Impact model: 3D-CMCC FEM

The 3D-CMCC FEM is hybrid between an empirical and a process-based model relying on the concepts of the LUE approach at canopy level for carbon fixation. The 3D-CMCC FEM 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.

Sector
Forests
Region
local
Contact Person

Information for the model 3D-CMCC FEM 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.

Basic information
Model Version: v.5.3.3-ISIMIP
Reference Paper: Main Reference: A. Collalti 1,2 , S. Marconi , A. Ibrom , C. Trotta , A. Anav, E. D’Andrea , G. Matteucci , L. Montagnani , B. Gielen , I. Mammarella , T. Grünwald , A. Knohl , F. Berninger , Y. Zhao , R. Valentini , and M. Santini et al. Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for 10 European forest sites. Geosci. Model Dev.,9,479-504,2016
Reference Paper: Other References:
Person Responsible For Model Simulations In This Simulation Round: Alessio Collalti
Output Data
Experiments: I, Ia, II, IIa, IIb, IIc, III, IIIa, IIIb
Climate Drivers: IPSL-CM5A-LR, GFDL-ESM2M, EWEMBI, MIROC5
Date: 2018-08-06
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 sets used
Simulated Atmospheric Climate Data Sets Used: MIROC5 (rcp45), HadGEM2-ES (rcp45), IPSL-CM5A-LR (rcp45), GFDL-ESM2M (rcp45), IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Climate Variables: ta, tasmax, tas, tasmin, rhs, 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
Model output specifications
Output Format: ascii txt per grid cell and species level
Output Per Pft?: data era species level
Key model processes
Dynamic Vegetation: based on changes in tree age, forest structure, (no natural regeneration)
Nitrogen Limitation: no
Co2 Effects: yes on photosynthesis and stomatal conductance
Light Interception: yes, Lambert-Beer for two sun and shaded leaves within canopy
Light Utilization: Light Use Efficiency (Monteith approach)
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: Yes, 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: 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
Additional Forest Information
Forest sites simulated: Bily_Kriz Collelongo, Kroof, Peitz, Le Bray Hyytiala, Solling beech Solling spruce Soroe
Basic information
Model Version: 3D-CMCC FEM (v.5.x)
Reference Paper: Main Reference: A. Collalti, S. Marconi, A. Ibrom, C. Trotta, A. Anav, E. D'Andrea, G. Matteucci, L. Montagnani, B. Gielen, I. Mammarella, T. Grünwald, A. Knohl, R. Valentini, and M. Santini et al. Validation of 3D-CMCC Forest Ecosystem Model (v.5.1) against eddy covariance data for ten European forest sites. Geoscientific Model Development,9,1-26,2016
Reference Paper: Other References:
  • 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 ItalyEcological Modelling,272,362-378,2014
Output Data
Experiments: historical (Hyytiälä, Peitz, Solling beech, Solling spruce, Sorø, KROOF, Collelongo)
Climate Drivers: GSWP3, Historical observed climate data, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2018-07-26
Resolution
Spatial Aggregation: country/region level
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 sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Climate Variables: tasmax, tas, tasmin, rhs, rsds, pr
Spin-up
Was A Spin-Up Performed?: No
Natural Vegetation
Natural Vegetation Partition: height, age and species classes
Natural Vegetation Dynamics: dynamic changes in forest structure
Natural Vegetation Cover Dataset: ancillary data
Management & Adaptation Measures
Management: basal area-related harvesting