Evaluating the impacts of greening scenarios on thermal comfort and energy and water consumptions for adapting Paris city to climate change

2018·7.4 MB·English

by  C. de Munck

UrbanClimate23(2018)260–286
ContentslistsavailableatScienceDirect
Urban Climate
journalhomepage:http://www.elsevier.com/locate/uclim
Evaluating the impacts of greening scenarios on thermal
comfort and energy and water consumptions for adapting Paris
city to climate change
C.deMuncka,*,A.Lemonsua,V.Massona,J.LeBrasa,M.Bonhommeb
aCentreNationaldeRecherchesMétéorologiques,GAME,Météo-France/CNRS,Toulouse,France
bLaboratoiredeRechercheenArchitecture,Toulouse,France
A R T I C L E I N F O A B S T R A C T
Articlehistory: Recentclimateprojectionspredictanamplificationofglobalwarmingandmorefrequent
Received31May2016 extremeeventssuchasheatwaves.Therefore,theadaptationofcitiestocounterbalance
Receivedinrevisedform29November2016 theseadversechangesisurgent.Amongavailableadaptationstrategies,urbangreeningisa
Accepted5January2017 measurethatisfrequentlyencouragedtoimprovethermalcomfortorenergydemand,but
Availableonlinexxxx
whoseimpactsarenotwellknownatthescaleofcities.Inthisstudyweevaluatetheeffects
ofvariousurbangreeningscenariosbasedonurbanclimatesimulationsacrosstheParisarea.
Keywords: The modelling relies on the Town Energy Balance model. The scenarios tested consist of
Climatechangeadaptation anincreaseinground-basedvegetationoranimplementationofgreenroofsoncompatible
Greeningstrategies
buildings,orthetwocombined.Resultsshowthatincreasingthegroundcoverhasastronger
Thermalcomfort
coolingimpactthanimplementinggreenroofsonstreettemperatures,andevenmoreso
Energyconsumption
Waterresources whenthegreeningrateandtheproportionoftreesareimportant.Greenroofsarehowever
themosteffectivewaytoreduceenergyconsumption,notonlyinsummerbutalsoonan
annualbasis.Theeffectsthevariousgreeningmeasuresmayhaveoverdifferentseasonsis
finallyaddressedinordertodrawupacomprehensiveinventoryoftheclimaticimpactsof
suchstrategies.
©2017ElsevierB.V.Allrightsreserved.
1. Introduction
Currentclimateprojectionsforecastanamplificationoftheglobalwarming(MeehlandTebaldi,2004;Stockeretal.,2013)
andanincreaseinextremeevents(Giorgi,2006).Thoseglobaltrendsthattranslateatregionalandlocalscales,arelikelytohave
significantimpactsincities,thatarehometothemajorityofthepopulation,namelyachangeintheintensityoftheurbanheat
island(highlydependentonthesoilwaterresources,suchashighlightedbytheworkofLemonsuetal.,2013),increasedhealth
risks(asattestedbythe15,000excessdeathsduetoextremeheatobservedinAugust2003,HémonandJougla,2004),notto
mentiontheincreaseintheuseofairconditioningtomeetthecomfortneedsoftheinhabitants(180%between2003and2020
accordingtoAdnot,2003a,b).Theseperspectivesmakecitiesterritorieswherenotonlymitigationissueswillbethestrongest,
butalsopotentiallyterritoriesmorevulnerabletoclimatechangethannaturalenvironments.
*Correspondingauthor.
E-mail address:[email protected](C.Munck).
http://dx.doi.org/10.1016/j.uclim.2017.01.003
2212-0955/©2017ElsevierB.V.Allrightsreserved.
C.Muncketal. /UrbanClimate23(2018)260–286 261
Therearedifferentformsofadaptationstrategiestoreducethisurbanvulnerability,involvingactionsonrequalificationof
surfaces,urbanforms,innerandouterbuildingenvelopes,oronindividualbehaviors.Urbangreeningstrategies,morespecifi-
cally,havemultipleinterests.Thankstovegetationcoolingpotential,theyarebothadaptationmeasurestoclimatechangeand
mitigationmeasuresforurbanheatislandandair-conditioningdemand.Beyondclimaticconsiderations,urbangreeningalso
deliversadditionalbenefitstotheurbanecosystem,suchasabettermanagementofstormwater(Ashleyetal.,2011;Stovin,
2010;Stovinetal.,2008),areductionofairpollution(CurrieandBass,2008;Hill,1971;Nowaketal.,2006;Pughetal.,2012)or
anincreaseinurbanbiodiversity(GetterandRowe,2006;Madreetal.,2014;Mullaneyetal.,2015)forexample.
This is by modifying its radiative, thermal, hydrological and aerodynamic properties that vegetation affects the urban
environment.Thesemodificationsresultfromthreephysicalprocesses:directevaporationofthewaterheldinthesoiland
interceptedbythefoliageofplants(daytimeandnighttime),thetranspirationofalltypesofplants(generallydaytime,butcan
alsooccuratnighttimeforsomeplantspeciesorundercertainclimates),andfinallytheinterceptionofsolarradiationfortree
species(daytime).Butthereisawiderangeofgreeningsolutionsthathavedifferenteffectsdependingonthescaleatwhich
theyareimplemented(building,street,neighbourhood,city),ontheplantspeciesselected,onthelocalclimaticconditions,but
alsoontheaspectsstudied(thermalcomfort,energetics,dynamics,hydrology,etc.).Thisisshownbythevastliteratureonthe
subject,whetherfromexperimentalornumericalstudies.
