The making of a metric: Co-producing decision ... - Isha Ray

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Themakingofametric:Co-producingdecision-relevantclimate1

science2

KripaJagannathana,b,AndrewD.Jonesa,IshaRayb3

aLawrenceBerkeleyNationalLaboratory4

Earth&EnvironmentalSciencesArea–BerkeleyLab5

1CyclotronRoad6

Berkeley,CA947207

USA8

9

bUniversityofCalifornia,Berkeley10

EnergyandResourcesGroup,11

Berkeley,CA9472012

USA13

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Correspondingauthor:KripaJagannathan([email protected])15

Otherauthors:AndrewJones([email protected]),IshaRay([email protected])16

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Early Online Release: This preliminary version has been accepted for publication in Bulletin of the American Meteorological Society, may be fully cited, and has been assigned DOI The final typeset copyedited article will replace the EOR at the above DOI when it is published. © 20 American Meteorological Society 20

10.1175/BAMS-D-19-0296.1.

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CapsuleSummary18

Understandinghowscienceisco-producedisascienceuntoitself.Usingthecaseof19

ProjectHyperion,weillustratehowco-productionworks(ordoesnotwork)inpractice.20

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Accepted for publication in Bulletin of the American Meteorological ociety. DOI S 10.1175/BAMS-D-19-0296.1.

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Abstract22

Developingdecision-relevantscienceforadaptationrequirestheidentificationof23

climaticparametersthatarebothactionableforpractitionersaswellastractablefor24

modelers.Inmanysectors,thesedecision-relevantclimaticmetricsandtheapproaches25

thatenabletheiridentificationremainlargelyunknown.“Co-production”ofsciencewith26

scientistsanddecision-makersisonepotentialwaytoidentifythesemetrics,butthere27

islittleresearchdescribingspecificandsuccessfulco-productionapproaches.This28

paperexaminesthenegotiationsandoutcomesfromProjectHyperion,wherein29

scientistsandwatermanagersjointlydevelopeddecision-relevantclimaticmetricsfor30

adaptivewatermanagement.Weidentifysuccessfulco-productionstrategiesby31

analyzingtheproject’snumerousback-and-forthengagementsandtracingtheevolution32

ofthescienceduringtheseengagements.Wefoundthateffectivemediationbetween33

scientistsandmanagersneededdedicated“boundaryspanners”withsignificant34

modelingexpertise.Translatingpractitioners'informationneedsintotractableclimatic35

metricsrequireddirectandindirectmethodsofelicitingknowledge.Weidentifiedfour36

indirectmethodsthatwereparticularlysalientforextractingtacitly-heldknowledge37

andenablingsharedlearning:developingahierarchicalframeworklinkingmanagement38

issueswithmetrics;startingdiscussionsfromtheplanningchallenges;collaboratively39

exploringtheplanningrelevanceofnewscientificcapabilities;andusinganalogiesof40

other‘good’metrics.Thedecision-relevantmetricswedevelopedprovideinsightsinto41

advancingadaptation-relevantclimatescienceinthewatersector.Theco-production42

strategiesweidentifiedcanbeusedtodesignandimplementproductivescientist-43

decision-makerinteractions.Overall,theapproachesandmetricswedevelopedcan44

helpclimatesciencetoexpandinnewandmoreuse-inspireddirections. 45


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Introduction46

Adaptationpractitionersacrossmanysectors,includingresourcemanagement,land-47

useplanning,andpublichealth,urgentlyneeddecision-relevantsciencetoplanforand48

managetheimpactsofclimatechange(ACCNRS2015;Mossetal.2013;Lemosand49

Morehouse2005;Kirchhoffetal.2013a;Kerr2011).Therehavebeenseveralefforts50

towardsdevelopingactionable(ordecision-relevant)sciencebroadly,andmore51

specificallytowardsprovidingscientificdetailsoftheclimateimpactsthatplanners52

needtoaccountfor(Machetal.2019;BremerandMeisch2017;Beieretal.2017).53

Resourcemanagers,however,stillreportthatclimateinformationthatcanhelpto54

developadaptationdecisions,isnotreadilyavailabletothem(Mossetal.2019;Barsugli55

etal.2013;USGAO2015;Vogeletal.2016).Thisispartlyonaccountofunresolved56

mismatchesbetweenscientists’anddecision-makers’perceptionsofwhatconstitutes57

‘actionable’climateinformation(Lemosetal.2012;McNie2007).Oneimportant58

exampleofthismismatchisthatcurrentclimatemodellingandmodelevaluationefforts59

typicallyfocusonbroadclimatologicalmetrics,suchasaveragesorextremesin60

temperatureandprecipitation.However,inordertobeactionable,resourcemanagers61

needinformationonmanagement-specificmetrics,suchasthestartdateoftherainy62

seasonornumberofextremeheatdaysinthesummer(Brileyetal.2015;Roncolietal.63

2009;Mossetal.2019;Bornemannetal.2019).Thislackoffocusonmanagement-64

specificclimatesciencecanprecludeitsuseinadaptationdecisions,aseventranslation65

orcommunicationofsuchbroaderinformationcannotmovethescience“offtheshelf”66

tomakeitusable(Mossetal.2019;Lemosetal.2012;Hackenbruchetal.2017).67

Theliteraturerecognizestheimportanceofdeterminingspecificclimaticmetricsthat68

couldbemostapplicableforspecificproblems(Hackenbruchetal.2017;Brileyetal.69


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2015;Bornemannetal.2019).Butthistaskisoftenassumedtobesolelythedecision-70

