D4.1 Preliminary report on causal mechanisms between climate change, climate service information, and socio‐economic behaviour
Abstract
More than 100 million people per year are affected by climate‐related extremes, such as floods and droughts.The frequency and the impact of these extreme events are expected to increase in many regions of the worlddue to global warming and socio‐economic changes. Climate services (CS) have been used over last decadesto support decision making processes and reduce risk associated with climate‐related extremes. However, therole of CS information in shaping decisions planning about adaptation options, such as climate‐resiliencepathways achieving a zero‐emission pathway by 2050, remains largely unexplored. In this preliminary report(D4.1), we describe different methods used in this project for unpacking the causal mechanisms betweenclimate change, CS data, socio‐economic behaviours, which are expected to pave the way for the upcominganalyses of human‐climate interactions (T4.2, T4.3, and T4.4).
Full text
ThisprojecthasreceivedfundingfromtheEuropeanUnion’sHorizon2020researchand innovationprogrammeundergrantagreementNo101037293 DeliverableD4.1 Preliminaryreportoncausalmechanismsbetweenclimate change,climateserviceinformation,andsocio‐economic behaviour October2022
ThisprojecthasreceivedfundingfromtheEuropeanUnion’sHorizon2020researchand innovationprogrammeundergrantagreementNo101037293 InnovatingClimateservicesthroughIntegratingScientificandlocalKnowledge DeliverableTitle:Preliminaryreportoncausalmechanismsbetweenclimatechange,climate serviceinformation,andsocio‐economicbehaviour Author(s):RiccardoBiella,LotteMuller,MaurizioMazzoleni,GiulianoDiBaldassarre ContributingAuthors(s): Date31‐10‐2022 Suggestedcitation: Availability:☒PU:Thisreportispublic[Pleaseselect] ☐CO:Confidential,onlyformembersoftheconsortium(includingthe CommissionServices) DocumentRevisions: AuthorRevisionDate RiccardoBiella,Lotte Muller,Maurizio Mazzoleni,GiulianoDi Baldassarre Firstdraft14‐10‐2022 MichaWernerReviseddraft19‐10‐2022 RiccardoBiella,Lotte Muller,Maurizio Mazzoleni,GiulianoDi Baldassarre Finalversion28‐10‐2022
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 1 ExecutiveSummary Morethan100millionpeopleperyearareaffectedbyclimate‐relatedextremes,suchasfloodsanddroughts. Thefrequencyandtheimpactoftheseextremeeventsareexpectedtoincreaseinmanyregionsoftheworld duetoglobalwarmingandsocio‐economicchanges.Climateservices(CS)havebeenusedoverlastdecades tosupportdecisionmakingprocessesandreduceriskassociatedwithclimate‐relatedextremes.However,the roleofCSinformationinshapingdecisionsplanningaboutadaptationoptions,suchasclimate‐resilience pathwaysachievingazero‐emissionpathwayby2050,remainslargelyunexplored.Inthispreliminaryreport (D4.1),wedescribedifferentmethodsusedinthisprojectforunpackingthecausalmechanismsbetween climatechange,CSdata,socio‐economicbehaviours,whichareexpectedtopavethewayfortheupcoming analysesofhuman‐climateinteractions(T4.2,T4.3,andT4.4).
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 2 TableofContents 1Introduction.................................................................................................................................................1 2Interplaysbetweenclimateservices,humanbehaviour,anddecision‐making.........................................3 3MethodsusedtorepresentCS‐humaninterplays......................................................................................6 3.1Modellingcomplexsystems................................................................................................................6 3.2Systemdynamics.................................................................................................................................6 3.2.1Systemarchetypes.......................................................................................................................7 3.2.2CausalLoopDiagrams..................................................................................................................8 3.2.3Applicationsofsystemdynamicsmodelsinwaterresources.....................................................9 3.3Agentbasedmodelling........................................................................................................................9 3.3.1ApplicationsofABMmodelsinwaterresources.......................................................................11 4CS‐humandynamicsinthedifferentlivinglabs........................................................................................13 5Conclusions................................................................................................................................................15
