Impact model: 3D-CMCC-CNR-BGC

Sector
Forests
Region
local

The 3D-CMCC-BGC is the fully biogeochemical version of the 3D-CMCC FEM. The 3D-CMCC-BGC is designed to simulate forest ecosystems at flexible scale (from hectare to 1 km per 1 km) and on a daily time step. The model simulates tree growth as well as carbon and water fluxes, at species level, representing eco-physiological processes in heterogeneous forest ecosystems including complex canopy structures.

Information for the model 3D-CMCC-CNR-BGC 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
Alessio Collalti: alessio.collalti@cnr.it, 0000-0002-4980-8487, Italian National Research Council (Italy)
Daniela Dalmonech: daniela.dalmonech@gmail.com, 0000-0002-1932-5011, Italian National Research Council (Italy)
Carlo Trotta: trottacarlo@unitus.it, 0000-0001-6377-0262, University of Tuscia (Italy)
Output Data
Experiments: I, Ia, II, IIa, IIb, III, IIIa, IIIb
Climate Drivers: None
Date: 2018-08-06
Basic information
Model Version: v.5.5-ISIMIP
Model Output License: CC0
Model Homepage: https://www.forest-modelling-lab.com/the-3d-cmcc-model
Model License: GNU General Public Licence v3.0 (GPL)
Simulation Round Specific Description: * Data in embargo period, not yet publicly available
Reference Paper: Main Reference: Collalti A, Perugini L, Santini M, Chiti T, Nolè A, Matteucci G, Valentini R et al. A process-based model to simulate growth in forests with complex structure: Evaluation and use of 3D-CMCC Forest Ecosystem Model in a deciduous forest in Central Italy. Ecological Modelling,272,362-378,2013
Reference Paper: Other References:
Resolution
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
Spin-up
Was a spin-up performed?: No
Natural Vegetation
Natural vegetation partition: based on species-level tree density
Management & Adaptation Measures
Management: dbh-related harvesting in forest models, fixed by protocol data
Extreme Events & Disturbances
Key challenges: drought
Key model processes
Dynamic vegetation: based on changes in tree age, forest structure, (no natural regeneration)
Nitrogen limitation: no
Co2 effects: photosynthesis and stomatal conductance
Light interception: Lambert-Beer for two sun and shaded leaves within canopy
Light utilization: Farquahar, von Cammaerer and Barry (1980) as modified in dePury and Farquhar (1992)
Phenology: different for deciduous and evergreen species, based on temperature, eliophany, LAI (pipe model theory) and others
Water stress: reduction in photosynthesis and stomatal conductance
Heat stress: reduction in photosynthesis and stomatal conductance
Evapo-transpiration approach: Penman-Monteith for sun and shaded leaves, stomatal conductance with Jarvis method
Differences in rooting depth: no
Root distribution over depth: no
Closed energy balance: not for sensible heat fluxes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: no
Respiration: mechanistic modellization
Causes of mortality in vegetation models
Age/senescence: age-dependent mortality function based on maximum attainable age
Fire: no
Drought: indirectly through reduction in growth efficiency (carbon starvation)
Insects: no
Storm: no
Stochastic random disturbance: no
Other: growth efficiency mortality, that happens when all reserves (non structural carbon pool) are depleted, self thinning (crowding competition)
NBP components
Fire: no
Harvest: yes, all harvested biomass is removed from the stand
Other processes: model accounts for replanting after harvesting
Species / Plant Functional Types (PFTs)
List of species / pfts: Picea abies Fagus sylvatica, Pinus sylvestris, Pinus pinaster
Model output specifications
Output format: ascii txt and netcdf per grid cell and species level
Output per pft?: data era species level
Additional Forest Information
Forest sites simulated: Bily_Kriz Collelongo, Kroof, Peitz, Le Bray Hyytiala, Solling beech Solling spruce Soroe
Person responsible for model simulations in this simulation round
Alessio Collalti: alessio.collalti@cnr.it, 0000-0002-4980-8487, Italian National Research Council (Italy)
Daniela Dalmonech: daniela.dalmonech@gmail.com, 0000-0002-1932-5011, Italian National Research Council (Italy)
Carlo Trotta: trottacarlo@unitus.it, 0000-0001-6377-0262, University of Tuscia (Italy)
Output Data
Experiments: historical (Hyytiälä, Peitz, Solling beech, Solling spruce, Sorø, Le Bray, Collelongo, Bily Kriz)
Climate Drivers: None
Date: 2018-07-26
Basic information
Model Version: v.5.5-ISIMIP
Model Output License: CC0
Resolution
Spatial aggregation: forest stand
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: GSWP3, Historical observed climate data, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Emissions data sets used: Atmospheric CO2 concentration
Climate variables: tasmax, tas, tasmin, rhs, rsds, pr
Spin-up
Was a spin-up performed?: No
Natural Vegetation
Natural vegetation partition: based on species-level tree density
Management & Adaptation Measures
Management: dbh-related harvesting in forest models, fixed by protocol data
Extreme Events & Disturbances
Key challenges: drought