makers’responsibility(Brileyetal.2015),andisnotconsideredaresearchproblemper71

se.However,resourcemanagersmaynotknow,apriori,thetypesofclimaticmetrics72

thatcouldbemostuseful,andscientistsmaynotalwaysknowwhethertheycan73

provideinformationondecision-relevantmetricswithreasonableskill(Brileyetal.74

2015;PorterandDessai2017;Lemosetal.2012).Thismeansthatdirectlyasking75

decision-makerstoexplainthetypesofclimateinformationtheyneedisrarely76

sufficient.Therefore,fewstudieshavesystematicallyidentifieddecision-relevant77

metricsforsectoraladaptations(Hackenbruchetal.2017;Vanoetal.2019;Bornemann78

etal.2019).‘Co-production’,oriterativeandcontinualengagementbetweenscientists79

anddecision-makers,isoftensuggestedasameanstoenablemutuallearningand80

reconciliationbetweenmanagers’needsandscientificpriorities(Lemos2015;Kirchhoff81

etal.2013a;Weaveretal.2014;Vogeletal.2016;Kolstadetal.2019).Itcanthushelp82

toidentifydecision-relevantclimaticmetricsthatarealsotractableformodellers.83

Thatbeingsaid,notallco-productioneffortshaveledtopositiveoutcomes(Lemosetal.84

2018),orhavebeensuccessfulatunderstandingandrespondingtoresourcemanagers’85

needs(Lemosetal.2018;PorterandDessai2017).Thesuccessofco-productionis86

predicatedonthelevelandqualityofinteractionsbetween(andwithin)different87

groups(PorterandDessai2017;Walletal.2017;Kirchhoffetal.2013b;Machetal.88

2019;Lemosetal.2018;Meinkeetal.2006).Whiletheliteratureprovidesrich89

guidanceonthegeneralprinciplesandprerequisitesforsuccessfulco-production90

(Heggeretal.2012;Meadowetal.2015;LemosandMorehouse2005;Beieretal.2017),91

thereisadearthofempirically-groundedguidanceonco-productionprocessesthat92

haveworkedinpractice(Djenontin2018;Lemosetal.2018;ParkerandLusk2019).93


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Hence,theprocessofco-productionisoftenablackbox;thereisnoclarityonthetypes94

ofscientist-decision-makerengagementprocessesthatcanbeexpectedtoresultin95

effectivetwo-waycommunicationsandtoenablethecreationofusableclimatescience96

(PorterandDessai2017;Machetal.2019;Jagannathanetal.2019a).97

Inthispaperwepresentboththeprocessof,andoutcomesfrom,acaseofco-98

production,ProjectHyperion,that(eventually)ledtotheidentificationofdecision-99

relevantclimaticmetricsforwatermanagementdecisions.Asaresponsetocallsto100

detailthepracticeof‘how’co-productionworks(PorterandDessai2017;Lemosetal.101

2018;Machetal.2019),wefocusthispaperonnotjusttheknowledgeoutcomesfrom102

theeffort(i.e.thedecision-relevantmetrics),butalsoonhowthemetricsevolved103

iterativelythroughmultipleengagementsoverthecourseofayear.Therestofthe104

paperdetailstheboundaryspanningandengagementstrategiesthatenabledthe105

projecttoovercomeinstitutionalandepistemologicalbarriers,andallowedashared106

understandingacrossprofessionalcommunitiestoemerge.107

ProjectHyperionandtheprocessofco-production108

ProjectHyperionisabasicscienceprojectthataimstoadvanceclimatemodellingby109

evaluatingregionalclimatedatasetsfordecision-relevantmetrics.Whiletherehasbeen110

anexplosivegrowthinthenumberofregionalclimatedatasetsavailabletousers,there111

islimitedunderstandingofthecredibilityandsuitabilityofthesedatasetsforusein112

differentmanagementdecisions(Mossetal.2019;Barsuglietal.2013;Jonesetal.2016;113

Jagannathanetal.2019b;Vandermolenetal.2019).Hyperionaimstoaddressthisneed114

bydevelopingcomprehensiveassessmentcapabilitiestoevaluatethecredibilityof115


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regionalclimatedatasets,understandtheprocessesthatcontributetomodelbiases,and116