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 3 ListofFigures Figure1.(a)DroughtconditionsintheAugust2022expressedascombineddroughtindicator(Toretietal., 2022).(b)Satelliteimagefrom9AugustshowingtheeffectofdroughtinnorthernFranceandsouthern England(EuropeanUnion,CopernicusSentinel‐3).............................................................................................1 Figure2:Plausiblefeedbacksbetweenclimateservices(CS),adaptationanddroughtrisk.Thisfigurehasbeen adaptedfromWensetal.(2019)toincludeclimateservices.............................................................................3 Figure3:Stylisedsummaryofexistingliterature.BoxAreferstotheworkbyCortekaretal.(2020),BoxBof Schlüteretal.(2017),BoxCofvanValkengoedandSteg(2019),BoxDofMeijeretal.(2021)........................4 Figure4:Conceptualmodelbasedonthe“successofthesuccessful”archetype..............................................8 Figure5.ExampleofCausalLoopDiagramshowinghowincreasingwatersupplycanincreasedemand,hence nullifyingtheeffectsoftheincreasedsupplyinthefirstplace.Atthesametime,vulnerabilityisincreasedas thesystembecomesmoreandmoredependentonthesupplyofwater.TakenfromDiBaldassarreetal.,2018 .............................................................................................................................................................................9 Figure7: AnExampleofABMinAgriculturePestControlStudies.(Source:UniversityofSurrey).................11 Figure8: ConceptualframeworkagriculturaldroughtriskABM(source:Schrieksetal.,2021).....................12
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 4 ListofTables Notableoffiguresentriesfound.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 1 1 Introduction Overthelast50years,climate‐relatedhazardssuchasdroughts,floods,andwildfires(Centreforresearchon theepidemiologyofdisasters(CRED),2019),havecausedaboutUSD1.5trillionineconomiclossesandmore than1milliondeathworldwide,withtheirseverityexpectedtoincreaseinmanyregionsoftheworldbecause ofclimaticandsocio‐economicchanges(Hoegh‐Guldbergetal.,2018;Swainetal.,2018).Justrecently,Europe experiencedoneoftheworstdroughtsofthelast500yearsduringthe2022summer(Toretietal.,2022),with 47%and17%oftheterritoryinwarningandalertconditions,respectively(seeFigure1).Overthelast75years, increasingtrendsofdeficitsinprecipitation,soilmoisture,andrunoffhavebeenobservedinEurope,leading toeconomiclossesofmorethan50billionEUR(Naumannetal.,2021).Ontheotherhand,floodevents occurredinPakistanduringAugust2022leadingtodevastatingconsequencesandmorethan1000fatalities. Recentclimateprojectionsshowthatinthefuturewewillexperiencemoreofsuchextremeevents,with Europeaffectedbyincreasingdrierandwarmerspringsandsummersduetoglobalanthropogenicwarming. Thiswillexposefarmers,households,andecosystemstoseverewatershortages,foodandeconomiclosses, andenvironmentaldegradation(Bastosetal.,2020;Mohammedetal.,2022;Quintonetal.,2022;Webberet al.,2018) . Figure1.(a)DroughtconditionsintheAugust2022expressedascombineddroughtindicator(Toretietal.,2022).(b) Satelliteimagefrom9AugustshowingtheeffectofdroughtinnorthernFranceandsouthernEngland(EuropeanUnion, CopernicusSentinel‐3). Improvingriskmanagementandadaptationstrategieswillallowstakeholdersandend‐userstoadaptand reducedroughtsandfloodsimpactsinthefuture.Overtheyears,numerousmonitoringandprediction systemshavebeendevelopedbyexploitingobservationsfromin‐situnetworksandremotesensingwithin advancedstatisticalapproaches(e.g.multivariateanalysis)andnumericalmodels(Haoetal.,2017).For example,short‐andlong‐termweatherforecastshavebeenusedasinputforlandsurfaceandhydrological modelstoprovidebothsoilmoistureandstreamflowpredictionandcontinuousmonitoringofcrucial hydrologicvariablesfordroughtandfloodcharacterization(Lidardetal2021). ClimateServices(CS)havebeensuccessfullyimplementedandusedformitigationandadaptationinviewof theconsequencesofclimatechange.TheconceptofCSincludes,butisnotlimitedto,climatesciencesand weatherservicesprovideindividuallytailoredinformationforriskmanagementbydecision‐makers.The EuropeanResearchandInnovationRoadmapforClimateServicesdescribesCS(EuropeanCommissions,2015) as:“thetransformationofclimate‐relateddata—togetherwithotherrelevantinformationintocustomised productssuchasprojections,forecasts,information,trends,economicanalysis,assessments(including technologyassessment),counsellingonbestpractices,developmentandevaluationofsolutionsandanyother
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 2 serviceinrelationtoclimatethatmaybeofuseforthesocietyatlarge.Assuch,theseservicesincludedata, informationandknowledgethatsupportadaptation,mitigationanddisasterriskmanagement”. AnumberofCShavebeendevelopedoverthelastyearsforbetterrepresentingtheatmospheric,oceanic, cryosphere,andlandsurfacephenomenaandtheirinteractionsoverdifferenttimehorizons(fromdaysto decades)andspatialscales(fromlocaltoglobal)andoverregions,fromhighlylocalizedtotheentireplanet. ExamplesofCSincludespastclimatedata,historicreanalysisdata,forecastsandpredictions,amongothers. CSareessentialinbuildingclimateresilienceandforclimateriskmanagementasabasisforadequatedecision‐ making(BrasseurandGallardo,2016).TheargumentforthebenefitofCSisthatknowledgeaboutthe consequencesofclimatechangeisneededforriskmanagementtoavoidarbitrarydecisionsandinadequate