandWRRdatasetsbasedoninsitudatafromtheirstudyareas?


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6 Resultsanddecisionsfromthediscussion

Severaldatasetsareincludedintheprojectdatarepository,eitherintheftpserverhttps://wci.earth2observe.eu/thredds-restricted/catalog-earth2observe.htmlorontheWaterCycleIntegrator(WCI)portalhttp://wci.earth2observe.eu/thredds/catalog.htmlAt present the Release 1 of the datasets is available (see the report D3.3 of theproject).A discussionwas conducted among the projectmembers on the availability of dataandtheirusageinWP4,WP5andWP6.Afewpointsemergedthatneededathoroughdiscussion,suchas:

1. Precipitationas thekeydataset in termsofuse inmodelling and in the casestudiesneedstobeconsideredbothfromthepointofviewofadequatelengthof the actual period of the datasets and of the possible need for furtherdatasetsforthesecondpartoftheproject.

2. Datasetsareavailabledescribingseveralvariablesofthewatercycle.Whileforsome of them the ongoing or future use is clear, there appears to be anuncertaintyontheuseofsomeotherdatasets.Discussionontheiruseshouldbeconducted.

3. Theuseofdatasets inthecasestudiesneedstobeencouraged.WP6has justfinished setting up the sites and the experiments are well on track. AdiscussionontheuseofdatasetsinthedifferentcasestudyareasistimelyfortherestoftheactivityofWP6.

A thorough discussion was conducted in the two separate session on Thursday 28afternoonandFriday29(seetimeschedule). Inthefollowingthemajoroutcomesofthediscussionontheseandotherpointsaresummarized.

6.1 Precipitationdatasetcharacteristics(leadWP3)

Ahandfulofquestionswereaskedandareheredetailed:

1. Whydowehavedifferentdatasets?Whynotonlyonethatcanbeusedbyalltheendusersoftheproject?

a. Havingonlyonedatasetdoesnotprovidethecompletepictureof theprecipitatingsystemsbecausetheretrievalmethodbehindtheproductmaybebiasedtowardsdetectingcertaintypesofprecipitatingsystemswhilemissingothers altogether. In fact, someof themethods relyonspecific spectral channelsof the available sensors anddo (ordonot)evolveintimeasnewchannelsbecomeeventuallyavailable.

b. Someoftheproductshaveaglobalcoverageandothersdonot.Inourcase we have global products, African-based products and researchproductswithlimitedcoverage.

c. Therefresh time isnotalways thesame,e.g.,daily,3-hourly,decadal,monthlyorinstantaneousorbital.

d. The time series are rather not uniform as their length varies fromproducttoproduct.


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e. The spatial ground resolution is quite different as the productswereoriginallyconceivedhavinginmind:

i. Differentsensorswithdifferentfieldsofview,and/or

ii. Different applications (weather monitoring, hydrology,climate,,..).

f. Research products as opposed the global/African products areincludedbecauseofseveralreasons:

i. Snow and low rain rate detection,which is not performedbytheglobalalgorithmstodate.

ii. Haildetection.

iii. Orographicenhancement,whichistheprimarycauseoferrorsoverthemountainareas.

iv. Someproductsareprovidedonashortterm,casestudybasis.An example is thePREC_X-SAR radarproduct. Suchdataset isparticularly interesting to push the limit of precipitationdetectionovertheseaatveryhighresolution.

v. Other datasets, such as the PRECIP/MR/DC, provide moreinformation than the simple precipitation intensity. Theywillbeusedtocomparewithsnowdetectionalgorithms(183-WSL)andworkonthephysicsoftheconvectiveelements.

g. Someof theproductsaregenerated innearreal time(NRT)andthusdo not include any form of bias correction. Other products areavailableofflineandincludegaugeadjustmentand/ormodeloutput.

h. TheprojectisconceivedaroundremotesensingofwaterresourcesinaEOcontextandthustheexploitationofdifferentplatformsisanaddedvalueoftheproject.

i. Itmayseemirrelevantortrivial,buthavingaseriesofdatasetsallinauniversalformat(inourcasenetCDF)isveryimportantsincedifferentformats represent awell knownobstacle for awidespreaduse of EOdatasetsbytheendusers.EartH2Observehasmadeasubstantialeffortinthisdirection.

