HomeMy WebLinkAbout11-04-24 Public Comment - A. Kociolek - Public commentFrom:Angela Kociolek
To:Bozeman Public Comment
Cc:Mitchell Overton; Jon Henderson; Erin George
Subject:[EXTERNAL]Public comment
Date:Tuesday, October 29, 2024 1:15:22 PM
Attachments:favicon.ico
CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you
recognize the sender and know the content is safe.
Hello, this is a general public comment for the City Commission and Departments & Citizen Advisory
Boards related to Community Development, Parks/Forestry, and Sustainability. I’d appreciate it if you
could kindly forward to them.
Thank you!
Angie Kociolek
_____________
Greetings City Commissioners, Department Heads, and Board Members,
My husband came across this article:
Las Vegas metro area could be the biggest winner
when it comes to potential cooling power of trees •
Nevada Current
nevadacurrent.com
Since ours is a semi-arid and drought-prone city, I thought I’d share it as well as these accompanying
articles with you all:
Trees in cities are beyond shady | U.S.
Geological Survey
usgs.gov
sciencedirect.com
A take home message from this research is, "Tree canopy is important in urban environments as it has
implications for city planning, public health, and climate resilience.”
More research is always better but my hope is that the City of Bozeman begins to recognize, and incentivize
the protection of, existing trees (public and private). Established trees are a critical part of our existing tree
canopy as well as being appreciating assets that serve us all.
Sincerely,
Angie Kociolek
Bozeman, MT
Las Vegas metro area could be the biggest winner when it comes to potential cooling power of trees • Nevada Current
https://nevadacurrent.com/...s/?emci=40e93afe-1196-ef11-88ce-000d3a98fa6b&emdi=bf81af46-1396-ef11-88ce-000d3a98fa6b&ceid=94461[11/4/2024 12:15:45 PM]
WORKING + THE ECONOMY
JUSTICE
SUSTAINABILITY
EDUCATION
HOUSING
POLITICS + GOVERNMENT
DECISION 2024
PART OF STATES NEWSROOM
GOVERNMENT
SUSTAINABILITY
Las Vegas metro area could be the
biggest winner when it comes to
potential cooling power of trees
Las Vegas metro area could be the biggest winner when it comes to potential cooling power of trees • Nevada Current
https://nevadacurrent.com/...s/?emci=40e93afe-1196-ef11-88ce-000d3a98fa6b&emdi=bf81af46-1396-ef11-88ce-000d3a98fa6b&ceid=94461[11/4/2024 12:15:45 PM]
BY: JENIFFER SOLIS - OCTOBER 29, 2024 5:00 AM
Researchers found Las Vegas was the worst offender in the study in terms of the prevalence of treescompared the prevalence of superheating man-made surfaces like buildings, roads, and sidewalks.(Photo: Ronda Churchill/Nevada Current)
Las Vegas is heating up faster than almost every other American city, but a new
multi-year study may provide local governments some direction for effective heat
relief.
According to a study published by the U.S. Geological Survey last week, Las
Vegas and other cities in hotter, drier regions may be the biggest winners when it
comes to the cooling effect trees can provide in sizzling temperatures.
In eight large cities across the country, scientists placed 80 to 100 sensors on trees
in each city and measured hourly air temperatures for three months during the
summers of 2016 to 2019. The study found that urban trees in arid cities amplified
the cooling of local air temperature significantly more than in more humid
locations.
The study covered Baltimore, Los Angeles, Phoenix, Portland, Miami, Tucson,
Denver and Las Vegas.
“We found that trees in every city are reducing air temperature. But we did find
that the hotter and drier the city, the greater the magnitude of that cooling power
was,” said Peter Ibsen, USGS research ecologist and the study’s lead researcher.
For example, in humid Miami, researchers found that trees cooled the surrounding
air temperatures by about 2 degrees C (Celsius), while trees in the Las Vegas
metro area accounted for a 7-degree C cooling effect. The study measured cooling
effects over a 60-meter buffer around each tree, indicating a broader impact of tree
canopy on air temperature.
“Trees, when you put up them all over the whole city, they have this larger effect
of being able to reduce air temperature at the neighborhood scale,” Ibsen said.
Trees in hotter, drier cities like Las Vegas also consistently mitigated air
temperature increases during periods of extreme heat, meaning trees can
Las Vegas metro area could be the biggest winner when it comes to potential cooling power of trees • Nevada Current
https://nevadacurrent.com/...s/?emci=40e93afe-1196-ef11-88ce-000d3a98fa6b&emdi=bf81af46-1396-ef11-88ce-000d3a98fa6b&ceid=94461[11/4/2024 12:15:45 PM]
substantially alter residents’ exposure to extreme heat in urban areas.
“We’re seeing a ton of cooling coming from trees in Las Vegas. And when we did
a whole model accounting for heat waves in Las Vegas, that cooling effect
increased during heat waves as well,” Ibsen said.
Average summertime temperatures in Las Vegas have increased by 5.8 degrees F
since 1970, ranking as the second fastest-warming city in the U.S.
Extreme heat waves in Southern Nevada have exacerbated heat-related
hospitalizations and deaths year-after-year. Last year, Clark County reported more
than 300 heat-related deaths. In 2024, the county said heat was a factor in more
than 400 deaths.
Las Vegas recorded its hottest temperature ever — 120 degrees F— on July 7,
2024. That same day, the youngest person in Clark County to die of a heat stroke
was a 27-year-old man, according to the Clark County coroner. The second hottest
day ever recorded in Las Vegas — 119 degrees F — happened two days later,
where the youngest person to die of heat stroke was a 28-year-old man.
Heat related deaths are often associated with the sick and elderly, but at a certain
temperature the human body can’t withstand extreme heat, and neither a person’s
state of fitness nor their levels of hydration can protect them from heat damage.
Finding trees that aren’t ‘all in
parks’
Human-caused climate change has turbocharged heat all over the country, but it’s
most intense in cities. That’s because buildings, roads and sidewalks radiate more
heat than grass and trees, in what’s known as the urban heat island effect.
Researchers found Las Vegas was the worst offender in the study in terms of the
prevalence of superheating human-made surfaces compared to tree coverage. Only
about 9% of the Las Vegas metro area is covered by tree canopy, while
impervious surfaces — buildings, roads and sidewalks — covered nearly 50% of
the area. Those trees were also rarely planted near those superheated surfaces.
Las Vegas metro area could be the biggest winner when it comes to potential cooling power of trees • Nevada Current
https://nevadacurrent.com/...s/?emci=40e93afe-1196-ef11-88ce-000d3a98fa6b&emdi=bf81af46-1396-ef11-88ce-000d3a98fa6b&ceid=94461[11/4/2024 12:15:45 PM]
Ibsen said researchers in the Las Vegas area had to install sensors across the
largest area of any other city studied compared to its size due to a widespread lack
of trees.
“To get around 80 to 100 sensors up, we needed to find 80 to 100 trees that are
also not necessarily all in parks,” Ibsen said. “There’s not that many places in Las
Vegas where you can find that.”
However, researchers found that even in sweltering cities with sprawling concrete
networks, trees were able to effectively mitigate heat and cool air temperatures
significantly in arid regions.
“It’s not that the areas are getting colder during heat waves, but trees are able to
cap that increase. So downtown may increase by like 12 degrees, but areas with
trees may only increase by 8 degrees. And we didn’t find that in every city,” Ibsen
said.
Ibsen says trees function in surprisingly similar ways to the human body, pumping
water through their leaves to cool down, the same way a human sweats to cool
down. That water vapor then cools the air surrounding the tree. In humid
environments, the air is already full of water vapor, so water doesn’t evaporate as
quickly or cool as effectively.
“In really arid cities, there’s more water getting pumped out of the soil by trees. So
we get this bonus cooling effect, in addition to shade that we don’t see in the more
humid cities,” Ibsen said.
Grass did not have the same large-scale cooling effect as trees, especially in arid
conditions, according to the study. The lack of shade provided by grass and its
proximity to the ground makes grass especially inefficient at cooling surrounding
air temperatures.
“Unlike grass, a tree can cool things off in multiple directions and at different
levels of height as well,” Ibsen said.
Ibsen said he hopes local agencies and municipalities will work with the data from
their research to create better urban planning.
Las Vegas metro area could be the biggest winner when it comes to potential cooling power of trees • Nevada Current
https://nevadacurrent.com/...s/?emci=40e93afe-1196-ef11-88ce-000d3a98fa6b&emdi=bf81af46-1396-ef11-88ce-000d3a98fa6b&ceid=94461[11/4/2024 12:15:45 PM]
Time for a water schedule
rethink?
However, researchers warn that maintaining trees in an urban setting requires
irrigation. The study also found that several tree species could not survive
intensifying heat waves and existing water restrictions, resulting in leaf death.
Cities should invest in well-trained urban foresters that can select the right species
of trees that can withstand extreme heat, Ibsen said.
The City of Las Vegas is working on establishing an expansive urban canopy
across urban areas, but that work is more complicated than just planting more
trees, said Steven Glimp, a board certified arborist, and the city’s manager of parks
and urban forestry.
“To get around 80 to 100 sensors up, we needed to find 80
to 100 trees that are also not necessarily all in parks...
There’s not that many places in Las Vegas where you can
find that.”
– Peter Ibsen, U.S. Geological Survey
The city aims to plant 2,500 to 3,000 trees annually, focusing on areas with the
greatest heat island effect, including downtown, the Historic Westside, and parts of
council wards one, three, and five in the southeast portion of the city’s boundaries.
Glimp said planting trees in built environments with hardscapes is challenging due
to compacted soil degraded by concrete and asphalt. The city has implemented
innovative soil volume strategies since 2016, including soil cells and engineered
soil-mixes to provide better oxygen and space for tree roots.
Less adaptive species first planted in Las Vegas are also failing in the midst of
higher temperatures and water restrictions.
“This year, we did see an increase in mortality with some old school species. The
mortality rate was much higher this year, because species are starting to fail in
Las Vegas metro area could be the biggest winner when it comes to potential cooling power of trees • Nevada Current
https://nevadacurrent.com/...s/?emci=40e93afe-1196-ef11-88ce-000d3a98fa6b&emdi=bf81af46-1396-ef11-88ce-000d3a98fa6b&ceid=94461[11/4/2024 12:15:45 PM]
these really hot summers,” Glimp said.
