Developing Climate Resilient Cities: From Heat
Islands to Urban Digital Twins
Dev Niyogi

The University of Texas Extreme weather, climate and Urban Sustainability (TExUS) Lab

William Stamps Farish Chair Professor
Jackson School of Geosciences
And Professor, Department of Civil, Architectural, and Environmental
Engineering. University of Texas at Austin
Email: happy1@utexas.edu
https://www.linkedin.com/in/texus/(Affiliated faculty – Oden Institute for Computational Engineering and Sciences; UT Center

for Space Research; UT Environmental Science Institute; Planet Texas2050; Good Systems/Smart Cities)[Former Indiana State Climatologist and Professor Purdue University/ Department of Agronomy andDepartment of Earth, Atmospheric Planetary Sciences/ Division of Environmental and EcologicalEngineering/ West Lafayette, IN]

Cities areimportantdrivers of economy,culture, creativity,branding,…

Cities while epitomizing

technological advances andopportunities can also represent
what can potentially go wrong
if technology is not effectively utilizedUrban problems: sprawl

4

Urban problems: pollution

5

Urban problems:
weather, flooding

6Growing Cities andUrbanization is a dominant

global change
underway.

Urbanization iscausing significant, anddetectable, changes inregional climate throughtemperature and rainfall
modification

http://webpages.scu.edu/ftp/jready/family_urbanizationand
modernization.html

Research Premise•Improved Scientific Knowledge ,Computational Tools and Models if blended
with community needs can help develop
tools that can help with predicting andmanaging climate extremes; help
develop climate-ready cities throughdesign and infrastructure planning.

Conceptual diagram of major Urban Climate Issues
Air Emissions,
Anthropogenic urban
activities
Infrastructure

UHI

Temperature
HeatStress

Shifts inLocation& Timing

RainfallUrbanFloods/Droughts

Water
Scarcity

Climate Change / Global & Regional
Slide from Dr. Akshara, CDAC

How do cities

affect weather/climate?

Urbanization and land use change leads to regional temperature changes(warming= Urban Heat Island)

90

Average Temperatures inJuly for Urban & Rural Areas

85
80
7570
65
La Porte Jul 01 Avg Temp
Midway Jul 01 Avg Temp

6051015202530

Examples ofUrban Heat

Islands

Kishtawal et al. 2010, IJOC

Change in Temperatureand MoistureGreater trapping of
Infrared radiation(Warmer air holds moremoisture)
Increased Convective
Energy, Stronger thermals,
Modified regional
convergence
Modified location,intensity, duration ofRainfall

Urban modification ofSurface energy and Radiation BalanceUrbanization Impacts Scale Beyond the Surface(temperature)

The enhanced weather model predicted radar reflectivity convection andrainfall is simulated only when urban – rural gradients boundary exists
0100 UTC

With Indianapolis
Urban Area

Indianapolis

Without
Indianapolis
Urban Feedback

0200 UTC

0300 UTC

Meta-analysis of urbanization impact on rainfall modification

Jie Liu & Dev Niyogi ; Scientific Reports, 9,
Article number: 7301 (2019) https://www.nature.com/articles/s41598-019-42494-2• Urbanization modifies rainfall,such that mean precipitation isenhanced by• 18% downwind of the city,• 16% over the city,• 2% on the left and 4% on theright with respect to the storm
direction.
• The rainfall enhancement
occurred approximately 20–
50 km from the city center.
• Rainfall increases not only
downwind of the city but also
over the city.

Can the urban impact scale up to affect regional climate?
Indian monsoon rainfall is becoming more extreme (Science,2006)

Reassessment…
Heavy rainfall trend only noted for urban grids

NewDelhi(10860000)

Mumbai(16250000)

Kishtawal et al. 2010, IJOC

BangaloreChen(4172nai000)(6995400)

Kolkata(14220000)

Cities affectclimate..

Can we design
cities to get the“desired” climate?

(e.g. cooling…Where should it rain)

Or use City Knowledge
to improve predictionof urban extremes?

How can we make smart
cities to be resilient cities?

(How to use technology effectivelyin making cities more liveable?)How can we get information
on cities and blend them intoweather models to improvecity-level forecasts?

CHICAGO’SURBAN INTEGRATEDFIELD LABORATORY
Slides courtesy Dr. Cristina
Negri, Chicago IFL PI

Community Driven Science
Existing community
planning documents

Community
meetings

OVERARCHINGSCIENCE QUESTIONS
Select questions
and tailor for inclusivity
and pertinence

JUST ENERGY
TRANSITION

Refined, specificscience question

Observations

Resiliency concerns
Energy andenvironmental justicePreferred energy solutions

Education andworkforce development

Modeling

Vision
The next five years
We will deliver a reliable
representation of thecomplex urban Chicago
Metro environment andits feedbacks with climate.
• A systems-based approach for
integrating, physical, biologicaland human dimensions• A framework to simulate, evaluate
and project the impacts andfeedbacks between climate andurban systems

MISSION
Community Research on
Climate and Urban Science
Work with communities todevelop, deploy and use a diverse
measurement and observational
infrastructure to reveal
microclimates in urban areas andtheir dynamics in theheterogeneous urban fabric.
Model these microclimates in theurban environment and theinteraction of physical climatefactors with human and economic
factors and decisions.

