Download Future urbanization in Asia and potential heat risk

Future urbanization in Asia and
potential urban heat risk:
Preliminary study
presented at the
GCP Workshop, University of Tokyo, Japan
7-10 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?
Millions
Urban and rural population in the World, 1950 - 2050
12 000
10 000
8 000
6 000
4 000
2 000
0
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Rural
Source: UN 2014
Urban
Billlions
World urban population by region
7
6
5
4
3
2
1
0
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
WORLD
More developed regions
Less developed regions
AFRICA
ASIA
EUROPE
LATIN AMERICA AND THE CARIBBEAN
NORTHERN AMERICA
OCEANIA
1950
2 525 779
812 943
1 712 836
228 827
1 395 749
549 043
167 869
171 615
12 675
Source: UN DESA, 2012, 2014, Medium Fertility Variant
Total Population (000)
2000
2050
6 127 700
9 550 945
1 193 355
1 303 110
4 934 346
8 247 835
808 304
2 393 175
3 717 372
5 164 061
729 105
709 067
526 278
781 566
315 417
446 201
31 224
56 874
2100
10 853 849
1 284 035
9 569 814
4 184 577
4 711 514
638 816
736 228
513 065
69 648
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 Grubler
et al (2007)
Research design
Research design
Data
• 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 design
Data
Variable
Population
Urban areas
National
population
Urbanization
Nations
Temperature
Database
Year
Coverage
Resolution
Source
30 arc-second grid
Gridded Population of the
cell (~ 1 km at
http://www.ciesin.columbia.edu/data/gpw-v4/
World 4 (GWP4) release 2
2010
Global
equator),
(2010)
0.008333333 DD
30 arc-second grid
Global Rural Urban
1990, 1995,
cell (~ 1 km at http://sedac.ciesin.columbia.edu/data/collection/g
Global
Mapping Project (GRUMP)
equator),
2000
rump-v1
(2005)
0.008333333 DD
World Population Prospects
1950-2100 Global
nations
http://esa.un.org/unpd/wpp/DVD/
(2014)
World Urbanization
1950-2050 Global
nations
http://esa.un.org/unpd/wup/
Prospects (2014)
30 arc-second grid
Global Rural Urban
cell (~ 1 km at http://sedac.ciesin.columbia.edu/data/collection/g
Mapping Project (GRUMP)
2000
Global
equator),
rump-v1
(2005)
0.008333333 DD
GCM downscaled GCM data
portal, Research Program
2.5 minutes (~ 4.5
1950-2000
on Climate Change,
Global km at equator),
http://www.ccafs-climate.org/data/
& 2050
Agriculture and Food
0.041667 DD
Security, CGIAR and CCAFS
Modeling Center (or Group)
Commonwealth Scientific and Industrial Research Organization
(CSIRO) and Bureau of Meteorology (BOM), Australia
Beijing Climate Center, China Meteorological Administration
Institute ID
CSIRO-BOM
Model Name
ACCESS1.0
ACCESS1.3
BCC
BCC-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
CanESM2
NCAR
CCSM4
National Center for Atmospheric Research
Community Earth System Model Contributors
Commonwealth Scientific and Industrial Research Organization in
collaboration with Queensland Climate Change Centre of
Excellence
EC-EARTH consortium
LASG, Institute of Atmospheric Physics, Chinese Academy of
Sciences and CESS,Tsinghua University
The First Institute of Oceanography, SOA, China
NSF-DOE-NCAR
CESM1(BGC)
CSIRO-QCCCE
CSIRO-Mk3.6.0
EC-EARTH
EC-EARTH
LASG-CESS
FGOALS-g2
FIO
FIO-ESM
GFDL-ESM2M
NOAA Geophysical Fluid Dynamics Laboratory
NOAA GFDL
GFDL-CM3
GFDL-ESM2G
NASA Goddard Institute for Space Studies
Met Office Hadley Centre (additional HadGEM2-ES realizations
contributed by Instituto Nacional de Pesquisas Espaciais)
Institute for Numerical Mathematics
NASA GISS
GISS-E2-H
GISS-E2-R
MOHC
HadGEM2-ES
(additional realizations by INPE)
HadGEM2-CC
INM
INM-CM4
IPSL-CM5A-LR
Institut Pierre-Simon Laplace
IPSL
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC-ESM
Japan Agency for Marine-Earth Science and Technology,
Atmosphere and Ocean Research Institute (The University of
Tokyo), and National Institute for Environmental Studies
MIROC
Atmosphere and Ocean Research Institute (The University of
Tokyo), National Institute for Environmental Studies, and Japan
Agency for Marine-Earth Science and Technology
MIROC
Max-Planck-Institut für Meteorologie (Max Planck Institute for
Meteorology)
MPI-M
MIROC-ESM-CHEM
MIROC5
MPI-ESM-MR
MPI-ESM-LR
Meteorological Research Institute
MRI
MRI-CGCM3
Norwegian Climate Centre
NCC
NorESM1-M
Data downloaded from
(http://www.ccafsclimate.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
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
Research Design
UN Population data
United Nations
Population Division
Department of Economic and Social Affairs
World Urbanization Prospects: The 2014 Revision
File 3: Urban Population at Mid-Year by Major Area, Region and Country, 1950-2050 (thousands)
POP/DB/WUP/Rev.2014/1/F03
July 2014 - Copyright © 2014 by United Nations. All rights reserved
Suggested citation: United Nations, Department of Economic and Social Affairs, Population Division (2014). World Urbanization Prospects: The 2014 Revision, CD-ROM Edition.