Lack of irrigation can also be an issue. The Southern Nevada Water Authority
four-season watering schedule doesn’t always align with the reality of summer
heat, including this year when 100-plus degree weather continued well into the
fall, said Glimp. But more resilient species should be able to handle less frequent
irrigation.
“We’re planting species that could basically survive on our existing rainfall. Once
established, after a few years being irrigated, they could survive extended
drought,” Glimp said.
Resilient tree species, including native and non-native varieties, are being planted
throughout the city to enhance urban canopy diversity. The city also promotes
these species to local nurseries and landscapers to increase availability for
homeowners looking to cool their homes and neighborhoods.
REPUBLISH
Our stories may be republished online or in print under Creative
Commons license CC BY-NC-ND 4.0. We ask that you edit only forstyle or to shorten, provide proper attribution and link to ourwebsite. AP and Getty images may not be republished. Please see
our republishing guidelines for use of any other photos andgraphics.
JENIFFER SOLIS
Jeniffer was born and raised in Las Vegas,
Nevada where she attended the University of
Nevada, Las Vegas before graduating in 2017 with
a B.A in Journalism and Media Studies.
Nevada Current is part of States Newsroom, the
nation’s largest state-focused nonprofit news
organization.
Sustainable Cities and Society 113 (2024) 105677
Available online 17 July 20242210-6707/Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Urban tree cover provides consistent mitigation of extreme heat in arid butnothumidcities
Peter C.Ibsena,*,Benjamin R.Crawford b,Lucila M.Corro a,Kenneth J.Bagstad a,BrandonE.McNellis c,George D.Jenerette d,Jay E.Diffendorfera
aU.S.Geological Survey,Geosciences and Environmental Change Science Center,Lakewood,CO,United States of AmericabDepartmentofGeographyandEnvironmentalSciences,University of Colorado Denver,Denver,CO,United States of AmericacUSDAForestService,Institute of Pacific Islands Forestry,Hilo,HI,United States of AmericadDepartmentofBotanyandPlantScience,University of California Riverside,Riverside,CA,United States of America
ABSTRACT
Urban land cover types influence the urban microclimates.However,recent work indicates the magnitude of land cover’s microclimate influence is affected byaridity.Moreover,this variation incoolingand warming potentials of urbanland covertypes can substantially alter the exposure of urban areas to extremeheat.Ourgoalistounderstandboththerelativeinfluencesofurbanlandcoveronlocalairtemperature,aswellas how these influences vary duringperiodsofextreme heat.Todosoweapplypredictivemachinelearningmodelstoanextensivein-situ microclimate and 1 m land cover dataset across eight U.S.cities spanning a wide ariditygradientduringtypicalandextremeheatconditions.We demonstrate how the cooling influence of tree canopy and the warming influence of buildings onmicroclimatelinearlyscaleswithregionalaridity,while the influence of turf and impervious surfaces does not.These interactions lead tree canopy to consistentlymitigatetoairtemperatureincreasesduringperiodsextremeheatinaridcities,while the influence ofurban tree canopy on extreme heat in humid regions is varied,suggesting that mitigation is possible,but tree canopy can also aggravate extreme heat or have no significant effect.
1.Introduction
Urban ecologists have studied differences between urban and ruralheatcharacteristics(commonly known as the urban heat island phe-nomena)for decades,but more recent attention has beengiven to urbanheatwithincities(Buo et al.,2023;Shandas et al.,2019;Shiflett et al.,2017).Within-city urban heat research also underpins the goal ofmitigatingextremeurbanheat,which has spurred municipalities andtheU.S.Federal Government to invest over one billion USD in urbanforestry(Garrison,2021).However,the degree to which planting treesorotherwisealteringurbanlandcovertypescanmitigatesurfaceandairtemperatureiscomplexandnotfullyunderstood.For example,regionalclimatecanmoderateurbanlandcover’s influence on urban heat (Lietal.,2019),making tree planting strategies to reduce heat potentiallymoreeffectiveinsomecitiesthanothersdependingontheregionalaridity,which drives evaporative demand and therefore transpiration(Shashua-Bar et al.,2023).Periods of extreme heat can potentiallyreduceheatmitigationprovidedbytreesinsomecases(e.g.,increasingtreedeaththroughheatstressinsomespecies),and possibly increaseheatmitigationinothers(e.g.,killing herbivorous pests or increasingtranspirationincertaintreespecies)(Ossola &Lin,2021;Winbourne
et al.,2020).As urban climate models point to more frequent andintenseperiodsofextremeheat,the complexities of responses toextremeheatarekeyuncertaintiesneedingtoberesolved.Therefore,tobestaddressurbanheatatthelocalandcontinentalscales,a compre-hensive understanding is needed of how different land cover types in-fluence within-city heat during typical summer climate and extremeheatconditionsacrossregionalclimates.Urban areas have extremely heterogeneous distributions of landcovertypes,all of which can influence urban temperatures (Cadenassoetal.,2007;Smith,et al.,2023).At neighborhood (~103 m)and microtomeso‑scales (~10�–103 m)(Barlow,2014),vegetated land covers canreducelocaltemperaturesthroughaninteractingmixofbiophysicalprocesses,such as trees’shading of surfaces and transpiring water fromleaves,but coolingpotential can vary basedon availablewaterresources(Winbourne et al.,2020).Turf,however cools air primarily throughtranspirationandismorelimitedbywateravailability,displayingthermalpropertiessimilartoimpervioussurfacesduringextendeddryperiods(Rahman et al.,2021;Smith et al.,2023).Impervious surfacesandbuiltstructuresincreasetemperaturesthroughamixofphysicalandanthropogenicproperties,for example,road and building materialspossesshighheatstoragecapacities,and buildings act as anthropogenic
*Corresponding author.E-mail address:pibsen@usgs.gov (P.C.Ibsen).
Contents lists available at ScienceDirect
Sustainable Cities and Society
fkqnj]h dkial]ca6 sss*ahoarean*_ki+hk_]pa+o_o
https://doi.org/10.1016/j.scs.2024.105677Received16February2024;Received in revised form 15 July 2024;Accepted 16 July 2024
Sustainable Cities and Society 113 (2024) 105677
2
heat sources to the urban environment (Alhazmi et al.,2022;Offerleetal.,2005).These urban land cover types also respond uniquely toregionalclimates,where vegetation will transpire more in arid climatesandlargerbuildingsmaystoremoreheatandhavegreateranthropo-genic heat fluxes in warmer weather and extreme heat (due to air con-ditioning usage)(Alhazmi et al.,2022;Ibsen et al.,2021;Mushore et al.,2017).Land cover influences local air temperature via modification ofsurface-atmosphere energy exchanges as defined by the urban energy
balance (Oke,1982)and surface radiation budget (Grimmond et al.,2010),where available net all-wave energy and additional energy inputfromanthropogenicactivity(e.g.,combustion,metabolism)is parti-tioned into sensible (QH)and latent (QE)heat fluxes and heat stored(ΔQS)in urban surface materials.Urban land cover types all contributedifferentlytotheseenergyfluxfactors,as well as tothe surface radiationbudget,where surface radiation is the sum of incoming and outgoingshortwavesolarradiation(K↓and K↑)and incoming and outgoing longwaveradiation(L↓and L↑).From meso-to neighborhood scale areas,
Fig.1.A:The eight United States citiesused in thestudy,withtheir corresponding high-resolutionland cover data inlaid.The U.S.mapdisplays themaximum vaporpressuredeficit(VPD,a metric of atmospheric aridity)in the month of August.The VPD data are modeled 30-year normals (PRISM Group,2007).B:RankedplacementofallU.S.cities with populations above 100,000 vs.mean August VPD,to display the range of the aridity gradient captured by the eight study cities.C:One study city (Portland)used as an example to display higher detail of the high-resolution land cover data,as well as the deployment of iButton sensors,stratifiedacrossagradientofurbanvegetation(displayed as the Normalized Difference Vegetation Index,NDVI).
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
3
variability in urban land cover composition creates many uncertaintiesineachlandparcel’s unique microclimate,and how the environmentrespondsduringextremeheatindifferentgeographicregions.Thesevariabilitiesarekeywhenconsideringhowexposuretoextremeheatandthesubsequenthumanhealtheffectsaredistributedwithincities.In this paper,we quantify the spatiotemporal dynamics of within-and between-city urban heat and the capability for urban land covertypestoinfluencehowairtemperatureincreasesaremitigatedoraggravatedduringperiodsofextremeheat,by synthesizing multiplehigh-resolution datasets,representative of a range of spatial scales.Weuse(1)a network of air temperature sensors representing micro-scaleconditionsdeployedacrosseightcitiesspanningagradientofregionalmeanAugustVaporPressureDeficit(VPD,a metric of atmosphericaridity)as derived from 30-year normals (PRISM Group,2007),com-bined with (2)city-specific high-resolution land cover data,(3)satellite-derived land surface temperature (representing meso-scaleconditions),and (4)regional climate parameters (representingmacro-scale conditions).We test two hypotheses –first,tree canopy andbuildingswillinfluencelocalmicroclimateinhotter,drier regional en-vironments more than turf and impervious surfaces due to a greaterthree-dimensional surface area,which will provide shade in the case oftreesandincreasedstoredheatenergyinthecaseofbuildings.Second,tree canopy and turf land covers will provide greater mitigation duringextremeheatinaridcitiesduringthedayandnightthaninhumidcitiesduetoanincreaseintranspiration-derived cooling.Collectively,tests ofthesetwohypothesesprovideaclearerunderstandingofnotonlythemechanismsbehindurbanlandcover’s cooling or warming influence onthelocalenvironmentinaregionalclimatesetting,but also how heatmitigationstrategiessuchasreplacingnon-vegetated land cover typeswithvegetation,or increasing albedo,can be enhanced during extremeheatevents.
