smart systems for water management · empowering water consumers through smart metering: evidence...

SMARTSYSTEMSFORWATERMANAGEMENTModelling,Simulation,AnalyticsandICTforBehaviouralChange

22-25August2016,MonteVerità–Switzerland

BOOKOFABSTRACTSEditedbyAndreaCastellettiandAndreaEmilioRizzoli

2

TheSmartSystemforWaterManagementSymposiumandSummerSchoolisaninitiativeoftheSmartH2Oproject

whichismemberoftheICT4Watercluster

TheSmartH2OprojectreceivesfundingfromtheEuropeanCommission

TheconferenceissupportedbyCongressiStefanoFranscini,adepartmentofETHZürichanditishostedatCentroMonteVerità,inAscona,Switzerland,withpartialsupportfromtheSwissNationalResearchFund

AndreaCastellettiandAndreaEmilioRizzoliacknowledgesupportfromtheirinstitutions,respectivelyPolitecnicodiMilanoandSUPSI

3

CONFERENCEPROGRAMMENote:thepresentationsallocatedintotheworkshopslotswillhavealengthof30minutesmaximum,theremainderofthetimeisforworkshopdiscussion.

Monday22ndofAugust

9:30–10:00-WelcomeaddressbythedirectorsofCentroStefanoFranscini

10:00–11:00-Waterresourceeconomicsandfinance-GregCharacklis,UniversityofNorthCarolinaatChapelHill,USA

11:00–12:00-Waterpricingpoliciesandconsumerbehaviour-JulienHarouandCharlesRougé,UniversityofManchester,UK

12:00–14:00Lunch

14:00–15:00Integratedmodellingofdemandandsupply.Theroleofhydroeconomicmodels-ManuelPulidoVelasquez,UniversitatPolitecnicadeValencia,Spain

15:00–16:00Behavioralinterventionstosuccessfullyreduceresidentialwaterconsumption-VerenaTiefenbeck,ETHZurich,CH

16:00–18:00Workshop-Thedriversofwateruserbehaviour:socialnormsandeconomicreasons

Empoweringwaterconsumersthroughsmartmetering:evidencefromafieldstudyinaresidentialsuburbofMontpellier(southofFrance)-MarielleMontginoul-Irstea–UMRG-Eau,MontpellierFrance

4

Tuesday–23rdofAugust

9:00–10:00-Synergisticwaterandenergydemandmodeling,management,andconservation-DavidRosenberg,UtahStateUniversity,USA

10:00–11:00-Economicandenergyanalysisofhouseholdwaterconservation-JayLund,UCDavis,USA

11:00–12:00-Forecastingwaterdemand-WojciechFroelich,UniversityofSilesia,Poland

12:00–14:00Lunch

14:00–15:00-Modellingwateruserbehaviour:fromsmartmetereddatatoagentbasedmodelling-MatteoGiuliani,PolitecnicodiMilano,Italy&AlessandroFacchini,SUPSI,CH

15:00–16:00-HardwareandsoftwaretoolsforpreciseEndUsedisaggregation-FranciscoArreguidelaCruz,UPV,Spain

16:00–16:30-Stochasticgenerationofresidentialwaterend-usedemandtraces–AndreaCominola,PolitecnicodiMilano,Italy

16:30-18:00Workshop-Understandingandmodellingthebehaviourofwaterusers

AFrameworkforReal-TimeSpatiallyDistributedDemandEstimationandForecasting,DominicL.Boccelli,UniversityofCincinnati,Cincinnati,OH,USA

Wednesday24thofAugust

9:00–10:00MultiobjectiveWaterManagementUnderUncertainty-PatrickM.Reed,CornellUniversityUSA

10:00–11:00ICTsolutionsforrealtimesmartwatermanagement-LydiaVamvakeridou&DraganSavic,UniversityofExeter,UK

5

11:00–12:00StandardizationActivitiesandGapsforSmartSustainableCities-GabrielAnzaldi,EURECAT,Spain

12:00-13:00Lunch

13:00–14:00Newcontroltechniquesforsmartwatersystems-PantelisSopasakis,IMTLucca,Italy

14:00–16:00-Workshop-InnovationinICTforwatermanagement

TheGIS-integratedFREEWATplatformasanICTtoolforsustainablewaterresourcesmanagement.GiovannaDeFilippis-ScuolaSuperioreSant’Anna,Pisa(Italy)

Hydra3D:ATooltoSimulateHydricResourcesScenarios.JavierDíaz,,LINTI,LaPlataNationalUniversity,BuenosAires(Argentina)