• 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/public
ations/manual/projection/index.shtml
Research design
methods
• Temperature
1. All annual files include 12 months of mean, max
and min temperatures
2. 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 design
methods
• Population
1. We started with GRUMP 2000 and GPW4 2010
spatial population data
2. We used the GRUMP rural-urban masks to
identified urban and non-urban areas
3. We allocated urban and rural population, based
upon UN figures, randomly by country,
Research design
methods
• Analysis
1. 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
Urban population under different summer temperature conditions,
2010 and 2050
1,400
1,200
Population (millions)
1,000
800
600
400
200
0
< 28 C
28-32 C
32-34 C
34-36
36-38 C
3 month mean temperature conditions (degrees C)
2010
2050
2050 (highest mean)
38-40 C
> 40
Preliminary results
Percent of sample Asian urban population experiencing 3 month
temperatures. 2010 and 2050
Year
2010
2050
2050 (highest mean)
< 28
28-32
32-34
34-36
36-38
38-40
>40
62.76
21.95
12.18
27.48
50.00
45.76
8.85
8.52
17.74
0.91
11.91
8.35
0.00
6.21
8.78
0.00
1.38
4.10
0.00
0.03
3.08
Between 252.5 and
528.8 million urban
residents
Discussion
Discussion
• Urbanization includes much more than
population expansion
Source: Romero-Lankao et al 2014
Projected future urban land use in Asia
2000 to 2030
Source: Seto et al. 2012. PNAS
Discussion
• Across the world’s continents, Asia suffers
from a disproportional distribution of “natural
hazard” impacts
Millions
Total population affected by ”natural disasters” by continent:
5-year moving averages, 1950-2014
300
250
Populatoin affected
200
Africa
150
Americas
Asia
Europe
Oceania
100
50
2014
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
1958
1956
1954
1952
1950
0
Year
Source: D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – Université
Catholique 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
Percent
Natural Disaster Number
Total
Asia
Drought
152
4.3
Floods
1,805
51.3
Storms
1,500
42.6
Heat waves
61
1.7
Total Asia
3,518
World
Drought
Floods
Storms
Heat waves
Total World
632
4,374
3,607
167
8,780
24.1
41.3
41.6
36.5
40.1
Affected
Number Percent
(millions)
Total
Deaths
Number Percent
(thousands)
Total
Damage
Cost Percent
(US$ billions)
Total
1,725
3,406
908
0.1
6,039
28.6
56.4
15.0
0.0
1,513
2,283
889
12.1
4,697
32.2
48.6
18.9
0.3
38
410
224
0.4
673
5.6
61.0
33.3
0.1
2,221
3,584
994
5
6,804
77.6
95.0
91.4
2.6
88.8
2,211
2,377
970
155
5,714
68.4
96.0
91.6
7.8
82.2
136
673
1,022
22
1,853
28.0
60.9
22.0
1.9
36.3
Source: D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – Université
Catholique 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)
3000
2500
Deaths from heat waves
2000
1500
Heat wave
Linear (Heat wave)
1000
500
Adj. R-sq. = 0.071
Sign. F = 0.018
0
Source: D. Guha-Sapir, R. Below, Ph. Hoyois - EM-DAT: The CRED/OFDA International Disaster Database – www.emdat.be – Université
Catholique 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