Greeningthebuildingenvelopescanreducesurfacetemperaturescomparedtoartificialsurfaces,whichlimitstheamount
ofheatthatcanpenetrateintobuildings(EumorfopoulouandKontoleon,2009;KontoleonandEumorfopoulou,2010;Onmura
etal.,2001;Sternbergetal.,2011;TakebayashiandMoriyama,2007;Wongetal.,2010).Indoingso,theydiminishtheseasonal
temperaturevariationsinsidebuildings(Castletonetal.,2010),generatingenergysavings,butwithouttotallyachievingthe
efficiencyofconventionalinsulationaccordingtoEumorfopoulouandAravantinos(1998)andEumorfopoulouandKontoleon
(2009).Urbangreeningtakesalsootherformsthroughstreettrees,urbanparksorforests.Onthebasisofameta-analysisofthe
literatureonthesetypesofstrategies,Bowleretal.(2010)showthaturbanparksgenerateanaveragedaycoolingintherange
of0.94◦C,withvariationswhichareexplainedinparticularbythesizeofthepark(Barradas,1991;Changetal.,2007)andits
composition(tree/lawnratio,Potchteretal.,2006)butalsothesurroundingclimate.Theyalsohighlightthatthetemperatures
under the trees are systematically cooler than the surroundings during the day, with variations depending on tree shading
capacityShashua-BarandHoffman(2000).Atnighttime,however,treecanopycouldretainheat(Liangmeietal.,2008;Souch
andSouch,1993;Tahaetal.,1991).Besides,astudybySouchandSouch(1993)suggeststhatthethermalperformanceoftrees
canvarydependingonthetypeofsurfaceonwhichtheyareplanted(grassorconcrete,forexample).Theevapotranspiration
potentialconditionedtowaterstatusofplantsandsoils,andirrigationpractices,isalsoacrucialpointwhichhasreceivedvery
littleattentiontodate,exceptbyShashua-Baretal.(2009)whoproposeanefficiencycoefficientwhichlinkscoolingeffectand
evaporation.
Foroperationalpurposeswithintheframeworksofurbanpolicies,theanalysisandevaluationofsuchstrategiesneedtobe
examinedatalargescale.Sofar,onlyafewnumericalstudiesinvestigatethecoolingpotentialoflarge-scalestrategiesofurban
planning,suchastheimplementationofgreenandbluecorridorsforParisurbanarea(GroupeDescartes,2009),orofgreen
roofsoverToronto(Bassetal.,2003)andNewYorkCity(Rosenzweigetal.,2009).
Ourstudyaimstocomplementthescientificliterature,butalsobettermeettheexpectationsofurbanplannersanddeci-
sionmakers,throughaphysically-basedanddetailedmodellingofurbanclimate,usingParisurbanareaasastudycase.The
originalityoftheworklies(1)inthedevelopmentofrealisticurbangreeningscenarios,implementedlocallybutassessedat
cityscale;(2)inthecomparisonofdifferentvegetationinstallationsinurbanenvironment,onroofs(withorwithoutirrigation)
andontheground(lawns,mixedvegetation);(3)inthemulti-criteriaevaluationofscenarioswhichcoverstheissuesofheat
exposureandthermalcomfort,energyconsumption,andwaterdemand;and(4)inthetimescalesthatareinvestigated(fora
specificheatwave,aswellasatseasonalandannualscales).
Thenextsection(Section2)presentsthebasisfortheconstructionofthedifferentscenariosthatwesimulated,thenumerical
configurationusedaswellasitsevaluationbeforegreening.Thenvariousimpactsareassessed,bothinextremesummercon-
ditions(heatwave,Section3)andfortherestoftheyear(Section4)toevaluatewhetherthesegreeningmeasuresdevisedto
meetheatwaveissueshaveadverseeffectsontherestoftheyearornot.Theimpactsstudiedarethermalcomfortandenergy
consumptionforuseofair-conditioning(AC)andheating,whicharegoodindicatorsofthevulnerabilityofcitiestoheatwaves,
butarealsoaboutthemanagementofurbanwater(volumesrequiredforwateringthevegetationandgeneratedbysurface
runoffthroughouttheyear).Thelastsection(Section5)drawstheconclusionsofthisworkandsuggestswaystodevelopthis
approach.
2. Methodologyforevaluatinggreeningscenariosatcity-scale
Theinitialaimofthisstudyistoevaluate,basedonmodelling,greeningscenarioswhichmayrepresentstrategiestoimprove
futuresummerconditionsincities.AstheEuropeanheatwaveof2003isidentifiedbythescientificcommunitytoberep-
resentativeinmeantemperaturesofsummersofthesecondhalfofthe21st century(Déqué,personalcommunication),the
meteorologicalconditionsassociatedtothisheatwavearetakenasastudycasetoevaluategreeningscenarios.Thismethod-
ology,focusingonPariscity,reliesonsimulatingtheurbanclimateofPariswiththeTownEnergyBalance(TEB)urbancanopy
model(HamdiandMasson,2008;Masson,2000).ThefirststepconsistsindesigningforPariscitygreeningscenariosthatare
atthesametimerealisticandpromisingandthatcanalsobetakenintoaccountbyourmodel.Inasecondstep,theurban
climateofParisissimulatedatthespatialresolutionof1kmwithTEBinofflinemode(withmeteorologicalforcings)foreach
262 C.Muncketal. /UrbanClimate23(2018)260–286
greeningscenario,alongsidesustainablestrategiesforenergyandwaterusage.Thisisdoneundera10-yearmeteorological
forcing(1999–2008)thatincludestheheatwaveeventof2003(8–13August2003).Thiswaybothcanbestudiedthebenefits
ofgreeningscenariosinaheatwavecontextaswellastheirrespectiveimpactsatthescaleofotherseasons.Thismethodology
allowsforacomprehensiveassessmentofgreeningstrategiestoberealised.Duringthesimulations,indicatorsidentifiedto
berepresentativeofcityvulnerabilitytoclimatechangearecomputedsothattheimpactsoftherespectivescenarioscanbe
quantifiedandcompared.