2.Materials and methods
2.1.Study locations
To capture a continental-scale gradient in regional climate,weselectedeighturbanextentslocatedintheUnitedStates(“city”here-after),covering a spectrum of regional VPD and representing multipleecoregions(Fig.1A &B,Table 1)(Omernik &Griffith,2014).While allcities in the study are major metropolitan urban areas withpopulationslargerthan1000,000,the study extent varies by each city(Table 1).We used iButton sensors (Maxim Integrated Products,Inc.iButton Thermocron)to capture hourly air temperature at multiple lo-cations within each extent.The exact number of sensors deployed andrecovered,as well as the mean distance between each sensor is found inTable1.We placed sensors at approximately 2 m height within treeswithfullcanopytopreventbiasfromdirectradiationhittingthesensors(Terando et al.,2017).We stratified sensor distribution across a vege-tation gradient,measured using the Normalized Difference VegetationIndex(NDVI)for a cloud free day ina summer month(July –September)the year prior to deployment in each city.Only one image was used foreachcityasNDVIwasnotincludedintheanalysisbutwasonlyusedtostratifysensorplacementtoensureeachcity’s deployment captured arangeofurbanvegetationexposures(Fig.1C).While initial sensordeploymentwasbinnedintofiveNDVIcategoriesusingcity-specificJenksnaturalbreaks.We have found this method resulted in sensorscapturingarangeofurbanlandcovertypesthatcloselyresembledthetotalcitywidefractionsoflandcovercomposition(Fig.S1 and Table 1).We reducedpotential biases through removal ofoutliers that were threetimesormorethantheabsolutederivationaroundthemedianoftherecordeddayornighttimetemperatures(Leys et al.,2013).While ac-curacy of iButtons(�0.5 �C depending on the specific sensor)is coarserthanairtemperaturemonitorsusedinmeteorologicaltowers,we areabletotrade-off a decrease in accuracy for a large-scale deployment tocapturegreaterspatialdetailofdifferentlandcovertypes(Shi et al.,
2021).Dates of deployment and summary statistics are provided inTable1.Each city selected has associated high-resolution (~1 m)land coverdata.We retrieved data for Baltimore,Los Angeles,Phoenix and Port-land from the U.S.Environmental Protection Agency’s EnviroAtlas(Pilant et al.,2020),Miami from Florida International University (Gannetal.,2020),Tucson from the Pima Country Geospatial Data Portal(Pima County Geospatial Data Portal,2023),Denver from the DenverRegionalCouncilofGovernmentsdataportal(DRCOG Regional DataCatalog,2018),and Las Vegas from the Nevada Division of Forestry.Asthedatasetswereindependentlyproducedwithdifferentclassificationmethods,we focused our study on the four types of urban land covercommontoalleightcities’dataand forwhich wehad adequate coverageforintra-and inter-city comparisons:tree canopy,turf,impervioussurfaces(e.g.,roads,parking lots,sidewalks),and buildings.
2.2.Land cover influence on air temperature
We applieda 60-meter buffer surrounding each sensor and used thatareatoextractthelandcoverfractionsaroundeachsensor.A 60-mbufferhasbeenshowntohaveastrongmodelfitwhenanalyzingurbanmicroclimateandlandcoverinteractionsinregardstoheatmitigatingecosystemservices(Crum &Jenerette,2017;Ziter et al.,2019).We determined the total potential of each land cover type’s in-fluenceon airtemperatureby a linear regression ofthedaily sensor’s airtemperaturedataatdaytime(13:00–15:00 h data mean)and nighttime(01:00–03:00 h data mean)against the land cover fraction found withinthebuffer.For example,a single tree canopy daytime point in Fig.2depictstheslopecoefficientforallthesensor’s mean air temperaturerecordingswithinacityduringdaytimehoursforasingleday’s daytimeornighttimeperiodofthestudy,regressed against the tree canopypercentagewithinthebuffer.The regression slope indicates themaximumpotentialinfluenceonairtemperaturebythatlandcovertypeforthatdayspecificweatherpattern(�C/Fraction of Land Cover).Co-efficients for each day and night of sensor deployment are then plottedagainsttheregionalVPDattimeofinthatcity(calculated from anaggregateofthethreenearestairports).We included only significantslopesintheanalysis.This method has been used previously to examinethecoolingefficiencyofvegetatedlandcoversonurbanheatexposure(Du et al.,2024;Ibsen etal.,2021;Zhouet al.,2021),though wearealsofocusedonthe“warming efficiency”of land cover as well,resulting infourslopecoefficients(one for each land cover type)for each dailydaytimeandnighttimeperiod.An example of this methodology is pro-vided in Fig.S2.We further examined the relationship between land cover andsummertimeairtemperaturebyimplementingarandomforestregres-sion analysis.Land cover influences air temperature in unique waysthroughouttheyear(Manoliet al.,2020).However,because exposure tourbanheathasstrongcorrelationstohumanhealthandmentalwell-being (Barboza et al.,2021;Hondula et al.,2018;Mullins &White,2019)and as our sensor network was only deployed during summermonths,we have kept our analysis focused on summertime conditions.In addition to using land cover variables as predictors,we includeddaytimelandsurfacetemperature(LST)as a local climate variable.WeusedthemeandaytimeLSTvalueat30-meter resolution from cloud-freeLandsatscenesavailableduringthesummertimesensordeployments,providedby the U.S.Geological Survey (Specific image dates used foundinSupplementalTable1)(Dwyer etal.,2018).For Miami however,thatwerenofullycloudfreeimagesavailableduringthestudyperiod.TokeepalltheCity-Specific models the same,we used the leastcloud-covered image and masked out any cloud-covered area.We useddaytime,Landsat-derived LST for both our daytime and our nighttimemodels.However,previous work has found significant correlation be-tween daytime LST and nighttime air temperature across multiple cities(Shiflettet al.,2017),as well as strong relationships between land covertypesandnighttimeLSTwhenusingrandomforestmodels(Logan et al.,
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
4
Table 1Studycityand sensor deployment dynamics,regional (three-yearsdata of the three nearestairport meteorological towers)and local (derivedfrom sensors used in thestudy)air temperature dynamicsand aridity (VPD,vapor pressure deficit),and summaryland cover dataatthe city scale (over the entirestudyextent)and within the60mbuffersurroundingeachsensor.
City BaltimoreMD Denver CO Las Vegas NV Los Angeles CA Miami FL Phoenix AZ PortlandOR Tucson AZ
Study City andRegionDynamicsEcoregion(LevelII)EasternTemperate Forest Great Plains NorthAmericanDesert
MediterraneanCalifornia TropicalWetForest North AmericanDesert Marine WestCoastForest NorthAmericanDesertPopulation(millions)2.7 2.9 2.2 13.1 6.1 4.8 2.4 1
MeanMaximimumAugustVPD
1.66 2.94 6.32 1.76 1.49 5.71 2.69 4.6
Dates of SensorDeployment 07/11/2017-09/30/2017 07/10/2018 -09/12/2018 06/11/2018 -08/19/2018 06/23/2017-09/14/2017 07/01/2018 -09/18/2018
06/11/2017-08/15/2017 06/20/2017 -08/24/2017 06/14/2019-09/07/2019SensorsDeployed/Analyzed 100/78 90/58 90/81 100/89 88/80 100/82 100/95 90/83
Mean SensorDistance(km)0.842 0.978 1.418 0.564 1.053 0.940 0.880 0.945
Area of StudyExtent(km2)173.48 322.42 360.36 167.9 368.21 277.1 194.76 237.37
Land Cover DataSource EnvironmentalProtectionAgencyEnviroAtlas
Denver RegionalCouncilofGovernmentsLandUseLandCoverData
NevadaDivision ofForestryUrbanTreeCanopyAssessment
EnvironmentalProtectionAgencyEnviroAtlas
Miami-DadeCountyUrbanTreeCover
EnvironmentalProtectionAgencyEnviroAtlas
EnvironmentalProtectionAgencyEnviroAtlas
Pima CountyGeospatialDataPortal
TemperatureSummaryDataPeriodofRegionalClimate Jun -Sep;2016 -2018 Jul -Sep;2017 -2019 Jun -Aug;2016 -2019 Jun -Sep;2016 -2018 Jul -Sep;2017 -2029
Jun -Aug;2016 -2028 Jun -Aug;2016 -2018 Jun -Sep;2018 -2020
Median DaytimeRegionalAirTemperature(�C)
28.8 28.8 38.3 24.4 32 38.1 24.8 37.2
Median NighttimeRegionalAirTemperature(�C)
21.1 17.2 29.4 19.3 26.7 30.6 15.6 26.7
95th PercentileDaytimeRegionalAirTemperature(�C)
34.4 35 43.3 29.6 33.9 42.8 33.9 41.7
95th PercentileNighttimeRegionalAirTemperature(�C)
24.4 21.6 34.4 22.8 28.5 35 20 31.1
Median DaytimeLocalAirTemperature(�C)
28.1 30.5 40.5 28.6 31.5 40.5 27.1 40
Median NighttimeLocalAirTemperature(�C)
20.7 18.1 30.1 20.1 26.5 31 17.1 27.5
95th PercentileDaytimeLocalAirTemperature(�C)
35.1 37 46 35.7 35.1 46 36.1 44.6
95th PercentileNighttimeLocalAirTemperature(�C)
26.5 23.5 34.7 25.1 28.5 35.5 23.1 31.5
Summary LandCoverDataTreeCanopyPercentage(Citywide scale)
28.6 12.7 10.1 20.1 14.5 6.5 27.3 17.1
Turf/GrassPercentage(Citywide scale)
16.5 22.1 5.7 10.1 18.5 7.6 22.3 0.02
(continued on next page)
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
5
2020).Prior research thus suggests that including daytime LST andurbanlandcoverinanighttimemodelcanstillprovidestrongpredictionaccuracy.To represent regional climate,we retrieved the dry bulb tempera-ture,wind speed,relative humidity,and atmospheric pressure from thethreeclosestairportmeteorologicalstationstoeachstudycity,duringthethree-year period around each city’s sensor deployment,specific tothehoursofanalysis(midday –13:00–15:00 –and midnight –01:00 to03:00).This three-year window enables representative regional climate
variability during the deployment,without including data from farbeyondthestudyconditions.We created two All-City and sixteen City-Specific random forest models to model the influence of predictors ondaytimeandnighttimetemperature.The All-City model uses all theparameterdatafromtheeightcities(four land cover types,threeregionalclimateparameters,and daytime LST),and each model wastunedtodeterminetheoptimalnumberofvariablesusedateachnode.However,we maintainedconsistency across models bynot adjusting thenumberofparameterstoimprovefitasourprimaryobjectivewasto
Table 1 (continued)
City BaltimoreMD Denver CO Las Vegas NV Los Angeles CA Miami FL Phoenix AZ PortlandOR Tucson AZ
ImperviousSurfacePercentage(Citywide scale)
36.3 38.7 46.2 40 29.1 33.9 30.1 23.6
Building SurfacePercentage(Citywide scale)
16.1 16.2 18 25 17 14.6 14 13.2
Median TreeCanopyPercentage (60msurroundingsensor)
19.1 11.4 13 24.5 17.1 7.9 21.1 18.1
Median Turf/GrassPercentage (60msurroundingsensor)
5.9 15.8 6.4 8.8 14.1 6.8 18.9 1.2
MedianImperviousSurfacePercentage(60msurroundingsensor)
44.3 45 41.3 39.5 41.9 42.1 27.3 30
Median BuildingSurfacePercentage(60msurroundingsensor)
13.3 13.3 2.6 19.9 9.6 8.9 13.4 2.4
Fig.2.Modelflow diagram depictingall variables,data sources,generalizedprocessing,models,andmain outputs described in the manuscript.ModifiedfromIbsen2022(Ibsen et al.,2022).
