D3.3 Benchmarking tailored climate services for local applications using local knowledge and data

ThisprojecthasreceivedfundingfromtheEuropeanUnion’sHorizon2020researchand innovationprogrammeundergrantagreementNo101037293              DeliverableD3.3 Benchmarkingtailoredclimateservicesforlocalapplications usinglocalknowledgeanddata  October2024 ThisprojecthasreceivedfundingfromtheEuropeanUnion’sHorizon2020researchand innovationprogrammeundergrantagreementNo101037293     InnovatingClimateservicesthroughIntegratingScientificandlocalKnowledge            DeliverableTitle:DL3.3Benchmarkingtailoredclimateservicesforlocalapplicationsusing localknowledgeanddata Author(s):LluísPesquer(CREAF),IliasPechlivanidis(SMHI),DanieleCastellana(RC510), VakhoChitishvili(CENN),KatherineEgan(ECWMF),PaoloMazzoli(GECO), AlexandrosZiogas(ENVIS),SchalkJanvanAndel(IHE),AmandaBatlle (CREAF),EsterPrat(CREAF),MichaWerner(IHE). DateOctober2024 Suggestedcitation:PesquerL.,PechlivanidisI.,etal.(2024)Benchmarkingtailoredclimate servicesforlocalapplicationsusinglocalknowledgeanddata Availability:☒PU:Thisreportispublic[Pleaseselect] ☐CO:Confidential,onlyformembersoftheconsortium(includingthe CommissionServices)   DocumentRevisions: AuthorRevisionDate LluísPesquerFirstdraft June2024 LluísPesquer,IliasPechlivanidis,etal.SeconddraftSeptember2024 MichaWerner,IliasPechlivanidisReviewOctober2024 LluísPesquer,IliasPechlivanidis,etal.FinalversionOctober2024 D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 1 ExecutiveSummary TheI‐CISKprojectisfocusingontheco‐creationprocessofhuman‐centredclimateservices(CS).Thescientific workconductedinI‐CISKaimstoexplorefit‐for‐purposemethodologies,tailoredtoaddresslocalneeds.This documentreviewsthecontributionofthetailoredmethods,localdata,localknowledgeandstakeholder feedbackinordertoachieveahigherusabilityoftheI‐CISKdevelopedCScomparedtotheglobalandnational ones(benchmarking).ThisanalysisisdoneforallsevenI‐CISKLivingLabs(LL),eachwiththeirownspecific contextsandpurposes. Mainconclusionsofthisworkare:  MostI‐CISKCSdevelopedhighspatialresolutionoutputmodelswhichareusefultounderstandthe localimpactsofclimatechange,andtheyallowtodesignbetteradaptationdecisionsandpolicy actions.  Differentdownscalingtechniques(specificformeteorologicalorhydrologicalapplications)areapplied inthespecifictailoredmethods.Thecontributionoflocaldataistotallyrelevantinthesetailoring processes;theroleoflocalknowledgeisstilllow.  Usersdemandseveralimprovementsonthevisualizationoftheclimatedata(speciallyforuncertainty inpredictionsystems)tosupportacorrectinterpretationofclimateinformationandtoachievethe maximumusabilitytothesectorsinvolved.  Keywords ClimateServices,tailoredinformation,usability,localdata,localknowledge.  D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 2 AboutI‐CISK I‐CISK’sambitionistoinnovatehowclimateinformationisused,interpretedandactedonthroughanext‐ generationofClimateServicesthatfollowahumancentred,socialandbehaviourallyinformedapproach; integratingtheknowledge,needsandperceptionsofcitizens,decisionmakersandstakeholderswithclimate informationatspatialandtemporalscalerelevanttothem. ClimateServices(CS)arecrucialtoempoweringcitizens,stakeholdersanddecision‐makersintakingclimate‐ smartdecisionsthatareinformedbyasolidscientificevidencebase,thatcontributetowardsasustainable Europeaneconomy,lifestyle,environmentalprotectionandresourceuse,andthatareresilienttoclimate changeandcompatiblewithachievingclimateneutrality.Europeanandinternationalcollaborativeresearch efforts,includingCopernicusandGEOSShaveestablishedasolidscientificfoundationforaneffectiveCSvalue chain,includingadvancedscientificknowledge,monitoringandmodellingofclimatechangeandtheimpacts ofclimateextremes.However,severalbarrierschallengethecurrentgenerationofCSinachievingthefull opportunityoftheirvalue‐proposition.Thesechallengesincludethefailuretoincorporatethesocialand behaviouralfactorsandthelocalknowledgeandcustomsofclimateservicesusers.Additionally,the effectivenessofclimateservicesischallengedby;thestillpoorlydevelopedunderstandingofthemulti‐ temporalandmulti‐scalardimensionofclimate‐relatedimpactsandactions;thetranslationofCS‐provided dataintoactionableinformation;considerationofreinforcingorbalancingfeedbackloopsassociatedtousers’ decisions;andthelackoftrans‐disciplinaryapproachesacrossthefullCSvaluechain. I‐CISKaimstoseizetheseuntakenopportunitiesthroughahuman‐centredframeworkforco‐productionof nextgenerationCSthatspansthefullCSvaluechaintakingthedownstreampartofthevaluechainasastarting point.TheI‐CISKframeworkrealisesthefullpotentialofinformationprovidedthroughCSbyempowering actorstotaketheimpactsofextremeclimaticeventsandclimatechangeintoaccountintheirdecisions. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 3 TableofContents  1Introduction.................................................................................................................................................1 1.1PurposeofthisDocument...................................................................................................................1 1.2StructureofthisDeliverable................................................................................................................1 2IntegrationofLocalDataandKnowledgetoProvideUser‐TailoredInformation.......................................3 2.1Definitions............................................................................................................................................3 2.2TheI‐CISKLivingLabsandtheirchallenges..........................................................................................3 2.3MethodsFollowedtoTransformLocalData/KnowledgeinTailoredInformationateachLivingLab5 3State‐of‐the‐artinTailoringClimateServicesforLocalApplicationsusingLocalKnowledgeandData.....8 3.1InternationalClimateServices.............................................................................................................8 3.2NationalClimateServices.....................................................................................................................9 3.2.1ClimateServicesinSpain................................................................................................................9 3.2.2ClimateServicesinGeorgia...........................................................................................................10 3.2.3ClimateServicesinHungary..........................................................................................................10 3.2.4ClimateServicesintheNetherlands.............................................................................................10 3.2.5ClimateServicesinItaly................................................................................................................10 3.2.6ClimateServicesinGreece............................................................................................................11 3.2.7ClimateServicesinLesotho..........................................................................................................11 3.3TowardstheNeedforTailoredClimateServices...............................................................................11 4BenchmarkingoftheImplementedClimateServicesacrosstheLivingLabs............................................13 4.1Andalucía‐SpainLivingLab...............................................................................................................13 4.1.1ClimateServicedescription...........................................................................................................13 4.1.2Integrationoflocaldataandlocalknowledge..............................................................................13 4.1.3Usabilityofthetailoredmethods.................................................................................................14 4.1.4Benchmarking...............................................................................................................................14 4.2Alazani‐GeorgiaLivingLab................................................................................................................15 