improvetheabilityofmodelstopredictmanagementrelevantoutcomes.117

Sincedecision-relevanceisacoremotivationfortheproject,Hyperionisdesignedon118

theprinciplesofco-production.Theprojectbringstogetherscientistsfromnine119

researchinstitutionswithmanagersfromtwelvewateragenciesinfourwatersheds:120

Sacramento/SanJoaquin,UpperColorado,SouthFlorida,andSusquehanna.Inaddition,121

theprojectstructureexplicitlyallowsforboththegroupstoco-developthescienceplan122

andresearchquestions,inadditiontoco-producingthescienceitself.Thescientists123

includeatmosphericandearthsystemscientistsaswellashydrologists.Thewater124

managers,dependingontheagency,havefunctionsincludingplanning,operatingand125

managingwaterquality,watersupply,stormwatermanagement,floodcontrol,and126

waterinfrastructuredesign.Thesewatermanagershavehighlevelsoftechnical127

expertiseinengineering,hydrologyorothersciences,andwerepurposefullyselected128

becauseoftheirinterestintheprojectconceptandtheirwillingnesstodedicatetimeto129

theengagementefforts.Inaddition,theprojectteamforHyperionincludesthree130

dedicated‘boundaryspanners’(includingtwooftheauthors),i.e.,peoplewhose131

primaryroleistofacilitateandmediatethescientist-watermanagerboundary.132

InthispaperwefocusonPhase1oftheproject,anddescribehowdecision-relevant133

metricsineachofthestudyregionswereco-producedbythisgroup.Fromthewater134

managers’perspective,suchmetricsquantitativelydescribeclimaticphenomenathat135

aredirectlyrelatedtopracticalmanagementproblems;changesinthesequantities136

wouldnecessitateshiftsinwaterinfrastructureplanningandoperations.Fromthe137

scientists’perspective,thesemetricscanbeusedtotestmodelfidelityfordecision-138

relevantphenomenaandhencepushmodeldevelopmentandscientificinquiryinmore139


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use-inspireddirections.Toidentifythesemetrics,aseriesofiterativeengagement140

methodswereused.Structuredengagementmethodsincludedworkshops,remoteand141

in-personfocus-groupdiscussions,andquarterlyprojectupdatecalls.Therewerealso142

continualless-structured,informalconversationsbetweenscientists,managers,and143

boundaryspannersoverphonecallsoremails.ApprovalfromLawrenceBerkeleyLab’s144

HumanSubjectsCommittee-InstitutionalReviewBoardwasobtainedforkey145

engagements.Thetimelineofengagementactivities,alongwithgoalsandmilestonesat146

eachstage,ispresentedinFig.1.147

Theroleofboundaryspanners148

TheboundaryspannersinProjectHyperionhadvaryingdegreesofsocialscience,149

climatescienceandadaptationexpertise;theyalsohadpriorexperienceinco-150

productionandsimilarparticipatoryresearchactivities.Itisgenerallyacknowledged151

thatboundaryspannersarenecessaryforthetranslationofjargonandassumptions152

amongdifferentactorsandacrossepistemicdivides(Bednareketal.2016b;Kirchhoffet153

al.2013b;Cashetal.2003).Atthesametime,theliteraturerecognizesthatthisroleis154

challenginginpractice(Bednareketal.2018;Saffordetal.2017)andthatthefunctions155

andattributesofeffectiveboundaryspanningarenotwellunderstood(Goodrichetal.156

2019;Bednareketal.2016a).157

Thechallengesofboundaryspanningareoftendiscussedininstanceswhereactorsare158

resistanttocrossingepistemicboundariesor“compromising”theirexpertise(Cashet159

al.2003).InHyperion,mostofthewatermanagerswantedtoincorporateclimate160

changeinformationintheirdecisions,andmostscientistswerecommittedto161

developingdecision-relevantscience.Thiscollectivegoodwillnotwithstanding,several162

roundsofdeliberationswereneededtomediatedifferencesinincentivesandpriorities,163


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andtotranslatethewatermanagers’needsintoquantitativemetricsandscientific164

researchquestions.Theboundaryspannersneededtoactivelyensurethatfeedback165

frombothgroupswasnotjustheardanddocumented,butalsoincorporatedintothe166

overallscienceplanfortheproject.167

Themediationofthescientist-managerboundarytoarriveatactionablerainfallmetrics168

illustratesthesetensionsandalsotheireventualresolution.Severalofthemanagers169

wantedinformationonIntensityDurationFrequency(IDF)curvesforrainfallevents170

(Srivastavaetal.2019)thatformedthebasisoftheirflood-relateddecisions.The171

scientists,basedontheirexpertiseandmodellingcapabilities,prioritizedmetricssuch172

asfrequencyandintensityofspecificstormevents(e.g.tropicalcyclones)and173

associatedrainfall.Whilethesestormmetricswererelatedtodecision-relevantrainfall174

quantities,theywereoftenonestep‘upstream’(inboththehydrologicaland175

metaphoricalsenses)ofwhatthewatermanagerswantedfordetailedplanning.The176

upstreammetricsrepresenteddriversofphenomenaofinterestratherthanthe177

decision-relevantphenomenathemselves.Recognizingthistension,theboundary178

spannersworkedwiththegrouptoco-createasharedunderstandingoftheterm179

‘metric’.Weintroducedahierarchicalframeworkthatdistinguisheddecision-relevant180

fromupstreammetrics,illustratingtheoverlapsandlinkagesbetweenthetwo,and181

showinghowbothtypesofmetricscouldfitwithintheproject’slargergoals.Withthe182

explicitlinkingofmetric-types,managerscouldbetterappreciatethescientists’focus183

