Impact model: IMAGE

ISIMIP Fast Track

Person responsible for model simulations in this simulation round

David Gernaat: david.gernaat@pbl.nl, Environmental Assessment Agency (Netherlands)

Tom Kram: tom.kram@pbl.nl, Environmental Assessment Agency (Netherlands)

Kathleen Neumann: kathleen.neumann@wur.nl, Wageningen University (Netherlands)

Elke Stehfest: Elke.stehfest@pbl.nl, Netherland Environmental Assessment Agency (PBL) (Netherlands)

Detlef van Vuuren: detlef.vanvuuren@pbl.nl, Environmental Assessment Agency (Netherlands)

Output Data

Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-17

Basic information

Model Version: 3.0

Reference Paper: Main Reference: Integrated modelling of global environmental change : an overview of IMAGE 2.4.

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 30 arcmin

Additional spatial aggregation & resolution information: land use on 5 arcmin, crop model on 30 arcmin

Temporal resolution of input data: climate variables: daily

Temporal resolution of input data: co2: annual

Temporal resolution of input data: land use/land cover: constant

Temporal resolution of input data: soil: constant

Input data

Climate variables: tasmax, tasmin, pr

Spin-up

Was a spin-up performed?: Yes

Spin-up design: 270 years

Management & Adaptation Measures

Management: internally determined growing season, therefore autonomous adaptation

Key input and Management

Crops: mai, whe(w,s), soy, rice

Land cover: potential suitable cropland area according to climatic conditions, current harvested areas

Planting date decision: Simulate planting dates according to climatic conditions

Planting density: Planting density=1

Crop cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature and vernalization requirements computed based on past climate experience (whe, sunfl, rapes); basetemperature computed based on past climate (mai); static (others)

Irrigation: limited water availability, water applied if water deficit and available

Initial soil water: 100 year spin up

Key model processes

Leaf area development: Dynamic simulation based on development and growth processes

Light interception: Simple approach

Light utilization: Simple (descriptive) Radiation use efficiency approach

Yield formation: fixed harvest index

Crop phenology: temperature or photoperiod (day length)?

Root distribution over depth: Exponential

Stresses involved: Water stress

Type of water stress: ratio of supply to demand of water

Water dynamics: soil water capacity with 5 soil layers

Evapo-transpiration: Priestly-Taylor

Soil cn modeling: C model

Co2 effects: Leaf-level photosynthesis-rubisco or on QE and Amax

Methods for model calibration and validation

Parameters, number and description: 2: maximum LAI under unstressed conditions, management factor

Calibrated values: Specific for each crop and region

Output variable and dataset for calibration validation: Yield (FAO yield statistics)

Spatial scale of calibration/validation: National

Temporal scale of calibration/validation: Average 1970-2005

Criteria for evaluation (validation): Iterative parameter fitting (management factor) to perfect fit