2. Whatarethemajorlimitationsoftheprecipitationproducts?

a. Opensciencequestions:

i. Snowdetection–Currentlydoneonlybyahandfulofresearchalgorithmsandwithveryvaryingperformance.

ii. Lowrainratedetection–Sameasaboveathighfrequenciesinthemicrowaves(MW).BothpointsarekeytasksoftheGlobalPrecipitationMeasurement(GPM)mission.

iii. Haildetection–Hereweareinno-man’sland!Justacoupleofalgorithms are conceived for this purpose but theirperformanceisTBD.

iv. Orographicenhancementisbeingheavilyresearchedbutonlyacouple of global algorithms are now being equipped withorography-relatedcorrections(e.g.,GSMaP).

b. Specificlimitationduetothealgorithmtype,e.g.


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i. IR-based algorithm (e.g., TAMSAT) may missstratiform/inconspicuousprecipitationsincetheyarebasedoncloudtoptemperature.

ii. Algorithms such as CMORPH were conceived for climatemonitoring(oratleastfornonrealtime)applications.Thishaschangedinthemeantime(Kalmanfiltering,gaugeadjustment),but some of the inherent limitations are still there. Forexample, the fact that the algorithm requires two successiveMWsensoroverpassestoapplythemorphingtechniqueclearlylimitsNRTapplicationssomehow.

c. Quantitative performance varies with accumulation time, beinginstantaneousvalueslessprecise,ingeneral.Thus,attentionshouldbepaid to validation activities (e.g., IPWG) that are ongoing all over theglobe.

d. MWdatahaveinherentlimitationssuchas:

i. Coarse resolution (16 × 16 km2 at the best) due toinstantaneousfieldofview(IFOV)andantennalimitations.

ii. Atlow-mediumfrequenciesthegroundemissivitymattersandthus the retrieval must be done using scattering rather thanemission.Thisimposesdifferencesbetweenlandandoceanorthe accurate knowledge of ground emissivity in the MW (ontrack,butstillexperimental).

iii. Large errors over ice- and snow-covered ground since a)snowfallandsnowonthegroundaredifficulttotellapart,andb) the atmospheric temperature profiles vary substantially intheseconditionsthusmodifyingtheweightingfunctionsofthechannels.

3. Howshouldweusetheprecipitationdatasetsintheproject?

a. To be discussed during the WP3+4+5+6 workshop, but a fewindicationsare:

i. Assimilation for the WRR2 needs necessarily to rely uponglobal products using ensemble and merging strategies.Datasets that show good performance in intercomparisonexercises should be used. The choice should then be: TRMM3B42v7, CMORPH, GSMaP). PERSIANN normally has lowerperformancesandthuscanbedroppedfromthelistasitwouldintroducefurthererrorsintheprocess.

ii. Errorcharacterizationneedstohaveadoubleoverarchinggoal

• Characterize the error for the WRR and hydrologicalmodelassimilation.

• Take the opportunity of eartH2Observe to refine theerror characterization of high-resolution products(research products) over small-scale basins andcomplexterrain.

iii. The error characterization needs necessarily to be onhydrological model output, but also per se as regards thephysicalcharacteristicsoftheresearchalgorithms.


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6.2 UseofprecipitationdatasetsforWRR2(leadWP5)

AdiscussionwasconductedonwhichdatasetsfromtheprojectrepositoryshouldbeusedfortheforcingofWRR2.TheWRR2resolutionwill be increased to0.25× 0.25andwill be composedof twoproducts.

o Baseline:1979-2015.§ ForcingwillbederivedfromERA-InterimwithdownscaledTair,

Qair, Psurf following altitude adjustments using a time/spatialvaryinglapserate.

§ Precipitation will be corrected using the Global PrecipitationClimatologyCenterv7 (GPCCv7)andGPCCmonitoring (2014-2015)productsanddownscaledusingCHPclim.

• However,thestrategyisnotyetfullydecided.• Therewas also a suggestionofusingMSWEP,but this

was suggested to be added as an extra ensemblememberforthe2000-2010period.

• WP6casestudiescouldevaluatetheprecipitationdataagainst in-situ observations on an early stage. To bedefinedwithWP6assoonasthedataisavailableintheserver.

o Ensemble:2000-2010:§ ThesameforcingasBaselinewillbeused,butwith3different

precipitationdatasets:• TRMM, CMORPH, GSMaP (baseline will be an extra

member by default). After discussion with the dataproducersofWP3PERSIANNwasdroppedfromthelistbecause it does not guarantee a sufficient overallperformancelevel.