2.1. Simulationconfigurationandevaluationofthereferencesimulation
2.1.1. Simulationconfiguration
Forthisstudy,thesimulationswereperformedwiththeSURFEXsurfacescheme(Massonetal.,2013),andmorespecifically
withTEB,theTownEnergyBalancemodel(HamdiandMasson,2008;Masson,2000)builtinSURFEXforurbanandartificial
surfaces,andISBA,theInteractionbetweenSoil-Biosphere-Atmospheremodel(Boone,2000;NoilhanandMahfouf,1996)for
naturalsurfaces(soilandvegetation).TheconfigurationchosenforthesimulationfollowsthatsetupintheMUSCADEproject
(Massonetal.,2014b),whichthisstudyispartof.ThisconfigurationisdetailedinAppendixA.Inthisconfigurationthatinvolves
aplatformofmodels,theinputdataforSURFEXareprovidedbyanarchitecturalmodel,theGENeratorofInteractiveUrban
blockS(GENIUS,Bonhomme,2013;Bonhommeetal.,2013).ThismodelprovidesSURFEXwithlandcoverinformationatthe
resolutionof250m(includingexistingurbangreenspaces),andmorespecificallyprovidesTEBwithamapdescribingurban
morphologybasedoneighttypesofurbanblocks(Continuouspavilion,Discontinuouspavilion,Continuousblock,Discontinuous
block,High-risetower,Ancientcentre,Industrialbuilding)withspecificgeometricfeaturesandbuildingdates.Then,TEBtrans-
latestheseeighturbanblocksintofivebuildingtypes(Haussmannianbuilding,Individualhousing,Densecollectivehousing,
Officetower,Warehouse)andaggregatesthemattheresolutionof1kmtoreducecomputationalcosts.Fivetypesofbuildinguse
(Residential,Office,Industrial,Agricultural,Commercial)completethisrepresentation.Theradiativeandthermalcharacteristics
ofbuildingsareprescribedonthebasisofbuildingtypeandage(characteristicsprovidedinAppendixA),buildingequipment
characteristics(air-conditioning,heating,ventilation)onthebasisofbuildinguse(seeAppendixA)andscenariohypothesis.For
example,inoursimulations,virtuoustemperaturesetpointsforair-conditioningandheatingarechosentobeinlinewiththe
sustainabilityofthegreeningscenarios:19◦Cforheatingand26◦Cforair-conditioning.Heatingandair-conditioningareactive
all-yearroundandtriggeredassoonastheirrespectivetemperaturesetpointsarereachedwhatevertheseason.
InthisconfigurationTEBisruninofflinemode(i.e.with1Dmeteorologicalforcingsasopposedtocoupledtoanatmospheric
model),atthespatialresolutionof1km,andwithmostofitsparameterizationsactivatedtoallowformorephysicalprocesses
andinteractionstobeaccountedfor:verticalturbulentdiffusionwithinmultilayerurbancanyons(HamdiandMasson,2008;
MassonandSeity,2009),buildingenergetics(Buenoetal.,2012;Pigeonetal.,2014),urbanvegetationontheground(Lemonsu
etal.,2012),greenroofs(deMuncketal.,2013a),aswellasamoduleforcomputingthermalcomfortindexes(Fialaetal.,2012;
Pigeon, 2011). In addition, the urban weather generator (UWG) implemented in SURFEX is applied in order to derive a 2D
temperatureforcingfroma1Dforcingandcityfeatures(LeBrasandMasson,2015).Despiteeffortstoimprovethemodel,some
physicalprocessesarenotyetrepresentedintheversionusedinthisstudy,suchastheshadingeffectoftrees,aswellasthe
radiativetrappingbytreecanopies.
Underthisconfiguration,themodificationstothesurfaceinducedbythegroundgreeningscenariostranslatewithinTEB
intothemodificationoftherespectivefractionsofroadsandgreenspaces,wherevergreeningispossibleandaccordingtothe
threegreeningratiostested.Inthecaseofscenarioswithroofgreening,thethermalandradiativecharacteristicsofthegreened
artificialroofs(thoseofwarehousesandindividualanddensecollectivebuildings)arereplacedbythoseofgreenroofs.Thetotal
densityofurbanvegetationinducedbythedifferentscenariosispresentedinFig.1.Additionalsurfacecharacteristics(building
fractionandtype,usageandheightofbuildings)whichmaybehelpfulfortheanalysisofsimulationresultsareprovidedin
AppendixA.
2.1.2. Evaluationofthereferencesimulation
Thereferencesimulation,whichtakesintoaccounttheexistingurbanvegetationandisthereforetheclosesttorealityin
termsoflandcover,isevaluatedtoappreciatetheperformanceofthemodelconfigurationsetupinthisstudy,bothduring
theheatwave(8–13August2003)andthedecade(1999–2008)studied.Thisevaluationexerciseisbasedonthe2-mairtem-
peraturesrecordedateightoperationalweatherstationsthatarelocatedwithinthespatialdomainstudied(seeAppendixB).
