Future urbanization in Asia and potential urban heat risk:

Preliminary studypresented at the

GCP Workshop, University of Tokyo, Japan7-10 December 2015

Peter J. Marcotullio, Carsten Kessler, Carson Farmer, Gabriel Schuster, Jonah Garnick & Douglas Price

Outline

• Introduction• Research design• Preliminary findings• Discussion• Summary, caveats and future work

Introduction

• “There are no real independent urbanization projections to the UN Urbanization Prospects, as alternative scenarios invariably use the UN historical and current data as model inputs and also deploy a comparable methodological framework” (Grubler, 2013)

• The UN currently projection urbanization to 2050, provides urban population numbers and growth rates (for nations) as well as a list of ~1700 cities of 300 thousand or greater (1950-2030)

Introduction to population growth

• What are the UN general trends and predictions for global urbanization?

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2 000

4 000

6 000

8 000

10 000

12 000

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Mill

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Urban and rural population in the World, 1950 - 2050

Rural Urban

Source: UN 2014

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Billl

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World urban population by region

OCEANIA EUROPE LATIN AMERICA AND THE CARIBBEAN NORTHERN AMERICA ASIA AFRICA

Introduction

• What are the UN general trends and predictions for global population?

Total population, 1950-2100

1950 2000 2050 2100WORLD 2 525 779 6 127 700 9 550 945 10 853 849

More developed regions 812 943 1 193 355 1 303 110 1 284 035Less developed regions 1 712 836 4 934 346 8 247 835 9 569 814

AFRICA 228 827 808 304 2 393 175 4 184 577ASIA 1 395 749 3 717 372 5 164 061 4 711 514EUROPE 549 043 729 105 709 067 638 816LATIN AMERICA AND THE CARIBBEAN 167 869 526 278 781 566 736 228NORTHERN AMERICA 171 615 315 417 446 201 513 065OCEANIA 12 675 31 224 56 874 69 648

Total Population (000)

Source: UN DESA, 2012, 2014, Medium Fertility Variant

Introduction

• What is the country level distribution of general trends and prediction for global population?

Total population change 1950 to 2000, by country

Total population change 2000 to 2050, by country

Total population change 2050 to 2100, by country

Introduction

• There are four urbanization scenario/projections to date:– Nicholls et al (2008): extend the UN projections to 2100 applying a

constant fraction (to port cities) at the national level to determine future port-city populations exposed to climate change risk

– Grubler et al (2007): extends the UN urbanization projections to 2100 and develops two additional scenario variants in which the asymptotic urbanization levels are varied to explore the implications of lower urbanization. The three urbanization rate scenarios are then combined with three alternative total population growth scenarios (low, medium and high) to determine the uncertainty range of future urban populations

– Balk et al 2012: applies Grubler et al (2007) data on low elevation coastal zones (LECZ) to 2100

– GEA (2012): extends urbanization scenarios with emphasis on energy issues and normative pathways (where economic, energy and security issues are simultaneously achieved). Based upon UN data and Grubleret al (2007)

Research design

Research designData

• Databases– Temperature (current & 2050)– Spatial population and land boundaries (2010)– Urban areas (2000)– National borders (2000)– UN total and urban population data 2000-2050

Research designData

Variable Database Year Coverage Resolution Source

PopulationGridded Population of the World 4 (GWP4) release 2

(2010)2010 Global

30 arc-second grid cell (~ 1 km at

equator), 0.008333333 DD

http://www.ciesin.columbia.edu/data/gpw-v4/

Urban areasGlobal Rural Urban

Mapping Project (GRUMP) (2005)

1990, 1995, 2000 Global

30 arc-second grid cell (~ 1 km at

equator), 0.008333333 DD

http://sedac.ciesin.columbia.edu/data/collection/grump-v1

National population

World Population Prospects (2014) 1950-2100 Global nations http://esa.un.org/unpd/wpp/DVD/

Urbanization World Urbanization Prospects (2014) 1950-2050 Global nations http://esa.un.org/unpd/wup/

NationsGlobal Rural Urban

Mapping Project (GRUMP) (2005)