onupstreamstormmetricsformodellingcausalprocessesthatcouldeventuallymake184

IDFpredictionsmoreaccurate.Scientistssawwhyitwasnecessarytoincludethe185

metricofinteresttomanagers,i.e.IDFcurves,inthescienceplan,andhowlinkingtheir186

stormmetricswithIDFresultsaddedtothenoveltyandimpactoftheirefforts.187


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Thisandsimilarresolutionswerehighlydependentonthepresenceofaboundary188

spannerwithdomainexpertiseinclimatemodelling.Whiletheliteraturerecognisesthe189

importanceof‘backgroundandexperience’inthesubjectmatter(Saffordetal.2017;190

Meadowetal.2015;Bednareketal.2016b),thereis,wewouldargue,lessappreciation191

ofthetechnicalexpertiserequiredtoexecutetechno-scientifictranslations(Bednarek192

etal.2018).Forourproject,havingaboundaryspannerwhowasalsoamodeller193

provedessential.GiventheaimsofHyperion,manyboundaryfunctionstowardsthe194

laterstagesoftheprojectneededin-depth(andoftenpainful)discussionsonmodel195

parameters,typesofsimulations,decision-relevantthresholds,statisticalmeasuresof196

modelperformance,etc.whichwerebeyondthetechnicalcapacitiesofthenon-197

modellerboundaryspanners(Fig.2).Inhindsight,webelievethataboundaryspanner198

withexpertiseinwatermanagementcouldhavebeenequallybeneficial,andmayhave199

augmentedoureventuallistofmetrics.Overall,wefoundthat,dependingonthenature200

ofwhatisbeingco-produced,boundaryspannersneedconsiderablyhigherlevelsof201

domainexpertisethanisgenerallyacknowledgedintheliterature.202

Directandindirectapproachesto‘making’metrics203

Acommonapproachtouserneedsassessmentsinconventionally-designedaswellas204

co-productionprojectsistodirectlyaskdecision-makersforthetypesofinformation205

theywant(Hudlicka1996;Brileyetal.2015).Thisapproachisbasedontheprevalent206

assumptionthatdecision-makersnotonlyknowtheclimaticmetricstheywant,butare207

alsoabletoarticulatetheirknowledgeinresponsetodirectquestions(Hudlicka1996).208

Neitheroftheseassumptionsistrueforeveryengagement.Wefoundthatdetermining209

thequantitativedetailsofdecision-relevantinformationrequiredbothdirectand210

indirectapproaches.Wedidexplicitlyaskmanagerstoidentifyanymetricsforwhich211


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theyrequiredprojections,andthisdirectapproachwaspartiallysuccessful.Butitput212

theonusofmetricidentificationonthewatermanagers,whodidnotalwaysknowwhat213

toaskfororwhatthescientistshadtoofferbywayofquantification.Forexample,the214

directapproachrevealedwatersupplyandfloodsaskeyclimate-relatedmanagement215

issuesinCalifornia,withsnowpack,snowmelt,streamflow,dryspellsandrainfallas216

hydroclimaticphenomenaofinterest.Butmanagerswerenotusedtotranslatingthese217

phenomenaintotractableparametersorthresholds(Brileyetal.2015;Hackenbruchet218

al.2017).219

Wethereforesupplementedthedirectapproachwithanindirectapproachthat220

assumedthatrelevantknowledgecannotberevealedbydirectquestions,butneedsto221

beextractedthroughmoreopen-endedscenarioanalysisandcontextualinquiry.222

Althoughsuchdiscussionsareatime-intensivewaytoaccessinternalknowledge223

structures(Hudlicka1996),combiningdirectandindirectconversationalmethodshave224

beenshowntobeaneffectivewayofelicitinguserneeds(Zhang2007).Thisindirect225

approachisusedinsoftwaredevelopmentforuserrequirementsengineering(Hudlicka226

1996;Zhang2007),butisnotcommonlyusedintheco-productionoractionable227

environmentalscienceliteratures.Partlyguidedbyresearchontacitly-heldknowledge,228

andpartlythroughtrialanderror,wedevelopedfourindirectstrategiesthatenabled229

scientistsandwatermanagerstocollaborativelyidentifydecision-relevantmetrics.230

1. Developinghierarchicalframeworks:Therewasoftenconfusionamongscientistsand231

managersonhowspecifica‘metric’needstobetohaveanunambiguous232

interpretationfromamodellingperspective.Forexample,intheinitialengagements,233

thewholegroupunderstood‘peakstreamflow’or'flooding'tobepotentialmetrics.234

However,whenmodellingmethodswerebeingdeveloped,thescientistshad235


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questionsastowhat‘peak’mightmeanorhow‘flooding’wasdefinedbythe236