• SuggestionwasmadetoalsoincludeMSWEP(c.f.HylkeBeck).

• Thesesatelliteproductswillbeavailablefromthedataportal.

§ Reduce the number of variables to report (only in theensemble).Atableonconfluencewillbeset-uptodecidewhichvariablestokeep.

o EncourageallmodelstoincludePET(potentialevapotranspiration)onthelistofvariablesforthebaselinesimulations.

o BothBaseline&Ensemble simulations shouldbeperformedwith theupgradedmodelversionsandwithoutdataassimilation.

o The groups carrying out data assimilation should perform an extrasimulationfortheperiod2000-2010whenpossible(iftheavailabilityofobservationsallowsit).


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o AtestsimulationusingWRR2baseline forcingandthemodelversionused inWRR1 should be performed in advance, by September 2016,andreportedduringtheWP5meetinginOctober.

o Adapting the spin-up period/setup in all the models for WRR2 wasdiscussed.Itwasdecidedtokeepthisdecisiontoeachmodellinggroupas differentmodels have different requirements. It was suggested toleave1979outofWRR2foranyanalysisasafurtheruniformspinupinall models. It was not decided if the 1979 simulations should beuploadedtothedataserver.

o Allmodellinggroupsareaskedtocarefullycheckthedataformatandconsistencybeforeuploadingthedatatotheserver.

6.3 Errorcharacterizationactivities(leadWP4)

6.3.1 Precipitationerrorpropagationinvestigationsinhydrologicalmodelling

ThefirstthingistheevaluationofthetranslationofprecipitationuncertaintyintotheWRRoutputs(streamflows,ET,SM,etc.).Precipitationuncertaintyisdefinedintermsofensembleprecipitationfieldsderivedintwoways:(i)usingvaryingprecipitationsdatasetsderived fromsatelliteproductsand the tier1&2 reanalysis fields; and (ii)usingastochasticerrormodelappliedonthemainsatelliteprecipitationfields.Thestudyareaforthistaskis:

• Spain;thedomainprovideshighqualityandhighresolution(hourly/8km)insituprecipitationfieldsandstreamflowobservationsovertheEbrobasinforaperiodrangingfrom2000to2010.

Forcingdatasets:• Tier1(0.5deg/daily)

o SAFRAN(aggregatedat0.5/daily).o Reanalysis(WRR1).o Satellite datasets (gauge adjusted CMORPH, 3B42v7, gauge adjusted

PERSIANN).• Tier2(0.25deg/3-hourly)

o SAFRAN(aggregatedto0.25/3-hourly).o Reanalysis(tier2).o Satellite datasets (gauge adjusted CMORPH, 3B42v7, gauge adjusted

PERSIANN).o Blendedsatelliteprecipitationensemble(20ensembles).

Modelsparticipatinginthistaskare:• WaterGAP(UniKassel)• PCR-GLOBWB(UniUtrecht)• ORCHIDEE(CNRS)• JULES(NERC)• H-TESSEL(ECMWF)


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Errorpropagationevaluations:• Evaluation of error propagation using distributed parameters (SM and ET)

usingasreferencethesatelliteSMandETfields• Evaluation of runoff simulations over the Ebro basin using observed runoff

data• Metrics to be used in the evaluation of uncertainty propagation from

precipitationtorunoff,soilmoistureandET:• Errormetric ratios (meanrelativeerror; central rootmeansquareerror;

correlation coefficient) between runoff and precipitation evaluated foreachensemblememberandpresentedinboxplots.

• Rank histogram to assess the reliability of the ensemble (rainfall andrunoff).Fordetailson thedefinitionof thescoreseeHamill (2001).Notethat thismetricwill be used only in the case of stochastic rainfall basedsimulations.

• Uncertainty ratios defined as the mean ensemble envelop widthnormalizedbya)themeanandb)thestandarddeviationofthereference(rainfall or runoff); uncertainty ratios can be determined for differentquantilethresholds.

