Impact model: PEPIC

ISIMIP3b
ISIMIP3a
ISIMIP2b
ISIMIP2a

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)

Additional persons involved: Mengxue Li

Output Data

Experiments: picontrol_2015soc_default, ssp370_2015soc_2015co2, ssp585_2015soc_default, historical_2015soc_default, ssp126_2015soc_default, ssp370_2015soc_default, ssp126_2015soc_2015co2, ssp585_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

Model Version: 1

Model Output License: CC0

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 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: IPSL-CM6A-LR (ssp119, ssp245, ssp460), GFDL-ESM4 (ssp245), MRI-ESM2-0, MPI-ESM1-2-HR, UKESM1-0-LL

Emissions data sets used: Atmospheric composition (ISIMIP3b)

Land use data sets used: Historical, gridded land use

Other human influences data sets used: Crop calendar

Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland)

Climate variables: hurs, tasmax, tasmin, rsds, pr

Exceptions to Protocol

Exceptions: No

Spin-up

Was a spin-up performed?: Yes

Spin-up design: 10 year spin-up

Natural Vegetation

Natural vegetation partition: No

Natural vegetation dynamics: No

Soil layers: 3

Management & Adaptation Measures

Management: Irrigaiton, fertilization, planting and harvest, tillage

Extreme Events & Disturbances

Key challenges: Extreme rainfall

Key input and Management

Crops: maize, rice1, rice2, soybean, spring wheat, winter wheat

Land cover: AgMIP GGCMI

Planting date decision: AgMIP GGCMI

Planting density: Crop specific

Crop cultivars: Based on GDD

Fertilizer application: AgMIP GGCMI

Irrigation: Automatic

Crop residue: 75% removel

Initial soil water: HWSD and spin-up

Initial soil nitrate and ammonia: HWSD and spin-up

Initial soil c and om: HWSD and spin-up

Initial crop residue: No

Key model processes

Yield formation: Yes

Methods for model calibration and validation

Parameters, number and description: Harvest index

Calibrated values: crop specific

Output variable and dataset for calibration validation: Yield

Spatial scale of calibration/validation: Country

Temporal scale of calibration/validation: Year

Criteria for evaluation (validation): R2

Attachments

719-2016_Liu_PET_AFM.pdf (4.1 MB)

719-2016_Liu_NLoss_STOTEN.pdf (2.9 MB)

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)

Additional persons involved: Mengxue Li

Output Data

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

Basic information

Model Output License: CC0

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/

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

Observed atmospheric climate data sets used: W5E5v1.0

Emissions data sets used: Atmospheric composition (ISIMIP3a)

Other human influences data sets used: Crop calendar, N-Fertilizer (ISIMIP3a)

Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland)

Climate variables: hurs, tasmax, tasmin, rsds, pr

Exceptions to Protocol

Exceptions: No

Spin-up

Was a spin-up performed?: Yes

Spin-up design: 10 years spin-up

Natural Vegetation

Natural vegetation partition: NO

Natural vegetation dynamics: NO

Soil layers: 3

Management & Adaptation Measures

Management: Irrigation, fertilizer, planting hand harvest dates

Extreme Events & Disturbances

Key challenges: The impacts of extreme rainfall on crop yields are hard to be simulated.

Key input and Management

Crops: Maize, Rice1, Rice2, Soybean, Spring wheat, Winter wheat

Land cover: No

Planting date decision: AgMIP GGCMI data

Planting density: crop-specific

Crop cultivars: Varied with GDD

Fertilizer application: AgMIP GGCMI

Irrigation: automatic irrigation

Crop residue: 75% removal

Initial soil water: HWSD data

Initial soil nitrate and ammonia: HWSD data

Initial soil c and om: HWSD data

Initial crop residue: No

Key model processes

Leaf area development: No

Light interception: No

Light utilization: No

Yield formation: R2

Crop phenology: No

Root distribution over depth: No

Stresses involved: No

Type of water stress: No

Type of heat stress: No

Water dynamics: No

Evapo-transpiration: No

Soil cn modeling: R2

Co2 effects: No

Methods for model calibration and validation

Parameters, number and description: Harvest index

Calibrated values: Crop specific

Output variable and dataset for calibration validation: Yield

Spatial scale of calibration/validation: Country

Temporal scale of calibration/validation: Year

Criteria for evaluation (validation): R2

Attachments

706-2016_Liu_PET_AFM.pdf (4.1 MB)

706-2016_Liu_NLoss_STOTEN.pdf (2.9 MB)

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

Resolution

Horizontal 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

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: 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

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

Observed atmospheric climate data sets used: GSWP3, WATCH (WFD)

Additional input data sets: N and P fertilizer application rates based on FertiStat (2007)

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

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