4.2.1ClimateServicedescription...........................................................................................................15 4.2.2Integrationoflocaldataandlocalknowledge..............................................................................16 4.2.3Usabilityofthetailoredmethods.................................................................................................16 4.2.4Benchmarking...............................................................................................................................16 4.3Budapest–HungaryLivingLab..........................................................................................................17 4.3.1ClimateServicedescription...........................................................................................................17 4.3.2Integrationoflocaldataandlocalknowledge..............................................................................18 4.3.3Usabilityofthetailoredmethods.................................................................................................18 D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 4 4.3.4Benchmarking...............................................................................................................................18 4.4Rijnland‐TheNetherlandsLivingLab................................................................................................19 4.4.1ClimateServicedescription...........................................................................................................19 4.4.2Integrationoflocaldataandlocalknowledge..............................................................................20 4.4.3Usabilityofthetailoredmethods.................................................................................................20 4.4.4Benchmarking...............................................................................................................................21 4.5EmiliaRomagna–ItalyLivingLab......................................................................................................21 4.5.1ClimateServicedescription...........................................................................................................21 4.5.2Integrationoflocaldataandlocalknowledge..............................................................................23 4.5.3Usabilityofthetailoredmethods.................................................................................................23 4.5.4Benchmarking...............................................................................................................................23 4.6Crete–GreeceLivingLab...................................................................................................................23 4.6.1ClimateServicedescription...........................................................................................................23 4.6.2Integrationoflocaldataandlocalknowledge..............................................................................24 4.6.3Usabilityofthetailoredmethods.................................................................................................25 4.6.4Benchmarking...............................................................................................................................25 4.7LesothoLivingLab..............................................................................................................................25 4.7.1ClimateServicedescription...........................................................................................................26 4.7.2Integrationoflocaldataandlocalknowledge..............................................................................27 4.7.3Usabilityofthetailoredmethods.................................................................................................27 4.7.4Benchmarking...............................................................................................................................27 5SummaryofLessonsLearntfromtheLocalApplications..........................................................................29 5.1LessonsLearntfromtheLocalApplicationsateachLivingLab.........................................................29 5.2DiscussionandMovingForward........................................................................................................31 References.........................................................................................................................................................33   D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 5 ListofFigures  Figure1MapofthesevenI‐CISKLLlocations.............................................................................................4 Figure2Screenshotofone(precipitationseasonalforecasts)oftheimplementedSpainLLclimate services.Itprovidesdifferentpercentilesoftheensemblesresults,medianleftupperfigureand uncertainty/dispersionintherightupper.Plotofthe6monthsforecastsofpercentileensemblesofclicked locationatthebottom......................................................................................................................................13 Figure3Left:exampleofinteractivesurveyaboutCSusabilityinbilateralmeetingswithLLstakeholders. Right:averagefeedbackfromallbilateralmeetings.........................................................................................14 Figure4Left:IndicatorsselectionlegendofEDOandGDO.Right:Differentexperimentandmodeloptions intheClimateDataStore(CDS)ofCopernicus.................................................................................................15 Figure5ScreenshotofforecastexampleoftheGeorgiaLLpilotclimateservice.....................................16 Figure6UrbanheatmapCS,Erzsébetvárosdistrict,Budapest.................................................................17 Figure7Dronecontroller(dronesequippedwiththermalcameras)fortheErzsébetvároscampaign, Budapest.18 Figure8ScreenshotofLLRijnlandclimateservicecomponentofseasonalriverdischargeforecastwith lowflow(drought)alertthresholdindicated(https://i‐cisk.dev.52north.org/living‐labs/rijnland—nl/app/,last visited2October2024).....................................................................................................................................19 Figure9ScreenshotofItalyLLCS;leftsection–identifyingtheriverstationofinterest.Ontheleftside theuseridentifiestheriverstationofinterestusingamapinterfaceoralist,whileontherightside(seenext figures)riverdischargeforecastsareprovidedeitherasdailyvaluesfortheincomingseason(averageand expectedvariability)orasmonthlycumulatevalues,thelatterismoreinterestingtotheLLuserswithwater storagecapacity.................................................................................................................................................21 Figure10ScreenshotofItalyLLCS;rightsection–browsingthehydrologicalforecasts. ..........................22 Figure11ScreenshotofGreeceLLCS:(a)Seasonalforecastingofsurfacewateravailabilityatabasinlevel, includinguncertaintyinformation,and(b)seasonalforecastoflandslidesusceptibilitylocallyandspatially variablycoveringthewholeisland....................................................................................................................24 Figure12ScreenshotoftheClimateServiceprototypefordroughts,displayingdroughtrisklevelsacross LesothodistrictsforSeptemberandJanuarybasedonuserinput.Additionallayerscanbeaccessedviathe Layersmenu.Pleasenotethatthisisaprototype,andthefinaldesignmaydiffer.........................................27   D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 6 ListofTables  Table1IntroductiontothelivinglabsandthechallengesaddressbyI‐CISK.....................................................4 Table2SummaryofthetailoredmethodsandtheusabilityofthelocalapplicationsforeachLL..................29  D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 1 1 Introduction 1.1 PurposeofthisDocument IntheDescriptionofWorkoftheI‐CISKprojectitisstatedthateffortswillbetargetedtowardsinnovationand enhancementofexistingclimateservices(CS)anddownstreamimpact‐basedproducts,andconsequentlyon thesupportofdecisionsandpoliciesinmultiplesectorsaccountingfortheirlocaltrade‐offs.InWorkPackage 3(WP3),oneoftheaimsistoaddressthelocalneedsandsectoralgapsofexistingCS,andtherefore,various state‐of‐the‐artmethodswillbeusedtogetherwithtools/methodstointegratelocalstate‐of‐the‐art observationsandlocalknowledge.Bothcontinental/globalandlocal‐scaleprocess‐basedimpactmodels,e.g. forthewaterandagriculturesectors,willbeusedtoassesssub‐seasonal,seasonalandcentennialchanges andimpactsattheLivingLab(LL)scale.Therefore,acontinuousdialoguewithvariousWPs,e.g.WP1,WP2 andWP4,hasbeenestablishedtoensureacontinuousexchangeandfeedbackofinformationrequiredto translatedatasetsintotailoredinformationandindicatorsforlocaluse. TheobjectivesofWP3are:  Toadvancelocalimpactpredictionsandprojectionsofclimatechangeandfutureextremesby developingmodellingchainsthatefficientlyintegrateexistingCSwhilecombininglocaldataand knowledgeforlocaltailoring.  