6.3.2 ImprovementsinhydrologicsimulationsfromV2EOdatasets

Thesecondstepistheevaluationoftheimprovementsfromerrorcorrectedsatelliteprecipitation products in hydrologic simulations (runoff, SM, ET, GW, inundatedareas). Evaluate the use of PMW derived rain rate in the representation of rainfallvariability and rainfall PDFs in land surfacemodels (e.g. calculationsof interceptionrates)EvaluateuseofsecondreleaseofEOproductswithmetadatainWRRreanalysis.Thestudyareasforthistaskare:

• Spain;Spaindomainprovideshighqualityandhighresolution(hourly/8km)insituprecipitationfieldsandstreamflowobservationsovertheEbrobasinforaperiodrangingfrom2000–2010.

• Ethiopia; Blue Nile case study provides in situ daily gauge rainfall and longtermstreamflowdatafortheperiod2000-2010.

• Colombia;Colombiacasestudycouldcontributeacasestudyevaluation.

Forcingdatasets:• WRR1(0.5deg/daily)

o Reanalysis(WRR1)o Satellite datasets (gauge adjusted CMORPH, 3B42v7, gauge adjusted

PERSIANN).• WRR2(0.25deg/3-hourly)

o Reanalysis(WRR2).o Blendedsatelliteprecipitationensembles(20ensembles).

Modelsparticipatinginthistaskare:• WaterGAP(UniKassel)• PCR-GLOBWB(UU)


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• ORCHIDEE(CNRS)• JULES(NERC)• H-TESSEL(ECMWF)

Errorpropagationevaluations:• Evaluation of error propagation using distributed parameters (soilmoisture

andET)usingasreferencethesatellitesoilmoistureandETfields.• Evaluationofrunoffsimulationsusingobservedrunoffdata.• Metrics to be used in the evaluation of uncertainty propagation from

precipitationtorunoff,soilmoistureandET:• Errormetric ratios (meanrelativeerror; central rootmeansquareerror;

correlation coefficient) between runoff and precipitation evaluated foreachensemblememberandpresentedinboxplots.

• Rank histogram to assess the reliability of the ensemble (rainfall andrunoff).Fordetailson thedefinitionof thescoreseeHamill (2001).Notethat thismetricwill be used only in the case of stochastic rainfall basedsimulations.

• Uncertainty ratios defined as the mean ensemble envelope widthnormalizedbya)themeanandb)thestandarddeviationofthereference(rainfall or runoff); uncertainty ratios can be determined for differentquantilethresholds.

6.3.3 Overallperformanceevaluationofthewaterresourcereanalysis

Provide an overall performance evaluation of WRR2 after correcting for EO dataerrors, improvingmodel parameterizations and incorporating satellite precipitationensemblesintothedataassimilationsystem.Globalanalyses:2-3satellitedatasets+reanalysis(2000-2010):

• EvaluateWRRmodel simulationsusingstreamflowsandglobal soilmoistureandETdatasets.

• EvaluateWRRsimulationsofsnowusingtheMFsnowdataset.• EvaluateWRRsimulationsusingWP2indices(drought,flood,warningskill).

Casestudies:Spain,Ethiopia,Colombia(2000-2010):• Evaluate improvements fromensembleerror-correctedsatelliteprecipitation

productsinhydrologicsimulations(runoff,SM,ET,GW).• EvaluateWRRinwaterquality.

6.4 Casestudiesactivities(leadWP6)

1) HowcanwestimulatetheuseandtestingofEOandWRRdatasetsinthedifferentcasestudies?


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a) EO-based lake levels will be provided by I-MAGE and used in the Ethiopian(Lake Tana) and Moroccan (reservoirs) case studies. This is particularlyinterestingasthesedatasetscomewitharatherhighresolution.

b) PML will work with the Ethiopian case study on EO-based water quality(suspendedsediments)inLakeTana.

c) EO-based rainfall products:Who is usingwhat? Testing of EO-based rainfallproducts is done in Ethiopia (Blue Nile), Colombia (Magdalena) andBangladesh (Brahmaputra). In Morocco information would be desirable onrainfall intensity on a half-hourly maximum intensity basis. CMORPH mayprovide this kind of information. A new rainfall product (IMERG fromGPM)thatcanbemadeavailablethroughCNRisthebestpossibleproductbutonlyavailable for 2 years. This could probably be used in the Moroccan andEthiopiancasestudiesanotherpossibilitywouldbetousetheCDRDandPNPRresearch datasets from CNR but only for very short time studies at highresolution.

