Impact model: PEPIC

PEPIC is a Python-based Environmental Policy Integrated Climate (EPIC) model. PEPIC 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

Sector
Agriculture
Region
global
Contact Person
  • Wenfeng Liu (Wenfeng.liu@eawag.ch), Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Switzerland)
  • Hong Yang (Hong.yang@eawag.ch), Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Switzerland)

Information for the model PEPIC 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
Reference Paper: Main Reference: Liu W, Yang H, Folberth C, Wang X, Luo Q. Schulin R et al. Global investigation of impacts of PET methods on simulating crop-water relations for maize. Agricultural and Forest Meteorology,221,164-175,2016
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Wenfeng Liu
Output Data
Experiments: I, II, III
Climate Drivers: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Date: 2017-10-16
Resolution
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: annual
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Soil: constant
Input data sets used
Simulated Atmospheric Climate Data Sets Used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Emissions Data Sets Used: CO2 concentration
Other Human Influences Data Sets Used: N-Fertilizer
Other Data Sets Used: Land-sea mask
Climate Variables: tasmax, tasmin, sfcWind, rhs, rsds, hus, pr
Spin-up
Was A Spin-Up Performed?: Yes
Spin-Up Design: 4 years as model warm-up period
Management & Adaptation Measures
Management: Crop calender is based on GGCMI phrase 2 dataset.
Key input and Management
Crops: Maize, Rice, Soy, Wheat
Land Cover: Whole land
Planting Date Decision: Based on GGCMI fullharm datasets.
Crop Cultivars: at the level of 2005
Fertilizer Application: Nitrogen based on isimip2b and no limitation on phosphorus.
Irrigation: No limitation.
Crop Residue: 75% removed.
Initial Soil Water: Based on ISRIC-WISE dataset
Initial Soil Nitrate And Ammonia: Based on ISRIC-WISE dataset
Initial Soil C And Om: Based on ISRIC-WISE dataset
Initial Crop Residue: No
Key model processes
Leaf Area Development: EPIC model
Light Interception: EPIC model
Light Utilization: EPIC model
Yield Formation: FAO yields at country level.
Crop Phenology: EPIC model
Root Distribution Over Depth: EPIC model
Stresses Involved: EPIC model
Type Of Water Stress: EPIC model
Type Of Heat Stress: EPIC model
Water Dynamics: EPIC model
Evapo-Transpiration: EPIC model
Soil Cn Modeling: EPIC model
Co2 Effects: EPIC model
Methods for model calibration and validation
Spatial Scale Of Calibration/Validation: country level
Temporal Scale Of Calibration/Validation: average between 1998 and 2002
Criteria For Evaluation (Validation): R2
Basic information
Model Version: Eawag (EPIC0810)
Reference Paper: Main Reference: W. Liu, H. Yang, C. Folberth, X. Wang, Q. Luo, R. Schulin et al. Global investigation of impacts of PET methods on simulating crop-water relations for maize. Agricultural and Forest Meteorology,221,164-175,
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: GSWP3, WATCH (WFD)
Date: 2016-02-11
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
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: GSWP3, WATCH (WFD)
Climate Variables: tasmax, tasmin, rlds, wind, rhs, rsds, pr
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: 20 years
Management & Adaptation Measures
Management: Planting and harvesting dates were based on SAGE dataset. PHU was estiamted based on planting and harvesting dates. After harvest, 75% of crop residue were removed from the field.
Key input and Management
Crops: mai, whe(w,s), soy, rice
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 (fraction of potential heat unit)
Planting Density: Crop-specific
Crop Cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature
Fertilizer Application: NP(N automatic application by stress threshold)
Irrigation: no restriction on actual water availability, irrigated water applied when water stres, MIRCA 2000: crop specific irrigated area (Portmann et al., 2010)
Crop Residue: Yes, 75% of crop residuces removed after harvest
Initial Soil Water: Spin up (climate-data specific, model start from 1951)
Initial Soil Nitrate And Ammonia: ISRIC-WISE, Spin up (climate-data specific, model start from 1951)
Initial Soil C And Om: ISRIC-WISE, Spin up (climate-data specific, model start from 1951)
Initial Crop Residue: ISRIC-WISE, Spin up (climate-data specific, model start from 1951)
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, partitioning during reproductive stages, total (above-ground) biomass
Crop Phenology: temperature, other water/nutrient stress effects considered, 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
Water Dynamics: soil water capacity approach with 5 soil layers
Evapo-Transpiration: P, PM (default), PT, HAR, Baier-Robertson
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 site-specific analyses of EPIC0810, Potential harvest index (maize)
Output Variable And Dataset For Calibration Validation: Yield (FAO yield statistics)
Spatial Scale Of Calibration/Validation: National
Temporal Scale Of Calibration/Validation: Average for 1998-2002
Criteria For Evaluation (Validation): R2