Toexploredifferentscientificstate‐of‐the‐artmethodstobridgedataandservicescurrentlyseparated ontemporalandspatialscales(fromforecaststoprojections)andincreasetrustinlocalpredictions.  Toevaluatetheusefulnessoftheintegratedimpactpredictionsandassessmentsforlocaloperations anddecision‐makingfrombothascientificandauserperspective.  Tounlockthebenefitsoftransformationofdatatoinformationforandwithintheclimate‐sensitiveLL regionsandsectorsbyimprovingtheconfidenceinformationofindicatorswhileenhancingtheir usability.  Todevelopuser‐drivenvisualisationtoolsthatensurerobustandseamlesstransferofproduced informationfromCS,andcommunicatepredictions,explicitlyincludinguncertainty,forinformed decision‐making.  Toproviderecommendationsforproductadaptations,extensionsandCSimprovements,anddeliver fit‐for‐purposetools,methodsandproductsforuser‐tailoredreal‐timeoperationalservices. Toachievepartoftheobjectiveslistedabove,thisdocumentpresentsthecurrentlyongoingworkandreports ontheadvancedstepsoftheprogressinWP3,whileitaddressesaseriesofspecificobjectivesthatinclude:  Reviewingdifferentapproachestotheintegrationoflocaldataandknowledgeatthescaleoftheliving labtoaddressthelocaluserneeds.  ComparingthedifferentusabilityoftheexistinginternationalandnationalCSwiththeI‐CISK dedicatedCS.  Describingthetailoredmethodstoaccomplishtheuserrequirementsinaco‐designandco‐develop process.  BenchmarkingthetailoredCSoverthespatialextentoftheLLs.  1.2 StructureofthisDeliverable Thisdeliverableisstructuredin5chapters:  Chapter1(current)istheintroductiontothedocumentpresentingthescope. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 8 3 State‐of‐the‐artinTailoringClimateServicesforLocalApplicationsusing LocalKnowledgeandData Inthischapter,wereviewthemaininternationalinitiativeswhichdevelopglobalCS,alongwithnational initiativesforthecountriesinvolvedinthesevenLL.Chapter3isimportanttosetthesceneofexisting benchmarksandjustifytheimportanceoftailoredclimateservices.LaterinChapter4,theexisting internationalandnationalCSwillbeaddressedandcomparedwiththeCSdevelopedwithinI‐CISK. Climateservicescanbedescribedasthegeneration,provision,andcontextualisationofconsistent, authoritative,andtimelyclimateinformationtosupportdecision‐making.Themaintaskistotransform climate‐relateddataintocustomisedproducts,adviseonbestpractices,anddevelopandevaluatesolutions thatmaybeusefulforsociety(Street2014).Theygenerallyinvolvetools,products,websites,orbulletins. (VaughanandDessai,2014).Theycanbeglobal,regionalorlocal,andforgeneralpurposes,multidisciplinary orfocusedonaspecificsector(health,tourism,agriculture,watermanagement,etc.)ordedicatedtoconcrete hazards(floods,droughts,forestfires,urbanheatwaves,etc.).Theycanbemultitemporaloraddressedto specifictimescale,includinghindcast,sub‐seasonalorseasonalforecasts,decadalorcentennialclimate projections.ThetailoredinformationprovidedbythecorrespondingCSshouldconsideralltheseaspects.  3.1 InternationalClimateServices AnumberofglobalandcontinentalclimateservicesareavailablebeingabletomeettherequirementsofI‐ CISKLLusers.ThesearedescribedindetailintheI‐CISKdeliverableD3.1“Preliminaryreportontheskill assessmentandcomparisonofstate‐of‐the‐artmethodsforforecastsandprojectionsofextremes”and summarizedbelow:  Copernicus:CopernicusistheEuropeanUnion’sEarthObservationProgramme(www.copernicus.eu). Itprovidesarangeofservicescoveringtheatmosphere,oceans,land,climatechange,securityand emergencyservices.MostoftheCopernicusCS,whichcouldbepotentialsolutionstoI‐CISKgoalsand activities,arehostedintheC3S(CopernicusClimateChangeService)https://climate.copernicus.eu/. Inaddition,theCopernicusEmergencyManagementService(CEMS)isofhighrelevanceforsomeof theproject’sLLs(seeChapter4),particularlytheCopernicusDroughtObservatoriesforEurope(EDO) andtheglobe(GDO);seehttps://drought.emergency.copernicus.eu/.  EuropeanClimateDataExplore:ThisCSisintheframeofaEuropeanClimate‐ADAPT(https://climate‐ adapt.eea.europa.eu)initiative.ThemostrelevantistheAgricultureCS,whichprovidesasetof agroclimaticvariablesat0.25degreespatialresolution(https://climate‐ adapt.eea.europa.eu/en/knowledge/european‐climate‐data‐explorer/agriculture).Also,theWater andcoastalCSscollecthydrologicalandmarinedatasetsat0.25degreespatialresolution https://climate‐adapt.eea.europa.eu/en/knowledge/european‐climate‐data‐explorer/water‐and‐ coastal.  FAO:TheFoodandAgricultureOrganization(FAO)hoststheAgriculturalStressIndexSystem(ASIS; https://asis.apps.fao.org/).Itmonitorsagriculturalareaswithahighlikelihoodofwater stress/droughtonaglobalscaleusingsatellitetechnology(Rojasetal.,2013).ASISprovidesallraster datasetsthroughtheFAOHand‐in‐HandGeospatialPortalandtheGoogleEarthEngine(GEE).The AQUAMAPS(https://data.apps.fao.org/aquamaps/)isAQUASTAT’sonlinegeospatial(regionaland global)databaseonwaterresourcesandagriculturewithgeospatialmodellingandanalytics functionalitiesforwatermanagement. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 9  GEOSS:TheGlobalEarthObservationSystemofSystems(GEOSS)hasdevelopedseveralCSsunder differentspecificinitiativessuchastheGlobalWaterSustainability(GEOGloWS; https://www.geoglows.org/pages/geoglows‐service),theGlobalDroughtInformationSystem(GDIS; https://earthobservations.org/organization/work‐programme/global‐drought‐information‐system), theCropMonitorbyGEOGLAM(https://cropmonitortools.org/tools/cmet/)andAquaWatch (https://www.aquawatchsolutions.com/).  WMO:TheWorldMeteorologicalOrganization(WMO)developedtheClimateServicesInformation System(CSIS)asthecoreoftheGlobalFrameworkforClimateServices(GFCS).Someselectedspecific CSswhichcouldfittotheneedsoftheI‐CISKLLusers,are:theIntegratedDroughtManagement HelpDesk(https://www.droughtmanagement.info/),theGlobalData‐processingandForecasting System(GDPFS,https://community.wmo.int/en/activity‐areas/global‐data‐processing‐and‐ forecasting‐system‐gdpfs)andthePublicWeatherServices(PWS)withtheWorldWeather InformationService(WWIS)(Ritterbush2006). AlltheseglobalCSsareusefulandrelevantformanypurposes,buttheydonotbenefitfromthecontribution oflocaldataandlocalknowledgeatLLlevel.Asdiscussedintheprevioussection,increasingthespatial resolutionoftheseglobaldatasets—whicharevisualizedanddeliveredbytheseinternationalCSs—isoneof themostdemandedfeatures.TheCoordinatedRegionalClimateDownscalingExperiment(CORDEX, https://cordex.org/about/)isarelevantinternationalinitiativeoftheWorldClimateResearchProgram (WCRP)specificallyaimedatachievingthisgoal.CORDEXanditsdifferentdomains,suchasEuroCORDEX, MedCorDEX,etc.,seektoadvanceandcoordinatethescienceandapplicationofregionalclimatedownscaling throughglobalpartnerships.TheCORDEXgoalsare:1)betterunderstandingofregional/localclimate phenomena,theirvariabilityandchanges,2)improvementofregionalclimatedownscalingmodels,3) generationofcoordinatedsetsofregionaldownscaledprojections,and4)communicationandknowledge exchangewithusersofregionalclimateinformation.Currently,CORDEXleadstheexperimentdesignforthe dynamicaldownscalingofCMIP‐6(Gutowskietal.,2016).  