16:00–18:00Publicevent–Increasingtheawarenessonwateruse

Thursday25thofAugust

9:00–10:00-WaterSavingsClustering:Whichtypesofhouseholdsrespondbesttosocialnormsmessaging?-WilliamHolleran,WaterSmart,CA,USA

10:00–11:00Gamificationforwaterutilities-PieroFraternali,PolitecnicodiMilano,Italy,IsabelMicheel,JasminkoNovak,EuropeanInstituteforParticipatoryMedia,Berlin

11:00–11:15Awardceremony:bestyoungresearcheraward

11:15–12:00Plenarysession:thefuturechallengesofurbanwatermanagementPaneldiscussionwithJayLund,LydiaVamvakeridou,PatrickReed,GregCharacklis,WilliamHolleran,andDavidRosenberg.ModeratedbyAndreaE.Rizzoli.

12:00Closing

6

ABSTRACTSOFTUTORIALS

Theme:Thedriversofwateruserbehaviour:socialnormsandeconomicreasons

Waterresourceeconomicsandfinance

GregCharacklis,UniversityofNorthCarolinaatChapelHill,USA

Theparticipantswillbeexposedtotheprinciplesofwaterresourceeconomicsinamanagement/policycontext,withadditionalattentiontothefinancingchallenges(andpotentialsolutions)thataccompanymoresophisticatedmanagementstrategies.

Waterpricingpoliciesandconsumerbehaviour

JulienHarouandCharlesRougé,UniversityofManchester,UK

Smartmetersandnewtypesofinteractionwithconsumersmakeemergenewhorizonsinwaterpricing,includingpricingstructureswheretheconsumerbillislinkedtoshortand/orlongtermwaterscarcity.Inthislecturewewillanalysehownewpricingstructurecanactuallyaffectconsumerbehaviour.

Integratedmodellingofdemandandsupply.Theroleofhydroeconomicmodels

ManuelPulidoVelasquezandFranciscoArreguidelaCruz,UniversitatPolitecnicadeValencia,Spain.

7

Overviewofapproachesfortheeconomiccharacterizationofurbanwaterdemands(eg.simplepoint-expansionmethod,econometricapproaches,mathprogramming,etc)anduseintheassessmentofconsumerandproducersurplus.Introductiontohydroeconomicmodelsintegratingdemandsandsupply;applicationtotheassessmentoftheimpactsofdroughts(scarcitycost)andtheconsequencesofdifferentwatermanagementandplanningpolicies(usingreliabilityindicatorsandeconomicnetbenefits).

Gamificationforwaterutilities

PieroFraternali,PolitecnicodiMilano,Italy,IsabelMicheel,JasminkoNovak,EuropeanInstituteforParticipatoryMedia,Berlin

Gamificationisgainingmomentumasatooltomotivateandengagecustomers.Inthisworkshopweshowhowitcanalsobesuccessfullyappliedinordertomodulateurbanwaterdemand,thusprovidinganinvaluabletooltowaterutilities.Intheafternoonworkshop,theparticipantswillexplorenewideasandsolutionsforeffectivecustomerengagementinaninteractivesession.

Behavioralinterventionstosuccessfullyreduceresidentialwaterconsumption

VerenaTiefenbeck,ETHZurich,CH

WaterheatingisthesecondlargestenergyenduseinEuropeanandU.S.households,makingshoweringoneofthemostenergy-intensivebehaviorsweengageinonadailybasis.Yetmostindividualshaveaverylimitedunderstanding-andfundamentalmisconceptions-oftheirenergyandwaterconsumptionathome.Inthisworkshop,wediscusstheimpactofdifferentfeedbackstrategiestoreduceenergyandwaterconsumptionintheshower.Inparticular,wewillcomparebehavior-specificfeedbackthatisprovidedin

8

realtimewithotherdemandmanagementstrategies(includingflowrestrictorsandfeedbackonahousehold'saggregatedresourceconsumption).Basedonaseriesofrecentrandomizedcontrolledfieldtrialsindifferentcountries,wewillinvestigatewhyreal-timefeedbackinducessuchalargebehaviorchange,analyzeeffectpersistenceandcosteffectiveness,andevaluatewhichsegmentsofthepopulationareparticularlyresponsivetothiskindofbehavioralintervention.

WaterSavingsClustering:Whichtypesofhouseholdsrespondbesttosocialnormsmessaging?

