Impact model: EPIC-IIASA

EPIC-IIASA is one of the 14 models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a agricultural sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Agricultural Sector, see this DOI link:

Contact Person
  • Juraj Balkovic (, International Institute for Applied Systems Analysis (IIASA) (Austria)
  • Nikolay Khabarov (, International Institute for Applied Systems Analysis (IIASA) (Austria)

Information for the model EPIC-IIASA is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Basic information
Model Version: EPIC0810
Reference Paper: Main Reference: Balkovič J, van der Velde M, Skalský R, Xiong W, Folberth C, Khabarov N, Smirnov A, Mueller N, Obersteiner M et al. Global wheat production potentials and management flexibility under the representative concentration pathways. Global and Planetary Change,122,107-121,2014
Output Data
Experiments: historical
Climate Drivers: WATCH (WFD), WATCH+WFDEI
Date: 2016-02-10
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: time constant
Temporal Resolution Of Input Data: Soil: Constant
Input data sets used
Observed Atmospheric Climate Data Sets Used: WATCH (WFD), WATCH+WFDEI
Climate Variables: tasmax, tasmin, wind, rsds, prsn, pr
Additional Input Data Sets: N and P fertilizer application rates based on Mueller et al. (2012)
Was A Spin-Up Performed?: Yes
Spin-Up Design: 20-yr spin up
Management & Adaptation Measures
Management: Planting dates and length of the growing season were estimated based on Sacks et al. (2010). Harvest day was scheduled automatically as a fraction of accumulated PHU. Hence, maturity in each year depends on the specific growing season temperature. No residue removal. P-fertilization scheduled together with tillage, N-fertilization scheduled based on N stress.
Extreme Events & Disturbances
Key Challenges: EPIC does not take floods and any physical damage to plants (e.g. hail or extreme winds) into account. Crops are not killed by extreme drought or temperatures, but only limtied in growth and yield formation.
Key input and Management
Crops: mai, whe(w,s), soy, ric
Land Cover: GLC2000 + Spatial Production Allocation Model dataset - You, L., et al., Spatial Produciton Allocation Model (SPAM) 2000 Version 3 Release 1. (Accessed Feb, 2012)
Planting Date Decision: fixed planting dates (Sacks et al., 2010) - for historical yields
Planting Density: Crop specific
Crop Cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature (4 cult for mai, multiple cult for ric)
Fertilizer Application: N and P fertilizer (Mueller et al. 2012); dynamic timing of N app, fixed timing of P app
Irrigation: no restriction on actual water availability, irrigated water applied when water stress; MIRCA 2000 crop specific irrigated area (Portmann et al., 2010)
Crop Residue: Yes
Initial Soil Water: Spin up of 20 years
Initial Soil Nitrate And Ammonia: Spin up of 20 years
Initial Soil C And Om: ISRIC-WISE
Initial Crop Residue: Spin up of 20 years
Key model processes
Leaf Area Development: prescribed shape of LAI curve as function of phenology, modified by water stress & low productivity
Light Interception: Simple approach
Light Utilization: Simple (descriptive) Radiation use efficiency approach
Yield Formation: Fixed harvest index modified by water stress, partitioning during reproductive stages, total (above-ground) biomass
Crop Phenology: temperature, heat unit index
Root Distribution Over Depth: exponential, actual water depends on water availability in each soil layer
Stresses Involved: Water stress, Nitrogen stress, Oxygen stress, heat stress (phosphorus, bulk density, aluminium (based on pH and base saturation)
Type Of Water Stress: ratio of supply to demand of water
Type Of Heat Stress: vegetative (source)
Water Dynamics: soil water capacity with 10 soil layers
Evapo-Transpiration: Hargreaves
Soil Cn Modeling: C model; N model; microbial biomass pool, 3 number of organic matter pools
Co2 Effects: Radiation use efficiency, Transpiration efficiency
Methods for model calibration and validation
Parameters, Number And Description: Default parameters from EPIC0810, Potential harvest index, optimal and min T adjusted for some cultivars
Output Variable And Dataset For Calibration Validation: Yield (FAO yield statistics)
Spatial Scale Of Calibration/Validation: National
Temporal Scale Of Calibration/Validation: 1997-2004
Criteria For Evaluation (Validation): R2