Theyprovidecompleteseriesofdailyminimumandmaximumtemperaturesoverthe10-yearperiod1999–2008,andhourly
temperaturesduringthesixdaysofthe2003heatwavearealsoavailableforsevenofthem.Ata1-kmhorizontalresolution,
themodelcannotaccuratelyreproducethesub-gridheterogeneityinthesurfacecharacteristics.Thereforeitismoresuitable
tocomparemodelestimatesandobservationsforgroupsofstationshavingsimilarsurfacecharacteristicsratherthantocon-
ductameshbymeshcomparison.Consequently,theevaluationexerciseisperformedfortwotypesofstations,thoseurban
andthoserural(seeAppendixB),dependingontheurbanisedfractionofthemodelmeshtheybelongto,onaverage81±16%
and21±16%respectively.Whilesimulatedandobservedhourlytemperaturesarecomparedforthe2003heatwave,minimum
andmaximumtemperaturesarecomparedintermsofthemonthlyclimatologyoverthe10-yearperiodstudied.AppendixB
providesgraphsshowingtheevolutionofsimulatedandobserved2-mtemperaturesduringtheheatwaveaswellasthatof
monthlymeansofsimulatedandobserved2-mminimumandmaximumtemperatures(referredtothereafterasTNmeanand
TXmean).TheassociatedstatisticalscoresaresummarizedinTable1.
C.Muncketal. /UrbanClimate23(2018)260–286 263
Fig.1. Urbanvegetationdensityineach1-km2gridmesh.RepresentsgroundvegetationforLV25,LHV25,LV50,LHV50,LV75,andLHV75scenarios,androoftop
vegetationforGRandIGRscenarios(greysquaresshowthelocationofbuildingswithoutgreenroof,i.e.Haussmannianbuildingsandofficebuildings).
Duringtheheatwave,althoughthemodelcorrectlysimulatesthedailydynamicsoftemperatures(seeAppendixB),thebias
valuesobtained(+1.33◦Cforurbanstationsand+1.14◦Cforruralones)suggestthatthemodeltendstosimulatetemperatures
alittlehigherthanthoseobserved,whateverthelocationofthestation.Withstandarddeviationsforsimulatedtemperatures
veryclosetothoseobservedatstations,5.22◦Cversus5.06◦Crespectivelyaturbanstationsand6.03◦Cversus5.51◦Catrural
stations,themodeliscapableofcapturingtheslightlyhighertemperaturevariabilityobservedatruralstations.Runningat
the spatial resolution of 1km, an average error on the six days of the heat wave of about 1.9◦C is made on the simulated
temperatures,whetherinurbanorruralareas.Thiserrorseemstobeduetoanoverestimationbythemodelofthetemperatures
264 C.Muncketal. /UrbanClimate23(2018)260–286
Table1
Rootmeansquareerrors(RMSE)andmeanbiaserrors(MBEModel-Obs)associatedwith2-mairtemperatureforthe
referencesimulationduringthe2003heatwaveandforthemeanclimatologyofthe1999–2008decade.
8–13August2003 1999–2008decade
T(hourly) TNmean TXmean
Urban Rural Urban Rural Urban Rural
RMSE(◦C) 1.93 1.90 1.26 0.77 0.73 0.86
MBE(◦C) +1.33 +1.14 +1.25 +0.76 +0.53 +0.71
duringthefirstthreedaysoftheheatwave(seeAppendixB),whichisprobablytheconsequenceofaninsufficientrepresentation
ofadvectionprocessesinthesimulationconfigurationused(discussedfurtherinAppendixB).
Onaverageoverthedecadesimulated,RMSEandMBEvaluesobtainedshowthatatthespatialresolutionof1km,beitat
nightorduringtheday,themodelconfigurationweusetendtooverestimate2-mairtemperatures.Atnight,themodelpresenta
RMSEof1.26◦CandaMBEof+1.25◦CforurbanstationsandaRMSEof0.77◦CandaMBEof+0.76◦Cforruralstations.Whatever
theseason,errorsontheTNmeanarelowerforruralthanforurbanstations(onaverageby0.5◦Cforbothscores),suggesting
thatthemodelfurtheroverestimatestemperaturesatnightinurbansettings.Thistendencyofthemodeltooverestimate2-m
temperaturesismuchlesspronouncedatdaytimethannighttime,withRMSEandMBEvaluestwotimeslessthoseatnight
(RMSEof0.73◦CandMBEof+0.53◦C).Contrarytonighttimeperiod,themodelpresentsaslighttendencytooverestimate
moremaximumtemperaturesforruralthanforurbanstationsduringtheday.Asfarastemperaturevariabilityisconcerned,
thecalculationofthestandarddeviationsforthemonthlyclimatologyshowedthattheydonotdiffermuchbetweenurbanand
ruralstations:fortheTNmean,theyareonaverage4.77◦Cforobservationsand4.63◦Cformodelestimations,andfortheTX
meanrespectively6.79◦Cand6.48◦C.Suchstandarddeviationvaluesthatareequivalenttoslightlylessthanforobservations
demonstratethatthemodelisquitegoodatcapturingthevariabilityfoundintheobservationsforthedecadestudied.