WilliamHolleran,WaterSmart,CA,USA

WaterSmartSoftwarehasdatafromover30RandomizedControlTrialimplementationsacrosstheUnitedStatesofitswaterconservationandcustomerengagementprogram.Leveragingthatdatawithasyntheticcontrolsmodelwillallowcalculationsofindividuallevelsavings.Applyingatime-seriesclassificationmodeltothesavingsatamonthly,weeklyanddailylevelwillrevealcharacteristicsthatwillimproveunderstandingofhowsocialnormsbasedmessagingvariesinitsimpactonindividualbehaviors.Thiswillinformfutureprogramdesignandallowfortargetingofspecificgroupstomaximizeprogramimpact.

9

Theme-Understandingandmodellingthebehaviourofwaterusers

Synergisticwaterandenergydemandmodeling,management,andconservation

DavidE.Rosenberg,UtahStateUniversity,UT,USA

Welinkdetailedend-useanalysisofresidentialwaterandassociatedenergyusestostochasticoptimizationtoidentifythecost-effectivemixofconservationactionsresidentialuserssuppliedbyawatersystemcanadopttomeetsystem-widesynergisticwaterandenergyconservationtargets.Theend-useanalysisdrawsfromadatabasecontainingtiming,duration,frequency,andvolumeinformationof1.4millionwater-useeventsrecordedfor800householdsin11U.S.andCanadiancitiesovertwoormoreweeksofhighfrequencywaterusemonitoringoverthelast15years.Energyusesincludetheembeddedenergytocollect,treat,convey,anddistributewatertohouseholds,energytoheatwaterwithinthehouseholdforhotwateruses,andembeddedenergytocollectandtreatwastewater.Thisdataisusedtosamplealarge,simulatedsetofhouseholdswithindoorandoutdoorwaterappliances,behaviors,andconservationpotentialspecifictothestudycity.Thestochasticoptimizationprogramthenidentifiesthemixofhouseholdapplianceandbehavioralchangeconservationactionsthatminimizecity-widecoststoachievesystem-widewaterandenergyreductiontargets.Theminimizationisalsosubjecttolowerandupperboundsonthenumberofconservationactionsimplementsandupperboundsonhouseholds’paybackperiodforconservationactions.Resultsidentifyfeasiblecity-widecollaborativewaterandenergyconservationtargets,selectandsizewaterandenergyconservationprograms,identifysynergiesandtradeoffsbetweenwaterandenergy,andquantifypaybackperiodsforhouseholdconservationactions.Additionally,thepresentationwillshareapproachesUtahStateUniversityisnowdevelopingtoorganizeandmanagehigh-frequencyend-

10

usedataanddeploylow-costsmartmeterstomonitorwateruseandsavingsbynon-residential,institutional,andresidentialusers.

Economicandenergyanalysisofhouseholdwaterconservation

JayLund,UCDavis,CA,USA

Householdwaterconservationwillbeexaminedforitsenergy,wateruse,andemissionimpacts.Theoptimizationofhouseholdconservationactivitiesfortheseobjectiveswillbeexaminedanddiscussed.Additionaldiscussionandanalysiswillincludehowchangesinprobabilisticwatersupplyreliabilityaffectoptimizedwateruserconservationactivities.

Forecastingwaterdemand

WojciechFroelich,UniversityofSilesia,Poland

Thistutoriallectureconsidersselectedissuesrelatedtowaterdemandforecasting.Attheurbanlevel,theforecastingisappliedtotheefficientmanagementofwaterresourcesandasapartofthesystemthatcontrolspressureinthewaterdistributionsystem.Atthehouseholdlevel,theforecastingisexploitedbythedecisionsupportsystem,encouragingconsumerstosavewater.Theoreticalpreliminariestotimeseriesarepresentedtogetherwithabriefoverviewofthemostknownforecastingmodels.Theonlineavailabilityofwaterdemanddataisassumed.Theconceptsofgrowingandslidingwindowsarepresentedtoillustratedifferentmodesoflearningmodels.Practicalmethodstodealwithmissingvaluesandoutliersarealsopresented.Inaddition,itisshownhowtorecognizestationarity,linearityandotherfeaturesofwaterdemandtimeseries.Theknowledgeofthesefeatureshelpstoselectthemosteffectiveforecastingmodelforthegiventimeseries.Differentmethodsofdealingwithseasonalityintimeseriesarepresented,includingautoregression,seasonaladjustment,andtheapplicationofdummyvariables.Anexampleofcombiningforecasts

11

generatedbydifferentmodelsispresented.Alltheoreticalpresentationsareillustratedbypracticalexamples.TheKNIMEtoolkitandtheRsoftwarepackageareusedforthispurpose.Anintroductiontobothtoolsismade.Inallofthepresentedexperiments,scale-independentforecastingerrorsarecalculated.

