Impact model: PEPIC

ISIMIP3b
ISIMIP3a
ISIMIP2b
ISIMIP2a

Confirm data for simulation round ISIMIP3b

Person responsible for model simulations in this simulation round

Wenfeng Liu: wenfeng.liu@cau.edu.cn, 0000-0002-8699-3677, China Agricultural University (China)

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

Output Data

Experiments: (*) ssp370_2015soc_default, historical_2015soc_default, ssp126_2015soc_2015co2, picontrol_2015soc_default, ssp126_2015soc_default, ssp585_2015soc_default, ssp585_2015soc_2015co2, ssp370_2015soc_2015co2
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2021-09-24

Basic information

Person responsible for model simulations in this simulation round

Wenfeng Liu: wenfeng.liu@cau.edu.cn, 0000-0002-8699-3677, China Agricultural University (China)

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

Output Data

Experiments: (*) obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-10

Basic information

Simulation Round Specific Description: * Data in embargo period, not yet publicly available. PEPIC 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/

Person responsible for model simulations in this simulation round

Nikolay Khabarov: khabarov@iiasa.ac.at, 0000-0001-5372-4668, International Institute for Applied Systems Analysis (IIASA) (Austria)

Additional persons involved: Wenfeng Liu

Output Data

Experiments: I, II, III
Climate Drivers: None
Date: 2017-10-16

Basic information

Model Output License: CC BY 4.0

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: Liu W, Yang H, Liu J, Azevedo L, Wang X, Xu Z, Abbaspour K, Schulin R et al. Global assessment of nitrogen losses and trade-offs with yields from major crop cultivations. Science of The Total Environment,572,526-537,2016

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 (ISIMIP2)

Other Data Sets Used: Land-sea mask

Climate Variables: sfcWind, hus, tasmax, tasmin, rhs, rsds, 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: mai, ric, soy, whe

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 and estimated by the model

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

Person responsible for model simulations in this simulation round

Wenfeng Liu: wenfeng.liu@cau.edu.cn, 0000-0002-8699-3677, China Agricultural University (China)

Output Data

Experiments: historical
Climate Drivers: None
Date: 2016-02-11

Basic information

Model Version: Eawag (EPIC0810)

Model Output License: CC BY 4.0

Simulation Round Specific Description: 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

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: 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
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

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: 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, 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 (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