OurresultsareinlinewithpreviousnumericalstudiesrunningwiththeTEBmodelthatarebasedonmoresophisticated
simulationconfigurationsthantheoneusedinourstudy.Whensimulatingthesamesix-dayperiodofthe2003heatwavewith
theTEBmodelcoupledtoamesoscaleatmosphericmodel,deMuncketal.(2013b)obtainatthespatialresolutionof1.25kma
similarMBE(+1.0◦C)andahigherRMSE(2.6◦C)valuesthanthoseobtainedwithoursimulationconfigurationfortheprediction
of2-mtemperatures.It’sonlybyrunningatahigherspatialresolution(250m)andwithaverydetaileddescriptionofsurface
characteristicsthattheperformanceofthemodelisimproved,withaMBEof+0.2◦CandaRMSEof1.5◦C,yetcomparableto
theoneobtainedwithoursimulationconfiguration.SimilarscoresareobtainedbyLemonsuetal.(2015)whosimulatethe2003
heatwaveatthespatialresolutionof1kmwithachainofmodelsincludingTEB:aMBEonnighttimetemperaturesranging
from+0.41to+3.72◦Cbetweenthemostandtheleasturbanisedlandcovers,witherrorsrangingfrom0.83to3.83◦C,and
atdaytime,anaverageMBEof−0.52◦Canderrorsrangingfrom0.78to2.06◦C.Inordertominimizetheimpactofthebiases
anderrorshighlightedinthereferencesimulation,theanalysisoftheimpactsofthegreeningscenariosinthepresentstudyis
carriedoutinrelativeterms(relativetothereferencesimulationbeforegreening).
2.2. DesigningrealisticgreeningscenariosforPariscity
Thegreeningstrategiesweevaluateconsistingreeningthecitywhereverpossiblebypartiallytransformingartificialurban
surfacesavailableintogreenspaces.Weassumeaspartlyavailableforgreening,theurbansurfacesatgroundlevelsuchas
pavements,squares,roundaboutsandcarparks(surfacesthatareneitherbuiltasbuildingsorroads,oralreadyvegetated),
aswellastheroofsofsomebuildingtypes.Atgroundlevel,acertainpercentageoftheurbansurfaceavailableforgreening
Table2
Characteristicsofthescenariossimulated.
Scenarioacronym Conversiontovegetation Additionalgreenareas(km2) Vegetationtype Irrigation
GR Roofsofsuitablebuildingsa 251(+28%) Sedums Noirrigation
IGR Roofsofsuitablebuildingsa 251(+28%) Sedums Dripirrigation
LV25 25%ofgroundavailableb 100(+11%) Lowvegetation:grassandsmallshrubs Sprinklersystems
LV50 50%ofgroundavailableb 199(+23%)
LV75 75%ofgroundavailableb 298(+34%)
LHV25 25%ofgroundavailableb 100(+11%) Lowandhighvegetation:60%grassand Sprinklersystems
smallshrubs40%ofdeciduoustrees
LHV50 50%ofgroundavailableb 199(+23%)
LHV75 75%ofgroundavailableb 298(+34%)
LHV75-IGR 75%ofgroundavailableb 549(+62%) Lowandhighvegetation Sprinklersystems
Roofsofsuitablebuildingsa Sedums Dripirrigation
a “Suitablebuildings”fortheimplementationofextensivegreenroofsrefertobuildingtypeswithflatroofsforwhichtheenergeticalperformancehasbeen
demonstratedintheliterature,i.e.individualanddensehousingandwarehouses.
b “Groundavailable”referstogroundsurfaceswhichareneithernatural,norbuiltasbuildingsorroads.Inarealcityitcorrespondstopavements,carparks,
squares,roundabouts,etc.
C.Muncketal. /UrbanClimate23(2018)260–286 265
is converted to green space, either 25, or 50, or 75%. This greening is realised with either low vegetation (grass and small
shrubs)oramixoflowandhigh(10m)vegetationcomposedof40%ofdeciduoustrees(oftenreferredtohereafterasmixed
wooded vegetation). At roof level, the surfaces available for greening are those of roofs of low-rise buildings (following the
conclusionsofNgetal.,2012)forwhicharchitectureorregulationwouldallowfortheirinstallation,whichmeansinTEB,roofs
ofIndividualandDensecollectivebuildingsandWarehouses(seeAppendixA).RoofsofHaussmannianbuildingsareexcluded
forarchitecturalandregulatoryreasonsandroofsofofficetowersfortheirinefficiency.Ontheroofsofthethreetypesofsuitable
buildingsareimplementedextensivegreenroofs(plantedwithsedum),whicharerecognizedfortheirenergeticalperformance.
ThecharacteristicssimulatedinTEBforthistypeofgreenroofaredescribedindeMuncketal.(2013a).Followingprevious
studieshighlightingthatvegetationisaneffectiveadaptationmeasureonlyifitiswatered(Danieletal.,2016;Kounkou-Arnaud
etal.,2013),wechosetowatergroundvegetationinsummerwhateverthescenario.Inthecaseofgreenroofstwoscenariosare
tested,withorwithoutirrigationinsummer.Alastscenario,consistingincombiningimplementationofirrigatedgreenroofs
andgreeningof75%withamixedwoodedvegetationisalsosimulated.
Forsummerirrigation,thechoiceofwateringvolumeandscheduleisbasedonasensitivityanalysisundertakenwithTEB
underthemeteorologicalconditionsofthe2003heatwave(deMunck,2013),whichaimwastooptimizeplantwaterusefor
anon-restrictedevapotranspiration,henceaircoolingeffect.Withregardtovolumes,wechoseforgreenroofs25Lperm2
perweek,avaluederivedaftertheguidelinesprovidedbySOPREMA(internationalmanufacturerspecializedinwaterproofing
solutionsforurbaninfrastructuresincludinggreenroofsandwalls,SOPREMA,2011)forFranceandindroughtconditions.For
groundvegetation,wetestedthreevolumes(25,50and75Lperm2 perweek),aswellasthreewateringschedules(in3,6
or8h),allthreeatnight,inordertooptimizebothplantwateruseandthenighttimeaircoolingeffect.Night-timewatering
waschosentoimprovenight’srestandreducenighttimethermalstress,amajorfactorrelatedtoexcessmortalityduringthe
2003heatwaveinFrance(Doussetetal.,2011;Ledransetal.,2005).Weconcludedthatauniquewateringschedule,overeight
hoursbetween21:00and05:00localtime,providingtheequivalentof25Lm−2 week−1 (i.e.410−5kgofwaterm−2 s−1)
wouldbesuitableforbothgroundandroofvegetation,theonlydifferencebeingthewateringsystem,withsprinklersforground
vegetationanddripirrigationforgreenroofs.ThewateringwithsprinklersisparameterizedinTEBasrain,whichisrealisticfor
lowvegetationbutmayslightlyoverestimatetheamountsofwaterinterceptedinthecaseoftrees.