Modellingwateruserbehaviour:fromsmartmetereddatatoagentbasedmodelling

AndreaCastelletti,MatteoGiuliani,AndreaCominola,PolitecnicodiMilano,Italy,AlessandroFacchini,AndreaE.Rizzoli,IDSIAUSI/SUPSI,CH

Smartmeteredconsumptiondataallowsophisticateddatadrivenmodellingofwateruserbehaviours.Inthissession,weanalysehowtodisaggregatewaterconsumptionintoenduses,howtobuildwateruserprofilesusingbigdataminingtechniqueandhowtoanalysesocialinteractionthroughanagentbasedplatform.Modelsareusedtounderstandandpredictuserbehavioursaswellastosimulatesocialnormsmechanismsactivatedbyexternalstimuli(e.g.awarenesscampaign,pricingschemes).

HardwareandsoftwaretoolsforpreciseEndUsedisaggregation

FranciscoArreguiDeLaCruz,UniversitatPolitecnicadeValencia,Spain

ResidentialwaterEndUsedisaggregationisoneofthemostpowerfultoolsavailabletounderstandwaterdemandandalsotodesigntrulyeffectivewaterconsumptionreductionstrategies.EndUsedisaggregationallowsforamuchbetterunderstandingnotonlyofthetechnicalcharacteristicsoftheappliancesbutalsothewaycustomersinteractwiththem.

ThedevelopmentandstandardizationofAMIandAMRtechnologieshasfacilitatedtheremotemeteringofcustomers.However,theirlowest-possible-costdesignandworkingliferequirementsonlyallow,inmostcases,forhourly/dailymonitoring.Unfortunately,areliabledisaggregation

12

requiresthattheflowsignalisavailableforanalysisatshortertimeintervals(10secondsorless).Thisreadingfrequencyexponentiallyincreasesthecommunicationcosts,theamountofdatatobeprocessedandthenumberofcommerciallyavailablehardwareoptions.

Takingintoaccountalltheseconstraintsaspecificcombinationofhardwareandsoftwarehasbeenmadeavailabletoreducethecostahighresolutionmonitoringofcustomers.Thissetincludes:

• Highresolutionpositivedisplacementmeterequippedwithapulseemitter.

• Anewloggercapableof:o Storingthetimeofoccurrenceofthepulseswitharesolutionof

1ms.Thisspecialfeaturegeneratesanundistortedflowtracesignalfromthemeterreadytobeprocessbythesoftware.

o SendingthedatatoadedicatedFTPserveronceeveryfewhours.

• Web-basedenduseanalysissoftwareo Alldata(theoriginalcomingfromtheloggersandtheprocessed

pulses)isstoredinanonlinedatabasewithouttheneedofhumanintervention.

o Advanceprocessingcapabilitiestoimprovetheanalysisofoverlappedconsumption.

o Flexibleandpowerfulconfigurationtoolstodefinedifferentendusesandtypesofusers.

ThesoftwareandhardwaredesignedallowforamuchpreciseEndUseclassificationwhichcanserveforresearchersandotherinterestgroupstodevelopmoreaccuratealgorithmsforautomaticEndUseclassificationtools.

13

Theme-InnovationinICTforwatermanagement

MultiobjectiveWaterManagementUnderUncertainty

PatrickM.Reed,CornellUniversityUSA

Computingpowerischeap,optimizationalgorithmshaveadvanced,visualizationshavegottenmoresophisticated,andmanysystemsoptimizationframeworkshavefoundtheirwayintocommercialuse,yetfornearly25yearswehavecontinuedtoformulateandsolvewatermanagementproblemslargelythesameway.Manyoftheinnovationsdiscussedabovehaveoccurredinisolation,andthedesignofcomplexengineeredwaterresourcessystemsremainsplaguedbyneglecteduncertaintiesandnarrowdefinitionsofoptimalitythatfailtoencompasscriticalperformancetradeoffs.Thissessionwillintroduceemergingmultiobjectivedesigntoolsthatenhancesystemsengineeringdesignunderuncertainty.Beyondthefocusonmultiobjectiveoptimization,thecoursewillcoverrelevantresearchrelatedtoconstructivedecision-aidingtheoryanddecision-biasesrelatedtorisk,andrecentadvancesinvisualanalyticsthathavebeendemonstratedtoenhancedesignprocesses.Acoregoalofthiscourseistoshowthestate-of-the-artforintegratinghumanintelligenceandcomputationalpowertoeffectivelyexploredesignhypotheses,discovercriticalsystemtradeoffs,andfacilitaterobustdesigndecisions.