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
6
compare results across cities using the same inputs.We divided outdatasetrandomly,allocating 70 %of the data totrain the modelwith thesensorairtemperaturevaluesastheresponsevariable,and theremaining30%of data was reserved for testing the trained model’sperformance.The 70/30 training/testing split ratio was selected basedoffpriorusageinurbanlandcoverandurbanheatmodels(Gardes etal.,2020;Liu et al.,2024;Mohammad et al.,2022).Feature importance ofparameterswasdeterminedbythepercentincreaseinmeansquareerror(%IncMSE).This metric measures how prediction accuracy changeswhenaparameterisexcluded,thus a higher %IncMSE implies greateroverallimportance,as removing that parameter would result in greatermodelerror.The combination of using sensor and land cover data inmachinelearningmodelstodeterminerelativevariableinfluenceandpredictwiderspatialtemperaturedynamicsiswellestablishedinthemodernurbanclimateliterature(Duncan et al.,2019;Lau et al.,2023;Shandas et al.,2019).Model fit is determined by the adjusted R2 of thelinearregressionbetweenthetestpredictedvaluesofthe30%and thecorrespondingobservedsensorairtemperaturevalues.We used thesametrainingandtestmethodstoproduceCity-Specific models for eachcity.The training data size was 89,046 and 92,424 observations for dayandnighttime,respectively,in the All-City Model and ranged from 7734to15,299 observations in the City-Specific Models.
2.3.Extreme heat event analysis
We derived air temperature maps of typical summer climate and anextremeheatconditioninourstudycitiesbyusingtheCity-Specificrandomforestmodelsdescribedintheprevioussection.Our “typical”summer climate parameterset representedthemeaninterquartile valuesofairtemperature,relative humidity,and wind speed over a three-yearperiodsurroundingtheyearofsensordeploymentforthatcity,and an
“extreme heat”regional climate parameter set,which uses the 95thpercentileofthesameparameters.To create city-wide values,weextractedthelandcoverfractionsandmeandaytimeLSTwithin60mgridpixelscoveringthecity.We then used these 60 m land cover frac-tions and mean daytime LST value as local area parameters in therandomforestmodel.The regional climate and local area parametersservedasatestdataset,for which we used the model trained on thesensor’s air temperature to predict air temperature for all 60 m gridpixelsineachcity.We then interpolated predicted air temperaturevaluesforeachcityusinganOrdinaryKrigingmodel.The specificsemivariogramparametersselectedbyfittingexponential,spherical,and Gaussian models to modeled air temperature results for each city,climate condition (typical,extreme heat),and time of day (daytime,nighttime).The nugget,partial sill,and range value used in the krigingwereselectedfromthesemiovargramfitwiththelowestsumofsquarederrorvalue(Table S2).We mapped results from kriging in ArcGIS Pro3.0.3 (ESRI Inc)and considered all points within a range of 600 m,acting as a blending factor to approximate wind mixing (Lonsdorf et al.,2021).We calculated land cover types’influence on air temperatureincreasesduringperiodsofextremeheatbytakingthecorrelationofthedifferencebetweenpredictedairtemperaturevaluesduringmodeledextremeheateventsandthemodeledtypicalclimatevalues,and thefractionoflandcovertypewithinthecitywide60-meter pixels,at bothdayandnighttimehours.To visualize the spatial pattern of land coverinfluenceontemperatureincreases,we used correlation coefficients foreachlandcovertype/city combination as a scaling factor for the landcoverfractionfoundineachpixel.A model flow diagram is provided inFig.2.Asweareusing correlations between landcover and air temperatureincreases,we can describe how closely related the land cover types arewithincreasesordecreasesinairtemperature,but not the effect size ofthatrelationship.We completed all statistical modeling analysis in RVersion4.2.2 (RStudio Team,2020)using the following packages(Semivariogram fitting and cross validation –package gstat;randomforest–package randomForest;extreme heat slope comparison –
package lsmeans)(Lenth,2016;Liaw &Wiener,2002;Pebesma,2004).All data used in analysis is openly available at (Ibsen et al.,2024).
3.Results
3.1.Regional drivers of urban land cover-air temperature influence
Across most cities,buildings and impervious surfaces were corre-lated with increasing air temperature during day and night,withbuildingscontributingameanmaximumwarmingpotentialof4.7 �Cand3.9 �C and impervious surfaces warming 2.6 �C and 2.4 �C duringthedayandnight,respectively.On the other hand,turf and tree canopywerecorrelatedwithdecreasingurbanairtemperature,with tree can-opy contributing a meanmaximum cooling potential of 4.2�Cand 3.4 �Candturfcooling3.2 �C and 3.0 �C during the day and night,respectively(Fig.S3).Across the continental aridity gradient,warming produced bybuildingsandthecoolingproducedbytreecanopyscaledlinearlywiththeregionalVPD.By contrast,the warming and cooling produced byimpervioussurfacesandturfremainrelativelyflatacrosstheariditygradient,especially during the day (Fig.3).In daytime hours,we foundregionalaridityexplainedthemajorityofvarianceinbothbuildingsandtreecanopy’s influence on air temperature,and a very small amount ofvarianceinturf’s air temperature influence (Buildings:R2 =0.58,p <0.001.Tree Canopy:R2 =0.61,p <0.001;Turf:R2 =0.02,p =0.03).Aside from buildings,the relationships between land cover-inducedtemperaturemoderationincreasedduringthenighttime(Buildings:R2
=0.56,p <0.001;Impervious Surface:R2 =0.22,p <0.001;TreeCanopy:R2 =0.66,p <0.001;Turf:R2 =0.17,p <0.001)(Fig.3).Theregionalinfluenceindaytimeairtemperaturemodificationcanbeinterpretedas,for each unit increase in daytime VPD,the maximumcoolingpotentialoftreecanopyincreasesby0.63 �C,and maximumwarmingpotentialofbuildingsincreasesby0.53 �C.During nighttimehourstheseVPD-driven increases to temperature modification areincreased,where tree canopy’s maximum cooling potential increased1.33 �C for every kPa increase to VPD,and buildings’maximumwarmingpotentialincreasedby1.20 �C.
3.2.Urban air temperature predictive modelling
We built anAll-City random forestmodel through thecombinationofregionalandlocalclimatedynamicsaswellaslocallandcoverchar-acteristics ofall eightstudy cities.This modelwasable toexplain mostofthevariationindaytimeandnighttimeairtemperatureattheconti-nental scale (Daytime R2 =0.96,Nighttime R2 =0.98)(Fig.S4A &C).When focusing on individual city predictions,the All-City algorithmdisplayedmorevariabilityinmodel-fit across each city,though overallmodel-fits were better during nighttime hours (Fig.S4B &D).Thesevalueswereclosetothepredictedvalueswhenbuildingsimilarmodelsoneachcityindependently(City-Specific models;Fig.S5).While ourmosthumidcity(Miami)exhibited thelowest amountof air temperaturevariabilityexplainedbyourlandcovervariables,there was no signifi-cant trend across all cities dependent on regional aridity.Overall,when evaluating the importance of variables in our All-Citymodel,we found regional and local climate characteristics were thestrongestpredictorsofdaytimetemperatures,while during nighttimeregionalclimateparameterswerethestrongestairtemperaturepre-dictors,and local climate was the weakest predictor (Fig.4A).Specif-ically,regional climate parameters of wind speed and air temperaturedisplayedhighimportanceinincreasingmodelaccuracywhenpredict-ing local air temperature variation in both our daytime and nighttimeAll-City models.In the All-City model,land cover parameters hadmoderateimportanceinbothdayandnight;though during the day,thegreenlandcovertypesoftreecanopyandturfexhibitedgreaterimportancethanthegreylandcovertypesofimpervioussurfaceandbuildings(Fig.4A).We observed more variability in parameter impor-tance when looking at individual City-Specific random forest models
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
7
(Fig.4B).Importance of land cover variables significantly increased inmorearidcitiesduringthedayandnight(Daytime:p value =0.002,adjusted R2 =0.26;Nighttime:p value =0.015,adjusted R2 =0.15;Fig.S6).
3.2.Land cover influences on extreme heat events
Through predictive modelling,we mapped air temperature duringplausibleaveragesummerdaysandextremeheatevents.This mappingallowedustocalculatetheincreaseinairtemperatureatevery60mpixelwithinourstudycitiesduringtheseextremeheateventsascomparedtoatypicalsummerclimate.The mapped outputs during thedayandnightforBaltimoreareprovidedinFig.5 and similarfigures fortheothersevencitiesareavailableinthesupplement(Figs.S7–S13).We then correlate the temperature increase during extreme heateventsbyfractionoflandcoverwithina60mgridcell.A significantpositivecorrelationindicatesthatduringheatevents,the air tempera-ture increases more rapidly as land cover type increases,while a nega-tive correlation indicates that air temperature increases less rapidly as alandcovertypeincrease.We found thatvegetated land cover fractionsinTucson,Phoenix,and Las Vegas were negatively correlated with tem-perature increase.Tree canopy,in particular,was strongly negativelycorrelatedtotemperatureincreasesduringthedayandnightinthosethreecities(Fig.6).Similarly in the two most arid cities (Las Vegas andPhoenix),the impervious surface fractions were positively correlatedwithtemperatureincrease.Unlike the arid cities,more humid citiesexhibitedagreatervariabilityinthecorrelationbetweentemperatureincreaseandlandcoverfractiondependingonlandcovertypeandtimeofday.For example,during the day in Portland,heavily treed areasexperienceagreaterincreaseindaytimeandnighttimeairtemperaturesduringheateventscomparedtoareaswithgreaterpercentagesofbuildings(Fig.6).
