Impact model: PROMET

Sector
Agriculture
Region
global

PROMET is a hydrological land surface process model, which has been extended by a biophysical dynamic vegetation component to model crop growth and yield formation. It uses first order physical and physiological principles to determine net primary production and respiration based on approaches from Farquhar et al. (1980) and Ball et al. (1987), combined with a phenology and a two-layer canopy architecture component of Yin et al (2005). PROMET takes into account the dependency of net primary production and phenology on environmental conditions including meteorology, CO2 concentration for C3 and C4 pathways as well as water and temperature stress. The mass and energy balance of the canopy and underlying soil surface are iteratively closed for each simulation time step. The canopy and phenology component allocates assimilates into the different plant organs of the canopy depending on the phenological stage of development. Assimilates that are accumulated within the fruit fraction during the growing period determine the dry biomass available for yield formation. The simulation is performed on an hourly time step to account for non-linear reactions of crop growth to environmental conditions (mainly light, water, temperature and wind). Conversion of daily climate model data to hourly values is done by the TeddyTool v1.1 (Zabel and Poschlod 2023). Depending on the reaction of the considered crop to meteorological and soil-specific conditions, the crop may either die due to water, heat or cold stress before being harvested or it may not reach maturity. In both cases, this results in total yield loss.

Zabel, F., Poschlod, B. (2023): The Teddy-Tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis. Geoscientific Model Development. doi: 10.5194/gmd-16-5383-2023.

Jaegermeyr, J., Müller, C., Ruane, A., Elliott, J., Balkovic, J., Castillo, O., Faye, B., Foster, I., Folberth, Ch., Franke, J., Fuchs, K., Guarin, J., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A., Kelly, D., Khabarov, N., Lange, S., Lin, T-S., Liu, W., Mialyk, O., Minoli, S., Moyer, E., Okada, M., Phillips, M., Porter, C., Rabin, S., Scheer, C., Schneider, J.M., Schyns, J., Skalský, R., Smerald, A., Stella, T., Stephens, H., Webber, H., Zabel, F., Rosenzweig, C. (2021): Climate change signal in agriculture emerges earlier in new generation of projections. Nature Food. doi: 10.1038/s43016-021-00400-y.

Zabel, F., Mueller, C., Elliott, J., Minoli, S., Jägermeyr, J., Schneider, M. J., Franke, J. A., Moyer, E., Dury, M., Francois, L., Folberth, C. Wenfeng, L., Ogh, T. A. M., Olin, S., Rabin, S. S., Mauser, W., Hank, T., Ruane, A. C., Asseng, S. (2021): Large potential for crop production adaptation depends on available future varieties. Global Change Biology. doi: 10.1111/gcb.15649.

Jägermeyr, J., Robock, A., Elliott, J., Müller, C., Xia, L., Khabarov, N., Folberth, C., Schmid, E., Liu, W., Zabel, F., Rabin, S.S., Puma, M.J., Heslin, A., Franke, J., Foster, I., Asseng, S., Bardeen, C.G., Toon, O.B., Rosenzweig, C. (2020). A regional nuclear conflict would compromise global food security. PNAS, 201919049. doi: 10.1073/pnas.1919049117.

Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, M., Václacík, T. (2019): Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nature Communications, 10:2844, 11. doi: 10.1038/s41467-019-10775-z.

Mauser, W., Klepper, G., Zabel, F., Delzeit, R., Hank, T. Putzenlechner, B., Cazadilla, A. (2015): Global biomass production potentials exceed expected future demand without the need for cropland expansion. Nature Communications, 6:8946, 11. doi: 10.1038/ncomms9946.

Hank, T.B., H. Bach, and W. Mauser, Using a Remote Sensing-Supported Hydro-Agroecological Model for Field-Scale Simulation of Heterogeneous Crop Growth and Yield: Application for Wheat in Central Europe. Remote Sensing, 2015. 7(4). 3934-3965. doi: 10.3390/rs70403934.