ICTsolutionsforrealtimesmartwatermanagement

LydiaVamvakeridou&DraganSavic,UniversityofExeter,UK

ThepotentialandthescientificchallengesfacingICTsolutionsforrealtimesmartwatermanagementarepresented.Forinstanceoneofthemainchallengesisthemanagementandextractionofinformationfromvastamountsofhighresolutionconsumptiondata;a“BigData”challenge,andhowitcanbefaced.

14

StandardizationActivitiesandGapsforSmartSustainableCities

GabrielAnzaldi,EURECAT,Spain

InthistutorialwewillexploreaproposedframeworkofstandardsandmethodologiesforinteroperabilitywithinSmartSustainableCities,andwewillalsoworkinaproposalofstandardizationroadmap,takingintoconsiderationtheactivitiescurrentlyundertakenbythevariousstandardsdevelopingorganizationsandforums.Thetutorialwillincludeareviewofthei)SSCneedsforstandardizationandinteroperability;ii)currentframeworksofSSCstandards;ii)SSCrelatedStandardDevelopmentOrganizationsactivities;iii)outlinesofSSCstandardneedsandgapanalysis.

Newcontroltechniquesforsmartwatersystems

PantelisSopasakis,IMTLucca,Italy

Inthistutorialsessionwewilldiscusshowdynamicalmodelingcombinedwithtime-seriesanalysisandoptimizationcanleadtoanefficientmanagementofcomplexwatersystems.Wewillintroducekeyperformanceindicatorstoevaluatetheperformanceofthecontrolledsystemandformulateaneconomicmodelpredictivecontrol(EMPC)schemetoaddresstheprescribedcontrolobjectives.Wewillalsoseehowwecanharnessthecomputationalpowerofgraphicscardstoacceleratecomplexcomputationsinvolvedinourcontrolproblems.

15

ABSTRACTSOFWORKSHOPPAPERS

Thefollowingabstracts,listedinalphabeticalorderofthepresentingauthor(underlined),summarisepresentationsthatwillbediscussedduringtheaccompanyingworkshops.Thislistisnotcomplete,andduringtheworkshopsitispossiblethatotherpresentationswillbedeliveredbesidesthoselistedhere.

16

AFrameworkforReal-TimeSpatiallyDistributedDemandEstimationandForecastingJinduanChen,IDModeling,Arcadia,CA,USADominicL.Boccelli,UniversityofCincinnati,Cincinnati,OH,USA

Short-termwaterdemandforecastsarevaluablefordistributionsystemoperatorscontrollingtheproduction,storageanddeliveryofdrinkingwater.Incertainproblems,likereal-timepumpscheduling,thecycleofdataacquisition,modelcomputation,anddecision-makingistime-sensitive,andrequiresanautomaticproceduretohandlethetransferofinformationbetweendatasources,aforecastingmodel,andtheoperator.OurinitialresearchfocusedontheTimeSeriesForecastingFramework(TSFF)-aflexibleframeworkthataccommodatesdifferentdatasourcesandforecastingmodels-wasperformedasthefirststeptobridgethegapbetweenforecastingalgorithmsandengineeringpracticewithrespecttototalsystemdemand.OurcurrentresearchisextendingtheTSFFfromtotalsystemdemandtospatiallydistributeddemandestimationandforecasting.Anautoregressiveintegratedmovingaverage(ARIMA)timeseriesmodelshasbeenvectorized(VARIMA)toincludethepotentialspatialcorrelationindemandsthroughtheerrorcovariancematrix,andtheresultingestimation/forecastingalgorithmisformulatedasaDynamicBayesianNetwork.UsingdatacollectedfromautilitySCADAsystem(e.g.,pressures,flowrates,tanklevels,etc),theparametersoftheVARIMAmodelaswellasthedemandsatagiventimestepwillbeestimatedwithanExpectation-Maximization(E-M)algorithm,whichwasdesignedtocomputethemaximumlikelihoodestimatesformodelswithincompleteobservations.AspartoftheE-Malgorithm,adistributionofdemandsisgeneratedthatreflecttheuncertaintyinthecurrentdemandestimates.Thisinformation,whencoupledwiththeupdatedVARIMAmodelparameterestimates,canbeutilizedtoforecastspatiallycorrelateddemandsaswellastheuncertaintyinfuturedemandestimates.Thisworkwillpresenttheoverallmodellingframeworkandourpreliminarystudiesintotheuseoftheframeworkforperformingsystemwidedemandestimation.