preparednessforclimatedisasters(Lemosetal.,2012).Moreover,ithasbeendemonstratedthatCSsuchas earlywarningsystemusedforfloodforecastingcanleadtopotentialmonetarybenefitsinreducingflood (Pappenbergeretal.,2015). DespitethesebenefitsofCS,itisstillnotclearhoweffectivelyCScanaiddecision‐makingforriskadaptation andmitigationpurposes,whichformatCSneedtohavetodoso,andhowtheinformationprovidedbyCS influenceuserbehaviourandconsequentadaptationactionstoextremeevents.Whilethephysicalandsocial linksbetweenclimatechangeandadaptationactionshavebeenwidelyanalysed,thefeedbacksgeneratedby theavailabilityofclimaticinformationandadaptationactionsareseldomconsidered.Forexample,aCScan provideinformationonthefuturewateravailabilityforacertainregionandend‐userscoulddecidetoadopt tailoredadaptationstrategiesbasedonthatinformation.Ontheotherhand,thesameend‐usercoulddecide upondifferentactionsiflimitedclimatic/hydrologicalinformationisavailable.Thoseadaptationactionsmay inturnaffectotherend‐usersandshapetheirfutureactionsandgoals. Thisdeliverablefocusesonidentifyingthedynamicsexplainingtheemergenceofdifferentfeedbacksbetween climatechange,CSdata,socio‐economicbehaviour,andadaptationmeasuresaimedatbuildingaresilient pathwayintheI‐CISKlivinglabs.ThiswillbeachievedbyfirstexploringtheinterplaysbetweenCSs,human behaviour,anddecision‐making(section2).WewillthenfocusonthemethodsusedtorepresentCS‐human interplays(section3)andtheirlinkswiththedifferentlivinglabsoftheI‐CISKproject(section4).Finally,we willsummarizetheresultsobtainedandprovideanoutlookoffutureresearchactivities.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 3 2 Interplaysbetweenclimateservices,humanbehaviour,anddecision‐ making AdditionaltothenumerousbenefitsprovidedbyCStousers,thedynamicsbetweenCS,humanbehaviourand decision‐makingrepresentsanimportantaspectforthedesignofresilientadaptationactions.Thereare severalchannelsthroughwhichhumanbehaviourcancause/contributetofeedbacks.Forexample,Wenset al.(2019)describeaframeworktoextendtraditionalriskmodellingtoincludetwo‐wayfeedbacksbetween adaptationanddroughtexposure,vulnerabilityandhazard.Figure2showsanadaptationoftheframework developedbyWensetal.(2019)toincludeclimateservices.Tomakethefigureexplicitlyrelevant,wehave includedclimateservicesexplicitlyasinputtoadaptiveaction(blueboxwithCS).Figure2Ashowstheinfluence ofadaptationondroughtrisk,yetdoesnotincludefeedbackmechanisms.WhileFigure2B‐Dhighlight channelsthroughwhichhumanbehaviourcancause/contributetofeedbacks.Namely,Figure2Bshowsabi‐ directionalinfluencebetweenadaptationandrisk,Figure2showstheinfluenceofadaptationonriskacross spatiotemporalscalesandlastlyFigure2Dtheinfluenceofriskonindividualdecision‐makingbehaviour.For furtherdetails,seeworkbyWensetal.(2019).TounderstandpotentialfeedbackchannelsshowninFigure 2B‐D(redarrows),firstFigure2Amustbeclearlyunderstood.Understandingbehaviouralresponsestoclimate servicesisessentialtoconsequentlyunderstandingpossiblefeedbackmechanisms. Figure2:Plausiblefeedbacksbetweenclimateservices(CS),adaptationanddroughtrisk.Thisfigurehasbeenadapted fromWensetal.(2019)toincludeclimateservices.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 10 ofthesystem(Schlüteretal.,2019).ABMsareconsideredaformofbottom‐upmodelling,astheyallowthe agentstointeractwithoneanotheronthebasisofsimplerulesappliedtothem.Emergingsystembehaviour areaconsequenceoftheseinteractions.ABMshavebecomeincreasinglyusedtomodelcoupledsocial‐ physicalmodels,forexample,floodriskassessment(ZhuoandHan,2020),landusechange(Groeneveldetal., 2017)ordroughtriskassessment(Wensetal.,2019). AmongthebenefitsofusingABMsaretheirabilitytoenablediverseagentstoberepresentedinmodelling (Troyetal.,2015)andofexploringemergentbehavioursandfeedbacksinasystembycreatingthemfromthe ground‐up.However,implementingrealisticdecisionrulesfortheseagentsisakeychallengeforABMasthese areoftenbasedonad‐hocassumptionsofhumanbehaviour(An,2012;Filatovaetal.,2013;Schlüteretal., 2017;Groeneveldetal.,2017).Inaddition,asthemodelisbottom‐up,resultsmaybedifficulttointerpretdue topotentiallyhighrelationalanddynamiccomplexity;asrelationshipsbecomeopaquethemodelbecomes morecomplex..Resultsmaynotbegeneralisable,andhavelimitedpredictivepower(An,2012;Schlüteret al.,2012;BlairandBuytaert,2016). ABMgenerallyimplementsthefollowinggeneralstepsfordevelopingamodel,asidentifiedbyBlairand Buytaert(2016): 1. Problemdefinition 2. Determinationofrelevantsystemagents 3. Descriptionoftheenvironmentinwhichagentsexist 4. Elicitationofagentdecision‐makingprocessandbehaviours 5. Determinationoftheinteractionsbetweenagents 6. Determinationoftheinteractionsbetweenagentsandtheenvironment 7. Developmentofcomputationalalgorithmstorepresentagents,environment,decision‐making processes,behavioursandinteractions 8. Modelvalidationandcalibration. AsABMisatimeandresourceintensiveprocess.Asaconsequence,itwillnotbecarriedoutwithinalltheLLs, butonlyinthoseinwhichcontextandneedsaremostsuitableforthistypeofstudy.Closecollaborationwith theLLswillbenecessaryforcarryingoutstep1to6ofthestepsdescribedbyBlairandBuytaert(2016).Step 7willbecarriedoutbyWP4incollaborationwithLLsandmainlywithWPs1,2and3,whilestep8,the validationofthemodel,willseetheinvolvementoftheLLstoensurethatthemodelisrealistic.However,this processisnotlinear,ratheritisiterative.Duringthevalidationofthemodel,themodelandtheLLwillco‐ createagentcharacteristicsandprocessestobetterreflecttherealityoftheLL.