managers.Furtherdirectquestionsthatprobedthemanagersfor“morespecific”237

metricswereunsuccessfulinelicitingthedetailsthatscientistswerelookingfor.At238

thesametime,scientistswerenotabletoclearlyarticulatewhatconstitutedan239

unambiguousmetric.Toresolvethisstalemate,theboundaryspannersaskedthe240

scientiststoprovideexamplesofwhatmightconstituteaspecificmetricfortheir241

modellingexercises.Thegroupthendecidedtocontextualizemetricsbydevelopinga242

hierarchicalframework:amanagementissuecamefirst,thenthehydroclimatic243

phenomenarelatedtotheissue,thentheaspectsofeachphenomenonthatwereof244

mostrelevancetothewatermanagers,andfinallyatractablemetricforeachaspect245

(Fig.3)(seealso(Maraunetal.2015)).ForHyperion,thehierarchyrepresenteda246

logicalframeworkthathelpedustounderstandthatpeakstreamflowcouldhave247

variedinterpretationsformodelling;itcouldbedailymaximumflow,orthehighend248

ofstreamflowdistribution,orvaluesabovecertainthresholds.Eachinterpretation249

representedaverydifferent‘metric’withuniqueresults.Throughtheframeworkwe250

collectivelyunderstoodthatpeakstreamflowwasbestcharacterizedasan‘aspect’ofa251

hydroclimaticphenomenon,andonestepaheadofbeinganunambiguousmetric,252

whichrequiredfurtherquantitativedetailsdescribingthecharacteristicsofthepeak253

thatwereimportanttomanagers.254

2. Startingfromtheplanningchallenge/goalratherthanthesciencequestion:Afocuson255

currentandfutureplanningchallengesorgoalsastheyrelatedtodifferent256

hydroclimaticphenomenawasaproductivepathtowardsmetricidentification.For257

example,whenaskedaboutplanninggoalswithrespecttostreamflowquantity,some258

managerssuggestedthattheaimwastohaveafullreservoironJuly1st.Throughthis259


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exchangeweidentifiedcumulativerun-offonJuly1asadecision-relevantmetric.260

Anotherdiscussioncentredonrecentclimate-orweather-relatedplanningchallenges261

(suchasHurricaneIrma,ortheOrovilledamfailure)inthemanagers’regions.Oneof262

themanagersdiscussedanice-jamrelatedfloodingeventanddescribedhowwarm263

temperaturesandheavyrainconditionsinearlyspringcausedthesnowtomelt264

rapidly,leadingtoflooding.Thispromptedacollectivediscussionaboutwhether265

frequencyofrain-on-snoweventsandtheassociatedrun-offcouldbeanactionable266

metrictohelpanticipateandmanagesuchevents.Theseresultssupport267

recommendationsfromotherstudiesthatalsosuggeststartingtheco-production268

processfromthemanagementgoalratherthanfromascientific“puzzle”(Beieretal.269

2017;Kolstadetal.2019).270

3. Collaborativelyexploringtheplanningrelevanceofnewmodels,tools,ordatasets:Itis271

oftenassumedthatpractitionersaremainlyinterestedinpragmaticsolutionsand272

maybelessopentoexploringnovelmodelsandtools(Vogeletal.2016).However,in273

Hyperion,collaborativelyandcriticallyexaminingwhetherandhownewmodels,274

datasetsortoolscouldberelevanttomanagers’contexts,provedtobeaproductive275

strategyforidentifyingmetrics.Forexample,oneofthescientistssoughtthewater276

managers’opiniononanewtypeofsatellitedataonterrestrialwaterstorage(TWS)277

thathadthepotentialtoaidinflood/droughtprediction.Managersrespondedthat278

theiragenciesmainlyused10-yeargroundwater(GW)baseflowasakeymetricfor279

droughtpredictions,butthatitwasnoteasytocollectdataforcomputingGW280

baseflow.Theywereinterestedinalternativestothismetric,whereuponthescientist281

explainedthatnewfindingssuggestedthatTWScanbeagoodpredictorofGWflow282

(insomeregions).ThegroupcollectivelyagreedthatbothTWSand10-yearGW283


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baseflowwouldbegoodmetrics,andthatTWSwouldbeexploredasapotentialproxy284

orupstreammetrictoGWbaseflow.285

4. Usinganalogiesfor‘good’metrics:Finally,someofthenewmetricsidentifiedinour286

projectcamefromdiscussionsofother‘good’metrics.Forexample,onewell-received287

setofmetricswasvisualizedthroughthe‘SnowWaterEquivalent(SWE)triangle’,288

whichusesafittedtriangletocharacterizetheannualcycleofsnowaccumulationand289

melt(Rhoadesetal.2018).TheSWEtrianglerepresentsacompositeofsixmetricsof290

managementrelevance:peakwatervolumeandtiming,snowaccumulationandmelt291

rates,andthelengthsoftheaccumulationandmeltseasons.Eachmetricistractable292

aswellasdecision-relevant,andthetriangleitselfpresentsavisuallydigestiblelinear293

approximationofallsixmetricscomprisingthesnowcycle(Rhoadesetal.2018).The294

watermanagersthoughtthiswasa“nifty”multi-metricrepresentationasitallowed295

forbothacomprehensiveandanindividualexaminationofthemanagement-relevant296

componentsofseasonalsnowdynamics.Theirresponseledtodiscussionsonwhether297

asimilarsetofmetricsdescribingtheannualcycleofrainfallwouldalsobeuseful.A298

newcompositeapproach,tentativelytermed‘rainfallgeometry’(tosignifywhatever299

geometricfigurefitstheannualcycleofrainfallinagivenlocation),andwhich300

includesthestartdateofthewetseason,peakrainfall,andlengthofthewetseason,301

wasco-developedasapromisingmulti-metricrepresentationofkeymanagement-302

relevantcomponentsofrainfall.303

Overall,wefoundthatthemakingofdecision-relevantmetricsneededaniteratively-304

derivedmixofdirectandindirectengagementapproachestocapturetheinformation305

needsofthewatermanagers,andtotranslatethemintotractablequantitativemetricsfor306


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thescientists.Fig.4showstheevolutionoftwodecision-relevantmetricsusingdifferent307

