Introduction to AIM/Impact model

Kiyoshi Takahashi
National Institute forEnvironmental Studies

Items of the presentationz Overview of AIM/Impact model– Structure– Examples of the assessed resultsz Introduction to AIM/Impact [Country]– Structure, Objective– Current status of development

AIM/Impact in AIM Framework

Objective of AIM/Impact
z Projection of potential impacts of climatechange on sensitive sectors.
z Consideration of linkages among affected
sectors.
z Proposition of effective adaptation measures
to cope with climate change.
z Accounting feedback effects on GHGs
concentration and climate system.

Framework of the AIM/Impact model
HEALTH
Health impacts ofEnvironmental
ChangeENERGY
Energy technology
and resources

AIM/Emission

AIM/Climate

POP
Population, Fertility and Mobility

CLIMATE
Radiation, Energybalance,Temperature and
Sea level rise

ENV
Environmental Pressure andcounter-measure

CYCLE
Chemistry of
GHGs

CGE

Energy andcarbon budget of
Ocean

Supply and demand equilibrium
Of goods, energy, water, land and labor

FOOD

WATER

Production and
DemandSupply infrastructures
demandHYDRO
Surface water balance
Routing module

OCEAN

LAND
Land-use allocation and
GHGs emission

VEG

Vegetation dynamics

Characteristics of AIM/Impact
z Area focused: Whole Asia to Global
z Spatial analysis (Modules run on GIS)z Consistency between socio-economic

scenario and climate change scenario.
z Integration of emission (WG3), climate(WG1) and impact and adaptation (WG2) inthe institute.

Computation framework
Original data
Climate data
GCM results
Soil property
Land-use
Population
etc.

Data import interface
GRASS commands

GRASS on UNIXGRASS database
Variable spatial resolution
Meshed raster data
GIS data

Climatescenario

Input
data

Outputdata

Outputdata

Climate scenario
creatorGRASS model
commands

GRASS Analysis
commands

UNIX shell program

Developed with
F77 or C language

Visualization
Average, etc.

Analysis on PC

Collaboration with climate model
Atmosphere

Emission model

Climate model

CCSR/NIES
CGCM
Ocean

Land Surface&
AIM Socio-Econ.
Emission Scenario(Asian-PacificIntegrated
Model)

LanduseSocio-Econ.
Factors

Water
Resource

Crop
Productivity
Food Demand
And Supply

Adaptation

Impact modelSimplified framework
Climate data GCM outputs

Global averagetemperature increase

Climate module
Socio-economic
scenario

Future climate change
Water balance module
Water resource module

Water demand module

Water scarcity evaluation
Water impact

Crop productivity module
Global trade module
Natural ecosystem module
Health impact module

Agricultural
impactCrop productivity
Climate data
Temperature
Precipitation
Radiation
Wind
Humidity

Soil data
Chemicalcharacteristics
Slope
TextureHuman Input

Changes in the potential productivity of rice
Irrigationfrom 1990 to 2050 under the climatic conditions -500 0 +500
projected using the CCSR/NIES GCM Machinery

Fertilizer(kg/ha)Agricultural trade
JPN
Producer price change (%)
Rice
Wheat
Other grains
Other crops
Livestock
Other agricultural products
Manufacture
Services
Production change (%)
Rice
Wheat
Other grains
Other crops
Livestock
Other agricultural products
Manufacture
Services
Consumer price index (%)
Income change per capita (%)
Social welfare change (%)

Production

CHN

-0.01
4.91
1.81
-0.01
-0.19
-0.15
0.03
0.03

-1.58
8.47
0.79
-0.28
-0.09
-0.01
-0.12
-0.16

0.001
0.026
0.022

-0.25
-3.97
-1.39
-0.07
-0.24
-0.27
0.31
0.00
0.001
-0.236-0.219Crop productivity change0.11-6.60
Tech. Improve-15.56
0.11
0.09
Labor
0.11
-0.01
Land
0.00

IDI

CAN

17.96
125.11
1.80
1.90
2.84
0.30
-1.10
-0.93

-40.16-13.10-43.59
2.76
-1.22
-0.35
0.61
0.69

USA

Demand

-0.06
4.76
-1.46
-0.10
-0.59
-0.07
0.03
0.02

-4.93
8.92
-3.36
-0.05
-0.04
0.04
-0.02
-0.02

0.25
0.03
0.04
0.03
0.01
0.017
0.026
0.009

2.03
-3.64
-6.50
-0.03
-0.22
-0.22
0.05
0.01
-0.010-0.0090.003

Population
Consumer-1.76
105.990.23-7.64
115.072.87
preference
-1.33
89.41
-4.04
-4.25
-2.27
-4.73
-0.37
-2.62
6.047
-0.617-4.892

Trade

-2.26
0.94
0.69
-1.62
-0.02
0.513
0.833
0.343

Tariff etc.