d) WRR/dataportal:guidanceisclearlyneeded.Nowtherearetoomanysimilarprecipitationdatasetsanditisalmostimpossibletochoosewhichonetouse.WP6partnerswouldliketohavetheWRRensemblemeanormedianvaluesofimportanthydrologicalvariables.Itissuggestedtomaketwoentrancestotheportal, one for scientists, who want to have access to all data, and one forusers,whoonlywantonedatasetforeachvariable.

e) A further need emerged from the Bangladesh case study. The Institute ofWater Modelling (IWM) is particularly interested in NRT precipitation dataand the discussion lead to the decision to try using the new IMERG GPMproductasitisthemostuptodateandavailableevery30min.However,thereisnoguaranteethatanunderestimationof intensetropicalrainfallwillshowup as no PMW retrieval scheme is capable at the moment to fully capturewarmrainepisodes.

2) WP6 teams to evaluate EO andWRR datasets based on in situ data from theirstudyareas?

a) HowwillWP6provide feedback?This isdone through theWP6deliverables6.2 and 6.4 (= mid-term and final reports on the use of E2O data for localapplications),andthroughpresentations/discussionsatprojectmeetings(liketheGAinBologna).

b) Can in situ data be fed back to globalwork? In situ rainfall data from Spain,EthiopiaandColombiaisusedintheerrorcharacterizationofWP4.Itwillalsobe tried tomake in situ discharge data from the case study basins availablethroughtheportal.

c) All case studieshaveused someof theavailable forcing,EOandWRR1data,butmorecouldbeandwillbedoneinthesecondhalfoftheproject.

3) Enduserrequirements


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a) In the secondhalf of theproject the interactionwith the stakeholders in thecasestudieswillbeintensified.Eachcasestudywillrewritetheworkplanforthe remaining 22months and include the foreseen interactionwith the casestudystakeholders.

6.5 Extendingtheprecipitationdatasetsforerrorcharacterizationandcasestudyusage

Asaresultof thediscussiononerrorcharacterizationexercisesandcasestudiesthefollowing activities will be performed by WP3 for extending the time span of thedatasetsandintroducingnewones:

1) TRMM 3B42 v7 bias corrected 3-hourly product will be extended as to include2014(CNR).

2) CMORPHv1biascorrected3-hourlyproductwillbeadded1999-2013(CNR).

3) GSMaPbiascorrected3-hourlyproductwillbemadeavailable2000-2010(CNR).

4) IMERGhalfhourlydata from2014till thepresentwilldeavailableontheportalforNRTusers,suchasIWMinBangladesh(CNR).


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7 Conclusionsandactions

Inthefollowingashort listofthemainconclusionsreachedbytheworkshopandoftheactionsisdetailed:

ü WP3andWP4.ApartfromtheuseofglobaldatasetsintheWRRconstruction(seebelow) high resolution datasets will be used for error characterization overselected case study areas (e.g., Spain, Morocco). Here TRMM 3B42v7, CMORPH,GSMaP, PERSIANN will be used together with CDRD and PNPR datasets. ThePREC_X-SAR dataset will be used whenever possible for pushing forward thedetectionlimitofprecipitationatveryhighresolution.Climatology-basedstudiesovertheMediterraneanwillbeconductedinvolvingPRECIP/MR/DC.

ü WP3 andWP5. Soil moisture, ET and other datasets will also be used to checkmodel performance. The triple collocation method will also be used tocharacterizetheWRRensemble.

ü WP4andWP5.Asuiteofmodels(WaterGAP,PCR-GLOBWB,ORCHIDEE,JULES,H-TESSEL)istobeusedbothfortheinvestigationofprecipitationerrorpropagationin hydrological models (ongoing over Spain) and in the investigation on theimprovements in hydrological simulations from v2 datasets (Spain, Ethiopia,Colombia).

ü WP4andWP3.Anoverallperformanceevaluationofthewaterresourcereanalysiswill be provided for 2000-2010 using 2-3 EO datasets on a global scale. Theimprovement for ensemble corrected satellite precipitation products inhydrological simulations (runoff, SM, ET, GW, inundated areas) will also becheckedoverSpain,EthiopiaandColombia.