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 11 Figure6: AnExampleofABMinAgriculturePestControlStudies.(Source:UniversityofSurrey) 3.3.1 ApplicationsofABMmodelsinwaterresources Inparallelwiththedevelopmentofsystemdynamicsapproaches(seeprevioussection),ABMhavebeen extensivelyusedtogainadeeperunderstandingofhuman‐watermanagementtodevelopsustainable managementstrategies(Dubbelboeretal.,2017;Filatova,2015;Haeretal.,2019;JanssenandOstrom,2006; TonnandGuikema,2018).However,systemdynamicsmodelscannotrepresenttheheterogeneityof individualriskperceptionduetotheirlumpedstructure.Forthisreason,ABMhasbeenextensivelyusedto gaindeeperunderstandingofcomplexityaboutimperfectheterogeneousactorsandtheirindividualdecisions, activitiesandinteractions(Wensetal.,2020).Thus,anABMseemssuitabletomodelhumandecision‐making inquantitativefloodriskassessment. ABMshavebeenrecentlyappliedtofloodanddroughtriskassessment,analysingmainlydifferentaspectsof humanbehaviour,theeffectsoffloodinsurance,droughtadaptationactions,andriskcommunicationon developmentoftimeoffloodanddroughtrisks(Filatova,2015;Wensetal.,2019).Inparticular,Haeretal. (2017)comparedthreedifferentbehaviouralframeworksforthedecision‐makingofhouseholdagentsabout investinginloss‐reducingmeasures,namelyexpectedutilitytheory,prospecttheoryandprospecttheory includingadaptationofbehavioursthroughBayesianupdating.Haeretal.(2020)quantifiedtheleveeeffect inEuropewithanABMandfurtherdiscussedpolicyimplications.Abebeetal.(2019)developedanew frameworkforintegratingahydraulicmodelintoanABMtorepresentindividualsandinstitutionsdecision‐ makingduringflooding.Justrecently,Michaelisetal.(2020)showedthattheirproposedagent‐basedmodel wasabletoexplainfloodadaptationandtheleveeparadox.Withrespecttodroughtriskmanagement,Wens etal.(2019)proposedanABMfocusedonindividualandcollectiveactionstosimulatetheadaptivebehaviours ofdifferentstakeholdersandexaminehowemergentactionsmightinfluenceprojecteddroughtrisk.Schrieks etal.(2021)proposedprovidesaschematicframeworkfordroughtriskusingABMinthecontextof agriculturalcommunitiesinwhichindividualdecision‐makingprocessofthefarmersisdrivenbybehavioural changes.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 12 Figure7: ConceptualframeworkagriculturaldroughtriskABM(source:Schrieksetal.,2021)
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 13 4 CS‐humandynamicsinthedifferentlivinglabs CSarefundamentaltoreduceriskandavoidfuturedisasters.WhilethebenefitsofCShavebeenwidely exploredbyexistingliterature,themainfeedbacksbetweenCS,adaptationmeasuresandclimate‐related extremeeventsarestillunclear.Inthissection,weprovideapreliminaryoverviewofsuchdynamicsandtheir characterizationwithinthedifferentlivinglabs(LLs)oftheproject. Tothisend,weprovideabriefdescriptionofeachLLalongwiththemainobjectivesandchallengesfromthe WP4perspectivethatwereidentifiedduringI‐CISKworkshopsandmeetingsheldinthefirstyearofthe project.ThesystemarchetypesdescribedinthissectionrefertotheaforementionedworkbyMoallemietal. (2022),whichisbasedonSenge’sbook(Thefifthdiscipline,1990). Budapest(Hungary).ThisLLconsistsofanurbanareachallengedbyrisingtemperaturesandheatwaves, exacerbatedbyurbanheatislands(UHIs).OneoftheaimsinthisLLisraisingawarenessaroundUHIand potentiallyinfluencepolicybye.g.promotingurbangreening.Intermsofhuman‐climateinteractions,thisLL showspotentialfortheanalysisoffeedbacksbetweenshort‐term(coping)measuresandlong‐term(adaptive) measures(includingunintendedconsequences)withpotentialemergenceofsystemarchetypes,including Fixesthatfail. AlazaniRiverbasin(Georgia).InthisLL,droughtsandwatershortageschallengemultipleneeds(e.g. agriculture,hydropower)aswellasseveralsectors(e.g.tourism,forestry).Inparticular,thereisinterestin expandingtheavailabilityofseasonalandsub‐seasonalforecastbydownscalingglobalinformationand integratingitwithin‐situobservations.CSsaremeanttoinformthedevelopmentofhydropowersectoras wellaspublicwaterallocationformultiplepurposes(farmersareexplicitlyconsidered),whileatthesame timereducingenvironmentalimpacts.Thereispotentialtounravelhuman‐climateinteractionsbybuilding uponarchetypaldynamics,suchastheTragedyofthecommonsorBand‐aidsolutions(forcompeting interests),aswellasSuccesstothesuccessful(forinequalityinaccess). SenquValley(Lesotho).ThefocusinthisLLisfoodsecurity,droughtandhumanitarianaid.Theprojectwill focusontheimplementationofaforecastsystemtoraisepreparednessforhumanitarianactions.More specifically,theRedCrossdeliversanticipatoryfinancingtofarmersbasedonseasonaldroughtforecastsasa waytoincreasepreparedness.Besidesthelimitationsofseasonaldroughtforecastinadata‐poorarea, challengesinthisLLincludethe“last‐mile”uptakeanddifferentlevelsofliteracy.Managementofcompeting