E_U

River discharge
Surface runoff

River routing

Precipitation
Evaporanspiration
Temperature
Soil characteristics
1990

Elevation

2100

Annual river discharge in 1990 and 2100 (UIUC climate model)

Water demand (withdrawal)
Driving force

Spatial distribution

Irrigated area
Population
Water supply
coverage
GDP or IVA

Population density
Cropland distribution
1990

2050
0.3 3 30 300(mm/year)Water consumption in India(scenario analysis)
1995
Baseline

2032
Fortress WorldWATER CONSUMPTION

2032
Policy Reform

700.0000

CONSUMPTION(km^3/year)

600.0000

500.0000

400.0000

300.0000

200.0000
MF
FW
PR
GT

100.0000

0.0000
1990

2000

2010

2020

2030

2040

YEAR

Change of water consumption from 1995 to 2032
(Domestic + Agriculture + Industry)1

m3/ha/year

40

200

1000

5000

Surface runoff as Water supply

Evapotranspiration
TemperatureWind speed
Radiation
Humidity

Field capacity
Vegetation
Soil

CCCma

NIES

Change of surface runoff
表面流出量の変化(2050s – 1980s)（2050s－1980s）-100 -10 0 10 100 (mm/year)
MPIRiver basin for water scarcity
assessment

Indus
GangesChiangjiang

MekongWater scarcity

Scarcity index= Withdrawal/ Surface runoff

1.2
0.25

1
0.2

0.8
0.15

0.6
0.1

0.4

0.05

0.2
0
2050(1980)

2055(1985)

02050(1980)

Ganges2055(1985)

Mekong
CCC
ECHAM4
CCSR/NIES

LINK (1980-89)
Ten-year average (1980-89)

Malaria

Reproduction rate ofmalaria vector
Temperature
Soil moisture

Expansion of the area affected by malaria

Diarrhea / capita
Water supply coverage
Temperature

Diarrhea
2000
B1

GDP/capita
Environmental
A1B A2
B2
consideration

EMRO_D

EURO_B

Water supply coverage

Diarrheal incidence per capita per year

1.5

1.2

0.9

0.6

0.3

WPRO_B

WPRO_ASEARO_D

EURO_C

EURO_A

EMRO_B

AMRO_D

AMRO_B

AMRO_A

AFRO_E

AFRO_D

0.0

Diarrhea incidence per capita
per year in 2000 (bar graph) and
GBD Regionin 2055 for 4 SRES scenarios (□A1B,△A2,◇B1,○ B2).

Forest vegetation
IS92cscenario
scenariowithclimatesensitivity
IS92c
with
low low
climatesensitivity

Forest diminishment
Temperature
Precipitation
Evapotranspiration
Max. velocity offorest movement

IS92a
scenariowith medium
climate climate
sensitivitysensitivity
IS92a
scenariowith medium

IS92e
scenariowith highclimateIS92e
scenario
withhigh sensitivity
climate sensitivity
Diminishof forest
Diminishmentof forest

Replacement of forest typewith the risk of diminishment

From global scale to national scalez Increasing attention to national-scale impact

studies.
– AIACC (Assessment of the Impact of and

Adaptation to Climate Change Project)– National Communicationz Concrete adaptation measures can beevaluated only on an appropriate spatial
scale which corresponds the stakeholders.

Features of AIM/Impact [Country]z Package of models, tools and data for

scenario analysis of national-scale climatechange impact assessment.
z Executable on PC-Windows (no need tolearn UNIX & GRASS)z Bundled datasets for basic assessment.
z Readily achievement of spatial analysis.
z Detailed manual documents.

