Urban Climate and Smart City Development

Jiachuan Yang 2020 September CONTENTS

 Background and Motivation  Experimental Investigation  Urban Canopy Model  Smart City Development  Future Work OngoingIntro Urbanization

1900 2008 2050 1.6 bill 6.7 bill 9.7 bill 0.2 bill 3.3 bill 6.8 bill

Source: http://www.museumofthecity.org/project/urban-air-pollution-in-chinese-cities/ https://agnux.wordpress.com/2009/06/22/ecofasa-turns-waste-to-biodiesel-using-bacteria/ 1/47 http://environment.nationalgeographic.com/environment/photos/urban-threats/ UrbanIntro Heat Island (UHI)

 From 1979-2003, excessive heat exposure causes more deaths than hurricanes, lightning, tornadoes, floods, and earthquakes in U.S. (Center for Disease Control and Prevention, 2006)

http://environment.nationalgeographic.com/environment/photos/urban-threats/ 2/47 http://www.mdpi.com/2071-1050/8/8/706 WaterIntro-Energy-Climate Feedback

Greenhouse Global gas emission warming Climate Increased Precipitation aridity pattern

Increased Increased UHI energy use water demand

Energy Water

More heat Less release vegetation

3/47 UrbanIntro Heat Mitigation

Technology Urban heat island Smart cities Policy  Large heat storage in  Novel engineering engineering materials material  Reduced evaporative  Urban green cooling due to small landscape vegetative cover  Built-up landscape traps  Resilient urban form radiation and inhibits design advective cooling  Waste heat released from  Green and renewable anthropogenic activities energy, efficient energy use

4/47 ResearchIntro Question

How to manage the water-energy-climate nexus to develop smart cities under global change?

 What needs to be done? Investigate the impact of potential adaptation/mitigation technology and policy on complex urban water-energy-climate nexus

 How? A synthesis of experimental and numerical approaches

Scale issue Complex processes

5/47 CONTENTS

 Background and Motivation  Experimental Investigation  Urban Canopy Model  Smart City Development  Future Work SensingIntro Engineering Materials

Sandstone Khaki Khaki Down to Earth White Down to Earth Sun baked clay Terracotta San Diego buff

Fawn Sandstone White

6/47 Omidvar, Song, Yang et al. Water Resour. Res. 2018 SensingIntro the Campus

Wireless Sensor Network

7/47 SensingIntro the City

Mobile Urban Sensing Technologies (MUST) Led by Maider Llaguno-Munitxa

8/47 HeterogeneousIntro Urban Environment

Spatiotemporal temperature variability

9/47 https://www.nasa.gov/centers/goddard/news/topstory/2005/nyc_heatisland.html SensingIntro the City

How many stations (mobile/fixed) do we need?

Spatial

42880 grids (500 m x 500 m)

Temporal

1440 time (30-min interval)

61,747,200

New York City 10/47 MonthlyIntro Mean Temperature

11/47 SensorIntro Network Design

Measurement network A: randomly distributed fixed (RDF) sensors assuming no prior knowledge of the urban land use

Yang and Bou-Zeid, Environ. Res. Lett. 2019

12/47 SensorIntro Network Design

Measurement network B: evenly distributed fixed (EDF) sensors with equal measurements over each bin of impervious fractions

Yang and Bou-Zeid, Environ. Res. Lett. 2019

13/47 SensorIntro Network Design

Measurement network D: mobile measurement network (MMN) with sensors moving randomly within the studied area

Yang and Bou-Zeid, Environ. Res. Lett. 2019

14/47 SensingIntro the City

How about extreme temperatures?

Yang and Bou-Zeid Environ. Res. Lett. 2019 Optimal sensing strategy by combing mobile and fixed stations 15/47 CONTENTS

 Background and Motivation  Experimental Investigation  Urban Canopy Model  Smart City Development  Future Work UrbanIntro Canopy Model

Urban surface energy balance: 푅푛 + 푄 = 퐻 + 퐿퐸 + 퐺

Rn is the net radiation, Q is the anthropogenic heat, H is the sensible heat flux, LE is the latent heat flux, G is the storage heat flux z

T a Ta za

HR Hcan . Urban vegetation . Hydrological modeling T z R R . Sub-surface heterogeneity GR HW T G can zT W h TW H G TB

soil heat storage GG TG w r Kusaka et al. Boundary-Layer Meteorol. 2001

16/47 UrbanIntro Hydrological Modeling

Current modeling system needs to be enhanced via a better representation of urban hydrological processes:

 Outdoor irrigation  Anthropogenic latent heat  Oasis effect  Evaporation over engineering materials

Yang et al. Boundary-layer. Meteorol. 2015

17/47 In-situIntro Data Collection

Vancouver 29-m tower (Crawford et al. 2013)

Beijing 325-m tower (Song and Wang 2012) Phoenix 22-m tower Montreal 25-m tower (Chow et al. 2014) (Leroyer et al. 2011) Urban canopy parameters for each site is estimated based on field measurements and remote sensing technique. 18/47 ModelIntro Evaluation at 4 Cities

150 250 Observation H Observation LE Old SLUCM H Old SLUCM LE 200 New SLUCM H Phoenix ) Beijing

