Impact model: GEPIC-BR

ISIMIP2a

Confirm data for simulation round ISIMIP2a

Person responsible for model simulations in this simulation round

Christian Folberth: folberth@iiasa.ac.at, 0000-0002-6738-5238, International Institute for Applied Systems Analysis (IIASA) (Austria)

Hong Yang: Hong.yang@eawag.ch, Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Switzerland)

Additional persons involved: Christian Folberth (folberth@iiasa.ac.at), International Institute for Applied Systems Analysis

Output Data

Experiments: historical
Climate Drivers: None
Date: 2020-06-24

Basic information

Reference Paper: Main Reference: Folberth C, Gaiser T, Abbaspour K, Schulin R, Yang H et al. Regionalization of a large-scale crop growth model for sub-Saharan Africa: Model setup, evaluation, and estimation of maize yields. Agriculture, Ecosystems & Environment,151,21-33,2012

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
J. R. Williams, C. A. Jones, J. R. Kiniry, D. A. Spanel et al. et al. The EPIC Crop Growth Model. Transactions of the ASAE,32,0497-0511,1989
Liu J, Williams J, Zehnder A, Yang H et al. GEPIC – modelling wheat yield and crop water productivity with high resolution on a global scale. Agricultural Systems,94,478-493,2007

Resolution

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

Input data sets used

Observed Atmospheric Climate Data Sets Used: WATCH-WFDEI

Additional Input Data Sets: N and P fertilizer application rates based on FertiStat (2007)

Spin-up

Was A Spin-Up Performed?: Yes

Spin-Up Design: Simulations were run for each decade separately with 20 years spin-up

Management & Adaptation Measures

Management: Planting dates were estimated similar to Waha et al. (2012). Time until maturiy was calculated as grid-specific average PHU for the whole simulation period. Hence, maturity in each will depend on the specific growing season temperature. After harvest, 80% of crop residue were removed from the field.

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: potential suitable cropland area according to climatic conditions; current harvested areas (Portmann et al. 2010)

Planting Date Decision: Simulate planting dates according to climatic conditions

Planting Density: Crop-specific

Crop Cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature, 2 cultivars for mai

Fertilizer Application: NP, FertiSTAT, dynamic application according to nutrient stress

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: Crop-specific

Initial Soil Water: 20 year spin up

Initial Soil Nitrate And Ammonia: ISRIC-WISE, 20 year spin up

Initial Soil C And Om: ISRIC-WISE, 20 year spin up

Initial Crop Residue: ISRIC-WISE, 20 year spin up

Key model processes

Leaf Area Development: prescribed shape of LAI curve as function of phenology

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: 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, aluminum (based on pH and base saturation))

Type Of Water Stress: Ratio of supply to demand of water

Type Of Heat Stress: vegetative

Water Dynamics: soil water capacity approach with 5 soil layers

Evapo-Transpiration: Hargreaves

Soil Cn Modeling: C model; N model; microbial biomass pool; 6 organic matter pools

Co2 Effects: Radiation use efficiency, Transpiration efficiency

Methods for model calibration and validation

Parameters, Number And Description: Default parameters from EPIC0810 partly adjusted based on prior studies, Potential harvest indext (maize and rice), Fertlizer application rate

Output Variable And Dataset For Calibration Validation: Yield (FAO yield statistics)

Spatial Scale Of Calibration/Validation: National

Temporal Scale Of Calibration/Validation: Average for 1997-2003

Criteria For Evaluation (Validation): R2