interestsgivesrisetoTragedyofthecommonsorShiftingtheburden,whileinequalityinaccesscanbe describedbytheSuccesstothesuccessfularchetype.Sustainabilitytrade‐offsincludeBand‐aidsolutionsand Fixesthatfail. Rijnlandbasin(TheNetherlands).ThisLLpresentstrade‐offsinwaterusebetweenagriculture,recreation, shippingandnatureconservation.IntermsofCSinformation,thedemandisfrome.g.farmersand recreationistsformeasurementsandforecastingofsaltwaterintrusionandfreshwaterlevel.InthisLL;there areratherdifferentneedsamongstakeholders,notonlybetweenfarmersandrecreationists,butalsowithin (differenttypesof)farmers.Therearealsodifferentdisseminationneeds:farmersaremoreeagertoget informedthantherecreationists.Thiscanraiseinequalityinaccesstoinformation(Successofthesuccessful), aswellascompetinginterestsandneedsthatmightleadtooverexploitation(Tragedyofthecommons). Guadalquivirbasin(Spain).ThefocusonthisLLisontheinterplaybetweenagriculture,livestock,forestryand waterscarcity.TheaimoftheprojectinthisLListocreateaplatformforthedisplayofvarioushydrological indexestomeettheneedsoffarmersandlivestockproducers.Inparticular,monitoringthelevelofthewater andsub‐seasonalforecast.Theseareexpectedbecomplementedbylong‐termprojections,whicharealsoof interestprimarilytoinformthepublicdecision‐makingprocess,includingwaterallocationplanningfor differentwaterusers.Thefarmershavealsoexpressedtheneedofaplatformthatcanbeaccessedthrough
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 14 anapp.Thereisroomtoexploretrade‐offsbetweenseasonalplanning(basedonmeasurementsand forecasts)andlong‐termadaptationstrategies(basedonprojections),aswellbetweenincrementalversus transformativeadaptation.VarioussystemdynamicsarchetypescanbeatplayinthisLL:Tragedyofthe commonsforcompetinginterests,Successtothesuccessfulbecauseofunevenaccessto information/resources. EmiliaRomagna(Italy).ThisLLdealswithagriculturalandindustrialwateruse.Theaimistodevelopatoolto managewaterallocationbetweenfarmers(representedbyaconsortium)andamulti‐industryconsortium. Theallocationprocessismanagedbythelocalpublicwaterbody.Inthisproject,thefocusisonimproving seasonalallocationofwaterresources,especiallyduringdroughtconditions.Onetechnicalaspectconsistsof implementingaforecastwithatemporalresolutionof3‐days(comparedtothecurrent7days).Competing interestsbetweenagriculturalandindustrialwaterusecangiverisetoTragedyofthecommonsarchetypes. Crete(Greece).TourismisonefocusofthisLLinrelationwithmultiplehazardsandclimatechange.Theproject isdealingwiththedevelopmentofaseasonalforecastatsmallerscaleforbothtourismandwaterallocation, aswellaslongtermprojectionsofclimatechange.ThisCSinformationisusedtosupporttheseasonaland long‐termplanningofcommercialactivities.Oneofthegoalsistoraiseawarenessabouttherisksassociated withsometouristactivities.Therearetrade‐offswithshort‐versuslong‐termplanningandconcreterisksfor unplannedexploitation(Tragedyofthecommons). TheTablebelowsummarisesthemainsystemdynamicsarchetypesthatcharacterisehuman‐climate interactionsandfeedbackmechanismsinthedifferentLLs.Itshowshowtheinterplaybetweenclimate change,CSinformation,policy,behaviourandextremeeventsoftengiveraisetoFixesthatfail,Successtothe successfulandTragedyofthecommons. ThepreliminaryknowledgeofthesystemdynamicsarchetypesandbehaviouraltheoriesusedineachLLto unravelthehuman‐climatedynamicswillbeusedasinputinsystemdynamicsandABMmodellingframework toquantitivelyassesspastandfuturerisktrajectoriesinordertoavoidtrade‐offsandunintended consequences.Co‐creationprocesses(WP2)willbeusedtodevelopthesemodels.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 15 5 Conclusions Understandingthefeedbackmechanismsunderlyingtheinterplaybetweenclimatechangeandadaptation actionsisofpivotalimportancetoachievingaresilientfuture.Thisreportaimstoprovidemoreinsightsinto thehuman‐climatedynamicsbyfirstreviewingpreviousstudiesfocusedonassessingthebehavioural responsesofpeopleandsocietiestodifferentavailabilityofCS.Wethenpresentthetwomainmodelling frameworksthatWP4hasandwilladopt:SystemDynamics(SD)andAgentBasedModelling(ABM).Firstly,a briefoverviewofSDisgivenandbenefitsandchallengesarelaid‐out.Twoapproachesrelatedtoconceptual modellinginSDareexplainedthereafter.Systemarchetypes,beingapowerfulscopingtoolinhighlycomplex Socio‐EnvironmentalSystems,andCausalLoopDiagrams,willbeusedtosetthepreliminarymodelstructure tobediscussedandimprovedduringforparticipatorymodellingactivitieswithstakeholdersandend‐usersin theLLs.Thesecondmodellingframeworkexplored,ABM,isapowerfultoolforbothquantitativemodelling andqualitativeexplorationofemergentsystembehaviours.ContrarytoSD,ABMhastheadvantageof constructingthemodelfromthebottomup,startingfromthecharacteristicoftheagentsandtheir interactions.Finally,wehighlightedthepresenceoffeedbackinteractionbetweenCSsandadaptationamong thelivinglabs.Weconductedapreliminaryscopingexercisediscussingsaidinteractionsutilizingsystem archetypesandpresentedthewaysinwhichtheothermodellingmethodologiescanbeusedtofurtherthe researchensuringtheco‐creationprinciplesattheheartofI‐CISK. ThispreliminaryreviewperformedinWP4showsthatindividualschangetheirperceptionandbehavioural