Transcript Introduction to AIM/Impact model

Climate Change Impacts Modeling
Ｈｉｄｅｏ Ｈａｒａｓａｗａ
National Institute for
Environmental Studies
Objective of AIM/Impact
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Projection of potential impacts of climate
change on sensitive sectors.
Consideration of linkages among affected
sectors.
Proposition of effective adaptation measures
to cope with climate change.
Accounting feedback effects on GHGs
concentration and climate system.
AIM/Impact in AIM Framework
Characteristics of AIM/Impact
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Area focused: Whole Asia to Global
Spatial analysis (Modules run on GIS)
Consistency between socio-economic
scenario and climate change scenario.
Integration of emission (WG3), climate
(WG1) and impact and adaptation (WG2) in
the institute.
Computation framework
Original data
Climate data
GCM results
Soil property
Land-use
Population
etc.
Data import interface
GRASS commands
GRASS on UNIX
GRASS database
Variable spatial resolution
Meshed raster data
GIS data
Climate
scenario
Input
data
Output
data
Output
data
Climate scenario
creator
GRASS model
commands
GRASS Analysis
commands
UNIX shell program
Developed with
F77 or C language
Visualization
Average, etc.
Analysis on PC
Characteristics of AIM/Impact
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Area focused: Whole Asia to Global
Spatial analysis (Modules run on GIS)
Consistency between socio-economic
scenario and climate change scenario.
Integration of emission (WG3), climate
(WG1) and impact and adaptation (WG2) in
the institute.
Collaboration with climate model
Atmosphere
Emission model
Climate model
CCSR/NIES
CGCM
Ocean
Land Surface
&
AIM Socio-Econ.
Emission Scenario
(Asian-Pacific
Integrated
Model)
Landuse
Socio-Econ.
Factors
Water
Resource
Crop
Productivity
Food Demand
And Supply
Adaptation
Impact model
Crop productivity
Climate data
Temperature
Precipitation
Radiation
Wind
Humidity
Soil data
Chemical
characteristics
Slope
Texture
Human Input
Changes in the potential productivity of rice
Irrigation
from 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
IDI
-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
-0.219
Crop productivity change0.11
-6.60
Tech. Improve
-15.56
0.11
0.09
Labor
0.11
-0.01
Land
0.00
17.96
125.11
1.80
1.90
2.84
0.30
-1.10
-0.93
CAN
USA
-40.16
-13.10
-43.59
2.76
-1.22
-0.35
0.61
0.69
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
-0.009
0.003
Population
Consumer
-1.76
105.99
0.23
-7.64
115.07
2.87
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
Irrigated area
Population
Water supply
coverage
GDP or IVA
Spatial distribution
Population density
Cropland distribution
1990
2050
0.3 3 30 300
(mm/year)
Water consumption in India
(scenario analysis)
1995
Baseline
2032
Fortress World
WATER 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
Field capacity
Precipitation
Snowfall
Vegetation
Soil
CCCma
Evapotranspiration
Temperature
Wind speed
Radiation
Humidity
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
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)
0
2050(1980)
Ganges
2055(1985)
Mekong
CCC
ECHAM4
CCSR/NIES
LINK (1980-89)
Ten-year average (1980-89)
Malaria
Reproduction rate of
malaria vector
Temperature
Soil moisture
Expansion of the area affected by malaria
Forest vegetation
IS92c
with
low low
climate
sensitivity
IS92cscenario
scenario
with
climate
sensitivity
Forest diminishment
Temperature
Precipitation
Evapotranspiration
Max. velocity of
forest movement
IS92a
scenario
with medium
climate climate
sensitivitysensitivity
IS92a
scenario
with medium
IS92e
scenario
with high
climate
IS92e
scenario
with
high sensitivity
climate sensitivity
Diminish
of forest
Diminishment
of forest
Replacement of forest type
with the risk of diminishment
From global scale to national scale
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Increasing attention to national-scale impact
studies.
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AIACC (Assessment of the Impact of and
Adaptation to Climate Change Project)
National Communication
Concrete adaptation measures can be
evaluated only on an appropriate spatial
scale which corresponds the stakeholders.
Development of AIM/Impact [Country]
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Package of models, tools and data for
scenario analysis of national-scale climate
change impact assessment.
Executable on PC-Windows (no need to
learn UNIX & GRASS)
Bundled datasets for basic assessment.
Readily achievement of spatial analysis.
Detailed manual documents.
Framework of AIM/Impact [Country]
GIS dat a t r immed f or
n at ion al scale assessmen t
Global GIS dat a
Inp ut GIS dat a f or 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 scenar ios
Model par amet er s
Penman-PET model
Thornthwaite-PET model
Potential crop productivity model
Surface runoff model
River 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 IDRIDI
Interface tool for visualizing
data on plain spatial data
viewer
Out p ut GIS dat a of
impact assessmen t model
PREF.ID
392010100
392020100
392020400
392020200
392020400
392020500
392020300
392040100
392020600
392040300
392030200
392030300
392050200
392040200
392030100
GIS dat a f or sub- n at ion al
spat ial ag g r eg at ion
NAT
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
JPN
REG
Hokkaido
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Tohoku
Hokuriku
Tohoku
Hokuriku
Kanto
Kanto
Chubu
Hokuriku
Kanto
PREF
Hokkaido
Aomori
Akita
Iwate
Akita
Yamagata
Miyagi
Niigata
Fukushima
Ishikawa
Tochigi
Gumma
Nagano
Toyama
Ibaraki
VALUE
12
-10
-5
-5
2
3
-13
-2
8
-6
-7
15
17
12
-1
Potential usage of AIM/Impact[Country]
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Outside AIM project.
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Researchers, governmental officers or others who are interested in
assessing 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.
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Model is improved by replacing the ready-made parameters and
data with the specific and detailed ones collected for each country.
Use of IDRISI-GIS is recommended.
Source code and the latest databases are shared among the
teams for flexible improvement.
Future Direction of Impacts Study
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Global to National, Local Impacts
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Vulnerability and Adaptation
Impacts of Extreme Climate Events
Asia Impacts Research Network
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Global Warming Research Initiative (Council for Science and
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Technology Policy, Cabinet Office of Japan)
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IPCC 4th Assessment Report & AIACC
Millennium Ecosystem Assessment (MA)
APN Network Activity for Capacity Building