4.Discussion
ThroughoutEurope (Maes etal.,2021)and the United States(UnitedStateWhiteHouse,2023),major investments are being made toincreaseurbanvegetationwithafocusonheatmitigation.However,our resultsprovideclearevidencethatsimplyaddinggreennessisnotaone-size-fits-all approach to urban heat management.Finding that thecoolingeffectsofvegetatedlandcovertypesandthewarmingeffectsofbuiltstructuresaremodifiedbyregionalclimatesuggestthatthebiomechanicalandphysicalprocessesthatdriveairtemperaturemiti-gation are city-specific,so city-specific heat mitigation planning isneeded.We found that increasing tree canopy provides air-coolingbenefitsinallourstudycities(Fig.S3),though the arid cities had thelargesteffects.A mainreason for these differencesof treecanopycoolinginaridcitiesistheadditivepropertiesofshadingandevapotranspirationonbothairandsurfacetemperatures.Evapotranspiration is determinedbythelocalaridity(Sulman et al.,2016),and is the likely cause of ourresults:in cities with greater regional aridity,tree canopy providesgreaterheatmitigationduetoasubsequentincreaseinlatentheatflux(the energy used to evaporate transpired water)while also reducingsurfacetemperaturethroughshading(Winbourne et al.,2020).Interestingly,this effect isprominent intree canopy but notturf.Thisisprimarilyduetothestructuralpropertiesoftreesversusturf.Whileturfevapotranspirationisfoundtobehigherinaridcities(Grijseelsetal.,2023),it is less likely to impart cooling at a height relevant tohumans(Crum &Jenerette,2017).While tree canopy not only providescoolingclosertotheheightofanaveragehuman,but trees’size alsomeansthereisadoublebenefitofshadingandincreasingtotaltran-spiration as trees become taller.Given that water for irrigation is alimitedresource,especially in arid environments (Jenerette et al.,2011),our results also underscore how focusing on trees in greenspacesoverirrigatedturfinaridcitiescanincreasetheheatmitigation
Fig.3.Air temperature modification of urban land covers as a factor of regional Vapor Pressure Deficitacross all eight study cities during daytime (13:00–15:00)h,and nighttime (01:00–03:00)h.Each point represents the slope coefficient of a significant linear regression between the color-specified land cover and the airtemperaturerecorderdacrossanetworkofmicroclimatesensorswithinoneofthestudycities.Regression lines are color coded to a specific urban land cover type,and shading represents the standard error.Line equations,adjusted R2,and p values of the land cover regressions are provided in top left.
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
8
potential of urban green infrastructure while reducing municipal waterloss.While tree canopy can require less irrigation than turf,supplementalirrigationisstillnecessarytomaintaincurrenttreecanopyinmanyofthesearidcities.Moreover,while vegetation in cities may have theabilitytotranspireunderhigherVPDthanruralplants(Winbourneetal.,2020;Yuan et al.,2019),alleviating atmospheric drought stress,recent work in urban Sydney,Australia,found that during a two-weekheatwaveevenwell-watered trees,which did not display any hydrau-lic failure,still experienced leaf death (Marchin et al.,2022).Thedie-back was also partially dependent on the biogeographic origins oftheplantedtreespecies,where precipitation during the driest month ofthespeciesgeographicalrangewassignificantlycorrelatedwithcrowndie-back.This indicates the important role of well-trained urban for-esters in selecting the right species to withstand extreme heat,andgreaterresearchintothevulnerabilityofurbantreespeciestoheatvulnerability.
4.1.Urban land cover and the urban energy balance
We can interpret observed land cover-air temperature relations intheframeworkoftheurbansurfaceenergybalanceandradiation
budget.Leaf area index (the amount of leaf per unit ground area,LAI)from complex three-dimensional tree canopies means greater activesurfaceareaforQEfromtreeleavesrelativetoturf.There is also addi-tional cooling benefit from shading by tree canopies,which is notavailableforturf.As regional aridity increases,we found that greater cooling is asso-ciated with tree cover during both daytime and nighttime periods(Fig.3).During daytime,as regional VPD increases,QE is enhanced,attheexpenseofQHandΔQS,by greater vegetation-atmosphere moisturegradients.Overnight,in all regional climate settings,vegetated areastendtocoolfasterthanbuiltareasinpartduetoalackofQHandΔQSsourcesthatcontributetowarmernighttimetemperaturesinmoredenselybuiltareas(Oke et al.,2017).With low humidity,vegetatedareasmaycooldownevenmorerapidlythanbuilt-up areas due to lackofmoistureandcloudcoverintheoverlyingatmosphere.A drier at-mosphere reduces absorption and re-radiation of longwave energy atcity-wide scales and thus enhances differential radiative cooling ratesbetweensurfacetypes.By contrast,inhumid regions,greater shortwaveradiationreceivedatthesurfacereducesdifferentialradiativecoolingratesanddampensmicro-scale temperature variations (Oke et al.,2017),resulting in the high variability of land cover correlations withheatincreasesinhumidcities(Fig.6).
Fig.4.Model parameter importance outputs from All-City (A)and City-Specific(B)random forest models.The All-City model analysis includes all eight study citiestogetherwithinthesamemodel,with models run using daytime and nighttime data,while the City-Specific model analyzes each city seperately,with models runusingdaytimeandnighttimedata.Model inputs are grouped by type(e.g.,regional climate,local climate,land cover).Model importance is measured asthepercentincreaseinmeansquarederror,which is a measure of how much the model accuracy decreases when leaving out that variable.
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
9
As building land cover increases,greater building surface areapreferentiallychannelsenergytowardsQHandΔQSinallregionalclimatesettingsduringdry,daytime conditions.Buildings are also aanthropogenicheatsourcetotheatmosphere(Alhazmi et al.,2022).Inmorearidregions,the heating impact of buildings appears enhanced,due to greater cooling magnitude from vegetation in areas with lowbuildingandhighvegetationlandcoverfractions(Fig.3).Building andvegetationlandcoverfractionsareinverselycorrelated,and linearslopesbetweenbuildinglandcoverandairtemperatureshowpositivecorrelationsindryclimatesbecauseofenhancedcoolinginlow
building/high vegetation land cover locations.In more humid areas,thevegetationcoolingimpactismutedinlowbuilding/high vegetationlocations,resulting inflatter building land cover-airtemperature slopes.These two dynamics—trees increasing QE in arid areas and buildingsactingaslargebatteriesofheatstorage—further reinforce the findingsofLietal.(2019),which provide evidence that urban heat intensity isstronglydrivenbydifferencesinthelandcovertypes’capacity toevaporatewater(e.g.,impervious surfaces vs.vegetation)rather thanaerodynamicsofurbanboundarylayers(Li et al.,2019).Overnight,ΔQS and QF releases from buildings generally promote
Fig.5.Studycity land cover and predictive temperaturemodelling resultsfor Baltimore,MD.(A)The sampled study extent displayingthe high-resolution(1 m)landcoverdatainputtoourpredictivemodel.Mapped All-City Model results for (B)typical daytime (13:00–15:00)summer climate conditions,(C)daytime summerextremeheatconditions,(D)the difference between daytime extreme heat and the typical (E)nighttime (01:00–03:00)summer climate conditions,(F)nighttimesummerextremeheatconditions,(G)the difference between nighttime extreme heat and the typical conditions.
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
10
elevated micro-scale temperatures relative to areas dominated by otherlandcovertypesinallclimatesettings(Oke et al.,2017).Additionally,relatively flat impervious and turf surfaces have greater sky-view factorcomparedtocomplexthree-dimensional street-canyon and buildingconfigurations,and thus experience more rapid cooling due to longwaveradiationenergylosses(Oke et al.,2017).As impervious surfacesgenerallyhavehigherΔQSthanturf,we can see the steeper slopes inimpervioussurfacewarmingversusturfcoolinginFig.3.In more aridregions,this overnight cooling mechanism may be enhanced due toreducedL‾resulting from less watervapor in the overlying atmosphere.As described earlier,this likely results in more rapid cooling of lowbuilding/high vegetation areas (i.e.,steeper building-air temperatureslopes)in arid regions relative to humid regions(i.e.,flatter building-airtemperatureslopes).
4.2.Land cover types’influence on extreme heat events
We predicted extreme heat values within each study city to map thespatialvarianceinextremeairtemperatureandquantifythemitigationoraggravationofairtemperaturewarmingprovidedbyeachlandcovertype.The variability in land cover types’influence on increasing tem-perature during extreme heat in humid cities shows that certain com-munities are at added risk during extreme heat scenarios.For example,the 2021 heat wave that broke records across northwestern NorthAmericaappearstohavebeenananomalousevent(Thompson et al.,2022),yet recent work points to regional humidity as having exacer-bated its associated atmospheric conditions,resulting in the extremeheatatthesurfacethatlingeredfordays(Schumacher et al.,2022).
Moreover,our finding of tree canopy providing greater heat mitigationbolstersrecentworkfindingthatwhencontrollingforregionalclimate,more temperate mesic cities experience greater increases in heat expo-sure during heat waves compared to hot arid cities (Hu et al.,2023).Our data show that vegetated surfaces are more associated withincreasingairtemperatureduringextremeheateventsinthenorth-western City of Portland,potentially due to the high humidity anddensertreecanopyresultinginincreasedabsorptionoflongwaveen-ergy.These results underscore that land cover conversions from grey togreeninfrastructureareonlyapartofanurbanheat-mitigation toolkit.In more temperate cities where extreme heat is less common,fewerhouseholdsarepreparedwithairconditioningtodealwithextremeheat,and those that have air conditioning are predominantly higher-income (Romitti et al.,2022).Our results reflect the recent trend ofmoretemperatecitiesinhigherlatitudesbuildingcoolingcenterstoprotectunderservedpopulationsfromextremeheat(Kim et al.,2021).Results shown in Fig.6 display how land cover either aggravates ormitigatesincreasesinairtemperatureduringextremeheat,not how alandcoveriscorrelatedwithairtemperaturealone.Notably,some variability in the correlation between land cover andextremeheat-derived air temperature increases found in humid citiesmayalsobeafunctionofotherregionalandlocaldynamicsnotincludedinourmodels.For example,we found no clear pattern in buildings’influence on extreme heat-derived temperature increase during day ornight,which is counterintuitive to sensor data that showed increases inbuilding-derived warming in arid climates.While buildings do increasetheQHinconcordancewiththeirheight,large buildings also shadesurfacesandalterwindflowpatternswithinthecity(Alhazmi et al.,
Fig.6.Pearson correlation coefficients for each urban land cover type and air temperature increases occurring during modeled days and nights of extreme heat.Correlation coefficientsfromthe relationship between land cover fraction andtheamount airtemperature increased duringmodeledextreme heat periods are plottedagainsttheregionalmeansummertimevaporpressuredeficitforeachstudycityduringdaytimeandnighttimehours.Negativevalues thus imply that the land covertypeprovidesheatmitigationduringextremeheatevents,while positivevalues imply that the land cover type aggravates heat during extreme events.Non-significantcorrelationsaregreyedout.Significance determined at p �0.05.