optionsovertime,basedonCSavailability.Together,anindividual’sperceptionandbehaviouraloptionsare influencedbytheirsubjectivenorms,attitudes,riskpreferencesandbiases.Despitethisrichknowledgefrom varioussocialsciences,whenmodellingwaterrisk,humanbehaviourisoftenomitted,ormodelledusing unrealisticassumptions(e.g.usinghomogeneousrationalagents).Tobeabletocapturethefeedbackbetween climateservices,humanbehaviourandtheenvironment,understandingandmodellingofbehavioural responsestoclimateservicesisessential. SystemdynamicsandABMarepowerfultoolstothehuman‐climatedynamicsbothqualitativelyand quantitatively.Asaninitialstep,theimplementationofsystemarchetypeswithinasystemdynamic frameworkallowedustoconceptuallyinfercomplexemergentsystembehaviours,suchasthelong‐term consequencesofunequalaccesstoCSs.ThisreportshowedthatthefeedbacksbetweenhumanandCSpresent inthedifferentlivinglabscanbesummarizedbytheexistingsystemarchetypes.Thisstepwillbefollowedby morein‐depthparticipatorymethodologiesandquantitativemodelling.Thesehuman‐climatemodelswillalso showpossibletrade‐offsbetweenmultipleadaptationgoalsandCSavailabilitywhenfocusingatdifferent spatial‐temporalscalesandavoidpossiblefutureunintendedconsequences.Thiswillallowustoinvestigate thecausaldynamicsineachlivinglabsandevaluatethebi‐directionalfeedbacksbetweenadaptationactions andCSavailabilitytodescribefuturerisktrajectoriesandshowtheeffectofCSusedforshort/mediumforecast onlongertimescales(T4.1). Theresultsofthispreliminaryreportareexpectedtopavethewayfortheimplementationofmodelling frameworkwithinthedifferentlivinglabs(T4.3andT4.4)inordertoformalizehumanresponsestoclimate changeandCSinformationbyalsobuildinguponresultsfromWP1andWP2.Asanextresearchstep,wewill assessif(aswellhowandforwhom)theadaptationactionsadoptedbytheend‐usersusingtheavailableCS inthedifferentLLsareeffectiveinachievingaresilientfuture.Participatorymodellingapproachesandserious gameswillbealsousedtoincreasestakeholders’awarenessontheclimatechangeimpactsandtheinterplays withthehuman‐climatesystem.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 1 References Abebe,Y.A.,Ghorbani,A.,Nikolic,I.,Vojinovic,Z.,Sanchez,A.,2019.Acoupledflood‐agent‐institution modelling(CLAIM)frameworkforurbanfloodriskmanagement.EnvironmentalModelling&Software111, 483–492.https://doi.org/10.1016/j.envsoft.2018.10.015 Ajzen,I.,2011.Thetheoryofplannedbehaviour:Reactionsandreflections.Psychology&Health26,1113– 1127.https://doi.org/10.1080/08870446.2011.613995 Akhtar,M.K.,Wibe,J.,Simonovic,S.P.,MacGee,J.,2013.EnvironmentalModelling&Software49,1–21. Albertini,C.,Mazzoleni,M.,Totaro,V.,Iacobellis,V.,DiBaldassarre,G.,2020.Socio‐HydrologicalModelling: TheInfluenceofReservoirManagementandSocietalResponsesonFloodImpacts.Water12,1384. Alcott,B.,2005.Jevons’paradox.Ecologicaleconomics54,9–21. Bastos,A.,Ciais,P.,Friedlingstein,P.,Sitch,S.,Pongratz,J.,Fan,L.,Wigneron,J.P.,Weber,U.,Reichstein,M., Fu,Z.,Anthoni,P.,Arneth,A.,Haverd,V.,Jain,A.K.,Joetzjer,E.,Knauer,J.,Lienert,S.,Loughran,T.,McGuire, P.C.,Tian,H.,Viovy,N.,Zaehle,S.,2020.Directandseasonallegacyeffectsofthe2018heatwaveanddrought onEuropeanecosystemproductivity.ScienceAdvances6,eaba2724.https://doi.org/10.1126/sciadv.aba2724 Blair,P.,Buytaert,W.,2016.Socio‐hydrologicalmodelling:areviewasking“why,whatandhow?”Hydrology andEarthSystemSciences20,443–478.https://doi.org/10.5194/hess‐20‐443‐2016 Brasseur,G.P.,Gallardo,L.,2016.Climateservices:Lessonslearnedandfutureprospects.Earth’sFuture4,79– 89.https://doi.org/10.1002/2015EF000338 Centreforresearchontheepidemiologyofdisasters(CRED),2019.EM‐DAT:TheCRED/OFDAInternational DisasterDatabase.UniversitéCatholiquedeLouvain,Brussels,Belgium. Cortekar,J.,Themessl,M.,Lamich,K.,2020.SystematicanalysisofEU‐basedclimateserviceproviders.Climate Services17,100125.https://doi.org/10.1016/j.cliser.2019.100125 DiBaldassarre,G.,Sivapalan,M.,Rusca,M.,Cudennec,C.,Garcia,M.,Kreibich,H.,Konar,M.,Mondino,E., Mård,J.,Pande,S.,Sanderson,M.R.,Tian,F.,Viglione,A.,Wei,J.,Wei,Y.,Yu,D.J.,Srinivasan,V.,Blöschl,G., 2019.Sociohydrology:ScientificChallengesinAddressingtheSustainableDevelopmentGoals.Water ResourcesResearch55,6327–6355.https://doi.org/10.1029/2018WR023901 DiBaldassarre,G.,Sivapalan,M.,Rusca,M.,Cudennec,C.,Garcia,M.,Kreibich,H.,Konar,M.,Mondino,E., Mård,J.,Pande,S.,Sanderson,M.R.,Tian,F.,Viglione,A.,Wei,J.,Wei,Y.,Yu,D.J.,Srinivasan,V.,Blöschl,G., 2019.WaterResourRes55,6327–6355. DiBaldassarre,G.,Viglione,A.,Carr,G.,Kuil,L.,Yan,K.,Brandimarte,L.,Blöschl,G.,2015.Debates— Perspectivesonsocio‐hydrology:Capturingfeedbacksbetweenphysicalandsocialprocesses.Water ResourcesResearch51,4770–4781.https://doi.org/10.1002/2014WR016416 DiBaldassarre,G.,Viglione,A.,Carr,G.,Kuil,L.,Yan,K.,Brandimarte,L.,Blöschl,G.,2015.Debates— Perspectivesonsocio‐hydrology:Capturingfeedbacksbetweenphysicalandsocialprocesses.Water ResourcesResearch51,4770–4781.https://doi.org/10.1002/2014WR016416 DiBaldassarre,G.,Wanders,N.,AghaKouchak,A.,Kuil,L.,Rangecroft,S.,Veldkamp,T.I.E.,Garcia,M.,vanOel, P.R.,Breinl,K.,vanLoon,A.F.,2018.NatSustain1,617–622. Dubbelboer,J.,Nikolic,I.,Jenkins,K.,Hall,J.,2017.AnAgent‐BasedModelofFloodRiskandInsurance.JASSS 20,6.