Impact model: EPIC-IIASA

Sector
Agriculture
Region
global

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.

Person responsible for model simulations in this simulation round
Juraj Balkovic: balkovic@iiasa.ac.at, 0000-0003-2955-4931, International Institute for Applied Systems Analysis (IIASA) (Austria)
Christian Folberth: folberth@iiasa.ac.at, 0000-0002-6738-5238, International Institute for Applied Systems Analysis (IIASA) (Austria)
Additional persons involved: Nikolay Khabarov: khabarov@iiasa.ac.at
Output Data
Experiments: ssp370_2015soc_2015co2, picontrol_2015soc_default, ssp585_2015soc_2015co2, ssp126_2015soc_2015co2, ssp370_2015soc_default, ssp585_2015soc_default, historical_2015soc_default, ssp126_2015soc_default
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2021-09-24
Basic information
Model Output License: CC0
Model Homepage: https://iiasa.ac.at/models-tools-data/epic-iiasa
Model License: Custom
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. EPIC-IIASA is one of the currently 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs.
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
Resolution
Spatial aggregation: regular grid
Horizontal 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: soil: constant
Input data
Simulated atmospheric climate data sets used: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4
Other human influences data sets used: N-deposition, Crop calendar, N-Fertilizer (ISIMIP3b)
Additional input data sets: Slope and elevation based on GTOPO30
Climate variables: hurs, sfcWind, tasmax, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 30-year spin-up to equilibrate soil profiles. For each simulation, another spin-up of 8 years was performed to equilibrate soil water and mineral nutrient contents.
Natural Vegetation
Soil layers: 10
Key input and Management
Crops: Yes. Winter and spring wheat, maize, rice, soybean.
Land cover: No, whole land mask simulated
Planting date decision: Using the constant prescribed growing season dataset specified by the protocol.
Planting density: Global uniform depending on crop.
Crop cultivars: One constant cultivar for each crop in each grid cell (up to two for rice) based on maturity requirement. This is determined based on the the mean growing degree-days accumulated in the growing period (derived from prescribed crop calendars specified in protocol) over 1980–2009 growing seasons.
Fertilizer application: Synthetic fertilizer applied according to protocol: 20% at sowing, 80% once accumulated growing degree-days reach 25% of maturity requirement. Manure N and atm. N deposition were added to mineral fertilizer application.
Irrigation: Automatically applied based on crop water deficit (10%).
Initial soil water: Equilibrated in each simulation with a 8-year spin-up cycling the first 8 years of climate data using soil profiles with a 30-year spin-up (see above).
Initial soil nitrate and ammonia: Equilibrated in each simulation Equilibrated in each simulation with a 8-year spin-up cycling the first 8 years of climate data using soil profiles with a 30-year spin-up (see above).with a 8-year spin-up cycling the first 8 years of climate data.
Initial soil c and om: Equilibrated in each simulation with a 8-year spin-up cycling the first 8 years of climate data using soil profiles with a 30-year spin-up (see above).
Person responsible for model simulations in this simulation round
Juraj Balkovic: balkovic@iiasa.ac.at, 0000-0003-2955-4931, International Institute for Applied Systems Analysis (IIASA) (Austria)
Christian Folberth: folberth@iiasa.ac.at, 0000-0002-6738-5238, International Institute for Applied Systems Analysis (IIASA) (Austria)
Additional persons involved: Nikolay Khabarov: khabarov@iiasa.ac.at
Output Data
Experiments: obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-10
Basic information
Model Output License: CC0
Model Homepage: https://iiasa.ac.at/models-tools-data/epic-iiasa
Model License: Custom
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. EPIC-IIASA is one of the currently 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs; for a full technical description of the ISIMIP3a Simulation Data from Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Additional spatial aggregation & resolution information: Natively run at level of simulation units that are based on 5' x 5' regular grid, aggregate based on homogeneity in climate, soil, and topography.
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: W5E5v1.0
Other human influences data sets used: N-deposition, Crop calendar, N-Fertilizer (ISIMIP3a)
Climate variables: hurs, sfcWind, tasmax, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 30-year spin-up to equilibrate soil profiles. For each simulation, another spin-up of 8 years was performed to equilibrate soil water and mineral nutrient contents.
Natural Vegetation
Soil layers: 10
Key input and Management
Crops: Yes. Winter and spring wheat, maize, rice, soybean.
Land cover: No, whole land mask simulated
Planting date decision: Using the constant prescribed growing season dataset specified by the protocol.
Planting density: Global uniform depending on crop.
Crop cultivars: One constant cultivar for each crop in each grid cell (up to two for rice) based on maturity requirement. This is determined based on the the mean growing degree-days accumulated in the growing period (derived from prescribed crop calendars specified in protocol) over 1980–2009 growing seasons.
Fertilizer application: Synthetic fertilizer applied according to protocol: 20% at sowing, 80% once accumulated growing degree-days reach 25% of maturity requirement. Manure N and atm. N deposition were added to mineral fertilizer application.
Irrigation: Automatically applied based on crop water deficit (10%).
Initial soil water: Equilibrated in each simulation with a 8-year spin-up cycling the first 8 years of climate data using soil profiles with a 30-year spin-up (see above).
Initial soil nitrate and ammonia: Equilibrated in each simulation Equilibrated in each simulation with a 8-year spin-up cycling the first 8 years of climate data using soil profiles with a 30-year spin-up (see above).with a 8-year spin-up cycling the first 8 years of climate data.
Initial soil c and om: Equilibrated in each simulation with a 8-year spin-up cycling the first 8 years of climate data using soil profiles with a 30-year spin-up (see above).
Person responsible for model simulations in this simulation round
Juraj Balkovic: balkovic@iiasa.ac.at, 0000-0003-2955-4931, International Institute for Applied Systems Analysis (IIASA) (Austria)
Junguo Liu: junguo.liu@gmail.com, 0000-0002-5745-6311, Southern University of Science and Technology (China)
Ganquan Mao: ganquan.mao@icloud.com, 0000-0002-6301-996X, Southern University of Science and Technology (China)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-02-10
Basic information
Model Version: EPIC0810
Model Output License: CC BY 4.0
Simulation Round Specific Description: 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: http://doi.org/10.5880/PIK.2017.006
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
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Input data
Observed atmospheric climate data sets used: WATCH (WFD), WATCH-WFDEI
Additional input data sets: N and P fertilizer application rates based on Mueller et al. (2012)
Climate variables: tasmax, tasmin, wind, rsds, prsn, pr
Spin-up
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. http://MapSPAM.info. (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