Framework of AIM/Impact [Country]
GIS data trimmed fornational scale assessment

Global GIS data

Input GIS data for impact
assessment models
GIS tool for input data
development(Scenario Creator)

GIS tool for trimming away
ex-focused area
GIS tool for spatial interpolation

Socio-economic and GHG
emission scenarios

Model parameters

Penman-PET model
Thornthwaite-PET model
Potential crop productivity model
Surface runoff modelRiver discharge model
Water demand model
Malarial potential model
Holdridge vegetation classification
Koeppen vegetation classification
Vegetation move possibility model(2) Impact assessment(1) Development of input GIS data for model(3) Analysis of GIS data and outputs

GIS tool for subnational aggregation

Interface tool for
visualizing data on IDRIDIInterface tool for visualizing
data on plain spatial data
viewerOutput GIS data ofimpact assessment model

PREF.ID NAT
REG
PREF
392010100 JPN Hokkaido Hokkaido
392020100 JPN Tohoku Aomori
392020400 JPN Tohoku Akita
392020200 JPN Tohoku Iwate
392020400 JPN Tohoku Akita
392020500 JPN Tohoku Yamagata
392020300 JPN Tohoku Miyagi
392040100 JPN Hokuriku Niigata
392020600 JPN Tohoku Fukushima
392040300 JPN Hokuriku Ishikawa
392030200 JPN Kanto
Tochigi
392030300 JPN Kanto
Gumma
392050200 JPN Chubu
Nagano392040200 JPN Hokuriku Toyama
392030100 JPN Kanto
Ibaraki

GIS data for sub-national
spatial aggregation

VALUE
12
-10
-5
-5
2
3
-13
-2
8
-6
-7
15
17
12
-1

Development of input GIS data for
impact assessment models
GIS data trimmed fornational scale assessment

Global GIS data

Input GIS data for impact
assessment models

Ready-made global GIS data

Additional GCM results
Observed climate fromother data sources

Originally imported GIS data
trimmed at focused area

Regional climate model
results
Region-specific soil data
Population distribution withfiner resolution

GIS tool for input data
development(Scenario Creator)

Originally imported global GIS data

GIS tool for trimming away
ex-focused area
GIS tool for spatial interpolation

Observed climate (LINK)GCM results (IPCC-DDC)
Soil (DSMW, FAO)
Population (NGCIA)
Cropland and Irrigated land

Monthly climate：
Temperature, Rainfall,
Cloudiness, Windspeed
Socio-economic:
Population distribution
Cropland / Irrigated
land
Soil:
Soil unit, soil texture,slope, soil phase, field
capacity, elevation,
albedoSocio-economic and GHG emission scenarios
GHG emission scenarios
Originally imported data

Change of annual mean global
temperature

Ready-made data bundled in the package
Tools and Models

Scenarios of population change
and other socio-economic factors

A1

A2

B1

B2

Impact assessment models
Input GIS data for impact
assessment models

Output GIS data of impact
assessment model

Monthly climate：
Temperature, Rainfall,
Cloudiness, Windspeed
Socio-economic:
Population distribution
Cropland / Irrigated
land
Soil:
Soil unit, soil texture,slope, soil phase, field
capacity, elevation,
albedoPenman-PET model
Thornthwaite-PET model
Potential crop productivity model
Surface runoff modelRiver discharge model
Water demand model
Malarial potential model
Holdridge vegetation classification
Koeppen vegetation classification
Vegetation move possibility model

Model parameters
Characteristics of crop growth
Soil constraints on crop production
Snow melt temperature
Rate of water discharge in river
Potential rate of vegetation move
Ready-madeOriginally imported

Penman-PET
Thornthwaite-PET
Potential crop productivity
Surface runoff
River discharge
Water demand
Malarial potential
Holdridge vegetation classification
Koeppen vegetation classification
Vegetation move possibility

Analysis of GIS data and outputs
Output GIS data of impact
assessment model

Global GIS data
GIS data trimmed fornational scale assessment
Input GIS data for impact
assessment models

GIS data of sub-national
administrative boundary

GIS tool for
sub-national
aggregation

GIS data for sub-national
spatial aggregation

Interface tool for
visualizing data on plain
spatial data viewer

Other GIS data

Interface tool for
visualizing data on IDRIDI

Penman-PET
Thornthwaite-PET
Potential crop productivity
Surface runoff
River discharge
Water demand
Malarial potential
Holdridge vegetation classification
Koeppen vegetation classification
Vegetation move possibility