2000 Global

30 arc-second grid cell (~ 1 km at

equator), 0.008333333 DD

http://sedac.ciesin.columbia.edu/data/collection/grump-v1

Temperature

GCM downscaled GCM data portal, Research Program

on Climate Change, Agriculture and Food

Security, CGIAR and CCAFS

1950-2000 & 2050 Global

2.5 minutes (~ 4.5 km at equator),

0.041667 DDhttp://www.ccafs-climate.org/data/

Data downloaded from (http://www.ccafs-climate.org/data/) GCM downscaled GCM data portal, Research Program on Climate Change, Agriculture and Food Security, CGIAR and CCAFS

RCP8.5, 2.5 arc minutes resolution for globe

Modeling Center (or Group) Institute ID Model NameACCESS1.0ACCESS1.3BCC-CSM1.1

BCC-CSM1.1(m)College of Global Change and Earth System Science, Beijing Normal University

GCESS BNU-ESM

Canadian Centre for Climate Modelling and Analysis CCCMA CanESM2National Center for Atmospheric Research NCAR CCSM4

CESM1(BGC)

Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence

CSIRO-QCCCE CSIRO-Mk3.6.0

EC-EARTH consortium EC-EARTH EC-EARTHLASG, Institute of Atmospheric Physics, Chinese Academy of Sciences and CESS,Tsinghua University

LASG-CESS FGOALS-g2

The First Institute of Oceanography, SOA, China FIO FIO-ESMGFDL-ESM2M

GFDL-CM3GFDL-ESM2G

GISS-E2-HGISS-E2-R

MOHC HadGEM2-ES(additional realizations by INPE) HadGEM2-CC

Institute for Numerical Mathematics INM INM-CM4IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LRMIROC-ESM

MIROC-ESM-CHEM

Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology

MIROC MIROC5

MPI-ESM-MR MPI-ESM-LR

Meteorological Research Institute MRI MRI-CGCM3Norwegian Climate Centre NCC NorESM1-M

NOAA GFDLNOAA Geophysical Fluid Dynamics Laboratory

NSF-DOE-NCARCommunity Earth System Model Contributors

NASA GISSNASA Goddard Institute for Space Studies

Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology) MPI-M

Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais)

Institut Pierre-Simon Laplace IPSL

Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies

MIROC

Commonwealth Scientific and Industrial Research Organization (CSIRO) and Bureau of Meteorology (BOM), Australia

CSIRO-BOM

Beijing Climate Center, China Meteorological Administration BCC

Data: Sample of urban extents and national boundaries

Global Rural Urban Mapping Project (GRUMP) both raster (30 arc seconds) and vector files for year 2000

July 2014 - Copyright © 2014 by United Nations. All rights reservedSuggested citation: United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects: The 2014 Revision, CD-ROM Edition.

Department of Economic and Social Affairs

World Urbanization Prospects: The 2014 RevisionFile 3: Urban Population at Mid-Year by Major Area, Region and Country, 1950-2050 (thousands)

POP/DB/WUP/Rev.2014/1/F03

Population DivisionUnited Nations

Research DesignUN Population data

• National scale data for population and urban population every 5 years from 1950 – 2050

• Urban areas are defined by national governments and may not conform to GRUMP urban extents

• Projections are based on UN DESA methodologies, see: http://www.un.org/en/development/desa/population/publications/manual/projection/index.shtml

Research designmethods

• Temperature1. All annual files include 12 months of mean, max

and min temperatures2. For each model, we created 3-dimensional arrays

and identified, for each cell, the highest three temperatures for three consecutive months and from this created a new raster

3. Among the 30 models for 2050 we created another 3-dimensional array and, for each cell, identified: a) the mean values and b) picked the highest mean

Research designmethods

• Population1. We started with GRUMP 2000 and GPW4 2010

spatial population data2. We used the GRUMP rural-urban masks to

identified urban and non-urban areas3. We allocated urban and rural population, based

upon UN figures, randomly by country,

Research designmethods

• Analysis1. We use a sample of urban areas (~3600) to

sample both urban population and temperature (61% of urban population in 2010 and 75% of urban population in 2050)

2. We find mean temperatures for the “current” (2010), 2050 and 2050 highest mean periods

3. We compare changes in populations exposed to these different temperatures

Preliminary results

Projected temperature change (RCP 8.5 highest 3 month means)

Population change over time

Asian population change by latitude

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1,400

< 28 C 28-32 C 32-34 C 34-36 36-38 C 38-40 C > 40

Popu

latio

n (m

illio

ns)