directandindirectstrategies.308

Decision-relevantmetricsandtheircharacteristics309

Table1presentsexamplesofthemetricsidentifiedintheproject(SupplementTable1310

hasthefulllistforallfourregions).Insomecases,thesemetricsalreadyexistedinother311

contexts(suchasinengineeringorhydrologymanuals),buthadnotbeenrecognizedas312

metricsrelevantforclimatemodellingpriortoourco-productionprocess.Wealso313

observedthatnoteveryidentifiedmetricmappedontoaspecificmanagementdecision.314

Somemetrics,suchasdeviationsfromhistoricalmeansnowpack,weremoreusefulfor315

understandingthefuturestateofwatershedsthanformakingdecisions.Theinterestin316

snowpackshowsthatthereareoverlapsbetweenupstreamanddecision-relevant317

metrics;severalwatermanagerswere,infact,interestedinunderstandingupstream318

processesinadditiontoworkingwithactionablemetrics(Vanoetal.2019).319

Finally,wefoundthattherelevanceofmetricsdependson,andevolveswith,the320

availabilityofclimateinformation.Inregionswithlimitedavailabilityofclimatedata321

evensimpleclimaticmetricssuchasmonthlyorannualrun-offwereconsidered322

relevantenough.Inregionswithmoreinformationsuchsimplemetricswerenotas323

useful;managersidentifiedmoredetailedmetrics,suchastherunoffassociatedwith324

highestsnowmeltrate,ormaximumdailyor3-dayflowvolumes,asactionable.An325

analysisofhowandwhythecharacteristicsofdecision-relevantmetricsdifferedamong326

thewatermanagementagenciesisplannedforthenextphaseoftheproject.327


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DiscussionandConclusions328

Inthispaper,weopenuptheblackboxofco-productionanddocumentindetailthe329

strategiesthatenabled(anddidnotenable)thecreationofdecision-relevantscience.330

Weillustratehowco-productionworksinpracticebyanalyzingthenumerousback-331

and-forthcollaborativeengagementsofProjectHyperion,anddescribinghowthe332

sciencechangedandevolvedduringtheprocess.Bydescribinghowclimatescientists333

andwatermanagers(eventually)crossedtheboundariesofbothmandateand334

epistemologytoco-producedecision-relevantmetrics,weaddtothesparseliterature335

on‘howandwhen’co-productionworks.Toourknowledge,thisisthefirststudyto336

documentindetailtheactionableclimaticmetricsforadaptivewatermanagement,and337

theco-productionprocessesneededtoarriveatsuchmetrics.Ouroutcomes(i.e.theco-338

produceddecision-relevantmetrics),canbeusedasinputsfordevelopingactionable339

climatescienceforadaptationinthewatersector.Ourlearningsonengagement340

approachesprovideco-productionscholarswithinsightsonhowtodesignand341

implementproductivescientist-decision-makerinteractions.342

Wefoundthatidentifyingproblem-specificclimaticmetricsisevenmoreiterative,and343

needsmoresocialandtechnicalnegotiations,thanisgenerallyimpliedintheliterature344

promotingco-production.Thesemetricsoftenrepresentnewscientificdirectionsfor345

thescientistsaswellasnewwaysofmanagementforthewatermanagers.The346

commonlyuseddirectapproachtoidentifyingdecision-makers’informationneedswas347

insufficientforgettingatthequantitativedetailsofclimaticmetrics,evenwhenthe348

decision-makershadhighlevelsofscientificknowledge.Wefoundthatthetaskof349

translatinguserneedsintoquantitativemetricsneedstheexpertiseofbothresource350

managersandclimatescientists,aswellasanenablingprocessforbothgroups’351


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knowledge(s)toevolve.Hence,ajudiciousmixofdirectandindirectapproacheswas352

neededto“make”thesemetrics.Theindirectmethods,inparticular,revealedthe353

groups’tacitly-heldknowledgeandallowedacomprehensivesetofsharedlearningsto354

emerge.Keyindirectstrategiesincludeddevelopingahierarchicalframeworklinking355

managementissueswithactionablemetricsandupstreamphenomena;starting356

discussionsfromtheplanningchallengesandthenmovingtothemodel-specific357

metrics;collaborativelyexploringtheplanningrelevanceofnewmodels,datasetsand358

scientificfindingsthatmanagersdidnotyetknowabout;andusinganalogiesofgood359

metricsfromotherhydroclimaticphenomena.Eventually,thetwinfunctionsofthe360

metrics--ofbeingdecision-relevantandextendingmodelcapability--spoketoboth361

thedecision-makers’andthescientists’priorities,andallowedbothgroupstoco-exist362

withintheproject.Additionally,theinstitutionalizationoftheboundaryspanningrole,363

