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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

naam, titel presentatie 2

• 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)


• 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


– 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):


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?

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


• 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


– 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


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


• 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


• 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 =


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


-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.)


• 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


• 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


• 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


(2)

Age

(101)

Region

(27)

Birth Death

TFRr MORTs,a,r

Partial urban-rural split-up


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


– 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


– 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


• 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


• IMAGE team just started

• Input from others very valuable

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