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
11
2022;Tian et al.,2023),and wind speed was a very strong modelparameterinbothourdayandnightpredictivemodels(Fig.4A).Inrecentyears,LIDAR data have become available for more cities,whichhasbeenusedtomodeltheeffectsofshadeinurbanheatmodels(Buoetal.,2023),and in some cases,the shade cast by buildings reduces thesurfacetemperatureincreasescausedbybuildingenvelope(area andheight)by approximately 50 %,with variability also depending onbuildingaspect(Park et al.,2021).Recent studies that select cities withLIDARdataareabletoincludebuildingheight,tree canopy height aswellaskSkyViewFactor(a metric of potential shading)in their heatwavemodels(Hu et al.,2023).As these data sources become morecommon,such as the recent application of the U.S.Geological Survey(USGS)3D Elevation Program (Snyder,2012)to model building heightinmultipleUnitedStatescitiestheywillproveusefultofurtherresolvingthecomplexitiesoftheurbanenergybalances,adding greater nuance tothemodelsofurbanairtemperatures.Our air temperature models suggest that maximizing the benefits ofheatmitigationecosystemservicesfromurbanlandcoverchange(i.e.,tree planting)will largely depend on the regional climate.Replacingbuildingsandimpervioussurfaceswithtreecanopyinaridtosemi-aridcitiescouldprovideboththelargestheatmitigatingservicesandaddthemostresistancetoextremeheattargetedtoareaswherethecurrentlandcoverismoreassociatedwithaggravatingheatduringextremeheatdays(Fig.5D &G).However,tree selection for heat and drought toleranceandwaterneedsareimportantconsiderationsandtradeoffs.While not afocusinthisstudy,heat could also be mitigated though increasing thealbedoofimpervioussurfaces(Smith et al.,2023).Increasing imper-vious surfaces’albedo could be especially useful in humid cities whereimpervioussurfacewarminghasanequalorstrongereffectsizecomparedtotree-derived cooling,such as inMiami or Baltimore,aswellaspotentiallydampeningtheextremeheataggravatingaspectsofimpervioussurfaces.In othercities,maps of increases intemperaturecorrelated with landcovercanprovideguidancefordeterminingareasofriskduringextremeheat.These areas are not necessarily the hottest in the city,but they areareaswhichwillundergothelargeschangeintemperatures.As interestinincreasingheatmitigationprojectsincitiesgrows,and as this interestaimstodirecton-the-ground mitigation activities to populations withthegreatestneed,these results can help guide future urban planning.Moreover,as more cities produce high-resolution land cover and airtemperaturedata,these models can help guide the development ofadditionalhigh-resolution models showing how vegetation and the builtenvironmentaffectwithin-city temperatures across the globe.
4.3.Limitations of interpretation
In interpreting our results,we note two main limitations that canguidefutureresearch.First,we used linear regressions of each landcoverandsensor-recorded air temperature and extracted anysignificantslopeofthatregressiontorepresentthemagnitudeofland-covermoderation.While recent studies have shown land cover classes likeimpervioussurfacehavealinearrelationshipwithairtemperature,others like tree canopy can have a non-linear relationship,where athresholdamountoftreecanopyisrequiredbeforetemperaturebe-comes modified (Ziter et al.,2019).Conversely,Alonzo et al.,2021displayedlineareffectsoftreecanopyonaircooling,and non-linearwarmingcomingfromimpervioussurfaces(Alonzo et al.,2021).Therelationshipcomplexitiesatthescaleatwhichtreecanopydensityre-duces air temperature deserve further analysis,especially whenconsideringhowdifferenttreespeciesmayalsosignificantlyaffecttemperaturemitigation(Rahmanet al.,2020).While these relationshipsarecomplex,we ultimately used linear relationships for all land covertypes.Our research aim was to compare land cover types’effects on airtemperatureacrossandwithincities,and maintaining a standardizedfunctionalformofanalysishelpedusachievethatgoal.Secondly,while the choice of a 60 m scale of our analysis has been
corroborated as a suitable spatial scale to examine the effects of urbanlandcoverontemperatures,our study does not expand into furtherscalesofanalysis.Alonzo et al.(2021)found a similar spatial scale (90m)having the strongest model fit when analyzing the effects of treecanopyandimpervioussurfaceonafternoonairtemperatureanomalies.However,in their study the model fit for evening anomalies was stron-gest at a 200 m scale.To keep our daytime and nighttime results com-parable we have kept the spatial scale of analysis the same for both.AdifferentspatialscaleofanalysisthoughmayaddexplanationtowhyinPortlandturfhasawarmingeffectonairtemperature(Fig.S3).Thiseffectdoesnotmatchwiththeothercitiesinthestudy,nor to the currentliteratureonturforgrass’s influence on air temperature.A futuremulti-city analysis of analytical spatial scale variability on land cover’sinfluenceonairtemperaturecouldproveusefulinexplainingthisfinding.
5.Conclusions
Our study investigated how ubiquitous urban land cover types in-fluence local air temperatures,as well as how those land cover typesmitigateoraggravateincreasesinairtemperatureduringperiodsofextremeheat.We found overwhelmingly that the cooling effects of treecanopyandthewarmingeffectsofbuildingdensityincreasewithregionalaridity,while the more “two-dimensional”land covers of grassandimpervioussurfacesprovidegenerallyconsistentcoolingandwarming,respectively.Tree canopy does mitigate heat inall study cities,but because the extent of that mitigation is dependent on regionalaridity,irrigation is crucial for maintaining those heat mitigating ser-vices.Our study also displayed how regional aridity not only increasesthecoolingpotentialoftreecanopy,but also that tree canopy canmitigateincreasesinalreadywarmtemperaturesinaridcitiesduringperiodsofextremeheat.However,these consistent arid-city patterns ofhowlandcovermitigatesoraggravatesextremeheatfoundbreakdowninhumidcities.These results imply how in more humid cities,the landcovereffectsonextremeheatarecomplex,and it is important toconsidercity-specificcomplexity when planning urban land conversionstocombaturbanheatexposure.
Disclaimer
Any use of trade,firm,or product names is for descriptive purposesonlyanddoesnotimplyendorsementbytheU.S.Government.
CRediT authorship contribution statement
Peter C.Ibsen:Writing –review &editing,Writing –original draft,Visualization,Validation,Project administration,Methodology,Inves-tigation,Formal analysis,Data curation.Benjamin R.Crawford:Writing –review &editing,Writing –original draft,Conceptualization.Lucila M.Corro:Writing –review &editing,Methodology,Formalanalysis,Data curation.Kenneth J.Bagstad:Writing –review &edit-ing,Conceptualization.Brandon E.McNellis:Writing –review &editing,Methodology,Conceptualization.George D.Jenerette:Writing
–review &editing,Resources,Methodology,Conceptualization.Jay E.Diffendorfer:Writing –review &editing,Supervision,Resources,Investigation,Conceptualization.
Declaration of competing interest
The authors declare the following financial interests/personal re-lationships which may be considered as potential competing interests:Peter Ibsen reports financial support was provided by US GeologicalSurveyClimateResearchandDevelopmentProgram.Peter IbsenreportsfinancialsupportwasprovidedbyUSGeologicalSurvey-CommunityforDataIntegration.Peter Ibsen reports financial support was providedbyNationalScienceFoundation.Jay E.Diffendorfer reports financial
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
12
support was provided by US Geological Survey Climate Research andDevelopmentProgram.KennethJ.Bagstadreports financialsupport wasprovidedbyUSGeologicalSurveyLandChangeScienceProgram.G.Darrel Jenerette reports financial support was provided by NationalScienceFoundation.Peter Ibsen reports a relationship with USGeologicalSurveythatincludes:employment.Kenneth Bagstad reportsarelationshipwithUSGeologicalSurveythatincludes:employment.Jay Diffendorfer reports a relationship with US Geological Survey thatincludes:employment.Lucila Corro reports a relationship with USGeologicalSurveythatincludes:employment.If there are otherauthors,they declare that they have no known competing financial interests orpersonalrelationshipsthatcouldhaveappearedtoinfluencetheworkreportedinthispaper.
Data availability
Data used intheanalysis andall tabular data canbeaccessedthroughUSGSScienceBaseathttps://www.sciencebase.gov/catalog/item/664fb9c9d34e702fe8748151.
Acknowledgments
We acknowledge andthankthe UnitedStates Forest Service’s DenverUrbanFieldStation(DUFS)as collaborativeurban research groupwherepreliminaryresultswerefirstsharedanddiscussed.Jeremy Havens oftheUSGSassistedwithimagecreation.
Supplementary materials
Supplementary material associated with this article can be found,intheonlineversion,at doi:10.1016/j.scs.2024.105677.