andthedomainexpertiseofatleastoneboundaryspanner(anunder-appreciated364

phenomenonintheco-productionliterature),provedtobecrucialforeffectivetrans-365

boundarytranslation.366

Althoughtheco-productionwastime-consuming,therichnessofourunderstanding367

camefromanalyzingthemanyiterativeback-and-forthengagements,whereeventhe368

processesthatdidnotfullyworkwereessentialtogettotheprocessesthatdid369

eventuallywork.Co-productionisoftenpresentedasanoutcomeinitself,ratherthanas370

ameanstoanend(Lemosetal.2018).Thisperspectivemayhaveitsmerits,butwe371

arguethattheabilitytoachievedesiredoutcomesisquitesensitivetohowtheco-372

productionprocessisstructuredandimplemented.Morecriticalassessmentsofspecific373

co-productionprocesseswouldhelptomovethepracticeforwardmoreefficiently,and374

tomeetthegrowingneedforactionableclimatescienceacrossmanysectorsofsociety. 375


17

Acknowledgements376

TheauthorsaredeeplygratefultoProjectHyperion’swatermanagersandscientists377

whopatientlyparticipatedinthemanyback-and-forthengagementsthatformthebasis378

ofthispaper.WearealsothankfultoBruceRiordanwhoco-ledtheengagementsand379

PaulUllrichforhisagileleadershipoftheproject.Theauthorswouldalsoliketothank380

theWater+GroupatUCBerkeley,JamesArnott,MargaretTornandAlastairIlesfor381

theirdetailedfeedbackonthemanuscript.ThisworkwassupportedbytheOfficeof382

Science,OfficeofBiologicalandEnvironmentalResearch,ClimateandEnvironmental383

ScienceDivision,oftheU.S.DepartmentofEnergyundercontractDE-AC02-05CH11231384

aspartoftheHyperionProject,AnIntegratedEvaluationoftheSimulatedHydroclimate385

SystemoftheContinentalUS(awardDE-SC0016605).386

387


18

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23

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519

520

521

522

523

524

525

526

527

528

529


24

FigureCaptionsList530

FIG.1.Co-productionprocessandtimeline.531

FIG.2.DialoguebetweenWatermanager(W)andBoundaryspanner(B).532

FIG.3.Hierarchicalframeworkwithexamples.533

FIG.4.Examplesshowingtheevolutionofdecision-relevantmetrics.534

535

536 537

538


25

FiguresandTables539

540

FIG.1.Co-productionprocessandtimelinesummarisingkeyengagementactivitiesoverthe541

courseofayear,alongwiththemostimportantoutcomesateachstage(depictedbytheblue542

documenticon).‘Sci’referstoScientists,‘WM’referstoWaterManagerand‘HCph.’refersto543

HydroclimaticPhenomena.FordetailsofeachoftheseactivitiespleaseseetheSupplement.544

Therewasconstantboundaryspanningworkduringandbetweeneachoftheseactivities.545

546


26

547

FIG.2.DialoguebetweenWatermanager(W)andBoundaryspanner(B)showingthebenefits548

ofhavingamodellerasa“translator”ofthewatermanager’sdescriptionofinformationneeds549

intoquantitativemetricsthatcanbepursuedbymodellers.550

551

552


27

553

FIG.3.Hierarchicalframeworkwithexamples.(a)illustratesthehierarchicalframework554

startingfromamanagementissueandendinginthemetrics.(b)and(c)areexamplesofmetrics555

andhowtheyfitwithintheframework.Thehierarchystartswiththe‘Issue’ortopicof556

managementrelevanceintheregion(e.g.Flooding),andmovestothe‘Hydroclimatic557

Phenomenon’relatedtotheissue(e.g.Precipitationisahydroclimaticphenomenonrelatedto558

theissueofFlooding),andthentothe‘AspectofthePhenomenon’thatisofspecificinterestfor559

themanagementdecision(e.g.Extremeprecipitationistheaspectofprecipitationthatisof560

specificmanagementinterest).Finally,thehierarchyyieldstheactual‘Decision-relevantmetric’,561

whichreferstoaquantitythathaspotentialuseforthewatermanagersandhasan562

unambiguousformulaoralgorithmthatcanbeappliedtobothobservation-baseddataand563

modeloutputs(e.g.ProbableMaximumPrecipitation(PMP)isametricrelatedtoextreme564

precipitation).Wealsoidentifiedupstreammetricsthatdescribephenomenahypothesizedto565

beimportantdriversofthedecision-relevantphenomena(e.g.Intensityoftropicalstormsof566

certaindurationsorreturnperiodsareanupstreamdriverofPMP).567

568

569


28

570

FIG.4.Examplesshowingtheevolutionofdecision-relevantmetrics.(a)showstheevolutionof571

themetricthatrepresentsthe3-yearcriticaldurationofOctober-Marchhighflowsata10-year572

recurrenceinterval.Theinitialdirectidentificationapproachgaveabroadunderstandingofthe573

importanceofrunofffornutrientsandsediments,andthenadiscussionofrunoff-based574

planningledtoidentifyinghydrologicextremesasoneoftheimportantcomponentsofrunoff.575