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 2 Dumont,A.,Mayor,B.,López‐Gunn,E.,2013.IsthereboundeffectorJevonsparadoxausefulconceptfor bettermanagementofwaterresources?InsightsfromtheirrigationmodernisationprocessinSpain. Elsawah,S.,Pierce,S.A.,Hamilton,S.H.,vanDelden,H.,Haase,D.,Elmahdi,A.,Jakeman,A.J.,2017. EnvironmentalModellingandSoftware93,127–145. EuropeanCommission,2015.AEuropeanresearchandinnovationroadmapforclimateservices.European Commission,Directorate‐GeneralforResearchandInnovation,PublicationsOffice. Filatova,T.,2015.Empiricalagent‐basedlandmarket:Integratingadaptiveeconomicbehaviorinurbanland‐ usemodels.Computers,EnvironmentandUrbanSystems54,397–413. https://doi.org/10.1016/j.compenvurbsys.2014.06.007 Forrester,J.W.,1970.IMR;IndustrialManagementReview(pre‐1986)11. Forrester,J.W.,1997.IndustrialDynamics.JournaloftheOperationalResearchSociety48,1037–1041. Garcia,M.,Portney,K.,Islam,S.,2016.Aquestiondrivensocio‐hydrologicalmodelingprocess.Hydrologyand EarthSystemSciences20,73–92.https://doi.org/10.5194/hess‐20‐73‐2016 Gohari,A.,Eslamian,S.,Mirchi,A.,Abedi‐Koupaei,J.,Bavani,A.M.,Madani,K.,2013.Watertransferasa solutiontowatershortage:afixthatcanbackfire.JournalofHydrology491,23–39. Gonzales,P.,Ajami,N.,2017.SocialandStructuralPatternsofDrought‐RelatedWaterConservationand Rebound.WaterResourcesResearch53,10619–10634.https://doi.org/10.1002/2017WR021852 Haer,T.,Botzen,W.J.W.,Aerts,J.C.J.H.,2019.Advancingdisasterpoliciesbyintegratingdynamicadaptive behaviourinriskassessmentsusinganagent‐basedmodellingapproach.Environ.Res.Lett.14,044022. https://doi.org/10.1088/1748‐9326/ab0770 Haer,T.,Botzen,W.J.W.,Moel,H.de,Aerts,J.C.J.H.,2017.IntegratingHouseholdRiskMitigationBehaviorin FloodRiskAnalysis:AnAgent‐BasedModelApproach.RiskAnalysis37,1977–1992. https://doi.org/10.1111/risa.12740 Haer,T.,Husby,T.G.,Botzen,W.J.W.,Aerts,J.C.J.H.,2020.Thesafedevelopmentparadox:Anagent‐based modelforfloodriskunderclimatechangeintheEuropeanUnion.GlobalEnvironmentalChange60,102009. https://doi.org/10.1016/j.gloenvcha.2019.102009 Hoegh‐Guldberg,O.,Jacob,D.,Taylor,M.,Bindi,M.,Brown,S.,Camilloni,I.,Diedhiou,A.,Djalante,R.,Ebi,K.L., Engelbrecht,F.,Hijioka,Y.,Mehrotra,S.,Payne,A.,Seneviratne,S.I.,Thomas,A.,Warren,R.,Zhou,G.,Halim, S.A.,Achlatis,M.,Allen,R.,Berry,P.,Boyer,C.,Brilli,L.,Byers,E.,Cheung,W.,Craig,M.,Ellis,N.,Evans,J., Fischer,H.,Fraedrich,K.,Fuss,S.,Ganase,A.,Gattuso,J.‐P.,Bolaños,T.G.,Hanasaki,N.,Hayes,K.,Hirsch,A., Jones,C.,Jung,T.,Kanninen,M.,Krinner,G.,Lawrence,D.,Ley,D.,Liverman,D.,Mahowald,N.,Meissner,K.J., Millar,R.,Mintenbeck,K.,Mix,A.C.,Notz,D.,Nurse,L.,Okem,A.,Olsson,L.,Oppenheimer,M.,Paz,S., Petersen,J.,Petzold,J.,Preuschmann,S.,Rahman,M.F.,Scheuffele,H.,Schleussner,C.‐F.,Séférian,R., Sillmann,J.,Singh,C.,Slade,R.,Stephenson,K.,Stephenson,T.,Tebboth,M.,Tschakert,P.,Vautard,R., Wehner,M.,Weyer,N.M.,Whyte,F.,Yohe,G.,Zhang,X.,Zougmoré,R.B.,Marengo,J.A.,Pereira,J., Sherstyukov,B.,2018.Impactsof1.5°CofGlobalWarmingonNaturalandHumanSystems,GlobalWarming of1.5°C.AnIPCCSpecialReportontheimpactsofglobalwarmingof1.5°Cabovepre‐industriallevelsand relatedglobalgreenhousegasemissionpathways,inthecontextofstrengtheningtheglobalresponsetothe threatofclimatechange,sustainabledevelopment,andeffortstoeradicatepoverty. IPCC,2021.Summaryforpolicymakers,in:Masson‐Delmotte,V.,Zhai,P.,Pirani,A.,Connors,S.L.,Péan,C., Berger,S.,Caud,N.,Chen,Y.,Goldfarb,L.,Gomis,M.I.,Huang,M.,Leitzell,K.,Lonnoy,E.,Matthews,J.B.R., Maycock,T.K.,Waterfield,T.,Yelekçi,Ö.,Yu,R.,Zhou,B.(Eds.),ClimateChange2021:ThePhysicalScience Basis.ContributionofWorkingGroupItotheSixthAssessmentReportoftheIntergovernmentalPanelon ClimateChange.CambridgeUniversityPress,p.3−32.
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 3 Jager,W.,Janssen,M.A.,DeVries,H.J.M.,DeGreef,J.,Vlek,C.A.J.,2000.Behaviourincommonsdilemmas: HomoeconomicusandHomopsychologicusinanecological‐economicmodel.EcologicalEconomics35,357– 379.https://doi.org/10.1016/S0921‐8009(00)00220‐2 Janssen,M.A.,Ostrom,E.,2006.EmpiricallyBased,Agent‐basedmodels.EcologyandSociety11. https://doi.org/10.5751/ES‐01861‐110237 Jongman,B.,Hochrainer‐Stigler,S.,Feyen,L.,Aerts,J.C.,Mechler,R.,Botzen,W.W.,Bouwer,L.M.,Pflug,G., Rojas,R.,Ward,P.J.,2014.Increasingstressondisaster‐riskfinanceduetolargefloods.NatureClimateChange 4,264–268. Kreibich,H.,Blauhut,V.,Aerts,J.C.J.H.,Bouwer,L.M.,Lanen,H.A.J.V.,Mejia,A.,Mens,M.,Loon,A.F.V.,2019. Howtoimproveattributionofchangesindroughtandfloodimpacts.HydrologicalSciencesJournal64,1–18. https://doi.org/10.1080/02626667.2018.1558367 Lemos,M.C.,Kirchhoff,C.J.,Ramprasad,V.,2012.Narrowingtheclimateinformationusabilitygap.Nature ClimChange2,789–794.https://doi.org/10.1038/nclimate1614 Mård,J.,Baldassarre,G.D.,Mazzoleni,M.,2018.Nighttimelightdatarevealhowfloodprotectionshapes humanproximitytorivers.ScienceAdvances4,eaar5779.https://doi.org/10.1126/sciadv.aar5779 Mazzoleni,M.,Odongo,V.,Mondino,M.,DiBaldassarre,G.