References
Alhazmi,M.,Sailor,D.J.,&Anand,J.(2022).A new perspective for understandingactualanthropogenicheatemissionsfrombuildings.Energy and Buildings,258,Article 111860.https://doi.org/10.1016/j.enbuild.2022.111860Alonzo,M.,Baker,M.E.,Gao,Y.,&Shandas,V.(2021).Spatial configurationandtime ofdayimpactsthemagnitudeofurbantreecanopycooling.Environmental ResearchLetters.https://doi.org/10.1088/1748-9326/ac12f2Barboza,E.P.,Cirach,M.,Khomenko,S.,Iungman,T.,Mueller,N.,Barrera-G´omez,J.,et al.(2021).Green space and mortality in European cities:A health impactassessmentstudy.The Lancet Planetary Health,5,e718–e730.https://doi.org/10.1016/S2542-5196(21)00229-1Barlow,J.F.(2014).Progress in observing and modelling the urban boundary layer.Urban Climate,10,216–240.https://doi.org/10.1016/j.uclim.2014.03.011Buo,I.,Sagris,V.,Jaagus,J.,&Middel,A.(2023).High-resolution thermal exposure andshademapsforcoolcorridorplanning.Sustainable Cities and Society,93,Article104499.https://doi.org/10.1016/j.scs.2023.104499Cadenasso,M.L.,Pickett,S.T.A.,&Schwarz,K.(2007).Spatial heterogeneity in urbanecosystems:Reconceptualizing land cover and a framework for classification.Frontiers in Ecology and the Environment,5,80–88.https://doi.org/10.1890/1540-9295(2007)5[80:SHIUER]2.0.CO;2Crum,S.M.,&Jenerette,G.D.(2017).Microclimatevariation amongurbanlandcovers:The importance of vertical and horizontal structure in air and land surfacetemperaturerelationships.Journal of Applied Meteorology and Climatology,56,2531–2543.https://doi.org/10.1175/JAMC-D-17-0054.1DRCOGRegionalDataCatalog,(2018).Land use land cover pilotproject section12 2018[WWW document].URL data.drcog.org.Du,M.,Li,N.,Hu,T.,Yang,Q.,Chakraborty,T.,Venter,Z.,et al.(2024).Daytimecoolingefficienciesofurbantreesderivedfromlandsurfacetemperaturearemuchhigherthanthoseforairtemperature.Environmental Research Letters,19,Article044037.https://doi.org/10.1088/1748-9326/ad30a3Duncan,J.M.A.,Boruff,B.,Saunders,A.,Sun,Q.,Hurley,J.,&Amati,M.(2019).Turning down the heat:An enhanced understanding of the relationship betweenurbanvegetationandsurfacetemperatureatthecityscale.Science of the TotalEnvironment,656,118–128.https://doi.org/10.1016/j.scitotenv.2018.11.223Dwyer,J.L.,Roy,D.P.,Sauer,B.,Jenkerson,C.B.,Zhang,H.K.,&Lymburner,L.(2018).Analysis ready data:enabling analysis of the landsat archive.Remote Sensing,10,1363.https://doi.org/10.3390/rs10091363GannD.,Benjamin A.,&Hochmair H.(2020).Miami-dade county urban tree cover2014.10.34703/GZX1-9V95/Q0RWHE.Gardes,T.,Schoetter,R.,Hidalgo,J.,Long,N.,Marqu`es,E.,&Masson,V.(2020).Statistical prediction of the nocturnal urban heat island intensity based on urban
morphology and geographical factors -An investigation based on numerical modelresultsforalargeensembleofFrenchcities.Science of The Total Environment,737,Article 139253.https://doi.org/10.1016/j.scitotenv.2020.139253Garrison,J.D.(2021).Environmental justice in theory and practice:Measuring theequityoutcomesofLosAngelesandNewYork’s “milliontrees”campaigns.Journal ofPlanningEducationandResearch,41,6–17.https://doi.org/10.1177/0739456X18772072Grijseels,N.H.,Litvak,E.,Avolio,M.L.,Bratt,A.R.,Cavender-Bares,J.,Groffman,P.M.,et al.(2023).Evapotranspiration of residentiallawns acrossthe United States.WaterResourcesResearch,59,Article e2022WR032893.https://doi.org/10.1029/2022WR032893Grimmond,C.S.B.,Blackett,M.,Best,M.J.,Barlow,J.,Baik,J.J.,Belcher,S.E.,et al.(2010).The international urban energy balance models comparison project:Firstresultsfromphase1.Journalof AppliedMeteorology and Climatology,49,1268–1292.https://doi.org/10.1175/2010JAMC2354.1Hondula,D.M.,Davis,R.E.,&Georgescu,M.(2018).Clarifyingtheconnections betweengreenspace,urban climate,and heat-related mortality.American Journal of PublicHealth,108,S62–S63.https://doi.org/10.2105/AJPH.2017.304295Hu,J.,Zhou,Y.,Yang,Y.,Chen,G.,Chen,W.,&Hejazi,M.(2023).Multi-cityassessmentsofhumanexposuretoextremeheatduringheatwavesintheUnitedStates.RemoteSensingofEnvironment,295,Article 113700.https://doi.org/10.1016/j.rse.2023.113700Ibsen,P.C.,Borowy,D.,Dell,T.,Greydanus,H.,Gupta,N.,Hondula,D.M.,et al.(2021).Greater aridity increases the magnitude of urban nighttime vegetation-derived aircooling.Environmental Research Letters,16,Article 034011.https://doi.org/10.1088/1748-9326/abdf8aIbsen,P.C.,Crawford,B.R.,Corro,L.M.,Bagstad,K.J.,McNellis,B.E.,Jenerette,G.D.,et al.(2024).Urbantree cover provides consistentmitigationof extreme heat in aridbutnothumidcities-data release.U.S Geological Survey Data Release.https://doi.org/10.5066/P1LIKCO3Ibsen,P.C.,Jenerette,G.D.,Dell,T.,Bagstad,K.J.,&Diffendorfer,J.E.(2022).Urbanlandcoverdifferentiallydrivesdayandnighttimeairtemperatureacrossasemi-aridcity.Science of TheTotal Environment,829,Article154589.https://doi.org/10.1016/j.scitotenv.2022.154589Jenerette,G.D.,Harlan,S.L.,Stefanov,W.L.,&Martin,C.A.(2011).Ecosystemservicesandurbanheatriskcapemoderation:Water,green spaces,and social inequality inPhoenix,USA.Ecological Applications,21,2637–2651.https://doi.org/10.1890/10-1493.1Kim,K.,Jung,J.,Schollaert,C.,&Spector,J.T.(2021).A comparative assessment ofcoolingcenterpreparednessacrosstwenty-five U.S.Cities.IJERPH,18,4801.https://doi.org/10.3390/ijerph18094801Lau,T.-K.,Chen,Y.-C.,&Lin,T.-P.(2023).Application of local climate zonescombinedwithmachinelearningtopredicttheimpactofurbanstructurepatternsonthermalenvironment.Urban Climate,52,Article 101731.https://doi.org/10.1016/j.uclim.2023.101731Lenth,R.V.(2016).Least-Squares Means:The R Package lsmeans.Journal of StatisticalSoftware,69,1–33.https://doi.org/10.18637/jss.v069.i01Leys,C.,Ley,C.,Klein,O.,Bernard,P.,&Licata,L.(2013).Detecting outliers:Do not usestandarddeviationaroundthemean,use absolute deviation around the median.Journal of Experimental Social Psychology,49,764–766.https://doi.org/10.1016/j.jesp.2013.03.013Li,D.,Liao,W.,Rigden,A.J.,Liu,X.,Wang,D.,Malyshev,S.,et al.(2019).Urban heatisland:Aerodynamics orimperviousness?ScienceAdvances,5,eaau4299.https://doi.org/10.1126/sciadv.aau4299LiawA.,&Wiener M.(2002).Classification and regression by random forest.R News 2,18–22.Liu,Y.,An,Z.,&Ming,Y.(2024).Simulating influences of land use/land covercompositionandconfigurationonurbanheatislandusingmachinelearning.Sustainable Cities and Society,108,Article 105482.https://doi.org/10.1016/j.scs.2024.105482Logan,T.M.,Zaitchik,B.,Guikema,S.,&Nisbet,A.(2020).Nightandday:Theinfluenceandrelativeimportanceofurbancharacteristicsonremotelysensedlandsurfacetemperature.Remote Sensing of Environment,247,Article 111861.https://doi.org/10.1016/j.rse.2020.111861Lonsdorf,E.V.,Nootenboom,C.,Janke,B.,&Horgan,B.P.(2021).Assessing urbanecosystemservicesprovidedbygreeninfrastructure:Golf courses in theMinneapolis-St.Paul metro area.Landscape andUrban Planning,208,Article 104022.https://doi.org/10.1016/j.landurbplan.2020.104022MaesJ.,Quaglia A.P.,Pereira A.G.,Tokarski M.,Zulian G.,Marando F.et al.(2021).BiodiverCities:A roadmap to enhance the biodiversity and green infrastructure ofEuropeancitiesby2030:Progress reportPublications Office,EuropeanCommission.Joint Research Centre.,LU.Manoli,G.,Fatichi,S.,Bou-Zeid,E.,&Katul,G.G.(2020).Seasonal hysteresis of surfaceurbanheatislands.Proceedings of theNational AcademyofSciences of theUnited StatesofAmerica,117,7082–7089.https://doi.org/10.1073/pnas.1917554117Marchin,R.M.,Esperon-Rodriguez,M.,Tjoelker,M.G.,&Ellsworth,D.S.(2022).Crowndiebackandmortalityofurbantreeslinkedtoheatwavesduringextremedrought.Science of The Total Environment,850,Article 157915.https://doi.org/10.1016/j.scitotenv.2022.157915Mohammad,P.,Goswami,A.,Chauhan,S.,&Nayak,S.(2022).Machine learningalgorithmbasedpredictionoflanduselandcoverandlandsurfacetemperaturechangestocharacterizethesurfaceurbanheatislandphenomenaoverAhmedabadcity,India.Urban Climate,42,Article 101116.https://doi.org/10.1016/j.uclim.2022.101116
P.C.Ibsen et al.