3.2 NationalClimateServices 3.2.1 ClimateServicesinSpain AEMET(thepublicSpanishMeteorologicalAgency)providesasetofdifferentCSforallofSpain,including seasonalforecasts,climateprojections,andadroughtobservatory.Theassociatedclimateinformationis availablethroughdifferentmapbrowsers:theAdapteCCaplatform (https://www.aemet.es/es/serviciosclimat*icos/cambio_climat/visor‐AdapteCCa)showstheEuroCORDEX (CORDEXatEuropeanDomain,Jacobetal.,2014)climateprojectionsmaps,thedroughtmonitor (https://monitordesequia.aemet.es/)isapaletteofmonthlydroughtindices’maps(historicalavailablein https://monitordesequia.csic.es/historico)andatimeseriesplotofachosenlocation,the https://www.aemet.es/es/portal/serviciosclimaticos/prediccion_estacional,currenthydrologicalvariables areavailableinhttps://www.aemet.es/es/serviciosclimaticos/vigilancia_clima/balancehidrico. ElTiempo(https://www.eltiempo.es/),anunofficialyetpopularCSplatform,isaprivatedigitalmediachannel ownedbyPelmorexCorp,andisspecializedinshort‐midrangeweatherforecasts,howeveritdoesnotcover seasonalforecastsnorclimateprojections. TheLCSCClimatologyandClimateServicesLaboratory(https://lcsc.csic.es/)contributestothestudyof climaticdroughts,theircauses,changes,andimpacts.Italsodevelopsfree‐accesssoftware,databasesand climateservicesfordroughtquantification.Thespatialresolutionsofmostproductsare:1degreeforglobal, 0.125degreesforEuropeanand1.1kmfornational(Spain). D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 10  3.2.2 ClimateServicesinGeorgia InGeorgia,preparationsareunderwaythroughvariousprojects,suchasGRAIL (https://projects.worldbank.org/en/projects‐operations/project‐detail/P175629)andtheMulti‐HazardEarly WarningSystem(https://www.undp.org/georgia/projects/early‐warning‐climate‐information),howeveruntil today(September2024)nothinghasbeenfullyestablishedyet. 3.2.3 ClimateServicesinHungary TheCSimplementedbytheHungarianMeteorologicalService(https://www.met.hu/en/idojaras/)provide essentialmeteorologicalvariablesinshortrecentpasttimeseriesandshort‐termforecasts. 3.2.4 ClimateServicesintheNetherlands ThenationalleveldroughtinformationavailabletotheLLRijnlandatthestartofI‐CISK,andstillto‐date (September2024)arethemainnationalservicesusedaretwodroughtmonitoringwebsites,onefromthe nationalmeteorologicaloffice(KNMI;https://www.knmi.nl/nederland‐nu/klimatologie/droogtemonitor),and anotherfromthenationalagencyforwaterresourcesmanagement(RWS; https://waterberichtgeving.rws.nl/owb/droogtemonitor). TheKNMIserviceprovidescountry‐averagepotentialprecipitationdeficitintimeseriesgraphsandinmap format.Theprecipitationdeficitiscalculatedasacumulativedifferencebetweenprecipitationandpotential evapotranspirationfrom1stofAprilonward.Themaponlyshowsthemostrecentobservedcumulative precipitationdeficit.Thetimeseriesgraphshowstheobserved,butalsoaforecastforthecoming14days basedonECMWFIntegratedForecastingSystem(IFS)EnsemblePredictionSystem(EPS).TheKNMIdrought monitordoesrefertofurtherinformation,suchasStandardisedPrecipitationIndex(SPI),whichcontainsan interactivemapwithgriddedinformation,alsowithamaximum2‐weekleadtime.TheRijnlandwater authorityisinterestedinusingthisinformationintheirlocaldroughtmonitoraswell,andarebuildingup experiencewithusingthisdata,butlocaldecisionguidelineswithalertthresholdsforSPIhavenotyetbeen developed. TheRWSdroughtmonitor'skeyinformationusedintheLLRijnlandistheobservedriverdischargeatLobith station,togetherwitha14‐daystreamflowprediction.Nationallow(andhigh)flowalertlevelsareindicated. TheRijnlandwaterauthorityusesthesefortheirlocaldroughtpre‐alertaswell,becauselowflowsintheRhine increasesalinityintrusionfromtheseatowardsthemainfreshwaterintakepoint.ThenationallevelRWS droughtmonitoringdoesrefertoanensemblepredictionforthesamestreamflowstation,whichareusedby theRijnlandwaterauthority,butalsotheseforecastsdisplayedarewithamaximum2‐weekleadtime. 3.2.5 ClimateServicesinItaly InItaly,nationalclimateservicesareprimarilyprovidedbytheMeteorologicalServiceoftheItalianAirForce (MeteoAM),whichoffersawiderangeofforecastsandclimatedata,includingbothshort‐termandlong‐term projectionsofmeteorologicalvariables.TheseservicesareaccessiblethroughtheofficialportalofMeteoAM (https://www.meteoam.it/it/). Additionally,theMeteoItalianSupercomputingPortal(MISTRAL;https://www.mistralportal.it/it/mistral‐ open‐services‐it/)providesopenaccesstoclimatedata,includingnear‐termweatherforecastsand environmentalmonitoring.Thesetoolsareessentialforvarioussectorsandareclosertotheneedsofthe ItalianLLbyprovidingmeteorologicalforecastthatcouldpotentiallybelinkedtotherequestedhydrological forecastofthelocalservice.However,themostrelevantservicesforthecurrentcontextaretheregional ratherthannationalones.Forinstance,themonthlymapswithdroughtindicatorsprovidedbyAgenzia PrevencioneAmbienteEnergiaEmilia‐Romagna(ARPAE;https://www.arpae.it/it/temi‐ambientali/meteo) andtheseasonalforecastsarecloserinscaleandfocusondroughtseason,tothespecificneedsoftheLL. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 11 ARPAEproducesanddistributesclimatic,meteorologicalandhydrologicaldata:climateprojectionsummary reportsandbulletins,historicaldataanddatafrommonitoring.Itisimportanttonotethatnoneofthesetools currentlyofferseasonalflowforecastsforrivers,whichisasignificantgap.Suchforecastsareprovided,albeit withoutprecisedownscalingandthuswithlimitedreflectionoflocalhydrology,bytheCEMS‐GloFASForecast (https://cds‐beta.climate.copernicus.eu/datasets/cems‐glofas‐forecast)asmentionedintheinternational sectionabove. 3.2.6 ClimateServicesinGreece TheNationalMeteorologicalServiceofGreece(HellenicNationalMeteorologicalService,HNMR; http://emy.gr/emy/en)istheofficialproviderofCSsinGreece.TheseCSsincludeinformativemaps,tables andreportsforshort‐andmedium‐rangeweatherforecastsforallofGreece,localisedatregional,andcity level,aswellasfortheentireEuropeanregion.Additionally,HNMRprovidesextremeweatherwarnings, climatologicaldata,andseasonalforecastshortreportsbasedonECMWFseasonalforecastreporting. However,itdoesnotincluderegionalorlocalisedinformationonseasonaldata,nordoesitincludeclimatic projections. Inadditiontoofficialservices,thereareseveralunofficialyetpopularnationalandlocalclimateservices. Meteo(www.meteo.gr)isanadditionalweatherforecastingserviceprovidedbytheNationalObservatoryof Athens(ResearchInstitute).Itoffersshort‐rangeforecastingthroughintuitivemaps,tablesanddiagrams, weatherwarnings,andmeteorologicalmeasurementsacrossGreece.Othersimilarservices,butless interactiveandwithlimitedinformation,includeFreeMeteo.gr(https://freemeteo.gr/),K24.net (https://gr.k24.net/m/),andlocalservicessuchasCretaWeather.gr(https://cretaweather.gr/). Inthethematicareaofclimaticprojections,anationalhubwasdevelopedrecentlyin2023bytheHellenic MinistryofEnvironmentandEnergy.Thishubcanbeaccessedthrough http://mapsportal.ypen.gr/thema_climatechangeorhttps://adaptivegreecehub.gr/.Itprovidesclimatic projectionsdataorganisedininteractivemapsandrasterdatawitharesolutionof5kmforapproximately25 climaticvariablesandindices,aswellasinformationonclimatechangeadaptation. 3.2.7 ClimateServicesinLesotho ThemainnationalinitiativesthatprovideClimateServicesare:  TheLesothoMeteorologicalServices(LMS)deliversseasonalprecipitationoutlookstonational stakeholdersprimarilythroughpresentationsatnationalroundtablemeetings.LMSpreparesits seasonalforecastusingtheNorthAmericanMulti‐ModelEnsemble(NMME),whichisdownscaled withreanalysisproducts(ClimateHazardsGroupInfraRedPrecipitationwithStationdata;CHIRPS)and datafromlocalweatherstations.Theythenrefinethisforecastbycomparingittotheregional seasonaloutlookproducedbytheSouthernAfricaRegionalClimateOutlookForum(SARCOF).Based onthiscomparison,LMSfinalizesitsseasonaloutlookTheyalsoprovideupdatesthroughoutthe seasonviaemailtokeepstakeholdersinformed.Additionally,deliversdailytemperatureforecastvia emailandbulletins.  TheLesothoVulnerabilityAssessmentCommittee(LVAC)providesassessmentsusingtheIntegrated FoodSecurityPhaseClassification,reportingonbothcurrentandprojectedfoodinsecurityforthe upcomingconsumptionyear,attheadministrativelevel1.Theanalysisissharedintheformofareport withnationalstakeholders.  