Impact model: 3D-CMCC-CNR-BGC

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.

ISIMIP2b
ISIMIP2a
ISIMIP Fast Track

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: Marconi S, Chiti T, Nolè A, Valentini R, Collalti A et al. The Role of Respiration in Estimation of Net Carbon Cycle: Coupling Soil Carbon Dynamics and Canopy Turnover in a Novel Version of 3D-CMCC Forest Ecosystem Model. Forests,8,220,2017
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
Collalti A., Trotta C., Keenan T., Ibrom A., Bond-Lamberty B., et al. et al. Thinning Can Reduce Losses in Carbon Use Efficiency and Carbon Stocks in Managed Forests Under Warmer Climate. Journal of Advances In Modelling Earth System,10,,2018
Collalti A, Marconi S, Ibrom A, Trotta C, Anav A, D'Andrea E, Matteucci G, Montagnani L, Gielen B, Mammarella I, Grünwald T, Knohl A, Berninger F, Zhao Y, Valentini R, Santini M et al. Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for 10 European forest sites. Geoscientific Model Development,9,479-504,2016
Collalti A., Tjoelker M.G., Hoch, G., Makela A. et al. et al. Plant respiration: Controlled by photosynthesis or biomass?. Global Change Biology,None,,2019

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

Attachments

437-Collalti_et_al_2016_GMD.pdf (1.8 MB)

437-Collalti_et_al_2018_James.pdf (1.4 MB)

437-Collalti_et_al_2019_Eco_Appl.pdf (1.4 MB)

437-Collalti_et_al_2019_Glob_Change_Bio.pdf (3.9 MB)

437-Collalti_et_al_2014_Ecol_Mod.pdf (2.0 MB)

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