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Downscaling drivers of global environmental change within the IMAGE 2.4 frameworkIMAGE 2.4 framework
Paul Lucas
Overview
• MNP modelling strategy
• IMAGE and GISMO
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• Downscaling of socio-economic scenarios
• Work in progress– Urban / rural split-up in demography model
– Urban and rural population and area maps
MNP model strategy
• No expert modelling (except indispensable for MNP tasks and not available in network)
• Integral modelling in support of SD analysis with focus on core expertise fields (energy & climate, land-use)
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• Co-operation in firm (inter-)national partnerships for complementary expertise
• In-house overarching models (“scanners”) to assess broader P-P-P relationships
Generations of IAMs
• First generation:– one-dimensional models describing cause-effect chain of
climate change
– developed by small group
• Second generation:– more detailed models, taking interrelations and feedbacks
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– more detailed models, taking interrelations and feedbacks into account
– developed by institutes
• Third generation:– coupling of complex modelling systems, taking into account
as many feedbacks as possible; deal with stochastic modelling
– developed by collaborating institutes
Vision for IMAGE “family”:
• Third generation; truly integral; intermediate complexity Earth System model
• Fit to address questions of Ecosystems Goods and Services (EGS) and Human Well-Being (HWB):
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Services (EGS) and Human Well-Being (HWB):1. How does HWB depend upon EGS?
2. How do human activities affect the provision of EGS?
3. How do EGS failures impact on HWB?
4. How can management reduce the EGS shortfalls?
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GISMO
• Human well-being, as overarching outcome– HDI and in more detail human health currently used as proxy
– EGS is link to rest of the system (MA)
• Identifying interactions between the domains
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• Identifying interactions between the domains– feedbacks, trade-offs now / here � then / there, co-benefits
• Not a new model, (mostly) using existing models and data
Linking regions to country and even grid scale
• Most scenario studies distinguish 10–20 world regions– SRES, GEO, MA, etc.
• Finer scale more often preferable – Post Kyoto climate policy (EU burden sharing)
– Impact, vulnerability and adaptation studies
– Link to more “micro-scale” processes
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– Link to more “micro-scale” processes
• Within IMAGE 2.4 a need for rural/urban split-up and gridded information– Land allocation
– N and P emissions from households
– Biodiversity impacts
– Population and Health (HWB)
– (Energy demand and supply)
Downscaling (currently) the preferred route
Any process in which coarse-scale data is disaggregated to a finer scale while ensuring
consistency with the original data set
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consistency with the original data set
Downscaling
• Important constraints– Consistency with existing local data (the base year)
– Consistency with the original source (the scenario data)
– Transparency of methodology
• We distinguish two methodologies
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• We distinguish two methodologies– Conditional modelling
– Clearly defined algorithms
Linear downscaling
Convergence downscaling
External input based downscaling
Population and income downscaling
• Population downscaling– Country: On the basis of existing national-scale UN World
Population Prospects
long-range � 2000-2300
– Grid: Linear downscaling using GPW1995 and country growth rates
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• Income downscaling– Country: On the basis of a partial convergence rule
Convergence in per capita GDP within a region in f.i. 2150
– Grid: Multiply population on grid with per capita GDP per country
Van Vuuren et al. (2007), Global Environmental Change, 17(1): 114-130
Population downscaling
)/(* RCRC AAPopPop =
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Per capita income downscaling
2.0
4.0
6.0
8.0
10.0
Gro
wth
rate
(%
/yr)
STEP 1
• Flat income growth rate from
initial income to income in
convergence Year
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-6.0
-4.0
-2.0
0.0
2.0
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Gro
wth
rate
(%
/yr)
Singapore flat
Vietnam flat
Singapore corrected
Vietnam corrected
South-East Asia
STEP 2
• Difference with regional total
divided according to share in
(absolute) growth
Overall performance
• The proposed methodologies are transparent and easy to apply
• They yield a consistent dataset that does not suffer from the “unsatisfactory results” as seen in linear downscaling methods (Gaffin et al. & Hohne et al.)
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• There are some problems– Still discontinuities in base-year for GDP per capita
– Ignore structural relations (f.i. size and quality of labour force)
– Ignore important determinants (f.i. infrastructure, level of education, urbanisation)
(possible) refinements
• Separate accounting for large groups – Population: Urban and Rural
– Economy: industry, services, agriculture and Urban / Rural
• More sophisticated grid data / modelling
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• More sophisticated grid data / modelling– Population: Urban-rural population maps
– Economy: use sub-national or grid GDP data (Nordhaus 2005)
Work in progress
• Urban / rural split-up in demography model
• Urban and rural population and area maps
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• Urban and rural population and area maps
Cohort component population model
Sex
(2)
Health Risk
Factors
Income &
Education
Malnutrition
Drinking water & Sanitation
Malaria
Indoor air pollution
Outdoor air pollution
HIV/AIDS
Life-style
Base mortality
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(2)
Age
(101)
Region
(27)
Birth Death
TFRr MORTs,a,r
Partial urban-rural split-up
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Full urban-rural split-up
Challenges
• Initialisation
– Full historic split-up
• International migration
– From urb/rur to urb/rur
Sex
(2)
Age
(101)
Birth
TFRr,u
Urban &
Rural
Income &
Education
Death
MORTs,a,r,u
Urban &
Rural
Health Risk
Factors
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– From urb/rur to urb/rur
• Urban-rural migration
– (Calibration factor)
• Urban reclassification
– From village to city
Region
(27)
Urb/Rur
(2)
Urban and rural population maps
• Create gridded population maps using linear downscaling and sub-national data
• Rural/urban population numbers per country– HYDE 3.0 for 1700 – 1950 (Klein Goldewijk, 2005)
– UN World Population Prospects (UNWPP) for 1950-2000
Klein Goldewijk, K. (2005), Population and Environment, 26 (5): 343-367
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– UN World Population Prospects (UNWPP) for 1950-2000
– UNWPP and own projections for future scenarios
• Assign Urban pop to cells with highest population numbers until total urban pop match per country
Klein Goldewijk, K. and G. van Drecht, 2006. HYDE 3: Current and historical population
and land cover. In: MNP (2006),
Urban area maps
• Determine Urban Population Density (UPD) per country– Overlay Landscan popcounts with combined urban area from
satellite maps and count number of people in urban areas
– Divide people in urban areas by total urban area per country
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• Use constant UDP to determine total urban area– Divide total pop per cell by UPD
– When total area exceeds cell-area use cell-area
Klein Goldewijk, K. and G. van Drecht, 2006. HYDE 3: Current and historical population
and land cover. In: MNP (2006),
Wrap-up
• Need for more detailed information
• Downscaling shows promising results
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• IMAGE team just started
• Input from others very valuable