2

- New SLUCM LE )

100 2 -

m m

W 150

(

W (

x x

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-50 -50 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Local time (hour) Local time (hour)

250 250

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) Vancouver )

2 2 Montreal

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m m W W

( 150 ( 150

x x

u u

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-50 -50 0 4 8 12 16 20 24 0 4 8 12 16 20 24 Local time (hour) Local time (hour) Yang et al. Boundary-layer. Meteorol. 2015 19/47 CONTENTS

 Background and Motivation  Neighborhood Scale  Experimental Investigation  Urban Canopy Model  Smart City Development  Future Work BuiltIntro Urban Jungle

Paved surfaces, including roads, parking areas and sidewalks, covers about 36- 45% of urban surfaces for a variety of metropolitan areas (Gray & Finster, 2009)

Source: http://www.scmp.com/magazines/post-magazine/short-reads/article/2059543/drone-photos-hong- kong-andy-yeungs-unique 20/47 https://www.phoenix.gov/waterservicessite/Documents/Landscape%20Watering%20Guidelines.pdf ReflectiveIntro Materials

65

a = 0.1

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o a = 0.3

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e 55 a = 0.5 r roof

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t a = 0.7

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f 35 open paved surface

o

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25 0 4 8 12 16 20 24 Local time (hours) Yang et al. Build. Environ. 2016

21/47 Source: http://newscenter.lbl.gov/2011/11/03/cool-roofs-really-can-be-cool/ ReflectiveIntro Materials

road

Building height = street width

Yang et al. Build. Environ. 2016

22/47 ImpactIntro of Material Thermal Property

45 Control 40 Reflective material 35 Low thermal conductivity 30 Large heat capacity

25

20

15

10 Heating and Cooling Energy Use Energy Coolingand Heating 5

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Reflective Material of low thermal Material of large material conductivity heat capacity Building energy efficiency 24.07% 40.30% 25.20% enhancement

23/47 NovelIntro Engineering Materials

Thermochromic material

Fabiani, Pisello, Bou-Zeid, Yang et al. Appl. Energy 2019 24/47 UrbanIntro Green Landscape

Wang, Zhao, Yang and Song Appl. Energy 2016

25/47 “Smart”Intro Irrigation Schemes

How can we better design outdoor irrigation for a desert city?

. Daily constant: based on irrigation practice in Phoenix (8 pm local time) . Soil-moisture-controlled: meet plant need (wilting point ~ 0.24) . Soil-temperature-controlled: meet threshold temperature of 22 oC, but maintaining residual soil moisture of 0.10

26/47 “Smart”Intro Irrigation Schemes

0.5 x Daily constant Soil-moisture-controlled 0.4 Soil-temperature-controlled No irrigation

)

3

 x x x x m x x 0.3 x x x x x x x 3 x x x x x x x x x x x x x m x x x ( x x x x x x p x x x x x x x o x x x t x x x x x x x x x x  x 0.2 x x x x

0.1

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Yang and Wang, 2015 Energy Build. 27/47 EnergyIntro-Water Tradeoff

Is there an optimal temperature that can maximize the combined saving of energy and water resources?

12 energy use

8 total The activating soil temperature

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2 needs to be carefully determined - 4

m

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( in order to achieve the optimal

g

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v irrigation scheme a 0

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-4 water use

-8 19 20 21 22 23 24 25 26 27 Activating top-soil temperature (oC)

Yang and Wang, 2015 Energy Build. 28/47 CONTENTS

 Background and Motivation  Regional Scale  Experimental Investigation  Urban Canopy Model  Smart City Development  Future Work CoupledIntro Atmosphere-Urban Modeling

Upscaling the neighbourhood-scale results brings uncertainty： . Spatial heterogeneity of land surfaces . Lack of land-atmosphere interactions

Weather Research and Forecasting Model

Land Surface Model Urban Canopy Model

29/47 PlanningIntro for a Growing Desert City

Water stress

Low Water-use Xeric neighbor

(Golden, 2004) Thermal comfort

High Water-use Mesic neighbor

30/47 UrbanIntro Policy Dilemma

Source: https://www.glendaleaz.com/waterconservation/landscaperebates.cfm http://www.mesaaz.gov/residents/water-conservation/residential-grass-to-xeriscape-rebate http://www.chandleraz.gov/default.aspx?pageid=746 http://www.tempe.gov/city-hall/public-works/water/water-conservation 31/47 http://www.scottsdaleaz.gov/water/rebates HighIntro-resolution Weather Simulation

Yang and Wang Landscape Urban Plan. 2017

Water-saving city Fully-greening city

32/47 SocialIntro-Environmental Tradeoff

8 3 2 A fully−greening city consumes 1.61 × 10 m more water during the summer 1.5 + + + + + + + + + + + + + + 1+ + + + + + + + + + +

) 0.5

C

o

( Mean annual water consumption about

2 T 0 75 m3 per person (Gober and Kirkwood n Water-saving

i

e Fully-greening g -0.5 2010)

n

a

h + + + + + + + C + + + 8 3 3 -1 + 1.61 × 10 (m ) / 75 (m /person) = + + + + + + + + + -1.5 + 6 + + + + 2.15 × 10 person -2 Projected population growth 2.62 million 0000 0400 0800 1200 1600 2000 2400 by 2050 in the medium series (ADOA Local time 2015) Yang and Wang Landscape Urban Plan. 2017

33/47 GreenIntro Roofs in Sustainability Blueprint

Tokyo, Japan: Private buildings larger than 1000 m2 and public buildings larger than 250 m2 required to have 20% of rooftop greened Basel, Switzerland: green roofs mandated on all new buildings with flat roofs and for roofs over 500 m2 Portland, Oregon: all new city-owned facilities include a green roof with 70% coverage

Can cities mitigate heat islands by their local plans and efforts?

