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Impact model: LDNDC

Sector
Agriculture
Region
global

LandscapeDNDC is one of the 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/

Information for the model LDNDC 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.

Person responsible for model simulations in this simulation round
Andrew Smerald: andrew.smerald@kit.edu, 0000-0003-2026-273X, KIT, Garmisch-Partenkirchen (Germany)
Additional persons involved: David Kraus (david.kraus@kit.edu), Clemens Scheer (clemens.scheer@kit.edu), Kathrin Fuchs (kathrin.fuchs@kit.edu)
Output Data
Experiments: ssp370_2015soc_default, ssp126_2015soc_default, ssp370_2015soc_2015co2, ssp585_2015soc_default, historical_2015soc_default, ssp126_2015soc_2015co2, ssp585_2015soc_2015co2, picontrol_2015soc_default
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: 1.33
Model Output License: CC0
Model Homepage: https://ldndc.imk-ifu.kit.edu
Model License: Contained in model
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. LDNDC 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.
Reference Paper: Main Reference: Jägermeyr J et al. Climate change signal in global agriculture emerges earlier in new generation of climate and crop models. Nature Food,2,873-885,2022
Reference Paper: Other References:
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: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4
Emissions data sets used: Atmospheric composition (ISIMIP3b)
Other human influences data sets used: Nitrogen deposition (ISIMIP3), Crop calendar, N-Fertilizer (ISIMIP3b)
Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland), Land-sea mask
Climate variables: hurs, sfcWind, tasmax, tas, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 10 year spin up, with the purpose of reaching an approximate steady state for soil C and N (the model has an accelerated spin up routine for reaching this steady state). CO2 levels were set to the historical level. The management was the same as in the main simulation.
Natural Vegetation
Natural vegetation partition: We didn't include any natural vegetation.
Soil layers: 13 soil layers covering a 1m depth profile. From top to bottom: 5 times 20 mm 2 times 50 mm 4 times 100mm 2 times 200mm
Key input and Management
Crops: Mono-cropping of maize, wheat, rice, soybeans (i.e. no rotations).
Land cover: Either a crop is on the field or it is fallow.
Planting date decision: According to crop calendar
Crop cultivars: Default cultivars for model
Fertilizer application: 20% at planting 80% after reaching 25% of the accumulated growing degree days required for maturity
Irrigation: For irrigated simulations of upland crops, water was supplied so as to maintain the soil at field capacity during the growing season. The total irrigation water supplied to irrigated rice is incorrect (too high) due to a bug in the way the model wrote the outputs. This did not affect rice yields or other model outputs.
Crop residue: 30% left on the field
Initial soil nitrate and ammonia: Unimportant due to spin up.
Initial soil c and om: Adapted from the values in HWSD, with higher C contents in higher soil layers. Subject to change during spin up.
Key model processes
Leaf area development: Dependent on carbon allocation to foliage and modified by drought stress.
Light interception: Dependent on foliage structure.
Light utilization: Photosynthesis routine is based on the approaches of Farquar and Berry-Ball
Yield formation: The allocation of carbon from photosynthesis is controlled by the accumulation of growing degree days.
Crop phenology: Linked to the accumulation of growing degree days.
Root distribution over depth: Exponential
Type of water stress: Drought
Water dynamics: tipping bucket
Evapo-transpiration: Thornthwaite
Soil cn modeling: Detailed process based modelling in soil.
Co2 effects: Via photosynthesis routine
Methods for model calibration and validation
Calibrated values: See LDNDC website for main calibration sites and variables. https://ldndc.imk-ifu.kit.edu
Spatial scale of calibration/validation: Field scale
Temporal scale of calibration/validation: Fitting daily measurement timeseries over multiple years.
Person responsible for model simulations in this simulation round
Andrew Smerald: andrew.smerald@kit.edu, 0000-0003-2026-273X, KIT, Garmisch-Partenkirchen (Germany)
Additional persons involved: David Kraus (david.kraus@kit.edu), Clemens Scheer (clemens.scheer@kit.edu), Kathrin Fuchs (kathrin.fuchs@kit.edu)
Output Data
Experiments: obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-10
Basic information
Model Version: 1.33
Model Output License: CC0
Model Homepage: https://ldndc.imk-ifu.kit.edu
Model License: Contained in model code
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. LDNDC 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/
Reference Paper: Main Reference: Jägermeyr J et al. Climate change signal in global agriculture emerges earlier in new generation of climate and crop models. Nature Food,2,873-885,2022
Reference Paper: Other References:
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: land use/land cover: 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: Nitrogen deposition (ISIMIP3), Crop calendar, N-Fertilizer (ISIMIP3a)
Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland), Land-sea mask
Climate variables: hurs, sfcWind, tasmax, tas, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Spin up started in 1891, so that 10 years are run before the 1901 start date, with the purpose of reaching an approximate steady state for soil C and N (the model has an accelerated spin up routine for reaching this steady state). CO2 levels were set to the historical level. The management was the same as in the main simulation.
Natural Vegetation
Natural vegetation partition: We didn't include any natural vegetation.
Soil layers: 13 soil layers covering a 1m depth profile. From top to bottom: 5 times 20 mm 2 times 50 mm 4 times 100mm 2 times 200mm
Management & Adaptation Measures
Management: As in the GGCMI model protocol.
Key input and Management
Crops: Mono-cropping of maize, wheat, rice, soybeans (i.e. no rotations).
Land cover: Either a crop is on the field or it is fallow.
Planting date decision: According to crop calendar
Crop cultivars: Default cultivars for model
Fertilizer application: 20% at planting 80% after reaching 25% of the accumulated growing degree days required for maturity
Irrigation: For irrigated simulations of upland crops, water was supplied so as to maintain the soil at field capacity during the growing season. The total irrigation water supplied to irrigated rice is incorrect (too high) due to a bug in the way the model wrote the outputs. This did not affect rice yields or other model outputs.
Crop residue: 30% left on the field
Initial soil nitrate and ammonia: Unimportant due to spin up.
Initial soil c and om: Adapted from the values in HWSD, with higher C contents in higher soil layers. Subject to change during spin up.
Key model processes
Leaf area development: Dependent on carbon allocation to foliage and modified by drought stress.
Light interception: Dependent on foliage structure.
Light utilization: Photosynthesis routine is based on the approaches of Farquar and Berry-Ball
Yield formation: The allocation of carbon from photosynthesis is controlled by the accumulation of growing degree days.
Crop phenology: Linked to the accumulation of growing degree days.
Root distribution over depth: Exponential
Type of water stress: Drought
Water dynamics: tipping bucket
Evapo-transpiration: Thornthwaite
Soil cn modeling: Detailed process based modelling in soil.
Co2 effects: Via photosynthesis routine
Methods for model calibration and validation
Output variable and dataset for calibration validation: See LDNDC website for main calibration sites and variables. https://ldndc.imk-ifu.kit.edu
Spatial scale of calibration/validation: Field scale
Temporal scale of calibration/validation: Fitting daily measurement timeseries over multiple years.




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