3.3 TowardstheNeedforTailoredClimateServices ThebenchmarkofthetailoredCScanbemeasuredbytheirusability,particularlyconcerningsolutions designedtoaddresstheexistingusabilitygap(Raaphorstetal.,2020).Usabilitydependsontheleveland D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 12 qualityofinteractionbetweeninformationproducersandusers.Itcanprovideinsightsintothegapbetween thepotentialusefulnessofclimateinformationperceivedbyscientistsandwhatusersfindusableintheir decision‐makingprocess(Lemosetal.,2012).Moreover,adisconnectionbetweenclimatedataproduction anditsapplicationcanexacerbatethisgap(Singetal.,2018). ItisimportanttonotethattheI‐CISKCSimplementationisstillunderdevelopmentor,atleast,withalimited testingperiodforusabilityvalidationbyendusers.Therefore,itisprematuretoconductacomprehensive benchmarkevaluationbyarepresentativegroupofI‐CSIKstakeholdersatdifferentLLs.Instead,wewill approachusabilitybyanalysingthesolutionsdesignedtoovercomethemostcommonandparticularbarriers thatimpactusereffectivenessandsatisfaction(Pimenteletal.,2022;BrasseurandGallardo,2016)toimprove CSusability:  lowlevelofuserengagementintheco‐developmentprocess  lowspatialresolutioninCSinformationandlackofappropriatemethodfortailoring  misunderstandingtheprovidedclimateinformation(e.g.uncertainty)  lackofaCSevaluationphase(notethattheCSevaluationisthescopeoftheoncomingI‐CISKD3.4 deliverable)  informationisnotactionableformanagementanddecision‐making. Giventhattheusabilityisdifficulttoevaluatebyquantitativemetric,giventhat“differentactorsperceivethe usefulnessofscientificinformationdifferently”(PorterandDessai2017),thereforethenextchapter qualitativelycomparestheusefulnessoftheI‐CISKCSsversusexistinginternationalandnationalCSswhich werebrieflydescribedintheprevioussubsection.WeaimtohighlightthesuccessoftheI‐CISKCSsin overcomingtheidentifiedbarriersandhighlightingtheaddedvalueandbenefitstheyprovidefortheLLs. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 13 4 BenchmarkingoftheImplementedClimateServicesacrosstheLivingLabs ThischapterreviewsthesevenI‐CISKLLsinordertounderstandthedesignedand/orimplemented improvementsappliedtotheircorrespondingtailoredCSforovercomingthemainbarriersandincreasingtheir usability.WerecommendadditionalreadingoftheI‐CISKdeliverablesD1.1“CharacterizationoftheI‐CISK LivingLabs”,foramoredetailedinformationoftheLivingLabscharacteristics,andD2.1(Preliminaryreport) andD2.4(final)“InformationonClimateServiceNeedsandGaps”forextendedexplanationsofthemain barrierstotheCSusabilityineachLL. 4.1 Andalucía‐SpainLivingLab TheAndalucíaLLismainlylocatedintheGuadalquivirRiverBasinDistrict(RBD),plusasmallpartofthe GuadianaRBD.Itmainlyfocusesinthecomarca(region)ofLosPedroches,aprimarilyagriculturalarealocated inthenorthoftheprovinceofCórdoba,intheautonomousregionofAndalucía,Spain.Italsoincludesthe SierradeCazorla,SeguraandLasVillasNaturalParkintheupperGuadalquivirRBDasacomplementarysite fortestingthedevelopedCSforforestrylandscapes. 4.1.1 ClimateServicedescription ThisCSavailableinhttps://i‐cisk.dev.52north.org/living‐labs/guadalquivir‐‐es/isanAgriculturalandForestry PlanningServiceandiscomposedbyasetofproducts:historicalclimateinformation,seasonalclimate forecasts,climateprojections,agroclimaticinformationandgroundwatercharacterization.Theirgoalsarethe reductionofthevulnerabilitytoclimaterisksto(1)supportsustainableagriculturalandenvironmental management,(2)buildsocietalresiliencetomultiplerisks,(3)counterruralexodusandabandoningof agriculturalactivities,(4)buildacultureofdecisionmakingbasedonup‐to‐dateandevidence‐based informationandscientificdata,and(5)strengththeadoptionofEuropeanclimatechangepolicies. Figure2 Screenshotofone(precipitationseasonalforecasts)oftheimplementedSpainLLclimateservices.It providesdifferentpercentilesoftheensemblesresults,medianleftupperfigureanduncertainty/dispersion intherightupper.Plotofthe6monthsforecastsofpercentileensemblesofclickedlocationatthebottom. 4.1.2 Integrationoflocaldataandlocalknowledge ThecontributionoflocaldataistotallyrelevantforthehistoricalandpredictionsCSinthisLL.Historicalrecords fromAEMET,timeseries(1975‐2022)ofmonthly,anddailyinareferencesubperiod(1993‐2007),ofmean temperatureandprecipitationallowedtogenerateahighspatialresolutionmonthlymaps(250m)aswellfor droughtindicators:StandardisedPrecipitationIndex(SPI),StandardisedPrecipitationEvapotranspiration D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 14 Index(SPEI).Wealsoconsideredauxiliarylocaldatasets(SAIH,CAPDR,PAART,etc.,Trojeretal.,2024)for testing/evaluationpurposes,theirtimesseriesarenotstillenoughlargeforacontributiontotherobust climatemodellingmethodologies. ThecontributionoflocalknowledgeisspeciallyveryrelevantinthisLLintwoCSs:  AgroclimaticCS:theexperienceoflocalfarmersguidedtheselectionofvariables,suchasaggregated springprecipitationandmeantemperatureinMayandJune,tobeintroducedasindependent explanatoryvariablesinthemultiplelinearregressionmodelforannualoilproduction.  HydrogeologiccharacterizationCS:thelocalfarmersandlocalpublicmanagerssuggestedthe locationsoffieldcampaignsandreviewandvalidatethecharacterizationreportswiththeirexpertise. 4.1.3 Usabilityofthetailoredmethods Afterstakeholderconsultationinformoflivequestionariesinbilateralandsectorialonlinemeetings,themain barriersofCScollectedinthisLLabouttheCSusabilityare:  thelackoftailoredinformationwithtworelevantaspects:insufficientspatio‐temporalresolution (theyprioritizedthespatialresolution)andlackofaccesstohistoricalmeteorologicaltimeseries.  effectivedisseminationtotargetaudiences.  misunderstandingofforecastuncertainty. MosteffortsinthisLLtodevelopCSsusingtailoredmethodsfocusedondownscalingtechniques.Theseefforts aimedtogeneratehigh‐resolution(HR)historicalmaps,HRseasonalforecasts,andmid/highresolution climateprojections,allappliedtomonthlyprecipitationandmeantemperature. Ingeneral,wecollectedgoodevaluationsintermsofusability(Figure3)fromtheLLusers(seeMAP compositioninD1.1CharacterizationoftheI‐CISKLivingLabs)abouttheCSbasedonthedeveloped downscalingmethods.Somerefinementsonvisualizationandtime‐respondarerequestedandweareworking tosolvethem,buttheytotallyagreewiththespatialandtemporalresolutionoftheprovidedclimate information.Regardinguncertainty,usersinterestedinriskmanagementoftenaskforreliablepredictions.  Figure3Left:exampleofinteractivesurveyaboutCSusabilityinbilateralmeetingswithLLstakeholders.Right: averagefeedbackfromallbilateralmeetings. 4.1.4 Benchmarking AcomparisonwithexistingCSsfrominternationalinitiativeswithsimilartoI‐CISKobjectivesrevelsthat CopernicusC3SandCEMS(GDOandEDO)providevaluableclimateinformation,butdonotaccomplishtwo mainrequirementsformostoftheSpainLLendusers:finerspatialresolutionandclearunderstandable information.Figure4showsthedifferentoptionsandindicatorsindroughtCSandclimateprojections’ downloadingservice.DuringtheCSco‐creationprocess,weselected(andsimplified)themaininformation D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 15 followingtheusers’demand.Inaddition,thelanguageisabarriertoproperlyunderstandtheclimate information.  Figure4Left:IndicatorsselectionlegendofEDOandGDO.Right:Differentexperimentandmodeloptionsin theClimateDataStore(CDS)ofCopernicus.  ThecomparisonwithnationalCSexhibitsthaneventhereareseveralexistingdownscaleddatasets,suchas http://www.meteo.unican.es/datasets/spain02at20km(Herreraetal.,2012)or5km(Hernanzetal.,2022) thesemaynotfitthedemandsofmostLLstakeholders.Inthiscase,thelanguage(Spanish)makeseasythe understandingoftheinterfaces,optionsandclimateinformationbyusers,howeverthededicated developmenttotheirlocalneedsincreasestheI‐CISKCSusability.Finally,thededicatedvisualfunctionalities toallowcomparisonofconditionsbetweendifferentmonth/year(seeFigure2),ordifferentlocationsare appreciatedbytheusers.  4.2 Alazani‐GeorgiaLivingLab TheAlazaniRiverBasinLLislocatedwithintheterritoryofGeorgia.Duetothecomplexmountainous topographyandhighlydiverseclimatesettings,Georgiaissubjecttoclimate‐relatedhazardssuchasfloods, flashfloods,landslides,debrisflow/mudflowsnowavalanches,hailstorms,windstormsanddroughts. 4.2.1 ClimateServicedescription TheI‐CISKCS(https://i‐cisk.dev.52north.org/living‐labs/alazani‐‐ge/)isaWaterResourceManagement Service(integratedmanagementtoEUWaterFrameworkDirective)withthemaingoals:(1)toimprove resiliencethroughincreasedfoodproductionandwaterresourcemanagementfordrinkingwaterand irrigation,and(2)toachievegreaterexploitationofrenewableenergy(hydropower)throughimproved management,policymaking,supplyanddemandbalancingandenergysaving. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 16  Figure5ScreenshotofforecastexampleoftheGeorgiaLLpilotclimateservice. 