Key model processes
Dynamic vegetation: based on changes in tree age, forest structure, (no natural regeneration)
Nitrogen limitation: no
Co2 effects: photosynthesis and stomatal conductance
Light interception: Lambert-Beer for two sun and shaded leaves within canopy
Light utilization: Farquhar von Caemmer and Berry (1980) as modified by dePury and Farquhar (1992)
Phenology: Yes, different for deciduous and evergreen species, based on temperature, eliophany, LAI (pipe model theory) and others
Water stress: Yes, reduction in photosynthesis and stomatal conductance
Heat stress: reduction in photosynthesis and stomatal conductance
Evapo-transpiration approach: Penman-Monteith for sun and shaded leaves, stomatal conductance with Jarvis method
Differences in rooting depth: no
Root distribution over depth: no
Closed energy balance: not for sensible heat fluxes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: no
Causes of mortality in vegetation models
Age/senescence: age-dependent mortality function based on maximum attainable age
Fire: no
Drought: indirectly through reduction in growth efficiency (carbon starvation)
Insects: no
Storm: no
Stochastic random disturbance: no
Remarks: growth efficiency mortality, that happens when all reserves (non structural carbon pool) are depleted, self thinning (crowding competition)
NBP components
Fire: no
Harvest: yes, all harvested biomass is removed from the stand
Other processes: model accounts for replanting after harvesting
Species / Plant Functional Types (PFTs)
List of species / pfts: Picea abies Fagus sylvatica, Pinus sylvestris, Pinus pinaster
Model output specifications
Output format: ascii txt netcdf per grid cell and species level
Output per pft?: data era species level
Additional Forest Information
Forest sites simulated: Bily_Kriz Collelongo, Kroof, Peitz, Le Bray Hyytiala, Solling beech Solling spruce Soroe
Person responsible for model simulations in this simulation round
Alessio Collalti: alessio.collalti@cnr.it, 0000-0002-4980-8487, Italian National Research Council (Italy)
Daniela Dalmonech: daniela.dalmonech@gmail.com, 0000-0002-1932-5011, Italian National Research Council (Italy)
Carlo Trotta: trottacarlo@unitus.it, 0000-0001-6377-0262, University of Tuscia (Italy)
Basic information
Model Version: V.5.5-ISIMIP
Reference Paper: Main Reference: Collalti A, Thornton P, Cescatti A, Rita A, Borghetti M, Nolè A, Trotta C, Ciais P, Matteucci G et al. The sensitivity of the forest carbon budget shifts across processes along with stand development and climate change. Ecological Applications,29,1-20,2019
Resolution
Spatial aggregation: forest stand
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: GCM atmospheric climate data (Fast Track)
Emissions data sets used: Atmospheric CO2 concentration
Climate variables: tasmax, tas, tasmin, rhs, rsds, pr
Spin-up
Was a spin-up performed?: No
Natural Vegetation
Natural vegetation partition: based on species-level tree density
Management & Adaptation Measures
Management: dbh-related harvesting in forest models, fixed by protocol data
Extreme Events & Disturbances
Key challenges: drought
Key model processes
Dynamic vegetation: based on changes in tree age, forest structure, (no natural regeneration)
Nitrogen limitation: no
Co2 effects: photosynthesis and stomatal conductance
Light interception: Lambert-Beer for two sun and shaded leaves within canopy
Light utilization: Farquhar von Caemmerer and Berry (1980) as modified by dePury and Farquhar (1992)
Phenology: different for deciduous and evergreen species, based on temperature, eliophany, LAI (pipe model theory) and others
Water stress: reduction in photosynthesis and stomatal conductance
Heat stress: reduction in photosynthesis and stomatal conductance
Evapo-transpiration approach: Penman-Monteith for sun and shaded leaves, stomatal conductance with Jarvis method
Differences in rooting depth: no
Root distribution over depth: no
Closed energy balance: not for sensible heat fluxes
Coupling/feedback between soil moisture and surface temperature: yes
Latent heat: yes
Sensible heat: no
Causes of mortality in vegetation models
Age/senescence: age-dependent mortality function based on maximum attainable age
Fire: no
Drought: indirectly through reduction in growth efficiency (carbon starvation)
Insects: no
Storm: no
Stochastic random disturbance: no
Other: growth efficiency mortality, that happens when all reserves (non structural carbon pool) are depleted, self thinning (crowding competition)
NBP components
Fire: no
Land-use change: no
Harvest: yes, all harvested biomass is removed from the stand
Other processes: model accounts for replanting after harvesting
Species / Plant Functional Types (PFTs)
List of species / pfts: Picea abies Fagus sylvatica, Pinus sylvestris, Pinus pinaster
Model output specifications
Output format: ascii txt netcdf per grid cell and species level
Output per pft?: data era species level
Additional Forest Information
Forest sites simulated: Bily_Kriz Collelongo, Kroof, Peitz, Le Bray Hyytiala, Solling beech Solling spruce Soroe