Forpracticalreasons,acronymsweregiventothosescenarios.TheyareprovidedinTable2alongsideasummaryoftheir
characteristics,andusedthereafter.Inallcases,scenariosincreasedthedensityofexistingurbanvegetation(representedbythe
REFscenario)intheproportionswhichareprovidedinTable2andillustratedbythemapsofFig.1.Overtheentiresimulation
domain,greening25,50of75%ofavailableground-basedurbansurfacescorrespondstoincreasingexistinggreenspacedensity
byrespectively11,23and34%.Also,ascanbeseenonFig.1,becausethegroundsurfaceavailableforgreeningisquitelow
inthecitycentre,themorewegreentheground(from25to75%),themorethegreeningisonlypossibleontheoutskirtsof
Paris.Onthecontrarygreeningsuitableroofs(GRandIGRscenarios)allowswithinParistocompensateforthelackofground
space.Theincreaseinurbanvegetationdensityobtainedthroughthesetwoscenariosisfarfromnegligibleatthescaleofthe
simulationdomain(+28%)andishigherlocallywithinPariscity(between+30to+40%).Thescenariocombiningthegreening
ofbothgroundandroofs(LHV75-IGR)presentsthemaximumvegetationdensityincreasewith+62%.
2.3. Choiceofindicators
Theimpactsofgreeningstrategiesareanalyzedbycomparingthreetypesofindicatorsofinterestwhicharecomputedby
TEB:levelsofthermalheatstressreached,energyconsumptionsforcoolingandheatingbuildings,waterresourcesrequiredfor
summerwateringaswellasurbansurfacerunoff(fromartificialandnaturalurbansurfaces)allyearround.
The 2-m air temperature is compared between scenarios as a first indicator of greening impact. Then levels of thermal
comfortorheatstress(andtimespentateachlevel)areevaluatedthroughtheUniversalThermalClimateIndex(UTCI,Fiala
etal.,2012)andthethresholdsestablishedbytheGlossary ofTermsforThermalPhysiology(TheCommissionforThermal
PhysiologyoftheInternationalUnionofPhysiologicalSciences,2003)andindicatedinTable3.TheUTCIiscalculatedinTEB
takingintoaccounttheclimaticconditionsforapersonplacedinthemiddleoftheurbancanyon(airtemperatureandhumidity,
windspeed,directandreflectedsolarradiationandinfraredsolarradiationemittedbythewallsofthecanyon).TheUTCIcan
becalculatedforapersonwhostandsinthesunorintheshade.Inthelattercase,thecalculationoftheUTCIwillnottakeinto
accountthedirectsolarradiation.
Tocomparetheenergeticalperformancesofthegreeningscenariosinthecontextofthe2003heatwave,wechoosefor
indicatorstheenergyconsumptioncumulatedaswellasthepeakofenergydemandduringtheheatwave(air-conditioning).
Table3
AssessmentscaleofUTCIforhightemperatures.Source:www.utci.org.
UTCI(◦C) Thermalstresslevel
Above+46 Extremeheatstress(EXS)
+38to+46 Verystrongheatstress(VSHS)
+32to+38 Strongheatstress(SHS)
+26to+32 Moderateheatstress(MHS)
+9to+26 Thermalcomfort
266 C.Muncketal. /UrbanClimate23(2018)260–286
Fig.2. Boxplotsofaverageimpactsofgreeningscenariosonminimumandmaximumdailyurban2m-temperaturesovertheentiresimulationdomainduring
the2003heatwaveforeachscenarioandcomparedtothesimulationbeforegreening(REF).
Fortheseasonalanalysis,wecomparethe10-yearmeanenergyconsumptioncumulatedduringeachofthefourseasons(air-
conditioningandheating).
3. Impactsinthecontextofheatwave
TheimpactsofgreeningscenariosareestimatedfortheheatwaveofAugust2003,andmorespecificallyforasix-dayperiod,
extendingfromthe8thtothe13thofAugust,chosentobethehottestperiodofthisexceptionalheatwavefortheregionIle-de-
France.Thesesixdaysweremarkedbysettledanticyclonicconditions,withdryandstableairmassesinthelowerlayersofthe
atmosphere,whichgotwarmerandwarmeruntilthe14thofAugust,markingtheendoftheheatwave.
3.1. Thermalimpacts
3.1.1. 2-mairtemperatures
Tohaveinmindtheorderofmagnitudesbetweentheimpactsofgreeningstrategiesintermsof2-mairtemperaturesand
theindicatorspresentedpreviously,wecomparedtheminimumandmaximum2-mairtemperaturesthroughtheanalysisof
thetemperatureanomaliesbetweeneachgreeningscenarioandthesimulationbeforegreening(namedREF).