Sustainable Cities and Society 113 (2024) 105677
13
Mullins,J.T.,&White,C.(2019).Temperature and mental health:Evidence from thespectrumofmentalhealthoutcomes.Journal of Health Economics,68,Article102240.https://doi.org/10.1016/j.jhealeco.2019.102240Mushore,T.D.,Odindi,J.,Dube,T.,&Mutanga,O.(2017).Understanding therelationshipbetweenurbanoutdoortemperaturesandindoorair-conditioningenergydemandinZimbabwe.Sustainable Citiesand Society,34,97–108.https://doi.org/10.1016/j.scs.2017.06.007Offerle,B.,Grimmond,C.S.B.,&Fortuniak,K.(2005).Heat storage andanthropogenicheatfluxinrelationtotheenergybalanceofacentralEuropeancitycentre.International Journal of Climatology,25,1405–1419.https://doi.org/10.1002/joc.1198Oke,T.R.(1982).The energetic basis of the urban heat island.Quarterly Journal of theRoyalMeteorologicalSociety,108,1–24.https://doi.org/10.1002/qj.49710845502Oke,T.R.,Mills,G.,Christen,A.,&Voogt,J.A.(2017).Urban climates.Cambridge:Cambridge University Press.https://doi.org/10.1017/9781139016476Omernik,J.M.,&Griffith,G.E.(2014).Ecoregions of the Conterminous United States:Evolution of a Hierarchical Spatial Framework.Environmental Management,54,1249–1266.https://doi.org/10.1007/s00267-014-0364-1Ossola,A.,&Lin,B.B.(2021).Making nature-based solutions climate-ready forthe 50�Cworld.Environmental Science &Policy,123,151–159.https://doi.org/10.1016/j.envsci.2021.05.026Park,Y.,Guldmann,J.-M.,&Liu,D.(2021).Impacts of tree and building shades on theurbanheatisland:Combining remote sensing,3D digital city and spatial regressionapproaches.Computers,Environment and Urban Systems,88,Article 101655.https://doi.org/10.1016/j.compenvurbsys.2021.101655Pebesma,E.J.(2004).Multivariable geostatistics in S:The gstat package.Computers andGeosciences,30,683–691.https://doi.org/10.1016/j.cageo.2004.03.012Pilant,A.,Endres,K.,Rosenbaum,D.,&Gundersen,G.(2020).US EPA enviroatlasmeter-scale urban land cover (MULC):1-m pixel land cover class definitions andguidance.Remote Sensing,12,1909.https://doi.org/10.3390/rs12121909Pimacountygeospatialdataportal[WWWdocument],(2023).URLhttps://gisopendata.pima.gov/.PRISM Group,(2007).PRISM climate group[WWWdocument].URL http://www.prism.oregonstate.edu/.Rahman,M.A.,Dervishi,V.,Moser-Reischl,A.,Ludwig,F.,Pretzsch,H.,R¨otzer,T.,etal.(2021).Comparative analysis of shade and underlying surfaces on cooling effect.Urban Forestry and Urban Greening,63.https://doi.org/10.1016/j.ufug.2021.127223Rahman,M.A.,Hartmann,C.,Moser-Reischl,A.,von Strachwitz,M.F.,Paeth,H.,Pretzsch,H.,et al.(2020).Tree cooling effects and human thermal comfort undercontrastingspeciesandsites.Agricultural and Forest Meteorology,287,Article107947.https://doi.org/10.1016/j.agrformet.2020.107947Romitti,Y.,Sue Wing,I.,Spangler,K.R.,&Wellenius,G.A.(2022).Inequality in theavailabilityofresidentialairconditioningacross115USmetropolitanareas.PNASNexus,1,pgac210.https://doi.org/10.1093/pnasnexus/pgac210Schumacher,D.L.,Hauser,M.,&Seneviratne,S.I.(2022).Drivers and mechanisms ofthe2021pacificnorthwestheatwave.Earth’s Future,10.https://doi.org/10.1029/2022EF002967Shandas,V.,Voelkel,J.,Williams,J.,&Hoffman,J.(2019).Integrating satellite andgroundmeasurementsforpredictinglocationsofextremeurbanheat.Climate,7,1–13.https://doi.org/10.3390/cli7010005
Shashua-Bar,L.,Rahman,M.A.,Moser-Reischl,A.,Peeters,A.,Franceschi,E.,Pretzsch,H.,et al.(2023).Do urban tree hydraulics limit their transpirationalcooling?A comparisonbetween temperate and hot arid climates.Urban Climate,49,Article 101554.https://doi.org/10.1016/j.uclim.2023.101554Shi,R.,Hobbs,B.F.,Zaitchik,B.F.,Waugh,D.W.,Scott,A.A.,&Zhang,Y.(2021).Monitoring intra-urban temperature with dense sensor networks:Fixed or mobile?An empirical study in Baltimore,MD.Urban Climate,39,Article100979.https://doi.org/10.1016/j.uclim.2021.100979Shiflett,S.A.,Liang,L.L.,Crum,S.M.,Feyisa,G.L.,Wang,J.,&Jenerette,G.D.(2017).Variation in the urban vegetation,surface temperature,air temperature nexus.Science of The Total Environment,579,495–505.https://doi.org/10.1016/j.scitotenv.2016.11.069Smith,I.A.,Fabian,M.P.,&Hutyra,L.R.(2023).Urbangreen space and albedoimpactsonsurfacetemperatureacrosssevenUnitedStatescities.Science of The TotalEnvironment,857,Article 159663.https://doi.org/10.1016/j.scitotenv.2022.159663SnyderG.I.(.2012).The 3D Elevation Program:Summary of program direction (ReportNo.2012–3089.Reston,VA:Fact Sheet.10.3133/fs20123089.Sulman,B.N.,Roman,D.T.,Yi,K.,Wang,L.,Phillips,R.P.,&Novick,K.A.(2016).Highatmosphericdemandforwatercanlimitforestcarbonuptakeandtranspirationasseverelyasdrysoil.Geophysical Research Letters,43,9686–9695.https://doi.org/10.1002/2016GL069416Terando,A.J.,Youngsteadt,E.,Meineke,E.K.,&Prado,S.G.(2017).Ad hocinstrumentationmethodsinecologicalstudiesproducehighlybiasedtemperaturemeasurements.Ecology and Evolution,7,9890–9904.https://doi.org/10.1002/ece3.3499Thompson,V.,Kennedy-Asser,A.T.,Vosper,E.,Lo,Y.T.E.,Huntingford,C.,Andrews,O.,et al.(2022).The 2021 western North America heat wave among themostextremeeventseverrecordedglobally.Science Advances,8,1–11.https://doi.org/10.1126/sciadv.abm6860Tian,W.,Yang,Y.,Wang,L.,Zong,L.,Zhang,Y.,&Liu,D.(2023).Role of localclimatezonesandurbanventilationincanopyurbanheatisland–heatwave interaction inNanjingmegacity,China.Urban Climate,49,Article 101474.https://doi.org/10.1016/j.uclim.2023.101474UnitedStateWhiteHouse,(2023).Biden administration mobilizes to protect workersandcommunitiesfromextremeheat[Fact sheet].Winbourne,J.B.,Jones,T.S.,Garvey,S.M.,Harrison,J.L.,Wang,L.,Li,D.,et al.(2020).Tree transpiration and urban temperatures:Current understanding,implications,and future research directions.Bioscience,70,576–588.https://doi.org/10.1093/biosci/biaa055Yuan,W.,Zheng,Y.,Piao,S.,Ciais,P.,Lombardozzi,D.,Wang,Y.,et al.(2019).Increased atmospheric vapor pressure deficit reduces global vegetation growth.Science Advances,5,1–14.https://doi.org/10.1126/sciadv.aax1396Zhou,W.,Huang,G.,Pickett,S.T.A.,Wang,J.,Cadenasso,M.L.,McPhearson,T.,et al.(2021).Urban tree canopy has greater cooling effects in socially vulnerablecommunitiesintheUS.One Earth,4,1764–1775.https://doi.org/10.1016/j.oneear.2021.11.010Ziter,C.D.,Pedersen,E.J.,Kucharik,C.J.,&Turner,M.G.(2019).Scale-dependentinteractionsbetweentreecanopycoverandimpervioussurfacesreducedaytimeurbanheatduringsummer.Proceedings of the National Academy of Sciences of theUnitedStatesofAmerica,116,7575–7580.https://doi.org/10.1073/pnas.1817561116
P.C.Ibsen et al.
Trees in cities are beyond shady | U.S. Geological Survey
https://www.usgs.gov/news/national-news-release/trees-cities-are-beyond-shady[11/4/2024 12:17:28 PM]
An official website of the United States governmentHere's how you know
By Communications and Publishing
October 21, 2024
DENVER — Hotter areas can actually be the biggest winners when it comes to
the difference a tree can make when temperatures are sizzling.
According to newly published U.S. Geological Survey research conducted in 8 large cities coast to coast,
urban trees in hot and dry cities can amplify the cooling of local air temperature.
The multi-year study was conducted in Baltimore, Los Angeles, Phoenix, Portland, Miami, Tucson, DenverWas this page helpful?
Trees in cities are beyond shady | U.S. Geological Survey
https://www.usgs.gov/news/national-news-release/trees-cities-are-beyond-shady[11/4/2024 12:17:28 PM]
and Las Vegas. Scientists placed 80-100 sensors on trees in each city and measured hourly air
temperatures for three months during the summers of 2016-2019.
“All trees have a cooling effect, but trees in hot, dry areas can have a greater impact than in humid cities,”
said Peter Ibsen, USGS research ecologist. “Trees in areas like Las Vegas, Phoenix and Tucson are
particularly effective at reducing heat.”
Tree canopy is important in urban environments as it has implications for city planning, public health, and
climate resilience.
The team found that trees have a more pronounced cooling effect in hotter and drier regions, a contrast to
buildings, which tend to have a warming effect in hotter and drier regions. Though in more humid cities
like Baltimore, Portland, and Miami, tree cooling is stronger than warming coming from impervious
surfaces, which highlights the potential for increasing local air temperature reductions by replacing
impenetrable surfaces with tree canopy.
Trees reduce heat in all studied cities, but their effectiveness was contingent on local water availability.
Irrigation is crucial for maintaining trees’ cooling effects in all studied areas.
Other surfaces had temperature changes more consistently across all regions. Flat surfaces like grass had a
cooling effect consistently across the cities in the study, while paved areas had a warming effect
consistently across the study.
The study, Urban tree cover provides consistent mitigation of extreme heat in arid but not humid cities
appears in the Oct.15 edition of Sustainable Cities and Society.
Trees in cities are beyond shady | U.S. Geological Survey
https://www.usgs.gov/news/national-news-release/trees-cities-are-beyond-shady[11/4/2024 12:17:28 PM]
Sources/Usage: Public Domain. View Media Details
Trees in cities are beyond shady | U.S. Geological Survey
https://www.usgs.gov/news/national-news-release/trees-cities-are-beyond-shady[11/4/2024 12:17:28 PM]
No FEAR Act
USA.gov
Vulnerability Disclosure Policy
U.S. Geological Survey
U.S. Department of the Interior
Contact USGS
1-888-392-8545
answers.usgs.gov