34/47 MultiIntro-level Mitigation Plans

Local plan City-scale plan Regional plan 25% green roof coverage over buildings

Chicago

Miami Los Angeles

Phoenix

New York City

Pittsburgh 35/47 RegionalIntro Cooling by Green Roofs

New York City

Local plan City-scale plan Regional plan

Yang and Bou-Zeid Landscape Urban Plan. 2019 36/47 RegionalIntro Cooling by Green Roofs

Chicago

Local plan City-scale plan Regional plan

Yang and Bou-Zeid Landscape Urban Plan. 2019 37/47 ScaleIntro Dependence of Cooling Benefit

0.3 NYC  Cooling benefit of green roofs

Pittsburgh C) o increases non-linearly with the Miami 0.2 Chicago intervention area

reduction reduction ( Phoenix 2 T LA 0.1 

Daytime Daytime The shape of metropolitan areas and its geoclimatic 0 0.1 1 10 100 1000 setting control the scaling Roof area (km2)

Yang and Bou-Zeid Landscape Urban Plan. 2019

38/47 UHIIntro under cold waves 2019 United States cold wave: Temperatures below −30.0 °C in the midwest of the United States during late January 2017 European cold wave: The lowest temperature was −45.4 °C in Central and East Europe on January 5, 93 people across Europe died 2016 East Asia cold wave: Caused over 100 known deaths across East Asia, South Asia and Southeast Asia.

To what extent will the UHI intensify or weaken under anomalously low regional temperatures?

Source: http://www.c3headlines.com/global-warming-urban-heat-island-bias/ 39/47 https://www.dnainfo.com/chicago/20150109/downtown/history-of-winter-chicago-it-could-be-worse-definitely-was EarlyIntro 2014 North American cold wave

Yang and Bou-Zeid 2018, J. Appl. Meteorol. Clim. Daytime

Night-time

UHI = Turb – Trul

Urban heat islands intensified during daytime (0.65 ± 0.34 oC, mean ± standard deviation among cities), and even more noticeably during night- time (1.32 ± 0.78 oC) 40/47 TemporalIntro evolution of UHI

 WRF simulation is able to reproduce the temperature variation across the cold wave event

 Intensification of UHI during the cold wave correlates weakly with incoming solar radiation

Yang and Bou-Zeid 2018, J. Appl. Meteorol. Clim.

41/47 MechanismIntro of intensified night-time UHI

Heat storage

Heat release

Yang and Bou-Zeid 2018, J. Appl. Meteorol. Clim.

Night-time surge in UHI is controlled by the heat release from urban fabric (engineering materials as “thermal battery”)

42/47 ClimateIntro change and human health

Implicit assumption: temporal variation of spatially aggregated temperature can pick up the risk signal

43/47 Gasparrini et al. 2015, The Lancet US residents’Intro exposure to extreme heat and cold

 Worker commute data from the 2006-2010 Census Transportation Planning Products (CTPP) (https://ctpp.transportation.org/)  16 major United States metropolitan areas  Three major heat waves (Jul 13 – Aug 29 in 2006, Jul 11 – Aug 10 in 2011, and Jun 18 – Jul 20 in 2012) and one cold wave (Jan 1 – Feb 1 in 2014) 44/47 TemperatureIntro anomaly under extreme weather

 Cold wave lowers the area-weighted mean 2-m air temperature by 11.5 ± 3.1 oC  Anomaly under heat waves (3.7 ± 1.5 oC) is much smaller than the cold anomaly

45/47 ImpactIntro of population dynamics

 Population dynamics lessen the exposure of urban residents to extreme cold by 0.4 ± 0.8 oC, but substantially increased the exposure to heat waves 2.0 ± 0.8 oC (more than half of the heat wave hazard 3.7 ± 1.5 oC)

46/47 CONTENTS

 Background and Motivation  Experimental Investigation  Urban Canopy Model  Smart City Development  Future Work ConclusionsIntro

 Challenges posed to the Water-Energy-Climate nexus in the urban environment could be managed through strategic urban planning and policy

 The environmental benefits of mitigation strategies exhibit strong variations with geographic and climatic conditions, and are subject to change with the scale

Building scale Neighborhood scale City and Regional scale (~10 – 100 m) (~100 – 1000 m) (~1000 – 10000 m)

Experimentation should be prompted at a case-by-case basis to test the overall value of individual measures for developing smart cities in different regions

47/47 Thank you!