ü WP5andWP3.Theprecipitationdatasets thatwill be used inWRR2areTRMM3B42v7,CMORPHandGSMaP, all 3-hourly andbias corrected. SuggestionsweremadetoincludeMSWEP.

ü WP3andWP6.EO-based lake levelswill beusedoverLakeTana (Ethiopia) andover the Moroccan reservoirs. Over Lake Tana also the EO-based water qualitydatawillbeused.

ü WP6andWP3.Severalprecipitationdatasetswillbeused(andarealreadyusedtosome extent) over Ethiopia (Blue Nile), Colombia (Magdalena River basin), andBangladesh (Brahmaputra). CMORPH and to some extent IMERG will beintroduced inMoroccoandBangladesh. InMoroccoalsohighresolutiondatasets(e.g.,CDRDandPNPR)willbeavailableforshorttermperiods.

ü WP6andWP5.FeedbackwillbeprovidedonEOproductsandalsoonthemodeloutputstosomeextent.


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8 Glossary

AAiT AddisAbabaInstituteofTechnologyCDRD CloudDynamicsandRadiationDatabaseCEH CentreforEcology&Hydrology(NERC)CHPclim ClimatHazardsgroup’sPrecipitationCLIMatologyCMORPH CPCMORPHingtechniqueCNR ConsiglioNazionaledelleRicercheCNRS CentreNationaldelaRechercheScientifiqueCPC ClimatePredictionCenter(NOAA)ECMWF EuropeanCentreforMedium-rangeWeatherForecastsEO EarthObservationERA ECMWFReAnalysisET EvapoTranspirationGA GeneralAssemblyGLEAM GlobalLandEvaporationAmsterdamModelGPCC GlobalPrecipitationClimatologyCenterGPM GlobalPrecipitationMeasurementmissionGSMaP GlobalSatelliteMappingofPrecipitationGW GroundWaterH-TESSEL Hydrology-TiledECMWFSchemeforSurfaceExchangesover

LandICARDA InternationalCenterforAgriculturalResearchinDryAreasIFOV InstantaneousFieldOfViewIMERG IntegratedMulti-satellitERetrievalsforGPMIPWG InternationalPrecipitationWorkingGroupIR InfraRedIWM InstituteofWaterModellingJRC JointResearchCentreJULES JointUKLandEnvironmentSimulatorKKTITC KentroKainatomonTechnologionInnovativeTechnologies

CenterSA.LSM LandSurfaceModelMSWEP Multi-SourceWeighted-EnsemblePrecipitationMW MicroWaveNECR NaturalEnvironmentResearchCouncilnetCDF NetworkCommonDataFormNOAA NationalOceanicandAtmosphericAdministrationNRT NearRealTimeORCHIDEE OrganisingCarbonandHydrologyInDynamicEcosystemsP PrecipitationPCRGLOBWB PCRasterGLOBalWaterBalancemodelPDF ProbabilityDensityFunctionPERSIANN PrecipitationEstimationfromRemotelySensedInformation

usingArtificialNeuralNetworksPET PotentialEvapoTranspirationPML PlymouthMarineLaboratoryPMW PassiveMicroWavePNPR Passive-microwaveNeural-networkPrecipitationRetrieval


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SAFRAN Systèmed’AnalyseFournissantdesRenseignementsAtmosphériquesàlaNeige

SEVEN SolutionsfortheEnvironmentSustainableEngineeringSM SoilMoistureTAMSAT TropicalApplicationsofMeteorologyusingSATellitedataand

ground-basedobservationsTBD ToBeDefinedTRMM TropicalRainfallMeasuringMissionTU-Wien TechnischeUniversitätWienUNAL UniversidadNacionaldeColombiaUNESCO-IHE UnitedNationsEducational,ScientificandCultural

Organization-InstituteforWaterEducationUSC UniversidaddeSantiagodeCompostelaUT UniversityofTartuVUA VrijeUniversiteitAmsterdamWATCH WATerandglobalChangeWaterGAP Water-aGlobalAssessmentandPrognosisWCI WaterCycleIntegratorWFDEI WATCHForcingDataERAInterimWP WorkPackageWRR WaterResourcesReanalysis

report of the joint workshop between wp3, 4, 5 and 6earth2observe.eu/files/public...

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