4.2.2 Integrationoflocaldataandlocalknowledge TheNationalEnvironmentalAgency(NEA)isthemainhydrologicalagencyinGeorgia,andoperatesthe networkofhydrologicalandmeteorologicalstations.ObserveddischargedataatstationsintheAlazaniand Ioribasinsisprovidedtoevaluateandbias‐adjusttheseasonalstreamflowforecasts.Thisincludeshistorical data,aswellasrealtimedata.Thelatterisavailableonlyatalimitednumberofsites,asthehydrological networkisstillbeingreconstructedfollowingitsvirtualcollapseafterthesovietperiod.Localpastexperience offarmers’associationscontributestotheimpactfarmingdecisions’planning. 4.2.3 Usabilityofthetailoredmethods ThetailoredmethodsthatarebeingimplementedintheCSofthisLLare:  Sub‐seasonalandSeasonaldroughtforecastsbasedondroughtindicatorstodisplayexpecteddrought conditionsacrosstheAlazaniandIoribasins(displayedatsub‐basinlevel).  Hydrologicalforecastsatkeylocations,downscaledtosinglesub‐catchmentandspecificpoint locationsofinterest. ThespecificbarriersdetectedinthenationalCSofthisLLare:  Servicediscontinuity(on‐demandasopposedtoregularproduction).  Lackoflong‐termnationalstrategy.  Demandsonsector‐tailoredinformationforagricultureplanning. ThemainbarriersfortheusabilityoftheinternationalCSsarethelanguageforsomeofthelocaldecision‐ makersandendusers. 4.2.4 Benchmarking ThereiscurrentlynoprovisionofstreamflowforecastsinGeorgia,andusersinsteadrelyonweatherforecasts offorinstanceprecipitation,whichismainlysourcesfromweatherapps. ItisnotpossibletocomparetheI‐CISKCSwiththenationalCS,becauseasexplainedinsubsection3.3.2these arecurrentlyunderdevelopmentatthenationallevel,incollaborationwiththeRuralDevelopmentAgency, D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 17 NEA,UNEPandotherpartners.Seasonalstreamflowforecastsarenotincludedinthosedeveloping capabilities.ThebenefitsversusInternationalCSthatarecurrentlyavailableoftheCSdevelopedintheGeorgia LLare:  Integrationofbasinmanagement(EUWaterFrameworkDirective).  Improvedresiliencethroughincreasedfoodproductionandwaterresourcemanagementfordrinking andirrigation.  Greaterexploitationofrenewableenergythroughimprovedmanagement,policymaking,supplyand demandbalancingandenergysaving.  Widerrangeofvariablesrelatedtoheatwaves. 4.3 Budapest–HungaryLivingLab TheBudapestLLislocatedintheErzsébetvárosdistrict,aninner‐cityareaofBudapest(thecapitalandmost populouscityofHungary).Theareaisdenselyconstructedwithmanyprotected‐heritagebuildingsmostly fromthelate19thandearly20thcenturies. Thisdistricthasalowpercentageofgreenspaces,withahighdensityofbuildings,andthereforeisparticularly exposedtoheatwaves,whicharealreadycausingissuesforarangeofsectorsinthecity.Thefocusofthe LivingLabisonurbanheatislandsinthetourismandpublichealthsectors. 4.3.1 ClimateServicedescription TheI‐CISKCS(https://i‐cisk.dev.52north.org/living‐labs/budapest‐‐hu/)isanUrbanHeatPlanningServicewith twospecifictools:  Time‐seriesanalysis:Utilizingorthophotosasahigh‐resolutionbaselinefortime‐seriesanalysisof thermaldata.Thismethodallowsfortrackingchangesinurbanheatovertimewithaclearreference tothephysicalchangesintheurbanlandscape.  Energybalancemodellingwithdetailedsurfaceinformation:Applyingenergybalancemodelsthatuse detailedsurfaceinformationfromorthophotos,combinedwiththermaldata,tointerpreturbanheat dynamicsmoreaccurately.  Figure6UrbanheatmapCS,Erzsébetvárosdistrict,Budapest. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 24 demand,especiallyforcoolingneedsduringthehotsummerdaysandnights,isanimportantconsideration forthetourismindustry.Extremeweatherevents(e.g.heavyprecipitationevents,highwinds)andflood impacts(coastalandriver)areprimarilyrelatedtotransportationinfrastructure(mainlyportsandroads), whichsupportstheeconomicactivityaswellastourism‐relatedinfrastructure(landslidepotentialdue,among others,toheavyrain,seeFigure11.b).Assuch,themainendusersoftheCSincludetourismenterprises, tourists,citizensandusersfromthewatersupply,transportationandenergysectors. TheCSdevelopedaddressesthesectorialneedsatdifferenttemporalandspatialscales.Theserviceis structuredbasedontwomaintemporalscales:(a)seasonalandsub‐seasonalinformationwhichaddress operationalneedsforinformedandimproveddecisionmaking,and(b)end‐of‐centuryclimaticprojectionsfor supportinglong‐termplanningadaptationmeasures. (a) (a)  (b) (b)   Figure11ScreenshotofGreeceLLCS:(a)Seasonalforecastingofsurfacewateravailabilityatabasinlevel, includinguncertaintyinformation,and(b)seasonalforecastoflandslidesusceptibilitylocallyandspatially variablycoveringthewholeisland. 4.6.2 Integrationoflocaldataandlocalknowledge Localhistoricaldata(useofprecipitationandtemperaturemeasurementsthroughouttheisland)havebeen usedforthedownscalingofmeteorologicalvariablestodrivehydrologicalimpactmodellingandprovide better,localizedinformation. Localinformationandlocalknowledgewerecrucialandactuallyshapedthedevelopedservices.Thisledto thedesignoftheservicetoprovide:(a)theinformationneeded,(b)forthetimeneeded,and(c)forthespatial scaleneeded: D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 25  Watermanagementsector:identificationofspecificneeds(informationneeded,e.g.4monthswet periodsurfacewateravailability)foroperationalaswellaslongtermplanning(periodofdecision making–whentheinformationisnecessary,targetperiodthattheinformationcovers–e.g. NovembertoFebruary,typeofinformationrequiredforbetterdecisionmaking,potentialgains,etc.).  Transportation:identificationofspecificneedsforoperationalaswellaslongtermplanning(what typeofinformationisrequiredforeachoftheperiodaddressed,whatdecisionsaremadeaddressing differentperiodsofoperations,climatichazardsidentified,typeofinformationrequiredforbetter decisionmaking,potentialgains,etc.).  Tourism,accommodation:identificationofspecificneeds(informationneedede.g.specificclimatic indexesorvariables)foroperationalaswellaslongtermplanning(periodofdecisionmaking–when theinformationisnecessary,targetperiodthattheinformationcovers). 4.6.3 Usabilityofthetailoredmethods ThetailoredmethodsimplementedintheCSoftheCrete‐GreeceLLare:  SeasonalforecastsofLandslideSusceptibilitybasedonprecipitationseasonalforecasts.  Seasonalforecastsofsurfacewateravailabilitytailoredtospecificreservoirmanagementneeds. Theindexproducedwasbasedonacollectionofinformationfromstakeholders(watermanagement) regardingthedecisionstobemade,theproblemstobeaddressedandtherelevanttimelines.Itaddresses waterallocationanddistributionseasonalplanningbasedontheexpectedwetyearsurfacewateravailability atareservoirbasinlevel.Theinformationproducedisbasedonseasonalforecastingofsurfacewater discharge(informationproducedwithintheI‐CISKproject),whenitsavailabilitycoversthewetperiodin question.However,whenthisproduct(seasonalforecastofsurfacewateravailability)isnotavailableforthe desiredperiod(e.g.earlyintheyear),estimationoftheindexisprovidedbasedoncomparisonofcurrent hydrologicalyearwithstatisticalanalysisofhistoricaldata. ThespecificbarriersdetectedintheexistingnationalandinternationalCSofthisLLare:  Climatechangeserviceslackcross‐sectorlinks.  Lackofsector‐tailoredinformationandsector‐specificindicators.  Lackofaccessibilityfornon‐expertusers.  Lowspatio‐temporalresolution. 4.6.4 Benchmarking ThedevelopedClimateServicesareaddressingdataneedsgapsunderacross‐sectoralapproach,providing informationonhighspatial(1kmx1kmisthekeyfeaturefortheirusability)andtemporalscalessuitablefor operationaldecisionsupport(seasonal)andlong‐termplanning.ThenewCSprovidenewinformationto support:  Improvedplanninginthetourismsector,tosupportadaptationofproductsanddestinationsand widenthespatialdistributionoftourismintheMediterranean.  Betterinformedandmoreagileplanningoftourismpolicyandbusinessactivities(shortandlong term).  ImprovedwaterresourcesplanninganduseefficiencyinCrete(targetSDG6–Cleanwaterand sanitation).  4.7 LesothoLivingLab TheLesothoLLisspreadinmultiplelocationsthroughoutthecountry(Lesothoisalandlockedcountryin SouthernAfrica)focusingonareasathighriskofdroughtsandcoldwaves.Thefrequencyofextremeevents islikelytoincrease,withclimateprojectionssuggestingahotteranddrierconditionsinthefuture,posing D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 26 higherandmorefrequentriskoffoodinsecurity.TheLivingLabfocusesontheDisasterManagementandas itiskeyinthepreparationandresponsetosuchextremeevents. 