Basedonoursimulations,ourresults(Figs.2,3and4)suggestthatgreeningsuitableroofsdoesnothaveanimpacton2-m
airtemperaturesunlessthoseareirrigatedandthatthisimpactonlyreallyappearsatdaytime.Andeveninthiscase,theaverage
daytimecoolinginducedduringthesixdaysoftheheatwaveatthescaleoftheentiresimulationdomainisrelativelylimited,
withamediancoolingof−0.17◦C(Fig.2).Lookingatthespatialdistributionofthiscooling,thepotentialofirrigatedgreen
roofsforcoolingmaximumtemperaturesgenerallyrangesfrom−0.25to−0.5◦Cdependingonbuildingtype,respectivelyfor
individualanddensecollectivehousing.ItisnoticeableeveninthecentreofParisandcanreachupto−0.67◦Clocally(Fig.4).
Themaximumcoolinginducedbyirrigatedgreenroofsinthesimulationdomaininthecourseoftheheatwaveis−0.97◦C.
Thetemperatureanomaliesgeneratedbytheincreaseingroundvegetation(systematicallywatered)areshownonFig.2.
Thesescenariosshow,asexpected,agreatercoolingof2-mtemperaturesthanthatgeneratedbyrooflevelvegetationwith
threesignificantresults:
1. Foragiventypeofvegetation(LVorLHV)nightanddaytimecoolingdonothavethesamemagnitude(Fig.2).Amaximum
effectisobservedatdaytimeforscenarioswiththeadditionoflowvegetation,withamediancoolingof−0.19◦C,−0.37◦C
C.Muncketal. /UrbanClimate23(2018)260–286 267
Fig.3. Spatialdistributionoftheimpactsofgreeningoverminimumdailyurban2m-temperaturesforintermediategroundgreening(LV50andLHV50),roof
greening(IGR)andmaximumgreening(LHV75-IGR),expressedastheaverageimpactsduringthesixdaysoftheheatwave.
and−0.55◦CrespectivelyforLV25,LV50andLV75(Fig.2).Conversely,theimplementationofamixedwoodedvegetation
leadstoamaximumeffectatnighttime,withamediancoolingof−0.38◦C,−0.45◦Cand−0.50◦CrespectivelyforLV25,LV50
andLV75(Fig.2).Whileitislogicalforthecoolingeffecttobeoptimumatdaytimewhenthevegetationisphotosynthetically
active(andthereforeevapotranspirationratesattheirmaximum),asisthecaseforlowvegetationscenarios,weobtain
ahighercoolingpotentialatnightforthemixedwoodedvegetationscenariosthatisexplainedbythewateringschedule
andthevegetationtype.Indeed,whenthetreesarewateredatnight,sprinklersbeingsimulatedinTEBasartificialrain,
alargepartofthewaterinterceptedbythefoliageandthesoilisdirectlyevaporatedandisnolongeravailableduring
theday,generatinglesscoolingatthattimeofday.Thisoccursalsoforlowvegetation,buttoalesserextentduetoits
smallerinterceptionsurfacesandlowertransferturbulentcoefficients.Notethatfortrees,thisphenomenonisprobably
morepronouncedthaninrealityduetosprinklerwateringsincegreateramountsofwaterareinterceptedbytreeleaves
andaredirectlyevaporated,hencearenotstoredintheground.Thisshowsthelimitsofthecurrentparametrization
forthewateringofgroundvegetation(thatwasduetoconstraintsinthecodestructureoftheISBAscheme),whichis
undifferentiatedforgrassesandtrees.Thesetimingandmagnitudedifferencesinthecoolinggeneratedbylowandmixed
woodedvegetationscenariosarewellvisibleonthemapsofFigs.3and4whencomparingLV50andLHV50scenarios.
Duringthenight,thestrongestcoolingisgeneratedbytheLHV50scenarioandrangesonaverageduringtheheatwave
between−0.25and−2◦C,withamaximumcoolingpotentialobservedinareasofdensecollectivehousing.Meanwhile,
theLV50scenarioonlygeneratedacoolingofaround−0.25to−0.5◦C.Inthecourseoftheheatwave,thegreatestcooling
ofminimumtemperaturesgeneratedbytheLVH50andLV50scenariosamountsrespectivelyto−2.2and−1.1◦C.
2. Whenlookingateachtypeofgroundvegetationsimulatedseparately,thehigherthesurfacegreeningrate,thegreater
the cooling generated. When greening 25, 50 or 75% of available surfaces with low vegetation, this corresponds to a
daytimecoolingof−0.19,−0.37and−0.55◦Candanighttimecoolingof−0.04,−0.08and−0.12◦C(medianvaluesof
boxplotsonFig.2).Similarly,foramixedvegetation,thiscorrespondstoadaytimecoolingof−0.08,−0.31and−0.49◦C
andanighttimecoolingof−0.38,−0.45and−0.50◦C.Lookinglocally,iftakingLVscenariosasanexample,thegreatest
268 C.Muncketal. /UrbanClimate23(2018)260–286
Fig.4. Spatialdistributionoftheimpactsofgreeningovermaximumdailyurban2m-temperaturesforintermediategroundgreening(LV50andLHV50),roof
greening(IGR)andmaximumgreening(LHV75-IGR),expressedastheaverageimpactsduringthesixdaysoftheheatwave.
reductioninmaximumtemperaturesestimatedatthescaleofthesimulationdomainamountstoabout−0.5◦CforLV25,
−1◦CforLV50and−2◦CforLV75.
3. Nighttimeresultssuggestthatforsimilargroundsurfacegreeningrates,thecoolingpotentialofamixedvegetationcom-
posedof40%oftreesismoreeffectivethanthatofavegetationcomposedonlyofgrassesandsmallshrubs.Forthetime
beingitishardertodrawconclusionsfromdaytimeresultssincethemodeldoesnotyetaccountfortreeshadingeffects.
