Demonstrative presentation of AIM/Impact model Mr. Kiyoshi - - PowerPoint PPT Presentation

Demonstrative presentation

• f AIM/Impact model
• Mr. Kiyoshi Takahashi, NIES, Japan
• Dr. Yasuaki Hijioka, NIES, Japan
• Dr. Amit Garg, Winrock International, India

APEIS Capacity Building Workshop on Integrated Environmental Assessment in the Asia Pacific Region October 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

• ther 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.

Procedure

AIM/Impact GIS data archive Dataset for assessment in the workshop

LINK historical

• bservation

NIES/CCSR GCM results Soil and surface field data Administrative boundaries Interpolated GCM results Climate scenario Potential productivity under rain-fed agriculture Potential productivity under 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.sh Potential productivity considering irrigation Figures of results Statistical data

• f 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 interpolation 0.5 x 0.5

• bservation

• f irrigated area

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

• bservation

• f irrigated area

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

• bservation

• f irrigated area

Example of parameter

cropname WheatSC WheatWC Whitepotato PhaseolusbeanTEC PhaseolusbeanT RC Soybean Rice Cotton Sweetpotato Cassav a Pearlmillet SorghumT RC MaizeTRC SorghumT EC MaizeT EC crop_kind 1 1 1 1 2 2 2 2 2 2 3 3 3 4 4 m_gp 100 200 150 90 120 120 130 160 150 330 90 120 120 110 110 min_gp 90 90 90 50 50 75 80 150 90 180 55 90 70 90 70 m_lai 5 5 5 4 4 4 5 3 4.5 3 4 4 4 3 4 m_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 0.35 bean 1 1 1 hi_kind 1 1 gp_kind 1 1 2 2 thres_l 5 5 7 7 7 13 12 15 10 10 15 15 12 15 12 thres_u 25 25 30 32 32 38 36 38 40 35 45 38 40 38 40

Characteristics of crop growth

Potential productivity

• f 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

• bservation

• f irrigated area

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 models GIS tool for trimming away ex-focused area GIS tool for spatial interpolation GIS tool for input data development (Scenario Creator) Socio- economic and GHG emission scenarios Output GIS data of impact assessment model 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 Model parameters Interface tool for visualizing data on IDRIDI Interface tool for visualizing data on plain spatial data viewer GIS tool for sub- national aggregation

PREF.ID NAT REG PREF VALUE 392010100JPN Hokkaido Hokkaido 12 392020100JPN Tohoku Aomori

• 10

392020400JPN Tohoku Akita

• 5

392020200JPN Tohoku Iwate

• 5

392020400JPN Tohoku Akita 2 392020500JPN Tohoku Yamagata 3 392020300JPN Tohoku Miyagi

• 13

392040100JPN Hokuriku Niigata

• 2

392020600JPN Tohoku Fukushima 8 392040300JPN Hokuriku Ishikawa

• 6

392030200JPN Kanto Tochigi

• 7

392030300JPN Kanto Gumma 15 392050200JPN Chubu Nagano 17 392040200JPN Hokuriku Toyama 12 392030100JPN 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