4.7.1 ClimateServicedescription InLesotho,twoCSsarebeingimplementedatRedCross‐ImpactBasedForecastingportal,withthegoalof enhancingthetimelyexecutionofanticipatoryactionsforcoldwavesanddroughts,usingimpact‐based forecasts. However,thereiscurrentlynocleardemandfromusersforaclimateserviceinformationsystem(i.e.,a platform)thatvisuallydeliversthisserviceforcoldwaves.Instead,thesupportprovidedfocusesonhelping theLesothoMeteorologicalServiceimprovetheaccuracyoftheirforecasts. Incontrast,theclimateservicefordroughtswillbeaninformationsystemdesignedtomonitordrought forecastsandsupportearlyactionplanningtomitigatedroughtimpacts.Theseactionscanbeautomatically triggeredwhenlinkedtopre‐approvedplansandfinancing,asdetailedintheEarlyActionProtocolagreed uponbyrelevantstakeholders. KeyactionsareplannedforOctober,markingthestartoftherainyseason,whenearlywarningmessagesare disseminatedtocommunitiesatrisk,andJanuary,whencashtransfersaremadetocommunities.TheClimate ServicewillprovideseasonalforecastsinSeptemberfromtheLesothoMeteorologicalServiceandinformation onprecipitationobservationsandforecastedfoodinsecurityinJanuary.TosupportLRCSoperations, additionaldatasuchaspopulationdensityandlivelihoodzoneswillalsobeincluded.  D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 27  Figure12ScreenshotoftheClimateServiceprototypefordroughts,displayingdroughtrisklevelsacross LesothodistrictsforSeptemberandJanuarybasedonuserinput.Additionallayerscanbeaccessedviathe Layersmenu.Pleasenotethatthisisaprototype,andthefinaldesignmaydiffer. 4.7.2 Integrationoflocaldataandlocalknowledge TheI‐CISKCSfordroughtsprimarilyreliesonlocaldataproviders.Currently,thereisnoautomatedmethodto integratethisdataseamlessly.TheAnticipatoryActionfocalpointatLRCSwillmanuallyupdatethelocal informationintotheCS.Globaldatamaybeincludedtoofferpreliminaryinsightsintotherainyseasonbefore thelocalseasonaloutlookisavailable. InLesothoLL,localknowledgecontributestofarmersdecisionmakingandsupportstheVulnerability AssessmentAnalysisreports.Inaddition,localknowledgeisintendedtoincorporatetheindigenous knowledgefromcommunities,atthisstagewithoutsuccess.Thiscontributioniscrucialinacollaborative processinCSco‐designing,withtheaimtoachievethattheoutcomesarenotperceivedasimposed. 4.7.3 Usabilityofthetailoredmethods TheClimateServicefordroughtsisunderdevelopment,withfeedbackonusabilityfromthemainuseralready incorporatedintothedesign.Thisiterativeprocessensuresthattheservicemeetstheneedsofitsusers. 4.7.4 Benchmarking Forcoldwaves,anEarlyActionProtocol(EAP)hasbeendraftedbyLRCSandiscurrentlyintheprocessof acceptanceandreview.However,questionsremainaroundtheactionthresholdsincludedforsnowand temperatureforecasts.Forsnow,thethresholdistoolooselydefined(occurrenceof‘moderate’snowinthe Lesothohighlands),whilstfortemperature,althoughthethresholditselfismorespecific(maximum temperature<=2Cforatleasttwoconsecutivedays)itsapplicationinrealityisnotclear(forexample spatially).Aparticularbarriertodevelopingtheactionthresholdsforsnowwasalackofobservedsnowdepth observations,whichideallywouldhavehelpeddefinethethresholdsthroughananalysisofhowmuchsnow fellduringpastcoldwaveeventswithknownimpacts.Toaidthresholdrefinement,andadditionallyto understandforecastperformanceatthosethresholds,user‐centredevaluationofboththresholdsandECMWF forecastsisbeingconductedusingERA5Landreanalysisdataasa‘truth’proxy.ComparisonofERA5Land temperatureandsnow‘records’toadatabaseofimpactioncold/snoweventsisexpectedtoprovidevaluable informationfortherefinementofEAPactionthresholds.Oncethesethresholdshavebeenthoroughlydefined, D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 28 auser‐centred,event‐basedassessmentofECMWFtemperatureandsnowforecastcanbeundertaken.Itis hopedthatthiswillprovidefurtherinformationtotheLesothoMeteorologicalService(LMS)onforecast performance,includingforexample,areeventstypicallytooearlyortoolate?Howoftendoweseefalse alarms?Takentogether,thisshouldsupportbothLMSandLRCSintheircoldwaveanticipatoryactions.Whilst thisisnotinitselfnewclimateservice,itsupportsandbolstersthecurrentprocessforcoldwavepreparation andanticipatoryaction.Resultswillbepresentedinafollow‐onI‐CISKdeliverableD3.4“Assessmentofexisting andtailoredclimateservicesusingarangeofuser‐drivenevaluationmetrics”. Fordroughts,noexistingclimateserviceoffersacentralizedvisualizationofmultipleinformationsources tailoredtouserneeds.Currently,usersreceiveinformationfromvariousstakeholdersintableorlistformats, oftenfilledwithtechnicaljargon,makingitchallengingtointerpret.Thereisaneedforacentralizedplatform thatconsolidatesinformation,makingiteasiertoaccessandunderstandtherelationshipsbetweendifferent datasets.Additionally,internalcommunicationwithinLRCSiscurrentlyinefficient,primarilyoccurringvia email.TheCSwillmakethisbetter,byprovidingaplatformeasilyaccessiblebyusers. D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 29 5 SummaryofLessonsLearntfromtheLocalApplications 5.1 LessonsLearntfromtheLocalApplicationsateachLivingLab Table1providesacomprehensiveoverviewofthevariousaspectsoftheI‐CISKCSsacrossthedifferentLL implementations:applieddomain,localdataandlocalknowledgecontribution,tailoredmethods implemented,mainbarriersfromauserperspective,andthebenefitsandaddedvaluedoftheseI‐CISKCSs. Fromthissummary,supportedbythecontentofthepreviouschapters,wecanconcludethefollowing:  MosttailoredI‐CISKCSsaimtogenerateoutputswithhigherspatialresolutionthantheavailablefrom global,regional,ornationalservices.  DownscalingtechniquesarewidelyappliedastailoredmethodsacrossmanyLLs,withlocaldata playingacrucialroleintheseefforts.  LocaldatafallsshortofmeetingFAIR(Findability,Accessibility,InteroperabilityandReuse)data principles.  Despitetheextensiveinformationcollectedduringtheco‐creationprocesses,onlyafewLLsfully benefitfromlocalknowledgecontributions.Thisknowledgeisprimarilyusedtoenhancethe understandingofclimateinformation,butnottobuildcomprehensiveclimateknowledge.  InsomeLLs,usingthelocallanguageisarequirementforacompleteunderstandingoftheclimate information,whileinothers,thetechnicalterminologyposesabarrier.  Theinterpretationoftheprovidedclimateinformation(particularly,theuncertainty)iskeyfor developingactionsforwaterresourcesplanning,tourismpolicy,climateadaptationandvulnerability reductionindifferentsectorsatdifferentLLs.  Sector‐tailoredinformationand/orsector‐specificindicatorsarerepeatedlydemandedinsomeLLs.  Table2SummaryofthetailoredmethodsandtheusabilityofthelocalapplicationsforeachLL. LivingLabDisciplineLocaldata contribution Local knowledge contributio n I‐CISK tailored methods Barriersinthe existingCSs Addedvalue fromI‐CISK tailoredCSs Andalucía‐ Spain Meteorolo gical Longtimeseries oftemperature and precipitation densenetwork observations Advisoryon explanatory variables, potential correlations anddesign of agriculture adaptation strategies Statistical downscaling, bias correction Lowspatial resolution, language, misunderstandi ngofforecast uncertainty, lackofsector‐ tailored information Highspatial resolution, language, visualization toolsaddressed touserneeds  Alazani‐ Georgia Hydrologic al Timeseriesof discharge, temperature and precipitation. Sparsenetwork, withmany Past experience from farmers Sub‐seasonal andseasonal drought forecasts, hydrological downscaled forecasts Service discontinuity, lackoflong‐ termnational strategy Integrationof basin management, improvedwater resourceand renewable D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 30 stationsnot operational energy managements Budapest– Hungary Meteorolo gical HRairborne thermalimages, VGI Noinfo. available Datafusion techniques, CNNfor enhanced pattern recognition, orthophoto aided vegetation indexing Lackofspecific heatwaves’ variables, limited informationon green infrastructure Detailedurban heatmappingat thestreetand blocklevel Rijnland‐ The Netherlands Hydro‐ meteorolo gical Groundstation observations Precip.,ETpot, andQ Advisoryfor themulti‐ level dynamic drought alert thresholds S2Sdrought forecastsand sectorspecific alerts,climate change information Droughtalert leadtime limitedto14 days,no referenceto climatechange