IDRISI

Plain spatial data viewer
PREF.ID NAT
REG
PREF
392010100 JPN Hokkaido Hokkaido
392020100 JPN Tohoku Aomori
392020400 JPN Tohoku Akita
392020200 JPN Tohoku Iwate
392020400 JPN Tohoku Akita
392020500 JPN Tohoku Yamagata
392020300 JPN Tohoku Miyagi
392040100 JPN Hokuriku Niigata
392020600 JPN Tohoku Fukushima
392040300 JPN Hokuriku Ishikawa
392030200 JPN Kanto
Tochigi
392030300 JPN Kanto
Gumma
392050200 JPN Chubu
Nagano392040200 JPN Hokuriku Toyama
392030100 JPN Kanto
Ibaraki

VALUE
12
-10
-5
-5
2
3
-13
-2
8
-6
-7
15
17
12
-1

Aggregation to
sub-national level

Analysis of GIS data and outputs- Visualization z

For IDRISI user––z

GIS data in AIM/Impact [Local] will
have genuine IDRISI format, and
AIM/Impact [country] visualize thedata with starting up IDRISI through
IDRISI-API functions.
Full IDRISI functions can be used toprocess and analyze the GIS data
in AIM/Impact [Local].

For Non IDRISI user–

Plain spatial data viewer software(COMPAC FORTRAN Array
Visualizer) is included in the
package, and user can see andprint out the results visually.

Analysis of GIS data and outputs- Regional aggregation zzz

Numerical grasp of the resultwith representative values isalso important and useful.
Input GIS data andassessed results of impacts
are aggregated spatially andmean values for subnational divisions aretabulated.
Ready-made GIS data of
sub-national divisions
incorporated in the package.

PREF.ID NAT
REG
PREF
392010100 JPN Hokkaido Hokkaido
392020100 JPN Tohoku Aomori
392020400 JPN Tohoku Akita
392020200 JPN Tohoku Iwate
392020400 JPN Tohoku Akita
392020500 JPN Tohoku Yamagata
392020300 JPN Tohoku Miyagi
392040100 JPN Hokuriku Niigata
392020600 JPN Tohoku Fukushima
392040300 JPN Hokuriku Ishikawa
392030200 JPN Kanto
Tochigi
392030300 JPN Kanto
Gumma
392050200 JPN Chubu
Nagano392040200 JPN Hokuriku Toyama
392030100 JPN Kanto
Ibaraki

VALUE
12
-10
-5
-5
2
3
-13
-2
8
-6
-7
15
17
12
-1

Integrated user interface of
AIM/Impact-country
Penman-PET model
Thornthwaite-PET model
Potential crop productivity model
Surface runoff modelRiver discharge model
Water demand model
Malarial potential model
Holdridge vegetation classification
Koeppen vegetation classification
Vegetation move possibility model

GIS tool for
sub-national
aggregation

Manual writing ofconfiguration file

Interface tool for
visualizing data on plain
spatial data viewer

Configuration file forcontrolling
tools and models

Interface tool for
visualizing data on IDRIDIz

GIS tool for input data
development(Scenario Creator)z

User-friendly MS Visual
Basic form similar to the
AIM-Trend.
The interface is used tocomplete a configuration filecontrolling data
management tools, models,visualization tool.
Configuration file can beedited manually, whichenables complicated model
simulation with batch
Integrated user interface
programming by expert
users.
GIS tool for trimming away
ex-focused area
GIS tool for spatial interpolationzPotential usage of AIM/Impact[Country]zOutside AIM project.
–
–
–

z

Researchers, governmental officers or others who want to assess
future national impact of climate change.
Interactive user interface and ready-made datasets are provided
for instant achievement of scenario analysis.
Spatial visualization is achieved with a plain spatial data viewer
controlled from AIM/Impact [Country] interface.

Inside AIM project.
–––

Model is improved by replacing parameters or using more detailed
data for specific countries.
Use of IDRISI-GIS is recommended.
Source code and the latest databases are shared among theteams for flexibility and further refinement.

Development schedulez First version :End of this year.
z

Presentation of preliminary assessments using
AIM/Impact [Country] is expected at the AIM
Workshop in March 2003.

z Public distribution: End of next year
– After the review process by thecollaborative researchers.