Mauser, W., et al., PROMET - Processes of Mass and Energy Transfer - An Integrated Land Surface Processes and Human Impacts Simulator for the Quantitative Exploration of Human-Environment Relations. Part 1: Algorithms Theoretical Baseline Document. http://www.geographie.uni-muenchen.de/department/fiona/forschung/projekte/promet_handbook/index.html. 2015, Department of Geography: Munich.

Information for the model PROMET 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
Florian Zabel: f.zabel@lmu.de, 0000-0002-2923-4412, Ludwig-Maximilians-Universität München (Germany)
Additional persons involved: Julia M. Schneider, Tobias Hank, Wolfram Mauser
Output Data
Experiments: ssp126_2015soc_2015co2, ssp370_2015soc_default, historical_2015soc_default, ssp126_2015soc_default, picontrol_2015soc_default, ssp585_2015soc_2015co2, ssp585_2015soc_default, 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
Model Version: 10.0
Model Output License: CC0
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. PROMET 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: Mauser, W., Klepper, G., Zabel, F., Delzeit, R., Hank, T. Putzenlechner, B., Cazadilla, A. et al. Global biomass production potentials exceed expected future demand without the need for cropland expansion. Nature Communications,6,11,2015
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: hourly
Temporal resolution of input data: co2: daily
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: N-deposition, Crop calendar, N-Fertilizer (ISIMIP3b)
Additional input data sets: HWSD soil data on cropland (own dataset)
Climate variables: hurs, sfcWind, tasmax, tas, tasmin, rlds, rsds, ps, pr
Additional information about input variables: Daily climate variables are converted to hourly values using the Teddy-Tool. Zabel, F., Poschlod, B. (2023): The Teddy-Tool v1.0: temporal disaggregation of daily climate model data for climate impact analysis. Geoscientific Model Development Discussion. doi: 10.5194/gmd-2023-36.
Spin-up
Was a spin-up performed?: No
Key input and Management
Crops: maize rice soy winter wheat spring wheat
Key model processes
Crop phenology: Development rate dependent on temperature, soil water (for germination), photoperiod, water stress
Root distribution over depth: dynamic
Stresses involved: water stress heat stress nitrogen stress
Co2 effects: Considered for C3 and C4 pathways.
Person responsible for model simulations in this simulation round
Florian Zabel: f.zabel@lmu.de, 0000-0002-2923-4412, Ludwig-Maximilians-Universität München (Germany)
Additional persons involved: Julia M. Schneider, Tobias Hank, Wolfram Mauser
Output Data
Experiments: obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2023-04-06
Basic information
Model Version: 10.0
Model Output License: CC0
Simulation Round Specific Description: PROMET 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: Mauser, W., Klepper, G., Zabel, F., Delzeit, R., Hank, T. Putzenlechner, B., Cazadilla, A. et al. Global biomass production potentials exceed expected future demand without the need for cropland expansion. Nature Communications,6,11,2015
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Vertically resolved: No
Temporal resolution of input data: climate variables: hourly
Temporal resolution of input data: co2: daily
Temporal resolution of input data: soil: constant
Input data
Emissions data sets used: Atmospheric composition (ISIMIP3a)
Other human influences data sets used: N-deposition, Crop calendar, N-Fertilizer (ISIMIP3a)
Climate variables: hurs, sfcWind, tasmax, tas, tasmin, rlds, rsds, ps, pr
Additional information about input variables: Daily climate variables are converted to hourly values using the Teddy-Tool. Zabel, F., Poschlod, B. (2023): The Teddy-Tool v1.0: temporal disaggregation of daily climate model data for climate impact analysis. Geoscientific Model Development Discussion. doi: 10.5194/gmd-2023-36.
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 6 years model spin-up with same settings than rest of the simulation.
Key input and Management
Crops: maize rice soy winter wheat spring wheat
Key model processes
Crop phenology: Development rate dependent on temperature, soil water (for germination), photoperiod, water stress
Root distribution over depth: dynamic
Stresses involved: water stress heat stress nitrogen stress
Co2 effects: Considered for C3 and C4 pathways.