Use this integrated canvas
to reveal the environmental
drivers that exacerbate
environmental injustice andsocial polarization in urban
communities and enable the codesign, with impacted
communities, of resilience andadaptation strategies.

Apply this approach to focus on
specific neighborhoods by testing
various climate change mitigation
actions and develop decision
support tools based on individualand community-scale decision
making.
Use the science program in its
mission to train and educate thenext generation of climate workforce in the region with hands onexperiences and innovativecurricula.

Sensing and modeling to understand urban climatechallenges
The Chicago region provides anexcellent focal point for understandingurban to regional climate science andhow to implement solutions that areequitable to communities.

JUST AND CLEAN
ENERGY TRANSITION

Team Crocus
RESEARCH
AND EDUCATION§ Argonne National Laboratory§ University of Illinois
at Chicago§ Chicago State University§ Northeastern Illinois University§ Community Collegesof Chicago§ North Carolina A&T§ Notre Dame University§ University of Chicago§ Northwestern University§ Wash U St. Louis§ University of Illinois
at Urbana Champaign§ University of Texas Austin§ U. Wisconsin Madison

COMMUNITIESINDUSTRY AND
LOCAL GOVERNMENT§ Blacks in Green§ Greater Chatham Initiative§ Puerto Rican Agenda§ Metropolitan Mayors Caucus§ Center for Neighborhood
Technology*§ Elevate*§ Environmental Law
and Policy Center*§ Local Government§ World Business Chicago§ CMAP – Chicago Metropolitan
Agency for Planning§ Museum of Science andIndustry§ Shedd Aquarium§ ComEd§ JC Decaux
Inequities that exist today have oldroots in Chicago and Cook County,
which have pushed communities into
the most physically challenging areas.

The Baltimore SoCialEnvironmental Collaborative IFL

Slide from Ben Zaitchik, JHU, BSEC PI

The BSEC Approach
Project Goal: produce the urban climate science needed to inform
community-guided potential equitable pathways for climate action.
Guiding premise: equitable adaptation is a product of place-based cocreation of knowledge. The IFL, then, has to be designed collaboratively,
with measurement objectives guided by community priorities.
Implication: the IFL is iterative, with observation and modelingcomponents designed to be both robust and flexible to collaborative
learning throughout the life of the project.
Indicators of success (example): integration of IFL analyses andparticipatory design methods to Baltimore City’s forthcoming Climate
Action Plan and Disaster Preparedness and Planning Project (DP3) andsubsequent implementation activities.

Overarching questions

Department of Energy Urban Field LabsUT Austin SETx: Southeast Texas• Which processes and variables need to be
captured in regional scale hydrological andatmospheric models so that they are
representative of the conditions
experienced by local communities and help
inform adaptation strategies?
• How can we understand the linkages
between and within natural, built, andsocial systems in urbanized regions tobetter support natural and humanresilience?
Slide from Prof. PaolaPassalacqua, SETx IFL PI

Input: Heterogeneous Data
LandScan Data (Population)

JAXA SRTM Data (Elevation)

Segmented Satellite ofTarget Area34

WUDAPT forweather models

Google Earth, Open Street Maps and Landsat Imagery based
reclassification of Cities, with Local Survey and Verification, andRendering. Map released to broader community.

Example of New Urban Climate Zone map

Control(b)WUDAPT(c)(a)Arabian Sea

Figure 1: (a) Domain configuration of WRF (b) Land use/ land cover for control (MODIS) and including LCZ classes (WUDAPT) Black line represents the studyarea.

Impact of Urban Parameterizationon Simulation of Hurricane Rainfall• Key Points:• The WRF model quantitative precipitation forecast over the urban domain wassensitive to urban physics for an intense, large-scale event such as a hurricane.
• The consideration of detailed urban physics in the WRF model simulation reduced
the simulated rainfall error over the urban region by 16.5%.
• The improved model rainfall appears to be in response to accurate simulation ofmesoscale surface gradients.