17

TheGIS-integratedFREEWATplatformasanICTtoolforsustainablewaterresourcesmanagementGiovannaDeFilippis-ScuolaSuperioreSant’Anna,Pisa(Italy)

IacopoBorsi-TEASISTEMIS.p.A.,Pisa(Italy)LauraFoglia-UCDavis,CA,USAMassimilianoCannata-ISTSUPSI,SwitzerlandVioletaVelascoMansilla-CSIC,Barcelona(Spain)RudyRossetto,ScuolaSuperioreSant’Anna-Pisa(Italy)

Duringthelastdecades,overexploitationoftheavailablefreshwaterresourcescauses qualitative and quantitative degradation of accessible ground- andsurface-watersupply,duetogrowinghumanpressureandclimatechanges.Forthisreason,addressingpropermanagementandplanningstrategiesisofparamount importance to restore unbalanced situations and/or preventfuturescenariosofdegradation.

As pointed out by regulations and recommendations of the EU and theEuropeanandEnvironmentAgency(EuropeanEnvironmentAgency,2004),developinginnovativesoftwaretoolstoaddresswatermanagementissuesisimperativeandnumericalmodelsrepresentvaluabletoolstoaddressthistaskas, thanks to their predictive function, they can help for the application ofplanningandmanagementstrategies.

TheEUHORIZON2020FREEWATproject (FREEandopensourcesoftwaretools for WATer resource management; Rossetto et al., 2015) aims atsimplifying the application of EU-water related Directives (EU, 2000), bydeveloping an open source and public domain, GIS-integrated platform forplanningandmanagementofground-andsurface-waterresources.

Suchplatformisexpectedtohelpinfacilitatingthewidespreaduseofcomplexmodeling environments and in producing scientifically and technicallysoundingdecisionandpolicymaking.Theseobjectiveswillbeachievedtakingadvantage of storing, managing and visualizing large spatial datasets andadoptingaparticipatoryapproach,toinvolvestakeholdersnotonlyinthefinalstageofresultdiscussion,butalsoduringthephaseofscenariosetting.

18

The FREEWAT platform is integrated within the open source QGIS GIS,allowing the simulationof thewholehydrological cycle and the analysis ofseveral water data, where input and output data are managed through aSpatiaLite Data Base Management System (DBMS).The FREEWAThydrological model is based on fully distributed and physically-basednumerical codes,mainly from the open source USGSMODFLOW family. Assuch, the FREEWAT platform is conceived as a canvas, where severalsimulationcodesmightbevirtuallyintegrated.

FREEWATcapabilities include:modelling solute transportprocesses in the

saturated and unsaturated zones;pre-processing tools for the analysis,interpretation and visualization of hydrochemical and hydrogeologicaldata;tools for time-series processing;a module for sensitivity analysis,

calibration and parameter estimation;a module devoted to water

management and planning.Within the FREEWAT project, fourteen casestudieswillbesetupthroughoutEU,Switzerland,Turkey,UkraineandAfrica(with the cooperation of UNESCO-IHP), to demonstrate the full platformcapabilitiesatdifferentscales.AmongthetoolsintegratedinFREEWAT,theFarmProcess(FMP)embeddedinMODFLOW-OWHM(Hansonetal.,2014)isaremarkablemodule,whichallowstosimulateconjunctiveuseofground-andsurface-water,withparticular focusonruralenvironments,underdemand-driven and supply- constrained conditions, taking also into accountconstraintsonwellabstractionandwater-rightsrankingofwateraccountingunits.TheFMPallowstodynamicallyintegrateinfiltration,surfacerunoffanddeeppercolationcomponents,toeffectivelybalancecropwaterdemandandsupplyfrombothsourcesofwater.ItisfurthercoupledwiththeCropGrowthModule,afreeandopensourcemodulebasedontheEPICfamilymodels,toestimatecropwateruptakeandprovidecropyieldatharvest.

REFERENCES

EU,2000.Directive2000/60/ECoftheEuropeanParliamentandoftheCouncilestablishingaframeworkfortheCommunityactioninthefieldofwaterpolicy.OfficialJournal(OJL327)on22December2000.

19

EuropeanEnvironmentAgency,2004.AgricultureandtheenvironmentintheEUaccessioncountries-ImplicationsofapplyingtheEUcommonagriculturalpolicy.EnvironmentalissuereportNo.37,55pp.

HansonRT,BoyceSE,SchmidW,HughesJD,MehlSM,LeakeSA,MaddockT,NiswongerRG,2014.One-WaterHydrologicFlowModel(MODFLOW-OWHM).U.S.GeologicalSurvey,Reston,Virginia.