3 month mean temperature conditions (degrees C)

Urban population under different summer temperature conditions, 2010 and 2050

2010 2050 2050 (highest mean)

Percent of sample Asian urban population experiencing 3 month temperatures. 2010 and 2050

Year < 28 28-32 32-34 34-36 36-38 38-40 >402010 62.76 27.48 8.85 0.91 0.00 0.00 0.002050 21.95 50.00 8.52 11.91 6.21 1.38 0.032050 (highest mean) 12.18 45.76 17.74 8.35 8.78 4.10 3.08

Preliminary results

Between 252.5 and 528.8 million urban

residents

Discussion

Discussion

• Urbanization includes much more than population expansion

Source: Romero-Lankao et al 2014

Source: Seto et al. 2012. PNAS

Projected future urban land use in Asia2000 to 2030

Discussion

• Across the world’s continents, Asia suffers from a disproportional distribution of “natural hazard” impacts

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Total population affected by ”natural disasters” by continent:5-year moving averages, 1950-2014

Africa

Americas

Asia

Europe

Oceania

Source: D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – UniversitéCatholique de Louvain – Brussels – Belgium.

Discussion

• Risk from extreme heat currently has one of the lowest impacts among hazards in the region

Distribution of selective cumulative natural disaster impacts in Asia and share of World, 1950-2014

Occurances Affected Deaths DamagePercent Number Percent Number Percent Cost Percent

Natural Disaster Number Total (millions) Total (thousands) Total (US$ billions) TotalAsia

Drought 152 4.3 1,725 28.6 1,513 32.2 38 5.6Floods 1,805 51.3 3,406 56.4 2,283 48.6 410 61.0Storms 1,500 42.6 908 15.0 889 18.9 224 33.3Heat waves 61 1.7 0.1 0.0 12.1 0.3 0.4 0.1Total Asia 3,518 6,039 4,697 673

WorldDrought 632 24.1 2,221 77.6 2,211 68.4 136 28.0Floods 4,374 41.3 3,584 95.0 2,377 96.0 673 60.9Storms 3,607 41.6 994 91.4 970 91.6 1,022 22.0Heat waves 167 36.5 5 2.6 155 7.8 22 1.9Total World 8,780 40.1 6,804 88.8 5,714 82.2 1,853 36.3

Source: D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – UniversitéCatholique de Louvain – Brussels – Belgium.

Discussion

• With expanding population and urban areas will come increasing energy use, infrastructure development and other activities that create and increase Urban Heat Island effects

• While those Asian affected by extreme heat are low in number compared to other risk, this hazard will grow in importance, not only because of the higher number of those exposed but also because of increased sensitivity (larger numbers of older people in cities)

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3000De

aths

from

hea

t wav

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Heat wave

Linear (Heat wave)

Adj. R-sq. = 0.071Sign. F = 0.018

Source: D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – UniversitéCatholique de Louvain – Brussels – Belgium.

Discussion• “Despite all of the heat-related risks that cities face

in the future, few have put heat-management plans in place” (Hoag, 2015, pp. 404)

• My guess is that solutions may come from new technologies, urban design and energy efficiencies AND from traditional sources including traditional housing and building technologies

Conclusions

Conclusions

• We have developed a simple baseline model for the creation of urbanization scenarios using a random distribution of urban and non-urban population across national space

• The output of this model is associated with temperature outputs from 30 models for RCP 8.5 pathways to 2050

• Results suggest increasing and large urban populations may experience very warm consecutive months in the mid-term (2050) future

Conclusions

• Selected caveats– These are preliminary findings of a baseline for

the development of scenarios. This baseline needs further validation and checking;

– The model method while “plausible” has not yet been compared to historical development patterns;

– We do not include UHI in our results;– We do not include urban physical expansion in the

model;

Conclusions

• Some thoughts on further work for Asia• Phase 1

– Calculate temperatures for RCP 8.5 for 2020, 2030, 2060 and 2080

– Project urbanization to 2100– Continue baseline analysis to 2100– Compare model outputs to historical results

Conclusions

• Phase II– Develop different models of urbanization for a

range of outputs• Allocation models for urban growth based upon, inter

alia, transportation corridors, physical barriers, and densities

• Varying urbanization and population levels

– Compare the results from these analyses against each other and the baseline “random” approach

End