Demonstrative presentation of AIM/Impact model Mr. Kiyoshi Takahashi, NIES, Japan Dr. Yasuaki Hijioka, NIES, Japan Dr. Amit Garg, Winrock International,

Demonstrative presentation of

AIM/Impact model

Mr. Kiyoshi Takahashi, NIES, JapanDr. Yasuaki Hijioka, NIES, Japan

Dr. Amit Garg, Winrock International, India

APEIS Capacity Building Workshop on Integrated Environmental Assessment in the Asia Pacific RegionOctober 2002

Outputs of AIM/Impact

-500 0 +500 (kg/ha)

1990

2050

0.3 3 30 300 (mm/year)

Change of crop productivity

Change of river discharge Water withdrawal

Objective of the course

● Introduction of standard procedures of impact assessment using AIM/Impact.

● Demonstration of specific procedures of assessment of potential crop productivity under anticipated climate change.– STEP1: Collection of input data– STEP2: Scenario development– STEP3: Parameter setting and simulation– STEP4: Display and analysis of the results

● Brief introduction of AIM/Impact [Country]

Standard work flow of impact assessment in

AIM/Impact● 1. Collection of input data and

importing them into GIS database– GRASS GIS

● 2. Future scenario development– Interpolation– Simple climate model and Pattern scaling

● 3. Simulation– Parameter setting

● 4. Display and analysis of the results– Visualization– Aggregation– Feedback or higher impact

GRASS (Geographic Reseoucres Analysis

Support System)● Gegraphical Information System

Software● Run on unix oprating systems (Solaris,

Linux, etc.)● Advantage

– Distributed on internet (Free)– Raster (gridded) data – Source codes available (C language)– Modules can be developed by users with

the GRASS developers' library.● Disadvantage

– Unix– Inexcelent graphical user interface

Example of spatial data managed in GRASS GIS

Obserbation climatology

GCM results

Population density

Assessment results

Data collection

● Current climate– Monthly or daily mean climatology

● CRU/UEA LINK climatology (1901-1996, 0.5x0.5, monthly)

● GEWEX/NASA ISLSCP (1987 and 1988, 1.0x1.0, monthly, daily or 6-hourly)

● Future climate projection– Output of General Circulation Models

(GCMs)● IS92a simulations (IPCC-DDC)● SRES simulations (IPCC-DDC)● Simulation by CCSR/NIES

– Output of Regional Climate Models (RCM)● Not available yet

Data collection

● Soil– Chemical and physical character of soil

● FAO Soil Map of the World● Landuse

– Landuse classification derived from remote-sensing data

● 1km x 1km GLCC (EDC/EROS/USGS)● Population

– Gridded population density● GPW2 (CIESIN/Colombia University, 2.5min)● LandScan2000 (1km x 1km)

Climate scenario

● Future changes of climate (temperature, rain, radiation, wind etc.) are deduced from GCMs results distributed at IPCC-DDC or provided by NIES/CCSR.

● In order to compromise with the very low resolution of GCM results, the results of GCMs are interpolated and current observed climatology is used for expressing spatial detail.

● For assessing various future path of GHG emission, "pattern scaling method” is employed to develop climate scenario.

Pattern scaling

GHG increase run (GCM)

Control run (GCM)

Spatial pattern of climate change (GCM)

＋＝

Minus

Current climate (observed)

Climate change scenario

＝―

Interpolated and scaled spatial pattern of CC

Simple climate model

Various emission path

⊿ Ｔ

t

Spatial interpolation Scaling

Simulation

● Simulation models in AIM/Impact are Unix shell files which consist of GRASS commands originally developed using GRASS-GIS library and standard GRASS commands included in the GRASS distribution.

● Some models refer to the model parameter files for reading assumption or information other than spatial input data managed in GIS.

Models in AIM/Impact

● Water balance model– Penman PET– Thornthwaite PET– Surface runoff

● River discharge model● Potential crop productivity model● Water demand model● Malaria potential model● Vegetation classification model● Vegetation move possibility model

Visualization and analysis

● Grasping spatial pattern of impact through visualization– Detection of critically damaged region– Time series analysis based on animation

● Spatial aggregation– Aggregation (spatial average) based on

administrative boundaries– Time series trend– Linkage with the other assessment

frameworks (ex. Economic model)

Demonstration of assessment of agricultural

impact● What will be done in the demonstration

– Calculate potential crop productivity of rice and winter wheat in Asia.

– Display some figures of the results focusing India.

● Objective– Demonstrate the procedure to assess

climate change impact using AIM/Impact with going through simplified assessment processes step by step.

ProcedureAIM/Impact GIS data archive

Dataset for assessment in the workshop

LINK historical observation

NIES/CCSRGCM results

Soil and surface field data

Administrativeboundaries

Interpolated GCM results

Climate scenario

Potential productivityunder rain-fed agriculture

Potential productivityunder perfect irrigation

(1) copygrassdata.sh

(2) interpolation.sh

(3) scenariocreate.sh

(5) nowaterstress.sh(4) rainfed.sh

(7) considerirrigation.sh

(8) indiafigure.sh

(6) createirigdata.shPotential productivityconsidering irrigation

Figures of results

Statistical data

of irrigated area

Spatial data of

irrigated ratio

Interpolation(interpolation.sh)

NIES/CCSR GCM (5.6 x 5.6)Monthly mean temperature in 2050s under IS92a scenrio.