RossettoR,BorsiI,FogliaL,2015.FREEWAT:FREEandopensourcesoftwaretoolsforWATerresourcemanagement.RendicontiOnlineSocietaGeologicaItaliana,35:252-255.DOI:10.3301/ROL.2015.113

20

Hydra3D:ATooltoSimulateHydricResourcesScenariosJavierDíaz,LauraFava,AnabellaDiGrazia,EduardoNola-LINTI,LaPlataNationalUniversity,BuenosAires(Argentina)JuanPabloZamora-INTA,Maimará.Jujuy(Argentina)

Freshwater is a vulnerable and a finite resource, essential to support life,urban development, and the environment. It is one of themost importantresources of the planet and also, one of the most damaged by pollution,overexploitation,populationgrowth,energycrisesandclimatechange.Onechallenge that the Water Resources Integrated Management has to facenowadaysisrelatedtothetaskofbuildingskillsindifferentworkspacesandeducational settings, whether formal or informal, to achieve populationawareness on efficient water use. Information and CommunicationTechnologies offer tools that enable the exchange of experiences andknowledgeamonglearninggroups.Despitethis,duringtrainingworkshopsonwater technical capabilities, debates are held using physical models andposterswithmapsthatareexplainedorally.SpecialistsonHydricResourcesusepaperandphysicalmodelstocarryouttheiractivitiesthataretoanalyze,buildmock-ups,modifyandcreatehydricscenariosondifferentareas(Setta,2014).Theuseofpapersandstaticscalephysicalmodelshasitslimitations,especiallywhen itbecomesnecessary toworkwithhigh complexityhydricscenarios.

ThispaperpresentsHydra3D,developedinDiGrazia(2015),acomputertoolthat enables the simulation and recreation of hydric scenarios on a virtualground.Thissoftwareallowsfortheanalysisofdifferentalternativesofwaterresource allocation taking into account the interaction between varioussectorsofdemand(agricultural,urbanandindustrial).Thiscomputertoolletstheuserbuildahydricscenariobyincorporatingpredefinedcomponentsandaddingelementsonavirtualgeographicspace.Thecomponentsthatcanbeaddedrepresentdifferentelementsthattakepartinahydricscenarioinreallife,forexample:ariver,anindustry,anagriculturaldistrict,acity,amountain,

21

amine,etc.Allofthesecomponentshavetheirownbehaviorthatwillaffectthescenarioandcanbeconfiguredbytheuser.Thegroundelevationandthegeneralstateofthescenario(theseason,forexample)canalsobemodified.Thetoolexecutesasimulationthat,dependingontheconfigurationmadebytheuser,allowsthevisualizationof theresulting indicators thatarepartofhydricresourcemanagement.Builtscenarioscanbesavedallowingtheuserthepossibilityofsharinghiscreationwithothers.ReportscanbeexportedinPDF format giving theuser a numeric representationof the scenariobeingexecuted.

It isexpectedthatthedevelopmentofthistoolwillprovidesupport fortheimplementation of capacity building processes in different areas andeducational levels, forresearchandforthetrainingofgraduatestudentsbyprovidingthemwithatoolthatletsthembuildmock-upsandexploredifferentscenariosofwatermanagementinavisualand3Denvironment.ThistoolwillbeusedinthetrainingprocessofHydricTechnicalCapabilitiesaimedatruralcommunitiesandruraldevelopment institutionsof thePunaJujeña(Bilbao,2012). Itmay also be used by other specialists during their training,morespecifically for the M.S. in Integrated Water Resources Management tocomplete exercises where the layout of water scenarios and its furtheranalysisisrequired(UNL,2016).

REFERENCES

Bilbao,L.,García,J.,Guzman,P.,Narmona,L.,Ariza,I.,Ochoa,V.,Zamora,J.P.,2012.PrimerprocesodeformaciónencapacidadestécnicashídricasdelaprovinciadeCatamarca-Intercambiodesaberesentretécnicosyproductoressobreelaprovechamientodelaguaencomunidadesrurales.EditorialCopiar.

DiGrazzia,A.,Nola.E.,2015.Hydra3Dunaherramientaparasimulacióndeescenariosdegestiónderecursoshídricos,availablefromhttp://edunola.com.ar/juegos/PruebaWin.rarorhttp://edunola.com.ar/juegos/SimulacionWeb.html

22

Setta,D.,2014.SelanzóelProcesodeFormaciónenCapacidadesTécnicasHídricasComunitariasenlapunajujeña.ComunicaciónIPAFRegiónNOA,available:http://inta.gob.ar/noticias/se-lanzo-el-proceso-de-formacion-en-capacidades-tecnicas-hidricas-comunitarias-en-la-puna-jujena/.

