Impact model: EPIC-Boku

EPIC-Boku is one of the 14 models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a agricultural sector outputs. In EPIC-Boku, the default estimation method for potential evapotranspiration (PET) is Penman-Monteith (PM). 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

ISIMIP2a
ISIMIP Fast Track

Confirm data for simulation round ISIMIP2a

Person responsible for model simulations in this simulation round

Hermine Mitter: hermine.mitter@boku.ac.at, 0000-0003-0799-9489, Universitat fur Bodenkultur Wien, Institute for Sustainable Economic Development (Austria)

Erwin Schmid: erwin.schmid@boku.ac.at, 0000-0003-4783-9666, Institute for Sustainable Economic Development, BOKU; University of Natural Resources and Life Sciences, Vienna (Austria)

Additional persons involved: Nikolay Khabarov: khabarov@iiasa.ac.at

Output Data

Experiments: historical
Climate Drivers: None
Date: 2016-05-04

Basic information

Model Version: epic0810

Reference Paper: Main Reference: Kiniry, J. et al. EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Canadian Journal of Plant Science,75,679-688,2011

Reference Paper: Other References: Izaurralde R, Williams J, McGill W, Rosenberg N, Jakas M et al. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecological Modelling,192,362-384,2005
Müller, C., Elliott, J., Kelly, D. et al. et al. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci Data,6,50,2019

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

Additional temporal resolution information: Land-use cover time constant unless crop rotations are used. Total cropland cover (GLC2000)

Input data

Observed atmospheric climate data sets used: GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI

Climate variables: tasmax, tasmin, wind, rhs, rsds, prsn, pr

Spin-up

Was a spin-up performed?: No

Management & Adaptation Measures

Management: The crops are simulated for three management/input systems (AI, AN, and SS): AN: automatic nitrogen fertilization – N-fertilization rates based on crop specific N-stress levels (N-stress free days in 90% of the vegetation period). The upper limit of N application is 200 kg ha-1 a-1. AI: automatic nitrogen fertilization and irrigation – N and irrigation rates are based on crop specific stress levels (N and water stress free days in 90% of the vegetation period. N and irrigation upper limits of 200 kg ha-1 a-1 and 500 mm a-1. SS: subsistence farming – no N fertilizations and irrigation.

Extreme Events & Disturbances

Key challenges: hail, and rainfall intensity e.g. mm/h

Key input and Management

Crops: mai, whe(s), soy, ric

Land cover: potential suitable cropland area according to climatic conditions, current harvested areas (data source: IFPRI)

Planting date decision: Simulate planting dates according to climatic conditions (fraction of potential heat unit)

Planting density: Planting density=0.9

Crop cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature

Fertilizer application: N automatic application by stress threshold for high input and irrigation systems, P, K (nutrient-stress factor from national stat. IFA), Subsistence farming no fertilization, (Only mineral fertilizer)

Irrigation: AQUA-STAT, Siebert et al. (2007)

Crop residue: No

Initial soil water: Sup(1)

Initial soil nitrate and ammonia: 1 year spin up

Initial soil c and om: Soil database, 1-year spin up

Initial crop residue: 1 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, partitioning during reproductive stages, total (above-ground) biomass

Crop phenology: Crop phenology is a function of temperature (heat unit index)

Root distribution over depth: linear partitioning from emergence to maturity

Stresses involved: Water stress, Nitrogen stress, Oxygen stress, Phosphorus Stress, Heat stress

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

Type of heat stress: vegetative (source)

Water dynamics: soil water capacity approach with 15 soil layers; water table dyn. (runoff, ET, percolation, sub surface flow

Evapo-transpiration: Penman-Monteith

Soil cn modeling: C model, N model, microbial biomass pool, 3 organic matter pools

Co2 effects: Radiation use efficiency, Transpiration efficiency

Methods for model calibration and validation

Parameters, number and description: Default parameters from site-specific analyses of EPIC

Spatial scale of calibration/validation: Field scale

Person responsible for model simulations in this simulation round

Hermine Mitter: hermine.mitter@boku.ac.at, 0000-0003-0799-9489, Universitat fur Bodenkultur Wien, Institute for Sustainable Economic Development (Austria)

Erwin Schmid: erwin.schmid@boku.ac.at, 0000-0003-4783-9666, Institute for Sustainable Economic Development, BOKU; University of Natural Resources and Life Sciences, Vienna (Austria)

Output Data

Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-13