APEIS Capacity Building Workshop on Integrated Environmental Assessment in the Asia Pacific Region October 2002 Demonstrative presentation of AIM/Impact model Mr. Kiyoshi Takahashi, NIES, Japan Dr. Yasuaki Hijioka, NIES, Japan Dr. Amit Garg, Winrock International, India
Outputs of AIM/Impact -500 0 +500 (kg/ha) Change of crop productivity 1990 2050 Change of river discharge Water withdrawal 0.3 3 30 300 (mm/year)
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 Population density GCM results 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 remotesensing 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) = Minus Control run (GCM) Spatial pattern of climate change (GCM) Interpolated and scaled spatial pattern of CC + Current climate (observed) = Climate change scenario Spatial interpolation Scaling T Various emission path t Simple climate model
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.
Procedure AIM/Impact GIS data archive (1) copygrassdata.sh Dataset for assessment in the workshop LINK historical observation NIES/CCSR GCM results Soil and surface field data Administrative boundaries (2) interpolation.sh Interpolated GCM results (3) scenariocreate.sh Climate scenario (4) rainfed.sh (5) nowaterstress.sh Potential productivity under rain-fed agriculture (7) considerirrigation.sh Potential productivity considering irrigation Potential productivity under perfect irrigation Spatial data of irrigated ratio (6) createirigdata.sh Statistical data of irrigated area (8) indiafigure.sh Figures of results
AIM/Impact GIS data archive (1) copygrassdata.sh Dataset for assessment in the workshop LINK historical observation NIES/CCSR GCM results Soil and surface field data Administrative boundaries (2) interpolation.sh Interpolation Interpolated GCM results (3) scenariocreate.sh Climate scenario (4) rainfed.sh (5) nowaterstress.sh (interpolation.sh) Potential productivity Potential productivity under rain-fed agriculture under perfect irrigation (7) considerirrigation.sh Potential productivity considering irrigation (8) indiafigure.sh Spatial data of irrigated ratio (6) createirigdata.sh Statistical data of irrigated area Figures of results NIES/CCSR GCM (5.6 x 5.6) Monthly mean temperature in 2050s under IS92a scenrio. Spline interpolation 0.5 x 0.5
AIM/Impact GIS data archive (1) copygrassdata.sh Dataset for assessment in the workshop LINK historical observation NIES/CCSR GCM results Soil and surface field data Administrative boundaries (2) interpolation.sh Temperature scenario Interpolated GCM results (3) scenariocreate.sh Climate scenario (4) rainfed.sh (5) nowaterstress.sh (scenariocreate.sh) Potential productivity Potential productivity under rain-fed agriculture under perfect irrigation (7) considerirrigation.sh Potential productivity considering irrigation (8) indiafigure.sh Spatial data of irrigated ratio (6) createirigdata.sh Statistical data of irrigated area Figures of results JAN FEB MAR JAN FEB MAR APR MAY JUN APR MAY JUN JUL AUG SEP JUL AUG SEP LINK historical temperature (1961-1990) Temperature scenario (2050s, CCSR/NIES model) OCT NOV DEC OCT -10 0 10 20 30 40 (C o NOV DEC )
AIM/Impact GIS data archive (1) copygrassdata.sh Dataset for assessment in the workshop LINK historical observation NIES/CCSR GCM results Soil and surface field data Administrative boundaries (2) interpolation.sh Precipitation scenario Interpolated GCM results (3) scenariocreate.sh Climate scenario (4) rainfed.sh (5) nowaterstress.sh (scenariocreate.sh) Potential productivity Potential productivity under rain-fed agriculture under perfect irrigation (7) considerirrigation.sh Potential productivity considering irrigation (8) indiafigure.sh Spatial data of irrigated ratio (6) createirigdata.sh Statistical data of irrigated area Figures of results JAN FEB MAR JAN FEB MAR APR MAY JUN APR MAY JUN JUL AUG SEP JUL AUG SEP LINK historical precipitation (1961-1990) Precipitation scenario (2050s, CCSR/NIES model) OCT NOV 1 DEC100 200 300 OCT (mm/month) NOV DEC
Example of parameter Characteristics of crop growth cropname WheatSC WheatWC Whitepotato PhaseolusbeanTEC PhaseolusbeanTRC Soybean Rice Cotton Sweetpotato Cassava Pearlmillet SorghumTRC MaizeTRC SorghumTEC MaizeTEC 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 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 hi_kind 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 gp_kind 1 0 0 1 0 0 0 0 0 0 0 0 0 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
AIM/Impact GIS data archive (1) copygrassdata.sh Dataset for assessment in the workshop LINK historical observation NIES/CCSR GCM results Soil and surface field data Administrative boundaries (2) interpolation.sh Potential productivity Interpolated GCM results (3) scenariocreate.sh Climate scenario (4) rainfed.sh (5) nowaterstress.sh of winter wheat Rain-fed assumption Potential productivity Potential productivity under rain-fed agriculture under perfect irrigation (7) considerirrigation.sh Potential productivity considering irrigation (8) indiafigure.sh Figures of results Spatial data of irrigated ratio (6) createirigdata.sh Statistical data of irrigated area Combined (weighted average) Irrigated ratio of wheat field Perfect irrigation (No water stress) 0 50 100 (%) Estimated winter wheat potential productivity under current climate 1 1000 2000 3000 4000 (kg/ha)
Change of potential productivity of rice and wheat Rice Winter wheat 1 1000 2000 3000 4000 LINK (1961-1990) 2050s, CCSR/NIES model (kg/ha)
AIM/Impact [Country] (1) Development of input GIS data for model Global GIS data GIS tool for trimming away ex-focused area GIS tool for spatial interpolation GIS data trimmed for national scale assessment GIS tool for input data development (Scenario Creator) Socio- economic and GHG emission scenarios Input GIS data for impact assessment models Model parameters 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 (3) Analysis of GIS data and outputs Interface tool for visualizing data on plain spatial data viewer Interface tool for visualizing data on IDRIDI Output GIS data of impact assessment model GIS tool for subnational 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
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