Kofi,A.,Matsuura,K.,Gordon,Y.,2007.WaterforPeopleWaterforLife.WorldWaterAssessmentProgramme.http://unesdoc.unesco.org/images/0012/001295/129556e.pdf

UNL,2016,MaestríaenGestiónIntegradadelosRecursosHídricos,FacultaddeIngenieríayCienciasHídricas,UniversidadNacionaldelLitoral,SantaFé,Argentina.Available:http://fich.unl.edu.ar/carrera.php?id=25

23

Empoweringwaterconsumersthroughsmartmetering:evidencefromafieldstudyinaresidentialsuburbofMontpellier(southofFrance)MarielleMontginoul-Irstea–UMRG-Eau,MontpellierFranceArnaudVestier-MontpellierMéditerranéeMétropole,France,

Inacontextcharacterizedbyincreasingwaterscarcity,Frenchenvironmentallaws highly incite water managers to improve network performances byreducingwaterleaksanduserstosavewater.Smartmeteringseemsanewinteresting solution to reach suchobjectives, by informingwatermanagersand users on real-time consumption (Kendel and Lazaric, 2015). ThiscommunicationaimstoexploreitscurrentadoptioninFrancebyurbanusersofthisnewtechnology,whichseemstheoreticallydeliverarangeofbenefitseitherforwatermanagersorforwaterusers.Inparticular,itallowsforurbanwaterusers to consult theirdailywater consumptionand to createSMSorEmail-alertmessagestoinformthemwhenwaterconsumptionexceedsapre-definedthreshold.Butresultsshowthatthistechnologyisnotadopted,withaverylowsubscriptionrate.Isitbecauseusersandespeciallyhouseholdsareill-informedorill-intentionedagainstthisnewtechnology?Inordertobetterunderstandreasonsunderlyingsuchalowrate(Darby,2010,Fischer-Lokouand al, 2004, Kendel and Lazaric, 2015), we conducted a natural fieldexperimentstudyinaresidentialsuburbofMontpellier(SouthofFrance).261households where equipped from January 2015 with smart meters andofficiallyinformedinJune-Julyoftheassociatedservices(readingdailywaterconsumption, defining warning system in case of own-defined level ofoverconsumption). We took special care to inform them, in particular bytestingtwomodesofindividualcommunication(bymailorface-to-face),buttheadoptionisstilllow.Wethenconductedahouseholdsurveytoidentifythepotentialobstaclesandtoknowtheirsocialrepresentations(Ajzen,1991)ofwords“water”and“remotereading”.77householdsanswered,revealingnotechnical obstacles (like low interests/levels in new technologies) andhighlighting that remote reading seems related mostly to positiveconnotations(likeuseful,simple,allowstobewarnedofawaterleakorofan

24

excesslevelofconsumption).Furtherresearcheshavethennowtofocusonthelaststep,thatonewhichgoesfromintentiontoaction.Inthatway,wewillinparticularexplorepotentialitiesofdifferenttypesofsmartwaterpricingbysimulatingtheimpactofsuchsystemsonhouseholds’waterbehaviorfromonadatasetof23,000watermeterslocatedinMontpelliermetropolis.

REFERENCES

Ajzen,I.,1991.TheoriesofCognitiveSelf-Regulation.Thetheoryofplannedbehavior.OrganizationalBehaviorandHumanDecisionProcesses,50(2),179-211

Darby,S.,2010."SmartMetering:WhatPotentialforHouseholderEngagement?"BuildingResearch&Information,38(5),442-57

Davies,K.;C.Doolan;R.VanDenHonertandR.Shi,2014."Water-SavingImpactsofSmartMeterTechnology:AnEmpirical5Year,Whole-of-CommunityStudyinSydney,Australia."WaterResourcesResearch,50(9),7348-58

Fischer-Lokou,J.;GuéguenN.,Lépy,N.,2004."EffetsDeLaCommunicationParRéseauxInformatiquesVersusEnFace-À-FaceSurLaReprésentationRéciproqueDesNégociateursEtLeurPriseDeDécision."BulletindePsychologie,57(5),525-3

Kendel,A.,Lazaric,N.,2015."TheDiffusionofSmartMetersinFrance.ADiscussionoftheEmpiricalEvidenceandtheImplicationsforSmartCities."JournalofStrategyandManagement,8(3),231-44