(2021)Watermanagement,hydrological extremes,andsociety:modelinginteractionsandphenomena.EcologyandSociety26(4):4. https://doi.org/10.5751/ES‐12643‐260404 Mazzoleni,M.,Odongo,V.O.,Mondino,E.,DiBaldassarre,G.,2021.Watermanagement,hydrological extremes,andsociety:modelinginteractionsandphenomena.Ecology&society26. Meadows,D.H.,Meadows,D.L.,Randers,J.,BehrensIII,W.W.,1972.TheLimitstoGrowth,5thed.Potomac Associates–UniverseBooks,ParkAvenueSouth,NewYork,NewYork10016. Meijer,K.S.,Schasfoort,F.,Bennema,M.,2021.QuantitativeModelingofHumanResponsestoChangesin WaterResourcesAvailability:AReviewofMethodsandTheories.Sustainability13,8675. https://doi.org/10.3390/su13158675 Michaelis,T.,Brandimarte,L.,Mazzoleni,M.,2020.Capturingflood‐riskdynamicswithacoupledagent‐based andhydraulicmodellingframework.HydrologicalSciencesJournal65,1458–1473. Mirchi,A.,Madani,K.,Watkins,D.,Ahmad,S.,2012.WaterResourcesManagement26,2421–2442. Moallemi,E.A.,Hosseini,S.H.,Eker,S.,Gao,L.,Bertone,E.,Szetey,K.,Bryan,B.A.,2022.EarthsFuture10, e2022EF002873. Mohammed,S.,Alsafadi,K.,Enaruvbe,G.O.,Bashir,B.,Elbeltagi,A.,Széles,A.,Alsalman,A.,Harsanyi,E.,2022. Assessingtheimpactsofagriculturaldrought(SPI/SPEI)onmaizeandwheatyieldsacrossHungary.SciRep12, 8838.https://doi.org/10.1038/s41598‐022‐12799‐w Musters,C.J.M.,deGraaf,H.J.,terKeurs,W.J.,1998.EcologicalEconomics26,243–258. Naumann,G.,Cammalleri,C.,Mentaschi,L.,Feyen,L.,2021.IncreasedeconomicdroughtimpactsinEurope withanthropogenicwarming.Nat.Clim.Chang.11,485–491.https://doi.org/10.1038/s41558‐021‐01044‐3 Newell,B.,Wasson,R.,2002.SocialSystemvsSolarSystem:WhyPolicyMakersNeedHistory,in:Castelein,S., Otte,A.(Eds.),ConflictandCooperationRelatedtoInternationalWaterResources:HistoricalPerspectives. UNESCO,Paris,France,pp.3–17. Palmer,P.I.,Smith,M.J.,2014.Earthsystems:Modelhumanadaptationtoclimatechange.Nature512,365– 366.https://doi.org/10.1038/512365a
D4.1–Preliminaryreportoncausalmechanismsbetweenhuman,climatechange,andclimateservices 4 Pappenberger,F.,Cloke,H.L.,Parker,D.J.,Wetterhall,F.,Richardson,D.S.,Thielen,J.,2015.Themonetary benefitofearlyfloodwarningsinEurope.EnvironmentalScience&Policy51,278–291. https://doi.org/10.1016/j.envsci.2015.04.016 Qi,C.,Chang,N.bin,2011.JEnvironManage92,1628–1641. Quinton,J.N.,Öttl,L.K.,Fiener,P.,2022.Tillageexacerbatesthevulnerabilityofcerealcropstodrought.Nat Food3,472–479.https://doi.org/10.1038/s43016‐022‐00533‐8 Richardson,G.P.,2011.SystDynRev27,219–243. Ridolfi,E.,Albrecht,F.,Baldassarre,G.D.,2020.Exploringtheroleofriskperceptionininfluencingfloodlosses overtime.HydrologicalSciencesJournal65,12–20.https://doi.org/10.1080/02626667.2019.1677907 Rogers,R.W.,1975.AProtectionMotivationTheoryofFearAppealsandAttitudeChange.TheJournalof Psychology91,93–114.https://doi.org/10.1080/00223980.1975.9915803 Sahin,O.,Mohamed,S.,2014.NaturalHazards395–414. Sawada,Y.,Kanai,R.,andKotani,H.,2022.Impactofcrywolfeffectsonsocialpreparednessandtheefficiency offloodearlywarningsystems,Hydrol.EarthSyst.Sci.,26,4265–4278,https://doi.org/10.5194/hess‐26‐4265‐ 2022. Schlüter,M.,Baeza,A.,Dressler,G.,Frank,K.,Groeneveld,J.,Jager,W.,Janssen,M.A.,McAllister,R.R.J., Müller,B.,Orach,K.,Schwarz,N.,Wijermans,N.,2017.Aframeworkformappingandcomparingbehavioural theoriesinmodelsofsocial‐ecologicalsystems.EcologicalEconomics131,21–35. https://doi.org/10.1016/j.ecolecon.2016.08.008 Schlüter,M.,Orach,K.,Lindkvist,E.,Martin,R.,Wijermans,N.,Bodin,Ö.,Boonstra,W.J.,2019.CurrOpin EnvironSustain39,44–53. Schrieks,T.,Botzen,W.,Wens,M.,Haer,T.,Aerts,J.,2021.IntegratingBehavioralTheoriesinAgent‐Based ModelsforAgriculturalDroughtRiskAssessments.FrontiersinWater3. https://doi.org/10.3389/frwa.2021.686329 Schrieks,T.,Botzen,W.J.W.,Wens,M.,Haer,T.,Aerts,J.C.J.H.,2021.IntegratingBehavioralTheoriesinAgent‐ BasedModelsforAgriculturalDroughtRiskAssessments.FrontiersinWater3. Shahid,S.,Behrawan,H.,2008.DroughtriskassessmentinthewesternpartofBangladesh.NatHazards46, 391–413.https://doi.org/10.1007/s11069‐007‐9191‐5 Srinivasan,V.,Lambin,E.F.,Gorelick,S.M.,Thompson,B.H.,Rozelle,S.,2012.Thenatureandcausesofthe globalwatercrisis:Syndromesfromameta‐analysisofcoupledhuman‐waterstudies.WaterResources Research48.https://doi.org/10.1029/2011WR011087 Sterle,K.,Hatchett,B.J.,Singletary,L.,Pohll,G.,2019.HydroclimateVariabilityinSnow‐FedRiverSystems: LocalWaterManagers’PerspectivesonAdaptingtotheNewNormal.Bull.Amer.Meteor.Soc.100,1031– 1048.https://doi.org/10.1175/BAMS‐D‐18‐0031.1 Sterman,J.,2014.BusinessDynamics,SystemThinkingandModelingforaComplexWorldClimateInteractive andtheC‐ROADSSimulationViewprojectDynamicModelsforPopulation(Routine)Screening:Understanding LongTermTrendsinPolicyDecisionsofClinicalPracticeGuidelinesViewproject. Sterman,J.D.,2001.SystemDynamicsModeling:TOOLSFORLEARNINGINACOMPLEXWORLD,California CMR. Swain,D.L.,Langenbrunner,B.,Neelin,J.D.,Hall,A.,2018.Increasingprecipitationvolatilityintwenty‐first‐ centuryCalifornia.NatureClimateChange8,427–433.https://doi.org/10.1038/s41558‐018‐0140‐y