Struggle between Simplicity and Complexity

Complexity

More Processes

Often Process
Understandingand DATA is a
missing feature

Simplicity

More Applications

Examples of data that you don’t have in complex models: Building heightFirst challenge or opportunity for Digital Twins-

Creating synthetic data from DT/ simple models for use in process/complex models
GLOBUS

Presented at IAUC 2022
Harsh Kamath, Manmeet Singh, Lori Magruder, ZongLiang Yang and Dev Niyogi

https://arxiv.org/ftp/arxiv/papers/2205/2205.12224.pdf

GLOBUS to WRF (G2W) Framework
GLOBUS derived urban parameters
Plan area fraction
Area averaged building height

Used by Urban Canopy Model (UCM)
Single-layer (Kusaka et al., 2001) and Multilayer (Martilli et al., 2002)+Building surface to plan area ratio
GLOBUS normalized
Digital Surface Model

Histogram of building heights
Mean building heights
Standard deviation of building heights
Frontal area index

Presented at IAUC 2022

Harsh Kamath, Manmeet Singh, Lori Magruder, ZongLiang Yang and Dev Niyogi

Multi-layer
Single-layer

Land cover derived from
ESA worldcover or Local
climate zones (LCZs)GEOGRID

https://arxiv.org/ftp/arxiv/papers/2205/2205.12224.pdf

NOAA NIHHIS Austin Heat Island Study with G2W (Globus to WRF)

Fig. 1 GLOBUS architecture (U-Net based)

Fig. 2 Scatter plots showing thevalidation of GLOBUS using
LiDAR as the ground truth at 300m spatial resolution.

Fig. 3 Histogram of buildingheights: percentage ofbuildings with heights less than
5-m in Austin downtown .

Fig. 4 WRF simulations of 2-meter
temperature over Austin

https://arxiv.org/ftp/arxiv/papers/2205/2205.12224.pdf

Interfacing Video Gaming Visualization with Urban
Weather Models
Terrain

Roads

Blocks + parcels

Buildings

47

Procedural
modeling ofcoupledurban land
atmosphericinteraction

Source: Garcia-Dorado, I. et al (2016)

Hyderabad, India

1990

http://www.atlasofurbanexpansion.org/

2014Hyderabad, India

Gathering Information on Typical DwellingsUSA© realtor.com

Second opportunity for DT*

Speed of computation
Urban science to Urban planningGreenscapes,
urban Greening

Changes intemperature andcity's climate

urban WRF

Computer visualization

Model initial conditions to WRF:hours to days

Seconds to minutes

https://www.linkedin.com/feed/update/urn:li:activity:68990156

Output: Procedural 3D Model
New Orleans: Procedural Model

New Orleans: Aerial Image53

Output: Procedural 3D Model
Dublin – 3D Urban Procedural Model

Dublin – 3D Google Earth54

Content Design Example

55

Inverse Urban Weather
Input Data
Land use/land cover

Population

ResultRoad Network

Buildings
Terrain[Aliaga et al. 2013]56

Inverse Urban Weather
Modified LULC (else same)

Modified Result

Land use/land cover

Road NetworkPopulation

Buildings
Terrain57

Inverse Urban Weather:Trees
Temperature-1°C+1°C

Humidity-5%+5%

Rainfall-20mm +20mm

Indianapolis“Hot city”

Northeast greening“Cool city”

Southeast greening58

Inverse Urban Weather:
Trees
Temperature
-1°C

+1°C

Humidity-5%+5%

Rainfall-20mm+20mm

Indianapolis“Dry city”

Circulargreening“Warm and…

Distributed greening…humid city””

59How would you design
the traffic of a city?

60

Results: Traffic Improvement
Initial Simulation
Travel Time: 60min
CO: 1012 gr
The user wants to optimize thecity to 50, 40, and 30 min as
maximum Travel Time61

Results: Traffic Improvement
Solutions:

Travel Time: 50 min
CO: 980gr
52 Lanes

Travel Time: 40 min
CO: 622gr
16% Jobs
31% People
34 Lanes

Travel Time: 30 min
CO: 484gr
29% Jobs
44% People
61 Lanes62

Third opportunity for Urban Digital Twins

Scalability

Application: Model coupling

Hydrologic
al System

What do we do
here?

Hydraulic System

CoastalSurge
SystemHurricane Ivan (2004) casestudyHigh spatial bias in CFSv2, Modified DLWP-CS
manmeet.singh@utexas.
overestimation of absolute substantially improved
eduvalues
spatial bias and also absolutevalues
https://arxiv.org/pdf/2206.11
Manmeet Singh, Vaisakh SB, Nachiketa Acharya, Suryachandra Rao, Bipin Kumar, Zong-Liang Yang, Dev Niyogi
669.pdf

Super resolution downscaling for urban
precipitation

Development of 300-m
precipitation product overCity of Austin from JAXAGsMAP satellite+Merged
product at 10 km, hourly
resolution
3 x3 deg box over Austin
Iterative SR-CNN – 300 m
grid out
Duration: 2001-2020

Manmeet Singh, Nachiketa Acharya, Sajad Jamshidi, Junfeng Jiao, Zong-Liang Yang, Marc Coudert,Zach Baumer, Dev Niyogi, submitted to Computational Urban Sciences

Inverse Urban Flooding

67

Looking forward●●Continued opportunity for simpler input conditions, developmentof ensembles, decision specific output
Challenges and precautions-

Framing (what is DT, what it is not)-Important to support physics based models not replace them-

Recognize meteorological science is exceptionally challenging-

Cities can be the solution for climate change – not the problem!

-Happy to discuss more(and have an email to prove it!)

Email: happy1@utexas.edu
dev.niyogi@jsg.utexas.edu