Spline interpolation0.5 x 0.5

AIM/Impact GIS data archive







Climate scenario








(8) indiafigure.sh


Figures of results

Statistical data of irrigated area

Spatial data ofirrigated ratio

Temperature scenario(scenariocreate.sh)

JAN FEB MAR

APR MAY JUN

JUL AUG SEP

OCT NOV DEC

LINK historical temperature(1961-1990)

Temperature scenario(2050s, CCSR/NIES model)

-10 0 10 20 30 40 (Co)

JAN FEB MAR

APR MAY JUN

JUL AUG SEP

OCT NOV DEC








Climate scenario








(8) indiafigure.sh


Figures of results



Precipitation scenario(scenariocreate.sh)

LINK historical precipitation(1961-1990)

Precipitation scenario(2050s, CCSR/NIES model)

1 100 200 300 (mm/month)

JAN FEB MAR

APR MAY JUN

JUL AUG SEP

OCT NOV DEC

JAN FEB MAR

APR MAY JUN

JUL AUG SEP

OCT NOV DEC








Climate scenario








(8) indiafigure.sh


Figures of results



Example of parameter

cropname WheatSC WheatWC Whitepotato PhaseolusbeanTEC PhaseolusbeanTRC Soybean Rice Cotton Sweetpotato Cassava Pearlmillet SorghumTRC MaizeTRC SorghumTEC MaizeTECcrop_kind 1 1 1 1 2 2 2 2 2 2 3 3 3 4 4m_gp 100 200 150 90 120 120 130 160 150 330 90 120 120 110 110min_gp 90 90 90 50 50 75 80 150 90 180 55 90 70 90 70m_lai 5 5 5 4 4 4 5 3 4.5 3 4 4 4 3 4m_hi 0.4 0.4 0.6 0.3 0.3 0.35 0.3 0.07 0.55 0.55 0.25 0.25 0.35 0.25 0.35bean 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0hi_kind 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0gp_kind 1 0 0 1 0 0 0 0 0 0 0 0 0 2 2thres_l 5 5 7 7 7 13 12 15 10 10 15 15 12 15 12thres_u 25 25 30 32 32 38 36 38 40 35 45 38 40 38 40

Characteristics of crop growth

Potential productivity of winter wheat

Rain-fed assumption

Perfect irrigation(No water stress)

Irrigated ratio of wheat field

1 1000 2000 3000 4000 (kg/ha)

0 50 100 (%)

Combined (weighted average)

Estimated winter wheat potential productivity under current climate








Climate scenario








(8) indiafigure.sh


Figures of results



Change of potential productivity of rice and

wheat

Rice

Winter wheat

LINK (1961-1990) 2050s, CCSR/NIES model

1 1000 2000 3000 4000

(kg/ha)

AIM/Impact [Country]

Global GIS data GIS data trimmed for national scale assessment

Input GIS data for impact assessment modelsG

IS tool for trim

ming aw

ay ex-focused area

GIS

tool for spatial interpolation

GIS

tool for input data developm

ent(S

cenario Creator)

Socio- economic and GHG emission scenarios

Output GIS data of impact assessment model

Penman-PET modelThornthwaite-PET model

Potential crop productivity modelSurface runoff model

River discharge modelWater demand model

Malarial potential modelHoldridge vegetation classificationKoeppen vegetation classificationVegetation move possibility model

Model parameters

Interface tool for visualizing data on ID

RID

I

Interface tool for visualizing data on plain spatial data view

er

GIS

tool for sub-

national aggregation

PREF.ID NAT REG PREF VALUE392010100JPN HokkaidoHokkaido 12392020100JPN Tohoku Aomori -10392020400JPN Tohoku Akita -5392020200JPN Tohoku Iwate -5392020400JPN Tohoku Akita 2392020500JPN Tohoku Yamagata 3392020300JPN Tohoku Miyagi -13392040100JPN HokurikuNiigata -2392020600JPN Tohoku Fukushima 8392040300JPN HokurikuIshikawa -6392030200JPN Kanto Tochigi -7392030300JPN Kanto Gumma 15392050200JPN Chubu Nagano 17392040200JPN HokurikuToyama 12392030100JPN Kanto Ibaraki -1

GIS data for sub- national spatial aggregation

(1) Development of input GIS data for model (2) Impact assessment

(3) Analysis of GIS data and outputs

Features of AIM/Impact [Country]

● 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

Documents

Demonstrative presentation of AIM/Impact model Mr. Kiyoshi Takahashi, NIES, Japan Dr. Yasuaki Hijioka, NIES, Japan Dr. Amit Garg, Winrock International,