Usingthehierarchy(Fig.3),wecametounderstandthat‘extremes’werean‘aspectof576

phenomenon’,andweprobedfurthertofindthatextremesactuallymeantflowsabovecertain577

thresholds.Wederivedthefinalunambiguousmetricatthenextiterationwherewe578


29

interrogatedthetypesofexceedancethresholdsthatimpactwaterqualitymanagementinthe579

region.(b)showsthemakingofarainfallmetric.First,thedirectapproachhighlightedthat580

changesinrainfallpatternswereanimportantchallengefortheregion.Inthenext2iterations,581

whichalsouseddirectengagements,weidentifiedthespecificaspectsofrainfallthatwereof582

importance.Finally,withtheanalogyofthe‘goodmetrics’oftheSWEtriangle,weidentified583

“RainfallGeometry”asapromisingconceptforadditionaldecision-relevantmetrics.584

585


30

Table.1.Examplesofdecision-relevantmetricsforeachregion,highlightingmanagement586

issues,hydroclimaticphenomena,aspectofphenomenaandtheneachdecision-relevantmetric.587

‘CA’referstotheSacramento/SanJoaquinwatershed,‘CO’isUpperColorado,‘FL’isSouth588

Florida,and‘SQ’isSusquehanna.Thelastcolumnalsodescribessomeofthepotentialdecisions589

orusesforthesemetricsthatwereidentifiedbythecasestudywatermanagers.Supplement590

Table1hasthefulllistforallfourregions.591

Region IssueHydroclimatic

Phenomenon

Aspectof

Phenomenon

Decision-relevant

MetricDecision/Use

CAWater

SupplySnowpack

Annualcycleof

snow

accumulation

andmelt

SnowWater

Equivalent(SWE)

triangle(Rhoades

etal.2018)-Peak

snow(amountand

timing),andits

relationshipwith

averagesnow-

accumulationand-

meltrates,and

timingandlength

ofaccumulation

andmeltseasons

On-streamreservoir

management,and

understandingfuture

streamflowcharacteristics.

Shapeofthetriangleshows

thechangingdynamicsof

thesnowseason,andwhat

toexpectintermsofrunoff

timingandamounts.

CA Flooding StreamflowPeakflow

{Pulseevents}

FrequencyofRain-

on-snowevents

andmagnitudeof

associatedrun-off

Reservoiroperationsand

floodmanagement.

CAWater

SupplySnowpack

Inter-annual

Variabilityin

Snowpack

Deviationsfrom

historicalmeanin

SWE,Snowpack

andSnowmelt

(amountand

timing)

Multi-yearwatersupply

planninganddrought

preparedness.


31

COWater

SupplyStreamflow

Seasonal

Streamflow

amount(in

snowmelt

season)

Cumulativerun-off

onJuly1and

August1

Annualwatersupply

planningfortheyeardone

basedonJuly1orAugust1

reservoirlevelestimates

(dependingonthe

reservoir).

CO Floods Streamflow

Seasonal

Streamflow

amount(in

snowmelt

season)

%ofaverage

annualinflowfor

Apr-July

Reservoirmanagement-

thismetricisaninputinto

somereservoiroperations

models.

COWater

SupplyStreamflow

Low-end

Streamflow

7-day10yearlow

flows

Waterqualitymanagement

(issuingdischargepermits),

andwatersupplyplanning

duringdryyears

(determiningpermitlimits

forwaterwithdrawals).

FL Flooding RainfallExtreme

Rainfall

IntensityDuration

FrequencyorIDF

curves,specifically,

1-day,3-dayandup

to7-dayrainfall

events,for10,25,

50and100year

frequency

intervals.

Tocalculateapplicable

dischargeratesfordifferent

stormwatermanagement

infrastructure.Design

criteriausedfordrainage

andfloodprotectionarein

termsofIDFs.Inother

words,designingof

standardengineering

practicesforinfrastructure.

FL Flooding RainfallExtreme

Rainfall

Probablemaximum

precipitation.For

1-day,3-dayand

maybeupto7-day

events

Largestorage

infrastructuredesign(like

highdams).

FLWater

SupplyRainfall

Variabilityin

Rainfall

Rainfallanomalies

atMonthlytime

scales

Watersupplyplanning,and

droughtmonitoring


32

SQWater

SupplyStreamflow Peakflow

10-yearfrequency

3-yearduration

highflowsforOct-

March


intermsofmonitoring

ChesapeakeBaywater

qualitystandards.

SQ Flooding Streamflow

Average/

cumulative

flows

Meanannualflow

andharmonic

meanflow

Watersupplyplanning,for

monitoringpassbyflows

andconservationreleases

associatedwithwater

withdrawalpermits.Water

qualitymanagementfor

calculatingdesignflowsfor

effluentlimitationsbased

onwaterqualitycriteria.

SQWater

SupplyStreamflow

Low-end

Streamflow

7-day10-yearlow

flow


intermsofwastewater

assimilationstandardsfor

dischargepermits.Water

supplyplanningintermsof

passbyflowsor

conservationreleasesfor

waterwithdrawalpermits.

592

593

594

595


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