AsforthemaximumgreeningscenariocombiningLHV75andIGRhypothesis,itsimpacton2-mtemperaturesisquitesimilar
tothatoftheLHV75sincethecoolingpotentialofIGRonstreetleveltemperaturesislimited(Fig.2).Itisonlyperforming
sensiblybetteratdaytimeinareasaroundPariscitycentrewithdensecollectivehousingwhereIGRmanagetofurtherenhance
thecooling,resultinginadomainmedianimpactof−0.65◦C.
3.1.2. UHIintensity
Alongthecross-sectiondefinedonthemapofFig.5,the2-mnighttimeUHIintensitybetweenPariscitycentreandthe50-
mawaycountrysidewasestimatedbeforegreeningataround5◦Cwiththestandardspatialstructurerepresentedbythegrey
line.Asexpectedfromprevioustemperatureresults,thegreeningofroofsdoesnotmodifytheUHIintensityatallsinceIGR
andREFtemperatureprofilesareidentical.ToevaluatethemaximumimpactofurbangroundgreeningontheUHI,previous
temperatureprofileswerecomparedtothescenarioswiththegreatestgreeningrate(75%,i.e.LV75,LHV75andLHV75-IGR).As
expectedfrompreviousresults,thescenariowithamixedwoodedvegetation(LHV75)inducedthegreatestreductioninUHI
intensity(−1.2◦C),whichlowerstheUHIto3.8◦C.TheimpactoflowvegetationonUHIisrelativelylowincomparison(inthe
rangeof0.25◦C).Asforirrigatedgreenroofs,theydonotallowforafurtherreductionoftheUHIintensityinducedbygreening
thegrounds(LHV75-IGR).
C.Muncketal. /UrbanClimate23(2018)260–286 269
Fig.5. 2-mmeannighttimeairtemperatureprofilesduringthesixdaysoftheheatwaveforthewest-to-eastcrosssectionidentifiedonthemapoftempera-
turespresentedfortheREFsimulation,estimatedacrossthreehourlyterms-2,3and4UTC.Theprofileforthebaselinesimulation(REF)showsaheatisland
intensityofaround5◦C.
3.1.3. Thermalcomfort
Toanalyzetherespectiveimpactsofthegreeningscenariosintermsofoutdoorthermalcomfortorstressexperiencedduring
theheatwaveviatheUTCI,wefirstcomputedtheUTCIforeveryhourofthesixdaysofthe2003heatwave(seeSection2.3).
TheUTCIvaluesobtainedwerethensortedinthefiveheatstressclassesdescribedearlier(Table3)tofinallycomputethemean
timespentdailyineachheatstressclass(overthe6:00to22:00timeslot)forapersonoutsideinthesunorintheshade.
Consideringlookingforshelterintheshadeafairlynaturalreflexinheatwaveconditions,wefocusedtheanalysisofour
greeningscenariosonthedailyimpactsthattheywouldhaveonthetimespentatleastinverystrongheatstress(referredto
asVSHSandcorrespondingtoUTCI>38◦C)byapersonintheshade,acrosstheentiresimulationdomainandacrossthethree
maintypesofurbanfabric.TheseresultsarepresentedinTable4.
Asexpectedfrompreviousimpactsonstreetleveltemperatures,non-wateredgreenroofs(GR)donotimproveoutdoor
thermalcomfort.Nevertheless,withoutmodifyingthetotaltimespentinthermalheatstress,theirwatering(IGR)allowsfor
thetimespentatleastinVSHStobemarginallydecreased(by15minonaverageacrossthewholecity).Notethatwatered
greenroofsappearmoreeffectivetoimprovestreetlevelthermalcomfortinthedensecollectivehousingfabricthaninany
otherurbanfabrics(22minlessspentatleastinVSHScomparedto11and9minlessrespectivelyintheHaussmannianand
individualhousingfabrics).Thisisexplainedbythehighroofgreeningrate(Fig.1)andtherathersmallstreetaspectratiosin
thisurbanfabric(seeAppendixA).
Onthecontrary,increasinggroundvegetationinthecityimprovesoutdoorthermalcomfort,bybothincreasingthetime
spentinthermalcomfortandbyreducingthatspentinthevariousheatstresslevels,withthemostmarkedeffectonVSHS.And
Table4
Meantime(inhoursandminutes)spentdailyduringthe2003heatwaveatleastinverystrongheatstress(UTCI>38◦C)forapersonintheshadebefore
greening(REF)andtimereductionsimulatedforeachgreeningscenarios.Meansarecalculatedoverdaytimehours(06:00–22:00LST),integratedacrossthe
entireurbanzoneandacrossthethreemainhousingtypesandweightedbypopulationdensity(estimatedfromnationalandpublicdatabasesbyBonhomme,
2013).
Scenario Town Haussmannianbuildings Densecollectivehousing Individualhousing
Meantimespentatleastinverystrongheatstress
REF 5:37 5:15 6:34 4:55
Impactofscenario
GR – −0:01 +0:02 –
IGR −0:15 −0:11 −0:22 −0:09
LV25 −0:20 −0:18 −0:26 −0:15
LV50 −0:39 −0:39 −0:49 −0:30
LV75 −0:55 −0:43 −1:10 −0:45
LHV25 −0:15 −0:21 −0:19 −0:10
LHV50 −0:37 −0:37 −0:49 −0:28
LHV75 −0:56 −0:51 −1:12 −0:43
LHV75-IGR −1:04 −0:53 −1:24 −0:50