information S2Sdrought forecasts Droughtalerts withsector specifictexts. Droughtforecast andclimate changeinfoin oneapplication Emilia Romagna– Italy Hydrologic al Timeseriesdata offlow measurements Experience frompast drought episodes, designof adaptation strategies HRseasonal hydrological forecasts Visualization barriers, misunderstandi ngofgraphical elements Supporting decision‐making, waterresources planning Crete– Greece Hydrogeol ogical Historicaltime seriesof precipitation temperature measurements Decisions relatedto climate hazards (droughts, wildfiresand heatwaves) andenergy demand Seasonal forecastsof landslide susceptibility andsurface water availability Lackcross‐ sectorlinks,lack ofaccessibility fornon‐expert users,low spatio‐temporal resolution Waterresources planning, tourismpolicy LesothoMeteorolo gical Rainfall observations Supportto vulnerability assessments Drought forecasts,cold wavesEarly Action Protocol Disconnected datasetsintable orlistformats, technicaljargon Impact‐based forecasts, centralized platform, harmonized climate information  D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 31 5.2 DiscussionandMovingForward Currently,globalandEuropeanorganizationsprovideawiderangeofCSbasedonhigh‐qualityclimatedata. Theseorganizationsgenerateandpresentvarioustypesofoutputs,fromhindcastsandsub‐seasonalto seasonalforecaststoclimateprojections,usinganensembleofmodelsandemissionscenarios.Theyproduce bothessentialmeteorologicalvariables(e.g.,precipitationandtemperature)aswellasimpactindicators,such asdroughtindices,bioclimaticindicators,andagroclimaticindicators,atdifferenttemporalandspatialscales. TheplannedCopernicusCSevolutionwillfocustorespondthesemainrequirements:  Climatepredictioninformationatdecadaltimescales  Linkingextremeweathereventstoclimatechange Theactionplantodevelop,explainedinAEuropeanresearchandinnovationRoadmapforClimateServices (EuropeanCommission2015)relatedto“Enhancingthequalityandrelevanceofclimateservices”includes manyactionswiththecentreonthelocalusersandwhichareaddressedtodecision‐making.Itconcludeswith therecommendationofensuringthatstakeholdersareinvolvedthroughouttheprocess(CSdevelopment). FromtheexperienceinI‐CISKLLs,basedontheneedsexpressedbystakeholders,themainrecommendations fromausabilityperspectiveforimprovingclimateinformation,withinandbeyondtheI‐CISKLLs,are: 1) Toincreasethespatialresolutionofclimate(impact)information: Informationfromregionalmodelsisnotadequatetounderstandthelocalimpactsofclimatechange. SomeI‐CISKLLsfeatureheterogeneouslandscapeswithhightopographicvariabilityandthe spatiotemporalpatternsofmeteorologicalandclimatevariablesareverycomplexatalocalscale; hencetheneedforincreasingthespatialresolution. 2) Toincreasetheprovisionofhydrologicalforecasts: Meteorologicalforecastsaremorewidelyavailablethanhydrological,andthosearehighdemanded insomeI‐CISKLLs.NotethatthewatersectorhasagreatrepresentationintheMAPcompositionon someI‐CISKLL,andmaybeothercompositionmaycallformoreextensionofbioclimaticor agroclimaticpredictors. 3) Toenrichthein‐situcomponentforenhancinglocaldata: Localdatashouldpopulateglobal(atleastEuropean)datasets.NationalAgenciesshouldsharetheir localobservationstoglobal/Europeaninitiatives.Thesedatasetsshouldbeaccessibleincommon repositories,andtheyshouldbeusedintheregionalmodelsindifferentprocesses(training, calibration,validation,etc.). 4) Toincreasetheprovisionofextremes: Modellingandpredictingextremeeventsisverycomplex;however,thescientificcommunityshould intensifyitsresearcheffortsinthisarea.Extremeeventsareofgreatinteresttostakeholders,asthey haveasignificantimpactonbothnaturalandhumanenvironments,aswellasontheiractivities. AndthemainrecommendationsfromausabilityperspectivefortheevolutiontonewlygeneratedCSsare: 1) Toinclude(ormaintain)theco‐creationstrategy: StakeholderengagementintheI‐CISKLLshasdemonstratedsignificantbenefitsfortheusabilityofCS. Theinvolvementoflocalstakeholdersinthedesign,development,deliveryandevaluationofCSsadds clearvalue.Thecontributionoflocalknowledgeanddataishighlyrelevantandessentialforthe serviceuptakeandimproveddecision‐making. 2) Toaddcomparisontoolsbetweenpastandfuture: D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 32 Theinclusionofsimpleanalysis/scenariotools,forinstance,acomparisonofpredictionsagainstapast benchmarkevent(withhighimpact)helpstounderstandfuturepredictionsandthepotentialimpact ofthesepredictions. 3) TodevelopsectorialCSswithtailoredclimateinformation: TheexistingessentialvariablesandtheirimpactindicatorsavailablefromCopernicusareusefulfor specificsectors;howevernewgenerationclimateservicesshoulddevelopspecificsectorialclimate informationtoforestry,agriculture,urbanplanning,tourism,watermanagement,etc.usingtailored information,methodsandtools. 4) Toreducelanguageandsemanticbarriersandtoprovideleaningmaterials: Therightinterpretationofclimateinformationisakeypointfortherightdecisionsbylocal stakeholders.Thereductionofclimatevulnerability,theadaptationmeasuresandthereductionof impactsinclimatehazardsdependsonthisinterpretation.Barriersinthisinterpretationshouldbe reduced,andconstantlyupdatedlearningmaterialsshouldbeprovided.  D3.3‐Benchmarkingtailoredclimateservicesforlocalapplicationsusinglocalknowledgeanddata 33 References Berg,P.,Bosshard,T.,Yang,W.,&Zimmermann,K.(2022).MIdAS—MultI‐scalebiasAdjuStment.Geoscientific ModelDevelopment15,6165–6180https://doi.org/10.5194/gmd‐15‐6165‐2022 Brasseur,G.P.,Gallardo,L.(2016)Climateservices:lessonslearnedandfutureprospects.Earth’sFuture,4(3) 79‐89,https://doi.org/10.1002/2015EF000338 Buontempo,C.,Hewitt,C.D.,Doblas‐Reyes,F.J.,Dessai,S.(2014).Climateservicedevelopment,deliveryand useinEuropeatmonthlytointer‐annualtimescales.ClimateRiskManagement6,1–5. https://doi.org/10.1016/j.crm.2014.10.002 Dixon,K.W.,Lanzante,J.R.,Nath,M.J.,Hayhoe,K.,Stoner,A.,Radhakrishnan,A.,Balaji,V.,Gaitán,C.F.(2016) Evaluatingthestationarityassumptioninstatisticallydownscaledclimateprojections:ispastperformancean indicatoroffutureresults?ClimaticChange135,395‐408https://doi.org/10.1007/s10584‐016‐1598‐0 EuropeanCommission:Directorate‐GeneralforResearchandInnovation,Street,R.,Parry,M.,Scott,J.,Jacob, D.etal.,AEuropeanresearchandinnovationroadmapforclimateservices,PublicationsOfficeoftheEuropean Union,2015,https://data.europa.eu/doi/10.2777/702151 Frické,M.H.(2018).Data‐Information‐Knowledge‐Wisdom(DIKW)Pyramid,Framework,Continuum.In: Schintler,L.,McNeely,C.(eds)EncyclopediaofBigData.Springer,Cham.https://doi.org/10.1007/978‐3‐319‐ 32001‐4_331‐1. GutowskiW.J.,Giorgi,F.,Timbal,B.,Frigon,A.,Jacob,D.,Kang,H.‐S.,Raghavan,K.,Lee,B.,Lennard,C.,Nikulin, G.,O'Rourke,E.,Rixen,M.,Solman,S.,Stephenson,T.,Tangang,F.(2016)WCRPCOordinatedRegional DownscalingEXperiment(CORDEX):adiagnosticMIPforCMIP6.GeoscientificModelDevelopment,9,4087– 4095,https://doi.org/10.5194/gmd‐9‐4087‐2016 Jacob,D.,Petersen,J.,Eggert,B.etal.EURO‐CORDEX:newhigh‐resolutionclimatechangeprojectionsfor Europeanimpactresearch.RegEnvironChange14,563–578(2014).https://doi.org/10.1007/s10113‐013‐ 0499‐2 JacobsK.L.,StreetR.B.(2020)Thenextgenerationofclimateservices.ClimateServices20, https://doi.org/10.1016/j.cliser.2020.100199 Hermans,T.D.G.,Šakić Trogrlić,R.,vandenHomberg,M.J.C.,Bailon,H.,Sarku,R.,Mosurska,A.(2022) Exploringtheintegrationoflocalandscientificknowledgeinearlywarningsystemsfordisasterriskreduction: areview.NaturalHazards114,1125‐1152.https://doi.org/10.1007/s11069‐022‐05468‐8 Hernanz,A.,García‐Valero,J.A.,Domínguez,M.,Ramos‐Calzado,P.,Pastor‐Saavedra,M.A.,&Rodríguez‐ Camino,E.(2022).EvaluationofstatisticaldownscalingmethodsforclimatechangeprojectionsoverSpain: Presentconditionswithperfectpredictors.InternationalJournalofClimatology,42(2),762‐776. https://doi.org/10.1002/joc.7271 Herrera,S.,Gutiérrez,J.M.,Ancell,R.,Pons,M.R,Frías,M.D.,FernándezJ.(2012)DevelopmentandAnalysis ofa50‐yearhigh‐resolutiondailygriddedprecipitationdatasetoverSpain(Spain02)InternationalJournalof Climatology,32,74‐85https://doi.org/10.1002/joc.2256 Kniveton,D.,Visman,E.,Tall,A.,Diop,M.,Ewbank,R.,Njoroge,E.,&Pearson,L.(2014).Dealingwith uncertainty:Integratinglocalandscientificknowledgeoftheclimateandweather.Disasters,39(1),35–53. https://doi.org/10.1111/disa.12108